Search Results (6,267)

This study presents a techno-economic analysis of deploying distributed energy resources (DERs), specifically photovoltaic (PV), battery energy storage systems (BESSs) and electric vehicles (EVs), in apartment buildings configured as Virtual Power Plants (VPPs). Utilizing cooperative game theory, the research models strategic collaboration between apartment residents (demand side) and utility operators (plant side) to maximize energy efficiency and economic returns. The VPP structure is analyzed over a 15-year life cycle, incorporating net present value (NPV), payback period (PBP), and government subsidy impacts. A cooperative game framework is applied using the Shapley value to ensure fair profit allocation based on each party’s contribution. Results indicate improved self-sufficiency, peak load reduction, and mutual financial benefits. Scenario analyses show that government subsidies to the plant side significantly increase the likelihood of successful cooperation, while declining DER costs enhance the VPP’s economic viability. The findings demonstrate that apartments configured as VPPs achieve strong economic viability (39% ROI, 10.5-year payback) and operational performance (70% self-sufficiency, 40% peak reduction) when grid arbitrage is enabled and moderate government subsidies (35% PV, 45% BESS) are provided. This research provides a replicable model for urban energy planning and policy development, promoting sustainable energy transitions through shared DER infrastructure and cooperative stakeholder engagement. Full article

Power electronic systems are often engineered through a sequential–iterative workflow in which hardware parameters are initially sized from steady-state, ripple, thermal, and electromagnetic-compatibility constraints, and controllers are subsequently tuned to satisfy dynamic and closed-loop performance requirements. While converters are inherently designed for closed-loop operation, increasing power density, uncertainty, and distributed interaction make the underlying design process resemble a strategic interplay among multiple decision-makers, including hardware designers, control algorithms, loads, disturbances, and manufacturing constraints. This paper develops a unifying game-theoretic perspective on the optimal design and control of power electronic systems. Classical concepts—such as robust control, worst-case design, droop-based load sharing, and tolerance allocation—are reinterpreted as equilibrium solutions of zero-sum, Stackelberg, non-cooperative, or cooperative games. Beyond a conceptual taxonomy, two illustrative simulation case studies are provided: (i) a Stackelberg hardware–controller co-design of a buck converter, demonstrating simultaneous passive-component reduction and improved transient performance relative to a conservative sequential design; and (ii) a droop-controlled parallel-converter example contrasting Nash and cooperative equilibria, explicitly quantifying trade-offs between bus-voltage regulation, current-sharing fairness, and conduction losses. By framing power electronic design and control as interacting strategic processes rather than isolated optimization stages, the paper aims to show that game theory can serve as a structured and practically interpretable framework for distributed and uncertainty-aware power electronic systems. Full article

UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV’s mobility to provide seamless spatial coverage. By jointly optimizing the UAV’s flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring quality of service-based fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes. Full article

Obesity is ranked as one of the biggest health challenges facing humanity today. Globally, the number of obese people has almost tripled since 1975, and this lifestyle disease currently affects hundreds of millions of adults who suffer from major health problems due to it, such as heart disease, type 2 diabetes and some cancers, that weigh heavily on the global health systems, In order to keep high standards for methods, anthropometric variables, i.e., Height and Weight have been intentionally excluded from the features, because labels for obesity classes are based on these measurements; thus, including them would introduce target leakage. All models were individually tuned with Optuna (50 trials, TPE sampler), and the class imbalance was managed by the synthetic minority over-sampling technique (SMOTE), which was done only in training folds. The models were evaluated by stratified five-fold cross-validation, with the macro-averaged F1-score being used as the main metric for evaluation. The best model was the fine-tuned XGBoost, which gave a test macro F1-score value of 0.872 and a macro-AUC of 0.977. The model was higher performing than others such as Random Forest (F1 = 0.869), MLP (F1 = 0.777), and Logistic Regression (F1 = 0.605). This means that behavioral and lifestyle variables may have a very strong and sufficient signal to identify obesity status, even when there are no direct anthropometric measurements available. However, it is worth noting that results here represent only performance on a single public benchmark dataset, so they cannot be taken as proof that the model would do well in real-world clinical settings. With the advent of ML methods for obesity prediction, rigorous, leakage-free evaluation becomes indispensable. Apart from external validation of the clinical models on independent datasets, the use of interpretability tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for understanding decision-making, as well as sex and gender subgroup analyses for evaluating fairness and equity, should also be pursued in the future. This study highlights the importance of rigorous, leakage-free evaluation in machine learning-based obesity research. Future work should focus on external validation using independent clinical cohorts, the integration of interpretability techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), and subgroup analyses by sex and gender to assess model fairness and clinical equity. Full article

Children with Autism Spectrum Disorder (ASD) frequently present impairments in executive functions and motor skills, which can negatively affect academic performance, adaptive behavior, and daily functioning. Exergames have emerged as a potentially engaging cognitive–motor intervention. The objective of this systematic review was to analyze the effects of exergame-based interventions on executive function components (particularly inhibitory control and cognitive flexibility) and motor skills in children with ASD. A systematic review was conducted in accordance with PRISMA guidelines, with the protocol registered in PROSPERO. Electronic searches were performed in PubMed, Scopus, Web of Science, and ERIC. Intervention studies published within the last five years and assessing exergame-based interventions in children with ASD were included. Methodological quality was evaluated using the PEDro scale. Six studies met the inclusion criteria. Exergame-based interventions were associated with improvements in executive functions, particularly inhibitory control (reported in two studies using Stroop- and Flanker-type tasks) and cognitive flexibility (assessed in two studies using the Wisconsin Card Sorting Test), although results varied depending on intervention duration and design. Acute interventions (single-session) primarily influenced inhibitory control, whereas longer-term programs showed broader cognitive and motor adaptations. Improvements in motor outcomes, including gross motor development, coordination, and fundamental motor skills, were reported in four studies. Methodological quality ranged from 4 to 6 points on the PEDro scale, indicating fair to good quality. Considerable heterogeneity was observed in intervention protocols, duration, and outcome measures. Exergame-based interventions may represent a potentially promising approach for targeting executive functions and motor skills in children with ASD; however, the current evidence is limited and heterogeneous. Not all included studies assessed both cognitive and motor outcomes, and findings should therefore be interpreted with caution. Further high-quality randomized controlled trials are needed to confirm these effects and establish optimal intervention parameters. Full article

Accurate size classification remains a persistent challenge for agricultural products with irregular morphology, such as bitter melon (Momordica charantia). Proper grading is essential for fair pricing, efficient packaging, and compliance with the Association of Southeast Asian Nations and Philippine National Standards, yet traditional manual sorting often results in inconsistencies. To address this, we introduce an automated classification framework built on the You Only Look Once Version 8 (YOLOv8) model. The system integrates Harris Corner Detection to enhance feature extraction and the Douglas–Peucker algorithm to simplify contour representations, thereby reducing noise and improving shape analysis. A dataset of Ampalaya images was trained and processed to detect and categorize fruit sizes, with evaluation conducted through a confusion matrix. Experimental results showed an overall classification accuracy of 93.75%, demonstrating that the combined approach effectively balances precision with computational efficiency. Beyond improving classification accuracy, the findings highlight the broader potential of combining deep learning and contour-based methods to advance agricultural automation, optimize post-harvest workflows, and strengthen competitiveness in both local and international markets. Full article

Silicon carbide (SiC) power devices are emerging as an alternative for electrical transportation systems to improve energy efficiency, reduce carbon emissions, increase power density, and enable long-term cost savings throughout the product life cycle. Thus, a fair comparison with state-of-the-art Silicon (Si) technology is required to justify the productization of SiC devices. This work performs a systematic investigation of both technologies at the device and system levels for distinct power module voltage classes (3.3 and 6.5 kV) and circuit topologies. Initially, experimental characterization of state-of-the-art power modules is performed, followed by energy efficiency characterizations at the power converter level. Then, an electrothermal simulation model was built and validated based on experimental results. Accurate system simulations of commercial two- and three-level traction topologies were developed, focusing on efficiency over the entire load range, loadability, potential energy savings under realistic train drive cycles, and a financial comparison of inverter prices per kW. SiC exhibits lower loadability degradation at high switching frequencies (>500 Hz) than Si technology. Energy-saving potentials of 40–70% in the traction inverter with a guaranteed return on investment during the converter’s lifetime are achieved by substituting Si with SiC inverters. In addition, massive energy savings of up to 200 MWh per inverter lifetime can effectively reduce the carbon footprint of railway systems (up to ~76 t CO₂-eq saved during the inverter lifetime). This paper provides essential information for distinct stakeholders to support the decision-making process and design considerations for future railway power conversion technologies. Full article

This article explores access intimacy as feeling, interactional practice, and political vision through an inclusive research project in Hong Kong, where 12 visually impaired adults and 35 university students collaboratively developed accessible board games. Drawing on Mingus’s interdependence framework and Valentine’s justice-based access, we position visually impaired participants as primary knowledge producers while critically examining vulnerability, power dynamics, and research ethics. Analysis of field observations and in-depth interviews reveals three key dimensions: (1) collaborative game design enabled visually impaired participants to experience emotional access by fostering friendship, recognition, and belonging beyond logistical accessibility; (2) negotiation around “independence” and “fairness” generated transformative empowerment for both visually impaired and sighted participants, reframing interdependence as strength; and (3) reciprocal vulnerability in sighted guiding practices disrupted ableist assumptions about autonomy, care, and risk, revealing care as mutual rather than unidirectional. We argue that access intimacy functions as a learnable relational skill, and that attending to it in research design, community planning, and accessibility policy fosters justice-based paradigms that move beyond accommodation toward genuine interdependence and solidarity. Full article

Machine learning-based systems are increasingly deployed in high-stakes domains, such as healthcare, finance, law, and e-commerce, where their predictions directly influence critical decisions. Although these systems offer powerful data-driven support, they also introduce serious concerns related to fairness, bias, and discrimination. As a result, detecting and addressing unfairness in machine learning software has become a central research challenge. This study presents a systematic mapping of research on software unfairness detection in machine learning systems, with the aim of consolidating existing fairness definitions, identifying major problem types, examining testing approaches, reviewing commonly used datasets, and highlighting open research gaps. A structured search was conducted across five major digital libraries and additional sources, covering publications from 2010 to 2025. From 1805 initially identified records, 67 primary studies met the inclusion and quality assessment criteria. The findings show that research activity has grown significantly since 2019, reaching a peak in 2022. Most studies were published in conference proceedings, accounting for 52% of the primary studies, followed by journals and workshop proceedings, which accounted for 42% and 6% of the primary studies. The literature encompasses multiple research themes, with 36% of the primary studies focusing on the analysis of existing fairness methods, 22% addressing bias mitigation strategies, 30% investigating testing techniques, and 12% proposing or evaluating evaluation frameworks. Fairness testing was conducted across multiple testing levels, including unit, integration, and system testing. Integration-level testing was the most prevalent, accounting for approximately 37.9% of the studies, followed by system-level testing at 27.3% and unit-level testing at 12.1%. Additionally, 22.7% of the studies applied fairness testing across more than one testing level. Frequently used datasets included COMPAS, Adult Census Income, and German Credit. Widely adopted tools, such as IBM AI Fairness 360, Themis, and Aequitas, were also identified. Overall, the systematic mapping study (SMS) highlights the progress made in fairness research while emphasizing the need for stronger integration of fairness into practical machine learning development. Full article

This paper proposes a collaborative optimal scheduling strategy based on asymmetric Nash bargaining for the integrated electricity–heat–hydrogen multi-microgrid system, which can minimize the overall system operation cost while guaranteeing the dynamic fairness of multi-microgrids energy transactions with full consideration of wind–solar uncertainty. First, a scenario generation method based on temporally correlated Latin hypercube sampling and Wasserstein probability distance-based scenario reduction is adopted to construct representative wind–solar uncertainty scenarios, which effectively mitigates the operational risks arising from wind and solar power output fluctuations in the coordinated dispatch of multi-microgrids. Then, an asymmetric Nash bargaining-based cooperative game model for energy trading is established, with each microgrid’s optimal independent operation cost as the negotiation breakdown point. The alternating direction method of multipliers is used for a distributed solution to obtain the optimal scheme that balances total system cost and trading fairness. Simulation results verify that the proposed strategy can effectively suppress operation risks from renewable uncertainty, significantly cut total system cost by 36.85%, and fully ensure trading fairness among multi-microgrid entities, with favorable engineering application value. Full article

The public deployment of electric vehicle charging stations must simultaneously balance construction economics, user accessibility, queueing pressure, feeder security, tail risk under demand uncertainty, and spatial fairness. These criteria are strongly coupled, yet most existing studies either rely on static optimization with limited behavioral realism or use multi-agent reinforcement learning for short-term charging operation rather than for long-term siting. This paper proposes a preference-conditioned multi-agent deep deterministic policy gradient (PC-MADDPG) framework for the urban charging station siting problem in a coupled traffic–distribution environment. Candidate charging sites are modeled as cooperative agents under centralized training and decentralized execution. Each agent outputs a continuous pile-allocation action, which is repaired into an integer expansion plan under a budget constraint. The environment evaluates each plan through attraction-based demand assignment, queue approximation, LinDistFlow-style feeder analysis, and a six-objective performance vector, including annual net cost, travel burden, service inconvenience, grid penalty, CVaR of unmet charging demand, and equity loss. On a reproducible benchmark with 12 demand zones, 10 candidate sites, an 11-bus radial feeder, and 16 stochastic daily scenarios, the proposed framework generates a non-dominated archive with 42 unique feasible plans. A representative PC-MADDPG solution opens 5 of 10 candidate sites and installs 20 fast-charging piles, achieving 99.88% mean demand coverage with an annual profit of 2.083 M$ and a maximum line utilization of 0.999. Relative to the NoGrid ablation, the selected full model reduces grid penalty by 23.87% and equity Gini by 51.08%, with only a 0.35% profit concession. Relative to the NoRisk ablation, the CVaR of unmet demand is lowered by 69.70%. Compared with a demand-greedy baseline, the proposed method reduces grid penalty by 11.72% and equity Gini by 25.19% while preserving similar demand coverage. These results provide proof-of-concept evidence, on a reproducible coupled benchmark, that preference-conditioned multi-agent learning can serve as a practical many-objective siting engine for charging-infrastructure planning when coupled traffic and feeder constraints are explicitly modeled. Full article

Marine turtle and cetacean strandings along the Italian coastline represent critical ecological events that require systematic documentation, yet historical data have suffered from fragmentation and poor accessibility across heterogeneous archives. GeoCetus addresses this gap by providing a unified national framework for the centralized collection, management, and open visualization of these data. The platform’s architecture integrates a spatially enabled database with a modern RESTful API, utilizing automated workflows to push data to a public GitHub.com repository. This system unifies historical and contemporary datasets, comprising over 4700 georeferenced records dating back to 1999, while ensuring data quality through structured validation, qualified contributors and reverse geocoding. The results demonstrate a significant improvement in data interoperability and democratization, with the dataset expanding by an average of 150–300 new records annually under a CC-BY-SA license. By adhering to FAIR Data Principles, GeoCetus offers the necessary infrastructure to support real-time operational responses and reproducible ecological analyses. We conclude that this standardized, machine-readable approach is essential for evidence-based national conservation strategies and effective environmental monitoring. Full article

Artificial intelligence (AI) has become increasingly embedded in technology-enhanced learning environments, where peer assessment now serves both instructional and analytic purposes. Beyond allocating feedback and grades, it also produces data that is later interpreted through learning analytics systems. In practice, visible indicators such as students’ fairness perceptions and the degree of agreement among peer raters are often treated as signs that the assessment process is functioning effectively. However, these indicators do not necessarily correspond to grading validity. Students may regard a peer assessment process as fair even when peer-generated ratings remain weakly aligned with expert judgement. This study, therefore, examines whether the socio-technical configurations associated with high perceived fairness in a digitally mediated peer assessment environment also correspond to criterion-referenced grading validity. Data were collected from 215 undergraduate students enrolled in an Artificial Intelligence Foundations course over two consecutive semesters at a university in Taiwan, with instructor ratings serving as an external expert reference within the course context, rather than as a universal ground truth. Because anonymity conditions and semester were fully confounded in the study design, differences linked to anonymity should not be interpreted as isolated causal effects. A two-stage fuzzy-set Qualitative Comparative Analysis (fsQCA) was used. In the first stage, three equifinal configurations associated with high perceived fairness were identified. In the second stage, these configurations were examined against four grading objectivity outcomes: peer–instructor alignment, peer convergence, familiarity bias, and leniency bias. The findings show that fairness perception and grading validity are only partially aligned. Configurations anchored in explicit criterion transparency consistently supported both experiential legitimacy and evaluative accuracy. By contrast, one configuration was associated with high peer convergence while remaining weakly aligned with instructor standards, a pattern described here as false objectivity; this context-dependent configurational finding warrants further investigation across other settings. The study contributes to research on digitally enhanced assessment and learning analytics by showing that fairness perception, peer convergence, and grading validity should be treated as analytically distinct dimensions of assessment quality. Full article

Large language models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various natural language processing (NLP) tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible artificial intelligence (AI) systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies. Full article

We present an updated overview of the symmetry-preserving contact interaction model in hadronic physics, which was developed a little over a decade ago to describe the mass spectrum and internal structure of mesons and diquarks composed of light and heavy quarks. Over the years, the contact interaction model has evolved into a framework capable of treating both ground and excited states, providing a simple yet consistent approach to nonperturbative QCD. In this review, we examine the mass spectrum and elastic form factors of forty mesons with different spins and parities, together with their corresponding diquark partners. Importantly, we update the comparison of contact interaction predictions using recent results from the literature, offering a fresh perspective on the model’s performance, strengths, and limitations. The analysis presented here refines previous conclusions and supports the contact interaction model as a practical tool for hadron structure studies, with potential applications to baryons and multiquark states. We also present comparisons with other theoretical models and approaches, including lattice quantum chromodynamics, and comment on future prospects in view of ongoing and planned experimental programs regarding hadron structure. In particular, forthcoming measurements at FAIR together with future studies at Jefferson Lab and the Electron Ion Collider are expected to provide key insights into hadron structure, with FAIR offering indirect constraints via hadron spectroscopy, hadronic interactions, and in-medium properties; high-precision data on meson structure and form factors from Jefferson Lab and the Electron Ion Collider will provide valuable benchmarks with which to confront predictions based on the contact interaction model. Full article