International Journal of Nanotechnology (118 papers in press)
A simulation-based study on the disc brake temperature distribution for optimizing hole geometry
by Shyam Sunder Sharma, Hariharan Raju, Pranay Singh Tomar, Rajesh Jangid, Rahul Khatri
Abstract: Disc brakes used in automotive are responsible for braking to ensure a smooth and safe ride. This study deals with the thermal analysis of a disc brake rotor under various geometry of holes cut on the disc rotor surface. The friction on the disc escapes in the form of heat from the surface of the disc rotor. The temperature observed on the surface of the rotor, because of the friction developed between the brake pads and the rotor is analysed using ANSYS 18.1. The rotor is designed by assuming appropriate parameters in SOLIDWORKS 17. The temperature distribution and total heat flux were observed using ANSYS 18.1. The analysis was carried out on different hole geometries i.e. circular, square, 3/4th circular, straight slots, and rotor without holes. The dissipation of heat was found better in disc rotor with holes as compared to rotor without holes. The simulation study shows that the slotted holes on the disc rotor has surface temperature i.e. 89.356
Keywords: Automotive disc brake; Simulation; Hole geometry; Heat dissipation.
Deep learning-based feature extraction coupled with multi class SVM for COVID-19 detection in the IoT era
by Mubarak Auwalu Saleh, Sertan Serte, Fadi Al-Turjman, R.A. Abdulkadir, Zubaida Sa’id Ameen, Mehmet Ozsoz
Abstract: The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organisation (WHO) in December 2019 Prompt and early identification of suspected patients is necessary to monitor the transmission of the disease, increase the effectiveness of medical treatment and as a result, decrease the mortality rate. The adopted method to identify COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR), the method is affected by the shortage of RT-PCR kits and complexity. Medical imaging using deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but efficient deep learning architecture and low data are affecting the performance of the deep learning models. To detect COVID-19 efficiently, a deep learning model based feature extraction coupled with support vector machine (SVM) was employed in this study, Seven pre-trained models were employed as feature extractors and the extracted features are classified by multi-class SVM classifier to classify COVID-19, Common Pneumonia and Healthy individuals CT scan images, to
improve the performance of the models and prevent overfitting, training was also carried out on augmented images. To generalised the models performance and robustness, three datasets were merged in the study. The model with the best performance is the VGG19 which was trained with augmented images, the VGG19 achieved an accuracy of 96%, the sensitivity of 0.936, specificity 0f 0.967, F1 score of 0.935, the precision of 0.934, Yonden Index of 0.903 and AUC of 0.952. the best model shows that COVID-19 can be detected efficiently on CT scan images.
Keywords: artificial intelligence; COVID-19; SVM; support vector machine; feature extraction.
Special Issue on: Intelligent Nano-Biotechnology for the Future Emerging Challenges and Advancements
Research on the application of new nanomaterial -Al2O3 in industrial product modelling design
by Wei Bi, Zidong He
Abstract: Gamma-Al2O3 is one of many aluminas. It has a series of remarkable characteristics, such as porosity, large specific surface area, high activity, and good thermal stability. Therefore, -Al2O3 is often used as adsorbents, catalysts and catalyst carriers, and has extremely wide applications in petrochemical industries, environmental protection and other fields. In industrial design, owing to its different physical properties, it is often widely used in different design products. In this research, aluminium nitrate and glycine were used as raw materials, and nano--Al2O3 was synthesised by low-temperature combustion. XRD and TEM test methods were used to analyse and characterise the reaction products, investigate the pH value of the solution, the molar ratio of the reactants, the calcination temperature, and the calcination time of the formation, particle size, crystal transformation and morphology of the reaction product and other aspects. Relevant data were obtained through experiments.
Keywords: nano-γ-Al2O3; environmental protection materials; new nanomaterials; industrial product modelling design.
Image-based automatic segmentation of leaf using clustering algorithm
by Shubham Mahajan, Shivalika Sharma, Davinder Paul Singh, Chinmay Chakraborty, Dr. Amit Kant Pandit
Abstract: This paper proposes a productive strategy to separate the leaf area from different plant images. The image analysis in plants plays a critical job in reasonable and gainful farming. Plant image analysis is mainly used to record the plant development plant area and leaf zone etc. Plant leaf segmentation has become a significant task to be examined among these plant characters. For this reason, a strategy for leaf area segmentation from different plant images is made. In this paper we have proposed an algorithm which will segment plant on the basis of L*a*b colour spaces and cluster formation. When our proposed technique was compared with the rosette tracker it was found that our proposed algorithm was more efficient in plant leaf segmentation. The results of our proposed algorithm are: accuracy is 79.23%, sensitivity is 85.84%, and specificity is 97.40%.
Keywords: colour space; leaf segmentation; plant development; plant image analysis.
Protective effect of nano-electrostatic composite material on miners skin and its influence on working state
by Shuicheng Tian, Ying Chen, Yuan Zou
Abstract: A nano-electrostatic composite material is proposed to promote the rapid healing of miners skin injuries and maintain their good working state in the harsh underground environment. The poly(lactic-co-glycolic acid) (PLGA) nano-particle therapy has a strong effect on maintaining the biological stability of the drug, so it is undertaken as a carrier to load the basic fibroblast growth factor (bFGF) into PLGA. The electrospinning technology is adopted to form a nano-electrospinning composite material (NECM), which is obviously interwoven into a network with uniform thickness. Sixty miners with skin injuries were treated with different dressings. It was found after 10 days that the wound-healing rate of miners using NECM reached 86.67%, which is much higher in contrast to that of the traditional medical dressings (43.3%). The main reason is that bFGF can promote the proliferation of wound granulation tissue, and loading it into nanofibre dressings can increase its bioavailability and promote wound healing. Therefore, the application of bFGF/PLGA NECM has a significant effect on promoting the healing of miners skin injuries, so it plays an important role in alleviating the psychological pressure of miners and enhancing their enthusiasm for work.
Keywords: nano-electrospinning composite material; skin protection; miner; working state.
Preparation of hydrophobic honeycomb films with an amphiphilic copolymer via the breath figure method
by Dejie Zhou, Qingzhao Wang
Abstract: Fabrication of honeycomb films with amphiphilic fluorinated ABC-type triblock copolymer via the breath figure method is introduced in this paper. The triblock copolymer poly(ethylene oxide)-block-polystyrene-block-poly(perfluorohexylethyl acrylate) (MeOPEO-b-PSt-b-PFHEA) was synthesised by atom transfer radical polymerisation (ATRP). The film with ordered honeycomb structure was fabricated under selective solvent, controlled humidity and polymer concentration. We analysed the influence of different factors on the film morphology. The triblock polymer we prepared exhibited a regular honeycomb surface structure in a carbon disulphide solution at 5 g/L concentration and 75% humidity. Surface wetting properties indicate that the amphiphilic triblock polymer can achieve high hydrophobicity by varying the concentration and humidity. The obtained polymer of the honeycomb pore structure is expected to be used in hydrophobic materials.
Keywords: honeycomb film; amphiphilic block copolymer; hydrophobic effect; ATRP.
Characteristic analysis of discontinuous function modelling-based nano-biosensor and its detection for hyaluronidase
by Fangfang Zhang
Abstract: This study draws attention to the characteristics of a nano-biosensor based on a discontinuous function algorithm and its application in hyaluronidase detection. The discontinuous algorithm model is introduced in the construction of sensor. Gold nanoparticles (AuNPs) and nanocomposite materials of Nation-Ru(bpy)32+-AuNPs and Nation-Ru(bpy)32+-AuNPs-TTX are prepared for chemical modification of the electrode. Finally, a new nano-electrochemical immunosensor is successfully constructed. Twenty ICR male mice aged 6-8 months were selected as research subjects. They were randomly divided into control group and observation group, with their sperm protein extracted for Western blot and enzyme activity detection. The results show that AuNPs nanomaterials have good biocompatibility and conductivity, and demonstrate good sensitivity in the detection process. The enzyme detection results show that the enzyme activity of the observation group is higher than that of control group, indicating that the prepared sensor system has relatively high accuracy, with the detection being simple, more sensitive, and quicker.
Keywords: discontinuous function; nano-biosensor; HAase detection; nano-particle.
Nano-drug release control microchip combined with biomaterials in the recovery of shoulder joint movement and contraction
by Zhigang Tong
Abstract: This study explores the effect of a new type of biological tissue engineering stent of kartogenin (KGN) loaded with polycaprolactone (PCL) combined with a microarray polylactic acid (PLA) nano-drug release (NDR) microchip based on magnetic nanoparticles to promote the healing of rotator cuff injury (RCI). Firstly, the ferroferric oxide (Fe3O4) magnetic nanoparticles (MNPs) were prepared and applied to PLA-loaded microchips to realise the remote magnetic control (RMC) of 1% lidocaine. Secondly, the directional PCL stent loaded with KGN (KGN-PCL) was prepared using the electrospinning technology. The results showed that the KGN-PCL biomaterial showed excellent in vitro drug release ability. The microarray PLA NDR microchip based on Fe3O4 MNPs combined with the KGN-PCL biological stent can promote the regeneration of cartilage in the tendon bone structure of the rotator cuff, and elevate the healing quality of rotator cuff tendon-bone by improving the biomechanical properties of the rotator cuff.
Keywords: Fe3O4 magnetic nanoparticles; nanodrug release microchip; kartogenin; biological tissue engineering stent; rotator cuff injury; biomechanical properties.
Cytological mechanism of nanowire reinforced biological bone cement in the treatment of vertebral osteoporotic fractures
by Sheng Xu
Abstract: This study analyses the effects of calcium phosphate cement (CPC) with magnetic composite nanomaterials on the adhesion, proliferation, apoptosis, and osteogenic differentiation of mesenchymal stem cells (MSC) in vitro. In this study, CNTs/Fe3O4 composites were prepared using different surfactants, and the best materials were selected through characterisation and analysis and then added to CPC to prepare the magnetic CPC (MCPC). The bone marrow MSCs of rats was selected to culture with CPC (CPC group), MCPC (MCPC group), and MCPC under 60 mT static magnetic field (MS group), respectively. Subsequently, the differences in the proliferation, apoptosis, and alkaline phosphatase (ALP) activity of MSCs in each group were detected. The results showed that the CNTs/Fe3O4 nanomaterials prepared by the combination of sodium acetate + polyethylene glycol 2000 surfactant had excellent properties; MCPC prepared with 9 wt% CNTs/Fe3O4 addition had the best stress-strain performance. Compared with the CPC group, the MSCs adhesion rate, proliferation activity, and ALP activity of the MCPC group and the MS group were obviously increased (P < 0.05), while the apoptosis rate was greatly reduced (P < 0.05). In contrast to the MCPC group, MSCs adhesion rate, proliferation activity, and ALP activity in MS group increased greatly (P < 0.05), and cell apoptosis rate was observably decreased (P < 0.05). It showed that CPC with magnetic composite material can promote MSC adhesion, proliferation, and osteogenic differentiation in vitro and reduce cell apoptosis, and the effect was more obvious under 60mT static magnetic field. The above results could provide an important cytological mechanism for the treatment of patients with vertebral osteoporotic fracture (VOF).
Keywords: magnetic composite nanomaterials; calcium phosphate cement; mesenchymal stem cells; vertebral osteoporotic fracture.
Effect of sodium hyaluronate-based nanoprobe in rehabilitation of tendon injury and tendon adhesion
by Siqi Lv
Abstract: This paper aimed to investigate the effect of nanoprobe fibres based on sodium hyaluronate in the rehabilitation of tendon injury and adhesion. A desolventisation method was adopted to prepare nanoparticle-encapsulated hyaluronic acid chitosan nanoprobes. There was an analysis on the cumulative release of basic fibroblast growth factor in different nanoprobe fibres, the difference in mechanical properties, its effect on the proliferation of human amniotic mesenchymal stem cells, and the difference in expression of tenomodulin and scleraxis proteins in hAMSCs cells. It was found that the NPs/HA-CS had a smooth surface with an average particle size of 136
Keywords: sodium hyaluronate; nanoprobe; tendon injury; tendon repair.
Graphene oxide nanomaterials in the recovery of shoulder joint movement Contraction
by Yayun Zhu
Abstract: Graphene oxide (GO) and pure titanium (Ti) samples were adopted to prepare the titanium dioxide (TiO2) nanotube films based on GO (GO-TiO2) by electrochemical anodisation. Then, the drug loading capacity, the drug releasing capacity, and the antibacterial ability of the GO-TiO2 nanotubes were analysed. The results showed that the number of MSCs on the surface of GO-TiO2 nanotubes was higher than that of pure Ti; the MSCs on GO-TiO2 nanotubes surface were in the form of polymerised colonies, while the MSCs on the surface of pure Ti were in the form of dispersed particles. Compared with pure Ti, the GO-TiO2 nanotubes could effectively prolong the drug release time. The antibacterial ability of pure Ti, GO-TiO2 nanotubes, and drug-loaded GO-TiO2 nanotubes was compared as pure Ti < GO-TiO2 nanotubes < drug-loaded GO-TiO2 nanotubes. GO-TiO2 nanotubes could promote the differentiation of MSCs such as osteoblasts, and also had a strong drug loading capacity for macromolecular drugs.
Keywords: graphene oxide; titanium dioxide nanotube; antibacterial ability; mesenchymal stem cells; shoulder joint.
Electrospinning nanofibres in exercise rehabilitation after fracture of anterior cruciate ligament
by Haiying Wang
Abstract: This study aimed to analyse the application of electrospinning nanofibres in the rehabilitation of fracture of anterior cruciate ligament (ACL). In this study, the polycaprolactone fibres were prepared by dissolving the PCL in a mixed solution of dichloromethane and N,N-dimethylformamide through an EP machine. The silk fibroin and PCL were dissolved and blended to prepare a nanofibre stent. Ninety patients with ACL injury were selected as the research objects and randomly rolled into a blank group, a control group, and an experimental group, with 30 cases in each group. The ACL was reconstructed through exercise rehabilitation therapy, ozone therapy combined with exercise rehabilitation therapy, and EP nanofibre stent, respectively. Results showed that the nanofibre stent was successfully prepared, which had good mechanical properties and hydrophilicity. The EP nanofibre material could effectively promote the proliferation and differentiation of stem cells, thereby increasing the rate of tissue regeneration at the injured site.
Keywords: electrospinning; anterior cruciate ligament fracture; polycaprolactone; silk fibroin.
Experimental and theoretical validation studies of ASnO3 (A = Ba, Ca, Sr) nanofibres for bioactivity applications
by Bradha Madhavan, A. Suvitha, Anand Steephen
Abstract: Barium, calcium and strontium stannate nano strands were blended through an electrospinning process. The as-prepared samples were calcined at 1000
Keywords: barium stannate; perovskite; nanofibres; bioactivity.
Preparation of reduced graphene oxide/nano hydroxyapatite nanocomposite scaffold and its adoption in sports fracture injury
by Danya Yang, Jianfang Wang, Jianquan Yang, Danya Fu
Abstract: This paper aimed to study the preparation of a nanocomposite scaffold composed of reduced graphene oxide and nano hydroxyapatite, and its adoption value in patients with sports injuries. First, graphene oxide and nHA nanocrystals were prepared, which were then combined to form RGO/nHA nanocomposites. Transmission electron microscopy and scanning electron microscopy were employed to characterise nanocomposites. The in vitro and in vivo experiments in mice were performed to detect the toxicity of the prepared RGO/nHA nanocomposite. Moreover, the physiological characteristics of the formed RGO/nHA nanocomposite in the tissues of mice were analysed, such as HE staining images. Then, the prepared nanocomposites were adopted to prepare RGO/nHA nano gel composite scaffolds, which were used in surgery of patients with bone injuries. The results showed that the prepared RGO/nHA nanocomposite was non-toxic in mice, and the RGO/nHA nanocomposite had good biocompatibility in mice.
Keywords: nanomaterials; composite scaffolds; sports injuries; reduced RGO-nHA.
Clinical value of injected calcium phosphate cement/recombined bone xenograft-based hybrid nano-biomaterials in the knee joint sports rehabilitation ability
by Wenting Zhou
Abstract: This paper aimed to analyse the application value of poly nano-microspheres and injected calcium phosphate cement/recombined bone xenograft hybrid biomaterials in knee joint sports rehabilitation. In this study, different types of hybrid nano-biomaterial, such as ICPC/RBX, ICPC/RBX + 4% PLGA, and ICPC/RBX + 8% PLGA, were prepared based on ICPC raw materials, and their physical and chemical properties were tested. The results showed that ICPC/RBX material, ICPC/RBX + 4% PLGA material, ICPC/RBX + 8% PLGA material could be formed in phosphate buffer saline buffer, and there was no collapse phenomenon. The initial setting time and final setting time of ICPC/RBX, ICPC/RBX + 4% PLGA, and ICPC/RBX + 8% PLGA materials increased compared with ICPC raw materials, but the difference was not marked. The compressive strength of ICPC/RBX + 4% PLGA and ICPC/RBX + 8% PLGA materials was always lower than the strength of ICPC and ICPC/RBX materials.
Keywords: PLGA nanospheres; ICPC/RBX hybrid biomaterials; new bone formation rate; biomechanics; ultimate compressive strength.
Preparation of nano biosensors for detection of intestinal flora and cellulose molecular weight
by Xiaoqian Liu
Abstract: This paper describes the preparation of a Fe2O3@Au nanoparticles (NPs)/glassy carbon electrode (GCE) biosensor to detect intestinal flora and cellulose molecular weight. Specifically, Fe2O3@Au and DNA-loaded NPs were prepared first, which were then attached to GCE to prepare Fe2O3@Au/GCE biosensor. The results showed that when the biosensor was hybridised with a base-matched target sequence, no oxidation peak was obtained; when the biosensor was hybridised with a mismatched target sequence and a completely non-complementary target sequence, an oxidation peak was obtained. The biosensor had a potential peak around 0.9 V at different polymerization temperatures, and the potential peak intensity results were expressed as 40
Keywords: biosensor; intestinal flora; cellulose molecule.
Neurotrophic factors combined with ordered collagen nano-modified materials in the recovery of exercise ability of patients with peripheral nerve transection
by Yiming Gu, Chaoyang Yang
Abstract: This study aimed to analyse the application of neurotrophic factor (NTF) combined with ordered collagen modified material (OCMM) (taking graphene oxide (GO) as example) modified nanofibre stent (NFS) in the recovery of exercise ability of patients with peripheral nerve transection (PNT). The graphene NFS was prepared by dissolving the graphite powder and preparing graphene nanomaterials by ultrasonic and centrifugal treatment methods. It indicated that the NFS was successfully modified, with good mechanical properties, hydrophilicity, and biocompatibility; the modified NFM could effectively enhance the migration and proliferation of neuronal cells, thereby promoting the nerve regeneration and recovery of exercise ability in patients with nerve injury.
Keywords: neurotrophic factor; peripheral nerve transection; water contact angle; silk fibroin; nerve regeneration.
Influence of gold nanoclusters biomaterials combined with upper limb rehabilitation robot on patients with limb ischemic injury
by Yan Zhai, Xiaobo Guo, Yong Zhang, Yongzheng He, Zhiqiang Li
Abstract: The reverse microemulsion was prepared and gold nanoparticles (AuNPs) were synthesised from it. Then, AuNC biomaterials were prepared by modifying folic acid with dendrimers, which were applied to patients with limb ischemic injury. It was found that the peak of AuNCs near the wavelength of 1700cm-1 corresponded to the infrared characteristic peak of C-O. The peak near the wavelength of 3400cm-1 corresponded to the N-H stretching vibration peak, and the peak near the wavelength of 3200cm-1 corresponded to the C-H deformation vibration peak. The arm supination score (40.2
Keywords: gold nanoclusters; sodium borohydride; surfactants; ischemic injury; reverse microemulsion.
Adoption of carbon nanomaterial-based flexible sensors in human exercise health monitoring
by Zhenwen Xu, Liou Liu
Abstract: To analyse the adoption of nanomaterials in flexible sensors and the monitoring value in human exercise health, carbon nanotube (CNT) was first prepared. Four different types of CNT sensors were subsequently prepared. After performance analysis, the optimal flexible CNT sensor was selected and used in human exercise health monitoring. The results showed that the diameter of the undoped nitrogen CNT was about 35.9 nm, and that of nitrogen-doped CNT was 51.42 nm. The height of the two CNT arrays was similar, and the arrangement was relatively uniform. The infrared response monitoring results of four different types of sensor revealed that the infrared sensor with silica substrate had a faster response speed than that with PMMA substrate, with remarkable difference (P < 0.05). The fitting curve of the bending angle of the wrist and knee and the resistance change showed that the greater the bending angle of the knee, the greater the resistance received, with a positive correlation between them. In short, the flexible sensor based on carbon nanomaterials had ideal infrared signal sensing and monitoring capabilities, and had high sensitivity.
Keywords: carbon nanomaterials; sensors; motion monitoring; smart products.
Adoption of three-resonant terahertz nano-biosensor in human sports health monitoring
by Xueqin Zhang, Fan Jiang, Benyu Xi, Yan Ma
Abstract: This work aims to analyse the adoption effect of three-resonant terahertz (THz) in nano-biosensors and its monitoring value in human sports health. First, the absorber-based three-resonant THz nanomaterial was prepared, which was applied to the preparation of sensors after its physical and chemical properties were detected. Then, three kinds of resonant sensor under modes A, B, and C were prepared, and the best nano-biosensor was selected and used in the monitoring of human sports health. The prepared flexible sensor was used for human motion detection, and the results showed that the sensor can accurately monitor the amplitude of human motion. Nano-biosensor based on three-resonant THz had good electrical signal monitoring capabilities and high sensitivity, which can conduct a comprehensive assessment of the human sports health status and is worthy of clinical promotion and adoption.
Keywords: multi-resonance terahertz; sensor; metamaterial; sports health.
Adoption of porous nanocomposite fibre scaffold in cartilage injury healing and rehabilitation
by Jucui Wang, Mingzhi Li
Abstract: To evaluate the effect of a porous nanofibre scaffold on the healing and repair of cartilage defects in animal models, three different PNFSs were prepared, namely gelatin/polylactic acid scaffold, thermally cross-linked Gel/PLA scaffold, and hyaluronic acid modified Gel/PLA-T scaffold. After characterisation and analysis, bone marrow stromal cells were taken as the research object. The detection of cell proliferation and apoptosis was performed to evaluate the biocompatibility of each scaffold after scaffold culture. Then, a New Zealand rabbit model of cartilage defect was constructed. The results showed that the density, mechanical properties, and in vitro degradation rate of HA@Gel/PLA-T scaffolds were remarkably increased relative to Gel/PLA and Gel/PLA-T scaffolds, while the porosity and water absorption rate decreased. It was verified that the prepared HA@Gel/PLA-T composite PNFS can promote the proliferation and chondrogenic differentiation of BMSCs cells, thereby promoting the repair and regeneration of cartilage damaged tissues.
Keywords: nanofibre scaffold; cartilage defect; wound healing; chondrogenic differentiation.
Effect observation and nursing of nano-silver antibacterial dressings in preventing catheter-related bloodstream infection
by Yan Zhang, Jing Wang
Abstract: This thesis takes patients with severe burns as a research case to discuss the therapeutic effects and nursing methods of nano-silver antibacterial dressings on patients with severe burns caused by central venous catheterisation through trauma. We found that the per-thousand-day infection rates of catheter-related bloodstream infections in the sterile application group and the Aner iodine dressing group were 25.48% and 20.83%, respectively. A total of 16 cases of catheter-related bloodstream infections occurred in all patients in the three groups, and a total of 16 pathogenic bacteria were isolated from the microbial culture of the catheter tip attachment and the blood microbial culture. For this reason, we can conclude that the use of nano-silver antibacterial dressings for maintenance of central venous catheters placed through the wound in patients with severe burns can effectively reduce the rate of central venous catheter-related infections and extend the number of days of catheter indwelling.
Keywords: nano-silver antibacterial dressing; catheter-related bloodstream infection; catheterisation; central vein; burns.
Polyacrylic acid coated nanomaterials and sports rehabilitation training on the therapeutic effect of patients with stroke
by Dan He, Yu Bai
Abstract: This article explores the protective effect of alteplase-loaded dextran-polyacrylic acid nanoparticles on hippocampal neurons in stroke rats. We randomly divided 24 stroke rats into sham operation group, stroke group, stroke + alteplase group, stroke + alteplase-loaded dextran-polyacrylic acid nanoparticle intervention group. We used the HE stains method to detect the number of neurons in the hippocampus and calculate the survival rate of neurons under an inverted microscope. The survival rates of neurons in the sham operation group, stroke group, alteplase group, and nanoparticle group were 100%, 11.0%, 16.6%, and 57.6%. The ischemic encephalopathy group and the neuron survival rate of the general enzyme group and the nanoparticle group were lower than that of the sham operation group. The neuron survival rate of the nanoparticle group was higher than that of the stroke group and alteplase group (P<0.05). There was no statistical difference between the alteplase group and the stroke group.
Keywords: stroke; polyacrylic acid nanoparticles; exercise rehabilitation; alteplase.
Study of the in vitro transdermal administration of surface anaesthetics based on dextran-based nano-injection lipid lidocaine
by Na He
Abstract: The paper explores the in vitro transdermal performance and in vivo characteristics of surface anaesthetics containing lidocaine (LID-HLD-BNs) using dextran-based nanoliposome as a carrier. Lidocaine-encapsulated traditional liposomes (LID-CLs) and lidocaine hydrochloride injection (LID-IJ) were used as controls, using isolated mouse abdominal skin. Using a Franz diffusion cell, high performance liquid chromatography was used to conduct in vitro transdermal experiments to observe the transdermal performance of LID-HLD-BNs. Using calcein as a probe and laser confocal microscopy (CLSM), with calcein-loading traditional liposomes (calcein-CLs) and free calcein as controls, the liposome preparation (calcein-HLD -BNs) fluorescence penetration in the abdomen of living mice was observed. The study found that the cumulative transdermal volume per unit area and transdermal rate of LID-HLD-BNs were higher than LID-CLs and LID-IJ. The transdermal rates of LID-IJ, LID-CLs, and LID-HLD-BNs were 39.15, 79.04 and 142.86 g/(cm2
Keywords: dextran-based nanoparticles; liposomes; lidocaine; chromatography; transdermal administration; in vitro studies.
Nano-targeted drugs in combined treatment and nursing of liver cancer
by Lan Li, Tingting Qiu, Yuqin Ni
Abstract: This article uses the new polymer material N-glycyrrhetinic acid (GA)-polyethylene glycol (PEG)-chitosan (NGPC) as the carrier material, and uses the ion cross-linking method to prepare Brucine-loaded chitosan nanoparticles (Brucine/NGPC-NPs) ).The uptake of Brucine solution and Brucine/NGPC-NPs by hepatocellular carcinoma cells is both time- and concentration-dependent. The uptake of NGPC-NPs is significantly stronger than that of the solution. It has significant active transport characteristics, and the intake follows the order of exogenous licorice. The addition of acid decreases, and its uptake pathway mainly depends on clathrin-mediated endocytosis and then internalisation by the cell. Therefore, we can conclude that NGPC-NPs can be used as a carrier for liver cell targeting, significantly increasing the amount of drugs entering liver cancer cells, and achieving the purpose of reducing toxicity and increasing efficiency.
Keywords: Brucine; nano-targeted drugs; chitosan nanoparticles; liver cancer cells; cell uptake; liver cancer care.
New nanoparticle computed tomography contrast agent in the treatment of neonatal hypoxic ischemic encephalopathy
by Yulin Chen, Wei Liu, Jingwei Sun, Linbao Wen
Abstract: The paper describes the preparation of iodine-containing polymer nanoparticles contrast agent by precipitation polymerisation method, used in the diagnosis of head CT in children with HIE, and the analysis of the CT manifestations of children with HIE. The article selected 36 children with HIE admitted from January 2019 to December 2020 as the observation group. In addition, we selected 36 patients who were born at the same time and had clinical symptoms but no abnormalities in CT examination as the control group. The brain CT values of the severe group were lower than those of the moderate group and the mild group, and the difference was statistically significant (P<0.05). For this reason, we have concluded that cranial CT examination with iodine-containing polymer nanoparticles as a contrast agent can reflect the scope, location and extent of neonatal ischemic and hypoxic encephalopathy, and has guiding significance for early diagnosis and treatment.
Keywords: head CT performance; new nanoparticles; iodine-containing polymer nanoparticles; hypoxic ischemic encephalopathy.
Application of bio-scaffold materials in the recovery of sports health of injured parts of fracture patients
by Min Li, Hong Ye, Xuefei Hu
Abstract: In order to analyse the application effect of bio-scaffold materials in the treatment of patients' fractures, the study first performed computed tomography scans of fracture patients to obtain fracture data. Secondly, the paper imports the fracture data of the patient into the model based on the finite element analysis software, and uses the finite element analysis software to analyse the stress situation of the fracture site. The study found that the orientation of peak strain on the loading surface of fracture patients appeared on the outside of the bone and stress concentration occurred. Finally, under the condition of finite element simulation, we installed the bio-stent at the fracture site and modelled and simulated it again, which better reflected the biomechanical situation of the fixed stent in the fracture. The research results provide relevant theoretical and practical support for the optimisation of clinical fracture stent installation materials.
Keywords: bio-scaffold material; finite element; fracture; biomechanics; sports injury; structural optimisation.
Study on preparation and technology of nano-porous platinum-based metal materials by dealloying method
by Chao Gao, Ping Yan, Zhongliang Tang, Shulong Liu
Abstract: Nano-porous metal materials have nano-scale pores and ligaments, which have the characteristics and excellent properties of both nano-porous (NP) materials and metal materials. Therefore, they have great application value in optics, chemistry, physics and so on. Compared with single metal catalysts, NP Pt-Ru alloy shows excellent activity for ammonia borane hydrolysis at room temperature. In this paper, a series of NP Pt-Ru-Al alloys with different components were prepared by the dealloying method, in which the active component Al was selectively corroded in acidic solution. The morphology and composition of the prepared NP Pt-Ru alloy were characterised, and the activity of the samples was tested.
Keywords: nano-porous; platinum; dealloying; Pt-Ru alloy; metal materials.
Antibacterial properties of PCL/silver NP-based nano-composites for potential food packaging applications
by Blessy Melapurakkal, Ahmed John
Abstract: A large number of non-renewable materials have been used for preparation of food packaging materials. These non-renewable based food packaging materials create negative impact on the eco system. Thus, biodegradable and biocompatible polymeric materials for food packaging materials are preferred. The addition of silver nanoparticles in biocompatible polymeric matrices enhances the physicochemical properties, and therefore, the food shelf life. Silver NPs were prepared by a green chemical approach, and the prepared silver nanoparticles were characterised by XRD, HRTEM and EDAX analysis. The composite film was prepared by incorporating silver nanoparticles in polycaprolactone. The composite film containing polycaprolactone (PCL) with various concentrations (0.1, 0.5, 1.0, and 1.5 wt%) of silver nanoparticles was prepared by the solution casting method. Furthermore, the composite film exhibited strong antibacterial activity against pathogenic micro-organisms Bacillus cereus, Bacillus pumilius, Bacillus subtilis, Clostridium perfringens and Pseudomonas aeruginosa. The enhanced properties were attributed to the incorporation of silver into the PCL matrix.
Keywords: silver nanoparticles; polycaprolactone; solution casting; antibacterial; food packaging.
Application of nanomaterials in martial arts sports injury patients
by Bingsi Xi, Bin Wang
Abstract: This paper discusses the curative effect and influencing factors of the implantation of nano-hydroxyapatite rods in the treatment of martial arts sports Schatzker type III tibial plateau fractures. Methods: The thesis research included 36 martial arts Schatzker type III tibial plateau fracture patients who were admitted to the hospital from July 2017 to July 2019. In the control group, 18 patients received traditional incision bone plate screw internal fixation. The HSS knee function scores at 1, 3, and 6 months after surgery were all higher than those of the control group. Arthroscopic nano-hydroxyapatite rod implantation for minimally invasive treatment of Schatzker type III tibial plateau fracture injury in martial arts sports can promote fracture healing, relieve pain, and promote joint function recovery.
Keywords: nanomaterials; fracture injury; arthroscopy; hydroxyapatite; tibial plateau fracture injury.
Research on application of new nanomaterials in repairing ligament injury of wushu athletes
by Mengjian Miao, Yan Lou, Changcheng Xin
Abstract: This paper explores the application value of nano-hydroxyapatite/polyamide 66 composite bioactive artificial vertebral body in anterior surgery of thoracolumbar ligament injury in martial arts athletes. In the experiment, 81 patients with thoracolumbar ligament injury who were treated by anterior menstrual route from January 2016 to September 2019 and were followed up, were divided into titanium mesh group and artificial vertebral body group according to different intervertebral bone graft support materials. All 81 cases were followed up for more than 12 months. There was no significant difference in the Cobb angle and the height of adjacent intervertebral space between the two groups at 1 week and 3 months after surgery (P > 0.05). Thoracolumbar ligament injury anterior decompression is thorough, and the reconstruction method conforms to the principle of spine biomechanical distribution, and the spinal cord function recovery is better.
Keywords: martial arts athletes; artificial vertebral body; nanomaterials; titanium mesh; thoracolumbar ligament injury; anterior surgery.
Modelling and analysis of fatigue physiological parameters of all tissues of football athletes under cervical vertebral stress based on biomechanical analysis
by Hui Ma, Tong Liu, Lijun Liu, WeiSong Chen
Abstract: Through the clinical observation of the curative effect of cervical spondylosis, the biomechanical effect and clinical value of the treatment of cervical spondylosis and the recovery of cervical physiological curvature were preliminarily discussed. The X-ray findings from 50 football players with cervical spondylosis were analysed. Under different physiological conditions, the changes of cervical curvature, cervical rotation, vertebral angular displacement and spondylolisthesis increased by 20%. The results showed that the curvature of the cervical spine of football players became straight, and the intervertebral activity was significantly reduced and redistributed. At the same time, the lower cervical facet joint and uncinate process joint are in a state of torsional instability, forming cervical instability and accelerating cervical degeneration.
Keywords: athlete's cervical vertebra; parameter change; cervical spondylosis; football players.
Analysis of the effect of modern biotechnology and nanotechnology on competitive sports
by Yunfei Lu
Abstract: Nanotechnology refers to the science and technology of making new materials or micro devices with thousands of molecules or atoms. With the development of science and technology, how to apply modern biotechnology and nanotechnology to sports, make sports training more scientific, and give full play to athletes' sports ability and technical level has become the research focus. Based on the characteristics of competitive sports, this paper expounds the application status of nanotechnology in sports. On this basis, according to the present situation and trend of modern biotechnology and nanotechnology, it analyses the effects of modern biotechnology and nanotechnology on competitive sports. Applying modern biotechnology and nanotechnology to the development of competitive sports will greatly change the face of sports, thus promoting the rapid development of competitive sports and creating opportunities for the substantial growth of competitive achievements.
Keywords: biotechnology; nanotechnology; competitive sports.
Review on feature selection technology of bioinformatics gene nano chip based on RSA algorithm
by Xiaohua Li, Siyu Yang
Abstract: With the implementation of the human genome project, computational intelligence has been widely used in computational biology and bioinformatics. The purpose of this study is to study the feature selection technique of gene chip in bioinformatics. In this paper, a binary key scanning method is used to decompose the model power algorithm into a series of modular multiplication operations. At the same time, the combination of genetic algorithm and RSA algorithm is used to select the feature of high dimensional space. The problem of sample classification of two categories is solved. The method is used for the design of re sequencing chip and gene expression chip. The final research shows that the RSA algorithm is applied to the technology of gene chip selection, and a high-density gene chip design software system and experimental result analysis system can be developed.
Keywords: RSA algorithm; gene chip; bioinformatics.
Research on optimisation strategy of athletes' training mental fatigue based on nanobiomechanical data fusion analysis
by Xiancheng Zhang, Xiaochang Lv, Huaizhao Zhang, Xina Chen, Dongyue Li
Abstract: Athletes need long-term, high-intensity, large amounts of exercise, and high load training, in order to successfully improve sports performance. In order to analyse the definition, characteristics, causes, performance and recovery measures of exercise-induced mental fatigue, through data fusion analysis, this paper points out the limitations affecting the recovery of exercise-induced mental fatigue under the current training system in China. The results show that, different from the concept of psychological exhaustion in the field of sports psychology, psychological fatigue is a predictable phenomenon, which accompanies athletes for a long time, is determined by many factors, and develops gradually. Athletes' age, professional years and other factors have an impact on their psychological fatigue. Sports mental state is related to mental fatigue, which can effectively predict athletes' mental fatigue.
Keywords: data fusion; athletes; mental fatigue.
Research on the relevant effects of Taijiquan exercise on the changes of lower limb muscle strength in the elderly based on nano biometric IC technology
by Jianqiang Fu, Chao Liu, Zhengyan Li
Abstract: With the improvement of peoples living standards and medical standards, the aging of population structure has become a global social problem. The author discussed and studied the influence of Taijiquan exercise on lower limb function of the elderly, and provided theoretical basis for preventing or delaying the decline of lower limb function of the elderly. Using the research method of sports biomechanics to analyse the gait data before and after Taijiquan practice in the elderly, and to explore the changes of the influence of Taijiquan on the kinematic parameters and kinetic parameters of the lower extremities, and reveal the physical fitness of Taijiquan for the elderly. Improve the role. Studies have shown that Tai Chi practice can significantly improve the lower limb muscle strength and proprioceptive function of the elderly.
Keywords: nano biometric IC technology; Taijiquan exercise for the elderly; changes of muscle strength; lower limbs.
Research on promoting the development of circular economy by using nano biopharmaceutical technology
by Haitao Zhu
Abstract: Biomedical nanotechnology is an important research field of the intersection of nanoscience and life science, which has developed rapidly in recent years. Nano drug carriers, nano biosensors and imaging technology and micro intelligent medical devices will play an important role in the diagnosis, treatment and health care of diseases. Circular economy should not only care about economic development, but also the survival of future generations. It requires that economic development should consider not only the increase of economic aggregate, but also the ecological carrying capacity, so as to realize the unity of economic, social and environmental benefits. This paper analyses the application of nano biotechnology in medicine, points out the importance of nano biotechnology in the development of medicine, and puts forward the ways of using nano biotechnology to promote the development of circular economy.
Keywords: nano drug carriers; nano biosensors; biotechnology; circular economy.
Analysis of the influence of physical training on skeletal structure based on biomechanical analysis
by Huasen Liu, Xinjian Wang
Abstract: From the perspective of biomechanics, this paper has done some work on the further research of sports biomechanics. The human body or a part of the human body should be simplified appropriately, and various mathematical, mechanical and anatomical models should be established. Combined with medical rehabilitation methods and sports biomechanics, this paper discusses the mechanical basis of sports injury prevention and sports rehabilitation in physical education and training. The muscle strength of each group was simulated by Adams software. When the initial excitation is reduced to zero, the muscle strength reaches the minimum. While studying bones, we should not forget the muscles and joints in the basic components of human motion system. There is an internal complementary relationship between bone and bone health.
Keywords: sports biomechanics; sports injury; simplification; skeletal structure.
Simulation analysis of sports training process optimisation based on motion biomechanical analysis
by Qingjuan Ma, Pengxiang Huo
Abstract: At present, the simulation results of some modelling methods are not very satisfactory. According to the trend of flexion angle and muscle force, the changes of knee joint three-dimensional motion angle and muscle force in each analysis step were applied to the finite element model as boundary conditions. According to the specific analysis of the biological characteristics of human body, muscle force and joint force were predicted by using the model of bone-muscle system combined with non-invasive biomechanical experiments. We found that females had slightly higher adduction and internal rotation than males. When the knee flexion angle is about 90 degrees, the average adduction and external rotation angle of the subjects is almost the same. Sports biomechanics plays an important role in our training work and is a symbol of modern training. We should use it to better train technology, tactics and competitions.
Keywords: sports biomechanics; motion system model; joint force; buckling angle.
Simulation study on changes of EMG and physiological parameters of athletes under training state based on nano biomechanics analysis
by Xiancheng Zhang, Shuang Li, Haiying Liu, Zhaolian Cao
Abstract: In order to further research and develop the application of EMG analysis technology in sports practice, combined with the physiological characteristics of muscle contraction, this paper uses time-frequency analysis and nonlinear analysis methods to process the athletes' surface EMG signal, and tries to evaluate the athletes' training level and competitive state. The results show that the intrinsic mode functions IMF and imf3 can effectively reflect the recruitment methods of fast muscle and slow muscle fibre motor units, and help to evaluate the level of muscle activity and analyse the physiological process of muscle contraction. The results show that the EMG signals of rectus femoris and medial femoris are strongly nonlinear, while the EMG time of biceps is weakly nonlinear. The results strongly support the hypothesis that the physiological changes of muscle strength are related to muscle mass.
Keywords: nano biomechanical analysis; physical exercise; electromyography.
Research and development of nano-lipid functional food and its impact on the sports health industry
by Hua Tian
Abstract: With the development of China's economy and the improvement of living standards, people have paid unprecedented attention to the quality of life and health, and healthy food and functional food are extremely popular. With the continuous development of science and technology, nanoscale substances are increasingly favoured by scholars because of their advantages, such as small size effect and surface effect. Nano-liposome technology is a liposome technology with phospholipid bilayer biofilm structure, which embeds active substances to improve bioavailability and maintain its original performance. Lipid nanocarriers can be widely used in food because of their good physical and chemical stability, biocompatibility and biodegradability. This paper analyses the present situation, gaps and technical requirements of nano-lipid functional food in China, studies the influence of nano-lipid functional food on the sports health industry, and puts forward the development ideas and key tasks of nano-lipid functional food.
Keywords: nano lipid; functional food; sports health industry.
Analysis and research on regeneration therapy of athlete tendon injury based on nanometre sensor technology
by Dongmei Wang
Abstract: Scholars have been working on how to prevent adhesion formation after tendon injury without affecting tendon inclusion. In order to ascertain the causes of athletes' injuries and to develop targeted measures to prevent and treat injuries, this study used literature review methods, questionnaire surveys, expert interviews, inductive statistics, and speed muscle strength test methods to analyse athletes' sports injuries. Investigation and analysis, to explore the characteristics of athletes' sports injuries and their occurrence factors. The comprehensive exercise rehabilitation treatment and nursing effect of sports athletes after tendon injury repair can effectively promote the rehabilitation of limb motor function, reduce the occurrence of postoperative tendon rupture and adhesion, and promote the improvement of patients' quality of life. It is worthy of popularisation and application.
Keywords: athletes; tendon injury; therapeutic analysis; nanometre sensor technology.
Analysis of the influence of nanotechnology developments on the sports health industry
by Fanxing Kong, Shixue Ren
Abstract: The special structure of the material gene made at the nanometre level makes it have special or abnormal characteristics that traditional materials do not have, so it has extremely wide application prospects in many fields. Nanotechnology is helpful to the selection and training of athletes, promotes the development of sports bioscience, and effectively improves athletes' athletic ability. The entry of nano-materials into the body will affect liver and kidney tissues, cells, brain tissues and lung tissues, and there are potential biosafety hazards. Therefore, when using nano-materials in sports engineering, it is necessary to study its biosafety, so as not to endanger human health. This paper analyses the influence of nanotechnology development in modern sports science and technology on China's sports health industry, and discusses the promotion of high technology to China's sports health industry.
Keywords: sports health industry; biosafety; athletes' training; nano-materials.
Effects of spray pressure, distance, angle and equivalent orifice diameter on spray uniformity for nano-pesticide application
by Shougen Li, Yalan Jia, Yaxiong Wang, Feng Kang
Abstract: In the application process of nano-pesticides, the toxicity of the active ingredients on ecological diversity has attracted great attention. Improving the spray uniformity is the important method to ensure the benign usage of nano-pesticides. The relative span is difficult to characterise the uniformity of the liquid volume and the droplet size at different points in the atomisation field. Therefore, we proposed new evaluation indices for the spray uniformity and further discussed the influence of different spray parameters on spray uniformity. The results showed that the equivalent orifice diameter, spray pressure, and distance had little effect on the uniformity of the liquid volume on the spray cross-section. The increase in the equivalent orifice diameter could improve the uniformity of the droplet size span. The increase in the spray pressure also reduced the uniformity of the droplet size span. In addition, this study has provided positive guidance for nozzle design and nano-pesticide application.
Keywords: phase Doppler particle analyser; droplet size; liquid volume; spray parameters.
Application value of new nano-drug carrier in biphasic 3D-PCASL in the evaluation of cerebral perfusion blood flow in symptomatic and asymptomatic unilateral middle cerebral artery subtotal occlusion
by Lihua Weng, Xuesheng Zheng, Ruigen Pan, Xian Zhang, Jing Jin, Jianju Feng
Abstract: We investigate the use of superparamagnetic iron oxide nanoparticles as a contrast agent for the cerebral perfusion of unilateral middle cerebral artery (MCA) subtotal occlusion by magnetic resonance three-dimensional pseudo-continuous arterial spin-labelled imaging (3D-pCASL) dual-phase scanning technology and the application value of blood flow to evaluate the difference of cerebral perfusion between symptomatic and asymptomatic patients. We found that the biphasic 3D-pCASL technology can accurately detect cerebral perfusion blood flow in patients with subtotal middle cerebral artery occlusion. Asymptomatic patients have higher cerebral perfusion than symptomatic patients with collateral circulation superparamagnetic iron oxide nanoparticles. Phase (PLD) equal to 1.5 s is more sensitive than 2.5 s, and PLD = 2.5 s can accurately reflect secondary collateral circulation's compensation status.
Keywords: arterial spin labelling; new nano-drugs; superparamagnetic iron oxide nanoparticles; middle cerebral artery occlusion; magnetic resonance perfusion imaging; cerebral blood flow.
Modelling and simulation analysis of training effect and electromyogram change of track and field athletes based on biomechanics
by Xuejun Wang, Shunjiang Ma, Yunyan Zhang
Abstract: Taking the serious knee injury of Chinese elite track and field athletes as the research object, this paper discusses the characteristics and mechanism of knee injury and its relationship with special sports and training methods. The changes of bioelectricity during the activity of local neuromuscular system were guided, amplified, displayed and recorded by surface electromyography (iEMG) through surface electrodes on the skin surface. The results show that fast muscle movement unit easily becomes fatigued, and the muscle working ability of fast muscle movement unit decreases after fatigue. It is suggested that, in the future training, we should strengthen the training of lower limb support and buffering ability, and gradually improve the level of athletes' special strength.
Keywords: exercise training; sports injury; muscle fatigue; myoelectric signal.
Nanomaterials in diagnosis and treatment of cardiovascular diseases
by Xiaoqi Zhao, Chunguang Wang, Zhiguang Sun, Nan Yao, Aiting Zhang, Shengyu Guo
Abstract: This paper prepared a nucleic acid aptamer functionalized magnetic ball/gold nanorod composite material. Thrombin and its pair of nucleic acid aptamers can be built into a closed-loop structure based on the hybridisation technology of adjacent surfaces. The measurement of human serum coagulation time proves that this inhibition and recovery method has very good clinical application prospects. In the application of multi-functional nanomaterials to assist sports rehabilitation training, the paper analyses the level of serum N-terminal brain natriuretic peptide precursor (NT-pro BNP), high-sensitivity myocardial troponin T (hscTnT) and heart function indexes in patients with chronic heart failure influences. The results show that the use of multifunctional nanomaterials combined with exercise rehabilitation therapy in patients with chronic heart failure can significantly reduce serum NT-pro BNP and hscTnT levels and improve cardiovascular function.
Keywords: cardiovascular disease; thrombin; nucleic acid aptamers; near infrared light; gold nanorods; exercise rehabilitation.
Research on gene therapy of neurons based on narcotic nanoparticles intervention in cerebral ischemia
by Fan Zhang, Fan Lei, Xingpeng Xiao
Abstract: To detect the efficiency of gene therapy of anesthetic nanoparticle drug materials -CD-PAMAM and -CD-PAMAM/pEGFP-N3 in vitro to intervene in cerebral ischemic neurons, in preparation for further in vivo gene therapy. Standard non-viral vector polyamidoamine dendrimer PAMAM was used as a positive control group. Cytotoxicity test: in the two types of cell, when N/P < 10, all anesthetic nanoparticle drug material carriers and their complexes showed low toxicity, and the cell survival rate was above 80% (P < 0.05); when N/P > 20, the cell survival rate of -CD-PAMAM and -CD-PAMAM/pEGFPN3 complex was higher than that of PAMAM and PAMAM/pEGFP-N3 complex (P<0.05). The cytotoxicity and gene transfection efficiency of -CD-PAMAM have been clarified, and it has been confirmed that -CD-PAMAM can be used as a carrier for gene transfer in Hek293 and SH-SY5Y cells, and can further interfere with gene therapy of cerebral ischemic neurons.
Keywords: anaesthetic nano-particle pharmaceutical materials; intervention of cerebral ischemic neuron gene therapy; cytotoxicity test.
Special Issue on: Nano Impact on Next-Generation Biomedical and Environmental Research
Study on the effect of carrier-free dual-drug nanocrystals against colon cancer cells
by Qing Jiang, Yi Zhang, Yilan Qiu, Meifang Quan, Shanyi Yang, Feiyi Chu, Rong Wang, Zhichao Ye, Xiaojun Tao, Rushi Liu
Abstract: Nano-delivery systems are often used for targeted therapy of tumours, but a single anti-tumour drug often causes drug resistance in tumour cells, thereby reducing the effectiveness of the drug in chemotherapy. Therefore, in order to overcome the drug resistance of tumour cells, nano-formulations of combined drugs have emerged. This work aims to design a carrier-free dual drug-loaded nanocrystalline HCPT-DOX NCs, and verify its possibility of treating colon cancer through experiments. And through the comparison with a single drug, it highlights its advantages in the treatment of colon cancer. The synthesised HD NCs are characterised by dynamic light scattering (DLS), and the best synthesis ratio is selected. Through the drug release experiment, the release efficiency of a single drug in HD NCs is explored. Through MTT experiment, cell scratch experiment, cell cloning experiment, etc., the possibility of HD NCs in the treatment of colon cancer at the cellular level is discussed. The HCPT/DOX (HD) molar ratio of 1:1 is the best choice for the preparation of analytically valuable nanocrystals. Drug release experiments showed that the cumulative release rates of HCPT and DOX in HD NCs at 48 hours were 94.03% and 74.15%, respectively. MTT experiments of the colon cancer cell HCT116 and RKO showed a stronger synergistic inhibitory in low-concentration HD NCs than in drugs that underwent a simple physical mixing, and the RKO cells exhibited a more obvious effect. HD NCs could significantly inhibit colony formation of colon cancer cells in cell cloning experiments. Cell migration experiments showed that drug combination therapy of HD NCs could effectively inhibit the migration of cancer cells. By cell uptake experiments, it was determined that at the same concentration, the drug absorption of HD NCs was much higher than that of the free drug. HD NCs with appropriate particle size can be synthesized at a certain feeding ratio. The dual-drug-loaded HD NCs showed better anti-tumour effects than a single drug and a simple mixture of two drugs.
Keywords: nanocrystals; dynamic light scattering; synergistic inhibitory; cell migration; colon cancer cells.
Effect of copper nanoparticles on growth parameters of maize seedlings
by Ali Raza, Saira Ahmad, Abdul Mateen, Adnan Arshad, Abdur Rehman, Helena A. L. Oliveira
Abstract: Nanoparticles (NPs) acknowledged great attention due to their unique properties and efficient applications in various sectors. Copper (Cu) is an essential micronutrient for plants, which acts either as the metal component of enzymes or as a functional structural or a regulatory co-factor of a large number of enzymes. To understand the possible benefits of applying nanotechnology to agriculture, an important first step is to analyse penetration and transport of NPs in plants. The main aim of this article is to explore the possible effects of CuO NPs on germination maize seedlings and growth of plant. Results showed that the germination rate, root and shoot length increased during uptake of Cu in the roots and the length of the shoots increased at high concentrations of CuO NPs. There was no toxic effect observed to inhibit seedling growth. In the sample in which the plant received a mixture of 800 mg/L of CuO NPs, the chlorophyll content decreased with photosynthetic activity and the catalase activity also decreased. While, in the sample in which the plant received a mixture of 550 mg/L CuO NPs, enzymatic and photosynthetic activities increased significantly along with root and shoot development in maize. The acquired results delivered conclusive evidence to indicate that the NPs considered under this study could enter into the maize plant cells, simply be assimilated by maize plants and also improved its growth by regulating the various enzyme activities.
Keywords: copper nanoparticles; growth restrictions; maize assimilation.
Synthesis, morphology and optical characterization of transition metal Oxide (Mn3O4) nanostructures and its antibacterial activities
by Syed Asif Naqvi, Ghulam Mustafa, Junaid Karim, Masood Raza, Saima Tauseef, Zaheer Uddin
Abstract: We studied the chemical growth of Mn3O4 nanostructures by employing the co-precipitation method. The size of the nanoparticles of the Mn3O4 sample through XRD was found to be 17-34 nm. By optical characterisation of Mn3O4, in addition to actual three sub-band gaps were found. It shows fractionalised band gaps followed by Schottky barriers, which would have different bandgap energies. It is related to fractional quantum behaviour and quantum jumps. EDX shows the exact percentages of elements that verify the Mn3O4 nanoparticles percentile. SEM results show Penrose-like structures with cylindrical entanglement, web, and granules morphology pores in each of the four different sizes of the granules. Mn3O4 nanoparticles also show antibacterial activities. The suppression of growth of Staphylococcus aureus, Enterococcus faecalis, Escherichia coli, and Citrobacter sp. was observed, showing the antimicrobial potential of manganese oxide nanoparticles. The optimal antibacterial concentration for Mn3O4 manganese oxide nanoparticles was found to be 400 ug/disc.
Keywords: Mn3O4 nanoparticles; antibacterial activity; Penrose-like structure; Staphylococcus aureus; Enterococcus faecalis; Escherichia coli; Citrobacter sp.
Special Issue on: Smart Bio-Signal Acquisition System
Smart approach to the impact of cumin powder on obesity among adults in the urban area of Puducherry, India
by J. Suganya, S. Singaravelu Ramasamy, U. Dinesh Babu, R. Vijayaraghavan, K. Emayavarman, T.P. Latchoumi
Abstract: This research study aimed to discover the effect of cumin powder on physical and biological parameters among adults with obesity in the urban area of Puducherry. Cumin may be effective in lowering cholesterol and in weight loss. Cumin may be helpful for people trying to lose weight and lower total cholesterol, LDL or "bad" cholesterol, and triglyceride levels with type 2 diabetes and also to help the body to handle stress. The following experimental design is used. A total of 40 adults with obesity for control and experimental groups, BMI value of 25, total cholesterol >200 mg/dl, and age group of 20 to 60 years were selected. After evaluating the lipid profile, a bag of cumin powder with warm water was administered for 70 days (10 weeks). Prior to and post action, all anthropometric and biochemical parameters were measured. Warm water and cumin powder decreased serum and total cholesterol, and slightly reduced BMI (p<0.05). In both anthropometric and biochemical parameters of overweight in adults, cumin powder with hot water showed a drop.
Keywords: overweight; obesity; cholesterol; body mass index.
Bioanalytical method devolpment and validation of a novel antiseizure agent Canobamate, using LC-MS/MS
by Yamarthi Venkateswara Rao, Rasheed Ahemad Shaik, Jithendra Chimakurthy
Abstract: To quantify Ceobamate in human plasma, a precise and sensitive electroionisation procedure in tandem mass spectrometry has been developed. The study was successfully confirmed by using Cenobamate-D4 as the internal norm and tert-butyl methyl ether in the testing of several positive ionic reactions. It has been used in liquid-liquid extraction to prepare samples as an extraction solvent. Cenobamate and Cenobamate-D4 (internal standard) were separated on an Eclipse C18 column (150 mm, 4.6 mm I.D., 5
Keywords: Cenobamate; human plasma; epilepsy; robustness; validation.
Analysing behavioral and academic attributes of students using educational data mining
by Muhammad Umer, Saima Sadiq, Arif Mehmood, Imran Ashraf, Gyu Sang Choi, Sadia Din
Abstract: Educational data mining has attracted significant consideration over the last few years. Information that is stored online using educational systems is increasing tremendously. The online learning environment can be improved by analysing and mining this information to extract representative features about students' behaviour and academic skills. Various classifiers are investigated to analyse the prediction of students' academic performance based on the attributes from the Kalboard360 learning management system (LMS). The selection of significant features can substantially improve the prediction, hence, ANOVA and chi-square filter approaches and forward feature selection and backward feature elimination wrapper approaches are examined for their efficacy. Results reveal that an extra tree classifier can achieve an accuracy of 0.8755 when trained on backward feature elimination selected features. Wrapper approaches prove to be effective in determining the most significant attributes for student performance prediction. 'Visited resources', 'raised hands', 'relation', 'parent answering survey', and 'student absent days' are regarded as the most significant attributes to determine student performance. Education specialists and institutions can leverage these findings to improve the student learning process and enhance their academic performance.
Keywords: metadata; educational data mining; feature extraction; ANOVA; chi-square filter; forward feature selection; backward feature elimination; extra tree classifier.
Multi to binary class size based imbalance handling technique in wireless sensor networks
by Neha Singh, Deepali Virmani, Gaurav Dhiman, S. Vimal
Abstract: Wireless sensor networks are used various disciplines including healthcare, banking, transportation, ocean and wildlife monitoring, earthquake monitoring, and numerous military applications. Now-a-days, there is escalation in size of data which makes it unfeasible to analyze it with accuracy. There are numerous problems that are faced when detecting patterns between structured and unstructured data that are unworkable by humans, so to make computation fast, easy and accurate, Machine Learning came in existence. Machine learning is extensively used in Wireless sensor networks. To make a machine learn, a training dataset is required and output is predicted by testing the dataset. A dataset in wireless sensor network has multi-class in its dependent variable. This multi-class classification causes class imbalance problem. This paper proposes MBSCIH (multi to binary class size based imbalance handling) technique in wireless sensor networks to solve the class imbalance problem in multi-class classification. MBSCIH converts multi-class classification into binary-class classification. MBSCIH is applied on WSN-DS, NSL-KDD and KDD-Cup 99 datasets and is tested with five major machine learning algorithm: Naive Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The test method used for testing is 10-fold cross validation. Results indicate that the proposed method increases the existing efficiency by 15.13%, 0.28%, 0.01%, 0.01%, 0.12% for Na
Keywords: wireless sensor network; multiclass classification; binary classification; intrusion detection; WSN-DS dataset.
A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images
by Jinhong Sun, Liang Qi, Yinglei Song, Junfeng Qu, Mohammad Khosravi
Abstract: Recently, with the explosive growth in the number of available medical images generated by medical imaging systems, content-based retrieval of medical images has become an important method for the diagnosis and study of many diseases. Most existing methods find medical images similar to a given one based on the extraction and comparison of crucial image features. However, similarity values computed with low level visual features of an image generally do not match the similarity obtained from human observation well. The overall performance of these methods is thus often unsatisfactory. This paper proposes a dynamic programming approach for content-based retrieval of medical images. The approach represents an image with three different histograms that contain both crucial intensity and textural features of the image. The similarity between two images is evaluated with a dynamic programming approach that can optimally align the peaks in the corresponding histograms from both images. Experiments show that the proposed approach is able to generate retrieval results with high accuracy. A comparison with state-of-the-art approaches for content-based medical image retrieval shows that the proposed approach can achieve higher retrieval accuracy in both ordinary and nano-scale medical images. As a result, higher retrieval accuracy may lead to more reliable results for the diagnosis and treatment of many diseases. The proposed approach is thus potentially useful for improving the reliability of many applications in health informatics.
Keywords: medical image retrieval; similarity; alignment of histograms; dynamic programming.
Small cell lung tumour differentiation using fluorine-18 PET and smoothening using Gaussian 3D convolution operator
by J. Vijayaraj, D. Loganathan, T.P. Latchoumi, M.V. Pavan, P.J. Lakshmi
Abstract: Nowadays, the most common disease for smokers is lung cancer. The deadly type of lung cancer is Small Cell Lung Cancer (SCLC). Tumour identification is complicated. It is only in the final stage that this form of lung cancer can be detected. When the patient has some of the earlier symptoms of SCLC, they can be subjected to preliminary tests for cancer. Two composite images, the maximum intensity projection and artificial intelligence, were automatically generated from the 4D image datasets. So, this paper presents the part of the identification of lung cancer by differentiating identified tumour cells using fluorine-18 Positron Emission Tomography (F-18 PET) and that can be smoothened using the smart Gaussian 3D convolution operator. The performance analysis shows the lung image dataset showing differentiation of tumour cells by applying this technique and simulation graphs for accuracy, sensitivity, and precision.
Keywords: small cell lung cancer; tumour cell; fluorine-18 PET; Gaussian 3D convolution operator.
Improved generalized fuzzy peer group with modified trilateral filter to remove mixed impulse and adaptive white Gaussian noise from colour images
by Akula Suneetha, E. Srinivasa Reddy
Abstract: In image processing applications, image denoising is an emerging area that recovers the original image from noisy image, which is essential in the applications like pattern analysis. The main aim of this study is to propose an effective filtering technique with peer groups for effective image restoration processes. In this research paper, a new approach is proposed that includes fuzzy-based approach and similarity function for filtering the mixed noise. In a peer group, the similarity function was adaptive to edge information and local noise level, which was utilized for detecting the similarity among pixels. In addition, a new filtering method (modified trilateral filter) was combined with improved generalised fuzzy peer group (IGFPG). The modified trilateral filter includes Kikuchi algorithm and loopy belief propagation to solve the inference issues on the basis of passing local message. In this research work, the images were collected from KODAK dataset and a few real time multimedia images like Lena were also used for testing the effectiveness of the proposed methodology. The collected colour images were contaminated with Adaptive White Gaussian Noise (AWGN) of standard deviation [05,20] and impulse noise of probability [0.05,0.20]. From the simulation, the proposed approach attained better performance related to the existing approaches in light of Normalised Color Difference (NCD), Peak Signal-to-Noise Ratio (PSNR), and Mean Absolute Error (MAE). Hence, the proposed approach achieved PSNR of almost 36 dB, which shows 0.2 dB to 2 dB improvement over the existing methods.
Keywords: adaptive white Gaussian noise; belief propagation technique; impulse noise; improved generalised fuzzy peer group; Kikuchi algorithm; modified trilateral filter.
Optimising pharmacokinetics via ADMET, bioactivity of Zr substituted samarium-doped ceria nanomaterials
by Bradha Madhavan, Suvitha A, Nagaraj Balakrishnan, Ananth Steephen, Ajay P
Abstract: The properties of ADMEs, bioactivity and restricted molecular medicinal chemistry were analysed via Swiss ADME in order to facilitate drug development. The important activities of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) in optimising pharmacokinetics and assessing the Ce0.8-xZrxSm0.2O2?? compounds were estimated. Six physico-chemical characteristics, bioactivity radar, donor acceptors and molar refractivity are being tested. In search of significant improvement in CeO2 fluorite structure, a promising doping, co doping approach was adopted. By varying the mole ratios of samarium dopant and Zr substitution, the Ce0.8-x ZrxSm0.2O2 nanomaterials were synthesised through simple chemical processes. Basic electronic structure calculation, bonding graph and crystallographic representation were analysed via a materials project tool. From the bonding graph, material design, synthesis glitches and stability assessments from the energies were calculated. ADMET Predictor is a product tool that rapidly and reliably forecasts more than 40 properties, including dissolvability, log P, CYP digestion positions and mutagenicity of Ames. Hence, these efforts are promising in monitoring pharmacokinetics via bio-electronics sensors and interfaces.
Keywords: bio sensors; bio-potential ceria; bio-electronics; bioactivity; pharmacokinetics.
EFFECT OF DIALYTIC STRETCH EXERCISES ON MUSCLE CRAMPS AND QUALITY OF LIFE AMONG HEMODIALYSIS PATIENTS
by P. Gophi, V. Shruthikamal, R. Danasu
Abstract: Chronic kidney disease is a serious scourge of non-communicable infections that influences the global population. The pervasiveness of end-stage kidney disease is increasing primarily because of diabetes mellitus and hypertension. Among hemodialysis patients, the most widely recognised difficulty is muscular cramps. It can occur anywhere in the body, however normally in the lower muscles of the legs, feet, toes, thigh, and middle section. This review evaluated the impact of dialytic stretching on muscle problems and the personal satisfaction of hemodialysis patients. A semi-exploratory scan pattern was selected. The investigation members were chosen by using the purposive inspecting method and during the second hour of hemodialysis the members were told to perform dialytic stretch exercise includes straight leg extension exercise (thigh hamstring muscle), seated marching (thigh front and back quadriceps muscle), leg stretch (leg front and back ankle), calf muscle stretching (gastronomies muscle) for a period of 12 sittings. The results of the muscle problems and personal satisfaction survey demonstrated improvement and were critical at a p < 0.05 level in light of mediation.
Keywords: muscle cramps; dialytic stretch exercises; hemodialysis.
Predictive protein module based on PPI network and double clustering algorithm
by Sicong Huo, Quansheng Liu, Tao Lu
Abstract: Protein-protein interaction (PPI) is a kind of biomolecular network which plays an important role in biological activities. In order to improve the accuracy of protein function module prediction, obtain the protein function module and run timely, this paper proposes a predictive protein module based on PPI network and double clustering algorithm (ISCC), which considers the characteristics of PPI network and considers nodes as two-dimensional data points. First of all, improved Density-Based Spatial Clustering of Applications with Noise (IDBSCAN) determines the central cluster, and then uses Spectral Clustering (SC) to redivide the weight; secondly, CFSFDP and chameleon algorithm are used to filter the similarity of the central cluster, and Support Vector Machine (SVM) is used to get the final clustering result. Finally, the experiments are compared with CDUN, EA, MCL and MCODE in terms of accuracy, sensitivity, F value and the number of protein functional modules. The experimental results show that the F value of ISCCD is 70% higher than that of EA, the number of recognition modules is 257 higher than that of CDUN, and the running time is 494 s faster than that of MCODE
Keywords: PPI network; protein function module prediction; clustering; SVM; IDBSCAN.
Diagnosing cardiovascular disease via intelligence in multimedia healthcare: a novel approach
by Geeitha Senthilkumar, Fadi Al-Turjman, Rajagopal Kumar, Jothilakshmi Ramakrishnan
Abstract: Diagnosing cardiovascular disease (CVD) in its early stages remains a challenge despite the existence of all the medical technologies and devices that are being used. Besides the digitized form of collecting and organizing data, prediction and diagnosis are two stumbling blocks in CVD. This study explores statistical machine learning models with a multimedia healthcare approach using artificial intelligence to predict risk factors of heart diseases associated with type 2 diabetes mellitus (T2DM). This study investigates the efficacy of a mathematical model to perform attribute evaluation using information criteria-based selection in LASSO regression. The present study implements a deep learning algorithm using a MultiLayer Perceptron (MLP) classifier with Gaussian Process Classification (GPC) that provides probabilistic predictions in terms of linear and nonlinear functions. The performance of the classifier is evaluated using precision, recall and accuracy metrics. The proposed classification model yields 93.59% accuracy of 10 cross-validations assorted with sigmoid function for better analysis.
Keywords: artificial intelligence; cardiovascular disease; multimedia healthcare; feature selection; type 2 diabetes.
Fishier mantis optimiser: a swarm intelligence algorithm for clustering images of COVID-19 pandemic
by Javad Rahebi
Abstract: In this study, an automated segmentation method is used to increase the speed of diagnosis and reduce the segmentation error of CT scans of the lung. In the proposed technique, the Fishier mantis optimiser (FMO) algorithm is modelled and formulated based on the intelligent behaviour of mantis insects for hunting to create an intelligent algorithm for image segmentation. In the second phase of the method, the proposed algorithm is used to cluster scanned image images of COVID-19 patients. Implementation of the proposed technique on CT scan images of patients shows that the similarity index of the proposed method is 98.36%, accuracy is 98.45%, and sensitivity is 98.37%. The proposed algorithm is more accurate in diagnosing COVID-19 patients than the falcon algorithm, the spotted hyena optimiser (SHO), the grasshopper optimisation algorithm (GOA), the grey wolf optimisation algorithm (GWO), and the black widow optimisation algorithm (BWO).
Keywords: meta-heuristic algorithms; Fishier mantis optimiser; COVID 19; coronavirus; clustering.
Wearable IoT enabled smart heart disease monitoring on WSN
by Xiaofei Wang, Xiaodong Li, Bing Zhang, Yanghua Liu
Abstract: The age profiles of many countries are increasing day by day with increasing population of individuals affected by the chronic diseases such as diabetes, cardiovascular disease, obesity and so on. In order to maintain the individual living remote health monitoring with daily activity by recognising people is a promising solution. Cardiovascular disease (CVD) is the major cause of mortality globally. Most of the deaths due to CVD are sudden and without any chance of medical help. In order to avoid this accidental death, precautions are required with continuous monitoring of body parameters such as heart rate, pulse rate and electrocardiogram (ECG) to show the current status of the health. IoT is rapidly growing industry in many disciplines including healthcare. In current research, heart disease is monitored with processing of electrocardiogram signals. The existing monitoring system lacks prediction accuracy and remote monitoring. In this proposed work, the gathered data from the wearable devices are preprocessed to remove the noise. The relevant features for better recognition are selected using the proposed LBPNet with PSO. Then a sequential minimal optimisation based SVM classifier recognises the abnormalities of heart disease from normal patients for diagnosis. These data are available in remote servers for doctors and carers with IoT application. The carers are notified about the patient health via smart phones. This proposed system is useful for cardiac patient monitoring and updating with high accuracy.
Keywords: cardiac disease; health monitoring; IoT; WSN; deep learning; PSO; LBPNet; ECG; SMO-SVM.
Experimental analysis of boost converter performance with non-ideals for sustainable energy applications
by Chegireddy Naga Kota Reddy, Choppavarapu Sai Babu
Abstract: In sustainable energy applications, boost converters are preferred owing to the low voltage profile of sustainable energy sources. Because of their light weight, high reliability, and small size, boost converters play a vital role in biomedical portable devices. In this paper, boost converter performance is examined using nonidealities of semiconductor devices. The impact of such nonidealities on the different parameters is investigated. To compensate for the damage incurred by nonidealities, expressions for output voltage and duty cycle are derived. The expression for voltage gain has been modified as a side effect of these nonidealities. The design equation for the inductor is modified based on these nonidealities. Further, nonidealities based expressions for the average value and ripple of current flowing through the inductor have also been established. Ultimately, these theoretical findings are validated with the results of simulation in MATLAB and hardware setup. In the hardware, the desired output voltage of 24 V is obtained from a 12 V input source by enhancing the duty cycle from 0.5 to 0.5174.
Keywords: sustainable energy sources; ideal boost converter; non-ideal Boost converter; output voltage; duty cycle; voltage gain; critical inductance; design of inductor; inductor current ripples.
Feature extraction based on motor imagery EEG
by Qingjun Wang, Zhihan Lv
Abstract: In order to study the entire process of EEG signals, the feature extraction and classification of motion imaging EEG signals becomes very meaningful. This article aims to study the feature extraction based on moving image EEG. Today, more mature motor imaging EEG signals are mostly two-dimensional signals, and the recognition results for 3D or 4D are low. In this paper, the wavelet transform is used as the feature extraction method, and support vector machine is used as the classifier to study the recognition of the four imaginary movements of left and right and tongue and leg. The simulation results of 20 subjects show that the four EEG signals have significant time-frequency domain differences and the recognition result is above 80%, with good recognition effect.
Keywords: motion imaging; electroencephalogram; wavelet transform; support vector machine; feature distribution.
Visible property enhancement techniques of IoT cameras using machine learning techniques
by Subbiah Narayanan, G.H. Sastry, Shobha Aswal, Venkatadri Marriboyina, R. Sankaranarayanan, Varsha Ph.D
Abstract: Visual perception in low light is challenging owing to low signal to noise ratio and photon counts. Deep learning is a kind of machine learning that is revolutionising picture identification and computer perception. In this study, deep learning will be used to enhance low-light picture filtering. To do this, a literature review will be performed to gather inspiration for methods and features that may be applied to the final networks. A fully functioning deep learning picture-filtering system will then be created, allowing networks to be trained using guided learning and the filtered resulting images to be recorded to files. With its output pictures plainly showing it was filtering low-light shots, the network functioned effectively. To maximise the network's potential, it must be run for a longer length of time.
Keywords: IoT cameras; visual improvement; signal to noise ratio; machine learning.
Real time crop field monitoring system using agriculture IoT system
by Pankaj Agarwal, Deepthi Gorijavolu, Hanumat Sastry G, Venkatadri Marriboyina, D. Vijendra Babu, Kishore C K
Abstract: Modern agriculture systems use features up to the maximum extent. In this paper, a propose wireless smart automation (WSA) with IoT system that interfaces with users' mobiles for real time crop field monitoring and control through the internet anytime from anywhere in the world. It is mainly focused on adapting smart methodology for farming, field monitoring and enhancing crop production. It provides a low cost and reliable monitoring towards field crops through IP connectivity for accessing and controlling devices from a smart mobile app. In addition to this, proper security authentication is given to this system through the Arduino UNO microcontroller. The user can operate drones for different control purposes, such as pesticide spraying, crop size monitoring, and field water level. The major advantage of the proposed system is adapting automation technology for continuous monitoring of crop fields with an IoT system.
Keywords: wireless smart automation; IoT system; Arduino; UNO microcontroller.
Image feature extraction algorithm based on parameter adaptive initialisation of CNN and LSTM
by Dong Li, Zai Luo, Xingmin Ma
Abstract: The selection and extraction of image features are the basis of image processing. For different types of image and different application requirements, the selection of image features will be different. This paper proposes an image feature extraction algorithm based on MMN linear activation function adaptive initialisation of convolutional neural network parameters. First, a parameter initialisation algorithm based on the multi-layer Maxout activation function is proposed, which solves the problem of poor recognition effect caused by improper parameter initialisation. Next, the selective dropout algorithm for shallow learning of long short-term memory networks is introduced to prevent overfitting. Finally, the SUSAN operator and random consensus algorithm are introduced for fine matching and purification. Experimental results show that the algorithm in this paper can obtain more detailed image information and edge information, and can better reflect image feature information.
Keywords: feature extraction; convolutional neural networks; long short term memory network; image matching; adaptive initialisation.
Implementation of intrusion detection system and improvement using genetic algorithm
by Ke Huang, Bichuan Sun, Xianming Sun, Mohammad Shabaz, Rijwan Khan
Abstract: The rapid evolution of computer technology due to the vast services and applications has made people dependent on computer technology. As a result, there could prevailing threats that need to be addressed, while dealing with the networking background. Therefore, we require the security to assist the networking technology in revealing the vulnerability abuses against the uses of a computer or its applications. In todays interacting environment, an Intrusion Detection System (IDS) is one of the major security components. It uses the security tools in the traditional way, and firewalls are also a significant method. The IDS is a security system that provides effective methods for computer network safety. This paper addresses the detection rate maximisation and false rate minimisation that are a major problem owing to its inability to discover a particular attack. This problem is tackled by the Genetic Algorithm (GA) approach presented in this paper, using the fuzzy methods for the IDS development. As a robust technology, it is most commonly used for IDS design and is based on machine learning. It is a search algorithm based on natural selection and genetics principles. For the GA specific problem solution, the fittest survival principle is used by GA functions for better approximation generation. In our approach, two datasets are used to perform the experiments. In the first dataset, 137 attacks and 840 normal connections, 977 connections in total, are kept in dataset 1; and in dataset-2, 234 attacks and 744 normal connections, 978 connections in total, are included. For both experiments, the presented technique manages a high detection rate, high accuracy, and a low false alarm. Therefore, the proposed technique outperforms the existing techniques with 96.57% detection rate and 3.12% false alarm.
Keywords: intrusion detection system; genetic algorithm; detection rate; false alarm; security system; intrusion prevention system; firewall; network-based intrusion. Detection(NIDS),Knowledge Discovery in Databases (KDD).
Analysis of resting state functional magnetic resonance images for evaluating the changes in brain function depression
by Hao Yu, Ye Yuan, Ashutosh Sharma, Abolfazl Mehbodniya, Mohammad Shabaz
Abstract: Continuous emotion of sadness is habitually considered as Major Depressive Disorder (MDD) that has parallel signs like other mental illnesses. These parallel indicative features can frequently lead to suffering of depression and other psychological conditions, and therefore involve experts to predict such symptoms and the timely treatment of MDD in order to evade the adverse effects. Magnetic Resonance Imaging (MRI) has a vital role in deducing the pathologies related to MDD. This paper deals with the application of data collection for the characteristics of spontaneous brain activity in the basic state of depression in patients, using resting state functional magnetic resonance images (fMRI), and discusses the changes in the brain function during a depression stage. In this paper, 16 patients with depression underwent 5 minutes and 12 seconds of brain functional MRI scan, and the Hamilton depression scale was used to evaluate the severity of the condition. The ReHo software was used to examine local brain regions on the image data. It is revealed that the resting brain fMRI-ReHo method found that the abnormal brain function area of patients with depression included: left thalamus, left temporal lobe, left cerebellum, occipital lobe, and the spontaneous activity consistency of patients in these areas was reduced. This work is done by SVM approach that uses AUC value of 0.885 for prediction, and it outperforms the state-of-the-art methods in a brain abnormality prediction by a maximum improvement of 22.24% and minimum improvement of 13.75%.
Keywords: data collection; magnetic resonance imaging; functional magnetic resonance images; resting state function; depression; brain function; Hamilton depression scale; default mode network.
Low area FPGA implementation of modified histogram estimation architecture with CSAC-DPROM-OBC for medical image enhancement application
by Koteswar Rao Bonagiri, Giribabu Kande, P. Chandrasekhar Reddy
Abstract: In this work, a Modified Histogram Estimation (MHE) architecture is proposed to verify the histogram count in the FPGA platform, and the Basic HE (BHE) architecture is also implemented for comparative purpose. The entire proposed MHE architecture is developed newly so as to reduce the logical elements involved in the HE process. In the MHE architecture, Dual Port Read Only Memory (DPROM), Carry Select Adder based Counter (CSAC), and Optimal Bin Counter (OBC) are used to evaluate the HE count with effective accuracy. The amount of percentage reduced by the 256-sample MHE is 17.62%, 15.41% and 23.01% for area, power and delay respectively. Additionally, the performance of the proposed MHE is compared with four existing methods: HOG, HBS, MBPA and DMH. The number of flip flops used by the MHE architecture is 2177 for the Vertex 6 device, which less when compared with the HOG and MBPA.
Keywords: application specific integrated chip; carry select adder based counter; dual port read only memory; field programmable gate array; medical image enhancement; modified histogram estimation; optimal bin counter.
Lightweight and secure IoMT edge device architecture through computing base minimization and computing process optimisation
by Prateek Mishra, Sanjay Kumar Yadav, Amit Kishor, Ravi Kumar Sachdeva
Abstract: Internet of Medical Things (IoMT) edge devices are resource-limited medical devices in terms of battery, footprint and processing power. Owing to limited resources, security and performance in presence of bulky hardware and software resources are challenging. Bulky resources are insecure owing to higher attack surface area. Performance optimisation of IoMT edge devices needs optimum resource surface area, known as trusted computing base. Minimisation of the trusted computing base in terms of number and size decreases visibility to the unauthorised world and increases lightweight and security of the architecture. This paper presents an Arduino IDE and ESP32 micro controller based IoMT edge device architecture to minimise the computing base in terms of least number and size of resources for lightweight and secure architecture. Computing process optimisation is proposed using lightest secure IoT algorithm with minimum size key. Comprehensive and direct comparisons between existing and proposed architectures result in a tightly secure and lightweight IoMT edge device architecture. Owing to the resource-constrained nature of the IoMT edge device, the aim of this work is to propose a lightweight and secure IoMT edge device architecture using minimum computing base and computing process optimisation.
Keywords: computing base minimisation; computing process optimisation; IoMT edge device; lightweight; secure IoT algorithm.
An improved model for unsupervised voice activity detection
by Shilpa Sharma, Rahul Malhotra, Anurag Sharma
Abstract: The antique way to express our selves is speech and nowadays speech is being used in many applications, especially in machine communication. As the application of speech is increasing at a rapid rate, various techniques are evolving to separate out the speech signals from audio signal, which is mixture of noise and speech. The method to distinguish voice and noise is known as voice activity detection. This method is gaining huge popularity as it removes background noise and is an acceptable approach in the area of speech coding, audio surveillance and monitoring. In this paper, a hybrid model of unsupervised classifier is investigated. The proposed approach is tested at different levels of noise signal and overlap window size. To validate the proposed approach, comparison with existing artificial neural network and support vector machine is presented. The outcomes of the proposed method are observed to be better than the existing methods, with an accuracy of 99.73% along with a better SNR of 25.61 dB. Also, the proposed model LFV-KANN efficiently handles increases in noise power by hybridisation of two classifiers: ANN and K-means clustering.
Keywords: voice activity detector; artificial neural network; support vector machine; K-means; unsupervised learning; machine learning; TIMIT database.
A novel hybrid model for automatic diabetic retinopathy grading and multi-lesion recognition method based on SRCNN and YOLOv3
by Prasanna Lakshmi Akella, Kumar Rajagopal, Fadi Al-Turjman
Abstract: Automatic grading and lesion identification of Diabetic Retinopathy (DR) is important for researchers because it is the leading cause of diabetes. Due to diabetes, the tiny blood vessels within the fundus are damaged and multiple lesions such as microaneurysms, haemorrhages, hard exudates, and soft exudates appear in the retina and cause multiple vision-related complications, which can drive to total vision loss without early examination and treatment. For clinical screening and diagnosis of DR, retinal fundus images are commonly used. Fundus images were taken by operators with different levels of experience, however, have a broad variance in quality. Low-resolution images of the fundus raise the risk of misdiagnosis and makes it more difficult to observe clinically. In order to avoid low resolution fundus images and to be able to diagnose DR carefully, authors developed a new hybrid structure in our proposed system to ensure that DR detection and classification processes become much more precise and faster compared with existing models. In the image pre-processing stage, the proposed model adopts a Super-Resolution Convolutional-Neural-Network to enhance the pixel density of low-quality fundus images. In the next step, to identify the DR grade, an advanced deep-learning model called You-Only-Look-Once Version 3 is used. Finally, another You-Only-Look-Once Version 3 network stage is applied using a bounding box to recognize the multiple lesions in the fundus images. The proposed system is evaluated on an openly accessible MESSIDOR dataset, and the results show that the system achieves 96.89% overall accuracy for DR grading and 97.6% accuracy for lesion detection with a high detection speed of 5.6 seconds.
Keywords: diabetic retinopathy; multiple lesions; diabetes; blood vessels; deep-learning; SR-CNN- YOLOv3.
Gas chromatography-mass spectrometry determination of polycyclic aromatic hydrocarbons in oil fried quail meat vs rabbit meat
by Rabia Siddique, Amna Sarfraz, Ameer Fawad Zahoor, Shazia Naheed, Muhammad Faisal Manzoor
Abstract: Processing improves the microbiological profile of food, but also introduces carcinogenic compounds, such as polycyclic aromatic hydrocarbons (PAHs) in food items. It has been noticed that generation of these carcinogenic compounds can be reduced by the marination process. There is a difficulty to check PAH levels and make comparison of results when many variables are involved. The study analysed the concentration of PAH standards in black and brown quail meat, checked the effect of different recipes (in which different spices are used), and also compared the recipe-wise PAH concentrations. This study also focused to compare the PAH values in both quails (brown and black) as well as their PAH results with a previous study of rabbits. PAHs were analysed by gas chromatography mass spectrometry from 42 samples of black and brown quail meat samples. The maximum levels of naphthalene were noticed in recipe V (country fried kebab) (1.71
Keywords: polycyclic aromatic hydrocarbons; quail meat; GC-MS; frying recipes; naphthalene; Tukey test.
Sleep stage based sleep disorder detection using single-channel electroencephalogram
by Vijaya Kumar Gurrala, Padmasai Yarlagadda, PadmaRaju Koppireddi
Abstract: The use of artificial intelligence in healthcare is the next generation of healthcare which can be the bridge between the physician and the patient. Sleep disorders hamper ones performance and are considered as a serious problem to overcome. There are several sleep disorders such as excessive sleep (hypersomnia), inability to sleep (insomnia), snoring, apnoea, etc., that are detected with the analysis of polysomnogram (PSG) signals. Considering many signals for PSG increases the systems memory and computation requirements. Hence, in this work, a machine learning model is proposed, considering single-channel EEG. Unique features are defined and extracted from sleep stage data to detect sleep disorders. The entire system consists of the sleep stage detection followed by sleep disorder detection. Accuracies of 98.8% using an SVM classifier for sleep stage detection and 95.9% for sleep disorder detection using ensemble bagged tree classifier are found.
Keywords: apnoea detection; machine learning; single channel sleep EEG; sleep stages; wavelet decomposition.
Groundwater quality index and human health risk assessment of heavy metals in and around Asansol Industrial Area, West Bengal, India
by Deepak Naresh Dhopte, Prasoon Kumar Singh, Jaydev Kumar Mahato, Shivam Saw
Abstract: The quality and quantity are equally important during the administration of water assets. Unscientific practices at Asansol industrial zone, which houses a diverse range of industries, releases a lot of untreated sewage along with the municipal waste that is adversely affecting the groundwater quality of this area. Poor quality water poses a risk of adverse effects on human health. Groundwater collected from 26 study locations has shown the prevalence of bicarbonate in the groundwater. Though some fluctuation in the parameters was observed both in pre- and post-monsoon seasons, the range is well within the permissible limit. Piper diagram shows the mixing of cations in the groundwater. Heavy metal analysis revealed that presence of copper was higher owing to the industrial discharges that reach the groundwater. Human health risk assessment data here clearly showed the non-carcinogenic risk associated with arsenic and cadmium in the region. Thus, this study will be helpful in reforming the municipal planning strategies for groundwater resources in this region accordingly.
Keywords: groundwater modelling; spatial distribution; Piper diagram; human health risk; GIS; water fluctuation; water pollution; WQI; heavy metals.
Remote IoT correspondence for coordinating end-devices over MANET via energy-efficient LPWAN
by Vikram Narayandas, Archana Maruthavanan, Raman Dugyala
Abstract: The Internet of Things (IoT) is associated with billions of gadgets and their collaborations with one another. The new phase of the network model in IoT indicates the association of diverse progressions of remote wireless developments in unlicensed bands with a massive number of advances. These are based on ZigBee, WiFi, and LoRa. The contemporary studies involve evaluating capacities and practices of these advancements for framing a MANET as for IoT using various estimations, including range, speed, and network size. IoT needs to work together with MANET to make it significantly more feasible for IT associations in building applications for the future. It is surmised that there is a need to develop a multi-layered innovative approach to manage interoperable IoT devices to frame a correspondence alongside the MAC layer to make a key course of action for the arrangement of a MANET for energy-efficient routing using LPWAN. In this work, we provide a comparative study between WiFi, Zigbee, and LoRa, based on cup carbon simulation using varying attributes distance, nodes and packet loss, etc. The results prove the better performance of LoRa in terms of packet loss and nodes usage.
Keywords: LoRa; ZigBee; WiFi; LPWAN; MANET; Bluetooth; cup carbon simulation.
Comparative approach for discovery of cancerous skin using deep structured learning
by Varun Kumar, T. Sucharitha, R. Priyadarshini, N. Rajendran
Abstract: Skin cancer incidences have increased recently owing to ozone layer deterioration. UV rays immediately enter the human skin, causing skin cancer. Thus, a novel approach for early skin cancer detection using digital data and image processing is required. Skin cancer detection research has been quite active since 2016. To increase patient detection accuracy and early diagnosis, we use both machine learning and deep learning algorithms to identify skin cancer. In this model, we employ naive Bayes, decision trees, and KNN. A convolutional neural network (CNN) is a deep learning technology that may be used to automate skin cancer screening. In this work, we compare 93.54% model accuracy.
Keywords: decision tree algorithm; KNN; skin cancer; deep structured learning; image processing.
Design and analysis of power-efficient hybrid full adder using static CMOS and transmission gates
by Priyank Sharma, Sanjay Sharma
Abstract: Energy-efficiency and high performance are the key requirements for any designer to achieve while making a device. For any controller or processor, the arithmetic logic unit is of utmost importance. The addition is the basic and necessary operation on which device performance depends. This paper presents the analysis of a low power hybrid full adder with the two commonly used logic techniques, Static CMOS adder and transmission gate adder circuit. This hybrid full adder circuit has been implemented by both the static CMOS and the transmission gate. This is a unique design of a hybrid full adder for the application of low power VLSI circuits. Using these two logics, the transistor count is reduced. The goal of this research paper is to influence the power, noise, and delay of the proposed full adder with the two commonly used TG and standard CMOS adders. All the three full adder circuits are simulated in the cadence virtuoso with GPDK at 45 nm.
Keywords: transmission gate; CMOS; noise; low power; full adder; delay.
A novel SVM and LOF-based outlier detection routing algorithm for improving the stability period and overall network lifetime of WSN
by Tripti Sharma, Amar Kumar Mohapatra, Geetam Tomar
Abstract: Wireless sensor network data are frequently erroneous owing to inevitable environmental factors such as intrusion attacks, signal weakness, and noise, which may vary depending on the situation. Outlier detection, often known as anomaly detection, is a technique for detecting anomalies and recognising noisy data in the aforementioned scenarios. In the proposed work, efforts have been made to design a routing algorithm that can detect anomalies based on LOF and SVM and is more energy-efficient. The primary objective of the proposed algorithm is to design an energy-efficient routing algorithm that is capable of detecting anomalies present in the environment with improved stability period and overall network lifetime. The sensor dataset provided by the Intel Berkeley Research Lab was simulated to assess the suggested approachs efficiency and competency. The simulation results reveal that this identification of anomalous nodes leads to the development of a more energy-efficient routing algorithm with a better stable region and a higher network lifetime. The proposed algorithm gives the best result with LOF. However, SVM with a gamma of 0.0005 could be used successfully in densely deployed wireless sensor networks. The LOF gives a 98% accuracy in finding anomalies present in the dataset chosen for the simulation.
Keywords: support vector machine; local outlier factor; clustering; wireless sensor network; routing; energy efficiency; anomalies; K-means clustering; outliers; data transmission.
Deep learning technique in CT image reconstruction and segmentation: a systematic literature review
by Shailendra Tiwari, Manju Devi, Sukhdip Singh
Abstract: Deep Learning (DL) in Computed Tomography (CT) is an important research area in computer vision and it provides fast advancement in the field of medical imaging. DL enables automated extraction of features and real-time estimation, whereas the traditional image reconstruction methods approximate the inverse function based on historian parameters to maintain reconstruction efficiency. This systematic literature review is based on last five-year data with the help of digital libraries (IEEE, ACM, Springer, Wiley, ScienceDirect) to find the research articles. The final work includes a systematic mapping report of the selected 88 research articles after applying the inclusion exclusion technique. This paper describes the basis of nine research questions, which include deep learning methods, framework, parameters etc., used in this literature. It concludes by highlighting the challenges of DL in the area of medical imaging, particularly in application of reconstruction and segmentation, and potential future development in the area.
Keywords: medical imaging; image reconstruction; image segmentation; deep learning.
A low power transistor level FIR filter implementation using CMOS 45nm technology
by M. Balaji, N. Padmaja
Abstract: Digital Finite Impulse Response (FIR) filters are widely used in signal processing fields, owing to their stability and linear-phase property. In this paper, the low area FIR filter is designed by proposing the Optimal Array Multiplier (OAM) and Optimal Full Adder (OFA) to minimise the resources. The number of adders used in the OAM is decreased by replacing all the half and full adders of conventional multiplier with the OFA. The buffer circuit is designed in the OFA for avoiding the noise, glitches and threshold issue. The performance of the OAM-OFA-FIR are analysed in terms of area, power and delay. The existing methods used to evaluate the OAM-OFA-FIR architecture are FIR filter design using Radix-2 algorithm & Look-up-table Carry Select Adder (LCSLA), and Vedic Design (VD) & Carry Look-ahead Adder (CLA). The area of the OAM-OFA-FIR architecture is 1755 um2, which is less than the existing methods.
Keywords: delay; finite impulse response filter; optimal array multiplier; optimal full adder.
Health information transmission system with less error rate based on wireless network
by Jing Zhang, Yanfang Liu
Abstract: Aiming at the problem of high bit error rate in traditional health information transmission systems, a health information transmission system of less error rate specialty based on a wireless network is designed. In terms of hardware, the accompanying cable, CI slot wireless network card, and information server are designed. At the same time, we obtain the information transmission time series of the final medical database, set the information transmission standard as IEEE 802.11b based on the wireless network, calculate the information transmission entry parameters, design the transmission status code, and then realise the information transmission of the final health data, and complete the system design. The experimental results show that the designed system has a lower transmission error rate and can solve the problem of high transmission error rate in traditional health information transmission system
Keywords: wireless network; healthcare and literature; final examination wireless information; information transmission system.
Design of IOT-aided prevention and control platform for major public health emergencies
by Yanfang Ma, Chunmeng Lu, Cunhong Li
Abstract: In view of the low traceability rate of traditional major public health emergency prevention and control platform, a new type of major public health emergency prevention and control platform based on the internet of things is designed. The information of major public health emergencies is collected, and the data is transmitted through the internet of things. The federal learning neural network is used to calculate the risk of major public health emergencies, analyse the data, and visually process the data to determine the type of prevention and control, so as to realise the auxiliary prevention and control of major public health emergencies through the internet of things. The experimental results show that the traceability rate of the experimental group is significantly higher than that of the control group, which can solve the problem of low traceability rate of traditional prevention and control platform.
Keywords: auxiliary prevention and control platform; internet of things; public health emergencies;.
Design of health system based on collaborative filtering algorithm
by Zheng Yi
Abstract: In view of the imperfect health system in Chinese society, this paper puts forward the design of a health management system based on collaborative filtering algorithm. In the original hardware system structure, we add automatic reset circuit, detect circuit state, ensure charge stability, avoid resource information outflow, reduce the interdependence between modules, use collaborative filtering algorithm, improve the system management function structure, use fitness function, refine the curriculum management scheme, and thus complete the design of the health management system. The test results show that the original system cannot meet the needs of users for the management of health and physical fitness test, and this system makes up for this defect and provides convenience for society to work and study.
Keywords: collaborative filtering; electronic engineering; physical education; health management; teaching management.
Interactive e-health care design system based on artificial intelligence technology
by Caibo Wang, Huanhuan Ge, Yaxing Lu
Abstract: This paper designs an interactive system of e-healthcare based on artificial intelligence technology, and optimises the hardware structure and software function of the system. In addition, through the introduction of roaming logic, navigation logic and other control logic, users are allowed to roam and navigate in the virtual scene. The system has the characteristics of a strong sense of reality, friendly interface and interactivity, which can meet the needs of an e-health system in external publicity, internal guidance, e-health planning and information management. Virtual reality technology and artificial intelligence technology are used to build a three-dimensional model and a virtual scene, with the introduction of the corresponding control logic to build a realistic and interactive, virtual city environment landscape that supports roaming, navigation and other operations, which can be deployed on different platforms to provide services for e-healthcare publicity, information management and other aspects.
Keywords: artificial intelligence; e-health environment; landscape design.
The combined study of improved fuzzy optimisation techniques with the analysis of the upgraded facility location centre for the Covid-19 vaccination by fuzzy clustering algorithms
by Rakesh Kumar, Gaurav Dhiman, Varun Joshi, Rutvij Jhaveri, Akash Kumar Bhoi
Abstract: With the latest production of vaccines at the conclusion of clinical trials in India, one of the next steps is to administer this vaccine to the consumer. The supply must be specifically positioned to ensure optimal distribution, and the transportation cost must be optimised. The significant concern of the location of facilities is a major logistic extent of decision-making for the vaccine distribution. How the material is passed to customers is one of the vital characteristics of a conversion process (manufacturing system). This fact involves deciding where the building or facility should be located. In this paper, the fuzzy cluster technology provides such optimised locations. Subsequently the optimal positions have been determined, the goal is to find the lowest transportation cost by fuzzy linear programming. We report on experimental studies by taking artificial data from the current warehouse to prove feasibility and showing that the proposed solution is applicable.
Keywords: Covid-19; facility location problem; optimisation; fuzzy clustering; fuzzy linear programming problem.
Unsupervised voice activity detection with improved signal-to-noise ratio in noisy environment
by Shilpa Sharma, Rahul Malhotra, Anurag Sharma Sharma, Jeevan Bala, Punam Rattan, Sheveta Vashisht
Abstract: To identify voiced and unvoiced signals, this research provides an extended voice characteristic detection strategy for noisy settings that uses feature extraction and unvoiced feature normalisation. In a high signal-to-noise ratio environment, the proposed method develops a recognition model by recovering characteristics for categorisation of spoken and unvoiced signals. The novelty of the suggested method is that it uses feature extraction to classify voiced and unvoiced signals with a higher signal-to-noise ratio. Furthermore, by combining two classifiers in a hybrid model, the model is less affected by noise for speech features, and identification performance improves. The model was tested for its ability to increase recognition accuracy. The proposed method produces better results than existing methods, with an accuracy of 99.7% and a signal-to-noise ratio of 25.61 dB. The proposed model LFV-KANN also handles increases in noise power efficiently through the hybridisation of two classifiers: artificial neural network and K-means clustering.
Keywords: TIMIT dataset; support vector machine; voice activity detector; unsupervised learning.
Optimisation of cache replacement policy using extreme learning machine
by Swapnita Srivastava, P.K. Singh
Abstract: In multiprocessors, all the cores ordinarily share the Last Level Cache (LLC). The memory systems of multi-core CPUs are often affected by irregular memory access patterns. The gap between the memory system and LLC prompts the research for an effective Cache Replacement Policy (CRP). Current processors use a variant of the Least Recently Used (LRU) policy to identify which should replace victims. However, there is a significant gap between the LRU policy and Belady's MIN policy, which is the ideal CRP in all the scenarios. Since Belady's algorithm needs future knowledge, it is optimal but not practically possible. This paper shows how CRP can be trained from recent cache accesses to guide future replacement decisions. Recent research on anticipating the reuse of cache blocks has enabled substantial improvement in cache speed and efficiency. This paper presents the ELM-SSO policy that uses Salp Swarm Optimisation (SSO) to optimise the weights coefficients of Extreme Learning Machine (ELM) to perform cache replacement classification. Furthermore, the use of SSO in optimising the ELM is examined to increase system accuracy and overcome the drawback of traditional ELM. The findings demonstrate that the proposed ELM-SSO policy outperforms the traditional cache replacement policy in terms of improvement rate, cache hit rate and cache miss rate. The proposed ELM-SSO policy improves the system performance by 36.66%, 6.25%, 11.71%, 11.35%, 10.32% and 10.99% over Optimal (OPT), Least Recently Used (LRU), Least Frequently Used (LFU), Logistic Regression (LR), K-Nearest Neighbour (K-NN) and Neural Network (NN), respectively.
Keywords: computer architecture; cache memory; latency; eviction set; hit ratio; Belady's replacement policy; least recently used; Salp swarm optimisation; extreme learning machine.
Research progress of adoption of hyperbranched polymer nano materials in textile industry
by Ruihang Huang, Xiaoming Yang, Wen Zhang
Abstract: Hyperbranched polymers have broad adoption prospects in coatings industry, rheological modifier, nanotechnology and the nearly spherical three-dimensional structure, many internal cavities, low viscosity, and high reactivity. From the perspective of the textile industry, the fabric dyeing and fabric antibacterial properties of hyperbranched polymer nano materials are studied, as well as their adoption as fibre leather fatliquoring agents in the process of leather fatliquoring, this paper reviews the adoption of hyperbranched polymers in the textile industry. From the perspective of dyeing, hyperbranched polymers can be applied to polypropylene dyeing, salt-free dyeing, dispersant-compound dyeing, and anti-staining through modification and polymerisation. Nano-Ag is modified by hyperbranched polymers to obtain a colloidal solution, after which the silk undergoes antibacterial treatment, and the finished fabrics all show strong antibacterial properties. Nano-Ag treatment of silk by steaming method can achieve better antibacterial properties. The polyamide-type hyperbranched polymer undergoes hydroxyl activation, and the linear-hyperbranched polymer is obtained by acylation reaction, which can be further modified by esterification reaction and compounding methods. It is then adopted in leather fatliquoring, when the collagen fibres of the leather are filled, thereby increasing the softness and thickening rate of the leather.
Keywords: hyperbranched polymer; textile industry; dyeing; antibacterial property; leather fatliquoring.
A hybrid WSN-based two-stage model for data collection and forecasting water consumption in metropolitan areas
by Mohammad Faiz, A.K. Daniel
Abstract: The improper distribution of in-house water consumption in the metropolitan regions of several Indian states has raised severe issues during the last few decades. Owing to increased human population and inefficient water usage, the average volume of water in the country's aquifers has begun to decline. Traditional water distribution and monitoring systems are unable to address this serious issue. The water crisis in the metropolitan area needs more efficient and reliable solutions to overcome this water distribution problem. The working of the proposed model is as follows. In the first stage, the data collection technique is proposed for water distribution in metropolitan areas through Energy Efficient Two-Phase Routing Protocol (EE-TPRP) using cloud-assisted wireless sensors. In the second stage, an efficient water demand prediction model (EWDM) using Backpropagation Feed-forward Neural Network (BP-FNN) is used for the prediction of water consumption for optimal distribution to users. The EE-TPRP protocol is compared to LEACH, MOD-LEACH, and DEEC protocols, where it has reduced overhead and enhanced network lifetime. The BP-FNN is compared to the regression model, fuzzy model, and ARIMA model, where it has improved the prediction efficiency of the water distribution in metropolitan areas.
Keywords: water distribution; WSN; artificial neural network; cloud; feed-forward; gateway; sensor node.
Detection of brain tumour using machine learning based framework by classifying MRI images
by P. Nancy, Murugesan G, Abu Sarwar Zamani, Karthikeyan Kaliyaperumal, Malik Jawarneh, Surendra Kumar Shukla, Samrat Ray, Abhishek Raghuvanshi
Abstract: The fatality rate has risen in recent years owing to an increase in the number of encephaloma tumours in each age group. Because of the complicated structure of tumours and the involution of noise in magnetic resonance (MR) imaging data, physical identification of tumours becomes a difficult and time-consuming operation for medical practitioners. As a result, recognizing and locating the tumour's location at an early stage is crucial. Cancer tumour areas at various levels may be followed and prognosticated using medical scans, which can be utilized in concert with segmentation and relegation techniques to provide a correct diagnosis at an early time. This article aims to develop image processing and machine learning based framework for early and accurate detection of brain tumours. This framework includes image preprocessing, image segmentation, feature extraction, and classification using the SVM, KNN, and Nave Bayes algorithms. Image preprocessing is performed using Gaussian Elimination, image enhancement using histogram Equalization, image segmentation using k Means and feature extraction performed using PCA algorithm. For performance comparison, parameters like- Accuracy, sensitivity and specificity are used. Experimental results have shown that the KNN is getting better accuracy for classification of brain tumour related images. KNN is performing admirably in terms of accuracy. In terms of specificity, SVM and KNN perform similarly well. KNN outperforms other algorithms in terms of sensitivity. Accuracy of KNN classifier is around 98 percent in brain tumour image classification.
Keywords: brain tumour detection; MRI images; machine Learning; Gaussian elimination; K means; KNN; SVM; image segmentation; feature extraction; image classification.
Credence-Net: a semi-supervised deep learning approach for medical images
by Pawan Kumar Mall, Pradeep Kumar Singh
Abstract: Deep learning uses a large-scale labelled dataset to ensure a high degree of accuracy. This technology is increasingly data-driven in medicine and biology imaging, and labelled data is more difficult and expensive to retrieve. Various studies are being conducted on semi-supervised deep learning models (SSDLM) and self-supervised deep learning. In order to increase the quantity of labelled data necessary for deep learning, researchers are increasingly looking at SSDLM and its applications. The motivation for the proposed Credence-Net is similar to how physicians handle uncertain or questionable instances in reality, based on their colleague's or senior's consultation. Proposed model Credence-Net has attained the best accuracy and specificity, sensitivity, precision, Matthews correlation coefficient, false discovery rate, false-positive rate, f1 score, negative predictive value, and false-negative rate 91.834%, 85.268%, 97.008%, 89.356%, 83.648%, 10.644%, 14.732%, 93.016%, 95.696%, and 2.992% for unseen dataset respectively. This research work leads to a more accurate and efficient semi-supervised deep learning model.
Keywords: deep learning; semi-supervised learning; shoulder fracture; X-ray; medical images.
Health evaluation system for hospitals based on big data and deep learning model
by Yuchen Xie
Abstract: Owing to the influence of redundant quality evaluation data, the existing college students academic quality evaluation system has too few hardware structure test points, which leads to the large power consumption of the evaluation system in actual operation. To solve this problem, an academic quality evaluation system for college students based on big data and deep learning model is designed. In the hardware part, microprocessor is used to build a CPU platform supporting big data. According to the discharge size of the hardware circuit, the test site position is set. In the software part, the deep learning model is used to construct the academic quality evaluation algorithm. The traditional evaluation system and the evaluation system designed in this paper are used to carry out experiments. The results show that the power consumption of the evaluation system designed in this paper is the minimum.
Keywords: big data; deep learning model; academic quality; hardware data parameters.
AI reconstruction method of health planning using IoT
by Y.A.O. Hang, IANG Yan, YANG Rongging
Abstract: With the increasing complexity of human settlements and the deterioration of the ecological environment, health systems are shouldering arduous responsibilities. However, owing to various reasons, the digital process of landscape planning and design has lagged behind the times. This paper discusses the current difficulties of AI reconfiguration design in landscape architecture, and points out the necessity of popularising this. The paper defines the definition, characteristics, and concept pedigree of landscape architecture AI reconfiguration design, analyses the main digital design methods and application software platform in the concept pedigree, and summarises the flow chart of landscape architecture AI reconfiguration design. At the end of this paper, the limitations and misunderstandings in the current AI reconfiguration design are analysed.
Keywords: VR technology; health; AI reconstruction; internet of things.
Detection and classification of brain abnormality by a novel hybrid Efficinentnet-Deep Autoencoder CNN model from MRI brain images in smart health diagnosis
by Dillip Ranjan Nayak, Neelamadhab Padhy, Ashish Singh, Pradeep Kumar Mallick
Abstract: This paper presents the novel smart hybrid EfficientNet-Deep Autoencoder (EF-DA) deep neural network model to classify brain images. This is the successor of modified EfficientNetB0 with a deep autoencoder to detect tumours. Initially, the feature extraction is done by modified EfficientNet, and then classification is done by the proposed smart deep autoencoder. The images are filtered, cropped by morphological operations, and augmented to train a deep hybrid EF-DA model in the first stage. In the second stage, a modified deep autoencoder is used for classification. The statistical result analysis of the hybrid model is assessed using seven types of degree metrics including F-score, precision, recall, specificity, kappa score, accuracy, and area under the ROC curve (AUC) score. It is compared with three types of pre-trained model, MobileNet, MobileNetV2, and ResNet50 for analysis. The EF-DA model has achieved an overall accuracy of 99.34% and an AUC score of 99.95%.
Keywords: hybrid; EfficientNet; data augmentation; deep autoencoder; deep neural network; AUC score; overfitting; recall; precision; F-score.
Dimension adaptive hybrid recovery with collaborative group sparse representation based compressive sensing for colour images
by Abhishek Jain, Preety D. Swami, Ashutosh Datar
Abstract: In this paper, a fast and efficient hybrid method of image compressive sensing (termed as HRCoGSR) is designed which can adaptively acquire grey or colour image and can faithfully recover it speedily. The proposed method combines and uses the approaches of recovery via collaborative sparsity (RCoS) and Group Sparse Representation (GSR). For fast convergence, Gaussian Pyramid (GP) is constructed at the front-end and then Block Compressive Sensing based RCoS recovery is applied. In the second phase, restricted Group sparse representation (GSR) process is carried out for further enhancing the perceptual quality. The collaborative sparsity-based CS solution is an iterative method and intends to improve SNR performance of the recovered image. It simultaneously enforces local 2D and 3D non-local sparsity in adaptive hybrid transform domain. Parametric performance of the proposed HRCoGSR method is tested over variety of standard grey and colour images and compared with 7 existing state-of-the-art methods. Experimental results show that the proposed HRCoGSR method is highly efficient and much faster than existing methods. The average computational time taken by the proposed method is only 26% to that of the standard RCoS method and 46% of the GSR method.
Keywords: compressive sensing; RCoS; recovery via collaborative sparsity; GSR; group sparse representation; hybrid recovery; gaussian pyramid; adaptive color image compression; collaborative recovery; image acquisition.
Intelligent overlay algorithm for medical data management based on wireless communication technology and feature fusion
by Changrong Peng, Xiaodong Zhang, Qian Liu, Xiaofang Zhao, Chenyang Dai
Abstract: Medical data management through wireless communication system become essential to make data available at all time. To address the problem of poor quality of management, a intelligent overlay algorithm based on wireless communication technology and feature fusion is proposed. The algorithm first uses sensors remote sensing equipment to collect patient data and transmit them by wireless communication, followed by image and data filtering, then feature extraction and feature fusion, and finally seamless overlaying by projection model. The results show that the spatial frequency and average gradient of the superimposed patient data management meets the requirements, indicating that the resultant data after the application of the sensing data is superimposition algorithm based on wireless communication technology and feature fusion retain the detail components of the patient data more realistically, with good clarity, and the image information is better maintained.
Keywords: wireless communication technology; feature fusion; medical data; sensors; intelligent overlay algorithm.
Research on health service technology based on multimedia imagery training method
by Wanchun Kang, Jian Wang, Hongseol Kim, Xinchao Du
Abstract: Health serving technique is one of the basic technical movements in serving patients. The traditional health technique pays attention only to the training of service technique, but ignores the patients' psychological state, which greatly limits the learning effect. The application of multimedia imagery training teaching method in hospital health service technology teaching not only conforms to the characteristics of modern health and mind learning, but also increases the stability and accuracy of patients health, helping patients quickly establish the power of correct action, so as to quickly and effectively master the action of service technology. This paper analyses the problems existing in the teaching of service technology in hospitals and medical colleges, and finally explores the specific application strategies of multimedia imagery training method in the teaching of nursing service technology in colleges and hospitals.
Keywords: multimedia imagery; health data; detection; knowledge data.
Evaluation method of resource fusion using artificial intelligence technology
by Juan Li
Abstract: Aiming at the problems of low application performance and long retrieval time of traditional resource database construction methods, this paper proposes a teaching resource database construction method based on neural network, which combines electronic engineering education with physical education. Firstly, the inventory structure of teaching resources is determined according to the type of hardware equipment. In the environment of a teaching platform andthe internet, the initial teaching resources are collected and preprocessed from two aspects of electronic engineering education and physical education. The neural network iterative algorithm is used to extract the characteristics of resources, realise the division of resource types, and fuse the same type of resource data. Finally, through the unified storage format, the teaching resources are substituted into the storage structure to realise the construction of the teaching resource database. It is found that the retrieval time of resources is reduced by 0.21 s, which improves the retrieval speed.
Keywords: neural network; electronic engineering education; physical education; teaching resources; database construction.
Evaluation method for colour matching using artificial intelligence technology
by Lijuan Yao, Ling Tang
Abstract: The existing colour matching evaluation methods have the problem of fuzzy colour attributes, which leads to high image distortion. This paper designs an evaluation method of public space indoor landscape colour matching based on artificial intelligence technology. The method quantifies the colour layout of the public space, determines the main colour of the space, identifies the colour attributes of the indoor landscape, deploys the combined colour phase ring, uses artificial intelligence technology to extract the colour matching features, calculates the colour distance combined with the transition colour frequency information, and adopts the colour quantisation algorithm to set the evaluation model. The experiment results show that the average distortions of the evaluation method and the other two evaluation methods is 30.12, 38.96, are 38.87, respectively, which proves that the colour matching evaluation method combined with artificial intelligence technology has higher use value.
Keywords: artificial intelligence technology; colour layout; colour matching; evaluation method; public space; interior landscape;.
Stereoscopic display of architectural design images based on virtual reality technology
by Ling Tang, Lijuan Yao
Abstract: The current image stereoscopic display method mainly displays images stereoscopically from the perspective of human left and right eye visual imaging, which not only displays images with distortion and missing details, but also makes it difficult to realise interaction for complex image stereoscopic display. This paper proposes a stereoscopic display method of architectural design images based on virtual reality technology. The images are drawn using DIBR technology and the depth images are processed using Gaussian filtering and so on. After designing the virtual interaction of the image stereoscopic display scene, EON is used to analyse the lighting of the building exterior and realise the stereoscopic display of the image. The simulation experimental data of the stereoscopic display method show that the proposed image stereoscopic display method relatively improves the display effect by about 66.7% and has good adaptability for different grey value images.
Keywords: architectural exterior; design images; image presentation; stereoscopic presentation; virtual interaction; virtual reality technology.
Studying the impact of anti-oxidant extracts of different vegetables on the formation of PAHs in rabbit meat
by Rabia Siddique, Ameer Fawad Zahoor, Sajjad Ahmad, Hamad Ahmad, Abid Hussain
Abstract: Polycyclic aromatic hydrocarbons (PAHs) are powerful noxious compounds which are produced in well-done processed meat products. Incomplete burning of fuels produces PAHs and soot, by the combination of combustion and pyrolysis. The PAH yields are connected with diverse factors, such as type of meat, heating temperature, cooking time, processing technique, additives and storage time. This research paper examines the formation of PAHs in cooked European rabbit meat. Additionally, extraction process and quantification procedure to evaluate the PAHs in these compounds are reported. Finally, an overview is represented on the impact of the anti-oxidant properties of vegetable extracts to decrease the generation of PAHs in cooked rabbit meat.
Keywords: rabbit meat; vegetables extracts; anti-oxidant; GC-MS; PAHs.
Lifetime of MOS degradation under reliability techniques (NBTI, HCI) in 8T Full Adder
by Priyank Sharma, Sanjay Sharma
Abstract: In this paper, we propose some reliability parameters to an 8T Full Adder at 45 nm CMOS technology. Decreasing the supply voltage with scaling down to threshold voltage is an effective logic to the low power circuits. However, the lifetime of the circuit gradually decreases. This paper focuses on this condition with providing various voltages 0.7 V, 1 V, and 1.2 V for the duration of 1 year. The major techniques in reliability are implemented such as Negative Bias Temperature Instability (NBTI) under PMOS and Hot Carrier Injection (HCI) under NMOS degradation and ageing processes are compared and evaluated. Simulation results predict the HCI effect at NM2 transistor is highly degraded at low voltages and gradually decreases at high voltages, but NM2 is effected soon to damage the circuit. The proposed schematics and reliability parameters are implemented through the Cadence Virtuoso tool. The proposed 8T Full Adder circuit is proposed with the reliability analysis of the NBTI and HCI at 45 nm CMOS technology. The stability was obtained in the operations of the transistor at 0.7 V, 1 V and 1.2 V.
Keywords: 8T Full Adder; negative bias temperature instability; hot carrier injection; degradation; low power; CMOS.
Deep learning-based feature extraction coupled with multi-class SVM for COVID-19 detection in the IoT era
by Auwalu Mubarak, Sertan Serte, Fadi Al-Turjman, Rabiu Aliyu, Zubaida Said, Mehmet Ozsoz
Abstract: The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organisation (WHO) in December 2019. Prompt and early identification of suspected patients is necessary to monitor the transmission of the disease, increase the effectiveness of medical treatment and as a result, decrease the mortality rate. The adopted method to identify COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR), the method is affected by the shortage of RT-PCR kits and complexity. Medical imaging using deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but efficient deep learning architecture and low data are affecting the performance of the deep learning models. To detect COVID-19 efficiently, a deep learning model based feature extraction coupled with Support Vector Machine (SVM) was employed in this study, Seven pre-trained models were employed as feature extractors and the extracted features are classified by multi-class SVM classifier to classify CT scan images from COVID-19, common pneumonia and healthy individuals. To improve the performance of the models and prevent overfitting, training was also carried out on augmented images. To generalise the model's performance and robustness, three datasets were merged in the study. The model with the best performance is the VGG19 which was trained with augmented images: it achieved an accuracy of 96%, a sensitivity of 0.936, a specificity 0f 0.967, an F1 score of 0.935, a precision of 0.934, a Yonden Index of 0.903 and AUC of 0.952. The best model shows that COVID-19 can be detected efficiently on CT scan images.
Keywords: artificial intelligence; COVID-19; SVM; feature extraction.
Analysis of high-dimensional data using feature selection models
by Shubham Mahajan, Amit Kant Pandit
Abstract: The determination of features assumes a significant part in the enhancement of output of AI models, limiting the computational time taken to make a learning model and improves the exactness of the learning cycle. Hence, the analysts give more consideration to the determination of features to expand the exhibition of AI calculations. The choice of the proper technique for the determination of features is significantly for a specific AI task through high-dimensional information. It is consequently important to complete an examination of various strategies for character determination for the exploration network, specifically with the end goal of improving effective techniques for choice. The method for choosing features improves the effectiveness of AI undertakings for high-dimensional information. To accomplish this target, this paper surveys the many different techniques for the choice of features for high-dimensional information.
Keywords: feature selection; signal processing; artificial intelligence; high-dimensional data; classification.
A novel and intelligent decision-making system for real-time healthcare tracking using commercial wearable data
by Anudeep Peddi, Teppala Venkata Ramana
Abstract: Wearable health devices became popular these days and have become genuinely intertwined with society. Smartwatches and other fitness devices fulfill the consumer needs in continuously tracking human activity, which can further decode to analyze the health parameters like heart rate, blood pressure, blood glucose levels, and many more. Internet of things (IoT) enabled techniques, mobile and desktop-based applications are ameliorating the ease of using these techniques. The applications of the wearables are also transforming the quality of virtual and tele-health care to improve, which is a substitute to the conventional medical practices. In this paper, we reported a descriptive analysis on the progress in modeling the healthcare wearable sensors that impact the imminent health care applications in different domains. Also, we made a comparative study on consumer fitness wearable devices to analyze how the device facilitates the ease of usage with other specification comparisons. We recorded data from a consumer wearable fitness device to observe and envisage the users effort to accomplish the activity goals each day for maintaining good health. We reported the exploratory analysis of the data obtained from the recordings. Supervised machine learning algorithms are applied to the recorded data and compared the results. Among the supervised algorithms applied, the random forest regression gave us the highest accuracy of 97.88% in predicting the subjects activity goal is perpetrated for the respective day.
Keywords: health care; wearables; smartwatches; fitness trackers; wellness activity trackers; wireless sensors; data processing; supervised machine learning; prediction; decision-making system.
Power optimisation of wireless sensing network through quantum deep learning
by Zhongzhen Yan, Kewei Kewei Zhou, Feng Guo, Na Hou, Jiangyi Du
Abstract: In current research, a secure wireless communication system with high data rate obtained by quantum computing (QC) is used. To speed up the data rate of transfer, the QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC-based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called Graph Convolutional Neural Network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of wireless communication systems, such as LTE/5G. Evaluated results show that our proposed approach consumes only 44.82J energy for computing 1000 samples. The proposed technique outperforms existing ones by consuming much less energy.
Keywords: wireless communication system; 5G; SPCA; convolution network; deep learning; energy efficiency; power optimisation.
Integrated agriculture IoT-based farm monitoring and management systems
by Satish Sampatrao Salunkhe, Aarti Amod Agarkar, Mandar Karyakarte, Jaison Mulerikkal, Prathiba Jonnala, Divyendu Kumar Mishra
Abstract: The internet of things (IoT) is a cutting-edge phenomenon in the digital realm, wherein objects connect, and processes are autonomous and managed via the internet. In this research, an architecture for integrating the IoT with crop production is designed, and various measurements and techniques for crop monitoring using cloud computing have been employed. The method enables real-time evaluation of data gathered from sensors put in crops that delivers a response for the farmer for crop production monitoring and saves the farmer's time as well as energy. The data acquired in the fields are saved in the cloud and analysed to allow for automation using IoT devices. The experimental results show the specifics of soil moisture, temperature, humidity, and water use in the field through a monitoring system, as well as managing the farmer's input.
Keywords: internet of things; agriculture; farming; monitoring system; management system.
COVID-19 detection and tracking using smart applications with artificial intelligence
by Geeitha Senthilkumar, Rajagopal Kumar, C. Nalini, V.R. Niveditha, Jothilakshmi Ramakrishnan
Abstract: Corona Virus Disease 2019 (COVID-19), a newly identified pandemic infection, threatened human life, and disrupted the entire world. Identifying and detecting this pathogenic virus is made essential as it is increasing the mortality rate day by day. In this scenario, alternative technologies play a vital role in monitoring, detecting and diagnosing the disease by deploying smart applications. Today smart applications are incorporated with AI techniques in detecting and monitoring the spread of infection. This work describes MLP techniques integrating the ANN model for extracting COVID-19. The model is equipped with a normalization process deploying Gaussian Process Regression (GPR) and Radial Based Function (RBF) for detecting the noise level. The proposed work exploits the publicly available COVID-19 datasets of July month from GitHub and Kaggle. The AI model is measured using the performance metrics in terms of precision, recall, F-measure and accuracy, and the MLP model produces higher accuracy.
Keywords: COVID-19; smart applications; sensors; smartphone; artificial intelligence; detection.
Research on intelligent city traffic management system based on WEBGIS
by L.I. You, J.I.N. Hui, Hong Jie, H.E. Chunmu, Wang Xianbing
Abstract: Aiming at the problem that the traditional health management system cannot effectively integrate the data, which leads to the low efficiency of data processing and the effect of data traffic management, a smart health management system based on WEBGIS is designed. Based on the traditional system, the dynamic health data acquisition hardware module is designed. According to the weekly similarity characteristics of health data flow, the data traffic flow in different time periods is predicted and the data traffic is controlled. The WEBGIS technology is used to deal with the traffic accidents, and the effective management of the urban traffic is realised. In this article, we used it to manage health data traffic. Simulation results show that the designed management system can effectively reduce the data congestion and improve the efficiency of health data traffic management
Keywords: WEBGIS technology; smart health; urban transportation; management system; system design.
Special Issue on: Eco-Friendly and Sustainable Cognitive Green Nano-Technologies for the Mitigation of Emerging Environmental Pollutants
Preparation of titanium dioxide composite nanomaterials using copper catalysis and their dynamic adsorption and photocatalytic performance in water treatment
by Ye Tian
Abstract: The aim is to investigate the dynamic adsorption performance of titanium dioxide (TiO2) nanocomposite materials in water treatment, providing direction for water purification. The copper-catalysed living free-radical polymerization method polymerizes the prepared TiO2 particles with tertiary amine polymer to manufacture the TiO2 polymer nanocomposite materials. The prepared TiO2 nanocomposite materials are then modified to obtain the quaternised TiO2 polymer nanocomposite materials (quaternised TiO2@poly(DEAEMA)), which are characterized and analysed. Finally, the water treatment performance of quaternised TiO2@poly(DEAEMA) is judged through photocatalysis and adsorption experiments, while the antibacterial performance of the prepared materials is judged using the common Escherichia coli and Staphylococcus aureus. Results demonstrate that the quaternised TiO2@poly(DEAEMA) polymer nanocomposite materials are completely and tightly wrapped, presenting a flower-like appearance, with a significantly-increased diameter and an average size of about 600nm, which can be utilized as the pollutant adsorbent. Water treatment simulation reveals the fastest adsorption rate and the highest adsorption capacity of quaternised TiO2@poly(DEAEMA), reaching 265 mg/g given the same reaction time. The catalytic removal rate in ultraviolet and visible light reaches 94%, and the photocatalysis of visible light reaches 69%. Until the reaction lasts for 45 minutes, its antibacterial activity is optimal, and the diameter of the inhibition zone against Escherichia coli and Staphylococcus aureus exceeds 16 mm. Therefore, the prepared TiO2 nanomaterials have high adsorption properties, good photocatalysis performance, and excellent antibacterial properties, which can provide an experimental basis for the treatment and purification of water resources in the industry.
Keywords: titanium dioxide; water treatment; dynamic adsorption; photocatalysis; nanocomposite material.
Special Issue on: Nano Impact on Next-Gen Biomedical and Environmental Research
Application of energy saving and environmental protection material in landscape construction of urban landscape wetland system
by Yifei Zhang, Yi Liu
Abstract: with the strengthening of energy saving and environmental protection requirements of buildings in China, the limitations of traditional landscape construction in energy saving and environmental protection in China are becoming more and more obvious. Based on this, this paper studies the application of energy saving and environmental protection materials in the landscape design of urban garden wetland system, and constructs a material performance analysis model based on logical regression algorithm. From six aspects, the information collection of materials in energy conservation and environmental protection is realized, and the logical regression algorithm is used for comprehensive analysis and evaluation. The material analysis model can make the construction of urban garden wetland system more ecological by comprehensive analysis of different types of materials, according to the environmental protection requirements of landscape construction and the planning scheme of urban garden wetland system. The experimental results show that the material analysis model based on logical regression algorithm has the advantages of quantifiable evaluation and good stability, which can improve the application efficiency of energy saving and environmental protection materials in landscape construction and strengthen the ecological nature of garden wetland system.
Keywords: logical regression algorithm; urban garden wetland system; energy saving and environmental protection materials; landscape construction.
Application analysis of green building materials in urban three dimensional landscape design
by Hongji Yue, Xuchen Jia
Abstract: The current urban three-dimensional landscape design process is mainly based on the three-dimensional simulation of an urban landscape for the overall design. How to achieve the innovation of urban three-dimensional landscape design process with the help of green building materials is the current development trend. Based on this, this paper studies the application of green building materials in urban three-dimensional landscape design. Firstly, the three-dimensional model of urban landscape based on SFM (Structure From Motion) algorithm is proposed, and the autocorrelation function is used to simulate the landscape signal. Through the maximum value of autocorrelation function curve in the process of three-dimensional reconstruction, the restoration of three-dimensional landscape is realised, and then the error of three-dimensional model is analysed. Secondly, the fuzzy evaluation method is used in the simulation evaluation, and the improved SFM three-dimensional reconstruction model is used to analyse the results of the three-dimensional landscape design. Finally, the application value of different types of green building material in urban three-dimensional landscape design is analysed through the design of three-dimensional simulation experiment.
Keywords: urban three-dimensional landscape design; environmental protection materials; SFM algorithm; three-dimensional model.
Effect of surfactants on mechanical and environmental geotechnical properties of loess and bentonite
by Kui Liu, Kangze Yuan
Abstract: In recent years, research on the influence of surfactants on the mechanical and environmental geotechnical properties of loess and bentonite has become a focus of the environmental geotechnical field. Based on this, this paper proposes a mechanical analysis model of loess and bentonite based on the mixed washing leapfrog algorithm, and studies the influence of surfactants on the mechanical and environmental geotechnical properties of loess and bentonite and its quantitative evaluation. Firstly, the data of mechanical strength, composition change, permeability, density change and load-bearing change of loess after adding surfactant are analysed, and then the comprehensive comparative analysis and evaluation of environmental geotechnical characteristics with or without surfactant are carried out by using the mixed washing frog leaping algorithm. Finally, the mechanical analysis model is verified by design experiment. The results show that the mechanical analysis model of loess and bentonite based on the mixed washing leapfrog algorithm has the advantages of high feasibility, high data accuracy and high analysis efficiency, which can realise the quantitative evaluation of the influence of surfactants on the environmental geotechnical characteristics.
Keywords: shuffle leapfrog algorithm; surfactant; environmental geotechnical characteristics; loess and bentonite; mechanical analysis model.
Application of micro and nano bubble technology in water level recovery of water conservancy construction engineering
by Dongling Zhang, Yuzhen Wang, Fei Luo
Abstract: In order to improve the removal efficiency of groundwater pollutants, and improve the ability of groundwater restoration and water level recovery, a micro and nano bubble enhanced remediation technology is proposed. A micro and nano bubble micro observation system was designed, including camera, micro lens, laser and image processing software. Combined with a particle size analyser and interface potential analyser, the basic physical characteristics and migration characteristics of micro and nano bubbles were analysed. According to the obtained characteristics, the particle size distribution and interfacial potential characteristics of micro bubbles are obtained, the existence time and gas mass transfer effect of microbubbles in water body are verified, and the migration rule of microbubbles in water bodies and porous media is obtained. The numerical simulation results show that the micro and nano bubbles have high mass transfer efficiency and long existence time, which can effectively improve the dissolved oxygen of groundwater, improve the activity of microorganisms, achieve the effective removal of pollutants, and promote the in-situ remediation of polluted groundwater.
Keywords: microbubble technology; water conservancy construction engineering; water level recovery; mass transfer effect; ozone; observation system.