Search Results (3,406)

Background/Objectives: Precise nuclei instance segmentation is a prerequisite for reliable digital pathology, yet the scarcity of pixel-level annotations remains a significant bottleneck for deep learning models. Methods: We propose a self-evolving framework for robust nuclei segmentation that uses only sparse point annotations, extending the Segment Anything Model (SAM). To overcome the limitations of static pseudo-labels, our method introduces a self-evolving labeling strategy via Exponential Moving Average (EMA), which adaptively refines learning targets. We also integrate instance-aware contrastive learning using point prompts as spatial anchors and implement a consensus-based filtering mechanism between prompt-guided and prompt-free decoders. Results: Extensive evaluations on CPM17, MoNuSeg, and the challenging CoNSeP datasets demonstrate that our framework achieves state-of-the-art performance across various backbones, including ViT-B and ViT-H. Conclusions: By enabling a seamless transition from general-purpose foundation models to specialized histopathology experts, this self-refining approach delivers a highly efficient, accurate solution for automated diagnostic workflows in clinical settings. Full article

Background: Regulatory restrictions and mandates typically offer short-term behaviour guidance, whereas interventions to improve knowledge and attitudes could result in more sustainable behavioural changes. Health authorities implemented awareness campaigns to enhance public knowledge and attitudes regarding COVID-19. This study explored the interplay between knowledge, attitudes and behaviours related to COVID-19 among university undergraduate students in Jordan, aiming to inform public health initiatives and educational programmes. Methods: A cross-sectional survey targeting undergraduate students enrolled at Yarmouk University in Jordan was conducted between January and May 2021. Participants consented to complete an anonymised validated self-administered questionnaire to evaluate their understanding of COVID-19 symptoms, treatment and transmission and attitudes and behaviours towards preventive measures. Data were analysed using descriptive and inferential statistics and structural equation modelling to investigate the associations between knowledge, attitudes and behaviours. Results: A total of 1375 undergraduate students participated in the survey. Knowledge of COVID-19 was low among most participants, with only 1.3% demonstrating high knowledge. Conversely, 58.5% exhibited good behaviour, and 31.4% reported full compliance with recommended behaviours. Significant differences were found in knowledge, attitudes and behaviours across different faculty clusters, with health faculties showing superior knowledge and more positive attitudes. Female participants (66.3%) were more likely to engage in positive behaviours than males (p-value = 0.02). Structural equation model (SEM) analysis showed that knowledge significantly influenced attitudes, which affected behaviours, confirming the model’s validity. Conclusions: The study highlights the critical role of knowledge and attitudes in shaping COVID-19-related behaviours among university students. Significant variations in knowledge and attitudes across different academic disciplines highlight the need for tailored educational interventions. The analysis supports the theoretical model linking knowledge, attitudes and behaviours, emphasising the importance of improving knowledge and attitudes to drive behaviour change. The findings suggest that comprehensive health education programmes targeting cognitive and affective aspects are essential for effective public health responses during pandemics. Full article

Mystical experiences are characterized by a profound sense of interconnectedness and transcendence of ordinary reality. These experiences can facilitate feelings of connectedness with oneself and others and have been documented as leading to significant positive changes in thoughts, emotions, and behavior. The purpose of the present study was to evaluate the extent to which the four mindfulness facets of psychological flexibility (i.e., experiential acceptance, present-moment awareness, cognitive defusion, and self-as-context) were associated with self-reported mystical experiences while controlling for established covariates. Using a sample of 150 individuals recruited online, a regularized regression with an elastic net—a computationally efficient machine learning algorithm—was used to model the relationships among mystical experiences, State of Surrender, frequency of psychedelic use, near-death experiences, and facets of psychological flexibility. State of Surrender, experiential acceptance, cognitive defusion, and present-moment awareness emerged as the most robust predictors of mystical experiences. Collectively, these findings underscore the role of psychological processes, including surrender-related processes and facets of psychological flexibility, in predicting mystical experiences. Full article

This sequential mixed-methods study examines when sustainability becomes a meaningful criterion in everyday consumption versus a widely endorsed discourse enacted selectively in Spain. Informed by research on the attitude–behavior gap in sustainable consumption, including work using TPB- and norm-based perspectives, the study explores how feasibility constraints and credibility concerns shape the translation of sustainability commitment into practice in a non-student adult sample. It addresses a recurring pattern in sustainable consumption research: strong normative endorsement often coexists with partial behavioral uptake, particularly when feasibility constraints (cost, convenience, perceived impact) and credibility concerns (skepticism/greenwashing perceptions) intervene. A focus group (n = 9) explored how participants define sustainability, justify conditional enactment, and interpret sustainability communication; these insights informed and refined an online survey (N = 317) capturing awareness, conceptual knowledge, concern, self-perceived behavior, practice adoption, willingness to change, and perceptions of sustainability as marketing/politics. Self-reported awareness was high (83.91%) and mean concern was 7.40/10, whereas mean self-assessed sustainable behavior was lower (6.20/10), indicating a commitment–practice gap. Most respondents reported at least one sustainable practice (98.42%) and expressed willingness to change habits (96.21%), yet intentions appeared stronger than current uptake for higher-effort changes. Associations between attitudinal endorsement and enactment were modest to moderate: concern was positively related to self-assessed sustainable behavior (Spearman’s ρ = 0.445) and to reported practice adoption (practice count; ρ ≈ 0.34), while self-assessed behavior was moderately related to practice adoption (ρ ≈ 0.48). Qualitative findings emphasized feasibility trade-offs and credibility discounting of sustainability claims. By combining interpretive evidence with survey patterns, the study shows that sustainability is widely endorsed in this sample but enacted unevenly, with feasibility and credibility helping to explain why commitment does not consistently translate into practice in the Spanish context. Full article

Name.Narrate.Navigate (NNN) is a trauma-informed program for justice-involved young people aged 12–18 years, recognising that experience and use of violence are often interconnected and may involve serious criminal behaviour, including vulnerability to criminal exploitation. NNN addresses a gap in evidence-based, culturally responsive tertiary interventions for this cohort in regional New South Wales (NSW), Australia, integrating dialectical behaviour therapy (DBT) principles with Aboriginal ways of knowing and doing, co-designed through community-based participatory research (CBPR) with Aboriginal community members, young people, and frontline practitioners. The program aims to strengthen skills for self-awareness, self-regulation and healthy connection through relational, creative, and participatory approaches. Using a realist evaluation framework, this paper examines what works in NNN, for whom, and under what circumstances. Drawing on participant session ratings, practitioner observations, program documentation, and interviews, findings are organised across four domains: effects, mechanisms, moderators, and implementation. Indicative findings show that engagement, emerging changes in the narratives of self, and developing skills for self-regulation were most evident when trauma-informed and culturally safe practice was enacted within genuinely relational, strengths-based encounters. These conditions are identified and discussed as transferable principles for the field, key amongst them that intervention readiness must be treated as a capacity to be actively built rather than a precondition to be screened for; and that creative, participant-led methods represent an epistemological commitment to whose knowledge counts in practice. This case study contributes to a critically underserved evidence base by documenting not only what a tertiary youth violence intervention looks like, but the conditions under which it begins to work and for whom. Full article

Construction waste minimisation remains a persistent challenge in developing country contexts, where technical and regulatory deficiencies are often compounded by limited behavioural evidence on how professionals understand and respond to waste generation. This study examines the awareness, attitudes, perceptions, and self-reported waste minimisation practices of construction professionals in Lagos, Nigeria, to clarify how these cognitive factors relate to waste minimisation. Using a quantitative cross-sectional survey design, data were collected from 243 construction professionals through a structured questionnaire and analysed using exploratory factor analysis, such as the relative importance index, the Kruskal–Wallis H test, and Spearman’s rank correlation. The findings indicate a high level of awareness of waste reduction strategies, with organised waste sorting for material reuse ranked the highest (RII = 0.868). However, 54.3% of respondents still perceived waste as an inevitable by-product of construction projects, revealing an important cognitive–behavioural gap. Spearman’s rank correlation showed no statistically significant association between awareness and attitudes (r = 0.113, p = 0.079) and no significant association between awareness and perceptions (r = 0.049, p = 0.452). A statistically significant but weak positive association was found between attitudes and perceptions (r = 0.204, p ≤ 0.001), which is consistent with the Theory of Planned Behaviour (TPB) theoretical expectations but does not constitute a direct test of the full TPB model. The study contributes context-specific behavioural evidence showing that awareness alone may be insufficient to support waste minimisation unless accompanied by more favourable perceptions of feasibility and value. These findings have implications for behaviourally informed policy, professional training, and circular construction strategies in Nigeria and similar contexts. Full article

This study developed and validated a specialised emotional intelligence (EI) scale for youth in the conflict-affected southern border provinces of Thailand. The primary objective was to establish a psychometric instrument tailored to this unique multicultural and sensitive context. Utilizing a sample of 500 local youth leaders, the instrument’s quality was rigorously evaluated through Second-order Confirmatory Factor Analysis (CFA) using Maximum Likelihood estimation. The final validated model comprises 25 indicators categorized into five dimensions: Self-Awareness, Self-Regulation, Self-Motivation, Social Awareness/Empathy, and Relationship Management. Results indicated an excellent model fit with empirical data (χ² = 284.15, df = 265, p = 0.198, CFI = 0.99, GFI = 0.97, RMSEA = 0.02). Factor loadings ranged from 0.72 to 0.92, while composite reliability (CR) and average variance extracted (AVE) values exceeded 0.88 and 0.61, respectively, confirming high internal consistency and construct validity. Social Awareness/Empathy emerged as the most significant dimension (B = 0.91). This study suggests that the scale is a robust tool for assessing EI in conflict zones, providing a critical foundation for targeted psychosocial interventions and sustainable peace-building initiatives among youth in the region. Full article

Higher Education Institutions (HEIs) are increasingly called upon to integrate sustainability across curricula and to prepare students to respond critically and responsibly to complex environmental challenges. While sustainability education is often associated with scientific, technological, or policy-oriented disciplines, the contribution of the humanities remains underexplored, particularly in digitally mediated university teaching. This paper argues that ancient texts, approached through the lens of the Environmental Humanities and supported by digital pedagogy, can offer a valuable framework for fostering sustainability literacy in higher education. Drawing on a humanities-based pedagogical model, this article explores how practices such as collaborative close reading, ecocritical discussion, narrative mapping, reflective writing, and digital storytelling can help students connect classical representations of nature, fragility, order, and human responsibility with contemporary ecological concerns. These activities encourage the development of sustainability-related competencies—including critical thinking, ethical reflection, interpretive complexity, and ecological awareness—while also supporting Inner Development Goals such as self-awareness, empathy, relational thinking, and responsible action. Based on a conceptual pedagogical model supported by exploratory qualitative evidence from a small-scale higher education course, this paper suggests that digital pedagogy can make sustainability learning in the humanities more dialogic and reflective. In doing so, this article proposes a practice-based pedagogical framework that may help Higher Education Institutions explore ways of embedding sustainability meaningfully beyond traditionally environmental fields. This article’s primary contribution is therefore pedagogical: it presents a humanities-based model for sustainability education while using exploratory qualitative evidence from one course context to illustrate how such a model may support interpretive, ethical, and sustainability-oriented learning. Full article

GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without addressing workflow-level integration overhead. We present GANSU (GPU Accelerated Numerical Simulation Utility), an open-source quantum chemistry framework written entirely in CUDA/C++ that integrates GPU-accelerated kernels for electron repulsion integrals, Fock matrix construction, and post-Hartree–Fock (post-HF) methods into a unified, GPU-resident execution pipeline. The key design principle is to eliminate host–device data transfers between computational stages by keeping all intermediate data, including density matrices, integral buffers, and Fock matrix replicas, on the GPU throughout the self-consistent field (SCF) iteration, combined with runtime-selectable integral strategies (stored ERI, resolution-of-the-identity, and Direct-SCF) that adapt to system size and available memory. On an NVIDIA H200 GPU, GANSU achieves end-to-end speedups of up to $52\times$ over PySCF for SCF, $45\times$ for MP2 on molecules with up to 470 basis functions, and $44\times$ for FCI, while outperforming GPU4PySCF by up to $34\times$ for FCI, across a range of molecular systems with up to 650 basis functions. The framework further provides analytical energy gradients and geometry optimization with nine algorithms, all operating within the same GPU-resident data flow. These results demonstrate that workflow-aware kernel integration, not just kernel-level optimization, is essential for realizing the full potential of GPU acceleration in scientific computing. GANSU is publicly available under the BSD-3-Clause license. Full article

As a common brain-computer interface (BCI) paradigm, electroencephalogram (EEG)-based motor imagery provides a critical pathway for both assistive technology to (restoring communication and control) and active rehabilitation (promoting neural plasticity and functional recovery). Domain adaptation has been shown to effectively enhance the decoding performance of motor intentions for target subjects by leveraging labeled data from source subjects. However, EEG data from source subjects often contains extensive personal privacy, and the direct access to source EEG data easily leads to privacy leakage issues. An important research topic is to achieve domain adaptation without directly accessing the source subjects’ raw data. To address this challenge, a privacy-preserving source-free domain adaptation framework, termed Transformer-based SFDA with Class-balanced Multicentric Dynamic Pseudo-labeling (T-CMDP), is proposed for cross-subject motor-imagery EEG classification. This framework consists of three coupled stages. In the source model training stage, a Transformer-based encoder combined with Riemannian manifold-aware feature extraction is employed to learn transferable and discriminative EEG feature representations. In the source-free target adaptation stage, only the pretrained source model is transferred to the target domain and adapted through knowledge distillation and information maximization, without accessing raw source EEG data. In the self-supervised learning stage, class-balanced multicentric prototypes and high-confidence pseudo-label updates are introduced to progressively refine the target-domain decision boundaries. Extensive experiments on three motor-imagery EEG datasets demonstrate that the proposed T-CMDP framework consistently outperforms eleven representative baselines from traditional machine learning, deep learning, and source-free transfer approaches, achieving average accuracies of 56.85%, 76.34%, and 74.49%, respectively. These results indicate that T-CMDP effectively alleviates inter-subject EEG distribution discrepancies and ensures the privacy preserving of source subjects, thereby facilitating more reliable and practical deployment of EEG-based BCI systems. Full article

When performing nighttime passive visible remote sensing of non-emissive land surfaces, illumination is typically dominated by weak moonlight that varies with lunar phase, producing low-radiance images with degraded textures and thus motivating low-radiance visible remote sensing image enhancement. We propose a Complementary Illumination–Semantic Prompt Diffusion framework (CISPD) that incorporates a semantic-invariant prompt and a self-learned illumination-aware prompt to guide diffusion-based low-light remote sensing image enhancement. During denoising, we sequentially inject two complementary prompts. We first retrieve a self-learned illumination-aware prompt from a learnable pool conditioned on the current latent context to correct non-uniform brightness, and then apply a semantic-invariant prompt extracted from a vision foundation model to reinforce geometric structures and suppress artifacts. To keep the two prompts complementary rather than redundant, we introduce a contrastive constraint that encourages their representations to remain distinct, and the dual prompts jointly steer the diffusion trajectory toward well-exposed results with faithful structures. Experiments on iSAID-dark and darkrs, together with LOLv1 and LOLv2, demonstrate that CISPD achieves the best PSNR and SSIM on iSAID-dark, strong qualitative generalization on darkrs, and competitive quantitative performance on LOLv1 and LOLv2. Full article

Early eye loss, congenital microphthalmia, and anophthalmia can significantly disrupt facial and psychological development in children. Timely intervention with conformers and ocular prostheses is essential for stimulating orbital growth and supporting healthy psychosocial development. This review presents evidence-based guidelines for ocularists, physicians, and allied professionals on fitting conformers and prostheses in young children, emphasizing the need for individualized treatment based on anatomical severity and age. Recommendations include initial conformer fitting within the first month of life for congenital cases or 4–6 weeks post-surgery in acquired cases, with frequent early replacements. For microphthalmia, moderate-to-severe cases require treatment similar to congenital anophthalmia cases; mild-to-moderate cases treatment within months; and mild cases are usually managed individually without urgency. A cosmetic prosthesis is advised the latest after the first year, as growth slows and self-awareness develops. Regular follow-up and adjustments support functional and psychosocial outcomes. We advocate for standardized care protocols to ensure equitable access and consistent long-term results across healthcare systems. Full article

Smoking detection in indoor work sites is challenging due to posture variability, object occlusion, poor lighting, and the small size of cigarettes. These factors hinder the extraction of reliable pose-aware features. Such features include hand–cigarette orientation and contours, which are critical for smoking detection. However, current mainstream detectors, such as YOLO-based methods, fail to capture pose-aware features under cluttered and low-visibility conditions. To address this, we propose the Contour-Driven Pose-Aware Network (CDPA-Net), which explicitly captures contour orientation and high-frequency appearance cues for robust smoking detection. Specifically, the Orientation-Driven Contour Extractor (ODCE) employs a Nonsubsampled Contourlet Transform to capture direction-sensitive posture and contour features, effectively suppressing background clutter. Additionally, the Frequency-Sensitive Attention Block (FSAB) highlights high-frequency discriminative signals under dim light via frequency-domain self-attention. Moreover, the Multi-Scale Frequency Integration Module (MFIM) fuses structural and spectral cues across scales to reinforce pose-aware representation. Experiments on both a public and a custom industrial dataset show that CDPA achieves 89.2% mAP50 at 112 FPS. This work provides a lightweight, interpretable, and accurate solution for smoking detection in industrial monitoring applications. Full article

This study explores the effectiveness of an AI-enabled career preparation platform in enhancing undergraduate students’ awareness of their career readiness and skill development. The research was conducted within a localized context at a comprehensive university in Singapore, introduced as part of a career-preparation exercise for internship exploration and selection, allowing students to self-assess their current competencies and identify gaps vis-à-vis industry requirements. Students first evaluate their perceived knowledge of their skills and the deficiencies they need to address. This platform leverages artificial intelligence to help students profile their skills and discover tailored internship opportunities. By uploading their resumes, students receive a personalized skills profile identifying their relevant competencies. The platform then suggests potential career roles and automatically shows skills for development. Using a retrospective pretest-posttest survey with Likert-scale responses, statistical tests revealed significant improvements across all measured areas. The platform was further assessed across two constructs with high internal consistency, reflecting strong user engagement and satisfaction. Lastly, we highlight the potential of AI-driven tools in supporting student career preparedness and offer insights for further platform improvements. The findings from this study are not assumed to generalize directly to other institutional, cultural, or national settings, but instead offer initial context-specific indications of how such tools may support students’ perceived skill awareness and career planning. Full article

The increasing complexity of next-generation wireless systems challenges the scalability and generalization capabilities of traditional model-driven physical layer (PHY) design, which relies on analytically derived channel models and optimization frameworks. This paper presents a comprehensive survey and critical review of deep learning (DL) architectures enabling the transition toward AI-native PHY design. A unified optimization perspective is developed in which all PHY tasks—including channel estimation, channel state information (CSI) feedback, massive MIMO processing, signal detection, channel coding, beamforming, resource allocation, and semantic-aware transmission—are formulated under a common empirical risk minimization (ERM) framework. Neural architectures such as autoencoders, convolutional and recurrent networks, transformers, and reinforcement learning models are examined through their underlying optimization formulations, loss functions, training methodologies, and representation learning mechanisms. The review compares model-driven and AI-native approaches in terms of performance metrics, computational complexity, robustness, generalization capability, and practical deployment constraints, including hardware limitations, energy efficiency, and real-time feasibility. The analysis highlights the conditions under which AI-native architectures provide adaptability and performance improvements while identifying trade-offs in complexity, latency, and interpretability. The study concludes by outlining prioritized research directions toward fully adaptive and self-optimizing wireless communication systems. Full article