Search Results (939)

Eating disorders are multifactorial mental health conditions that predominantly affect adolescent girls and young women and constitute a major public health concern due to their severe and often chronic impact on physical, psychological, and psychosocial functioning. Although existing research suggests that gender-related constructs and traditional gender roles may be associated with the development and expression of eating disorders, empirical evidence using validated measures remains limited. Accordingly, the present study examines health-related variables from a gender-sensitive perspective in a clinical sample of women diagnosed with an eating disorder. Forty women aged 14 to 50 years completed an assessment protocol including measures of gender norms, eating disorder symptoms, mental health, and self-perceived overall health. Results indicated that poorer mental health and self-perceived overall health were significantly associated with higher levels of eating disorder symptomatology. In an exploratory hierarchical regression analysis, overall conformity to traditional feminine gender norms was associated with eating disorder symptomatology after accounting for health-related variables. Exploratory analyses of individual gender norm dimensions indicated that only a small number of associations remained statistically significant after applying a false discovery rate correction. In sum, within the limitations of a modest and heterogeneous clinical sample, the findings suggest that conformity to traditional feminine gender norms is associated with less favorable health indicators and greater eating disorder symptomatology among women with EDs. These results underscore the potential value of incorporating gender-informed perspectives into future research and clinical reflection, while highlighting the need for replication in larger and longitudinally designed studies. Full article

Precise modeling and simulation of pedestrian flow are crucial for public space safety design and emergency management. This study proposes an interdisciplinary method integrating computer vision and cellular automata (CA). First, unidirectional pedestrian flow video data with different densities were collected from an overpass scene via controlled experiments. High-precision pedestrian trajectory extraction and tracking were achieved using the YOLO 11 model and DeepSORT algorithm, with image distortion corrected by perspective transformation. For the first time, the probability distribution of pedestrian turning angles derived from trajectory analysis was converted into data-driven transition probabilities for the Moore neighborhood in the CA model. An improved evacuation model was then constructed, comprehensively considering real-data-based transition probabilities, speed–density distribution, panic coefficient, individual life value, and hazard source dynamics. Multi-scenario simulations show that moderate panic may shorten evacuation time, while excessive panic causes behavioral disorders; group movement is constrained by the slowest individual, and increased hazard source speed reduces the proportion of safe pedestrians. This study provides new insights and methodological support for refined pedestrian evacuation simulation and safety management. Full article

Facial neuromodulation with botulinum toxin has traditionally been approached from the perspective of wrinkle correction. However, facial expressions primarily arise from coordinated muscular interactions that convey both positive and negative emotional valence. A conceptual framework focused on muscular equilibrium rather than wrinkle severity may therefore offer a more comprehensive, reproducible, and clinically meaningful approach. In this article, we propose the Index of Muscular Equilibrium (IME) Framework, a conceptual model for aesthetic neuromodulation that integrates functional muscle mapping, validated severity scales, and a composite IME score to support personalized treatment planning and outcome assessment. The framework is derived from a narrative review of PubMed-indexed literature on facial muscle activity, emotional expression, and validated clinical assessment tools. It combines a Valence Map to classify positive- and negative-valence muscle groups, a standardized evaluation of static and dynamic hypertonus, a conceptual Plan Score to guide selective neuromodulation, and a feedback-based longitudinal workflow (the IME Loop). Together, these components enable structured assessment of muscular imbalance, integration of established wrinkle severity scales, and translation into individualized, function-oriented treatment strategies, with intended benefits including improved objectivity, reproducibility, and patient communication. By reframing treatment success from the duration of muscle blockade to the duration of expressive harmony, the IME Framework introduces testable constructs for future validation and offers a functional perspective on facial neuromodulation aligned with contemporary affective science. Full article

Natural convection heat transfer is commonly described using dimensionless correlations relating the Nusselt number to the Grashof and Prandtl numbers. For gases and non-metallic liquids, these parameters combine into the Rayleigh number, $Ra=Gr·Pr$, while for liquid metals they form the Boussinesq number, $Bo=Gr·P{r}^2$, whose physical roles are often underrated. Focusing on the classical configuration of laminar flow over an isothermal vertical plate, the present work adopts a phenomenological perspective to examine how the governing variables influence the thickness of the thermal boundary layer, and, consequently, the heat transfer rate. Within this framework, the Rayleigh and Boussinesq numbers emerge naturally as the controlling dimensionless parameters. Once a power law functional dependence between the Nusselt number and the Rayleigh and Boussinesq numbers is assumed, a physically consistent range for the associated exponents is identified, together with the necessity of introducing a weak corrective function of the Prandtl number, which plays just a refining role. An overall order-of-magnitude analysis is further developed to recover the classical one-fourth power law structure of natural convection correlations and to clarify the origin of the Prandtl number corrective function. The primary contribution of this study is to demonstrate that the Rayleigh and Boussinesq numbers are not merely the outcome of solving the governing equations, but are the dimensionless groups that encapsulate the underlying physics of natural convection, in contrast to the Grashof and Prandtl numbers considered separately. This perspective provides a comprehensive physical interpretation of the existing heat transfer correlations, also offering guidance for identifying the appropriate dimensionless parameters when developing new correlations, useful both to researchers and educators. Full article

It has been reported that quantum correction modifies the topological charges of Anti-de-Sitter Reissner–Nordström (AdS-RN) black holes in Kiselev spacetime, yielding new perspectives on topological classification. This leads us to focus on how quantum corrections and other parameters collectively influence the long-term dynamic evolution of strings. First, we analytically examine whether the strings’ motion violates the Maldacena–Shenker–Stanford (MSS) bound. Then, we employ numerical integration to study the influence of various parameters on string chaotic dynamics. Our results demonstrate that the quantum-correction parameter a, the normalization factor c, and black-hole charge Q significantly influence chaotic behavior and the violation of the MSS bound. In particular, as a increases, the system undergoes an order–chaos–order transition, whereas an increase in c or a decrease in Q drives the system from order to chaos. Full article

This study empirically examines the relationship between digital banking and financial inclusion in Fiji, a small island developing state with geographically dispersed populations and limited access to traditional banking infrastructure. Using annual panel data from eight financial institutions—six commercial banks and two non-bank financial institutions—covering 2012–2024, the analysis accounts for cross-sectional dependence, heteroskedasticity, and serial correlation through Driscoll–Kraay panel-corrected standard errors, while robustness checks using the generalized method of moments (GMM) address potential endogeneity concerns. The results indicate that digital banking is positively associated with higher levels of financial inclusion in Fiji. Both the baseline model, which includes only digital banking, and the extended model, which incorporates banking-sector and macroeconomic controls, show consistent associations. From a policy perspective, the findings provide empirical support for strengthening digital financial infrastructure and regulatory frameworks to promote inclusive finance in small island economies. Overall, the study contributes to the limited empirical literature on digital finance in such contexts and offers insights for policymakers and financial institutions seeking to expand financial inclusion. Full article

Poly(ethylene glycol) (PEG) has long stood as the prevailing standard in drug delivery, celebrated for its capacity to enhance solubility, extend circulation, and improve pharmacological performance. Nevertheless, the emergence of anti-PEG antibodies, accelerated clearance, and limited biodegradability increasingly undermine its role as a universal solution. In response, a new generation of polymers has been developed to address these shortcomings, offering the potential to sustain or surpass PEG’s benefits while mitigating immunogenicity, improving biocompatibility, and enabling finer control over therapeutic fate. This review examines current research to articulate a coherent perspective on the replacement of PEG, tracing how advances in polymer design are reshaping the foundations of targeted drug delivery. Taken together, these developments signal not only a corrective to the limitations of PEG but also a broader paradigm shift toward safer, more versatile, and clinically translatable systems that define the next frontier in precision therapeutics. Full article

Building resilience is largely affected by the socioeconomic characteristics of the community as well as the physical and environmental local characteristics. The effectiveness of the adopted policies for resilience building partly relies on considering public concerns and insights. Insights from public narratives can enrich the resilience-building policies by sharing experiences or evidence from past disasters. Furthermore, it reveals priorities and concerns that society is expecting to be addressed. Even if the concerns are triggered by misinformation, addressing them (e.g., by disseminating corrective information) can increase the success of resilience-building policies. Tracing the public narrative over time shows how much people’s perspectives have changed after the disaster and how the relief and resilience-building efforts were compatible with society’s expectations. This study is aimed at extracting such insights from the public narrative on social media platforms after Morocco’s 2023 earthquake. Full article

Unsafe behavior remains a dominant contributor to accidents in complex socio-technical systems (STSs), yet it is still frequently interpreted as an individual-level information failure. This study argues that unsafe behavior is more accurately understood as a systemic outcome shaped by multi-level technological, organizational, and environmental conditions. To address this gap, an integrated human factor risk analysis framework is proposed by combining the STS perspective with safety information cognition (SIC) theory. The framework conceptualizes unsafe behavior as the result of risk transmission through safety information flows, linking system-level risk sources to individual perception, cognition, decision-making, and action. Within this perspective, human factor risk does not arise directly from individual error, but from deficiencies and asymmetries in the generation, transmission, and utilization of safety-related information embedded in the STS. Based on this conceptualization, a system-oriented human factor risk analysis (HRFA) approach is developed to support the identification, assessment, and control of unsafe behaviors across both accident scenarios and operational contexts. The framework is applied to road transportation of dangerous goods in China, a typical high-risk STS. The application results demonstrate that the proposed approach can effectively distinguish the comprehensive risk characteristics of different unsafe behaviors and reveal their underlying systemic causes. This study contributes to systems thinking in safety governance by shifting the analytical focus from individual behavior correction to upstream system conditions and information processes. The proposed framework provides a transferable approach for understanding and managing human factor risk in complex STSs and offers practical implications for proactive, system-oriented safety governance. Full article

Land subsidence poses a persistent challenge to Tianjin, a major coastal city in China, with implications for urban infrastructure and sustainable development. This study examines the spatiotemporal evolution of ground subsidence in Tianjin from 2003 to 2024 using multi-source SAR observations from Envisat ASAR (C-band), ALOS PALSAR (L-band), and Sentinel-1 (C-band). Surface deformation was derived using SBAS-InSAR with atmospheric phase correction. Due to limitations in data availability, SAR observations are temporally discontinuous; therefore, the long-term subsidence evolution was reconstructed by integrating multi-sensor deformation rates through a model-based time-series fitting approach. The results show pronounced subsidence during 2003–2010 in inland districts such as Wuqing, Beichen, Jinnan, and Jinghai, with maximum rates exceeding 50 mm/yr. After 2017, regional subsidence rates generally declined, while localized deformation became increasingly concentrated in coastal reclamation areas of the Binhai New Area, particularly around Dongjiang Port and Fuzhuang. Spatial and temporal patterns of subsidence exhibit clear correspondence with changes in groundwater use intensity and phases of urban construction and land reclamation. These observations suggest a transition in dominant subsidence controls over time. The results provide a long-term observational perspective on subsidence evolution in Tianjin and offer a geospatial basis for land-use planning and infrastructure risk assessment in coastal cities. Full article

To enhance the resilience and sustainability of urban metro systems under operational uncertainties and external disturbances, critical station identification and vulnerability assessment should be further investigated from the perspective of network science. In this paper, the presented comprehensive clustering algorithm and the Pearson correlation coefficient are adopted to explore the origin-destination (OD) passenger flow characteristics on different date classifications, and the different dates should be reasonably classified into three categories, including working day, weekends, and holiday. Meanwhile, this paper proposes the dynamic DomiRank algorithm and flow DomiGCN model to identify critical stations from network structure and function on different data classifications respectively, and further studies the vulnerability property of metro networks under simulated attacks. The Shanghai metro network is selected as case to prove the feasibility and correctness of the model. The results show that the dynamic DomiRank algorithm is relatively effective to identify critical stations from network structure, and the flow DomiGCN model is also relatively effective to identify critical stations from network function. Moreover, simulated attacks to these critical stations detected by the proposed methods can cause more damages than the other methods. These findings provide some supports for protection of metro infrastructure and contribute to the sustainable operation and development of urban rail transit systems. Full article

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate the use of an advanced deep learning (DL) model for the multiclass classification of diseases at the sub-diagnosis level using panoramic radiographs to resolve the inconsistencies and skewed classes in the dataset. Methods: To classify and test the models, rich data of 10,580 high-quality panoramic radiographs, initially annotated in 93 classes and subsequently improved to 35 consolidated classes, was used. We applied extensive preprocessing techniques like class consolidation, mislabeled entry correction, redundancy removal and augmentation to reduce the ratio of class imbalance from 2560:1 to 61:1. Five modern convolutional neural network (CNN) architectures—InceptionV3, EfficientNetV2, DenseNet121, ResNet50, and VGG16—were assessed with respect to five metrics: accuracy, mean average precision (mAP), precision, recall, and F1-score. Results: InceptionV3 achieved the best performance with a 97.51% accuracy rate and a mAP of 96.61%, thus confirming its superior ability for diagnosing a wide range of dental conditions. The EfficientNetV2 and DenseNet121 models achieved accuracies of 97.04% and 96.70%, respectively, indicating strong classification performance. ResNet50 and VGG16 also yielded competitive accuracy values comparable to these models. Conclusions: Overall, the results show that deep learning models are successful in dental disease classification, especially the model with the highest accuracy, InceptionV3. New insights and clinical applications will be realized from a further study into dataset expansion, ensemble learning strategies, and the application of explainable artificial intelligence techniques. The findings provide a starting point for implementing automated diagnostic systems for dental diagnosis with greater efficiency, accuracy, and clinical utility in the deployment of oral healthcare. Full article

The rapid development of rural and peri-urban areas increases the demand for decentralized wastewater treatment systems. Small wastewater treatment plants (SWTPs) with a capacity below 2000 PE are becoming an important element of local water protection and circular-economy strategies, yet clear guidelines for selecting appropriate technologies are still lacking. This study analyzes the criteria used in decision-making for SWTPs from a multi-stakeholder perspective and evaluates the relative importance of technical, economic, environmental and social factors. The research was conducted in Poland and included a survey of 130 respondents representing six stakeholder groups (officials, operators, designers, contractors, scientists and residents). Respondents allocated weights to four main groups of criteria and assessed eleven detailed parameters on a 1–10 scale. The data were analyzed using descriptive statistics, the Kolmogorov–Smirnov test with the Lilliefors correction to verify distribution assumptions, and the Kruskal–Wallis test to examine differences between stakeholder groups. The results show a consistent hierarchy of criteria, with technical reliability, treatment efficiency and operating costs ranked as the most important factors. Social and environmental aspects were assessed as relevant but secondary. Only minor differences between stakeholder groups were observed. The study highlights the need for integrated, multicriteria approaches in SWTP planning, particularly in dispersed rural areas. The findings may support local authorities, designers and investors in technology selection. The research is limited by the non-probability sampling strategy, the national scope of the dataset and the cross-sectional character of the survey. Full article