Search Results (6,963)

Social isolation and loneliness represent critical psychosocial stressors associated with profound hormonal dysregulation and adverse behavioral outcomes. This review synthesizes current evidence on neuroendocrine mechanisms linking perceived and objective social disconnection to health consequences, emphasizing hypothalamic–pituitary–adrenal axis dysfunction, altered glucocorticoid signaling, and inflammatory pathways. Loneliness activates conserved transcriptional responses with upregulated proinflammatory gene expression and downregulated antiviral responses, mediated through sustained cortisol elevation and glucocorticoid resistance. Neural circuit alterations in reward processing, particularly the ventral tegmental area-nucleus accumbens pathway, contribute to anhedonia, social withdrawal, and cognitive decline. Sex differences in neuroendocrine responses reveal distinct hormonal profiles and circuit-specific adaptations. Emerging interventions targeting oxytocin and arginine vasopressin systems, alongside behavioral approaches addressing loneliness-induced cognitive biases, show promise. Critical research gaps include a mechanistic understanding of epigenetic modifications, sex-specific therapeutic responses, and translational applications across diverse populations. Understanding the endocrine–behavior interface in social disconnection offers opportunities for targeted interventions addressing this growing public health challenge. Full article

Thermal limitations restrict the performance of low-cost, vertically stacked embedded systems. This paper presents a self-referencing digital twin framework for thermal and task management in a multi-device ESP32-S3 stack. The system combines a Mixture-of-Experts (MoE) model for task allocation with a neural network for short-term temperature prediction. Acting as a lightweight digital replica of the physical stack, the digital twin continuously monitors device states, forecasts thermal behavior 30 s into the future, and adapts workload distribution accordingly. The MoE model evaluates each device individually and asynchronously, estimating the portion of workload it should receive based on current state features including SoC temperature, CPU frequency, stack position, and recent task history. A separate neural network predicts future temperatures using real-time data from local and neighboring devices, enabling proactive thermal-aware scheduling. Training data for both models is collected through controlled experiments involving fixed-frequency operation and structured frequency switching with idle phases. All predictions and control actions are driven by in-built sensor feedback from the ESP32-S3 microcontrollers. The resulting digital twin supports distributed task scheduling based on temperature and works well in simple, low-cost edge systems with heat constraints. In one-hour experiments on a 6 ESP32-S3 stack, the proposed scheduling method completes up to 572 computation rounds at a $50{}^{\circ }$C temperature limit, compared with 493 and 542 rounds under logistic regression based control and 534 rounds at fixed 240 MHz operation, while keeping peak temperature at $51{}^{\circ }$C. Full article

Rapid evaluation of water toxicity requires biological methods capable of detecting sub-lethal physiological changes without depending on chemical identification. Conventional microscopy-based bioassays are limited by low throughput and difficulties in observing small, transparent and fast-moving microorganisms. This study applies a laser-biospeckle, non-imaging microbioassay to assess the motility responses of Paramecium caudatum and Euglena gracilis exposed to two organic pollutants, trichloroacetic acid (TCAA) and acephate. Dynamic speckle patterns were recorded using a 638 nm laser diode (Thorlabs Inc., Tokyo, Japan) and a CCD camera (Gazo Co., Ltd., Tokyo, Japan) at 60 fps for 120 s. Correlation time, derived from temporal cross-correlation analysis, served as a quantitative indicator of motility. Exposure to TCAA (0.1–50 mg/L) produced strong concentration-dependent inhibition, with correlation time increasing up to 16-fold at 500× PL in P. caudatum (p < 0.01), whereas E. gracilis showed a delayed response, with significant inhibition only above 250× PL. In contrast, acephate exposure (0.036–3.6 mg/L) induced motility enhancement in both species, reflected by decreases in correlation time of up to 57% in P. caudatum and 40% in E. gracilis at 100× PL. Acute trends diminished after 24–48 h, indicating time-dependent physiological adaptation. These results demonstrate that biospeckled-derived correlation time sensitively captures both inhibitory and stimulatory behavioral responses, enabling real-time, high-throughput water toxicity screening without microscopic imaging. The method shows strong potential for integration into automated water-quality monitoring systems. Full article

Optimizing the operating temperature of lithium-ion batteries is critical for safe, reliable, and efficient cell operation. Manufacturers’ recommendations vary in this area, which is primarily determined by the cells’ chemical composition and internal structural characteristics. Most manufacturers define the maximum charging voltage level as the same or close to the same value, but there are significant differences in the lower threshold voltage. Lithium-ion cells exhibit increased internal resistance at lower state-of-charge levels, resulting in elevated heat generation during operation, with intensity proportional to the depth of discharge. However, using a too low voltage threshold causes a significant loss of usable capacity, which reduces the cell’s energy utilization. The present research aims to define and analyze the optimal value of the lower voltage threshold more precisely, considering both thermal development and usable capacity aspects. A further objective is to determine an optimal energy safety margin level that provides a suitable compromise for longer-term storage. Different 18650 and 21700 standard lithium-ion cell types were tested using various load profiles. The results show that the two cell formats have different electro-thermal behaviors. The 21700 cells show a clear increase in thermal efficiency at around 3.1 V. In contrast, the 18650 cells have a heating pattern that depends heavily on the load. This requires selecting a cutoff that adapts to the discharge rate to prevent excessive thermal stress. These findings indicate that a fixed lower threshold voltage for all cells is not ideal. Instead, we need cutoff strategies that are specific to each cell and can change dynamically. The TER-based evaluation introduced in this work provides a practical framework for defining these adaptive limits. It may improve control in battery management systems in real-world applications. Full article

With the rapid development of the Internet of Things, self-powered sensing technology has become a crucial solution for scenarios where an external power supply is inconvenient or unavailable, such as wild monitoring and flexible wearables. The triboelectric nanogenerator (TENG)—an excellent self-powered sensor, particularly in the single-electrode mode—demonstrates broad application prospects due to its simple structure and ease of integration. However, a comprehensive understanding of the electron transfer behavior of TENGs for performance optimization remains insufficient. Here, we investigate such behaviors from three key aspects—the polymer functional groups, the configurations of TENGs, and corona polarization. It is found that polymer functional groups critically determine electron transfer ability, with fluorinated polymers exhibiting superior performance across all configurations. Moreover, the configuration significantly influences electron transfer efficiency, where the sliding configuration vastly outperforms contact–separation configurations. Furthermore, the effect of corona polarization is highly configuration-dependent, improving performance in contact–separation configurations while generally reducing it in sliding configuration. These findings provide valuable theoretical guidance and practical strategies for optimizing the design and selecting appropriate materials and configurations of TENG-based self-powered sensors. They also pave the way for a new generation of highly efficient, miniaturized, and adaptive self-powered systems. Full article

As artificial intelligence (AI) becomes an integral part of organizational operations, collaboration between humans and AI is transforming employees’ work experiences and behavioral patterns. This study examines the psychological challenges and coping responses associated with such collaboration. Drawing on Cognitive Appraisal Theory, we construct and test a theoretical framework that connects employee–AI collaboration to knowledge hiding via job insecurity, while considering AI trust as a moderating variable. Data were collected through a three-wave time-lagged survey of 348 employees working in knowledge-intensive enterprises in China. The empirical results demonstrate that (1) employee–AI collaboration elevates perceptions of job insecurity; (2) job insecurity fosters knowledge-hiding behavior; (3) job insecurity mediates the link between collaboration and knowledge hiding; and (4) AI trust buffers the positive effect of collaboration on job insecurity, thereby reducing its indirect impact on knowledge hiding. These findings reveal the paradoxical role of AI collaboration: although it enhances efficiency, it may also provoke defensive reactions that inhibit knowledge exchange. By highlighting the role of AI trust in shaping employees’ cognitive appraisals, this study advances understanding of how cognitive appraisals influence human adaptation to intelligent technologies. Practical insights are offered for managers aiming to cultivate trust-based and psychologically secure environments that promote effective human–AI collaboration and organizational innovation. Full article

Pedestrian evacuation efficiency is paramount to public safety and sustainable urban resilience. This study utilizes an agent-based model simulating evacuation dynamics in a built environment to assess the impact of route familiarity, interpersonal interactions, and storage layout on evacuation efficiency. The model incorporates an evolutionary game theory framework to capture strategic decision-making, featuring both symmetric and asymmetric interactions among evacuees with varying levels of exit information (complete, partial, or none). Results show that familiarity with exit location is the most decisive element for evacuation, significantly outweighing the influence of crowd interactions, imitation behaviors, group composition, or storage layout. Furthermore, the crowd composition exerts a significant moderating effect, so that asymmetric group structures yield superior evacuation performance compared to symmetric ones. The optimal storage layout for evacuation is contingent upon the availability of exit information. An orderly layout is superior when information is known, whereas a random layout proves more effective in the absence of information by preventing misleading paths. Thus, providing clear information, adaptable spatial designs and consciously constructing a heterogeneous population structure are more critical for evacuation. This work provides actionable insights for architects and safety planners, contributing directly to the development of safer, more sustainable built environments and supporting Sustainable Development Goal (SDG) 11, particularly Target 11.5. Full article

This article delves into the fuzzy finite-time adaptive control problem for uncertain nonlinear systems where state measurements are unavailable, nonlinear functions are unknown, and communication is limited. To emulate the unknown nonlinear relationships within the control methodology, we exploit fuzzy logic systems, while also proposing a state observer to address the challenge of unobservable states. To avoid the “complexity explosion” problem intrinsic to conventional backstepping techniques, the controller is developed based on the dynamic surface control methodology, which incorporates first-order filters to successfully alleviate this issue. An event-triggered approach is introduced to alleviate the computational and communication overhead. By leveraging the finite-time control approach, an adaptive finite-time fuzzy control algorithm is constructed using the adaptive backstepping technique. An event-triggered mechanism is designed to reduce communication frequency, while rigorously maintaining closed-loop stability and ensuring a positive minimum inter-event time to avoid Zeno behavior. The proposed finite-time controller achieves finite-time stability of the controlled systems, thereby guaranteeing that all system signals remain bounded within a finite time, despite the presence of unmeasurable states, unknown nonlinear functions, and limited communication constraints. This paper differentiates itself from recent related studies by proposing a co-designed observer–controller framework that rigorously guarantees finite-time stability under an event-triggered communication mechanism, thereby effectively addressing the multiple concurrent challenges of state estimation, rapid convergence, and limited network resources. Simulation examples are conducted to illustrate the effectiveness and feasibility of the derived control algorithm. Full article

Accurate short-term wind power forecasting plays a critical role in maintaining grid stability due to the inherently irregular and fluctuating nature of wind resources. Deep learning models such as LSTM, GRU, and CNN are widely used to learn temporal dynamics; however, their ability to capture or adapt to the underlying symmetries and asymmetries inherent in real-world wind energy data remains insufficiently explored. In this study, we evaluate and compare these models using authentic production and meteorological data from the Tokat Wind Farm in Türkiye. The forecasting scenarios were designed to reflect the temporal structure of the dataset, including seasonal patterns, recurrent behaviors, and the symmetry-breaking effects caused by abrupt changes in wind speed and operational variability. The results demonstrate that the LSTM model most effectively captures the temporal relationships and partial symmetries within the data, yielding the lowest error metrics (RMSE = 0.2355, MAE = 0.1249, MAPE = 25.16%, R² = 0.8199). GRU and CNN offer moderate performance but show reduced sensitivity to asymmetric fluctuations, particularly during periods of high variability. The comparative findings highlight how symmetry-informed model behavior—specifically the ability to learn repeating temporal structures and respond to symmetry-breaking events—can significantly influence forecasting accuracy. This study provides practical insights into the interplay between data symmetries and model performance, supporting the development of more robust deep learning approaches for real-world wind energy forecasting. Full article

Background/Objectives: Facial expressions are central to nonverbal communication and social cognition, and their recognition is shaped not only by facial features but also by contextual cues and prior experience. In high-threat contexts, rapid and accurate decoding of others’ emotions is adaptively advantageous. Grounded in neurocognitive models of face processing and vigilance, we tested whether brief perceptual adaptation to emotionally salient scenes, real-world disaster exposure, and pre-traumatic stress reactions enhance facial-emotion categorization. Methods: Fifty healthy adults reported prior direct exposure to catastrophic events (present/absent) and completed the Pre-Traumatic Stress Reactions Checklist (Pre-Cl; low/high). In a computerized task, participants viewed a single adaptor image for 5 s—negative (disaster), positive (pleasant environment), or neutral (phase-scrambled)—and then categorized a target face as emotional (fearful, angry, happy) or neutral as quickly and accurately as possible. Performance was compared across adaptation conditions and target emotions and examined as a function of disaster exposure and Pre-Cl. Results: Emotional adaptation (negative or positive) yielded better performance than neutral adaptation. Higher-order interactions among adaptation condition, target emotion, disaster exposure, and Pre-Cl indicated that the magnitude of facilitation varied across specific facial emotions and was modulated by both experiential (exposed vs. non-exposed) and dispositional (low vs. high Pre-Cl) factors. These effects support a combined influence of short-term contextual tuning and longer-term experience on facial-emotion categorization. Conclusions: Brief exposure to emotionally salient scenes facilitates subsequent categorization of facial emotions relative to neutral baselines, and this benefit is differentially shaped by prior disaster exposure and pre-traumatic stress. The findings provide behavioral evidence that short-term perceptual adaptation and longer-term experiential predispositions jointly modulate a fundamental communicative behavior, consistent with neurocognitive accounts in which context-sensitive visual pathways and salience systems dynamically adjust to support adaptive responding under threat. Full article

Healthcare faces a critical challenge: protecting sensitive medical data while enabling necessary clinical access. Evolving user behaviors, dynamic clinical contexts, and strict regulatory requirements demand adaptive access control mechanisms. Despite strict regulations, healthcare remains the most breached industry, consistently facing severe security risks related to unauthorized access. Traditional access control models cannot handle contextual variations, detect credential compromise, or provide transparent decision rationales. To address this, SAFE-GUARD (Semantic Access Control Framework Employing Generative User Assessment and Rule Decisions) is proposed as a two-layer framework that combines behavioral analysis with policy enforcement. The Behavioral Analysis Layer uses Retrieval-Augmented Generation (RAG) to detect contextual anomalies by comparing current requests against historical patterns. The Rule-Based Policy Evaluation Layer independently validates organizational procedures and regulatory requirements. Access is granted only when behavioral consistency and both organizational and regulatory policies are satisfied. We evaluate SAFE-GUARD using simulated healthcare scenarios with three LLMs (GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Flash) achieving an anomaly detection accuracy of 95.2%, 94.1%, and 91.3%, respectively. The framework effectively identifies both compromised credentials and insider misuse by detecting deviations from established behavioral patterns, significantly outperforming conventional RBAC and ABAC approaches that rely solely on static rules. Full article

This paper presents Greenopoli, an innovative framework for sustainability and waste management education that has engaged over 600 schools and 90,000 students since 2014. Greenopoli is founded on the idea that children and youth can grasp environmental issues as well as adults and act as agents of change within their families and communities. The Greenopoli approach combines scientific accuracy with playful, creative pedagogy to simplify complex topics and stimulate peer-to-peer learning. It includes storytelling, games, field visits, and “green raps” (original environmental songs co-created with students). The framework is adaptive, with content and activities tailored to education stages from kindergarten through university. Educators adopt the role of moderators or facilitators, encouraging students to discuss and discover concepts collaboratively. Greenopoli’s participatory method has been implemented across all age groups, yielding enthusiastic engagement and tangible outcomes in waste sorting and recycling behaviors. The program’s reach has extended beyond schools through collaborations with national recycling consortia, NGOs, municipalities, and media (TV programs, social media, TEDx talks). Numerous awards and recognitions (2017–2025) have highlighted its impact. A comparative analysis shows that Greenopoli’s use of peer-led learning, gamification, and creative communication aligns with global best practices while offering a unique blend of tools. Greenopoli is a novel best-practice model in environmental education, bridging theory and practice and contributing to the goals of Education for Sustainable Development and a circular economy. It demonstrates the effectiveness of engaging youth as change-makers through interactive and creative learning, and it can inspire similar initiatives globally. Full article

This study investigates the horizontal positioning accuracy of a low-cost, multi-frequency GNSS receiver operating in static mode using a newly released PPP-RTK correction service delivering localized corrections. To the authors’ knowledge, this represents one of the first performance evaluations of this service, which optimizes correction data based on the approximate receiver location. The results are compared against those from the previous version of the service, which provided non-localized corrections. Analyses were conducted in both the time and frequency domains, employing robust statistical tools to characterize error behavior. The localized service achieved a mean horizontal error of approximately 0.020 m and a 95% Circular Error Probable (CEP95) of 0.046 m, in line with its declared performance. By contrast, the earlier non-localized service yielded a mean horizontal error of approximately 0.074 m and a CEP95 of 0.124 m under comparable static conditions, confirming the significant improvement achieved by localized corrections. Spectral and wavelet analyses revealed a dominant 33 mHz harmonic in the positioning error, corresponding to the 30 s update period of atmospheric corrections, indicating a periodic influence arising from the correction stream. Continuous wavelet analysis further identified intervals in which this harmonic was absent, during which positioning accuracy improved markedly (CEP95 reduced to 0.019 m). To properly address the non-Gaussian nature of the error distribution, bias-corrected and accelerated (BCa) bootstrap methods were applied to estimate confidence intervals. Overall, the results demonstrate the benefits of localized corrections, while emphasizing the importance of accounting for the temporal structure of correction data in PPP-RTK performance assessments. Future developments will focus on kinematic scenarios and adaptive filtering strategies to mitigate periodic errors induced by correction updates. Full article

Background/Objectives: Feeding difficulties are more common in children with autism spectrum disorder (ASD) or other developmental conditions and are associated with nutritional risk and caregiver stress. However, they may be overlooked as growth tends to be preserved. We aimed to identify clinical and behavioral features associated with feeding difficulties among children with developmental conditions. Methods: This cross-sectional study included caregiver–child dyads, with children aged 1–7 years with ASD and other developmental conditions. Caregivers completed the Repetitive Behavior Questionnaire, Second Edition (RBQ-2) to assess child restricted and repetitive behaviors (RRBs) and the Behavioral Pediatrics Feeding Assessment Scale (BPFAS) to assess feeding difficulties. Demographics, anthropometric measures and cognitive and adaptive scores were retrieved from medical records. Results: Of the 132 participants (mean age 41.8 months, range 15–67; 74.2% male) included, majority had normal weight (87.7%) and height (89.2%) z scores. Among participants, 54.5% had autism, 26.5% language delay and 18.9% other developmental diagnoses. Over half (53.0%) had elevated BPFAS scores. Children not enrolled in school showed significantly more feeding difficulties compared to those who were enrolled (32.6% vs. 16.7%, p < 0.05). The RBQ-2 total score positively correlated with the BPFAS total frequency score (r = 0.33, p = 0.01) after adjusting for gender, age and developmental diagnosis. Conclusions: Feeding difficulties were common in this sample. Higher RRBs and absence of formal schooling were associated with higher rates of feeding difficulties. Longitudinal studies are needed to ascertain the role of RRBs and school enrollment as clinical indicators associated with feeding difficulties. Full article

This study investigates the aerodynamic and aeroacoustics behavior of automotive cooling modules in both conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), with a particular focus on installation effects. Numerical simulations based on the Lattice Boltzmann Method (LBM) are conducted to analyze noise generation mechanisms and flow characteristics across four configurations. The study highlights the challenges of adapting classical cooling module components to EV setups, emphasizing the influence of heat exchanger (HE) placement and duct geometry on noise levels and flow dynamics. The results show that the presence of the HE smooths the upstream flow, improves rotor loading distribution and disrupts long, coherent vortical structures, thereby reducing tonal noise. However, the additional resistance introduced by the HE leads to increased rotor loading and enhanced leakage flow through the shroud-rotor gap. Despite these effects, the overall sound pressure level (OASPL) remains largely unchanged, maintaining a similar magnitude and dipolar directivity pattern as the configuration without the HE. In EV modules, the inclusion of ducts introduces significant flow disturbances and localized pressure fluctuations, leading to regions of high flow rate and rotor loading. These non-uniform flow conditions excite duct modes, resulting in troughs and humps in the acoustic spectrum and potentially causing resonance at the blade-passing frequency, which increases the amplitude in the lower frequency range. Analysis of the loading force components reveals that rotor loading is primarily driven by thrust forces, while duct loading is dominated by lateral forces. Across all configurations, fluctuations at the leading and trailing edges of the rotor are observed, originating from the blade tip and extending to approximately mid-span. These fluctuations are more pronounced in the EV module, identifying it as the dominant source of pressure disturbances. The numerical results are validated against experimental data obtained in the anechoic chamber at the University of Sherbrooke and show good agreement. The relative trends are accurately predicted at lower frequencies, with slight over-prediction, and closely match the experimental data at mid-frequencies. Full article