The following International Journal of Learning Technology article, "Analysing student behaviour in the implementation of LAT using cloud technology in higher education institutions", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A new machine learning framework designed to detect malicious interference in unmanned aerial vehicles (UAVs), commonly known as drones, has shown strong performance in identifying both sudden and slow-developing sensor attacks, according to research in the International Journal of Automation and Control. The system, called GTF-MAD (Graph Time-Frequency Mixed Anomaly Detection), achieved a peak F1-score of 99.71% in detecting bias in tests on a quadrotor drone.
UAVs depend on sensors such as GPS (which provides satellite-based location data) and gyroscopes (which measure rotation and orientation). These act as the drone’s navigational senses. However, they are vulnerable to manipulation. GPS spoofing can feed false location signals to a drone, while gyroscope bias injection introduces small but persistent errors into motion readings. Both can accumulate into major navigation failures if undetected.
Traditional detection systems rely on fixed rules, physical flight models, or machine learning patterns in sensor data. However, they struggle with changing sensor relationships during flight, lack of frequency-based signal analysis, and difficulty detecting slow-burn attacks that evolve over time.
GTF-MAD addresses these issues through three components. An adaptive graph attention network models sensors as a dynamic system of relationships that change during flight. A dual time-frequency architecture analyses signals both as time sequences and as frequency patterns, capturing vibrations and periodic disturbances. A trend detection module combines statistical methods to identify slow, stealthy deviations.
Chen, J., Zhou, Y. and Xue, X. (2026) ‘Time series data-driven UAV sensor attack detection: an adaptive graphtime-frequency hybrid approach’, Int. J. Automation and Control, Vol. 20, No. 7, pp.1–25.
The following International Journal of Business Information Systems article, "Analysing the critical role of data governance in shaping Iraq's smart city future", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
A study covering 76 countries has found that people who are more trusting, patient, altruistic and cooperative tend to report higher levels of happiness and life satisfaction, suggesting that wellbeing depends on more than material prosperity alone. The work was published in the International Journal of Happiness and Development.
The research looked at behavioural preferences, stable patterns in how people make decisions and interact with others, and how these relate to subjective wellbeing. Subjective wellbeing is a metric that embodies both life satisfaction and emotional experiences such as happiness, enjoyment, and worry.
The researchers used data from the Global Preferences Survey and the Gallup World Poll They looked at five personality traits in the data: patience, risk-taking, reciprocity, altruism, and trust. The study combined survey responses with experimentally validated behavioural measures designed to reflect real-world behaviour, something that earlier studies had not generally done.
Across most countries and measures, stronger behavioural preferences were associated with higher wellbeing, the team found. People who were more trusting, altruistic, reciprocal and willing to take risks generally reported greater happiness and lower levels of worry.
What was particularly interesting about the findings is that there was consistency across different regions. Previous research on wellbeing has often focused on income, employment and health, mainly in wealthier countries. The new study suggests behavioural and social dispositions play an important role across cultures and economic systems in different parts of the world.
The team found that trust and reciprocity were especially important. They suggest that this is because cooperative societies foster stronger social bonds, and that reduces personal stress. Altruism may also improve wellbeing by increasing social connectedness and meaning. Patience may support healthier and more stable long-term choices, the team suggests.
It is worth adding that the findings are correlational rather than causal. The team cannot say whether the behavioural traits studied improve wellbeing or whether it is that happier people tend to become more trusting and altruistic.
Overdick, K. and De Neve, J-E. (2026) ‘Subjective wellbeing and behavioural preferences: evidence from global survey data’, Int. J. Happiness and Development, Vol. 10, No. 2, pp.140–171.
The following International Journal of Happiness and Development article, "Subjective wellbeing and behavioural preferences: evidence from global survey data", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
Artificial intelligence might now be used to address a less visible problem associated with renewable electricity production: the carbon footprint of the grid infrastructure itself. Details of how an AI-based forecasting system can predict the full lifecycle emissions of zero-carbon substations are provided in the International Journal of Business Intelligence and Data Mining. The approach is faster and more accurate than previous methods.
Substations convert high-voltage electricity into forms suitable for transmission and local distribution. Although often overlooked in climate debates, they generate emissions throughout construction, manufacturing, transport, maintenance, operation, and their decommissioning.
The study examines zero carbon substations, designed to minimise emissions through energy-efficient technologies, renewable integration, and offset measures such as carbon sinks. The researchers argue that only a full lifecycle perspective can properly assess their environmental impact, since supply chains and construction materials can account for substantial hidden emissions. Existing forecasting models, including deep reinforcement learning, recurrent neural networks, and random forest regression, usually cannot cope fully with the most important variables while maintaining speed and accuracy.
The new hybrid system, called Lasso-GRNN, combines statistical filtering with a neural network designed to model complex nonlinear relationships. Clustering techniques are also used to improve data quality before analysis.
The model achieves 98.51 per cent prediction accuracy with processing times of just 0.68 seconds. This could allow utility providers to make more timely and more informed infrastructure, maintenance, and investment decisions as electricity grids become increasingly decentralised and renewable focused.
Zeng, T., Chen, Y., Wang, L., Yuan, M., Lv, Z. and Wang, D. (2026) ‘Prediction of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model’, Int. J. Business Intelligence and Data Mining, Vol. 28, No. 8, pp.1–19.
The following International Journal of Automation and Control article, "Time series data-driven UAV sensor attack detection: an adaptive graph-time-frequency hybrid approach", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
The following International Journal of Services, Economics and Management article, "Applying the RBV theory to explore how fulfilment processes affect digital logistics performance in emerging economies", is freely available for download as an open access article.
It can be downloaded via the full-text link available here.
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.
