The Industrial Internet of Things (IIoT) refers to the multitude of connected devices and sensors used in industrial settings such as manufacturing plants, transportation systems, and energy grids. These devices can collect and exchange data with the goal of improving efficiency, productivity, and safety of the systems within which they are used and sometimes beyond.

IIoT devices are typically designed to monitor and control various aspects of industrial processes, such as machine performance, inventory levels, energy use, and environmental conditions. The data collected can be processed using conventional statistical tools or analyzed using artificial intelligence to detect trends and patterns and predict how changes in various parameters might change outcomes with a view to optimising the various industrial processes.

Overall, we can see the IIoT is an important part of the digitization and automation of industry, which is having an increasing impact on the economy and society.

But, there is an issue.

While the IIoT will be critical in making production more efficient and sustainable across various industries, it currently uses Wi-Fi for its connectivity (Standard IEEE 802.11), and Wi-Fi can consume a lot of energy because of the size of the data packets sent back and forth and the maximum transmission unit (MTU).

Researchers in Brazil have investigated data compression as a possible solution to this problem. Writing in the International Journal of Embedded Systems, the team describes two new methods they suggest can reduce significantly the amount of data sent by IIoT devices. Their methods use data compression to minimize the size of transmitted packets and the MTU. The first method involves using a customized binary Huffman-tree. This approach analyses the frequency of characters in a data stream and assigns a variable-length code to each in a way that minimizes the total number of bits required to represent the data. The second method utilizes a Lempel-Ziv-Welch algorithm with a flexible dictionary. This lossless data compression algorithm works by identifying repeated patterns or sequences of data in a given data stream and replaces those sequences with shorter codes.

The team's experiments with these compression techniques show that they can reduce energy consumption by 8% compared to existing solutions for IIoT. A manufacturing plant currently using an IIoT system might consume 1000 kilowatt-hours (kWh) of energy each month. With the proposed data compression methods in place reducing energy consumption by 8% that might result in a monthly savings of around 1 megawatt-hour per year, the equivalent energy consumption of hundreds of typical homes in the developed world.

Silva, M.V., Mosca, E.E. and Gomes, R.L. (2022) 'Green industrial internet of things through data compression', Int. J. Embedded Systems, Vol. 15, No. 6, pp.457–466.
DOI: 10.1504/IJES.2022.10055057

Research in the International Journal of Sustainable Agricultural Management and Informatics has demonstrated how machine learning can be used to predict water quality index. The work could have implications for the future of water management in drinking water and agricultural use.

Falling water quality has been a cause for concern in recent years, with its impact on both human health and agricultural production drawing increased attention. Indeed, at the time of writing, pollution of rivers and coastal waters caused by inappropriate release of untreated sewage is high on the environmental agenda while agricultural issues concerning water security are always on the agenda.

Various factors, such as acidity and alkalinity, pH level, turbidity, dissolved oxygen, nitrate content, temperature, and the presence of faecal microbes, are used to determine water quality. It is therefore crucial to develop effective methods for forecasting water quality, in order to monitor and control pollution.

Ahmad Debow, Samaah Shweikani, and Kadan Aljoumaa of the Higher Institute for Applied Sciences and Technology (HIAST) in Damascus, Syria, have developed 4-stacked LSTM models for predicting WQI. A 4-stacked LSTM (Long Short-Term Memory) is a type of recurrent neural network that can find long-term patterns in data that changes over time. Such models having analyzed the data can then make predictions about how that data might change in the future. By stacking four LSTM layers on top of each other, the model is better able to find nuanced patterns in the data.

To prepare the data and select features for analysis, the team used different algorithms, including K-NN (K nearest neighbours) and annual mean. K-NN is a well-known algorithm used in machine learning for classification and regression tasks. It is a non-parametric algorithm, whic makes no assumptions about the underlying data distribution. The basic idea underpinning K-NN is to classify new data points based on similarities between nearest neighbours in the training dataset.

The team's success with these models in replicating known data bodes well for real-world predictions and could make an important contribution to water management efforts. It should allow more proactive measures to be taken to minimize pollution in the water supply for both human consumption and agricultural use based on the predictions the models make.

Debow, A., Shweikani, S. and Aljoumaa, K. (2023) 'Predicting and forecasting water quality using deep learning', Int. J. Sustainable Agricultural Management and Informatics, Vol. 9, No. 2, pp.114–135.
DOI: 10.1504/IJSAMI.2022.10051380

Medical personal protective equipment (PPE) is essential infectious disease control, something that has in pandemic times become very apparent. Governments and organizations in affected areas generally recommend the wearing of medical PPE, including surgical masks, gloves, and face shields, especially in crowded environments. However, ensuring that medical personnel in severely affected areas comply with the recommendations requires a means to monitor in real-time whether PPE is being used.

Writing in the International Journal of Sensor Networks, a team from China has developed a system based on machine learning that can detect whether personnel are wearing the requisite PPE. The approach uses deep neural networks (DNNs) to carry out object detection in real scenarios.

Jianlou Lou, Xiangyu Li, Guang Huo, Feng Liang, Zhaoyang Qu, and Ndagijimana Kwihangano Soleil of the Northeast Electric Power University in Jilin and Tianrui Lou of Guangzhou University have used two novel modules, the Deformable and Attention Residual with 50 layers (DAR50) feature extraction module, and the Criss-Cross Feature Pyramid Network (CCFPN) feature fusion module, in order to address the two key problems that have so far limited performance in PPE detection. They have thus overcome the issues of interference from background information and detection target scales that vary in size.

By combining the two modules, the researchers were able to create an bject detection network, Attention and Multi-Scale Fusion-based Regions with Convolution Neural Network (AMS R-CNN). Their tests with medical PPE and The Visual Object Classes Challenge 2007 (VOC 2007) datasets, showed their system to work better than various state-of-the-art methods.

The development of AMS R-CNN could benefit those managing medical professionals and help ensure that the PPE rules are being adhered to with a view to minimising the risk of infectious disease transmission. The medical staff who work in high-risk environments, such as hospitals and laboratories, will themselves benefit from increased protection from colleagues and so improve overall safety and also reduce risk to patients.

The work highlights the potential of deep neural networks to revolutionize the way we detect objects. Accuracy can only be improved with further advances n this technology.

Lou, J., Li, X., Huo, G., Liang, F., Qu, Z., Lou, T. and Soleil, N.K. (2023) 'Medical personal protective equipment detection based on attention mechanism and multi-scale fusion', Int. J. Sensor Networks, Vol. 41, No. 3, pp.189–203.
DOI: 10.1504/IJSNET.2022.10052844

Research in the International Journal of Biomedical Engineering and Technology has revealed a promising area of research for the management of phantom limb pain, a common experience for amputees that can be challenging to treat. The research focuses on transhumeral amputees, who are missing a significant portion of one or both of their arms.

Phantom limb pain is a phenomenon that occurs in people who have had a limb partially or wholly amputated. Despite the loss of the limb, individuals may experience sensations such as pain, itching, and tingling as if the missing limb is still part of their body. It can be a persistent and distressing condition that has a significant and detrimental impact on quality of life.

The study, conducted by a team of researchers from the United Kingdom has utilized human motor control theory and Penfield homunculus to provide a comprehensive review and new perspective on phantom limb sensations and pain, and the potential for therapy and prosthetics. Control experiments were undertaken in the clinic with intact individuals using ultrasound imaging along the bone of the upper arm, the humerus, while the participants were instructed to produce a variety of hand movements.

Human motor control theory focuses on how the brain and nervous system control movement in the body. It seeks to understand the processes and mechanisms involved in the planning, execution, and control of movement, ranging from simple actions such as reaching for an object to complex activities such as playing a musical instrument. The famous Penfield homunculus is a neurological "map" of the human body developed in the 1930s. It represents the regions of the brain that control movement and sensation for different body parts, with the hands and face represented in greater detail at greater scale.

Ejay Nsugbe of Nsugbe Research Labs in Swindon and radiologist Carol Phillips of the University Hospitals Bristol, UK, found that compound gesture motions that involve bulk muscular recruitment can be detected along the humerus. This discovery could have significant implications for clinical rehabilitation prosthetists, who can use these gestures to explore the mobility and sensation of phantom limbs. The work could have implications for individuals struggling with phantom limb pain and it could provide a new avenue for therapy as well as leading to improved design of prosthetic limbs to make them more responsive to the user's needs.

There are implications for developing more effective treatments for phantom limb pain, such as physical therapy or medication, which may improve the quality of life of amputees. Additionally, by gaining a better understanding of the underlying mechanisms of phantom limb pain, researchers may be able to identify new targets for drug development, ultimately leading to better pain management for those experiencing this condition.

Nsugbe, E. and Philips, C. (2023) 'An insight into phantom sensation and the application of ultrasound imaging to the study of gesture motions for transhumeral prosthesis', Int. J. Biomedical Engineering and Technology, Vol. 41, No. 3, pp.258–271.
DOI: 10.1504/IJBET.2023.10055089

The Internet of Things (IoT) can be described as a loose network of physical devices that might be embedded with sensors, software, and connectivity. While a holistic view would see the IoT as being all the devices in the world with internet connectivity, it is often the case that these portable or remote devices are accessible within clusters or around hubs with specialist access and applications. Nevertheless, devices in the IoT can collect and exchange data with other devices or systems over the internet.

IoT devices can range from everyday consumer devices like smartphones, home appliances such as refrigerators, security cameras, and wearable monitors, and fitness devices, to industrial equipment and infrastructure in smart cities, factories, and transportation systems. The data generated by these objects can be analysed and used to gain insights, automate processes, and improve decision-making across various industries and domains.

Research in the International Journal of Cloud Computing, has looked at the need to improve technologies associated with database management in order to be able to better handle the large amounts of data generated by the IoT. The paper focuses on the use of IoT technology in the social and agricultural domains in rural sectors. In that context, there is a need for improvements that could benefit monitoring, farming conditions and practices. If it were possible to provide and implement adaptive, efficient remote and logistic operations using IoT devices, such as actuators and valves, then dynamic integration might be possible to improve various processes in farming, such as timely irrigation. This would allow savings on water, for instance, but also optimise irrigation to improve crop yields based on changing weather and other conditions.

The same analysis of IoT data might allow monitoring of pest activity and weed growth and so allow for more judicious application of pesticides and herbicides or even allow the farmer to avoid their use altogether by exploiting alternatives in a timely manner.

Zdzislaw Polkowski of the Jan Wyzykowski University in Polkowice, Poland, and colleagues in India, point out that there are many constraints and challenges facing farmers in the developing world. However, where technology can assist those in the developed world so too might it improve practices and conditions in the developing world.

Polkowski, Z., Mishra, S.K., Mishra, B.K., Borah, S. and Mohanty, A. (2023) 'Impact of internet of things in social and agricultural domains in rural sector: a case study', Int. J. Cloud Computing, Vol. 12, No. 1, pp.90–105.
DOI: 10.1504/IJCC.2023.10054989

Copper oxide nanoparticles might be used to provide an essential mineral nutrient to growing maize seedlings, according to work published in the International Journal of Nanotechnology.

Nanoparticles are tiny particles, which can range from 1 to 100 nanometres in diameter, sometimes a little bigger. Metallic nanoparticles smaller than 1 nanometre are considered to be atomic clusters. Particles exceeding 500 nanometres are usually thought of as microparticles, unless they are nanotubes or fibres, which can be longer, but are nanoscopic in cross-section. Being nanoscopic gives a particle unique properties when compared with atomic clusters or bigger particles. As such they have been researched extensively across many different sectors, including materials science, engineering, medicine, and agriculture.

Given that copper is an essential nutrient for plant growth, the idea that copper, or more specifically copper oxide, nanoparticles (CuO NPs) might have useful properties to help plants assimilate the mineral readily and so to grow better has been discussed. Physicist Ali Raza of the University of Agriculture in Faisalabad, Punjab, Pakistan, and colleagues have investigated the effects of dosing the growing medium of maize seedlings with Cuo NPS. The researchers wanted to see how well the CuO NPs could enter and move through the plants, and if they would affect the growth of maize seedlings. They also needed to know whether this type of nanoparticle would be toxic. Metallic copper nanoparticles, as opposed to CuO NPs can have a positive effect on seed germination but are phytotoxic to growing wheat, Triticum aestivum, seedlings.

The basic conclusion from their study is that CuO NPs are taken up by the seedling roots and lead to improved growth over the course of eighteen days. The seedlings produce longer roots and shoots when dosed with CuO NPs, even at the relatively high concentration of 800 milligrams per litre of CuO NPs the nanoparticles were not toxic to the seedlings.

However, chlorophyll content fell and catalase activity decreased, which would ultimately have a deleterious effect on photosynthesis above that concentration. Samples dosed at 550 milligrams per litre (mg/l) showed a proportionately lower enhanced growth rate when compared to the 800 mg/l samples and against undosed control, suggesting a dose-related uptake at these concentrations.

The team suggests that the nanoparticles have a beneficial regulatory effect on enzyme activity in the seedlings, given that copper is a component or co-factor in many plant enzymes.

Raza, A., Ahmad, S., Mateen, A., Arshad, A., Rehman, A. and Oliveira, H.A.L. (2022) 'Effect of copper nanoparticles on growth parameters of maize seedlings', Int. J. Nanotechnol., Vol. 19, No. 12, pp.1143–1157.
DOI: 10.1504/IJNT.2022.10050410

Data privacy issues have come to the fore in the world of cloud computing and there has been something of a backlash against this burgeoning area in some quarters. However, there are many individuals and organisations that rely on it on a daily basis. Unfortunately, the laws around data privacy in cloud computing have not kept apace with the technology and are often unclear and contradictory. This problem needs to be addressed urgently according to researchers writing in the International Journal of Cloud Computing.

Alaeldin Alkhasawneh of the Department of Private Law at Yarmouk University in Jordan and also the Department of Private Law at UAEU University in Abu Dhabi, United Arab Emirates, and Fawaz A. Khasawneh of the Department of Software Engineering and IT at the University of Quebec, Montreal, Canada, have explored the laws surrounding data privacy in cloud computing and identified several gaps in the legislation. The team offers several proposals as to how the laws might be improved to create a better experience and service for cloud consumers as well as increased protection for personal, sensitive, and private data.

Cloud computing is an essential service provided by many companies and used by many more. It offers various benefits to users by providing storage and computing services through remote servers rather than the user having to have their own on-site systems. There are costs, but the benefits of often distributed systems means that users within a multinational organisation can access those services anywhere in the world rather than overburdening a single-site server. Users of cloud computing might be private individual, small and medium-sized enterprises, governments and non-governmental organisations, as well as large companies and international corporate entities.

The team suggests that fundamentally cloud computing service providers should be required by law to take greater responsibility for the protection of their users' data. The team also suggests that the various local and regional laws need to work in concert to avoid contradictions and to allow cloud computing thrive without compromising the protections the various laws offer users.

Alkhasawneh, A. and Khasawneh, F.A. (2023) 'Legal issues of consumer privacy protection in the cloud computing environment: analytic study in GDPR, and USA legislations', Int. J. Cloud Computing, Vol. 12, No. 1, pp.40–62.
DOI: 10.1504/IJCC.2023.10054987

The concept of lifelong learning has been with humanity throughout history. There have always been those whose curiosity is forever piqued, who need new skills as they go through life, and those for whom change brings with it obstacles and opportunities that can be addressed with new knowledge. In the modern context, lifelong learning as a more formal concept and aspiration for society as a whole is probably newer, Indeed, we might see arguments for a new paradigm in learning beyond childhood and youth as emerging just 25 years ago or thereabouts. At that time, researchers began arguing for more innovative learning models that were personalized to those who wanted to learn and also giving these life students a chance to have a more active role in deciding what, when, and how to learn.

Writing in the International Journal of Grid and Utility Computing, a team from Spain discusses the current need for flexible, efficient, universal, and lifelong education especially given the rapid evolution of information and communications technologies.

Jordi Conesa, Montserrat Garcia-Alsina, Josep-Maria Batalla-Busquets, Beni Gómez-Zúñiga, María J. Martínez-Argüelles, Tona Monjo, and Enric Mor Universitat Oberta de Catalunya, Barcelona and María Del Carmen Cruz Gil Universidad de Zaragoza, Zaragoza, Spain, point out that lifelong learning needs to be integrated fully into society, but because it differs from regular learning in many ways, there are issues that must be addressed to allow this to happen, for the benefit of individuals and society as a whole.

It is worth noting, that lifelong learners are by definition older, and perhaps more mature, than those in conventional educational environments such as school and higher education. They may have much broader interests and have experience and skills that have not yet been achieved by younger learners. Lifelong learning may also work at many different levels and depths, not all lifelong learning will be aimed at passing exams or completing a dissertation to be presented to professors. Indeed, much lifelong learning may not be in any way vocational, it might not relate to work and could very well be more about family, leisure, sporting activities and other hobbies. Of course, for lifelong learners there is also the possibility of limited flexibility because of balancing commitments to home, work, and leisure, with that very learning.

As with many aspects of life, a personalised approach, tailored to fit the individual can be the most constructive way forward. Existing models of personalised learning have not yet been adapted to the needs of lifelong learners or society at large. The researchers have now examined the current state of lifelong learning, reviewed the relevant literature, and discussed the challenges we face in creating innovative electronic-learning models to promote self-determination life-students.

It is self-determination that is central to success for lifelong learners. It gives learners more control over how they are educated, and how they teach themselves, allowing them to make choices to fit their interests and goals better.

The team suggests that the development of innovative e-learning models that promote self-determination needs an interdisciplinary approach that brings expertise from education, psychology, technology, and other pertinent fields. Identifying the most effective ways to personalize learning and to develop appropriate tools and technologies is the way forward, for supporting self-directed learning, the team suggests. There is also a need to develop assessment frameworks to measure the efficacy of the personalized e-learning models being developed to ensure that they are working in the way the life-learners need and want them to work for them and for society.

Conesa, J., Garcia-Alsina, M., BatallaBusquets, J-M., Gómez-Zúñiga, B., Martínez-Argüelles, M.J., Monjo, T., Mor, E. and Cruz Gil, M.D.C. (2023) 'A vision about lifelong learning and its barriers', Int. J. Grid and Utility Computing, Vol. 14, No. 1, pp.62–71.
DOI: 10.1504/IJGUC.2023.10054826

Research from a team in India published in the International Journal of Public Sector Performance Management looks at the notion of "emotional intelligence" in the context of virtual teams. While it demonstrates an obvious relationship, the literature is still in the nascent stage and so precludes solid conclusions.

Anu Singh Lather of Ambedkar University in Delhi and Simran Kaur of Guru Gobind Singh Indraprastha University are well aware that research into emotional intelligence and its effects in virtual teams is still in its infancy and so hoped to offer new insights through a systematic review of the research literature as it stands. Emotional intelligence refers to the ability of individuals to recognize and manage their own emotions, as well as to understand and effectively respond to the emotions of others. Emotional intelligence can be broken down into several key components, including self-awareness, self-regulation, motivation, empathy, and social skills.

These components allow individuals to navigate complex social situations, build strong relationships with colleagues and stakeholders, and communicate effectively with others.

In the public sector, emotional intelligence is particularly important for managers and leaders, who need to be able to build trust and rapport with employees, collaborate effectively with other organizations, and respond to the needs and concerns of the public. By cultivating emotional intelligence, public sector employees can improve their ability to communicate, manage conflicts, and build strong, collaborative relationships with others, ultimately leading to more effective and efficient public services.

There is a wealth of information about how emotional intelligence affects our interactions in the "offline" world, but how it plays out in the virtual environments of online video conferencing, for instance, might well be different. Indeed, many virtual teams are built ad hoc and may exist only transiently unlike the more obvious teams present in the physical workplace. Even those virtual teams that are well-established and meet regularly will most likely have a very different dynamic to a team that meets face-to-face.

The team has reviewed a range of papers published during the first couple of decades of the 21st Century on the subject of emotional intelligence in virtual teams. They find that emotional intelligence is, of course, important. A relationship between virtual team performance and emotional intelligence of the team members was obvious from the research. However, there still remains a dearth of high-quality research published in this area and so we cannot yet extract a clear understanding of the factors that affect emotional intelligence in this online realm.

Given the pressures that arose during the COVID-19 pandemic, there will likely be research from the period following the team's original review that will fill some of the gaps in this research field in the coming months and years and provide new insights into the emotional intelligence of virtual teams. If, as they say, teamwork makes the dream work, then emotional intelligence is the back-end code to make the virtual team work.

Lather, A.S. and Kaur, S. (2023) 'Systematic review of emotional intelligence in virtual teams', Int. J. Public Sector Performance Management, Vol. 11, No. 2, pp.149–164.
DOI: 10.1504/IJPSPM.2023.10054876

Branding and image are usually something that those with something to sell are obsessed with. If you're marketing a product or service or even just touting your celebrity to gain traction, new opportunities, and lucrative endorsement contracts it is a given, you have to create a brand. So, why are so many consumers also obsessed with this idea of branding, of creating a personal brand, even when they have nothing to sell. Indeed, it is usually the opposite, they are following the marketers and the salespeople with a view to buying.

Writing in the International Journal of Internet Marketing and Advertising, a team from Germany has considered the notion of "My brand. My selfie"

Anne Mareike Flaswinkel, Markus Rump, and Reinhold Decker of the Department of Business Administration and Economics at Bielefeld University discuss how consumers often take brand-selfies. Photos of themselves with a brand for which they have a liking in close proximity, adorning their bodies, or otherwise prominently on display in the image. Given the modern wont for sharing anything and everything with all and sundry via the ubiquitous world of social media, brand-selfies and opinions can spread quickly. Indeed, it is this kind of activity that underpins something "going viral". Moreover, some consumers even position themselves as brand ambassadors and influencers with a view to gaining benefits associated with a given brand.

The team has carried out a cross-sectional study to identify brand identification as a strong predictor of consumer intention to portray themselves with a brand in a brand-selfie. They suggest that feelings of belonging and reward have a significant impact on such posts. The team has found a distinction between consumer motivations for posting brand-selfies and general selfies. They conclude that this kind of free marketing and advertising, for that is essentially what it is, has implications for marketers looking to incorporate selfie marketing into their marketing strategy.

"In an age when smartphones are an integral part of our daily lives, consumers are more reliant on information technology than ever before," the team writes. "Every day, an increasing number of people use social media and move around in the digital world. For many of us, the idea of life without technology is unfathomable." They add that their work could pave the way to a deeper understanding of the complexity of consumer motivations and offer suggestions on how marketers can benefit from brand-selfies and indeed encourage loyal consumers to increase their loyalty and activity in this context.

"Selfies [and brand-selfies] are a cultural phenomenon and a prevalent component of social media, and it remains relevant to understand why consumers display themselves with brands in such personal images," the team concludes.

Flaswinkel, A.M., Rump, M. and Decker, R. (2023) 'My brand, my self(ie) – why consumers portray themselves in brand-selfies', Int. J. Internet Marketing and Advertising, Vol. 18, Nos. 2/3, pp.310–334.
DOI: 10.1504/IJIMA.2022.10046943