Research pick: Hoarse? With no blame? Physics might explain! - "Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data"

A new approach to modelling vocal fold fatigue could improve detection and monitoring of problems as they arise, particularly among people whose jobs depend on sustained voice use. The research, published in the International Journal of Information and Communication Technology, uses a physics-based framework for tracking changes over time.

The vocal folds, colloquially referred to as vocal cords, are the vibrating muscular structures within the larynx at the top of the trachea that allow us to speak, sing, and make various sounds. Vocal fold fatigue involves declining vocal efficiency, poorer voice quality and greater effort when speaking and can arise in anyone at any time but is common among public speakers, teachers, call centre operators, singers, and others who use their voice more than average or at higher volume.

The new approach, called physics-informed joint simulation and identification (PJSF), treats fatigue as a continuously changing process rather than a fixed condition. It models the depletion and recovery of the vocal system using equations that describe how a system changes over time and combines these with acoustic recordings and laryngovibrography, measurements of vocal-fold vibration to give a diagnosis or predict issues that might arise.

In tests on 20 volunteers, the system was shown to balance between correctly identifying fatigue and avoiding false alarms. The work could lead to a system capable of monitoring vocal strain and supporting personalised voice-use guidance for users whose profession involves constant use of the voice.

Zhang, X., Sun, R., Zhao, C. and Zhao, Y. (2026) ‘Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data’, Int. J. Information and Communication Technology, Vol. 27, No. 97, pp.89–118.

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Free Open Access special issue on "Sustainable Education in the Age of Artificial Intelligence (AI) – Part 1" published by International Journal of Innovation and Sustainable Development

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

• Analysing the innovation game in school-enterprise cooperation for talent cultivation under the concept of industry-education integration
• A fuzzy evaluation of AI-assisted English teaching quality based on random forest regression algorithm
• Research on AI assisted teaching effectiveness evaluation method based on PSO-RBF
• Study on fine-grained classification of MOOC ideological and political teaching resources based on an improved switching ensemble algorithm
• Evaluation of public mathematics teaching quality in universities under the fuzzy matrix assessment model
• A deep semantic matching network-based method for retrieving Q&A data in online vocational education
• An AI-driven approach to quality evaluation for large-scale online teaching
• Dynamic evaluation and intervention mechanism of collaborative effectiveness of university research teams based on multimodal learning analysis

Research pick: Harder, better, safer, stronger - Three-way AI improves cybersecurity - "DNN and BiGRU-based hierarchical attention network for intrusion detection"

A new deep-learning architecture could deflect cyberattacks by combining several methods for analysing network traffic, according to research in the International Journal of Business Intelligence and Data Mining. The approach addresses a weakness in standard intrusion-detection systems, which struggle with the volume and complexity of modern network traffic.

The researchers combined a deep neural network (DNN), which identifies complex patterns in large sets of features, with a bidirectional gated recurrent unit (BiGRU), a type of neural network designed to identify relationships across sequences in both directions. Both components use an attention mechanism, which allows the system to concentrate on the features and points in a sequence that are most relevant to anomaly detection. The outputs are brought together and classified using a multilayer perceptron (MLP), another neural-network architecture used to assign data to categories.

Tests on two well-known benchmark datasets showed that the system could achieve validation accuracies of around 99 per cent. The researchers explain that the system is stable in training and has comparatively strong robustness and generalisation, which means it should be effective on data beyond its training data.

The results suggest that combining static-feature analysis with temporal pattern recognition can harden cybersecurity systems against denial-of-service attacks, network probing, and attempts to gain unauthorised access.

Yan, H., Liu, H., Yu, P., Xu, X., Li, M., Long, Y., Chen, H., Wang, Q. and Long, D. (2026) ‘DNN and BiGRU-based hierarchical attention network for intrusion detection’, Int. J. Business Intelligence and Data Mining, Vol. 28, No. 10, pp.1–24.

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New Open Access article available: "World uncertainty and the marginal value of cash: suggestive nonlinear evidence from six ASEAN technology markets"

The following International Journal of Economics and Business Research article, "World uncertainty and the marginal value of cash: suggestive nonlinear evidence from six ASEAN technology markets", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Research pick: Inferred information - "The legal and ethical implications of social media privacy concerns"

There is a modern adage: If you don’t want it on the internet, don’t put it on the internet!

However, a review of research into social media privacy in the International Journal of Management Concepts and Philosophy has found that legal and ethical protections are not keeping pace with the volume of personal information collected and shared online. Moreover, a lot of inferred information that people don’t deliberately share can now be gleaned by interested third parties from one’s social media activity.

Social media refers to online platforms, websites, and “apps” that people use to create, share, and interact with content and communicate with others. People use it to stay connected, share experiences, access information, and build professional or social networks.

The researchers examined privacy breaches, regulatory frameworks, and the responsibilities of social media companies and users. They argue that the central problem is not about what people choose to post but what can subsequently be inferred or indexed by others.

Social media use leaves a “digital trail" of one’s activities, locations, and interactions and can usually be stored, searched, and analysed easily by third parties, or at the very least by the platform providers themselves. The researchers suggest that even seemingly mundane online activities can reveal personality traits, shopping habits, political opinions and allegiance, and religious leanings.

The team argues that individual privacy and confidentiality support personal autonomy, civil liberties and democratic participation, strengthening the case for greater legal accountability and transparency from social media platforms. It also calls for improved digital literacy, so users are better equipped to understand and manage the risks of sharing information online.

Policymakers and other stakeholders sometimes argue that people who have “nothing to hide” have little reason to worry about privacy. But if that principle were applied consistently, there would be little need for frosted glass in bathroom windows. Privacy is not about concealing wrongdoing; it is about having control over who can see into the most personal parts of our lives, a principle that does not disappear simply because those lives have moved online.

Bilgrami, T., Singh, K. and Ahmed, S. (2026) ‘The legal and ethical implications of social media privacy concerns’, Int. J. Management Concepts and Philosophy, Vol. 19, No. 3, pp.306–322.

News media may use this press release as source material, in whole or in part, provided the content is not materially misrepresented. A link back to the original article is appreciated.

New Open Access article available: "English digital transformation algorithm for distributed big data based on Spark"

The following International Journal of Intelligent Information and Database Systems article, "English digital transformation algorithm for distributed big data based on Spark", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Research pick: Architecture and virtual reality - "Generation of virtual reality landscape scenes for digital urban architecture based on AI"

Artificial intelligence (AI) combined with virtual reality (VR) could guide us through tomorrow's cities before they are even built, according to research in the International Journal of Environment and Pollution. Such models could be useful for architects, planners, and policymakers.

The team describes a modelling method that uses computer vision, technology that enables computers to interpret images, to scan architectural drawings and buildings. Machine learning (ML), a form of AI that identifies patterns in data, then processes these scans to allow VR technology to construct realistic virtual architectural scenes.

The researchers suggest that greater automation of this process could reduce the labour and resources required by conventional 3D modelling, in which scanned material is often assembled and processed manually. This will be increasingly important as expanding cities place greater demands on infrastructure management and public services. Aside from applications in architecture and construction, the virtual city environments could be used to simulate transport conditions, investigate how tourism might be supported, and also allow responses to emergencies to be rehearsed. Such simulations would allow officials and other professionals to test scenarios in a controlled digital environment before acting in the physical city.

The work points towards digital representations of urban areas becoming increasingly useful in the management of infrastructure and the coordination of complex projects.

Dai, D., Tu, B. and Liu, J. (2026) 'Generation of virtual reality landscape scenes for digital urban architecture based on AI', Int. J. Environment and Pollution, Vol. 76, No. 8, pp.109-122.

News media may use this press release as source material, in whole or in part, provided the content is not materially misrepresented. A link back to the original article is appreciated.

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.

• Optimisation of English speech recognition using contrastive learning and feature reconstruction
• Impact of personalised learning system based on generative AI on students' higher-order thinking ability and reconstruction of teachers' roles
• Predicting athletes' physical condition using multimodal machine learning models
• Risk perception and traceability in higher education quality management using dynamic knowledge graphs and link prediction
• Quantum computing-driven portfolio optimisation framework for the intelligent economy

Research pick: Logical logistics - "Investigating the impact of AI and big data capabilities on supply chain performance: the mediating role of agility and resilience"

Artificial intelligence and big-data capacity might help logistics companies withstand supply-chain disruptions by making them more agile and resilient, according to research in the International Journal of Business Performance and Supply Chain Modelling. The results are based on an analysis of responses from almost 300 logistics professionals in Indonesia.

The researchers surveyed employees of logistics service providers and analysed their responses using partial least squares structural equation modelling. This statistical technique can be used to test relationships between multiple factors. The findings indicate that digital capabilities help companies detect disruptions, improve forecasting and decision-making, optimise routing, and respond more rapidly to changing conditions.

Supply chains face persistent uncertainty from geopolitical tensions, natural disasters, trade restrictions and labour disputes, regulatory changes, and energy insecurity. This study suggests that technology alone is insufficient to help companies cope, as its value depends on organisations being able to turn information and automated analysis into faster adaptation and resource reconfiguration.

The research is grounded in dynamic capabilities theory, which says that organisations need to continually identify changes, exploit opportunities, and rearrange resources as circumstances change. The survey captures one point in time and focuses on Indonesia but may well be applicable to other regions. The researchers suggest that longitudinal and cross-country studies are now needed to determine whether this is true.

Sudrajat, D., Nagari, A.L. and Apriyanto, D. (2026) ‘Investigating the impact of AI and big data capabilities on supply chain performance: the mediating role of agility and resilience’, Int. J. Business Performance and Supply Chain Modelling, Vol. 16, No. 5, pp.1–26.

News media may use this press release as source material, in whole or in part, provided the content is not materially misrepresented. A link back to the original article is appreciated.

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.

• Interactive mobile English translation teaching quality evaluation model based on particle swarm optimisation-neural network
• An explainable decision intelligence framework for consumer packaging evaluation in e-commerce: a hybrid entropy-weighted grey MCDM approach
• Metaheuristic-based talent configuration optimisation for enterprises under the new quality productive forces framework
• Hierarchical cluster analysis of the deviation degree between school discipline punishment levels and judicial sentences
• Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data

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.

• Intelligent voiceprint monitoring system for wind turbine blades using SVDD and LSTM
• Identification of new quality productivity levels in agricultural digital transformation using causal inference from confounded data
• Model predictive control-based frequency regulation and photovoltaic fluctuation suppression for high-inertia asynchronous grids
• Multimodal deep neural network inference for design colour emotion quantification
• The simulation modelling and dynamic response for a dual-drive vibratory pile hammer considering soil nonlinearity

Research pick: Info binding by the light - "Experimental demonstrations of visible light communication using quantum noise for high security"

Research in the International Journal of Sensor Networks has demonstrated a visible-light communication system that uses the inherent randomness of quantum noise to make transmitted data harder for eavesdropping hackers to intercept.

The experimental system was able to transmit data at 100 megabits per second (100 mbps) and applies encryption directly at the optical, or “physical”, layer of the communication system. 100 mbps is a modest data transfer speed when compared to modern broadband networks, equivalent to mid-90s Fast Ethernet speeds. Nevertheless, this is a valid initial baseline for a research demonstration, and fast enough for a wide variety of applications.

Moreover, optical wireless of this kind is emerging as a specialised complement to Wi-Fi and fibre connections, for situations with confined coverage and abundant optical spectrum where immunity to radio interference is essential. It could thus be attractive for settings such as hospitals, aircraft, and industrial facilities.

Visible-light communication (VLC) uses rapidly modulated light, typically from LEDs, to carry digital information through an open wireless channel. The researchers can convert data into a signal with many possible light-intensity levels and use a shared secret key to determine how the data is represented. Quantum noise, the random fluctuations arising from the fundamental properties of light, is then superimposed on to the signal. When the noise is at a sufficiently high level, a third party lacking the key cannot reliably distinguish between the signal and the noise. The current approach is based on the Y-00 quantum stream cipher, a technique previously demonstrated in fibre-optic communications and extends the approach to visible-light transmission.

The work could address a particular vulnerability in VLC, that unlike data protected only by higher-level network encryption, the optical signal itself remains exposed while in transit. This kind of physical-layer protection could therefore provide an additional security mechanism for optical wireless links in environments where interception is possible.

Xiao, N., Chen, S., Chen, F. and Shi, S. (2026) ‘Experimental demonstrations of visible light communication using quantum noise for high security’, Int. J. Sensor Networks, Vol. 52, No. 1, pp.1–6.

News media may use this press release as source material, in whole or in part, provided the content is not materially misrepresented. A link back to the original article is appreciated.

Free Open Access special issue on "Exploring AI: Methods and Applications for Data Mining – Part 2" 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.

• DNN and BiGRU-based hierarchical attention network for intrusion detection
• Rapid planning of multimodal transport path based on improved VSRB-RRT algorithm
• A mathematical model of attribute-based encryption for mining clusters in big data
• A resource allocation method for digital online teaching platform based on classification mining
• Design of lightweight human pose estimation network for rehabilitation training
• Evaluation and optimisation method for graphic advertising design effectiveness based on binocular vision
• Research on intelligent recognition of English machine translation errors using joint contrastive learning
• Study on high jump athlete error action recognition based on dual stream CNN BiLSTM
• An intelligent recommendation method for multimodal ideological and political education resources integrating with dynamic portrait modelling

New Open Access article available: "The sensitivity of variance risk premium estimates to grid and strike fineness: simulating the Heston model with jumps"

The following International Journal of Financial Markets and Derivatives article, "The sensitivity of variance risk premium estimates to grid and strike fineness: simulating the Heston model with jumps", is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

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 deep learning framework for sentiment-aware visual communication in digital media systems
• An optimisation model for the graphic design process based on generative AI
• Adaptation mechanism of animation short drama IP and e-commerce products based on multimodal emotion computing
• A system for generating tourism cultural symbols based on diffusion models and graph neural networks
• Deep learning-based semantic segmentation for colour language analysis: a computational framework for cinematic media

Five Inderscience journals announced as open access-only titles

We are pleased to announce that the following titles

• International Journal of Automation and Control
• International Journal of Innovation and Learning
• International Journal of Management and Enterprise Development
• International Journal of Mobile Learning and Organisation
• International Journal of Mathematical Modelling and Numerical Optimisation

are now Open Access-only journals. All accepted articles submitted from 8 September 2026 onwards will be Open Access, and will require an article processing charge of EUR €1700.

Free Open Access special issue on "Achieving Carbon Neutrality from Environmental Impact Monitoring and Assessment Technologies – Part 5" 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.

• Marginal emission reduction costs of regional energy system transformation enabled by digital economy from the perspective of supply chain
• Automatic completion algorithm for knowledge graphs in power market based on convolutional neural network
• Coupling coordination level and sustainable development of ecological environment and network economy based on data mining
• Calculation of carbon emission footprint of ecotourism energy driven by big data
• Research on the development of low-carbon digital economy transformation based on artificial intelligence and fuzzy synthesis algorithm
• Generation of virtual reality landscape scenes for digital urban architecture based on AI

Research pick: Emission control - "Marginal emission reduction costs of regional energy system transformation enabled by digital economy from the perspective of supply chain"

A digital scheduling framework combining machine learning, optimisation and blockchain technology could allow carbon emissions to be reduced at lower cost in regional energy systems, according to research in the International Journal of Environment and Pollution. The system cut the marginal emission-reduction cost, the additional expense of removing one more tonne of carbon, by about 12 per cent when the carbon quota was restricted to half the system’s baseline emissions.

The new framework addresses a problem facing those managing increasingly complex regional energy networks, where electricity, heat and other sources must be coordinated while emissions are controlled. The problem being that the differences in data quality can make it difficult to assign emissions to specific sources and to then respond to rapidly changing demand.

The researchers used a machine-learning algorithm known as extreme gradient boosting, XGBoost, to identify complex relationships in the data and so forecast daily energy loads using historical consumption, electricity prices, weather, and industrial activity. The forecasts continuously update a mixed-integer linear programming (MILP) model, an optimisation method that selects decisions subject to constraints such as energy balances and carbon limits.

In addition, the framework records encrypted emissions data using a blockchain platform. Blockchain technology is perhaps more familiar as a cryptocurrency system, but it is essentially an immutable digital ledger that can be used to record data and information exchanges of all kinds. The blockchain thus provides a traceable record of emissions and assigns responsibility across the energy network.

The researchers suggest that integrating forecasting, scheduling, and verification into a single system could provide regional energy systems with a more responsive mechanism for managing emissions as carbon constraints tighten.

Liu, W. and Ma, W. (2026) ‘Marginal emission reduction costs of regional energy system transformation enabled by digital economy from the perspective of supply chain’, Int. J. Environment and Pollution, Vol. 76, No. 8, pp.1–26.

News media may use this press release as source material, in whole or in part, provided the content is not materially misrepresented. A link back to the original article is appreciated.

New Open Access article available: "Investigating the impact of AI and big data capabilities on supply chain performance: the mediating role of agility and resilience"

The following International Journal of Business Performance and Supply Chain Modelling article, "Investigating the impact of AI and big data capabilities on supply chain performance: the mediating role of agility and resilience", 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 Hydromechatronics

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

• Investigation of non-homogeneous thick-walled cylinder ceramic matrix composites for biochemical and medical innovations
• Investigation into the thermal optimisation of low eddy current structure of high-speed on/off valve considering energy consumption and dynamic characteristics
• Intelligent selection of parameters for air-floating piston based on improved multi-objective grey wolf optimisation algorithm
• Automation and synchronisation on electro-hydraulic lifting system of tunnel boring machine segment assembly
• Design and modelling of multiple-air-chamber pneumatic soft bending actuators

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.

• Digital energy data analysis and risk assessment under precision agriculture financing model
• Research on multiphase flow simulation of cave grouting diffusion coupled with phase field method and convolutional neural network
• Fine-grained classification and health assessment of urban green spaces using U-net and transfer learning
• Reinforcement learning empowered RNN: exploring personalised learning path optimisation in nursing education
• Exposure dose reconstruction-guided imaging screening of tuberculosis close contacts using IoT-based localisation