Search Results (26)

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The integration of Artificial Intelligence (AI) into manufacturing is transforming the industry by advancing predictive maintenance, quality control, and supply chain optimisation, while also driving the shift from Industry 4.0 towards a more human-centric and sustainable vision. This emerging paradigm, known as Industry 5.0, emphasises resilience, ethical innovation, and the symbiosis between humans and intelligent systems, with AI playing a central enabling role. However, challenges such as the “black box” nature of AI models, data biases, ethical concerns, and the lack of robust frameworks for trustworthiness hinder its widespread adoption. This paper provides a comprehensive survey of AI trustworthiness in the manufacturing industry, examining the evolution of industrial paradigms, identifying key barriers to AI adoption, and examining principles such as transparency, fairness, robustness, and accountability. It offers a detailed summary of existing toolkits and methodologies for explainability, bias mitigation, and robustness, which are essential for fostering trust in AI systems. Additionally, this paper examines challenges throughout the AI pipeline, from data collection to model deployment, and concludes with recommendations and research questions aimed at addressing these issues. By offering actionable insights, this study aims to guide researchers, practitioners, and policymakers in developing ethical and reliable AI systems that align with the principles of Industry 5.0, ensuring both technological advancement and societal value. Full article

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. The primary control employs droop-based regulation, and a distributed secondary layer enables proportional power sharing via peer-to-peer communication. A modified IEEE 14-bus test system is implemented in PSCAD to validate the proposed strategy under scenarios including load disturbances, reactive demand variations, and plug-and-play operations. Compared to conventional droop-based control, the proposed framework reduces frequency nadir by up to 0.3 Hz and voltage deviation by 1.1%, achieving optimised sharing indices. Results demonstrate that consensus-based coordination enhances dynamic stability and power-sharing fairness and supports the flexible integration of heterogeneous assets without requiring centralised control. Full article

Glioblastoma, IDH-wildtype (GBM), is the most aggressive and complex brain tumour classified by the World Health Organization (WHO), characterised by high mortality rates and diagnostic limitations inherent to invasive conventional procedures. Early detection is essential for improving patient outcomes, underscoring the need for non-invasive diagnostic tools. This study presents a convolutional neural network (CNN) specifically optimised for GBM detection from T1-weighted magnetic resonance imaging (MRI), with systematic evaluations of layer depth, activation functions, and hyperparameters. The model was trained on the RSNA-MICCAI data set and externally validated on the Erasmus Glioma Database (EGD), which includes gliomas of various grades and preserves cranial structures, unlike the skull-stripped RSNA-MICCAI images. This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on p-values, confidence intervals, and statistical power analyses supporting its demographic fairness. The proposed model achieved an area under the curve–receiver operating characteristic (AUC-ROC) of 0.63 on the RSNA-MICCAI test set, surpassing all prior results submitted to the BraTS 2021 challenge, and establishing a reliable and generalisable approach for non-invasive GBM detection. Full article

Under the goal of “dual carbon”, the power market and carbon market are developing synergistically, which is strongly promoting the transformation of the power system in a clean and low-carbon direction. In order to realise the synergistic optimisation of multi-virtual power plants, economic and low-carbon operation, and the reasonable distribution of revenues, this paper proposes a multi-VPP power–carbon sharing operation optimisation strategy considering multiple uncertainties. Firstly, a cost model for each VPP power–carbon sharing considering the uncertainties of market electricity price and new energy output is established. Secondly, a multi-VPP power–carbon sharing operation optimisation model is established based on the Nash negotiation theory, which is then decomposed into a multi-VPP coalition cost minimisation subproblem and a revenue allocation subproblem based on asymmetric bargaining. Thirdly, the variable penalty parameter alternating directional multiplier method is used for the solution. Finally, an asymmetric bargaining method is proposed to quantify the contribution size of each participant with a nonlinear energy mapping function, and the VPPs negotiate with each other regarding the bargaining power of their electricity–carbon contribution size in the co-operation, so as to ensure a fair distribution of co-operation benefits and thus to motivate and maintain a long-term and stable co-operative relationship among the subjects. Example analyses show that the method proposed in this paper can significantly increase the revenue level of each VPP and reduce carbon emissions and, at the same time, improve the ability of VPPs to cope with uncertain risks and achieve a fair and reasonable distribution of the benefits of VPPs. Full article

The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monitored, and governed. For this reason, trustworthy AI (TAI) has received increased attention lately, primarily aiming to build trust between humans and AI. Due to the far-reaching socio-technical consequences of AI, organisations and government bodies have already started implementing frameworks and legislation for enforcing TAI, such as the European Union’s AI Act. Multiple approaches have evolved around TAI, covering different aspects of trustworthiness that include fairness, bias, explainability, robustness, accuracy, and more. Moreover, depending on the AI models and the stage of the AI system lifecycle, several methods and techniques can be used for each trustworthiness characteristic to assess potential risks and mitigate them. Deriving from all the above is the need for comprehensive tools and solutions that can help AI stakeholders follow TAI guidelines and adopt methods that practically increase trustworthiness. In this paper, we formulate and propose the Trustworthiness Optimisation Process (TOP), which operationalises TAI and brings together its procedural and technical approaches throughout the AI system lifecycle. It incorporates state-of-the-art enablers of trustworthiness such as documentation cards, risk management, and toolkits to find trustworthiness methods that increase the trustworthiness of a given AI system. To showcase the application of the proposed methodology, a case study is conducted, demonstrating how the fairness of an AI system can be increased. Full article

Pacing strategy is a complex self-regulation process, crucial for optimising sports performance. Athletes with Intellectual Impairments (IIs) face unique challenges due to cognitive limitations that may hinder their ability to pace effectively, impacting chronometric performance. This study analysed the pacing profiles and chronometric performance across 253 event entries by elite swimmers with II, divided into three groups: 100 entries for group II1 (intellectual disability), 85 for group II2 (Down syndrome), and 68 for group II3 (autism spectrum disorder). These results were compared with 112 event entries from athletes without disabilities (AWDs). Data were collected from the 2023 Virtus Global Games and the 2023 World Aquatics Championships, focusing on middle-distance and long-distance events. Performance metrics were assessed using 50 m split times, and within-group variability was evaluated through coefficients of variation. Swimmers with IIs showed slower overall chronometric performance than AWDs, with the largest deficits observed in II2 athletes. The II1 and II3 groups displayed more comparable results, with the II1 group outperforming the others slightly. Despite the slower times, pacing profiles were largely similar across all groups, following a parabolic pacing strategy, especially for longer distances. Greater within-group variability in both chronometric performance and pacing profiles was observed in II2 and II3 athletes, reflecting higher functional heterogeneity. In contrast, II1 athletes, and even more so AWDs, exhibited more consistent performance and pacing across all events. While swimmers with II recorded slower times, their pacing strategies resembled those of AWDs, suggesting that cognitive limitations may not significantly impair pacing regulation in swimming. However, the higher variability in II2 and II3 athletes highlights the potential need for revised classification systems to ensure fair competition. Full article

Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still shortcomings in terms of data discoverability, accessibility and interoperability. Open Data portals have emerged as a shift towards openness and discoverability. However, they do not impose any condition to the data itself, just stipulate how datasets have to be described. Alternatively, the NGSI-LD standard pursues harmonisation in terms of data modelling and accessibility. This paper presents a solution that bridges these two domains (i.e., Open Data portals and NGSI-LD-based data) in order to keep benefiting from the structured description of datasets offered by Open Data portals, while ensuring the interoperability provided by the NGSI-LD standard. Our solution aggregates the data into coherent datasets and generate high-quality descriptions, ensuring comprehensiveness, interoperability and accessibility. The proposed solution has been validated through a real-world implementation that exposes IoT data in NGSI-LD format through the European Data Portal (EDP). Moreover, the results from the Metadata Quality Assessment that the EDP implements, show that the datasets’ descriptions generated achieve excellent ranking in terms of the Findability, Accessibility, Interoperability and Reusability (FAIR) data principles. Full article

Background: The rapid production of gas during the colonic fermentation of highly soluble fermentable fibres may cause unpleasant gastrointestinal symptoms. In vivo feeding studies are often used to assess symptomatic response to fibres; however, in vitro fermentation studies are quicker, cheaper, and more reproducible. The aim of this study was to validate an in vitro colonic fermentation model of gas production against in vivo experiences of symptoms following inulin consumption. Methods: Healthy volunteers (n = 21, 18–65 y/o, M/F) provided a stool sample used to inoculate an in vitro colonic fermentation model. Fermentation bottles containing faecal slurry, a fermentation medium, and a fibre substrate (inulin) were incubated at 37 °C for 24 h in a shaking water bath. The total gas production (mL) over 24 h (minus control) was measured. Each stool donor added 15 g inulin to a low-fibre diet and recorded experiences of gastrointestinal symptoms for 48h. In vitro gas production and in vivo symptom experience were compared for each donor following tertile classification. Low in vitro gas production was classed as <45mL (<1st quartile of dataset), medium as 45–78 mL (1st quartile–3rd quartile), and high as >78 mL (>3rd quartile). In vivo symptom response was classed as low if symptoms were mild and/or short-lived (<1 h duration); medium if moderate and/or prolonged (1 h); and high when abdominal pain or multiple prolonged (3 h) symptoms occurred. Results: In vitro gas production was high in six cases (29%); medium in ten (48%); and low in five (24%). Symptom experience was high in seven cases (33%); medium in five (24%); and low in nine (43%). The same classification occurred in 57% of cases and classification into adjacent categories occurred in 43%; no complete misclassification occurred. Agreement between the methods was fair: weighted kappa = 0.378 (p < 0.01). Discussion: The level of agreement between the in vitro model of gas production and in vivo symptom reports, and the absence of any cases of complete misclassification, is promising. This simple in vitro batch-fermentation model may be used in future to screen fibres for their potential impact on gastrointestinal symptoms. This will help develop strategies to increase fibre consumption generally and optimise their use in food reformulation. Full article

The smart grid optimises energy transmission efficiency and provides practical solutions for energy saving and life convenience. Along with a decentralised, transparent and fair trading model, the smart grid attracts many users to participate. In recent years, many researchers have contributed to the development of smart grids in terms of network and information security so that the security, reliability and stability of smart grid systems can be guaranteed. However, our investigation reveals various malicious behaviours during smart grid transactions and operations, such as electricity theft, erroneous data injection, and distributed denial of service (DDoS). These malicious behaviours threaten the interests of honest suppliers and consumers. While the existing literature has employed machine learning and other methods to detect and defend against malicious behaviour, these defence mechanisms do not impose any penalties on the attackers. This paper proposes a management scheme that can handle different types of malicious behaviour in the smart grid. The scheme uses a consortium blockchain combined with the best–worst multi-criteria decision method (BWM) to accurately quantify and manage malicious behaviour. Smart contracts are used to implement a penalty mechanism that applies appropriate penalties to different malicious users. Through a detailed description of the proposed algorithm, logic model and data structure, we show the principles and workflow of this scheme for dealing with malicious behaviour. We analysed the system’s security attributes and tested the system’s performance. The results indicate that the system meets the security attributes of confidentiality and integrity. The performance results are similar to the benchmark results, demonstrating the feasibility and stability of the system. Full article

Unlocking flexibility on the demand side is a prerequisite for balancing supply and demand in distribution networks with high penetration levels of renewable energy sources that lead to high volatility in energy prices. The main means of fully gaining access to the untapped flexibility is the application of demand response (DR) schemes through aggregation. Notwithstanding, to extract the utmost of this potential, a combination of performance-, financial-, and technical-related parameters should be considered, a balance rarely identified in the state of the art. The contribution of this work lies in the introduction of a holistic DR framework that refines the DR-related strategies of the aggregator towards optimum flexibility dispatch, while facilitating its cooperation with the distribution system operator (DSO). The backbone of the proposed DR framework is a novel constrained-objective optimisation function which minimises the aggregator’s costs through optimal segmentation of customer groups based on fairness and reliability aspects, while maintaining the distribution balance of the grid. The proposed DR framework is evaluated on a modified IEEE 33-Bus radial distribution system where a real DR event is successfully executed. The flexibility of the most fair, reliable and profitable sources, identified by the developed optimisation function, is dispatched in an interoperable and secure manner without interrupting the normal operation of the distribution grid. Full article

In order to achieve rapid and fair distribution of emergency supplies after a large-scale sudden disaster, this paper constructs a comprehensive time perception satisfaction function and a comprehensive material loss pain function to portray the perceived satisfaction of disaster victims based on objective constraints such as limited transport, multimodal transport and supply being less than demand, and at the same time considers the subjective perception of time and material quantity of disaster victims under limited rational conditions, and constructs a multi-objective optimisation model for the dispatch of multi-cycle emergency supplies by combining comprehensive rescue cost information. For the characteristics of the proposed model, based on the NSGA-II algorithm, generalized reverse learning strategy, coding repair strategy, improved adaptive crossover, variation strategy, and elite retention strategy are introduced. Based on this, we use the real data of the 2008 Wenchuan earthquake combined with simulated data to design corresponding cases for validation and comparison with the basic NSGA-II algorithm, SPEA-II and MOPSO algorithms. The results show that the proposed model and algorithm can effectively solve the large-scale post-disaster emergency resource allocation problem, and the improved NSGA- II algorithm has better performance. Full article

This article investigates the connection between US logistics companies’ commitment to environmental, social and fair governance (ESG) strategy and their performance on the US stock market during the 2007–2022 period. The research considers historical data analysis, CAPM and a comparison of optimised portfolios. According to the results of the analyses, ‘green’ logistics stocks are less volatile, and hence less risky, and more profitable compared to ‘non-green’ logistics stocks. The Great Recession (2007–2009) and the COVID-19 pandemic (2020) had the greatest impact on stock volatility, in terms of the US stock market. Optimised during the time of the Ukrainian crisis, green logistics portfolios were shown to have higher returns, but also risks and Sharpe ratios, than ‘non-green’ ones. The results confirm there to be a connection between companies’ commitment to ESG strategy and enhanced stock performance, which contributes to the importance of the ESG agenda. Full article

Malware detection is a major security concern and has been the subject of a great deal of research and development. Machine learning is a natural technology for addressing malware detection, and many researchers have investigated its use. However, the performance of machine learning algorithms often depends significantly on parametric choices, so the question arises as to what parameter choices are optimal. In this paper, we investigate how best to tune the parameters of machine learning algorithms—a process generally known as hyper-parameter optimisation—in the context of malware detection. We examine the effects of some simple (model-free) ways of parameter tuning together with a state-of-the-art Bayesian model-building approach. Our work is carried out using Ember, a major published malware benchmark dataset of Windows Portable Execution metadata samples, and a smaller dataset from kaggle.com (also comprising Windows Portable Execution metadata). We demonstrate that optimal parameter choices may differ significantly from default choices and argue that hyper-parameter optimisation should be adopted as a ‘formal outer loop’ in the research and development of malware detection systems. We also argue that doing so is essential for the development of the discipline since it facilitates a fair comparison of competing machine learning algorithms applied to the malware detection problem. Full article

The Long-Term Evolution (LTE) system was a result of the 3rd-Generation Partnership Project (3GPP) to assure Quality-of-Service (QoS) performance pertaining to non-real-time and real-time services. An effective design with regards to resource allocation scheduling involves core challenges to realising a satisfactory service in an LTE system, particularly with the growing demand for network applications. The continuous rise in terms of the number of network users has resulted in impacts on the performance of networks, which also creates resource allocation issues when performing downlink scheduling in an LTE network. This research paper puts forward a review of optimisation pertaining packet scheduling performance through the LTE downlink system by introducing a new downlink-scheduling algorithm for serving video application through LTE culler networks, and also accounts for QoS needs and channel conditions. A comparison of the recommended algorithms’ performances was made with regards to delay, throughput, PLR, and fairness by utilising the LTE-SIM simulator for video flow. On the basis of the outcomes obtained, the algorithms recommended in this research work considerably enhance the efficacy of video streaming compared against familiar LTE algorithms. Full article