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.

Research pick: The “bear” essentials in teaching economics - "Bare necessities: understanding economics from The Jungle Book"

Research in the International Journal of Pluralism and Economics Education argues that “The Jungle Book” can serve as an unconventional teaching tool for undergraduate economics, using the original stories by Rudyard Kipling as well as the Disney film adaptations to introduce concepts ranging from scarcity and sustainability to happiness and development.

The researchers used qualitative content analysis, a technique that looks beyond the literal content of texts to identify underlying themes and patterns. They thus examined passages from the literary work and the 1967 animated film, as well as its 2016 live-action adaptation. They focused on the theme of the “bare necessities", the basic goods and conditions required for a decent quality of life. The concept was made punningly famous by the lyric sung by Phil Harris voicing the friendly bear of the story, Baloo.

The material is used to illustrate concepts including opportunity cost, the idea that choosing one option means giving up another; barter and monetary economies; sustainable production and consumption; and the relationship between income and happiness. The team suggests that the same material might allow teachers to develop more advanced lessons that even connect the stories to the United Nations Sustainable Development Goals and the “tragedy of the commons”, in which an individual’s pursuit of their own interests can deplete the resources the benefits of which everyone might otherwise share.

Laha, A. and Maji, S.K. (2026) ‘Bare necessities: understanding economics from The Jungle Book’, Int. J. Pluralism and Economics Education, Vol. 16, No. 2, pp.200–223.

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.

Research pick: Computer, protect thyself - "Prediction of computer network security situation based on machine learning"

A machine-learning model has been designed to predict emerging computer-network security threats with greater reported accuracy than support-vector-machine systems, according to research in the International Journal of Intelligent Information and Database Systems.

The approach described uses a radial basis function neural network (RBFNN). This is an AI, artificial intelligence, model designed to identify complex, non-linear relationships in data. The researchers trained the RBFNN on processed network-security information, including data that can be generated by intrusion-detection systems, firewalls and network traffic. The system ultimately makes network defence more proactive rather than relying on the predominantly reactive technology of firewalls and after-the-fact intrusion-detection systems. Instead of relying on predefined rules to recognise known threats, it can analyse incoming data and use its training to spot similar patterns and so act as an early-warning system.

In tests, the RBFNN correctly classified more than 95% of normal network activity and Heartbleed attacks and achieved 97% accuracy in identifying , denial of service (DoS) attacks. Its accuracy for brute-force attacks was somewhat lower, at up to 93%. However, it had an overall misclassification rate of less than 5%.

The researchers envisage a real-time service capable of automatically analysing data, detecting anomalies and generating alerts. Such systems could become increasingly important as attacks and advanced persistent threats become more varied and difficult to identify using fixed rules.

Liu, Y. (2026) ‘Prediction of computer network security situation based on machine learning’, Int. J. Intelligent Information and Database Systems, Vol. 18, No. 7, pp.1–18.

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.

Research pick: AI optimises energy production and cuts carbon emissions in seconds - "Modelling and predicting energy consumption patterns using generative adversarial networks for effective carbon management"

A new AI model for predicting energy consumption could help managers plan ahead as well as open up opportunities for cutting carbon emissions, according to research in the International Journal of Information and Communication Technology.

The system uses a transformer-based generative adversarial network (GAN) to learn complex patterns in energy use. GANs are machine-learning systems in which two neural networks work against each other to produce realistic data. By adding Bayesian statistical optimisation, the team could set the system for best performance.

They tested the model on 2000 hourly energy and emissions records drawn from smart meters, building management systems, and industrial grids. They removed references to missing data and deleted outliers to normalise the input. The model made predictions that closely matched the observed energy data while taking only a few seconds to train and run.

The researchers explain that their computer-generated scenarios will allow operators to test demand-shifting measures, integrate renewable generation, and identify inefficiencies ahead of actual power generation and emission formation.

The approach addresses several weaknesses in conventional predictive models, which are known to struggle with non-linear consumption patterns, sudden spikes and troughs, and multiple external factors. This carbon-aware energy optimisation could be used across sites with internal power generation, such as industrial sites and in wider energy systems.

Liu, Y. and Li, B. (2026) ‘Modelling and predicting energy consumption patterns using generative adversarial networks for effective carbon management’, Int. J. Information and Communication Technology, Vol. 27, No. 92, pp.79–110.

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.

Research pick: Not a waste! - "Developing community-based waste management for public spaces in coastal tourism villages: establishing institutions with local champions"

Local action may offer a model for waste management in rural tourism, according to research in the International Journal of Tourism Anthropology.

A study of waste problems in Kampung Nipah, a coastal tourism hamlet in North Sumatra, Indonesia, argues that rural communities with limited public services may need locally governed waste systems rather than relying solely on municipal provision. The researchers found that fly-tipping and littering in Kampung Nipah is linked to inadequate infrastructure. However, it also occurs because of established cultural practices, weak collective responsibility, and an absence of an institution responsible for managing waste across the wider village. The team explains that government waste-collection services do not reach the area, while rubbish from residents, tourists and rivers can accumulate along the coast.

The study involved ethnographic methods, a close, but extended, study of people and their everyday practices. It was able to show how social behaviour and institutional arrangements contribute to the problem of local waste in such a place. To overcome this problem, the team proposes a community-based model centred on local champions, residents who take an active leadership role, who work alongside tourism managers and other stakeholders to change the waste culture.

While the work focuses on only one location, it may well prove to have wider implications for rural coastal tourism elsewhere.

Zuska, F. and Zulkifli, Z. (2026) ‘Developing community-based waste management for public spaces in coastal tourism villages: establishing institutions with local champions’, Int. J. Tourism Anthropology, Vol. 10, No. 5, pp.1–22.

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.

Research pick: Reaching new heights, with safety in mind - "Safety risk perception of high-rise building construction process based on digital twin technology"

Research in the International Journal of Critical Infrastructures describes a digital-twin system that could make safety monitoring on construction sites for high-rise buildings more effective. The system could allow accident risks to be identified as they appear rather than the site simply responding to hazards after the fact.

The team combined three techniques to build their system. First, they used an improved form of grey relational analysis, a statistical approach that works with incomplete data. Secondly, the incorporated local linear embedding reduces data complexity without compromising the relationships. Finally, they embedded a digital twin, a virtual representation of the physical construction site, linked to live measurements from the real environment.

Tests across 90 cases produced recall rates of around 96 to 99 per cent in under a second of processing time. The accuracy of risk factor identification was up to almost 98 per cent.

Fundamentally, accounting for interactions between hazards could help address risks that conventional systems overlook, including falls, struck-by incidents, collapses, mechanical injuries, and electric shocks. The short processing time will allow interventions to be made on-site in a more timely manner when conditions can change rapidly.

Deng, R. and Zhou, L. (2026) ‘Safety risk perception of high-rise building construction process based on digital twin technology’, Int. J. Critical Infrastructures, Vol. 22, No. 12, pp.1–23.

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.

Research pick: Using AI to track bird migration - "Advancements and challenges in bird migration models: a comprehensive survey"

On hearing the first cuckoo of spring, the poet’s heart might sing, and while one swallow does not a summer make, the annual migrations of bird species across continents have fascinated us for centuries. Today, however, technology is giving ornithologists the means to follow these incredible journeys in ways that would have been unimaginable to earlier generations of naturalists.

Research in the International Journal of Global Environmental Issues shows that artificial intelligence (AI) is being used by conservationists to help predict bird migration timings and patterns. The technology might allow them to identify how climate change and habitat loss are affecting the journeys of many migratory bird species.

The team looked at research into machine learning and deep learning models used to analyse migration, habitat preferences, and bird populations. They explain that machine learning refers to computer systems that identify patterns in data, while deep learning is a form of machine learning that uses layered neural networks to detect more complex patterns. Increasingly, automated sound recording devices that pick up bird calls and songs as they migrate overhead are providing useful data where visual monitoring is simply not possible.

These remote monitoring systems can operate for long periods with relatively little human intervention, building up a picture of migration activity over many nights, seasons, and even years. They offer researchers a way to monitor birds at a scale that would be extremely difficult to achieve with conventional field observations. This technology could thus strengthen monitoring, as habitats and migration routes respond to rising temperatures, changing rainfall, and land development. More accurate forecasts will help determine which habitats require greater protection and inform policy to ensure migratory bird conservation remains central to climate adaptation strategies.

Musale, P.P. and Sonawani, S.S. (2026) ‘Advancements and challenges in bird migration models: a comprehensive survey’, Int. J. Global Environmental Issues, Vol. 25, No. 1, 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.

Research pick: Masking for a friend - "Fusion of periocular and forehead features for masked face recognition"

A face-recognition system that can see who you are even if you're wearing a face mask covering nose and mouth is discussed in the International Journal of Computational Vision and Robotics. The new system uses biometrics from around the eyes and forehead.

The research addresses a weakness in face recognition as a technology that was exposed during the pandemic. During that period when masks were often compulsory, their use caused conventional face-recognition systems to reject some authorised users. Unlike iris recognition, which can require specialised near-infrared imaging and controlled conditions, periocular recognition can operate with ordinary visible-light cameras.

The team has combined several types of visual information. First, a deep-learning model extracts features from around the eyes. Secondly, two image-processing techniques, local binary patterns and histograms of orientated gradients, analyse the person's forehead. The approach then combines the data to identify or verify an individual with an accuracy up to about 96 per cent in tests on one database but rather less on other testbeds.

Given that, the approach might be used as an initial screening step rather than as definitive proof of identity. A facial match could prompt further authentication with a second factor such as a password, PIN or security token. However, the team explains that they should be able to add other feature recognition into the same system, ears, face shape or profile, and those should improve performance significantly.

Agarwal, D. and Bansal, A. (2026) 'Fusion of periocular and forehead features for masked face recognition', Int. J. Computational Vision and Robotics, Vol. 17, No. 2, pp.133-158.

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.

Research pick: Don’t have me phubbing on the telephone - "This or that, which coworker phubb more; association between personality traits and phubbing behaviour through mobile phone addiction"

Personality traits may help predict whether a person will ignore others while using their smartphones, according to a study of healthcare workers in the International Journal of Business Innovation and Research.

The study involved 177 co-workers and looked at how prevalent “phubbing” is. Phubbing is a portmanteau of the words 'phone' and 'snub' and is the practice of ignoring someone in preference to using one’s phone despite being in a situation in which face-to-face conversation and interaction would be the norm. The study might help social scientists and others understand better the notion of smartphone addiction.

The research assessed participants using the so-called Big Five personality traits - openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. Personality and phone addiction were self-reported, while co-workers provided evidence of phubbing by others. The team found that people scoring higher in openness, extraversion and neuroticism were more likely to display phubbing behaviour. Conscientiousness, a tendency towards responsibility, self-discipline, and consideration of one’s obligations, was associated with less phubbing. There was no significant relationship between phubbing and agreeableness, oddly enough.

If a condition we might call mobile phone addiction exists, then it might be described as an inability to refrain from phone use despite the potential for offline human interaction. To be a true addiction, there has to be associated harm, and the researchers suggest that psychological harm may well occur, either to the phubber or the phubbed. This phenomenon partly explains the relationships between personality and phubbing, the team reports. That said, the researchers emphasise that understanding individual differences behind problematic smartphone use could help organisations address antisocial phone behaviour without treating smartphone use itself as inherently harmful.

Khan, M.N., Shahzad, K. and Shafi, M.Q. (2026) ‘This or that, which coworker phubb more; association between personality traits and phubbing behaviour through mobile phone addiction’, Int. J. Business Innovation and Research, Vol. 40, No. 4, pp.466–487.

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.

Research pick: Workplace social media reduces staff turnover - "Social media as a tool for employee engagement and retention: moderation of perceived ease of use and usefulness"

Social media used inside organisations may help engage employees and increase staff retention, according to a paper in the International Journal of Applied Systemic Studies, which looked at this phenomenon in China. Employee engagement refers to a worker’s emotional and motivational connection with their organisation, the team explains, while retention is the organisation’s ability to keep employees and reduce staff turnover rates.

The researchers used a statistical method known as structural equation modelling to test the relationships between various factors. They considered whether an employee’s perception of technology affected their working relationship. Indeed, perceived ease of use and perceived usefulness strengthened the impact of social media technology on engagement and retention.

The findings are particularly relevant to China, where social media platforms are important workplace communication channels. The distinctive cultural and regulatory environment there provided the researchers with a useful setting for studying digital employment practices.

Social media, the team points out, can facilitate communication, knowledge sharing, and recognition. It can thus help employees feel more connected to colleagues, their superiors, and the organisation in general. If this improves employee retention, then it has the benefit to the organisation of keeping hold of experienced and talented staff and reducing recruitment and training costs.

However, the study shows that introducing social media tools may not be sufficient for optimal employee engagement and retention. The team explains that organisations need to ensure employees find these tools easy to use and patently useful.

Liu, J. (2026) ‘Social media as a tool for employee engagement and retention: moderation of perceived ease of use and usefulness’, Int. J. Applied Systemic Studies, Vol. 13, No. 3, pp.220–236.

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.

Research pick: The internet of bodies and cultural tourism - "Neuroscientific effects of main melody films on audience patriotic sentiment in the context of culture tourism integration: an IoB perspective"

Research in the International Journal of Arts and Technology monitored electrical brain activity and heart rate in people watching patriotically themed Chinese films. They found that the main incidental music in such films can increase the positive emotional response and patriotic sentiment in the audience and at the same time reduce negative emotional responses.

The findings could be used by film-makers offering content in the context of tourism, specifically cultural tourism. Such insights have potential in boosting the impact of film-themed attractions, reconstructed locations, tourism and educational programmes. The researchers add that their approach essentially uses the “internet of bodies”, a human analogue of the “internet of things”, in which technologies collect and analyse data about human behaviour and physiological states. In the cultural tourism setting, this technology would combine information gathered while people watch films with data on their behaviour and interactions at related physical sites.

The extension into social media interactions might also be used to amplify emotional responses through discussion and engagement beyond the film itself.

The approach does raise ethical questions about how physiological and behavioural data should be collected and used when technologies designed to measure audience responses become part of cultural experiences. It might be that prior consent of audience members should be necessary before anyone is subsumed into an internet of bodies system, and individuals should have the ability to opt out.

Wen, L., Sun, W. and Ding, R. (2026) ‘Neuroscientific effects of main melody films on audience patriotic sentiment in the context of culture tourism integration: an IoB perspective’, Int. J. Arts and Technology, Vol. 16, No. 9, pp.45–64.

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.

Research pick: Such a lovely place, but so many leave - "Hotel interns’ career choice intentions from a high-versus-low climate changing region background"

Research published in the International Journal of Environment and Sustainable Development analysed interviews with over 300 Chinese hotel interns to see how perceptions regarding hotel work affect whether graduates pursue a career in the industry or not. The findings suggest that negative perceptions do indeed deter graduate interns from pursuing hospitality as a career.

Fundamentally, the team found that occupational stigma, the belief that a profession has low status or is undesirable, was most associated with weaker professional identity. Those graduates with this perception generally did not choose hotel work as a career.

The findings highlight a significant recruitment problem for China’s hotel industry. Despite its reliance on a large and stable workforce, only an estimated 10 to 20 per cent of graduates in hospitality and tourism enter the sector. Moreover, internships, which are intended to provide graduates with practical experience early in their career, sometimes reinforce negative perceptions and lead to many graduates dropping out of the sector before they have even taken more than a few tentative steps on this career path.

The study did find that vocational skills sometimes offset the stigma. Interns with additional skills were less affected by negative perceptions of the industry. This suggests that practical training and professional certification might boost confidence in hotel careers and improve professional identity in the sector.

Yu, F., Liu, L. and Zuo, Z. (2026) ‘Hotel interns’ career choice intentions from a high-versus-low climate changing region background’, Int. J. Environment and Sustainable Development, Vol. 25, No. 7, pp.64–83.

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.

Research pick: Lowering the scenic language barrier - "Multimedia artificial intelligence technology for accurate translation of scenic area public notices from Chinese to English in ecological translation studies"

A new artificial intelligence, or AI, translation model could improve the accuracy and cultural appropriateness of Chinese-English public signs in tourist attractions, according to research in the International Journal of Environmental Technology and Management. The approach treats translation as more than a word-for-word conversion and combines machine translation with principles from eco-translatology in which language, culture, and social context are considered.

The researchers have incorporated these principles into a neural translation system based on transformer architecture, a widely used AI system for processing relationships between words in a sentence. The model thus uses cultural-language databases together with sentiment analysis. The latter uses a computer to assess the emotional or evaluative language in a piece of text. The system can then tweak the translation for linguistic, cultural, and communicative context.

In tests, the team reports an improvement over older approaches for cultural adaptability, fluency, and completeness. The findings suggest that translation systems designed for specific functions may be better suited to public-facing texts where a culturally inappropriate phrase might confuse visitors or change the intended message. The same system could support multilingual urban signs, heritage-site interpretation, and educational notices.

At this time, the model is limited to a single language pair and a corpus concentrated on Chinese scenic areas. However, the researchers plan to expand the training data and incorporate knowledge graphs, situational modelling, and causal reasoning to improve the system’s ability to be used in different cultures and settings. There is also the potential to improve the handling of historical references, deeper cultural meanings, and linguistic variation by developing deeper reasoning rather than relying on simple rules and templates.

Zhang, C. and Wang, Y. (2026) ‘Multimedia artificial intelligence technology for accurate translation of scenic area public notices from Chinese to English in ecological translation studies’, Int. J. Environmental Technology and Management, Vol. 29, No. 7, pp.1–22.

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.

Research pick: War! Not that it's good for - "World War II and its lasting legacy: an overview of socio-metabolic transition, environmental impacts and resource flows"

Research in the International Journal of Sustainable Development has looked at eight of the nations involved in the Second World War, both Allied and Axis countries, and shows how that period of history helped set societies on the path to the resource-intensive modern economies we have today. The research links wartime mobilisation to the period known as the Great Acceleration in energy use, material consumption, and ultimately detrimental environmental impact.

The study looks at societal change from before the war, 1935, to the post-war recovery period and the boomer years up to 1960. Demographics, economic activity, power supply, material and resource flow, and environmental impact are all examined. Three major consequences of WWII are seen. First, acceleration, in which existing trends become even more intense. Secondly, redirection where development shifts towards new technologies and the opening up of novel resources. Thirdly, reset, in which destruction or political upheaval changed the direction of nations from the paths there were on before the war.

The researchers use the term “socio-metabolic transition” to describe the various changes in power consumption and physical resources in society. By adopting this almost biological model, they were able to connect wartime production and resource mobilisation with institutional and technological changes that persisted long after 1945.

Abrari, L., Rezaei, N. and Linnanen, L. (2026) ‘World War II and its lasting legacy: an overview of socio-metabolic transition, environmental impacts and resource flows’, Int. J. Sustainable Development, Vol. 29, No. 5, pp.1–62.

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.

Research pick: Emergency AI hops to it - "Intelligent decision system for urban emergency management based on combined deep learning and optimisation algorithm"

A hybrid AI, artificial intelligence, system that models itself on grasshopper behaviour could be used to help with the allocation of medical resources, transport, and power during an urban emergency, according to research in the International Journal of Environmental Technology and Management. Tests with the new hybrid model on historical emergency datasets show it to be highly effective

The team's LSTM-GOA system combines a long short-term memory neural network and the so-called grasshopper optimisation algorithm (GOA). LSTM is a machine-learning tool that can identify patterns in data as they change over time. GOA is an optimisation technique that searches for better solutions to complex problems based on how a swarm of grasshoppers forage and feed. In this hybrid approach, the GOA is used to tune the behaviour of the LSTM so that it makes better scheduling decisions in order to find the most appropriate solution.

The researchers compared the model with conventional rule-based approaches and other optimisation methods. The team was able to improve prediction accuracy for medical resource demand by almost 70 per cent. The system can respond to otherwise unpredictable spikes in demand following natural disasters, public health incidents, and major traffic accidents. Moreover, it can work with noisy or incomplete data sets.

The work points the way to the broad use of predictive AI in managing resources in an emergency. The team explains that city authorities could use historical data to anticipate pressures across several public services and adjust allocations accordingly. This would be more effective than relying on fixed rules or responding to changing demands after the fact.

Cai, X., Qiu, J., Cao, H., Wu, F. and Mei, X. (2026) ‘Intelligent decision system for urban emergency management based on combined deep learning and optimisation algorithm’, Int. J. Environmental Technology and Management, Vol. 29, No. 7, pp.59–85.

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.

Research pick: Danger! High voltage - "Enabling holistic insulation monitoring in secondary AC protection circuits: a diagnostic algorithm"

A new diagnostic method could allow electricity substations to detect hidden insulation faults in relay-protection circuits earlier than conventional testing, according to research in the International Journal of Energy Technology and Policy.

The researchers have combined three measurements into a diagnostic model: zero-sequence current, an electrical signal indicating unintended current paths, waveform similarity, which compares measured current patterns with those expected under normal conditions, and third-harmonic content, a component of the electrical waveform that can reveal non-linear grounding faults.

In tests, they saw a 98.6 per cent detection rate for insulation faults and 94.3 per cent accuracy in locating multiple grounding points. Average diagnostic latency was less than 45 milliseconds, with a 1.5 per cent false-positive rate during normal operation. The team adds that the system can also detect insulation deterioration when resistance falls to between 50 and 100 kilohms. This offers an early warning sooner than conventional, offline methods, potentially allowing maintenance to be carried out before faults become critical.

A six-month pilot test at a 500-kilovolt substation in Nanjing with linked inspection robots demonstrated rapid communication between substation devices. Overall, the average fault-resolution time was reduced from more than four hours to just over one hour, and manual inspections were cut by 76 per cent.

Shi, H., You, H., Chen, X., Xu, S. and Chen, J. (2026) ‘Enabling holistic insulation monitoring in secondary AC protection circuits: a diagnostic algorithm’, Int. J. Energy Technology and Policy, Vol. 21, No. 5, pp.20–49.

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.

Research pick: Big data signal boost - "Developing and operationalising a sector-specific big data maturity model for the telecommunications industry"

Research in the International Journal of Business Information Systems discusses a sector-specific framework to help telecommunications companies assess how effectively they use big data. It addresses limitations in existing models that are often designed for organisations across other industries. The research involved 50 experts from academia and industry and refined and tested a big data maturity model revealing how well an organisation can manage, analyse, and use data.

The framework has seven dimensions: data governance, market strategy, network performance, business development, customers, investments, and innovation. These are sub-divided and assessed through five maturity levels, allowing weighted scores to be obtained that show where an organisation is performing well and where its capabilities need to be improved.

The researchers argue that generic maturity models can overlook the particular demands of telecommunications. In this sector, data is vital to network management, predictive maintenance, traffic optimisation, fraud detection, and the analysis of customer behaviour. The sector also increasingly relies on real-time data, distributed infrastructure and automated decision-making, so any framework needs to be operational rather than purely conceptual.

The researchers say the new approach could also help in the development of sector-specific maturity models for other data-intensive industries.

Desku, F. and Besimi, A. (2026) ‘Developing and operationalising a sector-specific big data maturity model for the telecommunications industry’, Int. J. Business Information Systems, Vol. 52, No. 7, pp.1–27.

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.

Research pick: Use me once, more fool you - "Waste to wealth for single-use plastics: a literature review and future research agenda"

A review of research in the Interdisciplinary Environmental Review on single-use plastics argues that tackling the waste problem will require a shift from recycling alone to a broader circular economy approach, in which materials are kept in use through reuse, recovery, recycling, and redesign.

The researchers examined 336 research papers published over the decade 2015 to 2025. They then used bibliometric analysis, a kind of statistical pattern mapping, together with analysis of the content of studies, to follow the major themes in this area as well as the collaborations and research trends. They found that research into circular approaches to single-use plastics has grown rapidly since 2017. Recycling emerged as a dominant theme, while the literature also focused on plastic packaging, environmental impacts, and the effects of microplastics on human health.

The review identifies several factors that could determine whether circular systems will actually work in practice. These include public awareness and previous recycling behaviour, as well as policies such as green credit, which provides financial incentives for environmentally beneficial activity. There is an urgent need for effective reverse logistics to be put in place to allow used products and materials to be fed back into supply chains. There is also a need for better sorting, collection, and recovery of re-usable waste plastics.

Gupta, A., Kumar, D., Kaliyan, M. and Doreswamy (2026) ‘Waste to wealth for single-use plastics: a literature review and future research agenda’, Interdisciplinary Environmental Review, Vol. 25, No. 3, pp.259–292.

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.

Research pick: Don’t you know that they're toxic? - "Aflatoxin awareness and food security among smallholder farmers in Tanzania"

A study in the International Journal of Agriculture Innovation, Technology and Globalisation suggests that farmers know how aflatoxin spreads but only poorly understand the health risks. Smallholder farmers often recognise the conditions that cause aflatoxin contamination in stored crops, the research explains, but they have much less understanding of the health problems it can cause. The work has considered in detail the relationship between farmer awareness, crop practices, and food security.

Aflatoxins are toxic substances produced naturally by certain fungi, particularly Aspergillus species. They can contaminate maize, groundnuts, oilseeds, spices, nuts and animal products. Prolonged exposure to aflatoxin can contribute to serious illness, including liver cancer.

In the study, the team used survey data and a statistical method known as structural equation modelling to examine the relationships between various factors. They found that farmers in Tanzania were most knowledgeable about contamination during storage and were aware that moisture, dirt, and poor air circulation were serious risks of Aspergillus mould formation and so contamination of the food with aflatoxin. Unfortunately, awareness was weaker during crop preparation, planting, and harvesting, where factors such as soil fertility can influence contamination.

The team adds that many farmers knew the economic consequences, including lower yields and prices, but did not realise that contaminated food could cause severe health problems. Some reported feeding contaminated crops to livestock or mixing them with uncontaminated food.

The researchers suggest that there is an urgent need for practical agricultural education alongside better testing.

Waryoba, F.D. (2026) ‘Aflatoxin awareness and food security among smallholder farmers in Tanzania’, Int. J. Agriculture Innovation, Technology and Globalisation, Vol. 5, No. 3, pp.304–330.

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.

Research pick: Natural diamonds are not quite forever - "Comparative analysis and price modelling of natural and lab-grown diamonds"

Natural diamonds consistently command higher prices per carat than laboratory-grown, synthetic, diamonds, according to a large-scale analysis of online diamond sales in the International Journal of Electronic Marketing and Retailing. This perhaps not-surprising finding does suggest that the two products should be treated as distinct market segments.

The researchers analysed almost 200,000 diamond listings collected from an online retailer. Just over three-quarters of the listings were for natural diamonds, and the remainder were synthetic. They found that carat weight, the diamond’s mass, with one carat equal to 0.2 grams, was the strongest influence on price for both categories. Colour and clarity, which describe a stone’s hue and the presence of internal or external imperfections, respectively, were secondary to mass.

However, when they compared prices across groups defined by carat range, colour and clarity, examining both round and intricately shaped stones, they found that bigger natural diamonds had a higher per-carat value in every subgroup of the round-diamond analysis, while the same broad difference was also found among intricately shaped stones.

The findings bolster the need to make this increasingly important distinction in the jewellery trade. Synethetic diamonds have exactly the same chemical composition and crystal structure as natural diamonds but are produced artificially, using high-pressure, high-temperature synthesis, and chemical vapour deposition. Whereas natural diamonds are mined from natural sources that are millions of years old.

The researchers argue that the observed price differences provide a basis for treating lab-grown diamonds as a separate market and for further research into pricing, investment diversification, and consumer valuation. The analysis also offers a framework for examining how attributes traditionally used to value mined diamonds operate in the growing lab-grown market.

The findings might invite a broader question: why do we attach such extraordinary economic and cultural importance to what are essentially chunks of carbon when much of their value is socially constructed around scarcity, provenance, and the subjective concepts of luxury?

Huang, C., Wang, H.Y. and Zhang, L. (2026) ‘Comparative analysis and price modelling of natural and lab-grown diamonds’, Int. J. Electronic Marketing and Retailing, Vol. 18, No. 5, pp.1–25.

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.