Search Results (317)

Predominant fault diagnosis in industrial robots depends on dedicated vibration or acoustics sensors. However, their practical deployment is often limited by installation constraints, susceptibility to environmental noise, and cost considerations. Applying Energy-Based Maintenance (EBM) principles to achieve enhanced fault diagnosis under practical industrial conditions, we propose a hybrid deep learning framework, the Multi-head Graph Attention Network (MGAT) with Multi-scale CNNBiLSTM Fusion (MGAT-MCNNBiLSTM) for industrial robots. This approach obviates the need for additional dedicated sensors, effectively mitigating associated deployment complexities. The framework embodies four core innovations: (1) Based on the EBM paradigm, motor current is established as the most effective and practical choice for enabling cost-efficient and scalable industrial robot fault diagnosis. A corresponding dataset of motor current has been acquired from industrial robots operating under diverse fault scenarios. (2) An integrated MGAT-MCNNBiLSTM architecture that synergistically models multiscale local features and complex dynamics through its MCNNBiLSTM module while capturing nonlinear interdependencies via MGAT. This comprehensive feature representation enables robust and highly accurate fault detection. (3) The study found that the application of spectral preprocessing techniques yields a marked and statistically significant enhancement in diagnostic performance. A comprehensive and systematic analysis was undertaken to uncover the underlying reasons for this observed performance improvement. (4) To emulate challenging industrial settings and cost-sensitive implementations, noise signal injection was employed to evaluate model robustness in high-electromagnetic-interference environments and low-cost, low-resolution ADC implementations. Experimental validation on real-world industrial robot datasets demonstrates that MGAT-MCNNBiLSTM achieves a superior diagnostic accuracy of 90.7560%. This performance marks a significant absolute improvement of 1.51–8.55% over competing models, including LCNNBiLSTM, SCNNBiLSTM, MCCBiLSTM, GAT, and MGAT. Under challenging noise and low-resolution conditions, the proposed model consistently outperforms CNNBiLSTM variants, GAT, and MGAT with an improvement of 1.37–10.26% and enhanced industrial utility and deployment potential. Full article

Federated learning (FL) is emerging as a pivotal paradigm for environmental monitoring, enabling decentralized model training across edge devices without exposing raw data. This review provides the first structured synthesis of 361 peer-reviewed studies, offering a comprehensive overview of how FL has been implemented across environmental domains such as air and water quality, climate modeling, smart agriculture, and biodiversity assessment. We further provide comparative insights into model architectures, energy-aware strategies, and edge-device trade-offs, elucidating how system design choices influence model stability, scalability, and sustainability. The analysis traces the technological evolution of FL from communication-efficient prototypes to robust, context-aware deployments that integrate domain knowledge, physical modeling, and ethical considerations. Persistent challenges remain, including data heterogeneity, limited benchmarking, and inequitable access to computational infrastructure. Addressing these requires advances in hybrid physics–AI frameworks, privacy-preserving sensing, and participatory governance. Overall, this review positions FL not merely as a technical mechanism but as a socio-technical shift—one that aligns distributed intelligence with the complexity, uncertainty, and urgency of contemporary environmental science. Full article

This study proposes a methodological approach to the redesign of a 1980s vehicle, the Lancia Beta HPE, integrating the TRIZ (Theory of Inventive Problem Solving) and the Industrial Design Structure (IDeS) frameworks within the design process. The redesign process focused on both the external morphology of the vehicle and its propulsion system, aligning the outcome with contemporary trends in market evolution, societal shifts, and environmental considerations. The objective of the project was to reinterpret stylistic elements that were typical of 1980s automotive design through a contemporary lens, while incorporating characteristics of the current aesthetic of electric vehicles (EVs). A pivotal element of the research involved a comparative stylistic analysis of past and present vehicle design languages. This facilitated the identification of design guidelines for adapting formal and stylistic details to the electric mobility paradigm, with emphasis on contemporary aesthetics and energy efficiency. The transition from internal combustion to electric propulsion necessitated a comprehensive re-evaluation of the vehicle’s key exterior features, encompassing the front end, body shape, and lighting systems, in order to reflect a novel ecological identity and convey technological advancement. In order to inform stylistic choices, an in-depth exploration of electric propulsion principles was conducted, leveraging AI-based tools such as GPT to support TRIZ-guided problem-solving. Full article

In today’s digital world where presence is often equated with personal visibility, the choice of Emirati women to remain faceless on social media presents a powerful counter narrative—one that reveals the complexities of identity, modesty and belonging in a hyperconnected multicultural society. This study takes a closer look at how these women manage their online identities by intentionally choosing not to show their faces on Instagram. Using digital ethnography and thematic analysis, this article explores how they navigate the balance between global expectations of self-expression and the traditional values of modesty and honor. Over a three-month period, the study observes their activity on Instagram, analyzing shared images to see how facelessness becomes a form of agency. The findings highlight the tension between Western-centric paradigms of identity and selfhood, proposing digital veiling as a transferable framework for understanding how modesty, discretion and agency are negotiated across digital cultures. This article contributes to the broader conversation on digital identity, gendered representation and intercultural negotiation by foregrounding the silent yet strategic practices of women who remain unseen but not unheard. Full article

The rapid uptake of generative AI (e.g., ChatGPT, DALL·E and MS Copilot) is disrupting conventional notions of authenticity in assessment across higher education. The dominant response, surveillance and AI detection, misdiagnoses the problem. In an AI-mediated world, authenticity cannot be policed into existence; it must be redesigned. Situating AI within contemporary knowledge work shaped by digitisation, collaboration and evolving ethical expectations, we reconceptualise authenticity as something constructed in contexts where AI is expected, declared and scrutinised. The emphasis shifts from what students know to how they apply knowledge, make judgement, and justify choices with AI in the loop. We offer practical design for learning moves, i.e., discipline-agnostic learning design patterns that position AI as a collaborator rather than a cheating application: tasks that require students to critique, adapt and verify AI outputs, provide explicit process transparency (prompts, iterations, rationale) and exercise assessable demonstrations of digital discernment and ethical judgement. Examples include asking business students to interrogate a chatbot-generated market analysis and inviting pre-service teachers to evaluate AI-produced lesson plans for inclusivity and pedagogical soundness. Reflective artefacts such as metacognitive commentary, process logs, and oral defences make students’ thinking visible, substantiate attribute, and reduce reliance on punitive “gotcha” approaches. Our contribution is twofold: i. a conceptual account of authenticity fit for an AI-mediated world, and ii. a set of actionable, discipline-agnostic patterns that can be tailored to local contexts. The result is an integrity stance anchored in design rather than detection, enabling assessment that remains meaningful, ethical and intellectually demanding in the presence of AI, while advancing a broader shift toward assessment paradigms that reflect real-world professionalism. Full article

Women experience realistic and symbolic gender intergroup threats across diverse social contexts, which can profoundly influence their decision-making processes. Drawing on intergroup threat theory, this research investigated how perceived gender intergroup threats affect women’s intertemporal choice behavior and examined cognitive appraisal as a potential mediating mechanism. Study 1 (N = 281) found a negative correlation between gender intergroup threat perception and delay discounting through questionnaires. Study 2 (N = 154) experimentally manipulated threat perception and demonstrated that both realistic and symbolic gender threats enhanced consideration of future consequences, with cognitive appraisal serving as a complete mediator of these effects. Study 3 (N = 120) employed a recall paradigm, providing convergent evidence that heightened realistic threat perception and associated threat appraisal increased preferences for delayed, long-term outcomes. These findings suggest that perceived gender intergroup threats promote future-oriented decision-making among women, potentially as an adaptive strategy to manage threat-related risks, and the mediating role of cognitive appraisal further elucidates the psychological mechanisms underlying this behavioral shift. This research advances the theoretical understanding of how intergroup threat dynamics shape women’s economic behavior and extends knowledge of gender threat interactions in decision-making contexts. Full article

The development of Digital Twin (DT) technology in Battery Management Systems (BMSs) presents a transformative approach for maintenance, monitoring, and predictive diagnostics, especially in the demanding field of space applications. DTs, through their three-layer structure, provide an accurate and dynamic virtual representation of the physical entity, continuously updated via bidirectional data exchange provided by the communication link. Given the promising capabilities of the DT approach in real-time applications, its integration into BMSs is straightforward, as it can enhance monitoring and prediction of nonlinear electrochemical systems, such as space-grade lithium-ion batteries, supporting the mitigation of ageing effects under the unique constraints of the space environment. Despite notable progress in BMS technologies, the choice of estimation techniques consistent with the DT paradigm remains insufficiently defined. This survey examines the state of the art with the aim of bridging the conceptual framework of DTs and existing battery management algorithms, identifying the methodologies most suitable in accordance with DT architectures and principles. The scope of this paper is to provide researchers and engineers with a comprehensive overview of the advancements, key enabling technologies, and implementation strategies for Digital Twins in space BMSs, ultimately contributing to more reliable and efficient space missions. Full article

Although associative interference has traditionally been attributed to retrieval competition, emerging evidence suggests that interference may also arise from encoding-based representational processes. The present study examined whether retroactive interference (RI) and proactive interference (PI) can occur in the absence of explicit retrieval competition and whether they reflect distinct underlying mechanisms. Participants studied two lists of word–picture pairs in an AB/AC associative learning paradigm, followed by a non-competitive two-alternative forced-choice (2AFC) associative recognition test and a source memory task. Across both frequentist and Bayesian analyses, recognition accuracy revealed a significant RI effect—lower accuracy for earlier A-B pairs relative to non-overlapping controls—whereas PI manifested as longer reaction times (RTs) for later A-C pairs, despite comparable accuracy. Source judgments showed faster correct responses for overlapping than for non-overlapping pairs, suggesting that cue overlap facilitated more fluent retrieval rather than confusion. These findings indicate that interference can emerge independently of retrieval competition and that RI and PI are supported by dissociable mechanisms: RI reflects encoding-related reorganization that weakens earlier associations, whereas PI reflects increased retrieval effort following differentiation of overlapping traces. Together, the results support a process-interaction framework in which encoding-based reactivation and reorganization shape later retrieval dynamics, demonstrating that associative interference arises from the interplay between encoding and retrieval processes rather than retrieval competition alone. Full article

Retrieval-augmented generation (RAG) has established a new search paradigm, in which large language models integrate external resources to compensate for their inherent knowledge limitations. However, limited context awareness reduces the performance of large language models in RAG tasks. Existing solutions incur additional time and memory overhead and depend on specific positional encodings. In this paper, we propose Attention Head Detection and Reweighting (ADR), a lightweight and general framework. Specifically, we employ a recognition task to identify RAG-suppressing heads that limit the model’s context awareness. We then reweight their outputs with learned coefficients to mitigate the influence of these RAG-suppressing heads. After training, the weights are fixed during inference, introducing no additional time overhead and remaining agnostic to the choice of positional embedding. Experiments on PetroAI further demonstrate that ADR enhances the context awareness of fine-tuned models. Full article

As a key piece of equipment in badminton, the surface treatment technology of rackets has garnered significant attention in the fields of material science and sports engineering. This study is the first to systematically review research on racket coatings, integrating interdisciplinary knowledge on the classification of functional coatings, their performance-enhancing principles, and their relationship with competitive levels, thereby addressing a gap in theoretical research in this field. This study focuses on four major functional coating systems: superhydrophobic coatings (to improve environmental adaptability and reduce air resistance), anti-scratch coatings (to prolong the life of the equipment), vibration-damping coatings (to optimise vibration damping performance), and strength-enhancing coatings (to safeguard structural stability). In badminton, differences in player skill levels and usage scenarios lead to variations in racket materials, which, in turn, result in different preparation processes and performance effects. The use of vibration-damping materials alleviates the impact force on the wrist, effectively preventing sports injuries caused by prolonged training; leveraging the aerodynamic properties of superhydrophobic technology enhances racket swing speed, thereby improving hitting power and accuracy. From the perspective of performance optimization, coating technology improves athletic performance in three ways: nanocomposite coatings enhance the fatigue resistance of the racket frame; customized damping layers reduce muscle activation delays; and surface energy regulation technology improves grip stability. Challenges remain in the industrial application of environmentally friendly water-based coatings and the evaluation system for coating lifespan under multi-field coupling conditions. Future research should integrate intelligent algorithms to construct a tripartite optimization system of “racket-coating-user” and utilize digital sports platforms to analyze its mechanism of influence on professional athletes’ tactical choices, providing a theoretical paradigm and technical roadmap for the targeted development of next-generation smart badminton rackets. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article

Background/Objectives: Decision-making under socially evaluative stress engages a dynamic interplay between cognitive control, emotional appraisal, and motivational systems. Contemporary models of multi-level co-regulation posit that these systems operate in reciprocal modulation, redistributing processing resources to prioritise either rapid socio-emotional alignment or deliberate evaluation depending on situational demands. Methods: Adopting a neurofunctional approach, a novel dual-task protocol combining the MetaCognition–Stress Convergence Paradigm (MSCP) and the Social Stress Test Neuro-Evaluation (SST-NeuroEval), a simulated social–evaluative speech task calibrated across progressive emotional intensities, was implemented. Twenty professionals from an HR consultancy firm participated in the study, with concurrent recording of frontal-temporoparietal electroencephalography (EEG) and bespoke psychometric indices: the MetaStress-Insight Index and the TimeSense Scale. Results: Findings revealed that decision contexts with higher socio-emotional salience elicited faster, emotionally guided choices (mean RT difference emotional vs. cognitive: −220 ms, p = 0.026), accompanied by oscillatory signatures (frontal delta: F(1,19) = 13.30, p = 0.002; gamma: F(3,57) = 14.93, p ≤ 0.001) consistent with intensified socio-emotional integration and contextual reconstruction. Under evaluative stress, oscillatory activity shifted across phases, reflecting the transition from anticipatory regulation to reactive engagement, in line with models of phase-dependent stress adaptation. Across paradigms, convergences emerged between decision orientation, subjective stress, and oscillatory patterns, supporting the view that cognitive–emotional regulation operates as a coordinated, multi-level system. Conclusions: These results underscore the importance of integrating behavioural, experiential, and neural indices to characterise how individuals adaptively regulate decision-making under socially evaluative stress and highlight the potential of dual-paradigm designs for advancing theory and application in cognitive–affective neuroscience. Full article

Global waste generation is a ubiquitous challenge, driving a paradigm shift towards viewing waste as a valuable resource for a circular economy across diverse sectors. While innovative waste-to-resource pathways are crucial, rigorous Life Cycle Assessment (LCA) is essential to ensure the pathways are an important part of current practices. However, LCA application to waste valorization varies, leading to incomparable results due to differing methodological choices. This paper examines three key nuances in waste-as-resource LCAs: the zero-burden assumption, the biogenic carbon neutrality assumption, and the benchmark assumption for emissions avoidance. Using a waste gasification to hydrogen case study, we demonstrate how these methodological decisions impact LCA outcomes. Our findings reveal that waste composition significantly influences the results and highlight challenges associated with biogenic carbon accounting under various system boundary assumptions. Emissions avoidance accounting requires multi-functional unit perspectives and robust benchmark selection. This paper clarifies these accounting approaches, empirically illustrates their influence, and discusses broad implications for accurate sustainability assessment, emphasizing the critical role of transparent LCA choices for effective policy and investment in circular economy solutions. Full article

Background: The management of endometriosis-associated pain has traditionally focused on analgesic efficacy. However, with high-level evidence demonstrating therapeutic equivalence among principal hormonal classes, the paradigm has shifted towards a patient-centred approach prioritising long-term tolerability and shared decision-making. Objectives: This review critically synthesises the evidence for the three main hormonal therapies—gonadotropin-releasing hormone (GnRH) analogues, dienogest, and gestrinone—focusing on their distinct tolerability and safety profiles to inform this modern clinical framework. Methods: This narrative review followed the SANRA (Scale for the Assessment of Narrative Review Articles) guidelines. The literature search was performed in PubMed, Embase, and Web of Science in June 2025. Results: Our comparative analysis, based on a structured literature search adhering to SANRA guidelines, shows that while all three classes are effective, they present distinct benefit–risk profiles: GnRH analogues offer potent pain relief but induce a hypoestrogenic state requiring add-back therapy to mitigate bone loss and vasomotor symptoms; dienogest preserves bone mineral density but is associated with challenging bleeding patterns and potential mood disturbances; gestrinone provides robust efficacy with a favourable cardiovascular and skeletal safety profile, although its androgenic effects can significantly impact patient adherence. Conclusions: In the absence of a clear hierarchy of efficacy, the optimal therapeutic choice is not determined by potency, but by a collaborative process in which patient values and tolerance for specific adverse effects guide selection. This review provides a framework to facilitate this shared decision-making (SDM) in clinical practice. Full article

This paper presents a flexible approach to a multipurpose, heterogeneous archive and data management system model that merges the robustness of legacy grid-based technologies with modern cloud and edge computing paradigms. It leverages innovations driven by big data, IoT, AI, and machine learning to create an adaptive data storage and processing framework. In today’s digital age, where data are the new intangible gold, the “gold rush” lies in managing and storing massive datasets effectively—especially when these data serve governmental or commercial purposes, raising concerns about privacy and data misuse by third-party aggregators. Astronomical data, in particular, require this same thoughtful approach. Scientific discovery increasingly depends on efficient extraction and processing of large datasets. Distributed archival models, unlike centralized warehouses, offer scalability by allowing data to be accessed and processed across locations via cloud services. Incorporating edge computing further enables real-time access with reduced latency. Major astronomical projects must also avoid common single points of failure (SPOFs), often resulting from suboptimal technological choices driven by collaboration politics or In-Kind Contributions (IKCs). These missteps can hinder innovation and long-term project success. The principal goal of this work is to outline best practices in archival and data management projects—from policy development and task planning to use-case definition and implementation. Only after these steps can a coherent selection of hardware, software, or virtual environments be made. The proposed model—CTAARCHS (Cloud-based Technologies for Astronomical Archiving Research Contents and Handling Systems)—is an open-source, multidisciplinary platform supporting big data needs in astronomy. It promotes broad institutional collaboration, offering code repositories and sample data for immediate use. Full article