China's drive towards a low-carbon economy is showing clear signs of slowing, according to research in the International Journal of Energy Technology and Policy. The study has tracked the country's progress on replacing fossil fuels with renewable energy sources. While there is still a shift in gear towards cleaner growth, the pace has almost skidded to a halt, from a 92 per cent rise in 2010 to just 17 per cent in 2023. The research thus raises concerns regarding the way in which early gains from heavy state investment have lost their impetus.

The researchers analysed data from 2014 to 2020 and projected trends to 2023, they then developed a detailed framework to assess how effectively renewable energy is supporting China's economic transformation. By considering four main factors, renewable energy utilisation, ecological environment quality, economic development, and the quality of life of the population, they determined that carbon emission intensity carried the greatest weight in evaluating low-carbon performance, reflecting its importance as a direct measure of climate impact.

The work also shows that China's transition to a low-carbon economy remains uneven across regions. Southern provinces, with stronger renewable infrastructure and more advanced industries, are leading the shift. In contrast, the industrial north continues to depend heavily on coal and other fossil fuels, which has led to much slower progress and greater environmental strain. This regional imbalance highlights the challenge of aligning national energy goals with local economic realities.

As the world's largest energy consumer and carbon emitter, China's experience is seen as a test case for how large developing economies can move towards sustainability without undermining development. Beyond China, the research offers perspective on the many difficulties facing countries attempting to reconcile rapid growth with carbon reduction.

As advances in renewable technologies and energy storage continue, the researchers suggest that future assessments should incorporate social and market factors such as consumer behaviour, pricing mechanisms and public acceptance. Only by combining technological innovation with structural reform, they argue, can China regain the momentum in its push to low-carbon.

Li, C. (2025) 'Construction of a multiscale renewable energy economic evaluation system considering low-carbon economy and energy storage integration', Int. J. Energy Technology and Policy, Vol. 20, No. 6, pp.3–16.
DOI: 10.1504/IJETP.2025.149451

A new approach to controlling robotic arms that could make industrial and collaborative robots more precise, adaptable, and efficient is discussed in the International Journal of Systems, Control and Communications. The work uses a decentralized adaptive fuzzy sliding mode control (AFSMC) that controls the robotic arm with voltage-based commands rather than traditional torque-based methods. This, the researchers, explain, simplifies the control system while allowing the equipment to maintain normal function even with uncertainties and external disturbances.

Conventional torque-based controllers rely on highly accurate models of the robot's dynamics. This makes them computationally intensive and impractical for some real-time applications. By controlling the motor voltages directly, the AFSMC method sidesteps this issue by allowing it to handle fuzzy, or imprecise and approximate information within a sliding mode control framework. Sliding mode controllers are prone to rapid oscillations so the researchers have added a hyperbolic tangent function to their model to producing smoother and more reliable motion.

The AFSMC operates in the workspace, which also allows for more precise and flexible motion. Its decentralized design means that each joint of the robotic arm can be controlled independently while still working in coordination with the others. The team's simulations with a three-degrees-of-freedom robotic arm show that the approach achieves high tracking accuracy and strong resistance to disturbances. The reduced computational demands compared with standard methods such as proportional-integral-derivative or proportional-derivative controllers make the approach more efficient and effective overall.

Robotic arms are increasingly tasked with high-speed, high-precision operations, from assembling electronics to handling delicate laboratory samples. By reducing the need for exact dynamic models and velocity feedback, the AFSMC could cut costs and make advanced control techniques available in embedded robotic systems. By combining fuzzy logic and sliding mode control, the researchers offer a flexible and theoretically grounded framework capable of managing the complex, non-linear behaviour typical of robotic manipulators operating under uncertain conditions.

Wang, L. (2025) 'Decentralised adaptive fuzzy sliding mode control for robotic arms using a voltage control approach in workspace', Int. J. Systems, Control and Communications, Vol. 16, No. 6, pp.1–20.
DOI: 10.1504/IJSCC.2025.149423

A highly precise fibre-based sensing system that can monitor and protect critical infrastructure such as power plants, borders, and military installations is described in the International Journal of Energy Technology and Policy. The system uses advanced laser technology to detect minute disturbances along optical fibres, offering a secure, real-time means of surveillance and fault detection over vast distances.

The research achieves this high precision by using high-power, ultra-narrow linewidth single-mode fibre lasers. These devices can emit light at an extremely stable and well-defined wavelength. This stability allows the system to interpret subtle back-scattered signals within the fibre with exceptional precision, using a method known as optical time-domain reflectometry (OTDR). OTDR works by sending light pulses down a fibre and measuring the light that is scattered back, revealing changes in temperature, strain, or vibration along the length of the fibre.

Laboratory tests demonstrated remarkable performance: fluctuations in transmission and central wavelength were kept below a critical level, while repeated measurements deviated by a tiny amount. This level of consistency, the paper suggests, confirms both the sensitivity and reliability of the design, combining low operational cost with the inherent safety of optical systems. The nature of ultra-narrow linewidth lasers means the signal-to-noise ratio is kept sufficiently high that accurate detection and localisation of events across extended distances can be achieved.

Conventional sensor networks rely on exposed components and electrical wiring, but this fibre-based system can be embedded directly into the ground, integrated with fences, or coiled around pipelines. This makes them resistant to tampering and environmental interference, including electromagnetic noise, extreme temperatures, and corrosion. The approach might be used to detect strain or temperature shifts that precede equipment failures or leaks in pipelines or failing integrity of bridges or tunnels. The approach is particularly suited to remote or hazardous environments, from nuclear facilities to long, unguarded borders.

Li, L., Liu, M., Wu, Q., Zhang, X., Liu, Z. and Zhang, Y. (2025) 'Optical fibre distributed sensing system based on high-power ultra-narrow linewidth laser', Int. J. Energy Technology and Policy, Vol. 20, No. 6, pp.17–32.
DOI: 10.1504/IJETP.2025.149449

A high-precision approach to predicting university student academic performance and flagging those students at risk of dropping is reported in the International Journal of Internet Manufacturing and Services. The approach combines machine learning with advanced optimisation techniques inspired by natural processes to help universities identify students that are struggling sooner rather than later and allow them to tailor support before problems escalate.

The approach uses a Gaussian Process Classifier (GPC), a statistical model that estimates the likelihood of particular outcomes based on complex, multidimensional data. The GPC is enhanced using an optimisation algorithm inspired by nature, following the way in which organisms locate sources of scent to allow the system to home in on an accurate solution to the question. In addition, it uses a Particle Velocity Search Algorithm (PVSA), based on the collective movement of particles in a fluid. This combination allows the system to fine-tune its parameters to detect subtle patterns in the data regarding student performance, attendance, marks, and engagement levels. In tests, the system could accurately discern which students needed additional support or guidance.

Traditional monitoring methods, while invaluable, often fail to spot early signs of academic difficulty. By contrast, the new model, through the analysis of large datasets, can detect individual behaviour and achievement that change over time and indicate problems earlier.

The work might allow universities to make better data-driven decisions regarding academic support and the distribution of resources, as well as reducing student dropout rates. The researchers suggest that categorising students by learning patterns rather than simple grades could help institutions design more equitable and personalised educational experiences.

Huang, K. and Wang, C. (2025) 'Utilising a Gaussian process classifier integrating with meta-heuristic optimisers to predict and classify performance systems', Int. J. Internet Manufacturing and Services, Vol. 11, No. 5, pp.1–30.
DOI: 10.1504/IJIMS.2025.149393

The common vernacular of YOLO, you only live once, and FOMO, fear of missing out, lead many people to yo-yo up and down between anxiety states. Research in the International Journal of Economics and Business Research, has looked at the role of self-esteem when people have that FOMO feeling in the context of conspicuous consumption (COCO).

The research shows that FOMO can nudge people to spend more on goods that display status or success than they otherwise would. The research also shows that this effect operates partly by eroding self-esteem, prompting individuals to seek reassurance and validation through conspicuous consumption of material possessions. The conclusions were drawn after an analysis of survey results from 561 employed adults in mainland China.

The analysis revealed that people who feel stronger FOMO are markedly more likely to engage in such spending. At the same time, the researchers found that FOMO reduces a person's sense of worth, their self-esteem. One might think of it as being the psychological equivalent of comfort eating, but it is the unnecessary consumption of high-prestige and other possessions rather than the inappropriate ingestion of food. The acquisition of possessions becomes the means by which the consumer attempts to restore their self-worth and social standing. Rarely is comfort eating or such conspicuous consumption of long-term benefit to the individual.

The results suggest that the relationship between FOMO and conspicuous consumption operates on two levels: a direct urge to keep up with others, and an indirect process in which reduced self-esteem fuels the desire for visible affirmation. Those with stronger self-esteem appear more resilient to these pressures, relying on internal rather than external sources of validation.

Gender did not emerge as influencing factor, men and women were equally prone to the issues. However, age did have an effect with older participants less affected by FOMO than younger people, suggesting that age brings greater emotional maturity and less dependence on external validation.

Jiang, Z-W. and Chang, S-Y. (2025) 'The impact of fear of missing out on conspicuous consumption: the mediating role of self-esteem', Int. J. Economics and Business Research, Vol. 29, No. 18, pp.1–19.
DOI: 10.1504/IJEBR.2025.149392

Research in the International Journal of Industrial and Systems Engineering discusses an immersive packaging design system that brings together virtual reality (VR) and advanced image analysis to create a more intuitive and realistic way of developing new products. The research suggests that this new approach could allow designers and consumers to experience packaging in three dimensions, exploring how materials, textures, and layouts will look and work before the produced is actually manufactured.

The usual approach to design in this area relies on two-dimensional renderings that do not always capture the depth or tactile qualities of packaging. The new system overcomes these limitations by allowing users to enter a virtual environment where they can view and manipulate designs as if handling real objects. In this virtual world, the designers might look at how labelling fits around bottles or how colours behave under different lighting. From the consumers' perspective, testers can pick up and inspect products virtually, gaining a clearer sense of scale and usability and so feed back their feelings to the designers and developers.

The research uses semantic segmentation, which automatically identifies and separates different visual elements in packaging images, the logos, text, and materials, at the pixel level for optimal precision. This approach allows each component to be edited individually within the virtual space. This means that developers don't have to rely on static mock-ups, designers can test variations instantly, and marketing teams can assess how small adjustments might affect user perception or emotional response.

The use of virtual reality with intelligent design tools suggests a new direction for packaging design. The work marks a shift towards more interactive, human-centred design approaches that brings together creative intent and consumer experience.

The next step in the research would be to develop touch and sound feedback, so that the art of packaging design comes even closer to the sensory qualities of a real prototype in the real world.

Ye, X., Tan, J. and Du, B. (2025) 'Research on immersive experience of packaging design based on virtual reality and semantic segmentation algorithm', Int. J. Industrial and Systems Engineering, Vol. 51, No. 5, pp.1–18.
DOI: 10.1504/IJISE.2025.149319

A new artificial intelligence system discussed in the International Journal of Simulation and Process Modelling could help make school and university sports safer and more effective. The system can analyse video of human movements in real time and combine this with data from sensors to understand those movements. The analysis can identify movements that would be unclear to a human observer because they are fast, unpredictable or hidden from view. The researchers suggest it will be a boon in physical education and how it is taught and monitored.

Current "pose recognition" tools use computer vision to identify human postures but often struggle with the complexities of real-world sports. Movements are usually irregular and constantly changing in most sports. The new system tackles this underlying problem by combining visual information with data from motion sensors, and then building on embedded simulations to allow it to interpret and predict movements as they happen.

The team tested their system on established benchmark datasets, including Human3.6M and the team's own CollegeSports-200 collection. They achieved almost 97 per cent accuracy. They kept the error count caused by visual obstructions low and with a frame rate of 38 frames per second, the system is fast enough to deliver live feedback. In field trials, they also had encouraging results as students were guided based on the system's analysis and were seen to improve on poor posture and results when their teachers had a clearer picture of fitness patterns across groups.

The team suggests this novel approach could lead to more adaptive, evidence-based physical education. Such an approach uses data directly to plan improved exercise and practice, and even to cut the potential for injuries.

Chen, D., Ni, Z. and Huang, W. (2025) 'Multimodal pose estimation and simulation modelling for real-time human motion analysis', Int. J. Simulation and Process Modelling, Vol. 22, No. 5, pp.1–10.
DOI: 10.1504/IJSPM.2025.149326

Research in the International Journal of Automation and Control describes a novel mathematical framework that can estimate fault risk in complex industrial systems, such as petrochemical reactors. The research might improve reliability of automated processes in the petrochemical sector and in other areas of advanced manufacturing.

The work builds on a predictive defect estimation model. It works for switched non-linear the behaviour of which alternates between different operating modes depending on conditions or control inputs. This is a common modality of chemical reactors, robotic platforms, and automated processes. The accurate prediction of imminent issues with such systems has always been a challenge because system response can vary widely with changing inputs, environmental conditions, and internal dynamics.

By using a mathematical representation of the system, a so-called augmented state-space model, variables describing the current condition of the system and failure signals, which indicate the presence of faults, the new model can evaluate how closely a system's predicted behaviour matches its actual behaviour. Discrepancies between the two are analysed statistically by the model to home in on whether the system is stable or liable to fail.

The researchers point out that traditional fault detection methods have always been limited by restrictive assumptions about system dynamics. The new approach allows for continuous real-time monitoring in industrial environments. Tests with a stirred tank reactor as a standard benchmark for modelling chemical reactions gave fault-free accuracy within 0.05 and successfully detecting both constant and time-varying faults. The current system works to detect faults that develop gradually. Real-world industrial settings introduce additional complexities, such as abrupt faults, high-frequency disturbances, and measurement noise, all of which will require further refinement of the model.

Wang, L. (2025) 'Design of a model predictive-based fault estimator for faulty nonlinear switched dynamics with guaranteed recursive feasibility', Int. J. Automation and Control, Vol. 19, No. 7, pp.1–22.
DOI: 10.1504/IJAAC.2025.149327

A study published in the International Journal of Mobile Learning and Organisation has mapped the fast-growing field of generative artificial intelligence (GenAI) in education. The research highlights both the technology's transformative potential and gaps in understanding how it affects learning and cognition. Academic interest in GenAI has surged, from just a handful of papers to many hundreds now.

GenAI refers to systems using deep learning and natural language processing to generate content or responses that emulate human tasks. In education, these systems are increasingly seen as capable of performing roles once the preserve of teachers, tutors, peer mentors, and administrative staff. They can deliver personalized instruction, real-time feedback, and data-driven decision support.

Applications range widely from language learning, where AI can simulate conversation and assess fluency, to healthcare training, where it helps students build clinical reasoning and critical thinking. Critics warn, however, of ethical issues and biases in training data that may affect fairness and trustworthiness.

The study found that most research so far centres on learner perceptions, how students feel about using GenAI, their motivation, and their trust in its usefulness. These early studies, largely survey-based, provide useful foundations for future studies but leave deeper questions unanswered. Few investigations have explored the cognitive and behavioural mechanisms underlying how learners actually interact with GenAI, a gap that could hinder efforts to design more effective and ethical systems.

Some studies have examined GenAI as a teaching support tool, focusing on human-computer interaction and adaptive learning that tailors content to individuals. Others have explored its role in assessment, offering automated feedback and grading. However, reliability remains a major concern, especially given that GenAI outputs can be inconsistent, and decision-making processes are often opaque, raising questions about fairness and academic integrity. One term that often arises in any discussion of GenAI is "hallucinations" where the system produces what seems to be wholly fabricated output that is not based on facts.

The authors argue that more empirical research is now needed to understand how GenAI shapes learning, thinking, and evaluation. Without such evidence, there is a risk that GenAI will spread through education superficially, enhancing convenience but not necessarily deepening learning.

By mapping hundreds of studies, the paper provides valuable insights for policymakers and educators hoping to harness the potential of GenAI responsibly. It identifies both promising directions and critical blind spots in the effort to integrate AI into educational practice.

Tu, Y-F. and Lu, Y-C. (2025) 'Trends of generative AI applications in educational settings', Int. J. Mobile Learning and Organisation, Vol. 19, No. 4, pp.442–467.
DOI: 10.1504/IJMLO.2025.149024

Bio-inspired computational methods have gained popularity recently. These methods mimic the seemingly complex behaviour of organisms to tackle difficult and often overwhelming problems. For example, algorithms have been inspired by honeybees' flight patterns when searching for nectar, ants' social foraging strategies, the evasive murmurations of birds and fish, and even the growth patterns of slime moulds. By modelling these natural processes mathematically, researchers can develop innovative solutions to complex challenges.

Work published in the International Journal of Bio-Inspired Computation has turned to fireflies and how they seek out the brightest of their number to address the classic knapsack problem. This problem involves making optimal choices about resource allocation under specific constraints. Using the firefly algorithm, researchers have explored how this natural behaviour might be used to guide decision-making in modern financial systems.

Conventional optimization techniques, such as dynamic programming, often struggle with the scale and volatility of real-world finance. When objectives such as profitability, regulatory compliance, and ethical considerations must all be balanced, those methods often fall short. Inspired by the firefly's attraction to brighter individuals, the firefly algorithm provides an adaptive strategy that can explore and exploit potential solutions, even in complex, dynamic environments. The integration of machine learning helps handle noisy and rapidly changing data, both of which are characteristics of financial markets.

The researchers specifically used the dual search pattern firefly algorithm (DSPFA), which combines Gaussian distributions with Lévy flights. This mathematical approach models both small incremental adjustments and rare, large jumps. This allows the algorithm to adapt in real time to changing financial conditions. It can dynamically balance risk and return while also accounting for environmental, social, and governance considerations.

Simulations demonstrated that this approach can effectively handle a variety of constraints, such as liquidity limits and regulatory requirements. At the same time, it maintains computational efficiency and produces decisions that are relatively easy to audit.

Xiao, X., Chen, Z., Quan, L., Stojanovic, A. and Wu, L. (2025) 'A knapsack modelling approach to financial resource allocation problem using a dual search pattern firefly algorithm', Int. J. Bio-Inspired Computation, Vol. 26, No. 5, pp.1–13.
DOI: 10.1504/IJBIC.2025.149184