Search Results (638)

Objective: To explore the longitudinal association between BMI and depressive symptoms among college students and to explore whether this association differs by gender. Method: A quantitative longitudinal design was adopted, and 1336 college students were tracked for a period of three years. The SCL-90 and BMI calculated from objectively measured height and weight were used to assess depressive symptoms and body mass status among college students. Using random intercept cross-lagged model analysis, we analyzed the temporal lagged associations between BMI and depressive symptoms and constructed stratified models for different genders to examine differences in these associations. Result: The results showed that there was a negative bidirectional predictive effect between BMI and depressive symptoms. Within-person deviations in depressive symptoms at T1 were significantly and negatively associated with deviations in BMI at T2 (β = −0.126, p = 0.003). The path from T2 to T3 was also significant (β = −0.079, p = 0.004). The BMI fluctuation at T1 could significantly negatively predict the depressive symptoms fluctuation at T2 (β = −0.133, p = 0.004). The path from T2 to T3 was also significant (β = −0.149, p = 0.003). At the interpersonal level, there was a significant positive correlation between RI_X and RI_Y (β = 0.142, p = 0.012). This means that, at the individual difference level, individuals with higher stable BMI traits also tend to have higher stable depressive symptoms traits. At the intra-individual level, the correlation between the BMI fluctuation and the depressive symptoms fluctuation at the same time point was not significant. Gender differences suggested that the longitudinal association between BMI and depressive symptoms may differ by gender. The BMI–depressive symptoms dynamics among male students appeared more complex. Conclusions: In the college student population, the association between BMI and depressive symptoms mainly reflects stable individual differences rather than continuous intra-individual dynamic effects. These findings highlight the value of separating between-person differences from within-person change when studying the physical and mental health of college students. Full article

Water and sanitation governance sits at the intersection of global development ambitions and highly localized service realities. While SDG6 sets universal targets for clean water and sanitation, the institutional and fiscal arrangements that translate those targets into actual service outcomes operate primarily at the subnational level. The discrepancy between globally defined objectives and locally executed delivery creates a structural research gap: how do the fiscal architectures of local governments influence progress towards SDG6? This study addresses this question for a panel of OECD countries by developing a deep learning-based estimation framework that combines bidirectional long short-term memory (BiLSTM) networks with Tianji’s horse racing optimization (THRO) algorithm. Three distinct operationalizations of fiscal decentralization are tested against SDG6 outcomes: subnational expenditure share (EFDM), subnational revenue share (RFDM), and a composite index balancing both dimensions (CFDM). Model adequacy is assessed using a layered diagnostic protocol involving regression fit, country-level residual patterns, error density profiles, Bland–Altman limits of agreement and inter-annual error trajectories. Among the three configurations, CFDM consistently records superior performance ($R^2=0.9216$; $RMSE = 1.4465$; $MAE = 1.0712$), while even the weakest specification clears $R^2=0.89$, attesting to the overall robustness of the proposed architecture. The margin by which CFDM outperforms its alternatives highlights a key finding: neither spending authority nor revenue capacity alone accurately reflects the fiscal reality of local water and sanitation governance; it is their combined effect that is important. The expenditure dimension is further proven to be the more influential of the two unidimensional proxies, consistent with the capital-intensive and maintenance-heavy nature of water infrastructure. On the other hand, coefficient findings show that fiscal decentralization is positively associated with SDG6 achievement for all models. Beyond its empirical contributions, the study introduces a methodological template for applying hybrid AI optimization to policy-relevant sustainability panels. It also connects two largely parallel bodies of scholarship, fiscal federalism and SDG research, that have rarely been examined together. Full article

Teacher professional development (TPD) is increasingly mediated by online platforms, yet emotion analysis in this context remains underdeveloped because teachers’ professional discourse is often reflective, evaluative, and shaped by professional norms. To address this challenge, this study proposes a fine-grained, low-intrusion affective-appraisal analysis framework for TPD-oriented online professional learning that integrates textual evidence with platform interaction logs. The framework retains pleasure, arousal, and dominance from the pleasure–arousal–dominance (PAD) model and introduces utility as an appraisal-related dimension, capturing teachers’ perceived usefulness, value judgment, and professional learning gain. Methodologically, it combines textual representations based on Bidirectional Encoder Representations from Transformers (BERT), intra-week long short-term memory (LSTM) aggregation, interpretable behavioral-log features, and feature-level fusion. Data were collected from an authentic TPD-oriented online course involving 107 pre-service teachers, yielding 1276 teacher-week samples from 4300 texts and 264,028 interaction records. Results show that intra-week sequential modeling improves the macro-averaged F1 score (Macro-F1) over both the term frequency–inverse document frequency plus support vector machine (TF-IDF+SVM) baseline and BERT-based weekly text concatenation, with statistically significant gains over the non-sequential BERT-concat model across all four dimensions. Adding interaction logs improves accuracy across all dimensions and provides complementary process-based evidence, especially for arousal and utility. By linking a four-dimensional affective-appraisal framework with text-log fusion, this study offers a scalable and context-sensitive approach to affective-appraisal analytics in pre-service teacher professional learning. Full article

This study investigates the relationship between energy consumption and ecological sustainability in South Africa over the period 1990–2023, with a particular focus on the roles of renewable energy consumption, non-renewable energy consumption, and economic growth. Ecological sustainability is proxied by the Load Capacity Factor (LCF), a comprehensive measure that captures the balance between biocapacity and environmental pressure. The study employs the Nonlinear Autoregressive Distributed Lag (NARDL) model to capture both short-run and long-run asymmetric effects, decomposing renewable energy consumption into positive and negative shocks to identify nonlinear dynamics. Descriptive statistics reveal moderate stability in the LCF, increasing adoption of renewable energy, sustained economic growth, and persistent dependence on fossil fuels. Unit root tests confirm mixed integration orders, justifying the use of the NARDL framework. Empirical results indicate that positive shocks in renewable energy consumption significantly enhance ecological sustainability, while negative shocks reduce the LCF, highlighting the asymmetric impact of renewable energy. Non-renewable energy consumption exhibits a statistically significant long-run association with ecological sustainability, reflecting South Africa’s continued structural dependence on fossil-fuel-based energy systems during the study period. Granger causality tests show that renewable energy and non-renewable energy consumption are key drivers of ecological sustainability, whereas economic growth and environmental conditions exhibit bidirectional feedback. The findings provide evidence for the strategic importance of promoting renewable energy adoption, reducing fossil fuel reliance, and integrating sustainability considerations into economic planning. Policy recommendations emphasize investment in renewable energy infrastructure, incentives for green energy adoption, and the integration of environmental objectives into economic development strategies to enhance South Africa’s ecological resilience. Full article

In recent years, short video platforms have rapidly developed into important media for cultural dissemination. The interactions of netizens in short video comment sections not only reflect their focus on cultural content but also contain rich emotional attitudes. However, given the vast and fragmented nature of comment data, accurately extracting keywords, identifying cultural themes, and analyzing sentiment tendencies pose significant challenges in understanding netizens’ cultural perceptions. To address these challenges, this study proposes a text analysis framework that integrates keyword extraction, clustering analysis, and sentiment analysis to explore the core topics and emotional characteristics of cultural dissemination in short video comment sections. Firstly, to address the challenge of balancing statistical information and semantic understanding in short-text keyword extraction, this paper proposes the TF-IDF-KeyBERT Integrated Algorithm (TKIA) keyword extraction algorithm, which integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Key Bidirectional Encoder Representations from Transformers (BERT). Experiments on the CSL dataset demonstrate improvement in the F1@5 metric, showing its potential to enhance keyword extraction performance for short texts. Secondly, to address the difficulty of simultaneously considering semantic representation capability and clustering flexibility in short-text clustering analysis, this paper designs the Self-Supervised Contrastive Enhanced Clustering (SCEC) algorithm by integrating self-supervised contrastive learning with a soft clustering strategy. Compared to baseline methods, SCEC improves clustering accuracy (ACC) by 17.5% on AGNews and 6.8% on THUCNews, suggesting a more effective way to reveal the underlying structure of cultural topics. Finally, to address the challenge of effectively leveraging both text structural information and global semantic features in short-text sentiment analysis, this paper develops the BERT-GCN Cross-Attention (BGC) Model, integrating BERT embeddings and Graph Convolutional Network (GCN)-based structural features via a Cross-Attention mechanism. On the My_weibo_senti_100k dataset, the BGC model achieves a 2.45% increase in Macro-F1 and a 2.41% improvement in accuracy over strong baselines, offering its ability for high-precision modeling of user sentiment. This study offers effective data support and technical pathways for applications such as cultural content understanding, personalized recommendation, and user emotion guidance. Full article

Background and Motivation: Foreign direct investment (FDI) has been widely examined as a potential driver of economic growth, yet empirical evidence for Morocco remains inconclusive due to methodological limitations and endogeneity concerns. This study re-examines the FDI–growth relationship in Morocco over the period 1977–2022 using a five-equation simultaneous system estimated by Two-Stage Least Squares (2SLS). The framework jointly models GDP growth, FDI, exports, human capital, and domestic investment in order to account for bidirectional relationships among the main variables. Methods: Unit root and Johansen cointegration tests support the existence of long-run equilibrium relationships among the series, while a Chow test identifies a significant structural break in 2003 (F = 11.43, p = 0.003). Diagnostic tests confirm instrument relevance (Cragg–Donald F > 10) and fail to reject over-identification validity (Hansen J p > 0.10). Results: The results indicate a positive but statistically fragile association between FDI and economic growth—positive in sign across all specifications but sensitive to sample size and instrument choice (β = 2.179, t = 1.728, p = 0.092; 90% CI: [−0.341, 4.699] in constant 2015 USD billions). FDI is associated with growth primarily through indirect channels—particularly export expansion and human capital accumulation—rather than through direct capital deepening alone, consistent with an absorptive-capacity interpretation. The estimated structural break in 2003 reflects a broader package of concurrent institutional and macroeconomic reforms; the model cannot isolate the independent contribution of FDI within this composite effect. The results should therefore be interpreted as evidence of long-run reduced-form associations rather than definitive causal effects. Conclusions: Overall, the study contributes to the Morocco-specific literature by integrating simultaneous equations, indirect transmission channels, and structural break analysis within a unified long-run framework. Full article

Background: Platinum-resistant ovarian cancer with peritoneal metastases remains a therapeutic frontier marked by limited systemic efficacy and a persistent unmet clinical need for effective locoregional strategies. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) has emerged as a novel minimally invasive platform designed to enhance intraperitoneal drug distribution and overcome biological barriers to chemotherapy delivery. Methods: We performed a PRISMA-compliant systematic review and meta-analysis evaluating clinical outcomes of PIPAC in platinum-resistant ovarian cancer. Primary endpoints included histologic regression (PRGS ≤ 2), severe toxicity, and 12-month overall survival, complemented by exploratory analyses of treatment feasibility, disease burden dynamics, and bidirectional therapy strategies. Results: PIPAC demonstrated a consistent signal of biologic activity, with pooled histologic response rates indicating meaningful tumor regression despite advanced disease. Severe toxicity remained low across studies, supporting the favorable tolerability of repeated intraperitoneal treatment. Survival outcomes were clinically relevant for a heavily pretreated population, while feasibility analyses suggested that PIPAC may facilitate downstream surgical opportunities in selected patients. Exploratory findings further supported the concept of intraperitoneal disease modulation, reflected by reductions in peritoneal cancer index and integration within multimodal treatment pathways. Conclusions: Beyond a purely palliative intervention, PIPAC may represent a biologically active component of personalized treatment strategies for platinum-resistant ovarian cancer. These findings redefine the therapeutic narrative from symptom control toward disease modulation and treatment escalation, underscoring the need for prospective trials to refine patient selection and optimize multimodal sequencing. Full article

Late eighteenth-century Chosŏn Korea presents a distinctive case in the history of Christian missions: a Catholic community emerged without the sustained presence of foreign missionaries. This article examines that distinctiveness through the lens of text-mediated local reception. Since the seventeenth century, the writings of Matteo Ricci had rendered Christian doctrine intelligible within a Confucian framework through Jesuit accommodation. In late Chosŏn, these texts moved beyond scholarly curiosity and became a medium of criticism, moral reflection, and, for some readers, communal religious practice, particularly among politically marginalized Namin (Southern) circles and Silhak (Practical Learning)-oriented thinkers. The reception of Catholicism unfolded in stages. Sinographic texts composed by Jesuit missionaries were first understood within an existing Confucian horizon and then selectively appropriated by local readers. In some cases, this process led to baptism, early lay organization, and communal religious life. Through comparison with China, Japan, and Vietnam, this study argues that Chosŏn represents a distinctive case in which translated Christian texts, local appropriation, and community formation converged without a sustained missionary presence. It further shows that this process was shaped not by one-way transmission alone, but by the active agency of local readers and a bidirectional process of cultural translation. Full article

Epilepsy is a heritable neurological disorder that is frequently comorbid with sleeping difficulties, including short/long sleep duration and insomnia. Although epidemiological studies have consistently reported the comorbidity between sleep disturbances and epilepsy, the shared genetic architecture and molecular mechanisms underlying this relationship remain poorly characterized, hindering therapeutic development. In this study, we integrated large-scale genome-wide association study (GWAS) summary statistics of European ancestry to dissect the genetic and molecular links between sleep traits and epilepsy. Using LDSC and GWAS-pw, we identified modest but statistically significant (Bonferroni-corrected) global and local genetic correlations between sleep behaviors and epilepsy. Subsequent CPASSOC cross-trait meta-analysis and transcriptome-wide association studies (TWAS) pinpointed specific pleiotropic loci and shared candidate genes, including SPAG7, VRK2, and LINC00925, which are functionally associated with neuroimmune signaling. While preliminary Phenome-Wide Association Study (PheWAS) profiling of these candidate targets did not identify major adverse associations in current databases, we emphasize that rigorous in vitro and in vivo experimental validations are required before considering them for therapeutic strategies. Finally, pleiotropy-robust bidirectional Mendelian Randomization (MR) analyses suggested unidirectional causal liability from epilepsy to short sleep duration. Although the estimated causal effect size was minimal, it reflects lifelong polygenic architecture rather than acute clinical magnitude. In conclusion, our multi-omics approach unveils the shared genetic architecture of the sleep-epilepsy axis and highlights potential biomarkers for future functional investigation. Full article

Uveitis is a heterogeneous group of intraocular inflammatory diseases and an important cause of visual impairment worldwide. Although current treatments mainly target inflammation, many patients develop chronic or recurrent disease, suggesting that inflammation control alone may not fully restore intraocular homeostasis. Increasing evidence highlights the blood–ocular barrier (BOB), including the blood–retinal barrier and blood–aqueous barrier, as a key regulator of the intraocular microenvironment. This review aims to summarize the bidirectional interaction between intraocular inflammation and blood–ocular barrier dysfunction in uveitis, and to highlight the clinical significance of barrier dysfunction in disease monitoring and management. In addition, this review discusses the potential value of incorporating barrier assessment into dynamic disease evaluation and relapse-aware management strategies. Recent studies suggest that inflammation and BOB dysfunction are bidirectionally linked. Inflammatory mediators disrupt barrier integrity, while barrier breakdown facilitates immune cell infiltration and further amplifies inflammation, forming a self-reinforcing cycle that may drive disease persistence. Importantly, BOB dysfunction also has clinical implications. Findings such as aqueous flare, macular edema on optical coherence tomography, and vascular leakage on fluorescein angiography reflect barrier status and can serve as dynamic indicators for disease monitoring. Persistent abnormalities despite reduced inflammatory cell activity may indicate incomplete barrier recovery or subclinical inflammation, helping to explain discordant clinical findings and the relapse-prone nature of uveitis. Rather than viewing BOB dysfunction solely as a pathological consequence of inflammation, this review highlights the potential clinical value in disease assessment and management. Barrier-related findings may provide additional information beyond conventional inflammatory evaluation, particularly in cases where inflammatory cell activity appears controlled, but underlying barrier alteration persists. Incorporating barrier assessment into monitoring may help interpret discordant clinical findings, improve evaluation of disease control, and support a more relapse-aware management strategy in uveitis. In addition, therapeutic approaches aimed at restoring barrier integrity may provide a more comprehensive strategy for achieving sustained remission and reducing recurrence risk. Full article

The structural integrity of tight junction (TJ) networks in the retinal pigment epithelium is a key indicator of the function of the outer blood-retinal barrier (oBRB). However, traditional automatic segmentation methods often suffer from topological discontinuities, resulting in fragmented predictions that fail to accurately reflect the barrier’s state. In this study, we propose a topology-preserving deep learning framework specifically designed for TJ skeleton extraction and node identification. Our method employs a multi-task bidirectional architecture that simultaneously models both the midline structure and connecting nodes, and incorporates a composite loss function (clDice) constrained by soft skeleton similarity to explicitly enforce global structural connectivity. Quantitative evaluations indicate that the proposed method significantly improves topological consistency, with a Betti error of 3.3182, a Graph Connectivity Ratio (GCR) of 0.6858, and a Mean Node Degree Error (MNDE) of 1.6977. Although the F1 score for connectivity is 0.7830, the predicted network outperforms the standard model in terms of morphological fidelity and connectivity. These findings underscore the necessity of adopting topology-aware modeling in the process of biological network extraction, providing a solid computational foundation for the objective quantitative analysis of morphological stability in tightly connected networks in both clinical and experimental research. Full article

Stainless steel pipes are critical components in industrial systems such as oil and gas transportation and nuclear power cooling. Surface defects can severely degrade their mechanical performance and operational safety. However, existing inspection methods still face challenges including difficult feature extraction, strong reflection interference, and limited accuracy in small-target detection. To address these issues, this paper proposes an improved detection algorithm termed YOLOv8s-BISW (incorporating BiFPN, SGE attention, and WIoU loss), which introduces multidimensional optimizations based on the YOLOv8s baseline. First, an image enhancement module combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE) is designed to mitigate uneven illumination and blurred defect imaging. Second, a Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to strengthen multi-scale feature fusion and improve adaptability to defects of different sizes. Meanwhile, a Spatial Group-wise Enhance (SGE) attention module is embedded into the backbone to enhance defect feature representation while suppressing background interference. Furthermore, the Wise Intersection over Union (WIoU) loss function replaces Complete IoU (CIoU) to improve bounding box regression for irregular defects. Experimental results show that the proposed model achieves an mAP of 0.979 on a self-constructed Stainless-steel Tube Flaw (STF) dataset. Compared with the original YOLOv8s, precision, recall, and mAP are improved by 0.007, 0.010, and 0.033, respectively, while the average detection time per image is only 3.7 ms, achieving a favorable balance between accuracy and real-time performance. Compared with mainstream algorithms such as SSD, YOLOv3, and Faster R-CNN, the proposed method demonstrates superior overall performance, providing reliable technical support for automated surface defect detection of stainless steel pipes and offering practical value for intelligent manufacturing quality control. Full article

To address the issues in existing PCB defect detection models, including insufficient capability for capturing small defects, weaker global feature modeling, and inadequate multi-scale feature fusion, this paper proposes a C2f-FPN-PAN++-Mamba model based on an improved YOLOv8n. The Mamba state–space model is embedded into the C2f module to construct a C2f-Mamba feature extraction unit, which, while retaining the local perception capability of convolution, enhances long-range dependency modeling, accurately capturing global semantic information of subtle defects in complex backgrounds and significantly improving the model’s feature representation ability for small defects. Meanwhile, an FPN-PAN++ enhanced feature fusion structure is introduced, achieving efficient complementary interaction between high and low-level features through bidirectional cross-scale feature aggregation and path augmentation, thereby strengthening the model’s robustness in identifying multi-scale and multi-form defects. Finally, the C2f-Mamba and FPN-PAN++ are organically integrated, improving global modeling and multi-scale fusion capabilities while maintaining lightweight computational efficiency, effectively reducing the miss and false detection rates of small defects. Experimental results indicate that, compared with the original YOLOv8n model, the proposed method achieves significant performance improvements in PCB defect detection tasks. On the PCB defect dataset, the model’s precision increased from 96.4% to 98.5%, recall from 94.6% to 98.4%, and mAP@0.5 from 97.2% to 98.8%, with the mAP@0.5:0.95 metric, reflecting multi-scale detection performance, rising dramatically from 57.5% to 62.5%. Experiments demonstrate that this method effectively enhances detection capability for small and complex defects while preserving the advantages of a lightweight model and high inference speed, providing a reliable technical solution for high-precision, real-time PCB defect detection in industrial scenarios. Full article

Early stopping is a standard form of implicit regularization in neural sequence models, but criteria based solely on validation loss can become unstable or weakly informative in noisy, non-stationary, or weakly separated regimes. We propose Symbolic Early Stopping (SES), a representation-aware hybrid stopping criterion that monitors the evolution of validation hidden-state organization during training. At each epoch, SES constructs a Mapper-based symbolic abstraction of hidden representations extracted from a fixed monitored layer, transforms latent trajectories into symbol sequences, and summarizes them through a compact set of symbolic–dynamic descriptors capturing sequential complexity, transition uncertainty, and geometric dispersion. These descriptors are aggregated into a single symbolic stability score, which is combined with validation-loss monitoring to detect convergence of the learned representation. We evaluate SES on recurrent, bidirectional recurrent, and encoder-only Transformer architectures across multiple time-series regimes with different levels of structural regularity and noise. The results indicate that SES frequently terminates training substantially earlier than conservative loss-based baselines while preserving a competitive quality–efficiency trade-off relative to oracle validation-based stopping. Robustness experiments under additive input noise show that the symbolic monitoring signal remains informative under moderate perturbations, although its advantage is not uniform across all datasets and model classes. A layer-wise analysis further suggests that useful stopping signals may emerge before the final validation curve fully stabilizes, reflecting earlier organization of latent representations. Overall, SES provides an interpretable and computationally tractable framework for representation-level early stopping in neural sequence modeling. Full article

In an increasingly digital global economy, cybersecurity capacity has become a key determinant of national resilience, economic competitiveness, and digital trust. However, preparedness remains uneven across the Middle East and North Africa (MENA), where levels of economic integration, governance quality, and institutional stability vary significantly. This paper examines the relationship between cybersecurity capacity, governance indicators, and international trade in selected MENA countries over the period 2010–2023. It evaluates whether rule of law and political stability are associated with cybersecurity capacity, whether trade openness predicts cybersecurity development, and whether cybersecurity capacity is dynamically associated with trade openness. The empirical analysis applies panel-data techniques, including panel unit-root tests, Pedroni cointegration tests, and the Toda–Yamamoto predictive causality framework within a multivariate VAR structure. Panel fixed-effects regressions with Driscoll–Kraay robust standard errors are also estimated to capture contemporaneous relationships while accounting for heteroskedasticity, serial correlation, cross-sectional dependence, and country-specific heterogeneity. The findings provide indicative evidence of a statistically significant bidirectional predictive relationship between trade openness and cybersecurity capacity. Greater trade integration appears to stimulate investment in secure digital infrastructure, while enhanced cybersecurity capacity may support trade expansion by strengthening digital trust and reducing transaction risks. In contrast, governance indicators do not exhibit consistent dynamic predictive relationships within the causality framework. The absence of cointegration indicates that cybersecurity capacity, governance indicators, and trade openness do not evolve within a stable long-run equilibrium relationship during the sample period. This finding may reflect the heterogeneous and policy-sensitive nature of digital infrastructure development across MENA countries. Full article