Search Results (3,848)

Recognizing human actions in low-light industrial environments remains a significant challenge for safety-critical applications in power systems. In this paper, we propose a Low-Light Pose-Action Collaborative Network (LPAC-Net), an integrated framework specifically designed for monitoring scenarios in underground electrical vaults and smart power stations. The pipeline begins with a modified Zero-DCE++ module for reference-free illumination correction, followed by pose extraction using YOLO-Pose and a novel rotation-invariant encoding of keypoints optimized for confined industrial spaces. Temporal dependencies are captured through a bidirectional LSTM network with attention mechanisms to model complex operational behaviors. We evaluate LPAC-Net on the newly curated ARID-Fall dataset, enhanced with industrial monitoring scenarios representative of electrical infrastructure environments. Experimental results demonstrate that our method outperforms state-of-the-art models, including DarkLight-R101, DTCM, FRAGNet, and URetinex-Net++, achieving 95.53% accuracy in recognizing worker activities and safety-critical events. Additional studies confirm LPAC-Net’s robustness under keypoint noise and motion blur, highlighting its practical value for intelligent monitoring in challenging industrial lighting conditions typical of underground electrical facilities and automated power stations. Full article

In recent years, behavioral economics has revolutionized various fields, including finance, marketing, and public policy. Its application in people management, however, remains an emerging area of exploration. By integrating psychological insights into economic decision-making, behavioral economics offers a nuanced understanding of human behavior, essential for designing effective HR practices. While many of the concepts are not new in organizational behavior research and related fields, thanks to the incorporation of formalized models of choice, behavioral economics brings analytical clarity to domains traditionally studied through descriptive or qualitative methods in the behavioral sciences. This review article delves into how behavioral economics can shed light on key aspects of people management, focusing on five domains: incentives, decision-making, leadership, personalization, and organizational change. We offer a critical overview integrating some of the most well-known findings with applicability in these areas as well as promising avenues for future research. One of the main conclusions is that behavioral economics offers a powerful lens to approach people management, but also that behavioral principles need to be understood in depth (beyond average effects, for example) as generalization is often flawed, claiming for personalized solutions and interventions grounded on comprehensive perspectives. Full article

Early Childhood Mental Health Consultation (ECMHC) promotes children’s social–emotional development and reduces challenging behaviors in early care and education (ECE) centers, yet implementation barriers increase teacher stress and reduce confidence. Scalable, efficient, and accessible approaches are needed to meet ECE center demands. This quasi-experimental match-controlled study evaluated two ECMHC programs in promoting children’s social–emotional development and improving teachers’ skills/attitudes compared to an attention control condition in 22 ECE centers in lower-resourced areas of BLINDED. We compared Jump Start (JS; traditional human consultation model), Jump Start Go (JS Go; AI-enhanced consultation model), and Healthy Caregivers–Healthy Children (HC2; obesity-prevention consultation model). Child social–emotional development, teacher workplace stress/confidence, and classroom practices were assessed at pre-and post-intervention. Children in JS and JS Go interventions demonstrated significant social–emotional gains (F = 13.55, p < 0.001), with the largest reductions in internalizing problems observed in children who received JS Go (−2.91 points; F = 9.65, p < 0.001). JS Go classrooms also showed greater improvements in prosocial behavior (F = 5.05, p = 0.012) and resiliency (F = 8.95, p < 0.001) than HC2 classrooms. Findings suggest that both traditional and AI-enhanced ECMHC approaches can promote teachers’ capacity to support children’s social–emotional development. Full article

The increasing sophistication of social bots demands advanced simulation frameworks to model potential vulnerabilities in detection systems and probe their robustness.While existing studies have explored aspects of social bot simulation, they often fall short in capturing key adversarial behaviors. To address this gap, we propose a simulation framework that jointly incorporates both realistic behavioral mimicry and adaptive inter-bot coordination. Our approach introduces a human-like behavior module that reduces detectable divergence from genuine user activity patterns through distributional matching, combined with a coordination module that enables strategic cooperation while maintaining structural stealth. The effectiveness of the proposed framework is validated through adversarial simulations against both feature-based (Random Forest) and graph-based (BotRGCN) detectors on a real-world dataset. Experimental results demonstrate that our approach enables bots to achieve remarkable evasion capabilities, with the human-like behavior module reaching up to a 100% survival rate against RF-based detectors and 99.1% against the BotRGCN detector. This study yields two key findings: (1) The integration of human-like behavior and target-aware coordination establishes a new paradigm for simulating botnets that are resilient to both feature-based and graph-based detectors; (2) The proposed likelihood-based reward and group-state optimization mechanism effectively align botnet activities with the social context, achieving concealment through integration rather than mere avoidance. The framework provides valuable insights into the complex interplay between evasion strategies and detector effectiveness, offering a robust foundation for future research on social bot threats. Full article

Implantable intrabody communication (IBC) is a method that enables low-power, high-security communication between implanted in-body devices that could track biomedical signals and an on-body receiver by using the human body as a communication medium. As the human body consists of various tissues that each have different conductivity, this paper explores the effects of the conductivity of the communication medium on the channel gain over a wide frequency range from 10 MHz up to 300 MHz through the measurements and two models: an electrical circuit model and a FEM simulation model. Measurements are conducted using a liquid phantom with varying conductivity values from 0 S/m up to 1 S/m, covering most human tissues in the frequency range of interest. The circuit and FEM models are designed to mimic the measurement setup in order to verify the measurement results. Results show that the circuit model predicts the communication channel characteristics well at lower frequencies but cannot account for the influence of the measurement setup at higher frequencies. The influence of wire inductances, which can cause a resonant behavior when measuring at frequencies above 100 MHz, was observed using the FEM model. The results also show that the higher the conductivity of the tissue in which the device is implanted, the lower the gain of the signal, with the difference in gain being more prominent when capacitive termination with a high-impedance load is used instead of low-impedance termination. These findings provide valuable insight for selecting the appropriate interface (low-impedance vs. high-impedance termination) across specific frequency ranges for in-body to on-body (IB2OB) communication devices, while illustrating the effect of tissue conductivity on an IBC channel, thereby supporting the optimized design and implementation of reliable IB2OB communication systems. Full article

Adult hippocampal neurogenesis (AHN) is a dynamic process that sustains neural plasticity and contributes to cognition, emotion, and stress resilience. While its functional significance in humans remains debated, growing evidence suggests that AHN plays an important role in health and disease. In this review, we summarize intrinsic and extrinsic factors that modulate AHN, with particular emphasis on hormones, behavior, diet, and their impact along the hippocampal dorsoventral axis, where baseline neurogenesis is higher dorsally, but ventral neurogenesis exhibits greater plasticity and sensitivity to modulatory systems. We highlight how cognitive stimulation, physical activity, and rewarding experiences preferentially enhance dorsal hippocampal neurogenesis, whereas chronic stress and glucocorticoids mainly impair neurogenesis in the ventral hippocampus. Nutritional influences such as caloric restriction, high-fat diets, vitamins, and polyphenols are also considered, with evidence for region-specific effects. We further examine the relevance of AHN alterations in neuropsychiatric diseases, such as major depressive disorder, schizophrenia, Alzheimer’s disease, and addiction, highlighting both common mechanisms and disorder-specific vulnerabilities. Collectively, current findings suggest that AHN serves as a converging pathway connecting lifestyle, neuroendocrine regulation, and psychiatric or neurodegenerative disease. Recognizing the dorsoventral specialization of AHN could refine mechanistic models of brain function and inform the development of targeted and distinct therapeutic strategies for cognitive and affective diseases. Full article

Prader–Willi syndrome (PWS) is a rare, multisymptomatic genetic disorder caused by the absence or dysfunction of specific genes on chromosome 15. The genetic abnormality is anticipated to cause a dysfunction of the hypothalamus, which is also central in the regulation of the autonomic nervous system (ANS). Typical symptoms of PWS indicating a hypothalamic dysfunction include muscular hypotonia, poor growth, short stature, and feeding difficulties in infancy, which in early childhood are replaced by hyperphagia, leading to a high risk of obesity. Other characteristics, such as sleep difficulties, altered pain perception, delayed gastric emptying and constipation, blood pressure irregularities and dysregulated stress response, altered temperature regulation, delayed pupillary reaction, and urine retention and incontinence, all indicate a dysfunction of ANS. The ANS is usually divided into three parts: the sympathetic nervous system (SNS), which activates the fight-or-flight response during stress; the parasympathetic nervous system (PNS), which promotes calm and digestion; and the independent enteric nervous system (ENS), which regulates the gastrointestinal tract. Noradrenaline is the main neurotransmitter for the SNS, and acetylcholine for the PNS, while the ENS is regulated mainly by acetylcholine and serotonin. However, the ENS is modulated by both the SNS and the PNS, as well as many neuropeptides. Peptides regulating behavior, metabolism, appetite, and satiety have been extensively studied in PWS. However, studies of the role of neuropeptides in regulating other autonomic functions are limited and remain poorly understood. This review aims to synthesize current evidence from both animal models and human studies to explore potential mechanisms by which neuropeptides may contribute to autonomic dysfunction in individuals with PWS. Full article

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy. Full article

Background/Objectives: The USAID STOP Spillover project in Cambodia aimed to reduce the risk of zoonotic virus spillover from bats to humans in bat guano farming communities. Methods: Using participatory tools, such as Outcome Mapping and Trials of Improved Practices, a team of local experts and community members collaboratively designed, tested, and refined biosafety and hygiene practices that are acceptable and sustainable to mitigate the risk of bat-borne disease spillover. We tracked progress and rolled out interventions to promote the adoption of safe behaviors that strengthen the understanding of zoonotic disease and reinforce the adoption of safety practices among bat guano producers and their neighbors. The intervention’s effectiveness was evaluated after three-month trials. Results: An improvement in knowledge, attitudes, and risk reduction practices was observed among participants. The primary motivators for adopting these measures were fear of disease, families’ well-being, cost savings, and experience of the COVID-19 pandemic. Conclusions: The community-driven approach fostered a sense of ownership, enabling participants to find the best solutions for their circumstance for long-term sustainability of the intervention. The findings recommended continued community engagement, improved access to biosafety and hygiene resources, and reinforced routine zoonotic disease surveillance. This model can be applied to mitigate emerging infectious disease spillover risks in similar contexts. Full article

The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-like and human-like chess play patterns, integrating Stockfish’s deterministic evaluations with stylometric behavioral features derived from the Maia engine. Key metrics include Centipawn Loss (CPL), Mismatch Move Match Probability (MMMP), and a novel Curvature-Based Stability (ΔS) indicator. These features were incorporated into a convolutional neural network (CNN) classifier and evaluated on a controlled benchmark dataset of 1000 games, where ‘suspicious’ gameplay was algorithmically generated to simulate engine-optimal patterns, while ‘clean’ play was modeled using Maia’s human-like predictions. Results demonstrate the framework’s ability to discriminate between these behavioral archetypes, with the hybrid model achieving a macro F1-score of 0.93, significantly outperforming the Stockfish-only baseline (F1 = 0.87), as validated by McNemar’s test (p = 0.0153). Feature ablation confirmed that Maia-derived features reduced false negatives and improved recall, while ΔS enhanced robustness. This work establishes a methodological foundation for behavioral pattern discrimination in chess, demonstrating the value of combining deterministic and human-centric modeling. Beyond chess, the approach offers a template for behavioral anomaly analysis in cybersecurity, education, and other decision-based domains, with real-world validation on adjudicated misconduct cases identified as the essential next step. Full article

Public ecological products constitute the most fundamental public goods supporting human well-being. Enhancing the high-quality supply of public ecological products is critical for maintaining ecological safety, ensuring the ecological regulation function, and promoting the harmonious coexistence of humans and nature. To deeply investigate the supply process and behavioral mechanisms of public ecological products, this study constructs a simulation model based on Agent-Based Modeling (ABM) to simulate the behavior rules and dynamic processes of four main subjects involved in the supply of public ecological products: government, enterprises, the public, and environmental non-governmental organizations (ENGOs). After calibrating the model parameters with relevant data from the water production and supply industry in Beijing, the good fit of the model output results verifies the effectiveness of the model. This study reveals the operating mechanism of multi-subject collaborative supply of public ecological products, providing a basic model for investigating the mechanism and evolution process of ecological product supply under more complex conditions, and also providing a powerful tool for the ex-ante evaluation of the implementation effect of public ecological product supply policies. Full article

Traditional recommendation systems are largely built upon the “rational-agent” assumption, representing user preferences as static numerical vectors while neglecting the pivotal role of emotions in decision-making. However, according to the dual-system theory in cognitive psychology, human decisions are jointly governed by two interacting subsystems: a rational system responsible for deliberate reasoning and an affective system driven by emotion and intuition. Conflicts between these two systems often lead to inconsistencies between users’ preferences and emotional experiences in real-world recommendation scenarios. To address this challenge, we propose an Emotion-Enhanced Dual-Agent Collaborative Framework (EDACF) that explicitly models and leverages cognitive conflicts between users’ emotional experiences and rational preferences. EDACF introduces user and item agents equipped with separate natural language memories for preference, emotion, and conflict representations, enabling cognitive-level reasoning beyond static numerical modeling. The framework features three key innovations: (1) a conflict detection mechanism that identifies users’ cognitive inconsistency states; (2) a dual-memory update strategy that maintains preference stability while capturing emotional dynamics; and (3) an adaptive reasoning mechanism that adjusts decision weights based on detected conflicts. Extensive experiments demonstrate that EDACF outperforms state-of-the-art baselines by 9.9% in NDCG@10 and 13.1% in MRR@10, with improvements exceeding 32% among user groups with high conflict. These results highlight a paradigm shift in recommendation systems from behavior prediction toward cognitive-level understanding of user decision processes. Full article

Probiotics have been increasingly evaluated for their potential effect on anxiety and depression through the modulation of the gut–brain axis. Individuals with cancer experience a high prevalence of these symptoms. However, the effects of probiotics and their underlying mechanisms in this population have not been systematically evaluated. This review synthesizes current evidence regarding probiotic interventions for anxiety and depression in cancer and examines the associated mechanistic pathways. A systematic search for original trials in PubMed, Embase, CINAHL, Web of Science, and PsycINFO was conducted in May 2025. Eligible studies included animal models or adults with cancer who received probiotics alone or in combination with other treatments, with outcomes related to anxiety, depressive symptoms, or depression. Search terms included animal model, cancer, probiotics, anxiety, depressive symptoms, depression, gastrointestinal microbiome, gut microbiome, and microbiota. The review followed PRISMA guidelines. Risk of bias in trials was assessed using the SYRCLE and Cochrane RoB2 tool. Nine studies met the inclusion criteria, including seven human studies, one animal study, and one mixed human–animal study, with human sample sizes ranging from 24 to 266. The animal study reported reductions in depressive and anxiety-like behaviors, paralleled by modulation of the hypothalamic–pituitary–adrenal (HPA) axis, reduced inflammation, rebalancing of the gut microbiota, and improvements in neurotransmitter pathways. Findings from human studies were more variable. Some trials reported improvements in anxiety, and depressive symptoms, while others showed no significant differences compared with control groups. Studies that combined probiotics with antidepressants or exercise demonstrated the most pronounced reductions in anxiety and depression. Mechanistic insights from human studies partially aligned with animal evidence, with several trials showing reductions in inflammatory markers (IL-6, TNF-α), improvements in neuroendocrine measures (serotonin, dopamine, cortisol), stabilization of metabolic markers, and favorable shifts in gut microbiota, although these effects were not consistent across all studies. Probiotics appear to be safe within the intervention periods of the reviewed studies (<24 weeks), as no serious adverse effects were reported. Substantial heterogeneity across studies, including variations in cancer type, intervention duration, probiotic strains, formulations, dosages, and study design combined with small sample sizes, restricts the ability to draw definitive conclusions. Rigorously designed randomized controlled trials with larger sample sizes and mechanistic biomarkers are required to confirm the efficacy of probiotics for relieving anxiety and depression in the cancer population. Full article

The human virome represents a fundamental yet understudied component of the microbiome, influencing immune regulation and disease. Given the profound immune dysregulation and microbial imbalance associated with HIV infection, understanding virome alterations during HIV and antiretroviral therapy is essential. This narrative review seeks to integrate and discuss the latest evidence regarding the structure and behavior of the human virome in healthy individuals, in the context of HIV infection, and under antiretroviral therapy. A comprehensive literature search was performed in MEDLINE and Google Scholar for peer-reviewed English-language articles published up to November 2025. Studies describing virome composition, diversity, and interactions in people living with HIV, as well as antiretroviral-induced changes, were included. Reference lists of relevant papers were screened to identify additional sources. Data were extracted and synthesized narratively, emphasizing human studies and supported by evidence from primate models where applicable. HIV infection induces profound alterations in the human virome, notably an expansion of eukaryotic viruses such as Anelloviridae, Adenoviridae, and Parvoviridae, accompanied by reduced bacteriophage diversity. Antiretroviral therapy partially restores virome balance but fails to fully re-establish pre-infection diversity, with persistent enrichment of Anelloviridae reflecting incomplete immune reconstitution. Virome perturbations correlate with immune activation, microbial translocation, and inflammation, contributing to comorbidities despite virological suppression. Emerging evidence suggests regimen-specific effects, with integrase inhibitor-based therapies showing more favorable viromic recovery. HIV and antiretroviral therapy profoundly remodel the human virome, with lasting implications for immune homeostasis and chronic inflammation. The ongoing disruption of the virome highlights its promise as both a biomarker and a potential therapeutic target in the management of HIV. Longitudinal, multi-omic studies are needed to clarify the causal role of virome alterations and guide future interventions. Full article

Post-stress cognitive impairment (PSCI) is defined as a persistent neuropsychiatric condition characterized by deficits in memory consolidation, executive functioning, and environmental interaction following exposure to violent stress. Despite its high incidence, PSCI remains underdiagnosed and lacks effective therapeutic strategies, posing a substantial societal burden and highlighting a critical gap in neuropsychiatric research. A major constraint in mechanistic studies is the persistent reliance on conventional paradigms, notably the Y-maze and novel object recognition test. Their limited sensitivity and poor translational relevance to human cognitive dysfunction, compounded by slow methodological innovation, significantly impede progress. Furthermore, the specific brain regions or neuronal populations contributing to PSCI pathogenesis are insufficiently explored. To address this, we assessed post-stress cognitive impairment in mice using a triple approach: Skinner box assays, traditional behavioral paradigms, and integrated 3D ethological analysis. This multi-method framework provides novel insights for refining animal models and advancing mechanistic understanding. Using c-Fos-based whole-brain screening, we identified the dentate gyrus (DG) as a key region involved in PSCI. Stress caused by violence markedly increased activity in DG CaMKII-expressing neurons. Chemogenetic inhibition of these neurons effectively alleviated stress-induced mild cognitive impairment phenotypes. In summary, by applying novel behavioral assessment tools, this study demonstrates that DG CaMKII neurons play a critical role in regulating post-stress cognitive impairment. Full article