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.

This research examines the feasibility of using waste cooking oil (WCO) as a substitute for traditional diesel fuel in internal combustion engines, with a focus on biodiesel production. The aim of this research is to evaluate the effects of WCO–diesel blends on engine performance, with particular emphasis on critical metrics including brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The study utilizes artificial neural networks (ANNs) to model and forecast the performance and emission characteristics of engines operating with different fuel combinations. The study employs a methodology that involves conducting experiments to evaluate the mixtures of waste cooking oil (WCO) and diesel fuel in diesel engines. Furthermore, artificial neural networks (ANNs) are employed to develop models for predicting engine performance. The analysis focuses on critical metrics, including BSFC and BTE, under various operating conditions. This research aims to improve sustainable energy solutions by demonstrating the benefits of alternative fuels and advanced artificial intelligence (AI) prediction models in automotive applications.

In this study, we examine whether personality profiles moderate a recently proposed environmental behaviour–well-being model. The model is grounded in Self-Determination Theory, which suggests that pro-environmental behaviour may contribute to subjective well-being through the satisfaction of basic psychological needs. A total of 403 adolescents from four Lithuanian schools participated in this study. Participants were selected using a convenience sampling method, and the average participant age was 14.89 years (56.6% female). Structural equation modelling demonstrated acceptable model fit (CFI = 0.988, TLI = 0.986, NFI = 0.929, RMSEA = 0.043 [0.022–0.058], SRMR = 0.113, χ2 (353) = 416.92, p = 0.011) and indicated that personality profiles moderate several pathways within the environmental behaviour–well-being model. Additionally, the results showed that pro-environmental behaviour and connectedness to nature did not significantly predict subjective well-being across all personality profiles. The findings highlight the importance of considering personality differences when designing sustainability-related educational interventions aimed at promoting environmental citizenship and improving subjective well-being.

Background According to network theories, mental disorders, including prolonged grief disorder (PGD), comprise networks of dynamically connected symptoms. Examining how prolonged grief symptoms are connected over time could reveal the patterns driving their persistence. This study provides the first empirical investigation of prolonged grief symptom networks using self-reported data on prolonged grief assessed multiple times daily. Methods Adults whose partner, family member, or friend died on average 30 months ago (N = 229, 80 % women, M age  = 51) rated prolonged grief symptom intensity using 11 items (e.g., “In the past three hours, I found myself yearning for him/her”) five times per day for two weeks. We used a two-step multilevel vector autoregressive model to produce between-person, contemporaneous, and temporal networks. Results In the between-person network, yearning and sadness were the most strongly and positively connected symptoms. In the contemporaneous network, yearning, preoccupation, and sadness formed a cluster of positively connected symptoms. Simultaneously, difficulty reintegrating after the loss, emotional numbness, meaninglessness, and loneliness due to the loss formed another positively connected symptom cluster. In the temporal network, emotional numbness had the greatest positive influence on other prolonged grief symptoms at the subsequent timepoint. Conclusion We propose that targeting emotional suppression, promoting flexible emotion regulation, and supporting integrated continuing bonds (approach-behaviors) and targeting avoidance of the reality of the loss (avoidance-behaviors) may help people to adapt to loss.

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 The death of a significant other can lead to prolonged grief disorder (PGD), characterized by intense grief reactions interfering with daily life. This study aims to identify latent profiles, and predictors thereof, of severity and fluctuation indices in PGD using experience sampling methodology (ESM) data from a novel Findable, Accessible, Interoperable, and Reusable (FAIR) data-archive. Methods Bereaved adults (N = 228) completed PGD items five times per day for two weeks using ESM. Latent profile analyses were run to identify profiles of fluctuating PGD reactions using four person-specific indicators derived from ESM-PGD items: mean, standard deviation, autocorrelation indicating inertia, and the root mean square of successive differences indicating instability. Background, loss-related characteristics, and psychopathology levels assessed before the ESM period were explored as potential predictors of profile membership. Results Five distinct profiles were identified: moderate PGD/low inertia/moderate fluctuation (46 %), moderate PGD/low inertia/high fluctuation (20 %), low PGD/low inertia/low fluctuation (19 %), high PGD/low inertia/low fluctuation (8 %), and low PGD/high inertia/low fluctuation (7 %). The most consistent differentiating predictors of profiles were gender, time since loss, and baseline psychopathology levels. Conclusions We found that at comparable levels of PGD severity, different fluctuation profiles exist in daily life of bereaved people suggesting that ESM may be an important tool for understanding the dynamic nature of PGD. Identifying these profiles enhances our understanding of individual differences in response to loss and could help to personalize bereavement care.

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.

This paper examines the impact of industrial energy efficiency on household energy poverty in the twenty-seven Member States of the European Union for the period 2003–2023. Although the literature has widely discussed energy efficiency as an enabler of decarbonisation and economic performance, its direct link to energy poverty at the macro level has rarely been analysed, let alone with respect to structural changes in industry. Filling this gap, this paper evaluates whether reductions in industrial energy intensity result in reduced energy poverty, understood as the share of households unable to maintain adequate indoor thermal comfort. Empirical analysis relies on a balanced panel dataset and uses fixed-effects regression models to take into account unobserved country-specific and time-specific heterogeneity. In addition, potential endogeneity between industrial energy intensity and labour productivity is addressed by the instrumental variable approach using two-stage least squares. The main models also include key macroeconomic and social control variables: real GDP per capita, social benefit expenditure, electricity prices for households, and unit labour costs. The results yield a robust and statistically significant positive link between industrial energy intensity and energy poverty, suggesting that efficiency improvements in industry make a quantifiable difference in household energy deprivation. This effect even increases in strength after the correction for endogeneity, thereby corroborating the causal relevance of productivity-driven efficiency gains. The findings also show substantial heterogeneity between EU Member States, indicating that national structural features will determine baseline levels of energy poverty. However, no strong evidence is found for an indirect price-mediated transmission mechanism or for moderation effects bound to income levels or social expenditure. This study provides sound empirical evidence that industrial energy efficiency is an important but structurally conditioned lever to alleviate energy poverty in the European Union. The results emphasise the integration of industrial efficiency policies with social and institutional frameworks while designing strategies for a just and inclusive energy transition.