Science Citation Index Expanded (Web of Science)

Cross-cultural empirical evidence on the validity of measures of depression and anxiety symptoms in adolescents is scarce and inconclusive. The present study investigated the cross-national measurement invariance and convergent validity evidence of the Depression and Anxiety scales of the Depression Anxiety and Stress Scales (DASS-21). We sampled 16,802 adolescents (58.1% female) aged 14–19 years across 30 countries from five continents. Both exact and approximate measurement invariance were tested. The results provided evidence for the exploratory structural equation model (ESEM) as the best representation of the data in most countries. Full scalar invariance was not supported when tested across all countries, but only among two Southern Asian countries (Bangladesh, India), two Northern European countries (Estonia, Lithuania), the six former republics of Yugoslavia, and European and American countries grouped by UN subregions. Results of the alignment method supported the approximate invariance of the ESEM model across 29 countries. Associations between Depression and Anxiety ESEM factors and life satisfaction, positive affect, and negative affect conformed to the theoretical expectations in most countries. Our findings indicate that the Depression and Anxiety scales of the DASS-21 have potential for use in cross-national research on adolescent internalizing symptoms, but we highlight issues of using it outside of Western (European, North American) settings.

Background/Objectives: Accurate and reliable automated dermoscopic lesion classification remains challenging. This is due to pronounced dataset bias, limited expert-annotated data, and poor cross-dataset generalization of conventional supervised deep learning models. In clinical dermatology, these limitations restrict the deployment of data-driven diagnostic systems across diverse acquisition settings and patient populations. Methods: Motivated by these challenges, this study proposes a transformer-based, dermatology-specific foundation model. The model learns transferable visual representations from large collections of unlabeled dermoscopic images via self-supervised pretraining. It integrates large-scale dermatology-oriented self-supervised learning with a hierarchical vision transformer backbone. This enables effective capture of both fine-grained lesion textures and global morphological patterns. The evaluation is conducted across three publicly available dermoscopic datasets: ISIC 2018, HAM10000, and PH2. The study assesses in-dataset, cross-dataset, limited-label, ablation, and computational-efficiency settings. Results: The proposed approach achieves in-dataset classification accuracies of 94.87%, 97.32%, and 98.17% on ISIC 2018, HAM10000, and PH2, respectively. It outperforms strong transformer and hybrid baselines. Cross-dataset transfer experiments show consistent performance gains of 3.5-5.8% over supervised counterparts. This indicates improved robustness to domain shift. Furthermore, when fine-tuned with only 10% of the labeled training data, the model achieves performance comparable to fully supervised baselines. Conclusions: This highlights strong data efficiency. These results demonstrate that dermatology-specific foundation learning offers a principled and practical solution for robust dermoscopic lesion classification under realistic clinical constraints.

Network simulation is a cost-effective, widely adopted method for testing and improving new protocols and solutions in Mobile Ad Hoc Networks (MANETs). Despite the prevalence of simulation-based studies, the absence of unified, extensible frameworks limits the modelling of end-to-end communication and the generation of reusable datasets necessary for research reproducibility. Our paper introduces a simulation framework for evaluating MANETs in Search and Rescue (SAR) scenarios, developed entirely in NS-3 to streamline both simulation and dataset generation. The framework incorporates mobility modelling, energy tracking, communication dynamics, and quality of service (QoS) logging within a modular architecture. It implements heterogeneous nodes with role-specific behaviours and supports discovery mechanisms such as line-of-sight, beacon broadcasting, and signal-based detection. Two simulation versions are implemented using different propagation loss models to assess performance under ideal and obstructed conditions. Routing protocols, including AODV, OLSR, and DSDV, are evaluated across varying scalability levels. Our results confirm established trends in MANET routing and demonstrate the framework’s flexibility in simulating mission-driven environments. The work presents multiple datasets and a reusable, extensible, scenario-driven simulation environment for researchers investigating MANET behaviour in complex applications such as disaster relief and tactical communications.

Background As healthcare increasingly shifts toward the home, the burden of care often falls on informal caregivers—family members, friends, or acquaintances—who frequently lack formal training and systemic support. This transition has intensified the risk of safety incidents in home care settings, including medication errors, falls, and other preventable harms. While patient safety researchers have traditionally focused on clinical environments, far less attention has been given to safety incidents in home care and to their cascading consequences for both care recipients and informal caregivers, who can become double victims (i.e., experiencing both the emotional impact of harm to a loved one and distress associated with their own involvement in the event). Purpose To develop and internationally appraise a safety-oriented, multilevel explanatory framework for safer home care provided by informal caregivers, specifying how conditions across ecological levels shape everyday caregiving processes, safety incidents, and interdependent consequences for care recipients and informal caregivers. Methods In this discursive paper, we have described the development of a conceptual framework and its international expert appraisal using a modified Delphi approach. The framework was co-developed within the BetterCare European Cooperation in Science and Technology Action CA22152 through interdisciplinary expert deliberations and a relationship-oriented narrative synthesis of literature identified in PubMed and CINAHL in September 2024. The modified Delphi process appraised the content relevance and cross-contextual interpretability of the principal framework components. Results Thirty-six experts from 19 countries participated in the Delphi appraisal. Consensus was achieved across all framework components in the first round. The final conceptual synthesis, termed the Double Victim Ecosystem Framework for Home Care Safety (DOVE Model), integrates Donabedian’s structure–process–outcome logic with a social ecological perspective. At the micro-level, care-recipient needs, informal caregiver capacities and vulnerabilities, dyadic-relational conditions, and the home environment form an interdependent structural configuration that shapes everyday caregiving and care-coordination processes. Meso-level system capacity and professional support processes, together with macro-level policy, legal, sociocultural, and socioeconomic structures, may buffer or amplify these pathways. Safety incidents represent proximal safety outcomes that may generate cascading clinical, psychological, physical, and relational consequences, including the Double Victim Phenomenon. Conclusion Safety in informal home care is a dynamic, multilevel capacity to anticipate and reduce avoidable harm, respond effectively when harm emerges, and support recovery and learning. The DOVE Model highlights the need for coordinated multilevel strategies to prevent safety incidents, strengthen caregiver preparedness, and provide structured support following safety incidents.

The aim of this study is to investigate the disruption of learning organization principles within dental service organizations, focusing on the critical commitment to patient care and its impact on long‐term staff retention.

Background: Diagnosing CNS tumors through MRI is limited by significant variability in scanner hardware, acquisition protocols, and intensity characteristics at clinical centers, resulting in substantial domain shifts that lead to diminished reliability for automated models. Methods: We present a Domain-Adaptive MRI Learning Model (DA-MLM) consisting of an adversarially aligned hybrid 3D CNN-transformer encoder with contrastive regularization and covariance-based feature harmonization. Varying sequence MRI inputs (T1, T1ce, T2, and FLAIR) were inputted to multi-scale convolutional layers followed by global self-attention to effectively capture localized tumor structure and long-range spatial context, with domain adaptation that harmonizes feature distribution across datasets. Results: On the BraTS 2020 dataset, we found DA-MLM achieved 94.8% accuracy, 93.6% macro-F1, and 96.2% AUC, improving upon previously established benchmarks by 2-4%. DA-MLM also attained Dice score segmentation of 93.1% (WT), 91.4% (TC), and 89.5% (ET), improving upon 2-3.5% for CNN and transformer methods. On the REMBRANDT dataset, DA-MLM achieved 92.3% accuracy with segmentation improvements of 3-7% over existing U-Net and expert annotations. Robustness testing indicated 40-60% less degradation under noise, contrast shift, and motion artifacts, and synthetic shifts in scanner location showed negligible performance impairment (<0.06). Cross-domain evaluation also demonstrated 5-11% less degradation than existing methods. Conclusions: In summary, DA-MLM demonstrates improved accuracy, segmentation fidelity, and robustness to perturbations, as well as strong cross-domain generalization indicating the suitability for deployment in multicenter MRI applications where variation in imaging performance is unavoidable.

Introduction – Previous studies have shown that children’s exposure to potentially traumatic events and their trauma−related symptoms may not always be consistently identified. This study aims to examine differences in trauma exposure and related psychological outcomes between clinical and non−clinical Lithuanian children. Methods – This cross-sectional study included 10–17−year−old children and adolescents recruited from a clinical inpatient setting (Vilnius University Hospital Santaros Klinikos) and general−education schools in Vilnius and nearby districts. After parental consent and child assent, participants completed a secure mobile assessment covering exposure to potentially traumatic events (CATS), dissociation (A−DES), mood and feeling (SMFQ), post−traumatic cognitions (CPTCI), PTSD symptoms (CATS; PCL−5 for convergent validation), and perceived social support (CASSS). Data were collected in 2023–2024. Group differences were examined using Welch’s t−tests (with Mann–Whitney U as robustness checks), and associations were assessed using Pearson correlations. Results – In the clinical sample over 40% of children experienced physical violence, while in the non−clinical sample 82.9% children reported exposure to multiple traumatic events. The clinical sample showed significantly higher dissociation, negative mood, and PTSD symptoms compared to the non−clinical sample. However, among children exposed to more than one traumatic event, differences in dissociation, PTSD symptoms, and close−friend support were not significant. Across both samples, exposure to potentially traumatic events was strongly associated with PTSD symptoms, dissociation, and post−traumatic cognitions, and moderately associated with mood symptoms. In the non−clinical sample, parental support showed moderate negative associations with dissociation, mood symptoms, post−traumatic cognitions, and PTSD symptoms. Discussion – This study identified between−sample differences in exposure to potentially traumatic events and trauma−related psychological outcomes among Lithuanian children in inpatient and community settings, highlighting the need for trauma−informed assessment and attention to social support within child mental health and welfare services.

This study explores the direct and mediating relationships among Self-Determination Theory (SDT), Digital Transformation Adaptation (DTA 5.0) and Workforce Motivation Outcomes (WMO) in the context of Construction 5.0. The primary objective is to examine how SDT influences DTA 5.0 and, in turn, how DTA 5.0 impacts WMO.

This paper conducts a comprehensive systematic review of Scan-to-HBIM workflows from 2012 to 2025, examining methodological structures, algorithmic support, and application maturity within digital workflows for cultural heritage documentation and management. The review synthesises global literature to classify contributions across technical domains: geometric acquisition, condition assessment, AI-assisted modelling, performance benchmarking, semantic enrichment, and immersive VR/AR applications, alongside the emerging cultural value dimension. The findings indicate a progressive shift toward hybrid human–algorithm workflows, combining deep learning, rule-based modelling, and semantic knowledge structures, rather than fully automated pipelines. Accuracy benchmarking highlights persistent inconsistencies in reporting frameworks across RMSE, IoU, and level-of-detail metrics, underscoring the need for greater methodological transparency. The paper concludes by identifying standardisation, interoperable data governance, and selective, scalable semantic AI integration as critical priorities for advancing reliable and sustainable Scan-to-HBIM practice in cultural heritage contexts.

Artificial intelligence (AI) is increasingly applied in medical image analysis, although its application in forensic pathology remains limited. The assessment of ligature marks in hanging deaths is challenging and relies on forensic expertise. This pilot study evaluated a deep learning approach for morphological classification of hanging-related ligature marks. Methods: A Convolutional Neural Network (CNN) was trained on a dataset of 404 standardized JPEG images obtained from forensic medicine atlases and classified into hanging-related ligature marks and non-hanging lesions, including strangulation and post-mortem artefacts. Following internal validation, the model was tested on an independent set of forensic case images provided by forensic pathology experts from Messina, Italy, and Vilnius, Lithuania. Images were annotated and reviewed by a team of two forensic pathologists, with final labels assigned by consensus using morphological criteria. Results: The CNN demonstrated encouraging classification performance with an F1-score of 0.81 ± 0.04, distinguishing hanging-related ligature marks from morphologically similar lesions. The methodological framework and image standardization criteria for AI-assisted forensic analysis were also established. Conclusions: AI-based image analysis may support the evaluation of ligature marks during external examinations. Nevertheless, forensic diagnosis requires the integration of autopsy findings, physical examination, and circumstantial evidence. Larger datasets and multicenter protocols are needed to further assess reliability and applicability.