Search Results (6,358)

Background/Objectives: Physical and mental health symptoms commonly affecting children are often under-addressed given the limited availability of pediatric behavioral healthcare. Training school providers (e.g., nurses, mental health professionals) to address these concerns is a promising strategy to explore, considering the unique level of accessibility afforded by school settings. While our earlier work augmented school providers’ pain management skills, providers desired more comprehensive training and youth support tools. Our team of interdisciplinary academic researchers and community partners will bridge this gap by developing the PURSUIT (Preventing Use of Substances for the Underserved with Innovative Technology) provider training program and companion online self-management platform for youth and caregivers. This protocol paper describes our planned approach to developing, implementing, and evaluating the PURSUIT program. Methods: We will draw from evidence-based cognitive–behavioral, trauma-focused, and mindfulness protocols to develop a comprehensive provider training program and interactive online self-management platform for youth and caregivers. Content areas will include core cognitive–behavioral strategies and specific skills for pediatric pain management, trauma-focused care, and substance use prevention. Innovative technological approaches, such as live and animated videos, will be used to promote user engagement. Academic and community partners will have roles in material co-development. Outcomes of this project will include the PURSUIT training program and self-management platform feasibility and acceptability (e.g., completion/engagement rates, quantitative/qualitative reports), as well as the impact of the training program on provider knowledge and the impact of the self-management platform on youth/caregiver outcomes. Conclusions: Interdisciplinary collaboration and community engagement will be critical to developing and evaluating a provider training program and youth/caregiver self-management platform. Full article

To address the challenge of complex quality control in shared manufacturing arising from loose “partner” relationships, a quality disclosure mechanism is incorporated into a shared manufacturing supply chain. By developing a platform-led game-theoretic model, it compares four quality disclosure strategies under third-party and self-built shared manufacturing platforms, filling a theoretical gap on how quality disclosure aligns with different platform models. The findings indicate that: (1) Quality disclosure always increases platform profit, providing theoretical support for the economic incentives for platforms to promote quality transparency. (2) Under third-party shared manufacturing platforms, all manufacturers prefer unilateral disclosure by the high-quality manufacturer, indicating that this platform model naturally generates a high-quality-led signaling mechanism and reduces coordination costs. (3) Under self-built shared manufacturing platforms, strategy choice is conditional: when the disclosure level is very high, the high-quality manufacturer counter-intuitively induces the low-quality manufacturer to disclose in order to avoid excessive guarantee risk; when the market quality gap is large, bilateral disclosure is the equilibrium, jointly building market trust; when the quality gap narrows, the equilibrium returns to unilateral disclosure by the high-quality manufacturer to strengthen the quality signal.This study provides a new theoretical framework for understanding quality signaling in multi-actor collaborative settings and offers managerial insights for shared manufacturing platforms to design disclosure mechanisms and for manufacturers to choose cooperation modes. Full article

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article

Forgiveness and reconciliation pose significant challenges in post-conflict societies, especially for those directly affected by violence. While legal frameworks of transitional justice are often discussed, the personal and religious dimensions of forgiveness receive less attention. This article seeks to address this gap using René Girard’s mimetic theory to analyze the relationship between religious motivation and the processes of forgiveness and reconciliation. The study’s empirical findings stem from qualitative interviews with 22 individuals who were victims during the Homeland War (1991–1995) in Croatia. Girard’s theory posits that humans imitate not just behavior, but the desires of others, leading to mimetic rivalry that can escalate into violence—a concept known as “negative mimesis.” However, concept of “positive mimesis” is also achievable when individuals emulate Jesus Christ’s model of self-giving love, facilitating a pathway towards reconciliation. The primary research question guiding this inquiry asks how individuals affected by violence navigate forgiveness and reconciliation, especially the role of religion in this process. In this context, forgiveness is conceptualized as a deeply personal and spiritual journey, whereas reconciliation is defined as the restoration of interpersonal relationships within a societal framework that requires forgiveness. Consequently, this research prioritizes exploring individual narratives and personal accounts from participants rather than addressing broader societal implications. Insights from interviews reveal participants’ understanding of both negative and positive mimesis, indicating that mimetic mechanisms can foster tendencies towards violence as well as forgiveness. In this framework, Jesus Christ serves as a vital external mediator, providing a transformative route away from cycles of violence. Most participants indicated that their faith, prayer practices, and the example set by Christ significantly influenced their forgiveness decisions. This paper contributes original insights into the theological and anthropological aspects of forgiveness, demonstrating that Girard’s mimetic theory effectively illuminates the complexities of reconciliation in post-conflict settings. Full article

Iterative self-training of language models presents a promising avenue for realizing self-improving Artificial Intelligence systems; however, this process is often hindered by the fundamental challenge of “Model Collapse.” Existing research indicates that models undergo catastrophic performance degradation and diversity collapse when recursively trained on their own increasingly homogenized synthetic data. Although some data selection-based approaches attempt to mitigate this issue by enhancing diversity, they predominantly rely on static strategies, lacking a feedback mechanism capable of adapting in real-time to the dynamic evolution of the model state and data distribution. To address this limitation, we propose a dynamic data selection framework titled “DCES” (dynamic center-edge sampling). We conducted extensive experiments on iterative self-training tasks across multiple model architectures. The results demonstrate that our system significantly outperforms baselines in terms of Perplexity (PPL) and loss across various models and test sets. Simultaneously, the framework effectively mitigates the degradation of Expected Calibration Error (ECE) and entropy metrics, successfully preventing mode collapse. Our findings highlight that an adaptive system capable of intelligent data curation based on training feedback is pivotal for maintaining the dynamic balance of data distributions and achieving sustainable AI self-evolution. This work provides a systematic methodology for realizing this goal. Full article

This article presents an arts-based and polyvocal account of Gira Ingoma (One Drum per Girl), a women- and girl-led cultural initiative in Rwanda that reconstructs drumming, warrior dance, and self-praise poetry to advance gender equality and contribute to everyday peace power. Based on arts-based qualitative methods (workshops, rehearsals, festivals, interviews, and youth-led Monitoring, Evaluation, and Learning), we show how repetitive public performance materialises gender equality beyond policy texts. The article explores core theoretical frames—gender performativity, everyday peace power, spatial approaches to peace, and performance-as-knowledge—while aligning key findings to research questions concerning (1) negotiation of gender through performance, (2) micro-processes of everyday peace power, and (3) observable change in confidence, community engagement, and institutional practice. We conclude with policy measures to embed gender-responsive arts education, resource girls and women across the creative value chain, and set parity targets within cultural institutions. Full article

Smoking cessation after a cancer diagnosis is a key determinant of prognosis, yet the psychological mechanisms underlying cessation remain poorly understood. Building on a recently validated Health Action Process Approach (HAPA) scale, this study examined whether baseline HAPA constructs predicted short-term smoking cessation and tobacco dependence in patients with head and neck cancer. Eighty-nine patients completed assessments at diagnosis (T0) and one-month follow-up (T1). Six HAPA constructs were measured at T0: Risk Perception, Outcome Expectancies, Recovery Self-Efficacy, Behavioral Intention, Coping Planning, and Action Control Efficacy. Smoking outcomes at T1 included cigarette dependence (CDS-12) and smoking status. Hierarchical linear regression showed that sociodemographic and clinical variables did not predict dependence, whereas adding HAPA constructs significantly improved prediction (ΔR² = 0.28, p < 0.001). Higher Risk Perception and Outcome Expectancies were associated with greater dependence, while logistic regression identified Action Control Efficacy as the only independent predictor of smoking cessation. These findings provide the first longitudinal evidence supporting the application of the HAPA framework to smoking cessation after cancer diagnosis and underscore the critical role of volitional processes in early cessation. Targeting action control may therefore enhance the effectiveness of smoking cessation interventions in oncology settings. Full article

Although organizational behavior (OB) research on burnout has traditionally emphasized workload and resource imbalance, such explanations remain insufficient for understanding burnout in highly routinized industrial hierarchies, where work is standardized, autonomy is limited, and stress often arises from how tasks are assigned and justified rather than from workload intensity alone. Drawing on the concept of illegitimate tasks—tasks perceived as unreasonable or unnecessary given one’s formal role—we argue that burnout in industrial hierarchies is more fundamentally rooted in violations of role legitimacy embedded in work design. We employed an explanatory sequential mixed-methods design to understand how job demands, effort–reward imbalance, and illegitimate tasks collectively affect job satisfaction and burnout in large manufacturing organizations. We analyzed quantitative survey data collected from 504 employees and found that illegitimate tasks exert a stronger, more consistent effect on burnout than effort–reward imbalance. In addition to these findings, qualitative interviews with senior executives revealed how ambiguous role boundaries, intensive overtime practices, and limited advancement structures normalize illegitimate task assignments in industrial settings. The findings suggest that burnout in industrial settings reflects not only accumulated job demands but also employees’ evaluations of how their work roles are structured and valued. This study thus brings the stress-as-offense-to-self perspective into OB scholarship and demonstrates its relevance for theorizing burnout in routine industrial work. The findings indicate that burnout reflects deeper deficiencies in work design that may undermine the sustainability of industrial work systems. From an industrial workforce sustainability perspective, workplace burnout extends beyond an individual health concern and signals structural issues in job design and human resource utilization relevant to social sustainability and decent work. Burnout can serve as an early warning indicator of declining human capital sustainability. Full article

Recent laboratory experiments in an intermediate-scale Hele-Shaw cell, designed to represent a coarse sand aquifer, demonstrate that mixing-induced calcite precipitation leads to the formation of a self-organized, heterogeneous porous medium. This morphology, characterized by elongated carbonate structures and internal preferential flow channels, induces strong anomalous transport features, including early solute arrival, distinct double-peak breakthrough curves, and pronounced tailing. In this article, we investigate the link between this precipitation-induced heterogeneity and solute transport by implementing varying permeability scenarios, derived from experimental image analysis, into a transport model. Our analysis reveals that while a standard dual-permeability approach, which simply delineates the total precipitated area, captures the flow diversion responsible for the emergence of the double peak, it fails to reproduce the transition between peaks and the late-time tailing. To address this, we introduce a novel triple-permeability model that incorporates internal preferential flow channels within the high-precipitation zones. By resolving the internal structure of these zones, the triple-permeability model accurately captures the dual-peak transition and tailing behavior. These findings provide critical insights for applications such as geological carbon sequestration and enhanced oil recovery. Although determining exact internal structures in field settings is challenging, our results demonstrate that effective transport models must account for the internal heterogeneity of high-precipitation zones, rather than treating them as uniform barriers, to accurately predict the channeling effects that govern injectivity and long-term storage security. Full article

Background: Pain remains one of the most debilitating and prevalent symptoms in cancer patients. However, assessment based solely on subjective self-report tools is limited by cognitive impairment and the heterogeneous nature of cancer pain. Since evidence on the ability of physiological biosignals to discriminate cancer pain intensity and pain phenotypes in real clinical settings remains limited, this study explored the potential of biosignals to discriminate between pain intensity and pain type. Methods: Electrodermal activity (EDA) and electrocardiogram (ECG) signals were recorded in cancer patients using the BITalino (r)evolution board (sampling frequency 1000 Hz). EDA was processed to extract skin conductance responses (SCRs) using continuous decomposition analysis (CDA) and trough-to-peak (TTP) methods. Heart rate variability (HRV) features were extracted in both time and frequency domains, including low frequency (LF), high frequency (HF), and the LF/HF ratio. Non-parametric Kruskal–Wallis tests were performed to compare biosignal parameters across pain intensity (Numeric Rating Scale, NRS: low 1–3; medium 4–6; and high 7–10) and pain types (nociceptive, neuropathic, mixed, and breakthrough cancer pain—BTCP). Results: Data from 61 patients were analyzed. For EDA, the maximum skin conductance response amplitude (MaxCDA) significantly differed across intensity groups (p = 0.037). Post hoc analysis showed a significant difference between the low- and high-intensity groups (p = 0.015), with the low-intensity group exhibiting a higher mean MaxCDA (0.063 µS) than the high-intensity group (0.024 µS). Several EDA parameters were significantly associated with pain type. The number of SCRs (TTP) (p = 0.015) and maximum SCR amplitude (TTP) (p = 0.040) were significantly lower in the mixed pain group compared with the nociceptive and neuropathic groups. No HRV parameters showed significant associations with pain intensity or pain type. BTCP did not significantly affect any biosignal parameters. Subgroup analyses showed that EDA features discriminating mixed pain were preserved in patients without bone metastases, BTCP, or high opioid burden, whereas no clinical variable modified the association between biosignals and pain intensity and type. Conclusions: In this investigation, selected EDA parameters were associated with cancer pain intensity and pain type, whereas heart rate variability measures did not show significant discrimination under the present methodological conditions. These findings suggest that EDA may provide complementary information on pain-related autonomic alterations in oncology patients. However, biosignals should not be considered standalone indicators of pain, and their interpretation requires integration with clinical variables and pharmacological context. Further studies adopting multimodal and longitudinal approaches are needed to clarify their role in automatic pain assessment in cancer care. Full article

Self-assembling antimicrobial complexes are a promising new technology for the development of antimicrobial, antifungal, and other bioactive agents with targeted delivery, adaptability, and the regulation of processes over time. Ribosomally synthesized antimicrobial peptides (AMPs) are most frequently considered as the basis for such complexes; however, we suggest that non-ribosomally synthesized peptides (NRPs) should be considered as molecules that also hold potential for engineering and already possess a set of qualities that AMPs are still to be engineered to have. This review examines the key features of NRP structure and self-assembly that determine their potential as antimicrobial agents, as well as NRP engineering methods through which new, more advanced agents for combating antibiotic-resistant microorganisms can be created. Full article

Background/Objectives: Promoting healthy and sustainable food choices is critical to address environmental and public health challenges, prevent health issues and enhance psychological well-being. Technological tools have shown promising results in supporting the adoption of many sustainable practices and in improving dysfunctional eating behaviors in clinical psychological settings. However, their potential to encourage healthy and sustainable eating choices in the general population through psychological or behavioral strategies remains understudied and unsystematically observed. Methods: A systematic review was conducted following PRISMA guidelines to (1) investigate digital tools and interventions aimed at improving healthy and sustainable eating behaviors, and (2) categorize the psychological or behavioral strategies they implemented. Four databases (PsycINFO, PsycArticles, PubMed, ProQuest) were searched combining keywords on sustainable diets (e.g., “sustainable diet,” “food sustainability”) and technological tools (e.g., “virtual reality,” “mobile app,” “web app”). Results: N = 16 studies were included. N = 7 (44%) used mobile app-based tools, n = 6 (38%) were virtual reality, n = 2 (12%) were web platforms, and n = 1 (6%) was an instant-messaging system. Digital tools and interventions were useful in promoting healthy and sustainable eating behaviors by implementing psychological or behavioral strategies like awareness (n = 10, 63%), decision-making (n = 6, 38%), emotion regulation (n = 3, 19%), nudging (n = 5, 31%), self-efficacy (n = 5, 31%) and self-monitoring (n = 4, 25%). Only a few studies included follow-ups (n = 5, 31%). Conclusions: Findings suggest that digital technologies have the potential to improve healthy and sustainable eating behaviors in the general population. However, given heterogeneity and methodological issues of studies, more longitudinal and rigorous research is needed to confirm the effectiveness and long-term benefits of different technological tools. Full article

Background: Due to communication problems, people with primary progressive aphasia (PwPPA) are often affected in their self-image and experience a reduced quality of life (QoL). Biographic-narrative therapy is an effective approach to improve QoL in post-stroke aphasia. This study describes how PwPPA experienced their participation in the biographic-narrative intervention called Cope PPA. Methods: The intervention comprised a combination of five individual and seven group therapy sessions as well as the use of music and art therapy elements. Inclusion criteria were a capacity to give consent and sufficient visual/auditory abilities of PwPPA. Exclusion criteria were the presence of severe depression (MADRS > 35) or severe cognitive deficits (MMST < 10). After the therapy, PwPPA and their family members took part in half-hour semi-structured interviews. Interviews were analysed according to the reflexive thematic analysis by Braun and Clarke. Results: The qualitative analysis was based on a data set of 34 interviews. A total of six themes were identified: (1) Participation required adherence; (2) Materials were considered remarkable; (3) Storytelling was conducted in an aphasia-free area; (4) Group participation created a sense of belonging; (5) Experiences encouraged self-reflection and (6) Coping is lengthy and ongoing. Conclusions: The findings of our reflexive thematic analysis suggest that PwPPA experienced the intervention as meaningful. Some PwPPA described the effects of our intervention on their self-image. Others emphasised that coping with their condition was an ongoing process requiring continuous support. Full article

The management of dental caries has evolved from the traditional mechanical approach of “extension for prevention” to a biologically oriented philosophy centered on preserving natural tooth structures. Minimally invasive dentistry (MID) emphasizes early detection, risk assessment, prevention, and conservative intervention based on the lesion’s activity and depth. This review outlines current evidence on non-operative, micro-invasive, and minimally invasive strategies, including fluoride therapy, remineralizing agents such as casein phosphopeptide–amorphous calcium phosphate (CPP-ACP), self-assembling peptides that promote biomimetic enamel repair, sealants, and resin infiltration. Minimally invasive operative methods employ advanced technologies for selective tissue removal—chemomechanical systems (Carisolv, Papacarie, Brix3000), sono-and airabrasion, and new-generation polymeric and ceramic burs (SmartBur, Cerabur) designed to preserve sound dentin. Laser photoablation, particularly with erbium lasers (Er:YAG, Er,Cr:YSGG), enables precise cavity preparation with minimal thermal and mechanical stress. These approaches enhance patient comfort, reduce anesthesia requirements, and maintain tooth vitality. Despite limitations related to cost, equipment, and operator sensitivity, MID represents not only a set of refined clinical techniques but also a comprehensive, evidence-based treatment philosophy founded on biological principles, structural preservation, and the promotion of long-term oral health. Full article

The increasing penetration of renewable energy and the frequent occurrence of extreme weather events have significantly heightened the uncertainty in power system operations. Simultaneously, the scarcity of renewable energy output samples under extreme meteorological conditions constrains the accurate assessment of extreme risks in system planning and dispatch. To bridge this gap, this work aims to propose a method for generating extreme wind power output scenarios that possess both diversity and statistical accuracy under limited sample conditions. To address this, this paper proposes a method for generating scenarios of extreme wind power output guided and constrained by statistical features. First, multidimensional statistical features are extracted from historical wind power output scenarios and combined, and a quantile threshold method is applied to screen out extreme wind power output scenarios. Subsequently, based on differentiated application requirements of the power system, extreme scenarios undergo preliminary classification followed by category-specific clustering analysis, achieving refined classification of the scenario set. Building on this, an improved generative adversarial network model is constructed, and the Wasserstein distance and gradient penalty mechanism are introduced to enhance training stability. Additionally, a statistical feature self-attention mechanism and feature loss function are designed to effectively constrain the consistency between generated scenarios and real scenarios in key statistical features. Results demonstrate that the proposed method can generate a set of extreme wind power output scenarios with both diversity and statistical accuracy under limited sample conditions, providing effective data support for system safety operation and risk prevention and control. Full article