Search Results (22,946)

This paper presents a performance evaluation of IEEE 802.11ax (Wi-Fi 6) networks using a combination of real-world testbed measurements and simulation-based analysis. The paper investigates the combined effect of received signal strength (RSSI), application bitrate, and network topology on video playback delays of 802.11ax. The effect of frequency band and client density on system performance is also investigated. Testbed measurements and field experiments were conducted in indoor environments using dual-band (2.4 GHz and 5 GHz) ad hoc and infrastructure network configurations. OMNeT++ based simulations are conducted to explore scalability by increasing the number of wireless clients. The results obtained show that the infrastructure-based deployments provide more stable video playback than the ad hoc network, particularly under varying RSSI conditions. While the 5 GHz band delivers higher throughput at a short range, the 2.4 GHz band offers improved coverage at reduced system performance. The simulation results further demonstrate significant degradation in throughput and latency as client density increases. To contextualize the observed performance, a baseline comparison with 802.11ac is incorporated, highlighting the relative improvements and remaining limitations of 802.11ax within the evaluated signal and load conditions. The findings provide practical deployment insights for video-centric wireless networks and inform the optimization of next-generation Wi-Fi deployments. Full article

As unmanned aerial vehicles (UAVs) are increasingly utilized in urban inspection and emergency rescue missions, path planning under strong wind conditions persists as a critical challenge. Traditional algorithms frequently exhibit deficiencies in environmental adaptability or encounter difficulties in balancing exploration and exploitation. This paper presents a dynamic-proportion Bat–Cuckoo Search (BA-CS) Hybrid Algorithm enhanced with wind field perception to tackle the challenges of UAV path planning in urban environments with strong winds, specifically addressing the issues of insufficient environmental adaptation and the exploration–exploitation imbalance. The algorithm integrates a dual-feedback mechanism that dynamically modifies the ratio of the BA/CS subpopulations in accordance with real-time iteration progress and population diversity. By incorporating wind field perception into population initialization, interpopulation information exchange, and wind resistance perturbation strategies, it attains efficient path optimization under multiple constraints. Experimental results under strong winds with speeds ranging from 10.8 to 13.8 m/s indicate that the proposed algorithm generates paths that are smooth, continuous, and entirely collision-free. It achieves a superior average wind resistance cost of 0.92, which is 9.8%, 17.1%, and 52.6% lower than those of the A*, RRT, and PSO algorithms, respectively. With a planning time of 3.95 s, it satisfies the path wind resistance stability requirements stipulated in the GB/T 38930-2020 standard, providing an effective solution for UAV inspection and emergency rescue operations in urban wind scenarios. Full article

Disinformation has become established as a strategic tool in political communication, with the capacity to erode public trust and undermine democratic quality. In an information environment increasingly mediated by artificial intelligence, it is essential to understand how the media articulates disinformation discursively. This study analyses, using a mixed design of quantitative and qualitative content analysis, 178 articles published in the five main Spanish digital newspapers (El País, El Mundo, La Vanguardia, El Español and Eldiario.es), comparing the treatment of two cases of alleged political corruption. The results show significant differences in volume, journalistic genre, tone, framing, and use of disinformation terminology, confirming that the media do not act as neutral transmitters but rather as discursive actors that use disinformation lexicon for the purposes of attack, defence, or ideological legitimisation. There is also a predominance of emotional tones and rhetorical strategies that favour polarisation. Full article

Research on muscle dysmorphia (MD), currently conceptualized as a clinical specifier for body dysmorphic disorder (BDD), is rapidly expanding. Although personality traits and disorders have been proposed as relevant risk factors for the development of BDD, their role in MD remains insufficiently understood. This scoping review aims to synthesize the existing empirical literature on the associations between MD and personality, while identifying key research gaps and clinical challenges. Following the PRISMA-ScR guidelines, a systematic search was conducted across PsycArticles, PubMed, Scopus, Web of Science, and Google Scholar between 1 October and 1 December 2024. A total of 15 studies met the inclusion criteria and were analyzed. Findings highlight the significant contribution of narcissism, neuroticism, and perfectionism to the development and severity of MD. In particular, traits associated with vulnerable narcissism consistently emerged as predictors of MD symptomatology. Sociocultural factors—such as the competitive environment of elite sports and early relational experiences—were also found to interact with personality-based vulnerabilities in shaping the onset and clinical expression of MD. However, most available studies relied on self-report measures, cross-sectional designs, and convenience samples predominantly composed of men, limiting the generalizability of the results. Despite these methodological limitations, this review emphasizes the importance of identifying personality-based vulnerabilities to enhance the understanding of MD and inform the development of person-centered prevention and intervention strategies. Full article

As a product of the integration of information and communication technologies, smart grid has greatly enhanced the efficiency of power system. However, with the development of the smart grid towards deep digitalization and interconnection, state estimation (SE) of power systems is facing dual challenges of a complex measurement environment and threat of cyber-attacks. The integrity and reliability of measurement data are affected by sensor failure, complex environmental noise, and data packet loss, causing state estimation deviations. Meanwhile, in recent years, malicious cyber-attacks, mainly in the form of false data injection into (FDIA) and denial-of-service (DoS), have also threatened the stable operation of power systems. This paper systematically reviews research achievements in related fields. Firstly, an analysis is conducted on the causes and mechanisms of measurement anomalies such as measurement loss, complex noise, and cyber-attacks. Then, the existing identification methods of measurement anomalies are reviewed, and state estimation methods for power systems under measurement anomaly conditions are analyzed from three perspectives: model-driven, data-driven, and hybrid-driven. Finally, advantages and disadvantages of various methods are analyzed, and future research directions are prospected, aiming to provide a reference for building a highly resilient and adaptive smart grid monitoring system. Full article

In the last decade, the interdisciplinary field of neuroarchitecture has grown significantly, revealing measurable links between architectural features and human neural processing. This review synthesizes current research at the nexus of neuroscience and architecture, with a focus on how emerging virtual reality (VR) and artificial intelligence (AI) technologies are being utilized to understand and enhance human spatial experience. We systematically reviewed literature from 2015 to 2025, identifying key empirical studies and categorizing advances into three themes: core components of neuroarchitectural research; the use of physiological sensors (e.g., EEG, heart rate variability) and virtual reality to gather data on occupant responses; and the integration of neuroscience with AI-driven analysis. Findings indicate that built environment elements (e.g., geometry, curvature, lighting) influence brain activity in regions governing emotion, stress, and cognition. VR-based experiments combined with neuroimaging and physiological measures enable ecologically valid, fine-grained analysis of these effects, while AI techniques facilitate real-time emotion recognition and large-scale pattern discovery, bridging design features with occupant emotional responses. However, the current evidence base remains nascent, limited by small, homogeneous samples and fragmented data. We propose a four-domain framework (somatic, psychological, emotional, cognitive-“SPEC”) to guide future research. By consolidating methodological advances in VR experimentation, physiological sensing, and AI-based analytics, this review provides an integrative roadmap for replicable and scalable neuroarchitectural studies. Intensified interdisciplinary efforts leveraging AI and VR are needed to build robust, diverse datasets and develop neuro-informed design tools. Such progress will pave the way for evidence-based design practices that promote human well-being and cognitive health in built environments. Full article

Object detection systems deployed in industrial environments are often constrained by limited annotation budgets, severe class imbalance, and heterogeneous visual conditions. Active learning (AL) aims to reduce labeling costs by selecting informative samples; however, existing strategies struggle to simultaneously ensure robust performance, rare-class coverage, and stability under realistic industrial constraints. In this work, we propose a rarity-aware, stratified AL framework for industrial object detection that explicitly aligns sample selection with class imbalance and annotation efficiency. The method relies on a composite image-level score that jointly captures model uncertainty, informativeness, and complementary diversity cues, while adaptively emphasizing rare classes. Crucially, a stratified querying mechanism is introduced to explicitly regulate class-wise sample allocation during selection, playing a key role in improving performance stability and rare-class coverage under severe imbalance, without sacrificing global informativeness. The proposed approach operates purely at the data-selection level, making it detector-agnostic and directly applicable to modern object detection pipelines. Experiments conducted on two real-world industrial datasets involving lobster and snow crab parts, using YOLOv10 and YOLOv12, demonstrate improved training stability and annotation efficiency across balanced, imbalanced, and noisy settings over multiple active learning cycles up to 15% labeled data. Complementary comparisons with fully supervised training further show that using only 45–65% of the labeled data is sufficient to retain more than 97% of full-supervision mAP@50 and over 90% of mAP@50:95. Full article

Hair dyes are widely used across all socioeconomic groups and regions worldwide. However, some studies indicate that these products contain substances known to be toxic to a wide variety of organisms. Moreover, dyeing practices generate effluents that may carry the toxicity of hair dyes into the environment. Due to these facts, there is great concern about the impacts these products may have on the environment, as well as on the health of their users and professionals in the field of cosmetology. This scoping review analyzed 184 publications from major databases (PubMed, SciELO, Scopus, Google Scholar, and MEDLINE). Ultimately, 126 scientific studies published between 1981 and 2024 were included based on methodological rigor and their relevance to the One Health framework. According to the literature, the components of hair dyes can induce adverse responses in biological systems, ranging from reversible topical irritations to severe systemic effects. Among the studies evaluated, more than half reported significant toxicological or genotoxic associations related to oxidative dye components such as p-phenylenediamine and its derivatives. These compounds are frequently associated with various types of human cancers, including breast, prostate, bladder, skin, ocular cancers, and brain tumors. In addition to their effects on humans, hair dyes exhibit ecotoxicity, which may threaten the maintenance of ecosystems exposed to their residues. The reported environmental impacts result from effluent emissions after successive hair washes that release unreacted dye residues. Due to the low biodegradability of these compounds, conventional wastewater treatment methods are often ineffective, leading to environmental accumulation and changes in aquatic ecosystems, soil fertility, and trophic balance. Data on the toxicity of hair dye effluents remain scarce and sometimes contradictory, particularly regarding the effects of their transformation products and metabolites. Overall, the evidence underscores the need for continuous monitoring, updated risk assessments, and the adoption of advanced treatment technologies specific to beauty salon effluents. The information presented in this work may support further studies and guide public management agencies in developing policies for mitigating the impacts of hair dye pollutants within the One Health perspective. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Leading agribusiness firms are pivotal to modernizing agricultural supply chains, yet evidence on how digital transformation translates into high-quality development remains fragmented. Using a 2024 sample of 30 Chinese national agribusiness leaders and the technology–organization–environment (TOE) framework, we integrate grey relational analysis with DEMATEL to quantify interdependencies among conditions, and combine fuzzy-set QCA with necessary condition analysis to identify both configurational pathways and binding constraints. The results of the analysis indicate that high-quality development rarely stems from a single driver; it emerges from complementary bundles linking digital technologies and R&D investment with organizational readiness (e.g., talent and governance) under supportive external conditions (e.g., policy incentives and market pressure). The findings provide a configurational explanation of digital upgrading in agribusiness and inform differentiated digital strategies for managers and policymakers. Full article

Recently, how to improve the aquaculture efficiency of sea cucumber Apostichopus japonicus and promote the sustainable development of its artificial cultivation has become an increasingly important issue. The pond water environment plays important roles in the survival rate and growth of A. japonicus seedlings. This study investigated the changes in water quality and plankton from June to November in A. japonicus ponds. The seawater temperature, pH, dissolved oxygen, salinity, nitrogen, and active phosphate contents were measured, and the planktonic species were detected and identified. The results showed that the seawater temperature ranged from 11.2 to 29.9 °C, and the highest temperature did not exceed the tolerance survival limits of A. japonicus. The changes in pH, dissolved oxygen, and salinity were also suitable for growth. A total of six phyla and 14 species of planktonic algae were detected, among which diatoms were dominant, and the dominant species changed over time. In the early stage, it was Chroomonas acuta, then, after it was Nitzschia sp., and then it returned to C. acuta again later. The biomass and density of algae peaked in week 5 (p < 0.05), but decreased to their lowest in week 18. The changes in chlorophyll-a content were consistent with the biomass of algae. Both the chlorophyll-a and pheophytin contents peaked in weeks 5 and 10 (p < 0.05). The changes in suspended particulate matter (SPM) and particulate organic matter (POM) were synchronized, and they peaked in weeks 5 and 12. These results suggested that the planktonic algae have the functions of a food supply and an environmental indication, and changes in chlorophyll-a, pheophytin, SPM, and POM support the food source reserve for A. japonicus. This study provides important information for the artificial cultivation of sea cucumber seedlings in a pond, and it is useful to promote the sustainable development of the sea cucumber industry. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

The mitigation of traffic noise is essential for the development of sustainable and livable urban environments, a goal that is directly contingent on addressing tire-pavement interaction noise (TPIN) as the dominant acoustic pollutant at medium to high vehicle speeds. This comprehensive review addresses a critical gap in the literature by systematically analyzing the damping properties of pavement systems through a unified, multi-scale framework—from the molecular-scale viscoelasticity of asphalt binders to the composite performance of asphalt mixtures. The analysis begins by synthesizing state-of-the-art testing and characterization methodologies, which establish a clear connection between macroscopic damping performance and the underlying viscoelastic mechanisms coupled with the microscopic morphology of the binders. Subsequently, the review critically assesses the influence of critical factors, such as polymer modifiers including rubber and Styrene-Butadiene-Styrene (SBS), temperature, and loading frequency. This examination elucidates how these variables govern molecular mobility and relaxation processes to ultimately determine damping efficacy. A central and synthesizing conclusion emphasizes the paramount importance of the asphalt binder’s properties, which serve as the primary determinant of the composite mixture’s overall acoustic performance. By delineating this structure-property-performance relationship across different scales, the review consolidates a foundational scientific framework to guide the rational design and informed material selection for next-generation asphalt pavements. The insights presented not only advance the fundamental understanding of damping mechanisms in pavement materials but also provide actionable strategies for creating quieter and more sustainable transportation infrastructures. Full article

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

People with developmental disabilities, who represent about 2% of the global population, encounter diverse functional and cognitive challenges that adversely impact their navigation and accessibility experiences in the built environments. Historically, accessibility standards predominantly focused on physical barriers, with less attention given to sensory and cognitive barriers. This multipart study delves into barriers faced by individuals with intellectual/developmental disabilities in the built environment. The first part reviews the state of existing literature on developmental disabilities. The second part captures insights from individuals with intellectual/developmental disabilities as they navigate various public buildings. The final part synthesizes the collected information, including lessons learned from autism-friendly architectural and other design requirements, to assess the current state of development of the Canadian accessibility standard, CSA/ASC B651:23, in meeting the needs of people with developmental disabilities. The study culminates in recommendations aimed at enhancing the accessibility standard for the built environment, specifically in addressing sensory and cognitive barriers faced by people with developmental disabilities. Full article