Template-Type: ReDIF-Article 1.0 Author-Name: S. Sakthivel Author-X-Name-First: S. Author-X-Name-Last: Sakthivel Author-Name: Charu Virmani Author-X-Name-First: Charu Author-X-Name-Last: Virmani Author-Name: S. Silvia Priscila Author-X-Name-First: S. Silvia Author-X-Name-Last: Priscila Author-Name: Ravindra Pathak Author-X-Name-First: Ravindra Author-X-Name-Last: Pathak Author-Name: S. Prasath Alias Surendhar Author-X-Name-First: S. Prasath Alias Author-X-Name-Last: Surendhar Author-Name: Bobur Sobirov Author-X-Name-First: Bobur Author-X-Name-Last: Sobirov Title: Smart technical control infrastructures in electrical automation through digital application systems Abstract: Both technological and social systems combine to construct the infrastructure and processes of digital technologies, ensuring that an organisation's aims and objectives are achieved. The firm created and employed access controls and measures to protect its data and information systems. The exploitation of information systems and disregard for internet security protocols are the main causes of computer security breaches. Non-compliance with information security regulations is a serious risk for businesses. It is crucial to identify, investigate, and consider the elements that affect compliance and the deployment of computer security for successful conformity and human adoption of computer security technology and compliance with computer practices. Computer engineering is increasingly automated with high tech. Technology and engineering in technical control systems have improved. The study examines clever technical control in electrical automation and intelligent technologies. It also analyses this technology's potential applications and future development trends in electrical engineering. Reviewing machine learning methods for technical control issues, we concentrate on the deterministic situation to illustrate the numerically complex issues. Journal: Int. J. of Critical Infrastructures Pages: 1-23 Issue: 1 Volume: 21 Year: 2025 Keywords: computer security abiding; stiffness adjusting; evaluating and monitoring; levelling; technical controls; controlling impedance. File-URL: http://www.inderscience.com/link.php?id=143947 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:1:p:1-23 Template-Type: ReDIF-Article 1.0 Author-Name: Sunday Anderu Keji Author-X-Name-First: Sunday Anderu Author-X-Name-Last: Keji Author-Name: Josue Mbonigaba Author-X-Name-First: Josue Author-X-Name-Last: Mbonigaba Author-Name: Gbenga Wilfred Akinola Author-X-Name-First: Gbenga Wilfred Author-X-Name-Last: Akinola Title: The economic effects of infrastructure investment on industrial sector growth in Sub-Sahara Africa: a disaggregated system-GMM approach Abstract: Investment in economically inclined infrastructure is pertinent to industrial sector growth in Sub-Sahara Africa (SSA), especially during this period of financial belt-tightening recovery due to the recent global pandemic. Findings suggest a dilapidated infrastructure spread across SSA, which has mired productivity growth, hence slow industrial sector growth. This study fills a vacuum in the literature by investigating the economic effects of infrastructure investment on industrial sector growth in SSA using disaggregated system-GMM approach. Diverse significant effects from various types of infrastructural tech on industrial growth across sub-regional countries were unravelled. Similarly, post estimations analysis via robust Arellano-Bond Autocorrelation and Hansen tests were adopted to establish the absence autocorrelation. The study uniquely disaggregated system GMM to provide valuable insights to policymakers. Hence, sub-regional countries should draft more policy support to prioritise economically motivated factor inputs such as information techs, access to energy, transport and water resources to expedite industrial sector growth. Journal: Int. J. of Critical Infrastructures Pages: 24-43 Issue: 1 Volume: 21 Year: 2025 Keywords: industrial sector growth; infrastructural investment; system generalised methods of moments; GMM. File-URL: http://www.inderscience.com/link.php?id=143948 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:1:p:24-43 Template-Type: ReDIF-Article 1.0 Author-Name: A. Anandhavalli Author-X-Name-First: A. Author-X-Name-Last: Anandhavalli Author-Name: A. Bhuvaneswari Author-X-Name-First: A. Author-X-Name-Last: Bhuvaneswari Title: Game of life-based critical security key mechanism infrastructure in internet of things Abstract: Modern technology's blessing, the internet of things (IoT), has made remote monitoring and automation a reality. IoT devices are now the most economical option for wireless sensor networks. These gadgets were created with a specific purpose; therefore, computing power and power sources are restricted to meet that need. Due to power limitations, providing security for this type of network is a real issue. The game of life-based security key mechanism (GLSKM) technique is designed to leverage more low-level hardware bitwise operations during the key generation and exchanging phase instead of more computationally integrated energy-starving activities. This work presents two modules: the game of life-based key exchange mechanism and the random seed and iteration limit selector. Both modules are built to use simpler bitwise hardware-targeted instructions to achieve minimal power consumption without sacrificing security. The GLSKM approach also recognises the network's overall performance. Journal: Int. J. of Critical Infrastructures Pages: 44-69 Issue: 1 Volume: 21 Year: 2025 Keywords: energy efficient; internet of things; IoT; game of life; security key exchange; wireless sensor networks; WSNs. File-URL: http://www.inderscience.com/link.php?id=143949 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:1:p:44-69 Template-Type: ReDIF-Article 1.0 Author-Name: C. Anish Author-X-Name-First: C. Author-X-Name-Last: Anish Author-Name: R. Venkata Krishnaiah Author-X-Name-First: R. Venkata Author-X-Name-Last: Krishnaiah Author-Name: K. Vijaya Bhaskar Raju Author-X-Name-First: K. Vijaya Bhaskar Author-X-Name-Last: Raju Title: Application of silica fume, pumice and nylon to identify the characteristics of LWC after critical infrastructure analysis Abstract: Finding lucrative building designs has been the major problem the construction industry has been experiencing lately. This issue can be fixed by dramatically lowering the structural part's self-weight and sizing it down. Lightweight concrete (LWC) is the sole material that can be used to achieve this. In earlier tests, various lightweight aggregates were utilised to lower the density. The primary benefits of LWC columns are that they do not require a reinforced cage or forms because their steel tubes can be used just as well as scaffolding and are fireproof. Based on the numerous research projects undertaken, it can be concluded that circular poles should be favoured over a square LWC to boost stability and satisfy various design needs. This study defines LWC while considering strength component development. Thus, this experiment examines silica fume and pumice stone as entire substitutions. After moulding samples with the desired mix ratio, compression, tensile, and bending capacities are assessed. This specially designed LWC mix of M30 grade concrete has 0.6 to 0.7 times the strength of regular concrete, according to tests. The strength measures dramatically increased by adding 20% silica fume and 1.5% nylon fibre. Journal: Int. J. of Critical Infrastructures Pages: 70-86 Issue: 1 Volume: 21 Year: 2025 Keywords: critical infrastructure; lightweight concrete; LWC; pumice; silica fume; nylon fibre; waste rubber powder; mechanical properties; thermal properties. File-URL: http://www.inderscience.com/link.php?id=143950 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:1:p:70-86 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaoli Yu Author-X-Name-First: Xiaoli Author-X-Name-Last: Yu Title: Study of corporate management financial early warning combining BP algorithm and KLR Abstract: China has a large number of small and micro enterprises, which are an important part of our market economy. The study analyses the causes of enterprise financial crises from internal factors and external factors, and constructs an early warning system for enterprise management financial crises (FCWS) based on the analysis results. To address the shortcomings of traditional early warning methods in terms of low accuracy and efficiency, the study combines signal analysis model (KLR) and BP neural network (BPNN) to build a KLR-BP enterprise management financial crisis early warning model. The performance of the KLR-BP model was tested using the financial data of 50 micro and small enterprises over the years, and the accuracy of the model exceeded 95%. Thus, the KLR-BP model can be practically applied to enterprise management financial early warning and make a certain contribution to the development of China's market economy. Journal: Int. J. of Critical Infrastructures Pages: 87-103 Issue: 1 Volume: 21 Year: 2025 Keywords: BPNN; KLR model; financial early warning; market economy. File-URL: http://www.inderscience.com/link.php?id=143951 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:1:p:87-103 Template-Type: ReDIF-Article 1.0 Author-Name: Jie Kong Author-X-Name-First: Jie Author-X-Name-Last: Kong Title: GRA-based study on the vulnerability and sustainable development of economic systems in tourist cities Abstract: The vulnerability of China's tourism city economies due to natural disasters, infectious diseases, and emergencies has become a hindrance to their sustainable development. To this end, the study takes Dali city as the research object and constructs a corresponding grey correlation degree model of the fragility of tourism city economic system based on the objective entropy value method and GRA. The study uses this model to systematically analyse the causes and mechanisms of action of the economic system fragility of tourism-oriented cities. The results show that Dali's economic subsystem has a relatively homogeneous industrial structure, and its coping capacity is growing flatly while its sensitivity is generally on the rise. The fragility of the social and economic subsystems correlates highly with the vulnerability of the city's economic system. This study provides targeted suggestions for sustainable development of tourism cities through a comprehensive analysis of their economic system fragility. Journal: Int. J. of Critical Infrastructures Pages: 128-145 Issue: 2 Volume: 21 Year: 2025 Keywords: tourist cities; economic system vulnerability; sustainable development; entropy method; GRA. File-URL: http://www.inderscience.com/link.php?id=145189 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:2:p:128-145 Template-Type: ReDIF-Article 1.0 Author-Name: J. Helen Arockia Selvi Author-X-Name-First: J. Helen Arockia Author-X-Name-Last: Selvi Author-Name: T. Rajendran Author-X-Name-First: T. Author-X-Name-Last: Rajendran Title: Hyper chaotic Chen model-based medical image encryption and DNA coding framework for secure data transfer critical infrastructures Abstract: Image encryption in the healthcare sector is used to protect sensitive medical images, such as X-rays, MRI scans, and CT scans, from unauthorised access and disclosure. This is important because medical images often contain personal and confidential information that can be used for malicious purposes if it falls into the wrong hands. The proposed research utilises a hyperchaotic system along with DNA coding for the secure data transfer of medical images. The closed hash table method was used to scramble the random chaotic sequences produced by the Chen system. The DNA substitution approach and DNA coding and decoding principles were used to perform the diffusion. The encryption approach breaks down the robust pixel correlation and allows safe data transfer for teleradiology applications. The two-stage scrambling followed by a single-stage diffusion ensures security in data transfer and robustness against attacks. The real-time medical images are used in this research and validated by the performance metrics. Journal: Int. J. of Critical Infrastructures Pages: 146-167 Issue: 2 Volume: 21 Year: 2025 Keywords: encryption; chaotic function; teleradiology; decryption; data transfer critical infrastructures. File-URL: http://www.inderscience.com/link.php?id=145190 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:2:p:146-167 Template-Type: ReDIF-Article 1.0 Author-Name: Deepak Kumar Sharma Author-X-Name-First: Deepak Kumar Author-X-Name-Last: Sharma Author-Name: Adarsh Kumar Author-X-Name-First: Adarsh Author-X-Name-Last: Kumar Title: A blockchain based solution for efficient and secure healthcare management Abstract: Healthcare, being a vital and rapidly evolving field, necessitates robust systems for managing medical records and ensuring data security. The article proposes a blockchain based healthcare management system that addresses critical challenge of secure medical data sharing. The system incorporates zero trust principles and blockchain technology to verify compliance with patient records and facilitate secure data exchange among research institutions, patients, and servers. The proposed distributed zero trust based blockchain structure (DZTBS) effectively meets the privacy and security requirements of availability, integrity, and confidentiality. Notably, compared to traditional systems, DZTBS achieves a remarkable reduction of approximately 20% in both total execution and block-generation time. Furthermore, our system outperforms existing encryption algorithms, including the advanced encryption standard and elliptic curve digital signature algorithm with a mean encryption time of 0.001053 seconds and a decryption time of 0.00365 seconds. These results show improved security and efficiency offered by proposed healthcare management system. Journal: Int. J. of Critical Infrastructures Pages: 168-186 Issue: 2 Volume: 21 Year: 2025 Keywords: blockchain technology; data sharing; electronic medical records; security; zero trust principle. File-URL: http://www.inderscience.com/link.php?id=145191 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:2:p:168-186 Template-Type: ReDIF-Article 1.0 Author-Name: Yongcun Zhang Author-X-Name-First: Yongcun Author-X-Name-Last: Zhang Author-Name: Zhe Bai Author-X-Name-First: Zhe Author-X-Name-Last: Bai Title: Prediction of the fracture energy properties of concrete using COOA-RBF neural network Abstract: Evaluating the energy requirements for crack propagation in concrete structures has been a subject of considerable interest since applying fracture mechanics principles to concrete. Concrete fracture energy is important for safe structural design and failure behaviour modelling because it is quasi-brittle. The complex nonlinear behaviour of concrete during fracture has led to ongoing debates regarding fracture energy prediction using existing estimation techniques. Using the previous dataset, prediction approaches were developed to measure the preliminary (<i>G<SUB align="right"><SMALL>f</SMALL></SUB></i>) and total (<i>G<SUB align="right"><SMALL>F</SMALL></SUB></i>) fracture energies of concrete utilising mechanical properties and mixed design elements. Two hundred sixty-four experimental recordings were gathered from an earlier study to construct and analyse ideas. This study combines the radial basis function neural network (RBFNN) with the Coot optimisation algorithm (<i>COOA</i>) and whale optimisation algorithm (<i>WOA</i>). The computation and analysis of G<SUB align="right"><SMALL>f</SMALL></SUB></i> and <i>G<SUB align="right"><SMALL>F</SMALL></SUB></i> used five performance measures, which show that both optimised <i>COOA-RBFNN</i> and <i>WOA-RBFNN</i> evaluations could execute superbly during the estimation mechanism. Therefore, even though the <i>WOA-RBFNN</i> approach has unique characteristics for simulating, the COOA-RBFNN analysis seems quite dependable for calculating. <i>G<SUB align="right"><SMALL>f</SMALL></SUB></i> and <i>G<SUB align="right"><SMALL>F</SMALL></SUB></i> given the rationale and model processing simplicity. Journal: Int. J. of Critical Infrastructures Pages: 187-208 Issue: 2 Volume: 21 Year: 2025 Keywords: concrete; fracture energy; neural network; estimation; radial basis function; coot optimisation algorithm; whale optimisation algorithm; WOA. File-URL: http://www.inderscience.com/link.php?id=145192 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:2:p:187-208 Template-Type: ReDIF-Article 1.0 Author-Name: Fayza Jallouli Author-X-Name-First: Fayza Author-X-Name-Last: Jallouli Author-Name: Tarek Yalouli Author-X-Name-First: Tarek Author-X-Name-Last: Yalouli Author-Name: Sonali Vyas Author-X-Name-First: Sonali Author-X-Name-Last: Vyas Author-Name: Sunil Gupta Author-X-Name-First: Sunil Author-X-Name-Last: Gupta Title: A prospective study of the determinants of economic growth in Tunisia 1990–2030 Abstract: The research reported in this work is to shed light on the nature of the relationship between economic growth and many economic variables in Tunisia between 1990–2021. This study is based on the theoretical framework of economic growth, and it aimed to build an econometric model of economic growth based on interpreted economic variables. The findings of our research showed that both public expenditure, human capital, and trade openness were considered the most influential factors on economic growth in Tunisia during the study period. However, the two economic variables such as physical capital and money supply were considered as the least influential factors on economic growth. It is worth noting that we tried to build a standard model of economic growth based on explanatory economic variables to make a forecast study of the growth of the gross domestic product and knowledge of the expectations and forecasts for the period 2021–2030. Journal: Int. J. of Critical Infrastructures Pages: 105-127 Issue: 2 Volume: 21 Year: 2025 Keywords: economic growth; gross domestic product; GDP; ARDL model; ARIMA model; Eviews; Tunisia. File-URL: http://www.inderscience.com/link.php?id=145207 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:2:p:105-127 Template-Type: ReDIF-Article 1.0 Author-Name: Nadeem Akbar Najar Author-X-Name-First: Nadeem Akbar Author-X-Name-Last: Najar Author-Name: D. Parthasarathy Author-X-Name-First: D. Author-X-Name-Last: Parthasarathy Author-Name: Arnab Jana Author-X-Name-First: Arnab Author-X-Name-Last: Jana Title: From shovels to snowploughs: the evolution of snow clearance infrastructure in Kashmir, India Abstract: This research examines the evolution of snow clearance infrastructure in the Kashmir Valley and its direct link to critical infrastructure-transportation. The study analyses numerous data sources such as snow removal action plans, departmental letters, notes, presentations, requisition letters, and official communications using a qualitative research approach, specifically content analysis. The research demonstrates the severe influence of snow removal on critical infrastructure by applying the theoretical framework of punctuated equilibrium theory and analysing its components, including pluralism, conflict expansion, policy image, and venue shopping. The data show a major shift from manual snow removal practices to mechanised operations between 1987 and 2022, which was driven by significant punctuations. Furthermore, the study emphasises the continual evolution of snow removal practices in Kashmir, with a focus on the incorporation of cutting-edge technologies and globally popular methodologies to ensure the resilience and functionality of critical transportation networks. The study provides important insights for policymakers and winter road maintenance managers involved in managing essential infrastructure in snowy regions. Journal: Int. J. of Critical Infrastructures Pages: 209-227 Issue: 3 Volume: 21 Year: 2025 Keywords: critical infrastructure; snow clearance; evolution; punctuations; policy; action plans; India. File-URL: http://www.inderscience.com/link.php?id=146871 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:3:p:209-227 Template-Type: ReDIF-Article 1.0 Author-Name: B.S. Rajath Author-X-Name-First: B.S. Author-X-Name-Last: Rajath Author-Name: G. Abhilash Author-X-Name-First: G. Author-X-Name-Last: Abhilash Author-Name: Kavya Shabu Author-X-Name-First: Kavya Author-X-Name-Last: Shabu Author-Name: M.D. Deepak Author-X-Name-First: M.D. Author-X-Name-Last: Deepak Author-Name: Shridev Devji Author-X-Name-First: Shridev Author-X-Name-Last: Devji Author-Name: Rajesh Kalli Author-X-Name-First: Rajesh Author-X-Name-Last: Kalli Title: Systematic literature review and future research trends on building information modelling using bibliometric analysis Abstract: The advent of building information modelling (BIM) has increased as a defined methodology for improving construction work processes. Despite the significance of its usage, there is dearth of studies that comprehend the applications of BIM and its potential benefits for construction work. The present work aims to understand the recent developments and applications of BIM research in the construction industry. In this regard, a systematic nine-step approach using bibliometric analysis is performed to scrutinise articles published in Scopus database. Based on the scrutinised articles, a detailed examination using thematic and cluster analysis was applied to explore the potential BIM areas. Findings indicated key clusters: 1) architectural design aspects; 2) sustainable development; 3) project management knowledge areas. The outcome of the study provides a holistic understanding of these clusters and suggests exploration of potentially challenging areas for future BIM applications. Journal: Int. J. of Critical Infrastructures Pages: 293-315 Issue: 3 Volume: 21 Year: 2025 Keywords: building information modelling; BIM; construction industry; bibliometric analysis; thematic analysis; cluster analysis; sustainable development. File-URL: http://www.inderscience.com/link.php?id=146876 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:3:p:293-315 Template-Type: ReDIF-Article 1.0 Author-Name: M. Suresh Author-X-Name-First: M. Author-X-Name-Last: Suresh Author-Name: S. Manju Priya Author-X-Name-First: S. Manju Author-X-Name-Last: Priya Title: IoT-based intelligent infrastructure decision support system with correlation filter and wrapper framework for smart farming Abstract: Agriculture is the backbone of the Indian economy in a world where the market is battleground, and technology is constantly changing. More than 75% of the population relies on this ancient craft. Each farmer must produce high-quality harvests despite water shortages and plant illnesses. They must delicately balance soil nutrients, sustaining fertility like a nation's lifeline. From these trials emerged the modern Indian farmer's hero: an IoT-based decision support system, a smart agricultural beacon. This miracle anticipates agricultural yield and guards their livelihood like a sentinel. It monitors soil fertility, stops soil degradation, and considers excessive irrigation a crime against nature. Wireless sensor devices elegantly communicate data to a central server to arrange this technology symphony. In the digital world, a machine learning system does predictive irrigation. The weather, soil, rainfall, seed damage, drought, and alchemical pesticides and fertilisers are considered. Many pioneers in this growing industry have failed, resulting in incorrect estimates and low crop yields. CBF-SF, an artisanal hybrid correlation-based filter (CBF) and sequential forward wrapper architecture is the solution. This clever technique turns parched areas into bountiful goldmines by predicting crop yields with precision, making farmers contemporary alchemists. Journal: Int. J. of Critical Infrastructures Pages: 267-292 Issue: 3 Volume: 21 Year: 2025 Keywords: correlation filter; sequential forward; prediction; IoT-based intelligent infrastructure; decision support system; correlation filter; precision agriculture; crop yield prediction; soil monitoring; wireless sensor devices; data analytics; agricultural technology. File-URL: http://www.inderscience.com/link.php?id=146877 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:3:p:267-292 Template-Type: ReDIF-Article 1.0 Author-Name: Ali Akbar Shafikhani Author-X-Name-First: Ali Akbar Author-X-Name-Last: Shafikhani Author-Name: Mostafa Pouyakian Author-X-Name-First: Mostafa Author-X-Name-Last: Pouyakian Author-Name: Amir Abbas Najafi Author-X-Name-First: Amir Abbas Author-X-Name-Last: Najafi Author-Name: Behrouz Afshar-Nadjafi Author-X-Name-First: Behrouz Author-X-Name-Last: Afshar-Nadjafi Author-Name: Amir Kavousi Author-X-Name-First: Amir Author-X-Name-Last: Kavousi Title: Safety plan modelling for resource-constrained construction projects to optimise cost, time, and safety risk Abstract: The intense competition to achieve project goals has increased due to limited resources in construction projects. No studies have compared the trade-off between time, cost, and safety while considering resource and equipment constraints. Equipment constraints may affect project scheduling and increase safety risks. Therefore, a project scheduling model that considers equipment constraints, time, cost, and safety risks is needed. This study aims to optimise cost, time, and safety risk by modelling safety plans in project scheduling problems with resource constraints. By solving this model, feasible solutions for time, cost, and safety risk trade-offs are provided. In addition, the model could also evaluate the risks of project activity, the risk of equipment and overtime, and minimise the overall safety risk of the project. Journal: Int. J. of Critical Infrastructures Pages: 228-251 Issue: 3 Volume: 21 Year: 2025 Keywords: safety risks; equipment planning; project-scheduling; construction; RCPSP; NSGA-II. File-URL: http://www.inderscience.com/link.php?id=146879 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:3:p:228-251 Template-Type: ReDIF-Article 1.0 Author-Name: Tedan Lu Author-X-Name-First: Tedan Author-X-Name-Last: Lu Title: Design of an intelligent financial management framework for enterprises based on big data Abstract: With the rapid development of information technology and the arrival of the big data era, enterprise financial management is facing increasingly complex challenges and opportunities. In order to improve the efficiency of enterprise financial management, this article combines big data technology to study a big data based intelligent financial management framework for enterprises. This article first introduces the importance of financial management. Then, an analysis was conducted on the design platform of a financial management framework based on big data. Finally, the design and implementation of the framework were discussed. To verify the effectiveness of the framework, this article tested it. The results showed that compared with traditional financial management frameworks, the response speed of this framework increased by 3.87 seconds during peak periods. The conclusion indicates that a big data-based enterprise intelligent financial management framework helps to achieve accurate analysis of financial data and intelligent decision-making. Journal: Int. J. of Critical Infrastructures Pages: 252-266 Issue: 3 Volume: 21 Year: 2025 Keywords: big data; intelligent finance; financial management; risk profile. File-URL: http://www.inderscience.com/link.php?id=146887 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:3:p:252-266 Template-Type: ReDIF-Article 1.0 Author-Name: Yi Cao Author-X-Name-First: Yi Author-X-Name-Last: Cao Title: Investigation of the effect of inlet and outlet of the water flow on the productivity of a solar collector Abstract: The paper makes it possible to harvest energy from solar radiation by using collectors containing fluid flow. Using this method, the radiant energy of the sun is absorbed by the fluid in the collector, and this energy is then conveyed to a thermal exchanger and used for a variety of purposes. Therefore, the influence of the inlet and outlet valve positions on the thermal productivity of the collector has been studied using computational fluid dynamics (CFD) in this study. The simulation of the problem was performed by COMSOL software by numerically solving the governing equations and establishing suitable boundary conditions. There are three different configurations investigated: input and output in the same direction, input and output perpendicular to each other, and input and output in the same direction and the opposite direction. Results indicate that the best thermal conduction rate occurs when the inlet and outlet are aligned (inlet and outlet are positioned opposite each other) and on the same side of the collector. Journal: Int. J. of Critical Infrastructures Pages: 1-25 Issue: 7 Volume: 21 Year: 2025 Keywords: solar collector; fluid flow; thermal performance; computational fluid dynamics; CFD; heat exchanger; boundary conditions; heat transfer; solar thermal systems. File-URL: http://www.inderscience.com/link.php?id=148168 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:21:y:2025:i:7:p:1-25 Template-Type: ReDIF-Article 1.0 Author-Name: Debabrata Datta Author-X-Name-First: Debabrata Author-X-Name-Last: Datta Author-Name: S. Seema Author-X-Name-First: S. Author-X-Name-Last: Seema Author-Name: S. Suman Rajest Author-X-Name-First: S. Suman Author-X-Name-Last: Rajest Author-Name: Biswaranjan Senapati Author-X-Name-First: Biswaranjan Author-X-Name-Last: Senapati Author-Name: S. Silvia Priscila Author-X-Name-First: S. Silvia Author-X-Name-Last: Priscila Author-Name: Deepak K. Sinha Author-X-Name-First: Deepak K. Author-X-Name-Last: Sinha Title: Ensemble machine learning regression technique to select the type of concrete as radiation shielding material Abstract: The selection of exact material for shielding analysis is challenging in radiation protection. The primary objective of shielding analysis is to reduce radiation exposure to the occupational worker at their workplace. Generally, high-density concrete is selected as the shielding material to prevent accidental exposure to gamma and neutron radiation. Composite material or multilayer shielding materials are generally used to optimise the cost of concrete with maximum benefit to the society of occupational radiation workers. A surrogate model for concrete's overall strength using cement, fly ash, and coarse and fine aggregates is created using machine learning and ensemble learning. Ensemble learning in machine learning solves underfitting and overfitting problems when fitting a regression model for shielding analysis. As density increases, concrete overall strength decreases. Several samples of various types of concrete (different compositions) are collected as input data. Finally, a multi-attribute decision-making method is applied to select the appropriate type of concrete. The research presents the ensemble learning based regression technique coupled with multi attribute decision making method to recommend the exact variety of concrete for shielding gamma and neutron radiation. Journal: Int. J. of Critical Infrastructures Pages: 317-337 Issue: 4 Volume: 21 Year: 2025 Keywords: gamma and neutron; technique of order preference for similarity ideal solution; TOPSIS; type of concrete; radiation shielding material; ensemble machine learning; regression technique; mean square error; MSE; root mean square error; RMSE; landscape of materials science. File-URL: http://www.inderscience.com/link.php?id=148322 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:4:p:317-337 Template-Type: ReDIF-Article 1.0 Author-Name: Abhishek Basu Author-X-Name-First: Abhishek Author-X-Name-Last: Basu Author-Name: Arpita Ghosh Author-X-Name-First: Arpita Author-X-Name-Last: Ghosh Author-Name: Anirban Mukherjee Author-X-Name-First: Anirban Author-X-Name-Last: Mukherjee Title: Logic realisation of a spatial domain image watermarking with single electron transistors – an innovative approach Abstract: Multimedia articles exchanged over the digital network are increasing day by day causing enhanced threats of losing authenticity or copyright of those contents. As a result, requirement for low power and high speed copyright protection system for multimedia objects is hovering. In this article, authors have projected one spatial domain-based image watermarking structure for multimedia copyright protection and its hardware level implementation based on field programmable gate array (FPGA). Moreover, single electron transistor (SET) implementation for the structure has also been presented. The technique uses least significant bit (LSB) plane-based information hiding and all the modules of embedding and extraction block are realised with SET. It has been observed that this scheme shows noteworthy imperceptibility along with robustness. The result of SET execution confirms significantly low power consumption. Journal: Int. J. of Critical Infrastructures Pages: 338-358 Issue: 4 Volume: 21 Year: 2025 Keywords: image watermarking; multimedia copyright protection; field programmable gate array; FPGA; single electron transistor; SET; least significant bit; LSB; low power. File-URL: http://www.inderscience.com/link.php?id=148324 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:4:p:338-358 Template-Type: ReDIF-Article 1.0 Author-Name: Sudarsan Biswas Author-X-Name-First: Sudarsan Author-X-Name-Last: Biswas Author-Name: Diganta Saha Author-X-Name-First: Diganta Author-X-Name-Last: Saha Author-Name: Rajat Pandit Author-X-Name-First: Rajat Author-X-Name-Last: Pandit Title: A state-of-the-art prefix-based frequent pattern mining without candidate generation and compact FP tree generation Abstract: Without the candidate generation approach, it is still dominating and gaining a good research impact to find the desired association rules. The FP tree is a memory resident that sometimes memory overfits for high-volume datasets. The issue with the FP growth deals with numerous pointers. It generates a massive number of conditional pattern base and conditional FP trees that pursue notable performance degradation with specific datasets. FP growth needs to maintain many pointers operations for large datasets during the rule mining process. We present an efficient frequent patterns approach known as prefix-based frequent pattern mining (PBFPM). A straightforward novel array-based key-value pair approaches for finding frequent patterns efficiently from large-volume datasets. We induce an array structure table (AST) rather than an FP tree structure for storing the dataset's pattern. The proposed method does not generate duplicate frequent patterns and avoid numerous pointer dealings, which saves time in the rule-generation process. We compared the performance concerning time and memory complexity with the FP tree and state-of-the-art boss tree. Journal: Int. J. of Critical Infrastructures Pages: 359-384 Issue: 4 Volume: 21 Year: 2025 Keywords: association rule mining; ARM; frequent pattern mining; array structure table; key value pair; hash map table. File-URL: http://www.inderscience.com/link.php?id=148325 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:4:p:359-384 Template-Type: ReDIF-Article 1.0 Author-Name: Jianying Guo Author-X-Name-First: Jianying Author-X-Name-Last: Guo Title: Ideal planning of power grid integrating various small-scale power generating with biogeography-based optimisation Abstract: Gasoline cars are being replaced by electric vehicles (EVs), which add to the strain on the power grid due to their charging needs. Uncontrolled EVs can disrupt the grid; therefore, reliable planning is necessary. Increased distributed generation (DG) resources, especially renewable energy, may disrupt the electrical system. Effective mitigation requires demand-side planning and wise utilisation of emerging technologies, including energy storage. This study recommends optimising EV and DG charging and discharging schedules to fulfil regulated planning needs. Power company schedules depend on parking lot traffic to meet grid goals. The primary objectives are to maximise vehicle holders' and companies' earnings, minimise losses, and reduce parking lot travel time. Investigating critical load sensitivity improves charge and discharge control. The proposed approach utilises a hybrid biogeographic harmony search (BHS). BHS models island species movement, speciation, and extinction using biogeographical mathematics. A sample test system illustrates the method and concept in various settings. Optimal distribution resource management increases network profitability by 8.4% and dependability by 6.63% in outage indices. This holistic strategy highlights flexible models facing greater EV integration and DG resource usage, with numerical figures demonstrating over an 8% network performance gain. Journal: Int. J. of Critical Infrastructures Pages: 385-412 Issue: 4 Volume: 21 Year: 2025 Keywords: electric vehicles; parking zone; renewable energy sources; distributed generation; DG; harmony search algorithm; HS; biogeography-based optimisation algorithm; BBO. File-URL: http://www.inderscience.com/link.php?id=148327 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:4:p:385-412 Template-Type: ReDIF-Article 1.0 Author-Name: Sharad Nigam Author-X-Name-First: Sharad Author-X-Name-Last: Nigam Title: Signalling solution for railway diamond crossing using weight sensor for passenger safety Abstract: Railway double diamond crossing is a complex junction where four trains can approach the junction at the same time, but only two parallel opposite trains can cross the junction at the same time and non-parallel trains must wait for clear junction. The concurrent access of diamond crossing by multiple trains caused accidents from last decades due to signalling conflicts. This article is proposing a wireless sensor network model with LoRa communication technique and weight sensor to automate all signals related to double diamond crossing. Weight sensor is used as a train detection method to measure the threshold weight of the incoming train, then all diamond crossing signals change their aspect according to input data. Reliability and accuracy of weight sensor in any atmospheric and flood condition is shown. A novel weight sensor-based algorithm is proposed in the presented manuscript to automate all related signal aspects for the safe movement of a train with minimum time delay through double diamond crossing. Journal: Int. J. of Critical Infrastructures Pages: 413-432 Issue: 4 Volume: 21 Year: 2025 Keywords: double diamond crossing; weight sensor/load cell; LoRa; Arduino; WSN. File-URL: http://www.inderscience.com/link.php?id=148330 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:4:p:413-432 Template-Type: ReDIF-Article 1.0 Author-Name: Jianjian Wang Author-X-Name-First: Jianjian Author-X-Name-Last: Wang Author-Name: Zhigang Liu Author-X-Name-First: Zhigang Author-X-Name-Last: Liu Author-Name: Guanglei Zhao Author-X-Name-First: Guanglei Author-X-Name-Last: Zhao Title: Applying artificial rabbit optimisation-LSSVR analysis for HPC's compressive strength estimation Abstract: High-performance concrete (HPC) functions stronger because it contains more components than ordinary concrete. The compressive strength (CS) of HPC prepared with fly ash (FA) and blast furnace slag (BFS) was assessed using several artificially-based analytics. In this study, the artificial rabbit optimisation (ARO) technique, abbreviated as AROR and AROLS for the radial basis function (RBF) neural network and the least square support vector regression (LSSVR) analysis, accordingly, was employed to identify the optimal values for the parameters that could be adjusted to enhance performance. The CS was used as the predicting objective, and 1,030 experiments and eight input parameters were used to construct the suggested techniques. After that, the outcomes of the enhanced model were compared to those documented in the corpus of current scientific literature. The calculations suggest that combining AROLS with AROR research might be advantageous. The AROLS demonstrated much higher R<SUP align="right"><SMALL>2</SMALL></SUP> (R<SUP align="right"><SMALL>2</SMALL></SUP><SUB align="right"><SMALL>Train</SMALL></SUB> = 0.9853 and R<SUP align="right"><SMALL>2</SMALL></SUP><SUB align="right"><SMALL>Test</SMALL></SUB> = 0.9912) and lower error metrics when compared to the AROR and previous papers. Finally, the offered technique for computing the CS of HPC increased by BFS and FA may be created using the recommended LSSVR analysis enhanced by ARO. Journal: Int. J. of Critical Infrastructures Pages: 1-28 Issue: 8 Volume: 21 Year: 2025 Keywords: high-performance concrete; compressive strength; artificial neural network; least square support vector regression; artificial rabbit optimisation. File-URL: http://www.inderscience.com/link.php?id=148538 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:21:y:2025:i:8:p:1-28 Template-Type: ReDIF-Article 1.0 Author-Name: Yang Huang Author-X-Name-First: Yang Author-X-Name-Last: Huang Author-Name: Xinyu Li Author-X-Name-First: Xinyu Author-X-Name-Last: Li Author-Name: Dan Li Author-X-Name-First: Dan Author-X-Name-Last: Li Title: Design of manufacturing enterprise FEW system based on ML from the perspective of circular economy Abstract: This study designs a financial early warning system for manufacturing enterprises, focusing on machine learning and the circular economy. The random forest model is used as the base model, optimised by the artificial jellyfish algorithm to enhance prediction accuracy. Financial and non-financial indicators are selected through significance testing and feature screening methods. The results show that the optimised model achieves the highest accuracy of 88.42% and AUC of 0.918. Key warning indicators include inventory turnover rate, accounts receivable turnover rate, Herfindahl index, and liquidity ratio. The study highlights the importance of timely warnings for maintaining financial stability in manufacturing enterprises, helping them manage financial crises and supporting sustainable growth. The proposed system provides valuable support for policymakers and industry leaders in managing financial risks and advancing circular economy goals. Journal: Int. J. of Critical Infrastructures Pages: 1-20 Issue: 9 Volume: 21 Year: 2025 Keywords: circular economy; CE; manufacturing enterprises; financial early warning; FEW; random forest; RF; artificial jellyfish algorithm. File-URL: http://www.inderscience.com/link.php?id=148646 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:21:y:2025:i:9:p:1-20 Template-Type: ReDIF-Article 1.0 Author-Name: Jin Lu Author-X-Name-First: Jin Author-X-Name-Last: Lu Title: A smart rural tourism resources recommendation based on audience preference Abstract: How to provide users with more accurate smart rural tourism recommendation services has become a hot research topic at present. To address the short-term audience preference issue caused by data scarcity, firstly, graph convolutional networks (GCN) are applied to recommend smart rural tourism resources. For long-term tourism audiences with sufficient data, use long short-term memory (LSTM) to construct a recommendation model based on users' long-term dynamic preferences. The results showed that in the case of data scarcity, the recall and accuracy of the GCN recommendation method increased by 17.9% and 11.8%, respectively. In long-term rural tourism applications, the hits ratio (HR)@10 and HR@20 of the dynamic preference recommendation model were as high as 42% and 50%, respectively. The results indicate that the proposed method provides more reliable technical support for intelligent rural tourism recommendation and can more effectively discover audience preferences. Journal: Int. J. of Critical Infrastructures Pages: 1-18 Issue: 10 Volume: 21 Year: 2025 Keywords: audience preference; rural tourism; resource recommendation; long short-term memory; LSTM; graph convolutional network; GCN. File-URL: http://www.inderscience.com/link.php?id=148769 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:21:y:2025:i:10:p:1-18 Template-Type: ReDIF-Article 1.0 Author-Name: Li Fang Author-X-Name-First: Li Author-X-Name-Last: Fang Title: Design of an augmented unknown input estimator for the lithium-ion battery state of charge and sensor fault estimation Abstract: This research proposes a novel method to enhance the accuracy of state of charge (SoC) estimation in lithium-ion packs by utilising nonlinear battery models in combination with an estimator for the unknown input. It is crucial in optimising performance, ensuring safety, and increasing operational life in situations involving electric vehicles and renewable power systems. A key enhancement of this approach is the inclusion of sensing faults as state variables, allowing simultaneous estimation of both SoC and sensor faults. This improves system reliability by detecting and correcting sensor inaccuracies, ensuring stable battery management. The fault-tolerant design reduces errors and enhances real-world applicability. The methodology presented was validated through experimental tests, demonstrating a significant improvement in battery state estimation. The results verify the advancement in battery management systems and aim to develop efficient and reliable energy storage for diverse uses. Journal: Int. J. of Critical Infrastructures Pages: 30-54 Issue: 11 Volume: 21 Year: 2025 Keywords: lithium-ion battery; LIB; SoC and UIE estimator; electric vehicles; sensor fault; nonlinear model; terminal voltage; system reliability; energy efficiency; renewable energy; optimising battery operation. File-URL: http://www.inderscience.com/link.php?id=149028 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:21:y:2025:i:11:p:30-54 Template-Type: ReDIF-Article 1.0 Author-Name: Xiaowei Zhang Author-X-Name-First: Xiaowei Author-X-Name-Last: Zhang Title: Precise state-of-charge estimation for LIBs: a cutting-edge nonlinear model approach with enhanced robustness and reliability Abstract: Precise state-of-charge (SoC) prediction is essential for optimising the performance, safety, and longevity of lithium-ion batteries (LIBs) in battery management systems (BMS). However, traditional prediction tactics, including Kalman filters and sliding mode observers (SMOs), struggle with sensor noise, model uncertainties, and external disturbances, leading to inaccuracies in real-world applications. This study proposes a nonlinear battery framework integrated with a Luenberger observer enhanced by H-infinity (H∞) optimisation to boost SoC prediction accuracy and robustness. The H∞ framework effectively mitigates disturbances, while sensor fault prediction enhances reliability under varying operational conditions. The recommended tactic is computationally efficient and suitable for real-time SoC prediction. Empirical outcomes validate the superior accuracy and stability of the recommended approach, achieving prediction errors that are up to 3.8% lower than those of conventional SMOs. The findings demonstrate potential for next-generation BMS applications, particularly in electric vehicles (EVs) and energy storage systems. Future work will focus on adaptive parameter prediction techniques to boost performance under real-world battery ageing conditions. Journal: Int. J. of Critical Infrastructures Pages: 1-29 Issue: 11 Volume: 21 Year: 2025 Keywords: lithium-ion battery; LIB; state-of-charge; SoC; Luenberger estimator; H-infinity theory; battery reliability; energy storage systems; battery management systems; BMS; electric vehicles. File-URL: http://www.inderscience.com/link.php?id=149029 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijcist:v:21:y:2025:i:11:p:1-29 Template-Type: ReDIF-Article 1.0 Author-Name: MohammedShakil S. Malek Author-X-Name-First: MohammedShakil S. Author-X-Name-Last: Malek Author-Name: Rupesh Vasani Author-X-Name-First: Rupesh Author-X-Name-Last: Vasani Author-Name: Viral Bhatt Author-X-Name-First: Viral Author-X-Name-Last: Bhatt Title: Investigating and validating the critical risk factors in PPP: confirmatory factor analysis of the Indian road sector Abstract: Critical risk factors (CRFs) may considerably impact PPP project success, hence they must be recognised and analysed. This study examines how private and public sectors affect PPP road project performance at different stages of development and throughout the construction life cycle. The literature review and survey of private and public professionals to identify and verify CRFs may provide insights from industry experts. CFA may disclose PPP road project dynamics by comparing the six phases and private and public sectors. The study's findings that building project phases positively affect public and private sectors' CRFs may help professionals focus on essential aspects to increase PPP road project efficiency. A mitigation handbook for avoiding and correcting issues may result from the study. Risk allocation, project management, and PPP success increase with this study. The study discusses Indian PPP road projects and the need of locating and assessing CRFs. Journal: Int. J. of Critical Infrastructures Pages: 433-454 Issue: 5 Volume: 21 Year: 2025 Keywords: public-private partnership; PPP; confirmatory factor analysis; CFA; critical risk factors; CRFs; roads; AMOS. File-URL: http://www.inderscience.com/link.php?id=149107 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:5:p:433-454 Template-Type: ReDIF-Article 1.0 Author-Name: Jiaojie Yuan Author-X-Name-First: Jiaojie Author-X-Name-Last: Yuan Author-Name: Jiewen Zhao Author-X-Name-First: Jiewen Author-X-Name-Last: Zhao Title: IoT-aided smart city architecture for anomaly detection Abstract: Anomaly detection in smart cities is critical for mitigating human fall-related injuries and fatalities, particularly within IoT devices. Despite numerous vision-based fall detection methods, challenges persist regarding accuracy and computation costs, especially in resource-constrained IoT environments. This paper proposes a novel fall detection approach leveraging the Yolo algorithm, known for its efficiency in minimising computation costs while maintaining high accuracy. By utilising a diverse fall image dataset, the method undergoes rigorous training and evaluation, employing standard performance metrics. The results reveal impressive precision, recall, and mean average precision (mAP) values of 93%, 89%, and 95%, respectively. Notably, the Yolo algorithm's computational efficiency ensures minimal resource utilisation, making it suitable for real-time deployment in IoT devices within smart city infrastructures. Consequently, this method presents a promising solution for enhancing fall detection accuracy while optimising computational resources, thus advancing safety measures in urban environments. Journal: Int. J. of Critical Infrastructures Pages: 495-514 Issue: 5 Volume: 21 Year: 2025 Keywords: anomaly detection; fall detection; vision system; Yolo; smart city; internet of things; IoT; mean average precision; mAP; algorithm's computational efficiency. File-URL: http://www.inderscience.com/link.php?id=149108 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:5:p:495-514 Template-Type: ReDIF-Article 1.0 Author-Name: Thirukumaran Subramani Author-X-Name-First: Thirukumaran Author-X-Name-Last: Subramani Author-Name: Priyanka Mathur Author-X-Name-First: Priyanka Author-X-Name-Last: Mathur Author-Name: Sohail Imran Khan Author-X-Name-First: Sohail Imran Author-X-Name-Last: Khan Author-Name: P. Suganya Author-X-Name-First: P. Author-X-Name-Last: Suganya Author-Name: Sukhwinder Sharma Author-X-Name-First: Sukhwinder Author-X-Name-Last: Sharma Author-Name: Sunita Sachin Dhotre Author-X-Name-First: Sunita Sachin Author-X-Name-Last: Dhotre Title: Environmental and social governance issues in AI-era electric power management and information disclosure Abstract: Artificial intelligence (AI) has dramatically transformed the electric power management sector, ushering in higher levels of efficiency, sustainability, and intelligent energy distribution. This shift has enabled more optimised consumption patterns and significantly reduced waste. However, AI complicates power management, particularly environmental and social governance (ESG). This study analyses the pros and cons of AI-powered electric power sector ESG issues. While AI improves power management through predictive maintenance and demand-response optimisation, it also presents transparency issues related to its decision-making algorithms, complicating ESG adherence. To address these concerns, we introduce a novel architectural framework designed to enhance transparency and directly confront ESG challenges associated with AI in power management. Our thorough trials validate the concept, presenting a potential strategy to harmonising technical advancement with ESG principles. The findings demonstrate the need for a balanced approach, embracing AI's potential to transform power management and ESG challenges. A sustainable and equitable future for power management technology requires this balance. Our research shows the importance of proactive ESG engagement in the AI era and the framework's ability to create a more open, accountable, and sustainable power management paradigm. Journal: Int. J. of Critical Infrastructures Pages: 470-494 Issue: 5 Volume: 21 Year: 2025 Keywords: artificial intelligence; electric power management; environmental and social governance; ESG; transparency and information disclosure; technological advancements; architectural framework; efficiency and challenges. File-URL: http://www.inderscience.com/link.php?id=149109 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:5:p:470-494 Template-Type: ReDIF-Article 1.0 Author-Name: Ganeshprabhu Parvathikumar Author-X-Name-First: Ganeshprabhu Author-X-Name-Last: Parvathikumar Author-Name: Brintha Sahadevan Author-X-Name-First: Brintha Author-X-Name-Last: Sahadevan Author-Name: Deepa Sree Pandiaraj Author-X-Name-First: Deepa Sree Author-X-Name-Last: Pandiaraj Author-Name: Marshal Raj Author-X-Name-First: Marshal Author-X-Name-Last: Raj Title: Investigation on cost effective smart construction techniques for quality monitoring and risk management in small scale construction sites in India Abstract: The challenges and risks involved in construction sites varies depending upon the building size, economy, materials used, tools or equipment's availability for safety measures, height, and geographical location. In this work, smart construction techniques are implemented and investigated for risk management and quality monitoring in a cost-effective manner in a small-scale construction site in India. The proposed work focuses on the general hazards and the risks faced by engineers in such sites. To mitigate the challenges, cost effective and reusable smart solutions set up is implemented and validated in a real-time small construction site. The smart solution setup provided support to the construction site engineers to predict the damages in the Scaffolds and Formwork, and testing the quality of concrete, verticality check, surface levelling and formwork deflection. The proposed solutions can be used to improve building critical infrastructures in a cost-effective manner especially in middle- and lower-income economies. Journal: Int. J. of Critical Infrastructures Pages: 455-469 Issue: 5 Volume: 21 Year: 2025 Keywords: formwork; labour safety; quality monitoring; risk management; scaffoldings; smart construction; India. File-URL: http://www.inderscience.com/link.php?id=149110 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:5:p:455-469 Template-Type: ReDIF-Article 1.0 Author-Name: Linhui Yang Author-X-Name-First: Linhui Author-X-Name-Last: Yang Title: Impact of market incentive-based environmental regulations on corporate financial performance in a circular economy Abstract: In the Chinese capital market, environmental regulations based on market incentives will have a significant impact on the economic activities of enterprises. To understand the impact of market incentive based environmental regulations on corporate financial performance, this study proposes a financial performance calculation model based on an improved long short-term memory network to evaluate corporate financial performance. On the basis of making assumptions, impact analysis is conducted through regression analysis and other methods. The experimental results indicate that the difference between output and expected financial performance is only 0.023. Technological innovation (TI) was significantly negatively correlated with market-based environmental regulation (p < 0.05), and significantly positively correlated with corporate financial performance (p < 0.01). The research method can effectively analyse the impact of environmental regulations on corporate financial performance based on market incentives. Most existing research analyses national or regional data, with less emphasis on the perspective of individual enterprises. Journal: Int. J. of Critical Infrastructures Pages: 515-531 Issue: 5 Volume: 21 Year: 2025 Keywords: circular economy; market incentives; environmental regulation; financial performance; FP; LSTM. File-URL: http://www.inderscience.com/link.php?id=149111 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:5:p:515-531 Template-Type: ReDIF-Article 1.0 Author-Name: S. Belina V.J. Sara Author-X-Name-First: S. Belina V.J. Author-X-Name-Last: Sara Author-Name: A. Jayanthiladevi Author-X-Name-First: A. Author-X-Name-Last: Jayanthiladevi Title: Efficient marine debris infrastructures on optimising SVM with LoG segmentation for enhanced IoR, DC and Hausdorff distance performances Abstract: In the face of escalating threats to aquatic ecosystems posed by marine debris, the demand for precise and efficient classification techniques becomes paramount. This study employs image segmentation methods Canny edge detection, Sobel operator, and Laplacian of Gaussian (LoG) to partition photographs of maritime trash. A notable addition is the integration of SVM-based classification, offering promising avenues for environmental surveillance and disaster management. By incorporating the LoG process, the identification of blob-like structures enhances the accuracy of debris segmentation. Comparative analysis utilising metrics like intersection over union (IoU), dice coefficient, and Hausdorff distance underscores the efficacy of the combined LoG and SVM approach. This synergistic method adeptly detects edges via the LoG operator and ensures accurate debris classification through SVM modelling. The results demonstrate significant improvements, yielding higher IoU (0.993), dice coefficient (0.996), and minimal Hausdorff distance (0.0000977). Executed in Python, this research propels marine debris analysis forward by furnishing a robust framework for automatic image categorisation, which is vital for initiatives aimed at environmental preservation. Journal: Int. J. of Critical Infrastructures Pages: 533-554 Issue: 6 Volume: 21 Year: 2025 Keywords: marine debris infrastructures; image classification; SVM method; segmentation techniques; canny edge; Sobel operator; SO; Laplacian of Gaussian; LoG; IoR evaluation; DC Metric Hausdorff distance; HD; improved results; automated analysis. File-URL: http://www.inderscience.com/link.php?id=150791 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:6:p:533-554 Template-Type: ReDIF-Article 1.0 Author-Name: M. Gomathy Author-X-Name-First: M. Author-X-Name-Last: Gomathy Author-Name: A. Vidhya Author-X-Name-First: A. Author-X-Name-Last: Vidhya Title: Detecting malware in linguistic data using malware detection deep belief neural network method Abstract: The widespread usage of high-end digital technologies has greatly increased cyber risks. To fight cybercrimes, a smart model should categorise and learn from data autonomously. Internet connectivity has made people's lifestyles more intertwined, and virtual collaboration is happening across regions. Pop-up messages also entice users and enable fraud. We use a neural network to predict unexpected pop-up message content in this paper. Modern malware and its powerful obfuscation algorithms have made traditional malware detection methods ineffective. However, deep belief neural networks (DBNNs) have garnered attention from researchers for malware detection to fight conventional cybercrime prevention methods in the long run. MDDBNN (malware detection deep belief neural network), based on file properties and contents, is proposed in this research for malware classification. The CLaMP Integrated dataset provided 5210 instances for training and testing. MDDBNN beats GaussianNB, LDA, logistic regression, and support vector machine (SVM). This study found that MDDBNN has the highest accuracy of 97.8%. Journal: Int. J. of Critical Infrastructures Pages: 640-662 Issue: 6 Volume: 21 Year: 2025 Keywords: deep belief networks; cyber security; cybercrime; spam and deep learning; DL; support vector machine; SVM; malware detection deep belief neural network; MDDBNN; logistic regression; LR. File-URL: http://www.inderscience.com/link.php?id=150792 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:6:p:640-662 Template-Type: ReDIF-Article 1.0 Author-Name: Hosny Abbas Author-X-Name-First: Hosny Author-X-Name-Last: Abbas Author-Name: Ibrahim E. Ibrahim Author-X-Name-First: Ibrahim E. Author-X-Name-Last: Ibrahim Author-Name: Hamada Esmaiel Author-X-Name-First: Hamada Author-X-Name-Last: Esmaiel Author-Name: Bassem Abd-El-Atty Author-X-Name-First: Bassem Author-X-Name-Last: Abd-El-Atty Title: Critical infrastructures challenges and requirements meet blockchain features and benefits: a literature review Abstract: Since its invention by Satoshi Nakamoto in 2008 (Nakamoto, 2008) as the backbone of the first successful bitcoin digital cryptocurrency, blockchain technology has evolved and experienced several innovative breakthroughs. It has become a disruptive solution for developing distributed and decentralised applications in many domains beyond cryptocurrencies. One example of these domains is the contemporary, riskily interdependent ICT-based critical infrastructure. This multi-domain literature review explores the literature of blockchain and critical infrastructure domains, attempting to match the features and benefits provided by the former to the challenges and requirements encountered in the latter. The review concludes that despite the known limitations of blockchain technology regarding scalability, interoperability, implementation complexity, and real-time requirements, it represents a promising enabling technology for addressing several challenges and requirements in the design and development of contemporary integrated and highly interdependent CIs. Future research directions are also highlighted. Journal: Int. J. of Critical Infrastructures Pages: 592-639 Issue: 6 Volume: 21 Year: 2025 Keywords: critical infrastructures; critical infrastructures requirements and challenges; interdependency modelling; risk assessment; complexity; blockchain technology; consortium blockchains; cross-sector integration. File-URL: http://www.inderscience.com/link.php?id=150793 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:6:p:592-639 Template-Type: ReDIF-Article 1.0 Author-Name: Yu Wang Author-X-Name-First: Yu Author-X-Name-Last: Wang Title: Application of machine learning and neural network technology in art design Abstract: In the digital art domain, the integration of intelligent design and analytical capabilities necessitates effective methods for automatically discerning and evaluating artworks. This research suggests a machine learning-based neural network method to the challenge. To investigate emotional resonance in numerous art forms across disciplines, a deep recurrent neural network is built. A new cross-domain edge cloud model uses cloud computing advances. This architecture offloads streaming media services to edge network sub-clouds, revolutionising storage and compute. Edge networks make cross-media data collecting easy, enabling analysis. Deep neural networks analyse visual and linguistic input to classify viewer emotions via multimodal classification. Experimental results show that the model can accurately identify unlabeled cross-media data. The technique also mitigates the possibility of erroneous emotion representation in AI systems by addressing artificial emotion simulation. The MMBT model outperformed others with 66.33% accuracy and 62.24% F1 value. This research provides a complete framework for discovering emotional nuances in cross-media art and intelligent art design and analysis. Journal: Int. J. of Critical Infrastructures Pages: 555-573 Issue: 6 Volume: 21 Year: 2025 Keywords: convolutional neural network; CNN; cross-media; emotion analysis; art design; machine-learning; neural network technology; streaming media services; artificial neural networks; ANNs. File-URL: http://www.inderscience.com/link.php?id=150794 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:6:p:555-573 Template-Type: ReDIF-Article 1.0 Author-Name: Vandana Bhavsar Author-X-Name-First: Vandana Author-X-Name-Last: Bhavsar Author-Name: Pradeepta Kumar Samanta Author-X-Name-First: Pradeepta Kumar Author-X-Name-Last: Samanta Author-Name: Sagar Malsane Author-X-Name-First: Sagar Author-X-Name-Last: Malsane Author-Name: M.D. Deepak Author-X-Name-First: M.D. Author-X-Name-Last: Deepak Title: A conceptual framework for adoption of digitalisation in construction organisations Abstract: Organisations worldwide are grappling with substantial difficulties following the current technological developments, environment related issues, and socioeconomic disruptions. Consequently, organisations have embraced Industry 4.0 to overcome these challenges and devise digital integration. Numerous frameworks, models, and tools have been developed to gauge the digital adoption or digital readiness of various sectors/organisations. However, though the adoption rates of various digital tools in construction firms have increased significantly since 2020, there is a paucity of systematic frameworks with construction-specific digitalisation dimensions and indicators required for successful technology adoption and readiness in the construction organisation. The study therefore proposes a holistic framework comprising dimensions and indicators specific to digitalisation readiness for construction organisations. The developed framework of the study will help construction organisations develop a concrete strategic graduation that sets up the roadmap for digital transformation and also ensures the identification of appropriate digital measures and investments. Journal: Int. J. of Critical Infrastructures Pages: 574-591 Issue: 6 Volume: 21 Year: 2025 Keywords: Industry 4.0; Construction 4.0; construction sector; digitalisation; digital transformation; digital adoption; digital readiness; maturity models; digitalisation adoption frameworks. File-URL: http://www.inderscience.com/link.php?id=150795 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijcist:v:21:y:2025:i:6:p:574-591