Search Results (1,917)

This study examines the impact of job commitment on the adoption of strategic entrepreneurship within organizations, with leadership support considered as a moderating variable. Focusing on information technology companies in Jordan, we integrate perspectives from organizational behavior and strategic management to explore how employees’ commitment (affective, normative, continuance) influences their engagement in entrepreneurial initiatives, and whether supportive leadership environments amplify this effect. This study draws on social exchange theory and organizational support theory to propose that committed employees will reciprocate the organization’s support by innovating and taking initiative, especially when they feel backed by leadership. A quantitative survey was conducted, gathering 384 valid responses from employees across Jordan’s IT sector. Data were analyzed using structural equation modeling. The findings reveal that all three forms of commitment positively affect the propensity to engage in strategic entrepreneurship, with affective commitment showing the strongest link. Notably, leadership support significantly moderates these relationships: in high-support contexts, committed employees exhibit substantially greater entrepreneurial behavior. These results indicate that committed employees are more likely to pursue innovative ideas and strategic opportunities, especially when leaders encourage and back their efforts. Theoretical implications include an enhanced understanding of commitment’s role in corporate entrepreneurship and the contingent value of leadership, while practical implications suggest actionable steps for IT firms and others in emerging economies to stimulate innovation. This research contributes to the literature by highlighting human and leadership factors as key drivers of strategic entrepreneurship in organizational settings, and by providing empirical evidence from the Middle East context. Full article

This study investigated the bioaccumulation patterns of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and per- and polyfluoroalkyl substances (PFASs) in the liver and muscle tissues of wild boars (n = 39) and domestic pigs (n = 38) from Northern Italy. This research addressed a critical gap in our understanding of how different ecologies and diets influence the uptake of persistent organic contaminants in two closely related species, one domestic and one wild. Significant differences in contaminant profiles were observed, largely attributable to distinct exposure routes and feeding behaviors. Wild boars displayed different quantities and families of environmental contaminants, with higher PCB levels in muscle and PFASs in liver. Conversely, domestic pigs exhibited markedly higher PAH concentrations, primarily linked to contaminated feed in controlled agricultural settings. The liver consistently demonstrated a central role in toxicant retention across both species. Notably, concentrations of several regulated PFAS compounds in both wild and farmed animals exceeded EU maximum levels (sum of PFOS, PFOA, PFNA, and PFHxS: 1.3 µg/kg), raising significant food safety concerns. These findings underscore the critical need for continuous environmental biomonitoring, stricter control of contaminant sources in agriculture, and updated risk assessments for both wild and domestic meat products to protect animal welfare and human health. Full article

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of the dynamical system governing the subject vehicle’s motion. Unlike conventional machine learning models that typically seek a single generalized representation across all drivers, our framework develops individualized models that explicitly preserve driver heterogeneity. Using both synthetic data from multiple traffic models and real-world naturalistic driving datasets, we demonstrate that DMDc accurately captures nonlinear vehicle interactions and achieves high-fidelity short-term predictions. Analysis of the estimated system matrices reveals that DMDc naturally approximates kinematic relationships, further reinforcing its interpretability. Importantly, this is the first study to apply DMDc to model and predict car-following behavior using real-world driving data. The proposed framework offers a computationally efficient and interpretable tool for traffic behavior analysis, with potential applications in adaptive traffic control, autonomous vehicle planning, and human-driver modeling. Full article

Understanding visitors’ motivations is essential for enhancing the perceived well-being of urban residents and promoting overall human welfare. Grounded in an integrated framework combining Self-Determination Theory and the Theory of Planned Behaviour, this study investigates how different types of motivation, i.e., amotivation, extrinsic motivation, and intrinsic motivation, influence visitors’ subjective well-being through the mediating role of behavioral intention. The theoretical model was tested using primary data collected via structured questionnaires from three urban forest parks in Fuzhou, China. Exploratory factor analysis identified latent constructs, and confirmatory factor analysis validated the measurement model. Structural Equation Modeling (SEM) was employed to test the proposed hypotheses. The results reveal that intrinsic motivation significantly enhances behavioral intention, whereas extrinsic motivation and amotivation have a negative association. Behavioral intention has a strong and positive influence on subjective well-being. Both intrinsic and extrinsic motivations indirectly affect well-being through the complete mediation of behavioral intention. In contrast, amotivation follows a dual pathway: it negatively influences well-being through partial mediation and also exerts a direct positive association. These findings underscore the central mediating role of behavioral intention in connecting motivation and well-being outcomes in urban forest park visitation. The study highlights the importance for policymakers and managers of considering how different forms of motivation affect the attainment of subjective well-being, and of incorporating these factors into future decisions concerning urban forest park so as to facilitate comparable findings and support further generalizations. Full article

Autism Spectrum Disorder presents one of the most complex challenges in contemporary neuroscience. This review adopts an unconventional narrative structure, drawing inspiration from song titles by The Beatles to explore the multifaceted biological, developmental, and social dimensions of autism. Spanning historical perspectives to embryonic origins and adult cognition, we examine critical topics including cortical folding, sensory processing, and the contributions of various brain regions such as the cerebellum and brainstem. The role of mirror neurons and other neural systems in shaping social behavior is discussed, alongside insights from animal models that have advanced our understanding of autism’s underlying mechanisms. Ultimately, this manuscript argues that autism is not merely a biomedical challenge, but a broader societal issue intersecting with education, human rights, and identity. Following the long and winding road of scientific discovery, we advocate for a more empathetic, interdisciplinary, and human-centered approach to autism research. Though the path ahead remains uncertain, every step informed by evidence and driven by collaboration brings us closer to deeper understanding, greater inclusion, and more effective support. Full article

Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as data-driven, high-fidelity agents capable of simulating individual opinions under varying informational conditions. Conditioning LLMs on real survey data from the 2020 American National Election Studies (ANES), we investigate their ability to predict individual-level responses across a spectrum of political and social issues in a zero-shot setting, without any training on the survey outcomes. Using Jensen–Shannon distance to quantify divergence in opinion distributions and F1-score to measure predictive accuracy, we compare LLM-generated simulations to those produced by a supervised Random Forest model. While performance at the individual level is comparable, LLMs consistently produce aggregate opinion distributions closer to the empirical ground truth. These findings suggest that LLMs offer a promising new method for simulating complex opinion dynamics and modeling the probabilistic structure of belief systems in computational social science. Full article

Collective robotics systems hold great potential for future education and public engagement; however, only a few are utilized in these contexts. One reason is the lack of accessible tools to convey their complex, embodied interactions. In this work, we introduce the Light-Augmented Reality System (LARS), an open-source, marker-free, cross-platform tool designed to support experimentation, education, and outreach in collective robotics. LARS employs Extended Reality (XR) to project dynamic visual objects into the physical environment. This enables indirect robot–robot communication through stigmergy while preserving the physical and sensing constraints of the real robots, and enhances robot–human interaction by making otherwise hidden information visible. The system is low-cost, easy to deploy, and platform-independent without requiring hardware modifications. By projecting visible information in real time, LARS facilitates reproducible experiments and bridges the gap between abstract collective dynamics and observable behavior. We demonstrate that LARS can serve both as a research tool and as a means to motivate students and the broader public to engage with collective robotics. Its accessibility and flexibility make it an effective platform for illustrating complex multi-robot interactions, promoting hands-on learning, and expanding public understanding of collective, embodied intelligence. Full article

The Anthropocene, marked by rapid and extensive environmental changes, poses distinct evolutionary pressures and opportunities for species adaptation. Insects, among the most diverse and resilient taxa, exhibit notable dietary plasticity and the ability to convert low-value biomass—such as agro-industrial and urban waste—into usable nutrients. This review explores how these traits serve as adaptive strategies, enabling insects to thrive and expand into novel, human-altered habitats. We examine the evolution of insect nutritional requirements and how alternative diets influence physiological, behavioral, and reproductive traits, ultimately enhancing resilience to anthropogenic stressors. The capacity of insects to metabolize diverse substrates not only supports their role in food security and circular economy initiatives but also provides valuable insights into detoxification pathways and metabolic flexibility in environments rich in xenobiotics. By synthesizing key studies, we highlight the pivotal role insects play in redefining ecosystem functions under human influence. This review underscores the intersection of nutritional and evolutionary biology in understanding insect success in the Anthropocene, emphasizing the importance of nutritional knowledge for both ecological research and applied insect farming systems. Full article

Remote work, as a technologically possible and widely applicable working mode, gained renewed attention during lockdowns amidst the COVID-19 pandemic. On one hand, remote work ensured that working remained sustainable; on the other hand, the unexpected and widespread nature of the immediate shift to remote work led to issues in terms of practicing and adapting to the process. Moreover, remote work can have strong social, economic, and environmental effects that have to be comprehensively understood. The high interest of employees in continuing with full or hybrid remote work calls for effective coping strategies at the individual and organizational levels in the future. This article focuses on academic studies documenting the peculiarities of remote work during the COVID-19 lockdowns. The aim is to identify the issues relating to remote work during the COVID-19 lockdowns that are documented in academic studies and thematically classify them into a range of factors. In this study, bibliometric and content analyses were employed, leading to comprehensive insights into the following areas: (1) remote work as a cause for changes in physical and psychological health; (2) remote work as a cause for changes in daily behavior, routine, and lifestyle; (3) factors that affect the process and productivity of remote work; (4) societal, economic, and environmental consequences of remote work; and (5) the distribution of the effects of remote work on individuals, economic subjects, and sectors. In conclusion, this study on working practices during the COVID-19 lockdowns that were documented in academic studies offers several benefits and areas of novelty: first, a comprehensive overview of the widespread process of adjusting to this new working mode; second, a classification of factors that affected the process at different stages and in different areas; and third, common factors that had more widespread effects during the remote working period. The findings also offer the following theoretical and practical implications: For researchers, this article can be a reference offering a holistic view of remote working during these lockdowns. For practitioners, it can provide an understanding of the impacting factors and their contextualization in terms of health, sociodemographic, and sectoral aspects can allow for more accurate human resource management strategies. Full article

For monitoring animal adaptation when facing environmental challenges, and more specifically when addressing the impacts of global warming—particularly responses to heat stress and short-term fluctuations in osmotic regulations in the different organs influencing animal physiology—there is an increasing demand for digital tools to understand and monitor a range of biomarkers. Microneedle arrays (MNAs) have recently emerged as promising devices minimally invasively penetrating human skin to access dermal interstitial fluid (ISF) to monitor deviations in physiology and consequences on health. The ISF is a blood filtrate where the concentrations of ions, low molecular weight metabolites (<70 kDa), hormones, and drugs, often closely correlate with those in blood. However, anatomical skin differences between human and farm animals, especially large animals, as well as divergent tolerances of such devices among species with behavior specificities, motivate new MNA designs. We addressed technological challenges to design higher microneedles for farm animal (pigs and cattle) measurements. We designed microneedle arrays composed of 37 microneedles, each 2.8 mm in height, using dextran-methacrylate, a photo-crosslinked biocompatible biopolymer-based hydrogel. The arrays were characterized geometrically and mechanically. Their abilities to perforate pig and cow skin were demonstrated through histological analysis. The MNAs successfully absorbed approximately 10 µL of fluid within 3 h of application. Full article

The effective use of nursing assistant robots requires an understanding of key acceptance factors. The study examined the differences in attitudes among 58 nursing students while performing ambulation tasks with and without an Adaptive Robotic Nursing Assistant (ARNA) robot. An ARNA is driven by tactile cues from the patient through a force–torque-measuring handlebar, whose signals are fed into a neuro-adaptive controller to achieve a specific admittance behavior regardless of patient strength, weight, or floor incline. Ambulation tasks used two fall-prevention devices: a gait belt and a full-body harness. The attitude toward the robot included perceived satisfaction, usefulness, and assistance, replacing the perceived ease-of-use construct found in the standard technology acceptance model. The effects of external demographic variables on those constructs were also analyzed. The modified technology acceptance model was validated with the simultaneous estimation of the effects of perceived usefulness and assistance on satisfaction. Our analysis employed an integrated hierarchical linear mixed-effects regression model to analyze the complex relationships between model variables. Our results suggest that nursing students rated the ARNA’s performance higher across all model constructs compared to a human assistant. Furthermore, male subjects rated the perceived usefulness of the robot higher than female subjects. Full article

Recent vision–language models (VLMs) achieve strong performance across multimodal benchmarks but suffer from high inference costs due to the large number of visual tokens. Prior studies have shown that many image tokens receive consistently low attention scores during inference, indicating that a substantial portion of visual content contributes little to final predictions. These observations raise questions about the efficiency of conventional token pruning strategies, which are typically applied after all attention operations and depend on late-emerging attention scores. To address this, we propose V-PRUNE, a semantic-aware patch-level pruning framework for vision–language models that removes redundant content before tokenization. By evaluating local similarity via color and histogram statistics, our method enables lightweight and interpretable pruning without architectural changes. Applied to CLIP-based models, our approach reduces FLOPs and inference time across vision–language understanding tasks, while maintaining or improving accuracy. Qualitative results further confirm that essential regions are preserved and the pruning behavior is human-aligned, making our method a practical solution for efficient VLM inference. Full article

Dog parents have a strong attachment to their pets and treat them as children. Similarly to the guilt experienced by the parents of human children, dog parents feel guilty when they have to travel or work long hours and leave their dogs at home alone. This study examined the empathic tendencies among dog parents and how they are related to dog parent guilt and other mental health symptoms. A sample of 332 dog owners from the United States, stratified by age and sex, was recruited for this study. The sample comprised 168 female and 164 male participants. The Guilt About Dog Parenting Scale (GAPS-D), Depression Anxiety and Stress Scale-21, Interpersonal Reactivity Index, and Dog Anthropomorphism Scale were administered via an online survey. Demographic variables, such as age, sex, race, education level, household income, relationship, and parental status, were also collected. Data were analyzed using descriptive statistics, correlation, and linear regression. As predicted, this study found that empathic tendencies in dog parents are related to guilt, anthropomorphizing, and mental health symptoms. This is the first study to examine empathetic tendencies among dog parents in relation to dog parent guilt. Researchers, educators, social workers, mental health professionals, and veterinarians should inform and educate pet owners about dog parent guilt. This will increase the knowledge of professionals, organizations, and pet owners suffering from such guilt. Cognitive behavior therapy (CBT) may offer a promising approach for helping pet parents identify, normalize, and better understand their thoughts, feelings, and behaviors related to empathic and anthropomorphic tendencies. By addressing these cognitive patterns, CBT could potentially help reduce associated feelings of guilt, depression, anxiety, and stress. Full article

With the growing use of digital technologies and interactive games, there is rising interest in how people respond to challenges, stress, and decision-making in virtual environments. Studying human behavior in such settings helps to improve design, training, and user experience. Instead of relying on complex devices, Virtual Reality (VR) creates new ways to observe and understand these responses in a simple and engaging format. This study introduces a lightweight method for monitoring stress levels that uses VR as the primary sensing platform. Detection relies on behavioral signals from VR. A minimal sensor such as Galvanic Skin Response (GSR), which measures skin conductance as a sign of physiological body response, supports the Sensor-Assisted Unity Architecture. The proposed Sensor-Assisted Unity Architecture focuses on analyzing the user’s behavior inside the virtual environment along with physical sensory measurements. Most existing systems rely on physiological wearables, which add both cost and complexity. The Sensor-Assisted Unity Architecture shifts the focus to behavioral analysis in VR supplemented by minimal physiological input. Behavioral cues captured within the VR environment are analyzed in real time by an embedded processor, which then triggers simple physical feedback. Results show that combining VR behavioral data with a minimal sensor can improve detection in cases where behavioral or physiological signals alone may be insufficient. While this study does not quantitatively compare the Sensor-Assisted Unity Architecture to multi-sensor setups, it highlights VR as the main platform, with sensor input offering targeted enhancements without significantly increasing system complexity. Full article

Since the emergence of SARS-CoV-2, the interplay of human behavior, environmental factors, viral evolution, and public health interventions has resulted in unexpected changes in the timing, intensity, and geography of respiratory virus outbreaks. For example, respiratory syncytial viruses (RSV) exhibited a surge during atypical summer months in several countries. Influenza, on the other hand, nearly vanished in the early years of the pandemic, but returned with unusual strength and altered seasonal patterns. Concurrently, new variants of concern in coronaviruses have demonstrated increased airborne transmissibility, greater resilience to environmental conditions, and the ability to evade both natural and vaccine-induced immunity. In this review article, we have synthesized the current understanding of the aerobiology of respiratory infectious viruses, with a particular emphasis on the paradoxical trends observed in recent years. We examined various aspects, including viral morphology and environmental survivability, shifts in seasonality, the drivers of mutation and resistance, and the impact of environmental and climatic factors. Key issues we explored include viral morphology adaptation in response to airborne selective pressures and climate variability influence on the ecology of airborne viruses. Lastly, we investigated future risks and proposed an interdisciplinary framework for monitoring and mitigating airborne viral threats in an ever-changing world. Full article