We need blockchain education

Research in the International Journal of Information and Communication Technology has looked at potential security and privacy weaknesses in remote higher education systems, focusing on centralised virtual learning platforms.

The researchers explain that these platforms usually rely on a single administrative infrastructure for authentication, records, and content delivery. This, they suggest, creates a single point of failure, where disruption or compromise of the central system might then affect the entire environment. This could open up the possibility of data tampering, credential fraud, and unauthorised access, while undermining trust in online degrees.

The team suggests that blockchain technology, usually associated with digital, or crypto, currencies, has the potential to protect education systems, making them tamper-proof. Earlier work has been tried allowing simple static credential storage. But the new approach is dynamic and could be used for enrolment, course access and ongoing assessment, rather than being mainly a certificate verification system.

The team’s proposal of Blockchain-Enabled Secure Distance Learning (BESDL) represents a lifecycle-based framework covering the full education process. It uses smart contracts, self-executing rules on a blockchain, to manage decentralised identity management, secure content-based access control, and encrypted content delivery.

Tests suggest improved authentication speed, better security, and greater scalability under high concurrent student loads compared with conventional systems.

Chen, J. and Chang, X. (2026) ‘Blockchain-enabled secure distance learning platforms for higher education‘, Int. J. Information and Communication Technology, Vol. 27, No. 56, pp.1-31.

Research pick: Factory-in-factory systems for greener industry - "Industry note: Innovative waste-to-energy pyrolysis technology for sustainable biowaste utilisation"

A waste-to-energy system designed for palm oil mills could turn agricultural waste into electricity, industrial fuels and carbon-storing materials while generating commercially viable returns, according to an “Industrial Note” in the International Journal of Agriculture Innovation, Technology and Globalisation.

The authors examined Factory in Factory (FiF) systems wherein an integrated biomass treatment system built around two linked technologies can be used to convert organic waste into usable energy and saleable by-products. The work argues that the approach could help industries reduce greenhouse gas emissions while addressing mounting pressure on landfill capacity and waste disposal.

The system is aimed particularly at palm kernel cake (PKC). This is a waste residue from palm oil production that is generated in vast quantities at mills across Malaysia. The material is already concentrated at these industrial sites, so using FiF means transportation and collection costs are avoided almost entirely.

At the centre of the process is Pyrolysis Molecularisation Extraction Technology (PMET), which uses pyrolysis. Pyrolysis is the thermal decomposition of organic material without oxygen. This approach can process around 300 kilograms of biomass per hour. The process generates combustible gas, carbon-rich biochar and a liquid bio-oil known as green tar.

Biochar, a charcoal-like substance, can be used either to sequester carbon for long periods or as a soil improver and for pollution treatment. The bio-oil could be used as industrial fuel or as a feedstock for chemical, pharmaceutical, and biomedical products as an alternative to fossil products from the petrochemical industry.

The authors explain that a second component, the Gas Generator Assemble Cabinet (GGAC), can use pyrolysis-generated gas in electricity production. Such units can generate around 130 megawatts of electricity per month. This would allow mills either to offset their own power use or sell electricity to the national grid.

Lee, C-W. and Kao, W. M-W. (2026) ‘Industry note: Innovative waste-to-energy pyrolysis technology for sustainable biowaste utilisation‘, Int. J. Agriculture Innovation, Technology and Globalisation, Vol. 5, No. 2.

Editorial statement from Prof. Luna Leoni, Editor in Chief of the International Journal of Information and Operations Management Education: Time for a Renewed Vision

Organisations worldwide are redefining how managerial knowledge, operational capabilities, digital competencies and learning processes are developed and transferred. Accelerated technological change, AI-driven transformation, new workforce expectations and increasing organisational complexity are reshaping both management practice and management education at an unprecedented pace.

Within this evolving landscape, the International Journal of Information and Operations Management Education (IJIOME) is entering a renewed phase of development to strengthen its relevance, international visibility and interdisciplinary contributions.

Since its foundation, IJIOME has provided a valuable platform for research at the intersection of information systems, operations management and education. The journal has advanced our understanding of how organisations and individuals learn, adapt and manage information and operational processes in dynamic environments.

Today, these foundational themes are becoming even more strategically important. Organisations increasingly require future-ready managerial capabilities, digitally enabled learning systems and adaptive operational models that can respond to continuous transformation.

Rather than redefining the journal’s mission, this renewed direction strengthens and modernises IJIOME’s original interdisciplinary foundations. The journal will continue to serve as a rigorous international forum for research addressing the evolving relationships between information systems, operations management, organisational learning and management capability development. In particular, IJIOME will place growing emphasis on four interconnected domains that reflect both the journal’s historical strengths and the emerging priorities of contemporary management research:

• Digital transformation and information-driven organisations, including AI-enabled management, digital capabilities, smart operations and data-driven organisational systems.
• Future-oriented management education, including digital skills, workforce transformation, competency-based education and technology-enhanced learning environments.
• Organisational learning and knowledge development, including learning organisations, knowledge transfer, intellectual capital and managerial capability building.
• Sustainable and responsible organisational transformation, including ESG integration, responsible leadership, sustainable operations and human-centred organisational development.

By reinforcing these directions, IJIOME seeks to support a broader international research community while remaining fully consistent with its core identity and mission.

We warmly invite scholars, educators, practitioners and policymakers from around the world to contribute rigorous, relevant and forward-looking research addressing the future of organisations, management education, information systems and operational transformation.

We are particularly interested in contributions that bridge academic rigour and managerial relevance, as well as proposals for special issues devoted to emerging and high-impact themes.

We look forward to a renewed period of growth, visibility and international engagement for IJIOME, and we sincerely thank our authors, reviewers, editorial board members and readers for their continued support and trust.

Research pick: Track and trace for fake reviews - "Precise identification and traceability of fake e-commerce reviews integrating multimodal semantic understanding"

Research in the International Journal of Information and Communication Technology discusses the development of an artificial intelligence (AI) system that combines text, images and reviewer behaviour to detect and trace fake e-commerce reviews. The system could address the growing challenge faced by online marketplaces as deceptive feedback becomes increasingly sophisticated.

The team used a multimodal approach to analyse several types of data at once rather than relying solely on an examination of written comments. Existing systems often focus on review text or simple behavioural indicators, making them vulnerable to fabricated reviews paired with misleading images.

To improve detection, the researchers used a text convolutional neural network. This is a machine-learning model designed to identify patterns in language. In parallel, a pre-trained language model was employed that captures broader semantic meaning. The team adds that information about reviewers was also incorporated into the analysis as well as images attached to reviews. The images were analysed using a residual network, a deep-learning architecture used in computer vision.

The system then brings together these various signals to work out whether a particular review is genuine or not. A Transformer model, widely used in modern AI systems, could then be used to trace the origins and spread of a review flagged as suspicious. Tests on large-scale datasets showed measurable gains over existing methods, the team reports.

Duan, B. (2026) ‘Precise identification and traceability of fake e-commerce reviews integrating multimodal semantic understanding’, Int. J. Information and Communication Technology, Vol. 27, No. 35, pp.81–102.

Research pick: Mother Goose and Rikki-Tikki-Tavi secure software networks - "Traffic anomaly detection with wild geese dwarf mongoose optimisation_DQNN"

Researchers have developed a new artificial intelligence-based system designed to improve cyberattack detection in software-defined networks (SDNs), a networking architecture widely used in data centres and enterprise systems.

The system combines a deep quantum neural network with a novel optimisation technique inspired by the behaviour of wild geese and dwarf mongooses. Its aim is to identify abnormal network traffic, including distributed denial-of-service (dDoS) attacks, while preventing network controllers from becoming overloaded.

SDNs differ from traditional networks by separating the control plane, which makes routing decisions, from the data plane, which forwards traffic. While this design improves flexibility and centralises management, it also creates potential targets for attackers seeking to disrupt communications between controllers and network devices.

In the new approach outlined in the International Journal of Heavy Vehicle Systems, network traffic is analysed using a deep quantum neural network, a machine-learning model designed to recognise complex patterns. When suspicious traffic is detected, the system assesses controller workloads and automatically transfers network switches from overloaded controllers to those with spare capacity.

In simulations, the researchers demonstrated a detection accuracy of 93.7%. They obtained a true positive rate of 91.6% and a true negative rate of 87.5%. The researchers argue that combining traffic anomaly detection with automated load balancing could strengthen increasingly centralised network infrastructures.

Ahsan Shariff, M. and Nelson Kennedy Babu, C. (2026) ‘Traffic anomaly detection with wild geese dwarf mongoose optimisation_DQNN’, Int. J. Heavy Vehicle Systems, Vol. 33, No. 2, pp.147–172.

Research pick: Wear and tear it up the track - "Application of wearable motion tracking devices in training, monitoring, and evaluation"

Researchers have developed an enhanced wearable motion-tracking system that could improve the accuracy of fitness trackers used to monitor exercise and training. The team provides details in the International Journal of Data Mining and Bioinformatics.

Current wearable devices often show inconsistencies in heart-rate monitoring and can miscalculate calories burnt, speed, and distance travelled. Such inaccuracies limit their usefulness for health-conscious consumers, but particularly for athletes and their coaches who need precision.

The new work hopes to improve both data collection and sensor calibration. Researchers used fuzzy algorithms, computational methods designed to handle uncertain or variable information, to analyse real-time exercise data. They also applied filtering techniques to remove noise and improve data quality before calibrating the device’s sensors.

In their tests, they found that measurements of heart rate, calorie expenditure, movement speed, and distance closely matched those obtained through standard laboratory procedures. The researchers suggest that their main advance lies in combining improved sensor calibration with more sophisticated data processing. This allows the device to generate a more reliable picture of an athlete’s training performance in real time.

The findings could be used beyond competitive sport to help users develop personalised fitness programmes for health monitoring and injury prevention by giving them more dependable information about their physical activity.

Wu, F., Yang, S., Zhang, C. and Wu, H. (2026) ‘Application of wearable motion tracking devices in training, monitoring, and evaluation’, Int. J. Data Mining and Bioinformatics, Vol. 30, No. 6, pp.71–91.

Free Open Access issue published by International Journal of Procurement Management

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

• Stakeholder engagement and sustainable procurement among multinational enterprises in developing countries: a case of Nigeria and Kenya
• Innovation co-development forms in adapted, technological and experimental public procurement

Free Open Access special issue on "Big Data Industrial Application and Computing Innovation – Part 1" published by International Journal of Data Mining and Bioinformatics

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

• Establishment of artificial intelligence pathological feature diagnosis model and molecular mechanism
• Integrating data mining techniques for analysing implicit user behaviours in online courses
• Dynamic load-balancing optimisation with bidirectional edge detection under multi-scale feature fusion
• Application of wearable motion tracking devices in training, monitoring, and evaluation
• Design of a multimodal information visualisation and analysis model based on improved graph embedding network
• Nonlinear stress-strain prediction method for pipeline steel based on multi-scale adaptive network

Research pick: Can AI beat breast cancer? - "Establishment of artificial intelligence pathological feature diagnosis model and molecular mechanism"

An artificial intelligence (AI) system that combines breast cancer tissue images with molecular marker data achieves high accuracy in diagnosis, tumour classification, and survival prediction. Details are reported in the International Journal of Data Mining and Bioinformatics.

A common limitation of breast cancer care is that medical imaging and molecular markers as well as hormone receptor status are usually analysed separately. The researchers suggest that this can reduce the effectiveness of early detection, subtype classification, and personalised treatment planning. Their new addresses this issue.

In testing, the system achieved an accuracy of 96.3 per cent and an F1 score of 0.95, a measure that balances precision and recall. The system could also successfully classify eight breast cancer subtypes, with accuracy remaining above 90 per cent across all categories.

The approach combines two forms of AI. A Vision Transformer (ViT), a deep-learning model that identifies patterns across entire images, extracts features from biopsy slides. A fully connected neural network (FCNN) analyses molecular marker data. The resulting information is combined to give a clearer diagnosis.

The team says the method improves on many existing AI systems, which usually focus on image analysis and overlook molecular information that influences tumour behaviour and treatment response. The model also incorporates clinical data regarding survival trends and so can help support treatment decisions.

Zhang, Y., Zhang, Y., Xu, H. and Wang, Y. (2026) ‘Establishment of artificial intelligence pathological feature diagnosis model and molecular mechanism’, Int. J. Data Mining and Bioinformatics, Vol. 30, No. 6, pp.1–20.

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.

• Blockchain-enabled secure distance learning platforms for higher education
• A neural network-based quality assessment model for English-to-Chinese text translation
• Personalised learning path recommendation and knowledge tracing model for large-scale online education
• Oil painting emotion recognition using multi-modal adaptive deep network
• Generative adversarial network and grammar rule constraint optimisation for English interlanguage error correction

Free sample articles newly available from International Journal of Economics and Business Research

The following sample articles from the International Journal of Economics and Business Research are now available here for free:

• The impact of investor sentiments on stock returns and volatility: do economic forces and herding behaviour matter?
• Performance expectancy of generalised audit software: a developing country perspective
• The impact of organisational structures on management compensation packages and investment decisions - a principal-agent approach investigating formula apportionment and separate accounting
• The impact of economic policy uncertainty on the real exchange rate: evidence from the UK
• Exploring UAE's female leadership styles in the digital era: motivators and barriers

Free Open Access issue published by International Journal of Economics and Business Research

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

• Short-term financing structure and trade credit dynamics in an emerging market: a nonlinear perspective
• Enhancing bank performance through loan performance: the role of technology and internal control in credit risk management in Vietnam
• Non-performing loans and capital adequacy ratio in Vietnamese commercial banks: moderating effects of ownership structure - a dynamic GMM analysis

Free sample articles newly available from International Journal of Monetary Economics and Finance

The following sample articles from the International Journal of Monetary Economics and Finance are now available here for free:

• Stochastic dominance performance comparison of optimal fiat and cryptocurrency portfolios
• Research on stock market anomalies: a systematic literature review, synthesis and framework for future research
• Application of the modified organic benchmarks model in assessing performance of university endowment portfolios
• Impact of Russia-Ukraine invasion on commodity prices in South Africa
• A VAR analysis of the macroeconomic shocks on the non-performing loans ratio in Slovakia

Research pick: Keep to the beat - "An extraction method of pop music singing beats based on audio features"

A study in the International Journal of Computer Applications in Technology has developed an improved way to determine the underlying beat, or tempo, in recorded music. It addresses persistent issues in analysing modern popular music where vocals, multiple instruments, and background noise overlap. A beat is the regular pulse that structures rhythm and guides how music is perceived and organised in time. While humans detect it naturally, machines struggle when audio is complex or when tempo changes during a track.

Existing beat detection systems often perform well only under simplified conditions. Many rely on limited audio features or assume relatively clean recordings, making them less effective in real-world music. Even advanced machine learning approaches can be unstable when audio conditions vary and may require high computational power, limiting their use in real-time applications, where latency can be a serious problem in music production and recording.

The researchers have used a multifeature fusion approach, which combines multiple types of audio information instead of relying on a single signal. The system first pre-processes the audio by segmenting it, reducing noise, and normalising volume levels to ensure consistent input. It then tracks changes over time and the frequencies present.

Features such as short-term energy and zero-crossing rate help identify rhythmic changes, while additional analysis separates rhythmic structure from melody and harmony. These signals are combined into a unified model that detects repeating patterns corresponding to beats and adapts when tempo changes occur.

Tests show reduced missed beats and false detections compared with traditional methods. The approach could be used to improve music recommendation systems, automated accompaniment tools, performance synchronisation, and music education software.

Kong, Z. and Liu, G. (2026) ‘An extraction method of pop music singing beats based on audio features’, Int. J. Computer Applications in Technology, Vol. 78, No. 6, pp.1–10.

New Open Access article available: "Analysing student behaviour in the implementation of LAT using cloud technology in higher education institutions"

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.

Free sample articles newly available from International Journal of Financial Services Management

The following sample articles from the International Journal of Financial Services Management are now available here for free:

• Electronic mobile service quality and customer loyalty: the conditional indirect effect of relationship quality and customer satisfaction
• Financial contagion and volatility spillover financial stock market: a statistical review of the literature
• An empirical study on financial well-being during the COVID-19 in India

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.

• AI enabling mechanism of 'lighthouse factory' from the perspective of complex system theory
• Design and development of mobile learning UI based on situational cognition theory
• Research on intelligent generation and interactive display method of traditional art for immersive experience
• Music generation controllable dance based on improved transformer model and style consistency
• Design of a cross-domain resource integration learning path generation model for innovative talent cultivation using bi-directional GAN and deep contrastive clustering network