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.

• Real-time customer segmentation using big data and cluster analysis in enterprise marketing strategies
• Enhancing tourism routes optimisation accuracy as well as dynamic adjustments using data analytics approach
• Intelligent assessment method of Japanese Kana writing trajectory based on ConvLSTM and CRF
• Research progress on discrete element method for ship navigation in broken ice areas
• A load-aware replica selection strategy with multi-armed bandits and adaptive redundancy in ICN

Collaborative education for solving climate challenges

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.

Free Open Access special issue on "Smart and Continuing Education and Life-Long Learning: Part III" published by International Journal of Continuing Engineering Education and Life-Long Learning

The International Journal of Continuing Engineering Education and Life-Long Learning has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Design of distance English translation teaching system based on digital multimedia intelligent equipment
• Design and implementation of a real-time intelligent translation system for network language based on incremental learning
• Development and testing of a teaching quality assessment and examination data collection system based on artificial intelligence
• Knowledge graphs to build a networked teaching system for Chinese grammar
• Interactive teaching practice of music classroom based on human-computer interaction situation
• Low-cost smart devices and personalised learning for AI-driven preschool education
• Optimisation of hybrid teaching mode of college dance based on human-computer interaction technology
• AI-driven personalised English learning path planning algorithm and blended learning platform construction
• Digital twin technology for building immersive learning environment for English education
• Exploration and practice of human-computer interactive open education based on OBE education concept
• English learning behaviour analysis and intelligent recommendation system driven by big data
• Application of deep reinforcement learning in intelligent interaction design of virtual practice scenarios for labour education in colleges and universities
• Simulation of multimodal education mode based on artificial intelligence
• Teaching quality management in vocational training based on evaluation data processing and improved BPNN
• Big data-driven personalised lifelong learning model for English education

Free sample articles newly available from International Journal of Work Organisation and Emotion

The following sample articles from the International Journal of Work Organisation and Emotionare now available here for free:

• The impact of professional isolation on emotional exhaustion with psychological capital as the moderator among Finnish knowledge workers
• The emotional side of collecting: disgust and attraction in the art market
• Impact of emotional intelligence and social intelligence on employee performance: is there an overlap?
• Workgroup and social inclusion through a blend of responsible leadership with universal-diverse orientation and virtual interaction
• Exploring the relationship between work from home and employee wellbeing: an SLR and cross-country perspective

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.

• Real-time error correction system of spoken English based on multimodal transformer-GCN framework
• Research on the design of interactive three-dimensional book for China-Laos railway based on AI technology
• Optimal placement and sizing of energy storage systems in distribution networks: a stochastic optimisation framework
• Research on a collaborative calculation framework for cross-regional power grid carbon emissions based on federated learning and adaptive graph convolution
• A virtual human generation method combining user emotional preferences with implicit reconstruction

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.

• Application of quantum optimisation osprey algorithm in English translation quality improvement model
• Multidimensional assessment of employment competence of Jiangxi graduates by BPNN
• Optimising the online marketing effectiveness perception using deep neural network integration with semantic mining
• Deep learning-based innovative product design driven by social network data
• Counterfactual causal inference for attribution of L2 Chinese grammatical errors

Research pick: Economic boost from financial inclusivity - "A survey of impact of financial inclusion for various sectors in different countries"

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.

New Open Access article available: "Empirical study of consumer-to-consumer social commerce users with a structural equation modelling approach"

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.

Free sample articles newly available from European Journal of International Management

The following sample articles from the European Journal of International Managementare now available here for free:

• Blockchain implications for management and international business theories: toward a new paradigm
• Two decades of foreign direct investment in Africa: a systematic literature review, integrative framework, and agenda for future research
• Team climate and performance in global virtual teams: exploring the effects of cultural intelligence and emotional intelligence on team climate satisfaction
• Dynamic capabilities and international performance: a meta-analytic regression analysis
• China's industrial policy and its implications for international business
• Openness towards language differences and cultural differences in multicultural teams: how do they interact?
• How to sample in necessary condition analysis (NCA)
• A state-of-the-art review on international strategic alliances: do we really know what we are researching?
• Global value chains and liability of international connectivity: MNE strategy post COVID-19

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.

• Building energy efficiency intelligent scheduling integrating big data analysis and artificial intelligence
• Brand value fluctuation prediction and risk management of rural characteristic industries based on GAN-LSTM
• Research on multi-view attitude measurement method for shipboard equipment with multiple feature points
• Optimisation of resource scheduling in English translation teaching platform based on greedy heuristic task migration algorithm and corpus
• MICPO: a modified crested porcupine optimiser with dynamic balancing for superior PV parameter accuracy and convergence

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

Research pick: Predicting HE higher and higher - "Analysis of factors affecting college students’ academic performance based on linear regression"

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.