Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Development of Xiamen's tourism industry based on GIS spatial analysis and grey correlation analysis method
• Personalised knowledge recommendation system for English teaching based on MoE-RAG algorithm
• Data security collaboration mechanism of college student innovation and entrepreneurship education platform combining federated learning and differential privacy
• Dynamic evaluation of professional core OBE based on meta-learning and knowledge distillation
• Joint modelling and governance of knowledge graphs and reasoning rules for vocational skill assessment

Predicting HE higher and higher

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.

Free Open Access special issue on "Achieving Carbon Neutrality from Environmental Impact Monitoring and Assessment Technologies – Part III" published by International Journal of Environment and Pollution

The International Journal of Environment and Pollution has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Monitoring and sustainable management of soil microbial environmental quality based on machine learning
• Evaluation of factors affecting expansion of weak-base ASP flooding based on grey correlation analysis combined with BP neural network
• Ecological services and improvement strategies of forest healthcare space environment under the background of carbon neutrality
• Optimisation of rural green supply chain promoting social sustainable development: a case study based on intelligent environmental impact assessment
• Investigation of the coordinated development of carbon emissions, energy, and sustainable growth based on fuzzy system theory
• Optimised bidding strategy for data centres participating in the electrical energy and fast frequency regulation market under the background of carbon peak and carbon neutrality
• Public service system for green and sustainable development in marketing based on blockchain technology

Free Open Access special issue on "Digitalisation, Information Systems and Artificial Intelligence in Business Processing" published by International Journal of Business Intelligence and Data Mining

The International Journal of Business Intelligence and Data Mining has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Prediction of carbon emissions throughout the lifecycle of zero carbon substations based on Lasso-GRNN neural network model
• A comprehensive management method of audit data based on knowledge graph
• Research on safety risk perception of electrochemical energy storage power station under the background of environmental sustainable development
• Study on multimodal ideological and political teaching material push on MOOC online learning platform

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Multi-cluster data mining and analysis of tourist behaviour patterns for scenic area management
• Basketball player tracking method based on multi-source data and attention mechanism
• Optimisation of railway logistics high quality development path based on new quality productivity
• Personalised digital course recommendation system for higher vocational colleges based on deep learning
• LDCIR-Trans: a lightweight dependency-constrained iterative refinement model for machine translation

Research pick: Battery boost - "Bayesian optimised route and SOH estimation effect for Li-ion battery management system of electric vehicles based on LSTM"

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.

International Journal of Business Governance and Ethics invites special issue proposals

The editorial team of the International Journal of Business Governance and Ethics has released a call for special issue proposals for their journal. Details are available here.

Research pick: Modelling Alzheimer’s from Amyloid to Tau - "Tau protein transmission simulation modelling in Alzheimer’s disease integrated with neuro-symbolic learning"

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 pick: From coal face to the green race - "From coal to green: skills pathways for key emerging sectors in just transition regions"

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.

Research pick: Sandpiper model predicts rainfall - "Optimised deep convolutional spiking neural network for accurate long-term and short-term rainfall forecasting in climate prediction systems"

AI can predict rainfall intensity better than several widely used forecasting models in tests using historical weather data from India. The new model reported in the International Journal of Mobile Communications shows that combining different forms of AI, along with advanced data-cleaning and optimisation techniques, can make rainfall prediction more accurate and reliable, particularly when expressed in practical categories such as light, moderate, or heavy rain.

The system uses a deep convolutional spiking neural network to identify spatial patterns in weather maps. The spiking aspect of the neural network was inspired by how brain cells communicate using short electrical pulses over time. Before the network training step, the researchers cleaned the data using a method called anisotropic diffusion Kuwahara filtering. This process reduces noise, random errors, while preserving important patterns. This is important in weather datasets, which often contain missing or uneven measurements.

The new model was evaluated using the India Rainfall Analysis dataset, which contains historical records from selected regions. Instead of predicting exact rainfall amounts, the system classifies conditions into rainfall categories. This type of classification is often more useful in practice, because decisions in agriculture, water management, and disaster response are frequently based on thresholds rather than precise measurements.

In the performance tests, the system worked better than established AI methods such as machine learning tools, like recurrent neural networks and gradient-boosting models. The new system raised fewer false alarms and did not miss major rainfall events, as was a problem with earlier models.

The team has improved the model using the sandpiper optimisation algorithm. This additional tweak models the behaviour of foraging waders (shorebirds) known as sandpipers. In machine learning terms, this additional tweak helps the model reduce prediction errors by optimising its internal settings.

Amanullah, M., Ananthajothi, K. and Agoramoorthy, M. (2026) ‘Optimised deep convolutional spiking neural network for accurate long-term and short-term rainfall forecasting in climate prediction systems’, Int. J. Mobile Communications, Vol. 27, No. 3, pp.300–315.

Research pick: Industrial ecosystems and innovation - "Nexus between innovation ecosystem and innovation performance"

A study of Kenya’s manufacturing sector suggests that industrial innovation there depends more on exogenous factors rather than what happens inside a firm. The findings, published in the International Journal of Business Innovation and Research, show there is a strong relationship between an “innovation ecosystem” and how well companies develop new products, improve their processes, and stay competitive.

An innovation ecosystem is the wider network in which a company operates. It includes government policies, access to finance, access to transport and energy, relationships with suppliers and customers, and links to universities and research institutions. These various elements determine how easily a company might generate new ideas and turn them into commercially viable goods or services. Innovation performance measures the outcomes of all these efforts.

The findings suggest that firms embedded in a strong ecosystem with reliable business services, effective trade support, and opportunities for knowledge sharing perform better in terms of innovation than companies without this external support. Fundamentally, companies in this kind of environment can adapt to changing market conditions and sustain growth.

Companies interact continuously with regulators, customers, suppliers, and research bodies, and innovation emerges from these interactions, rather than being due simply to internal research and development. The new perspective offered by this research challenges traditional management approaches and shows that companies ought to prioritise collaboration, learning, and flexibility rather than conventional management controls and hierarchy.

The researchers point out that the implications of their study are particularly acute for Kenya, where manufacturing has struggled to maintain competitiveness. Historically, Kenya has focused on exporting raw or semi-processed materials rather than higher-value finished goods. But this has limited both profitability and job creation, and there has been a decline in growth in manufacturing in recent years. The researchers explain that low levels of innovation may be to blame and suggest that responsibility for improvement does not rest solely with individual companies but with the industrial ecosystems discussed.

Gachanja, I.M. (2026) ‘Nexus between innovation ecosystem and innovation performance’, Int. J. Business Innovation and Research, Vol. 39, No. 9, pp.1–20.

Prof. Jiageng Ruan appointed as new Editor in Chief of International Journal of Transport Technology and Innovation

Prof. Jiageng Ruan from Beijing University of Technology in China has been appointed to take over editorship of the International Journal of Transport Technology and Innovation.

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Visual element layout generation for packaging design driven by human-machine collaborative reinforcement learning
• Attribute-based sharing method for cloud data with fine-grained dynamic access control
• Bayesian-optimised multiscale image inpainting for digital preservation of murals
• Augmented reality mobile real-time assistance system for sports training
• Sports social media influence prediction model with temporal transformer and causal reasoning

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• A study on compensation for voltage unbalance in distribution transformer areas considering three-phase load imbalance using hybrid PV-ESS inverters
• Student mental health assessment based on social sentiment analysis and multi-branch neural networks
• A generative adversarial error correction system for English writing
• Optimising English translation vocabulary selection based on corpus statistics and probabilistic modelling
• Multimodal generative adversarial networks for dynamic mitigation of foreign language anxiety

Research pick: Encryption and intrusion detection close to the edge - "Dual-modal system for real-time encryption and anomaly detection of 5G communication data integrating AES-GCM and LSTM"

Research into 5G cellular network security suggests that we need to unify encryption and intrusion detection to better protect those networks rather than treating encryption and detection as separate processes. The research in the International Journal of Information and Communication Technology focuses on the demands of 5G networks, which offer high data speeds, very low latency, and massive device connectivity. These capabilities allow us to use sophisticated mobile applications and have autonomous vehicles, smart cities, and industrial automation. But they come at a cost of increased exposure to fast-changing security threats from malware and malicious third parties.

The researchers have identified a structural limitation in conventional security design. Encryption typically protects data confidentiality, while intrusion detection systems independently monitor network traffic for malicious behaviour. In high-speed 5G environments, this separation can introduce delays and reduce the system’s ability to respond to attacks in real time.

To address this, the researchers have developed a dual-modal architecture that combines AES-GCM with a Long Short-Term Memory (LSTM) neural network. AES-GCM is a symmetric encryption method that scrambles data to prevent unauthorised access while also verifying that information has not been altered during transmission. The LSTM component is a type of deep learning model designed to analyse sequences of data over time, allowing it to identify patterns in network traffic and detect anomalies.

The system integrates these functions so that encryption and anomaly detection operate in parallel. Data is secured while being continuously monitored, rather than processed in separate stages. According to the researchers, this combined approach offers a detection accuracy of 98.1% and a false positive rate of just 0.5%, meaning it rarely mislabels normal activity as malicious. Encryption and decryption times are reported at 18.4 milliseconds and 21.7 milliseconds, respectively, performance levels considered suitable for real-time communication systems.

The team adds that this new model works under varying network loads. In high-bandwidth conditions, encryption delays are lower, suggesting the system adjusts dynamically to traffic intensity. They also add that energy consumption is reduced compared with encryption-only methods. This could be critical for edge computing environments where processing occurs on the device and where power resources might be limited.

Wang, H. (2026) ‘Dual-modal system for real-time encryption and anomaly detection of 5G communication data integrating AES-GCM and LSTM’, Int. J. Information and Communication Technology, Vol. 27, No. 41, pp.21–44.