The following International Journal of Electronic Security and Digital Forensics article, "Rule of law education for cybersecurity governance in higher education institutions: a framework for policy and practice", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Research in the International Journal of Information and Communication Technology suggests that so-called knowledge graphs, a form of AI-based data organisation, could improve the reliability and maintenance of power communication systems that help keep the lights on and modern electricity grids running smoothly.
The researchers report that such a system works better than a conventional database in query efficiency, fault diagnosis, and operational decision-making. They explain that this technology could be used to help utility operators anticipate equipment failures earlier and manage increasingly complex power networks more effectively.
Power communication equipment functions as the information backbone of electricity grids, enabling substations, sensors and control centres to exchange data in real-time. However, as grids are becoming more digitalised through smart sensors, distributed energy systems and private 5G networks, operators are generating far larger volumes of interconnected data that somehow has to be managed.
The researchers argue that conventional relational databases struggle with this level of complex data. Relational databases organise information into rigid tables linked by predefined relationships. While suitable for simpler systems, the researchers say they create information silos in large infrastructure networks, where maintenance records, fault reports, environmental conditions, and operational data are fragmented across separate systems.
The proposed AI framework instead uses a knowledge graph, which represents devices, faults, maintenance activities, and communication links as interconnected nodes. By explicitly mapping relationships between all these different pieces of information, the system can identify dependencies and hidden correlations more effectively. In order to integrate this information from different sources, the researchers used natural language processing (NLP), an AI technique that extracts meaning from human language.
NLP enables the system to analyse unstructured materials such as maintenance reports and technical documents alongside structured operational data. The resulting information is stored in the graph database designed specifically for highly connected data. This approach allows the utility operator to have in place predictive infrastructure management. Now, instead of relying mainly on manual inspections and operator experience when faults occur, they can predict failures in advance and carry out preventative maintenance.
Zhang, J., Chen, S., Guo, L., Xie, J., Li¸ B. and Zhong, R. (2026) ‘Research on intelligent management of the full lifecycle of power communication equipment based on knowledge graphs’, Int. J. Information and Communication Technology, Vol. 27, No. 42, pp. 72–92.
The following International Journal of Business and Emerging Markets article, "Monetary policy's impact on Vietnamese stock market bubbles", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Research in the International Journal of Collaborative Engineering has found that universities that bring together environmental engineering and landscape architecture students in joint projects produce stronger design outcomes and better-prepared graduates for the world of work. These students can face real-world infrastructure challenges more effectively, the research into interdisciplinary teaching in sustainability-focused disciplines found.
The researchers focused on a persistent mismatch between professional practice and higher education. In the workplace, environmental engineers and landscape architects frequently collaborate on projects such as urban drainage systems, flood mitigation schemes, and climate adaptation plans. However, most university courses teach these two subjects separately, with few connections made between the disciplines to allow students to learn about each other’s methods, terminology, and priorities.
Environmental engineering is a discipline concerned with designing systems that protect environmental quality, including water treatment, stormwater infrastructure, and flood control. Landscape architecture focuses on shaping outdoor and urban spaces with ecological processes, human use, and aesthetics in mind. These two disciplines overlap often in practice but those working in each field will commonly have followed separate educational paths.
To test their hypothesis of whether structured collaboration might address this silo effect, the researchers embedded joint learning activities into two existing courses: an environmental engineering watershed engineering module and a landscape architecture urban design studio. Students were put into small interdisciplinary groups and given the task of developing climate-adaptive stormwater and flood management strategies for a real city. External partners introduced practical constraints, such as budgeting, planning regulations, and community requirements. This meant the students had to move beyond abstract design exercises and engage with realistic decision-making and work together to do so.
Feedback from students and instructors and an assessment of the design outcomes of the project showed that the collaboration led to a higher standard of outcome than previous iterations completed within a single discipline. Avoiding professional siloing in these two fields and other related areas is increasingly important in the context of climate change, rapid urbanisation, and growing flood risk. The challenges are inherently complex, involving environmental systems, built infrastructure and social behaviour simultaneously, and so interdisciplinary approaches to problem-solving are increasingly needed in the real world.
Georgakakos, C.B., Cerra, J.F., Allred, S.B., Williams, K., Walter, M.T., LoGiudice, E. and Smith, G. (2026) ‘Cross-disciplinary learning in environmental engineering and landscape architecture’, Int. J. Collaborative Engineering, Vol. 2, No. 5, pp.1–35.
Financial inclusion has emerged as a driver of development rather than a secondary outcome, according to research in the International Journal of Intelligent Enterprise. Financial inclusion defines the extent to which individuals and firms have fair, affordable, and reliable access to financial services such as banking, credit, insurance, and equity markets.
The IJIE paper reviewed the research literature in this area and found that a clearer understanding of impact can be drawn if a distinction is made between financial development and financial inclusion. Financial development refers to the size, depth, and efficiency of a country’s financial system, in other words, how effectively it mobilises savings and allocates capital to productive uses. Financial inclusion, by contrast, focuses on who is able to participate in that system. A financial sector can be highly sophisticated while still excluding large parts of the population due to income, geography, gender, and social status.
Various studies show that the effects of inclusion are identified at multiple levels. At the household level, access to formal financial services allows people to save securely, borrow for emergencies or investment, and finance a family member’s education or assist with the startup of a small business. This reduces dependence on informal lending networks, which are often expensive, unstable, and unregulated in the developing world. At the company level, limited access to credit constrains expansion. Businesses without formal finance tend to rely on retained earnings or potentially risky informal borrowing, which restricts productivity growth and innovation.
The research also found a link between financial inclusion and broader distributional outcomes. By widening access to financial tools, groups that were once excluded can build assets and smooth income over time. Ultimately, this reduces inequality and poverty. Numerous papers reviewed also showed that gender inclusion increases female participation in economic activity and leadership roles, which then has an effect on institutional performance and policy design.
Rani, V.S., Sundaram, N. and Prasad Babu, P. (2026) ‘A survey of impact of financial inclusion for various sectors in different countries’, Int. J. Intelligent Enterprise, Vol. 13, No. 2, pp. 128–146.
The following International Journal of Business Excellence article, "Empirical study of consumer-to-consumer social commerce users with a structural equation modelling approach", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Academic success at university could depend on the changing interaction between students’ habits over time rather than fixed traits such as intelligence or total study hours. This conclusion is discussed in the International Journal of Computational Systems Engineering in a paper that challenges the conventional methods of predicting and measuring educational success.
In the research, the team looked at why some students consistently perform better than others and have developed a statistical model that treats learning behaviour as dynamic rather than static. The study suggests that standard approaches to educational analysis commonly overlook the fact that student routines, motivation, and workloads change during their time at university. Student habits frequently fluctuate in response to deadlines, stress, extracurricular commitments, and changing levels of engagement. Moreover, these factors influence each other dynamically from term to term, and static models cannot, by definition, take this into account.
The research used an extended linear regression model to estimate how strongly particular variables, such as attendance, study time, and motivation, affect examination results or scores. One of the clearest findings from this kind of analysis involved cramming before examinations. Educational advice often portrays intensive last-minute revision as inherently inferior to consistent long-term study. The study’s findings suggest a more nuanced relationship. Short-term intensive study was associated with stronger immediate improvements in results than long-term study habits alone. However, the researchers stress that cramming was only really effective when supported by stable routines and regular review throughout the term. The study also found that too many extracurricular activities reduced the effectiveness of cramming by limiting both available time and mental energy.
The study raises questions about how educational institutions understand student achievement. Universities frequently rely on static indicators such as attendance rates, exam results, and cumulative study hours when assessing academic potential. The researchers argue that these measures may overlook the importance of timing, behavioural change, and the interaction between short-term and long-term learning strategies.
Huang, R. (2026) ‘Analysis of factors affecting college students’ academic performance based on linear regression’, Int. J. Computational Systems Engineering, Vol. 10, No. 8, pp.1–13.
An AI model that combines Long Short-Term Memory (LSTM) neural networks with Bayesian optimisation can improve both the accuracy and efficiency of electric vehicle battery state-of-health (SOH) estimates, a key measure used in battery management systems to track degradation over time. Details are provided in the International Journal of Vehicle Information and Communication Systems.
Lithium-ion batteries gradually lose capacity through repeated charging cycles. SOH expresses this decline as a percentage of the original charging capacity. Accurate SOH estimation is important for drivers charging the vehicles ahead of a road trip. If SOH has fallen, then the distance they will be able to travel will be less than when the vehicle’s battery was new. It is also a matter of safety, as degraded batteries are more vulnerable to overheating, electrical faults, and, in rare cases, thermal runaway, a self-reinforcing reaction that can lead to fire.
Electric vehicles have Battery Management Systems (BMS) to monitor voltage, current, and temperature. However, converting this data into a reliable SOH estimate is difficult because battery degradation is influenced by complex chemical processes, temperature changes, and driving behaviour.
The new model can retain earlier patterns in a sequence, helping capture long-term behaviour in battery performance. The model links “health features” extracted from the vehicle data to standardised battery capacity. By using the probabilistic statistical technique of Bayesian optimisation, the new model can home in on particular data points rather than processing all possibilities. This reduces unnecessary computation while maintaining performance and gives a useful improvement on accuracy and halves the average error rate.
By obtaining a more accurate SOH estimate, the vehicle can manage its battery better and indicate when maintenance and replacement are needed. The BMS system can thus operate closer to safe performance limits. There is also the potential for extending battery life by adjusting charging rates and extent as the battery ages.
Xiao, Z. (2026) ‘Bayesian optimised route and SOH estimation effect for Li-ion battery management system of electric vehicles based on LSTM’, Int. J. Vehicle Information and Communication Systems, Vol. 11, No. 2, pp.146–162.
AI can be used to model the spread of Alzheimer’s disease through the brain and has now provided researchers with a more biologically grounded way to predict cognitive decline. Details are reported in the International Journal of Simulation and Process Modelling. The work takes into account a shift in neuroscience that now seeks to treat dementia as a dynamic network disorder rather than a static accumulation of toxic proteins.
Nevertheless, the research focuses on Tau, a protein increasingly seen as central to the progression of Alzheimer’s disease. Although the condition is also associated with amyloid plaques, scientists now believe Tau pathology correlates more directly with neurone death and the deterioration of memory and reasoning. Amyloid plaques are perhaps the trigger, but the accumulation of misfolded Tau proteins, which multiply like prions, is thought to be the abnormality that leads to the cognitive problems seen in Alzheimer’s disease.
The new model, NSTP-Net, combines two forms of AI. One is a graph neural network, a type of deep learning designed to analyse interconnected systems. In this case, the brain is represented as a network of linked regions, enabling the model to simulate how disease-related signals travel across neural pathways. The second component uses symbolic reasoning, in which established biological knowledge is encoded directly into the system as logical rules. These include the tendency of Tau to spread along synaptic connections, the vulnerability of highly active brain regions, and the role of genetic risk factors.
The researchers validated their model against data from 428 participants in the Alzheimer’s Disease Neuroimaging Initiative. NSTP-Net was able to reduce prediction error by about 22 per cent compared with existing methods when forecasting Tau spread over an 18-month period. It also showed strong performance in predicting which patients with mild cognitive impairment, measurable memory problems not yet severe enough to qualify as dementia, would later progress to Alzheimer’s disease.
Huo, M., Chen, Y. and Wang, H. (2026) ‘Tau protein transmission simulation modelling in Alzheimer’s disease integrated with neuro-symbolic learning’, Int. J. Simulation and Process Modelling, Vol. 23, No. 6, pp.1–12.
Research in the World Review of Entrepreneurship, Management and Sustainable Development has looked at changes in the labour market in regions of Greece affected by the rapid phasing-out of coal and the move to renewables. The research suggests that current European Union approaches to green skills risks underestimating how unevenly job skills are spread across different sectors undergoing this energy transition.
The research was done in the context of the European Green Deal and its Just Transition Mechanism. These both aim to support workers and regions shifting away from fossil fuels. The research used survey data from more than 500 companies across three sectors, energy, construction, and ICT, to build a skills gap index. This statistical measure comparing existing workforce capabilities with those required by employers could help avoid many of the emerging problems of the energy transition.
The work shows that there is a big divergence between sectors. The energy sector, undergoing the most direct structural change away from fossil fuels, has the largest and most complex skills gaps. Specifically, employers report shortages in the necessary financial expertise needed to structure investments in emerging technologies such as hydrogen systems, alongside technical and strategic capabilities for managing evolving energy networks. In construction, there is a narrower but still important gap that is concentrated in green building practices. In ICT, there are also smaller skills gaps overall, but this might simply be a reflection of limited awareness of the problem among those surveyed.
A central finding of the work is that almost all skills identified (over 91 per cent) are not easily transferable between the three sectors being considered. This, the researchers say, challenges the big assumption that green skills can be treated as a single, unified labour category suitable for broad training programmes. There is much to be done at the energy coalface, as it were, in terms of awareness and training to ease the transition to a low-carbon future despite grand political statements and policies.
Galanos, G., Agiropoulos, C., Kyrlis, I. and Zlatini, K. (2026) ‘From coal to green: skills pathways for key emerging sectors in just transition regions’, World Review of Entrepreneurship, Management and Sustainable Development, Vol. 22, No. 2, pp.1–37.
