Search Results (820)

Poly(lactic-co-glycolic acid) (PLGA) sustained-release systems for intra-articular (IA) delivery aim to extend joint residence time and reduce the reinjection frequency of conventional IA therapies. This review synthesizes current understanding of PLGA degradation, the acidic microenvironment inside degrading microspheres, and release behavior in joints, and surveys clinical experience with extended-release corticosteroid depots alongside emerging platforms for nonsteroidal and biologic agents. To situate PLGA within the broader IA field, we briefly summarize selected non-PLGA sustained-release approaches—such as multivesicular liposomes, hyaluronic acid conjugates, and hybrid matrices—to contextualize comparative performance and safety. For proteins and peptides, central barriers include acidification inside degrading microspheres, aggregation during fabrication and storage, and incomplete or delayed release, as illustrated by glucagon-like peptide-1 analog formulations. Mitigation strategies span pH buffering, excipient-based stabilization, and gentler manufacturing that improve encapsulation efficiency and preserve bioactivity. Translation hinges on manufacturing scale-up and quality systems that maintain critical particle attributes and enable informative in vitro–in vivo interpretation. Clinically, prolonged symptom relief after single dosing has been demonstrated for corticosteroid depots (e.g., ~50% pain reduction over 12 weeks with a single PLGA–triamcinolone injection), whereas repeat-dose safety and indication expansion beyond the knee remain active needs best addressed through multicenter trials incorporating imaging and patient-reported outcomes. Consistent real-world performance will depend on controlling batch-to-batch variability and implementing pharmacovigilance approaches suited to long dosing intervals, enabling broader clinical adoption. Full article

Teacher innovation is critical for fostering student creativity, enhancing school effectiveness, and advancing national talent strategies. Grounded in the broaden-and-build theory of positive emotions and social information processing theory, this study develops a moderated mediation model to explore the motivational mechanisms underlying teachers’ innovative work behavior. Using survey data from 508 teachers in mainland China, the analysis reveals that teacher well-being positively influences innovative work behavior, and this relationship is mediated by perceived insider status. Furthermore, principal authentic leadership enhances the impact of perceived insider status on innovation and strengthens the indirect effect of well-being through this mediator. These findings underscore the importance of both emotional pathways and contextual signals in shaping teacher innovation, offering theoretical contributions to education leadership and teacher work behavior research while providing practical implications for creating supportive and innovation-conducive school environments. Full article

The banking sector faces increasing pressure to balance financial performance with sustainability goals amid ongoing digital transformation, regulatory reform, and societal expectations for ethical responsibility. Entrepreneurial leadership has emerged as a pivotal approach for addressing these challenges; however, the behavioral and contextual mechanisms through which it shapes sustainability remain insufficiently understood. Drawing on Social Learning Theory (SLT), this study investigates how and when entrepreneurial leadership enhances sustainable bank performance through the mediating role of employee creativity and the moderating influence of an innovation-oriented climate. A two-wave multi-source survey was conducted among 459 employees and managers from Turkish banks, and the hypothesized model was tested using structural equation modeling to ensure robust empirical validation. The results indicate that entrepreneurial leadership significantly fosters employee creativity, which serves as a critical behavioral mechanism linking leadership behaviors to sustainability-oriented outcomes. Moreover, an innovation-oriented climate strengthens both the direct effect of entrepreneurial leadership on creativity and its indirect effect on sustainable bank performance, emphasizing the contextual importance of supportive organizational environments. Theoretically, this study extends the leadership and sustainability literature by illustrating how learning and behavioral modeling processes translate leadership vision into sustainable performance. Practically, it offers actionable guidance for bank executives to develop innovation-oriented climates, empower employees’ creative engagement, and design incentive systems that align leadership behavior with sustainability imperatives, thereby enhancing resilience and long-term competitiveness. Full article

Oculomotor reading behavior is influenced by both universal factors, like the “big three” of word length, frequency, and contextual predictability, and language-specific factors, such as script and grammar. The aim of this study was to examine the influence of the “big three” factors on L2 reading focusing on a typologically distant L1/L2 pair with dramatic differences in script and grammar. A total of 41 native Chinese-speaking learners of Russian (levels A2-B2) and 40 native Russian speakers read a corpus of 90 Russian sentences for comprehension. Their eye movements were recorded with EyeLink 1000+. We analyzed both early (gaze duration and skipping rate) and late (regression rate and rereading time) eye movement measures. As expected, the “big three” effects influenced oculomotor behavior in both L1 and L2 readers, being more pronounced for L2, but substantial differences were also revealed. Word frequency in L1 reading primarily influenced early processing stages, whereas in L2 reading it remained significant in later stages as well. Predictability had an immediate effect on skipping rates in L1 reading, while L2 readers only exhibited it in late measures. Word length was the only factor that interacted with L2 language exposure which demonstrated adjustment to alphabetic script and polymorphemic word structure. Our findings provide new insights into the processing challenges of L2 readers with typologically distant L1 backgrounds. Full article

Plate food waste (PFW) in early childhood education is a critical yet understudied issue in Eastern Europe, with implications for nutrition, sustainability, and food security. This study examined PFW in a kindergarten in the Republic of Moldova, encompassing all 58 enrolled children and generating 14,292 meal-level observations through direct weighing of served meals and leftovers. Variance analysis (ANOVA) was used to test the influence of weekday, meal type, age, and gender, while Principal Component Analysis (PCA) explored latent structures of waste determinants. Results showed significant effects of weekday and meal type on PFW, with lunch consistently generating the highest waste levels and snacks the lowest. Gender differences were modest, while the interaction between age and gender indicated heterogeneous developmental patterns in waste behavior. PCA reduced the dataset to three main components: Portion Control, Menu Design, and Serving Strategy, explaining 84.7% of the total variance. These findings provide novel evidence for understanding how contextual and nutritional variables shape children’s PFW in early education and offer a replicable framework for reducing PFW and improving dietary adequacy in kindergartens. The study’s implications extend to sustainable nutrition planning and early behavioral interventions in preschool settings. Full article

Japanese narcissism refers to a culturally embedded form of narcissistic personality that emerges within collectivist societies, particularly in Japan, where self-worth is maintained through emotional over-adaptation, perfectionism, self-sacrifice, and conformity to internalized moral obligations. Within the framework of Schema Therapy, this construct is characterized by dominant coping modes, such as Armor mode and Demanding Community mode, that suppress vulnerable emotional states and promote socially sanctioned compliance. Although narcissistic personality disorder (NPD) has been extensively studied in individualistic Western cultures, its manifestation in collectivist cultures remains underexplored. Japanese narcissism offers a culturally contextualized model that integrates psychoanalytic and Schema Therapy perspectives to explain thin-skinned narcissistic vulnerability, disguised as adaptive functioning. Clinical observations and case analyses indicate that patients often develop Armor mode (fusing Detached Protector and Perfectionistic Over-controller functions) and Demanding Community mode (internalizing collective moral expectations). These adaptive-appearing modes mask core maladaptive schemas—Emotional Deprivation, Defectiveness/Shame, Enmeshment, and Self-Sacrifice—while being mistaken for mature or healthy functioning. Historically, such patterns have been reinforced by moral-collectivist ideals, exemplified by the Imperial Rescript on Education, which valorized loyalty, endurance, and self-denial. Japanese narcissism may therefore represent a culturally specific clinical configuration, suggesting the need for contextually adapted Schema Therapy interventions that recognize both the harmony-preserving and narcissism-reinforcing functions of adaptive behavior. This framework contributes to the cross-cultural extension of Schema Therapy by theorizing how narcissistic structures manifest in collectivist societies, and highlights the need for empirical validation of culturally sensitive treatment protocols. Full article

The use of generative artificial intelligence (GenAI) chatbots in brands is growing exponentially, and higher education institutions are not unaware of how such tools effectively shape the attitudes and behavioral intentions of students. These chatbots are able to synthesize an enormous amount of data input and can create contextually aware, human-like conversational content that is not limited to simple scripted responses. This study examines the factors that determine chatbot brand trust in the adoption of GenAI in higher education. By extending the Technology Acceptance Model (TAM) with the construct of brand trust, the study introduces a novel contribution to the literature, offering fresh insights into how trust in GenAI chatbots is developed within the academic context. Using the convenience sampling technique, a sample of 609 students from public universities in North Central and Southwestern Nigeria was selected. The collected data were analyzed via partial least squares structural equation modelling. The results indicated that attitudes toward chatbots determine behavioral intentions and GenAI chatbot brand trust. Surprisingly, behavioral intentions do not affect GenAI chatbot brand trust. Similarly, the perceived ease of use of chatbots does not determine behavioral intention or attitudes toward GenAI chatbot adoption but rather determines perceived usefulness. Additionally, the perceived usefulness of chatbots affects behavioral intention and attitudes toward GenAI chatbot adoption. Moreover, social influence affects behavioral intention, perceived ease of use, perceived usefulness and attitudes toward GenAI chatbot adoption. The implications of the findings for higher education institutions are that homegrown GenAI chatbots that align with the principles of the institution should be developed, creating an environment that promotes a positive attitude toward these technologies. Specifically, the study recommends that policymakers and university administrators establish clear institutional guidelines for the design, deployment, and ethical use of homegrown GenAI chatbots, ensuring alignment with educational goals and safeguarding student trust. Full article

Background/Objectives—Traditional forensic investigations often analyze digital, physical, and criminological evidence separately, leading to fragmented timelines and reduced accuracy in reconstructing complex events. To address these gaps, this study proposes the Digital Stratigraphy Framework (DSF), inspired by archaeological stratigraphy, to integrate heterogeneous evidence into structured, temporally ordered layers. DSF aims to reduce asynchronous inconsistencies, minimize false associations, and enhance interpretability across digital, behavioral, geospatial, and excavation evidence. Methods—DSF employs Hierarchical Pattern Mining (HPM) to detect recurring behavioral patterns and Forensic Sequence Alignment (FSA) to synchronize evidence layers temporally and contextually. The framework was tested on the CSI-DS2025 dataset containing 25,000 multimodal, stratified records, including digital logs, geospatial data, criminological reports, and excavation notes. Evaluation used 10-fold cross-validation, Bayesian hyperparameter tuning, and structured train-validation-test splits. Metrics included accuracy, precision, recall, F1-score, and Stratigraphic Reconstruction Consistency (SRC), alongside ablation and runtime assessments. Results—DSF achieved 92.6% accuracy, 93.1% precision, 90.5% recall, 91.3% F1-score, and an SRC of 0.89, outperforming baseline models. False associations were reduced by 18%, confirming effective cross-layer alignment and computational efficiency. Conclusions—By applying stratigraphic principles to forensic analytics, DSF enables accurate, interpretable, and legally robust evidence reconstruction. The framework establishes a scalable foundation for real-time investigative applications and multi-modal evidence integration, offering significant improvements over traditional fragmented approaches. Full article

Accident investigations in high-risk industries frequently focus on attributing unsafe acts to individual operators, often neglecting the organizational conditions that shape such behaviors. This study adopts an exploratory perspective to examine how communication, resource adequacy, and procedural design influence the potential for unsafe acts in refinery maintenance operations within the oil and gas sector. Building on the HFACS-OGI framework, unsafe acts were classified into perception errors, decoding errors, model errors, decision errors, and violations. Data were collected through a survey (n = 46) and analyzed using ordinal logistic regression with 10,000 bootstrap replications, complemented by partial correlation analysis to capture indirect associations. The results provide preliminary evidence that organizational factors operate both as direct predictors of unsafe acts and as systemic pathways linking broader contextual conditions with operator behavior. In particular, deficiencies in communication emerged as a transversal determinant, partially explaining the relationship between organizational context and both perception and decision errors. While limited by sample size and exploratory design, the study contributes to safety science by extending the empirical application of HFACS-OGI beyond post-accident analysis and offering actionable insights for safety governance. The findings underscore the need for proactive organizational interventions that enhance communication systems, ensure resource adequacy, and promote the usability of procedures in order to mitigate the potential for unsafe acts. Full article

Background: Inter-brain synchrony (IBS)—the temporal alignment of neural activity between individuals during social interactions—has emerged as a key construct in social neuroscience, reflecting shared attention, emotional attunement, and coordinated behavior. Enabled by hyperscanning techniques, IBS has been observed across a range of dyadic contexts, including cooperation, empathy, and communication. This systematic review synthesizes recent empirical findings on inter-brain synchrony (IBS)—the temporal alignment of neural activity between individuals—across psychological and neurodevelopmental conditions, including stress, anxiety, depression, and autism spectrum disorder (ASD). Methods: Drawing on 30 studies employing hyperscanning methodologies (EEG, fNIRS, fMRI), we examined how IBS patterns vary by clinical condition, dyad type, and brain region. Results: Findings indicate that IBS is generally reduced in anxiety, depression, and ASD, particularly in key social brain regions such as the dorsolateral and medial prefrontal cortices (dlPFC, mPFC, vmPFC), temporoparietal junction (TPJ), and inferior frontal gyrus (IFG), suggesting impaired emotional resonance and social cognition. In contrast, stress elicited both increases and decreases in IBS, modulated by context, emotional proximity, and cooperative strategies. Parent–child, therapist–client, and romantic dyads exhibited distinct synchrony profiles, with gender and relational dynamics further shaping neural coupling. Conclusions: Collectively, the findings support IBS as a potentially dynamic, condition-sensitive, and contextually modulated neurophysiological indicator of interpersonal functioning, with implications for diagnostics, intervention design, and the advancement of social neuroscience in clinical settings. Full article

Background: Psychological vulnerabilities, including early maladaptive schemas (EMSs), emotion regulation difficulties, perceived stress, and limited social support, are increasingly recognized as drivers of maladaptive eating and obesity. These findings underscore the need for health education and health promotion strategies that address psychological determinants of eating behavior. However, few studies integrate these psychological mechanisms with dietary and lifestyle indicators in both community and medical populations. Methods: A total of 1500 adults (aged 18–65 years; 53% women) recruited from community and medical settings participated in the study. Data were collected between January 2018 and February 2025 using standardized paper-based questionnaires. Participants completed validated measures of EMSs (YSQ-S3), emotion regulation (DERS), stress (PSS-10), social support (MSPSS), eating-related behaviors (QERB), diet (FFQ-6; Unhealthy Diet Index [UDI]), and physical activity (IPAQ-SF). Anthropometric indices included body mass index (BMI) and waist circumference (WC) as an indicator of central adiposity. Analyses involved multivariate regression, mediation, and moderation models. Results: EMSs were associated with emotional overeating and higher UDI scores. Difficulties in emotion regulation mediated the EMS–eating relationship (β_indirect = 0.27, p < 0.001). Perceived stress amplified, while social support attenuated, the association between EMSs and emotion regulation difficulties. UDI was inversely related to physical activity (β = −0.14, p < 0.01) and positively to sedentary time (β = 0.12, p < 0.01). Both BMI and WC were higher among participants reporting greater stress, emotion dysregulation, and unhealthy eating. All effects remained robust after adjustment for age, gender, and BMI. Conclusions: Early maladaptive schemas and emotion regulation difficulties contribute to unhealthy dietary patterns and central adiposity, with stress and social support acting as contextual moderators. Integrating psychological assessment with validated dietary and lifestyle measures provides a comprehensive framework for obesity prevention and schema-informed interventions. From a lifespan perspective (18–65 years), these findings highlight the need for multidomain strategies targeting cognitive–emotional and behavioral mechanisms of weight regulation. Full article

This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data processing in a secure cloud infrastructure. At its core, the gateway evaluates the trustworthiness of sensor nodes by computing reputation scores based on behavioral and contextual metrics. This design offers operational advantages, including reduced latency, autonomous decision-making in the absence of central command, and real-time responses in mission-critical scenarios. Our system integrates supervised learning, specifically Decision Tree Regression (DTR), to estimate reputation scores using features such as transmission success rate, packet loss, latency, battery level, and peer feedback. The results demonstrate that the proposed approach ensures secure, resilient, and scalable trust management in distributed battlefield networks, enabling informed and reliable decision-making under harsh and dynamic conditions. Full article

Advances in smart animal husbandry necessitate efficient pig behavior monitoring, yet traditional approaches suffer from operational inefficiency and animal stress. We address these limitations through a lightweight YOLOv8n architecture enhanced with SPD-Conv for feature preservation during downsampling, LSKBlock attention for contextual feature fusion, and a dedicated small-target detection head. Experimental validation demonstrates superior performance: the optimized model achieves a 92.4% mean average precision (mAP@0.5) and 87.4% recall, significantly outperforming baseline YOLOv8n by 3.7% in AP while maintaining minimal parameter growth (3.34M). Controlled illumination tests confirm enhanced robustness under strong and warm lighting conditions, with performance gains of 1.5% and 0.7% in AP, respectively. This high-precision framework enables real-time recognition of standing, prone lying, lateral lying, and feeding behaviors in commercial piggeries, supporting early health anomaly detection through non-invasive monitoring. Full article

This study applies psychological network analysis to explore the structure and dynamics of parental stress, offering a novel perspective beyond traditional latent variable approaches. Rather than treating parental stress as a unidimensional construct, network analysis conceptualizes it as a system of interrelated emotional, behavioral, and contextual symptoms. Using cross-sectional data from Latinx parents of children with intellectual and developmental disabilities (IDD), we compared and identified key central and bridge stress symptoms of Latinx parents of children with autism versus other disabilities that hold influential positions within the stress network. These findings suggest that certain stressors may act as hubs, reinforcing other stress components and potentially serving as high-impact targets for intervention. Network analysis also highlights how symptom relationships vary by types of disabilities, offering insight into tailored support strategies. Overall, this approach provides a dynamic and clinically actionable framework for understanding parental stress, with implications for assessment, early intervention, and personalized mental health care for parents. Full article

Swarm Robotics (SR) is a relatively new field, inspired by the collective intelligence of social insects. It involves using local rules to control and coordinate large groups (swarms) of relatively simple physical robots. Important tasks that robot swarms can handle include demining, search, rescue, and cleaning up toxic spills. Over the past decade, the research effort in the field of Swarm Robotics has intensified significantly in terms of hardware, software, and systems integrated developments, yet significant challenges remain, particularly regarding standardization, scalability, and cost-effective deployment. To contextualize the state of Swarm Robotics technologies, this paper provides a systematic literature review (SLR) of Swarm Robotic technologies published from 2014 to 2024, with an emphasis on how hardware and software subsystems have co-evolved. This work provides an overview of 40 studies in peer-reviewed journals along with a well-defined and replicable systematic review protocol. The protocol describes criteria for including and excluding studies and outlines a data extraction approach. We explored trends in sensor hardware, actuation methods, communication devices, and energy systems, as well as an examination of software platforms to produce swarm behavior, covering meta-heuristic algorithms and generic middleware platforms such as ROS. Our results demonstrate how dependent hardware and software are to achieve Swarm Intelligence, the lack of uniform standards for their design, and the pragmatic limits which hinder scalability and deployment. We conclude by noting ongoing challenges and proposing future directions for developing interoperable, energy-efficient Swarm Robotics (SR) systems incorporating machine learning (ML). Full article