Search Results (25,127)

Human storytellers often orchestrate diverse narrative orders (chronological, flashback) for crafting compelling stories. To equip artificial intelligence systems with such capability, existing methods rely on implicitly learning narrative sequential knowledge, or explicitly modeling narrative order through pairwise event temporal order (e.g., take medicine <after> get ill). However, both suffer from imbalanced narrative order distribution bias and inadequate event temporal understanding, hindering generating high-quality events in the story that balance the logic and narrative order. In this paper, we propose a narrative-order-aware framework, TimeWeaver, which presents an event–order bidirectional pretrained model integrated with temporal mixture-of-experts to orchestrate diverse narrative orders. Specifically, to mitigate imbalanced distribution bias, the temporal mixture-of-experts is devised to route events with various narrative orders to corresponding experts, grasping distinct orders of narrative generation. Then, to enhance event temporal understanding, an event sequence narrative-order-aware model is pretrained with bidirectional reasoning between event and order, encoding the event temporal orders and event correlations. At the fine-tuning stage, reinforcement learning with multi-granular optimal transport reward is designed to boost the quality of generated events. Extensive experimental results on automatic and manual evaluations demonstrate the superiority of our framework in orchestrating diverse narrative orders during story generation. Full article

Detecting surface defects in steel products is essential for maintaining manufacturing quality. However, existing methods struggle with significant challenges, including substantial defect size variations, diverse defect types, and complex backgrounds, leading to suboptimal detection accuracy. This work introduces ELS-YOLO, an advanced YOLOv11n-based algorithm designed to tackle these limitations. A C3k2_THK module is first introduced that combines a partial convolution, heterogeneous kernel selection protocoland the SCSA attention mechanism to improve feature extraction while reducing computational overhead. Additionally, the Staged-Slim-Neck module is developed that employs dual and dilated convolutions at different stages while integrating GMLCA attention to enhance feature representation and reduce computational complexity. Furthermore, an MSDetect detection head is designed to boost multi-scale detection performance. Experimental validation shows that ELS-YOLO outperforms YOLOv11n in detection accuracy while achieving 8.5% and 11.1% reductions in the number of parameters and computational cost, respectively, demonstrating strong potential for real-world industrial applications. Full article

Objective: The objective of this exploratory pilot study was to examine the relationship between coping strategies and perceived quality of life in adolescents diagnosed with oncological diseases, with attention to the potential role of psychosocial factors in emotional adaptation. Method: The study included 20 adolescents (12 boys, 8 girls), aged 12–18 years, enrolled in the hospital school program in Bucharest, Romania, while receiving active oncological treatment. Participants completed two validated instruments: the Pediatric Quality of Life Inventory (PedsQL—Cancer Module) and the KidCOPE questionnaire. Results: The mean quality of life score was 70, indicating a moderately good level of quality of life. Emotion-focused and avoidance-based strategies (distraction, social withdrawal, and acceptance) were most frequently reported, while problem-focused coping was less common. Regression analysis showed that coping dimensions explained approximately 26% of the variance in quality of life (R² = 0.26, F(3,16) = 1.83, p = 0.183). Although the overall model was not statistically significant, an observed negative association was found between avoidant coping and quality of life (p = 0.037). These results should be interpreted with caution given the small sample size and cross-sectional design. Discussion: The findings suggest that adolescents with cancer may maintain a functional level of adaptation despite medical and emotional challenges, supported by medical staff and social resources. The predominance of avoidant strategies highlights the need for further investigation of their long-term implications. Conclusions: These preliminary results generate hypotheses and underline the importance of future research on psychological and educational interventions aimed at fostering more active coping strategies and supporting resilience in adolescents with cancer. Full article

Meeting the demands for advanced technology and superior quality in the automotive industry has become essential. Continuous evolution requires a rigorous analysis of every step taken. Customers demand high performance in the technology, design, and digitalization, as well as, of course, quality at a competitive price. To meet these expectations, engineers ensure transparency and trust at every stage of the project, guaranteeing flawless execution. This paper aims to highlight a clear and transparent approach to the 8D analysis method, demonstrating its effectiveness in identifying and solving engineering problems. Furthermore, quality planning and 8D analysis are fundamental pillars of quality management in the automotive industry. To ensure a comprehensive and well-founded approach, this paper combines several research methods: a review of the specialized literature, a hypothetical case study approach, and comparative analysis. The proposed methodology allows for a deep understanding of the concepts addressed, facilitating their applicability in real situations. The main conclusions drawn from this research are that quality planning in an automotive buckle development project has proven to be an essential and complex process, directly influencing the success of the project, the safety of end users, and their satisfaction. The analysis of the implementation of the quality planning process, as previously described, has highlighted several fundamental aspects that must be considered to ensure the success and performance of such a project. Full article

With the widespread deployment of deep neural networks in real-world physical environments, assessing their robustness against adversarial attacks has become a central issue in AI safety. However, the existing two-dimensional adversarial methods often lack robustness in the physical world, while three-dimensional adversarial camouflage generation typically relies on high-fidelity 3D models, limiting practicality. To address these limitations, we propose CAM3D, a cross-domain 3D adversarial camouflage generation framework based on single-view image input. The framework establishes an inverse graphics network based on the Mamba architecture, integrating a hybrid non-causal state-space-duality module and a wavelet-enhanced dual-branch local perception module. This design preserves global dependency modeling while strengthening high-frequency detail representation, enabling high-precision recovery of 3D geometry and texture from a single image and providing a high-quality structural prior for subsequent adversarial camouflage optimization. On this basis, CAM3D employs a progressive three-stage optimization strategy that sequentially performs multi-view pseudo-supervised reconstruction, real-image detail refinement, and cross-domain adversarial camouflage generation, thereby systematically improving the attack effectiveness of adversarial camouflage in both the digital and physical domains. The experimental results demonstrate that CAM3D substantially reduces the detection performance of mainstream object detectors, and comparative as well as ablation studies further confirm its advantages in geometric consistency, texture fidelity, and physical transferability. Overall, CAM3D offers an effective paradigm for adversarial attack research in real-world physical settings, characterized by low data dependency and strong physical generalization. Full article

Background and Objective: Postpartum depression (PPD) is a prevalent psychiatric condition with significant consequences for maternal, paternal, and infant well-being. In Saudi Arabia, some reported prevalence rates exceed global averages. This narrative review synthesizes the current literature on the prevalence, risk factors, awareness, and quality-of-life impact of PPD in Saudi Arabia. The aim is to identify methodological inconsistencies, highlight the risk factors, and guide future research and policy. Method: A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Studies published between 2010 and May 2025 were included if they addressed PPD in Saudi Arabia and the inclusion criteria were met. 38 articles were selected for full-text analysis and incorporation in the study. Results: PPD prevalence in Saudi Arabia ranges from 5.1% to 75.7%, with regional variation attributed to inconsistent methodologies, screening instruments, and diagnostic cutoffs. Risk factors encompass psychiatric history, marital conflict, limited social support, low income, cesarean delivery, unplanned pregnancy, anemia, and sleep disturbance. Nutritional and newborn-related predictors were inconsistently reported. Awareness among the public and healthcare professionals remains limited, and paternal postpartum depression is underrecognized. PPD exerts a pronounced negative impact on maternal quality of life, spanning physical, psychological, and social domains. Conclusions: PPD poses a substantial public health burden in Saudi Arabia. Routine screening with validated tools, integrated perinatal mental health services, and targeted public education campaigns may help address diagnostic delays and stigma. Future studies must adopt standardized diagnostic criteria and longitudinal designs to generate nationally representative prevalence estimates and evaluate preventive strategies. Full article

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well as the uniqueness of each printed component. Building upon our prior work in developing 3D-printable self-sensing cementitious materials by incorporating graphite powder and carbon microfibers into a cementitious matrix to enhance its piezoresistive properties, this study aims at enabling condition assessment of cementitious 3DP by integrating the self-sensing materials as sensing nodes within conventional components. Three different 3D-printed strip patterns, consisting of one, two, and three strip lines that mimic the pattern used in fabricating foil strain gauges were investigated as conductive electrode designs to impart strain sensing capabilities, and characterized from a series of quasi-static and dynamic tests. Results demonstrate that the three-strip design yielded the highest sensitivity (${\lambda }_{\mathrm{stat}}$ of 669, ${\lambda }_{\mathrm{dyn}}$ of 630), whereas the two-strip design produced the highest signal quality (SNR_stat = 9.5 dB, SNR_dyn = 10.8 dB). These findings confirm the feasibility of integrating 3D-printed self-sensing cementitious materials through hybrid manufacturing, enabling monitoring of print quality, detection of load path changes, and identification of potential defects. Full article

China and Italy, both ancient civilizations, have numerous traditional villages that bear witness to history and support the transmission of cultural heritage. However, these villages face challenges such as homogenized development, population outflow, and disruptions in cultural continuity. While both Chinese and Italian traditional villages have received considerable scholarly attention, their comparative study remains relatively limited, leaving the transferability of respective solutions across different legal, heritage and planning contexts to be fully explored. This study aims to adapt and transfer Italy’s contiguous protection, integrated operation, national park designation, and community partnership policies to China in order to establish a comprehensive mechanism for preservation and revitalization of traditional villages. A cross-case study of Cinque Terre (Italy) and Jiande (China), incorporating on-site mapping, governance analysis, and interviews, reveals that Italy’s integrated community-based approach markedly outperforms China’s fragmented state-led model in sustaining population, culture and tourism quality. These findings provide a globally replicable paradigm for traditional village preservation. Full article

Ensuring the stability of surrounding rock in underground excavations is a critical prerequisite for safe mining operations. This study examines the mechanisms of wedge failure formation and determines the performance of a combined support system (rock bolts + shotcrete) through probabilistic analysis. Field investigations in the Zhylandy ore field (Kazakhstan) included fracture mapping, rock mass quality assessment (RQD), fracture frequency (FF), and in situ stress measurements, which confirmed a thrust-faulting regime. Numerical modeling with Dips ver.8 and UnWedge ver.6 software (Rocscience) identified critical excavation orientations of 120° and 141° associated with maximum-volume wedge formation, as well as a “safe orientation window” of 70° ± 10°. The probabilistic analysis showed that rock bolts alone yield a factor of safety (FS) < 1.2, whereas the combined support system increases FS to 2.4–3.5, significantly reducing the likelihood of wedge failures. An adaptive framework integrating numerical modeling with intelligent monitoring (“monitor → update model → adjust support”) is proposed, allowing real-time adjustment of support parameters and optimization of material consumption. The practical significance of this work lies in providing design-ready recommendations for support selection and excavation orientation, contributing to accident prevention and sustainable mining operations. Full article

Accurate classification of optical communication signal quality is crucial for maintaining the reliability and performance of high-speed communication networks. While existing supervised learning approaches achieve high accuracy on laboratory-collected datasets, they often face difficulties in generalizing to real-world conditions due to the lack of variability and noise in controlled experimental data. In this study, we propose a targeted data augmentation framework designed to improve the robustness and generalization of binary optical signal quality classifiers. Using the OptiCom Signal Quality Dataset, we systematically inject controlled perturbations into the training data including label boundary flipping, Gaussian noise addition, and missing-value simulation. To further approximate real-world deployment scenarios, the test set is subjected to additional distribution shifts, including feature drift and scaling. Experiments are conducted under 5-fold cross-validation to evaluate the individual and combined impacts of augmentation strategies. Results show that the optimal augmentation setting (flip_rate = 0.10, noise_level = 0.50, missing_rate = 0.20) substantially improve robustness to unseen distributions, raising accuracy from 0.863 to 0.950, precision from 0.384 to 0.632, F1 from 0.551 to 0.771, and ROC-AUC from 0.926 to 0.999 compared to model without augmentation. Our research provides an example for balancing data augmentation intensity to optimize generalization without over-compromising accuracy on clean data. Full article

Background/Objective: Chronic constipation is a common gastrointestinal disorder that can be caused by a variety of factors, such as demographic, lifestyle, and medical disorders like hypothyroidism. Its prevalence varies worldwide, affecting quality of life and leading to specialized management strategies. To explore hypothyroidism patients’ knowledge and practice regarding constipation and evaluate the perceived effectiveness of pharmacological and non-pharmacological management approaches. Methods: A descriptive, cross-sectional design was used to collect the data from a private hospital in the eastern region of Saudi Arabia from January to May 2025. A convenient sample of 300 individuals with hypothyroidism completed the Bowel Habits Questionnaire. Results: Most participants knew that hypothyroidism could cause constipation, but they reported that they did not have more knowledge about it. Both pharmacological and non-pharmacological interventions, especially increase water intake, fiber intake, and exercise, were commonly used by the participants, and they perceived these approaches to be effective. There were strong correlations between constipation frequency and age, disease duration, and the use of constipation management methods. A strong association was found between constipation management strategies and treatment effectiveness. Conclusion: Age, disease duration, and constipation management strategies significantly affect constipation in hypothyroidism patients. Drinking plenty of water and eating more fiber are two very effective non-pharmacological strategies. It is recommended that nurses who integrate routine bowel health education and lifestyle guidance into care plans consider the gap in patient knowledge regarding the relationship between hypothyroidism and constipation, to enhance patients’ self-management and contribute to better health outcomes. Full article

Practicing medical research based on the best evidence is gaining increased value and popularity among most medical societies in the current era. Good clinical practice (GCP) is internationally recognized as the scientific and ethical standard for the design, conduct, performance, auditing, recording, analysis, and reporting of clinical trials involving human subjects. GCP ensures the accuracy and credibility of trial while safeguarding the rights, integrity, and confidentiality of participants. Adherence to GCP facilitates the generation of high-quality studies that can be incorporated in Evidence-Based Medicine (EBM). The clinical practice of EBM seeks to integrate robust medical literature into daily medical practice. This process involves systematically searching for high-quality evidence, critically appraising the retrieved literature, applying sound clinical principles and finally evaluating the efficacy of the chosen approach. Although EBM has been evaluated in many resource settings, it has not been addressed sufficiently in the field of Pediatrics and more specifically in indigenous populations. In this review, we briefly explain the EBM approach and its applications in Pediatrics, in order to help physicians care for young subjects more efficiently by integrating the best available information into their routine clinical practice. Also, the basic good practice principles for conducting clinical trials in children and adolescents are highlighted, emphasizing the importance of applying high ethical principles in this vulnerable population. Full article

This study aimed to evaluate the effects of specific LED light spectra on the growth and physiology of Changchuan No. 3 Capsicum annuum L. seedlings. The experimental design involved exposing pepper seedlings to six different spectral light combinations for 7, 14, and 21 days, with the treatments consisting of 2R1B1Y (red/blue/yellow = 2:1:1), 2R1B1FR (red/blue/far-red = 2:1:1), 2R1B1P (red/blue/purple = 2:1:1), 4R2B1G (red/blue/green = 4:2:1), 2R1B1G (red/blue/green = 2:1:1), and 2R1B (red/blue = 2:1). The results demonstrated distinct spectral regulation of seedling development: compared to the white light (CK), the 2R1B1FR (far-red light supplementation) treatment progressively stimulated stem elongation, increasing plant height and stem diameter by 81.6% and 25.9%, respectively, at day 21, but resulted in a more slender stem architecture. The 2R1B1G (balanced green light) treatment consistently promoted balanced growth, culminating in the highest seedling vigor index at the final stage. The 2R1B1P (purple light supplementation) treatment exhibited a strong promotive effect on root development, which became most pronounced at day 21 (126% increase in root dry weight), while concurrently enhancing soluble sugar content and reducing oxidative stress. Conversely, the 2R1B1Y (yellow light supplementation) treatment increased MDA content by 70% and led to a reduction in chlorophyll accumulation, while 2R1B (basic red–blue) resulted in lower biomass accumulation compared to the superior spectral treatments. The 4R2B1G (low green ratio) treatment showed context-dependent outcomes. This study elucidates how targeted spectral compositions, particularly involving far-red and green light, can optimize pepper seedling quality by modulating photomorphogenesis, carbon allocation, and stress physiology. The findings provide a mechanistic basis for designing efficient LED lighting protocols in controlled-environment agriculture to enhance pepper nursery production. Full article

This study explores the convergence in greenhouse gas emissions (GHGs) and its determinants across 38 OECD countries during the period 1996–2022, employing the novel approach which combined club convergence method with supervised machine learning algorithm Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) method. The findings reveal the presence of three distinct convergence clubs shaped by structural economic and institutional characteristics. Club 1 exhibits low energy efficiency, high fossil fuel dependence, and weak governance structures; Club 2 features strong institutional quality, advanced human capital, and effective environmental taxation; and Club 3 displays heterogeneous energy profiles but converges through socio-economic foundations. While traditional growth-related drivers such as technological innovation, foreign direct investments, and GDP growth play a limited role in explaining emission convergence, energy structures, institutional and policy-related factors emerge as key determinants. These findings highlight the limitations of one-size-fits-all climate policy frameworks and call for a more nuanced, club-specific approach to emission mitigation strategies. By combining convergence theory with interpretable machine learning, this study contributes a novel empirical framework to assess the differentiated effectiveness of environmental policies across heterogeneous country groups, offering actionable insights for international climate governance and targeted policy design. Full article

Dendrobium officinale is an important medicinal and edible plant in China, widely used in the dietary health industry and pharmaceutical field. Due to the different geographical origins and cultivation methods, the nutritional value, medicinal quality, and price of Dendrobium are significantly different, and accurate identification of the origin is crucial. Current origin identification relies on expert judgment or requires costly instruments, lacking an efficient solution. This study proposes a Variational Inference-enabled Data-Efficient Learning (VIDE) model for high-precision, non-destructive origin identification using a small number of image samples. VIDE integrates dual probabilistic networks: a prior network generating latent feature prototypes and a posterior network employing variational inference to model feature distributions via mean and variance estimators. This synergistic design enhances intra-class feature diversity while maximizing inter-class separability, achieving robust classification with limited samples. Experiments on a self-built dataset of Dendrobium officinale samples from six major Chinese regions show the VIDE model achieves 91.51% precision, 92.63% recall, and 92.07% F1-score, outperforming state-of-the-art models. The study offers a practical solution for geographical origin identification and advances intelligent quality assessment in Dendrobium officinale. Full article