Search Results (1,352)

Maintaining sustainable mental health is an increasing societal challenge in Japan, where psychological distress and sleep problems have become major public health concerns. This study examined how three culturally grounded dimensions of self-construal—Subjective Self (SS), Objective Self (OS), and Autonomous Self (AS)—relate to both positive and negative indicators of psychological adjustment among Japanese adults. This study aimed to examine whether internally guided forms of self-regulation (SS and AS) function as psychological resources, whereas externally guided self-regulation (OS) operates as a potential vulnerability factor in a culturally tight social context. By simultaneously examining multiple indicators of adjustment, this research clarifies how culturally shared self-regulatory patterns are linked to distress and sleep difficulties that affect large segments of the population. From a public health perspective, the findings highlight socially reinforced risk and protective patterns that can inform population-level prevention and mental health promotion in settings such as schools, workplaces, and communities, rather than relying solely on individual clinical intervention. These results underscore the importance of integrating cultural psychology into public health frameworks aimed at promoting sustainable mental health in contemporary and increasingly diverse social environments. Full article

This study investigates the influence of toxic language in hate speech targeting immigrants, particularly through narrative formats like first-person X (Twitter) threads. Hate speech, defined as promotion of hatred based on personal or group characteristics, increasingly escalates on social media, impacting public attitudes and behaviors. While previous research has primarily focused on measuring the scope of hate speech through content analysis and computational methods, there has been limited attention to its effects on audiences. This study presents the results of an online experiment (N = 339) with a 2 × 2 between-subjects design that manipulates the presence of toxic language and message popularity. Results indicate that hate messages lacking toxic language promote greater identity fusion with the author of the message, which in turn increases the intention to share the message, reinforces negative attitudes toward immigrants, and increases support for harsh policies against irregular immigration. Moreover, non-toxic hate messages significantly enhance narrative transportation exclusively for individuals with conservative political views, thereby further increasing their intention to share the message. These findings highlight that subtler forms of hate speech can create strong audience connections with hostile perspectives, emphasizing the need for anti-hate campaigns to address both overt and subtle hate content. Full article

To address complex connections in prefabricated concrete structures, a novel joint connecting a prefabricated concrete-filled steel tubular column and a composite beam is proposed. Pseudo-static tests on six scaled specimens and ABAQUS finite element analyses were conducted to investigate seismic mechanisms, focusing on slab effects and beam-bottom configurations. Experimental results show the joints exhibit plump hysteretic curves. The composite beams displayed distinct shear-dominated failure, while the stiffened column remained intact. With an average ductility coefficient of 2.84 and an ultimate equivalent viscous damping coefficient of 0.207, the specimens demonstrated excellent deformation and energy dissipation capabilities. The slab’s flange effect significantly enhanced negative bearing capacity, causing mechanical asymmetry. Comparatively, the steel plate beam bottom configuration offered superior stiffness and stability over the reinforcement beam bottom configuration. Sensitivity analysis revealed that bearing capacity is highly sensitive to beam parameters (e.g., longitudinal rebar strength, connector length) but less sensitive to column parameters. Notably, the bearing capacity of the beam bottom configuration using reinforcement increases significantly with concrete strength and reinforcement ratio, whereas the beam bottom configuration using a steel plate shows marked insensitivity to these factors. These findings clarify the load transfer mechanism and support the seismic design of prefabricated structures. Full article

To address the high volatility of spot market prices and the feature extraction limitations of single models, a short-term electricity price forecasting method based on a reinforcement learning dynamic soft ensemble strategy is proposed. First, a complementary dual-branch architecture is constructed: the CNN-LSTM-Attention branch mines local temporal features, while the Transformer branch captures long-range global dependencies. Second, the Q-learning algorithm is introduced to model weight optimization as a Markov Decision Process. An intelligent agent perceives fluctuation states to adaptively allocate weights, overcoming the rigidity of traditional ensembles. Case studies on PJM market data demonstrate that the proposed model outperforms advanced benchmarks in MAE and RMSE metrics. Notably, prediction accuracy is significantly improved during price spikes and negative price periods. The results verify that the strategy effectively copes with market concept drift, supporting reliable bidding and risk mitigation. Full article

This study examined the association between bullying and cyberbullying, both in victims and aggressors, and students’ perceived competence in English as a foreign language. A cross-sectional study was conducted with 444 Spanish students (50.00% boys, mean age = 13.27 ± 1.64 years). Perceived English competence was assessed using the Questionnaire of English Self-Efficacy (QESE), while involvement in bullying and cyberbullying was measured with the European Bullying Intervention Project Questionnaire (EBIP-Q) and the European Cyberbullying Intervention Project Questionnaire (ECIP-Q). Associations were analyzed using ANCOVA and binary logistic regression, controlling for age, BMI, maternal education level, and weekly physical activity. The results showed that cyberbullying victimization was consistently associated with lower self-perceived competence in all English skills, particularly among boys, who reported scores up to 12.1% lower and were up to 6.3 times more likely to report low self-efficacy in writing. Girls also showed a higher risk, with up to 5.6 times more likelihood of low scores in oral expression. As for aggression, boys involved in both traditional and cyberbullying showed significant reductions in all language domains, especially in writing and reading. Girls demonstrated a more specific pattern, with negative associations mainly in cyberaggression, showing significantly lower self-efficacy competence in all four skills. These findings highlight the need for gender-sensitive interventions that promote emotional safety and reinforce students’ self-efficacy in language learning. Educational programs involving students, teachers, and families are recommended to foster confidence, reduce fear of errors, and create supportive environments for communicative practice. Full article

The development of innovative cosmetic ingredients has driven growing interest in emulsion systems that combine performance, stability, and sustainability. Pickering emulsions can form physically stable systems by adsorbing solid particles at the oil–water interface. In this study, bacterial cellulose nanofibers (CNFs) and nanocrystals (CNCs), obtained via acid hydrolysis, were evaluated as stabilizing agents in Pickering emulsions containing jojoba, castor, and grape seed oils for hair conditioning applications. Structural and physicochemical characterization revealed that CNCs exhibited higher crystallinity, a narrower size distribution, and a higher negative surface charge than CNFs, resulting in enhanced colloidal stability. Emulsion analyses showed that CNCs more effectively reduced interfacial tension and produced smaller, more homogeneous droplets. Stability assessments under pH variation, thermal stress, and storage demonstrated that CNC-stabilized emulsions, particularly with castor oil, maintained stability indices above 95% for up to 60 days, whereas CNF-based systems showed greater sensitivity to environmental conditions. The incorporation of CNCs into a prototype conditioning cream resulted in a creamy texture and improved physical stability without compromising formulation performance. Overall, these results highlight CNCs as robust and efficient stabilizing materials for Pickering emulsions, reinforcing the potential of bacterial nanocellulose in advanced cosmetic formulations. Full article

Grounded in knowledge recombination theory and innovation tension theory, this study develops a novel measurement framework for scientific and technological innovation (STI) risks that captures the dynamic and systemic equilibrium between novelty and adaptation. We first analyze the endogenous mechanisms through which STI risks emerge from knowledge recombination processes, and then propose a classification framework for knowledge recombination, along with quantifiable metrics for novelty and adaptation. Next, we introduce a risk classification system for STI and corresponding quantitative evaluation metrics, facilitating dynamic monitoring of innovation risk states. Finally, we validate the framework through an empirical case study in natural language processing (NLP). Our results reveal a persistent innovation tension within the STI system between novelty and adaptation. Emerging phrases and reinforced phrases demonstrate distinct risk profiles and distribution patterns, corresponding to differentiated structural and evolutionary regimes. These differences stem from their distinct mechanisms in the novelty–adaptation interaction within a complex innovation system. Specifically, in emerging phrases, novelty shows a stable positive linear correlation with Z-score, while adaptation exhibits a significant negative linear correlation with Z-score. In reinforced phrases, novelty displays a significant bimodal association with Z-score, and adaptation demonstrates a robust inverted U-shaped relationship with Z-score. Emerging knowledge combinations show significantly higher risk scores than reinforced combinations, with high-novelty–low-adaptation combinations consistently in the highest risk quantile across stages. Moreover, the risk threshold for emerging phrases increases monotonically across developmental phases. Thus, our framework advances innovation risk assessment from static categorization to dynamic, system-level evaluation, enabling tiered risk management and optimized resource allocation for high-potential innovation pathways. Full article

Unsafe disposal of pesticide waste remains a critical environmental and public health issue in developing agricultural systems. This study examined cocoa farmers’ disposal behaviours and their determinants across Nigeria’s major cocoa-producing regions using an explanatory sequential mixed-methods design. Quantitative data were collected from 391 farmers, followed by 23 in-depth interviews to contextualise behavioural drivers. Results showed that knowledge of pesticide risks and availability of disposal facilities significantly predicted safer disposal practices (R² = 0.469, p < 0.05), whereas age had a negative influence. Qualitative findings revealed that negative attitudes, social norms, and limited infrastructure reinforced unsafe behaviours, while membership in farmers’ associations promoted safer practices through peer learning. A joint display demonstrated convergence between structural enablers (collection cages, extension support) and behavioural factors (knowledge, attitudes, norms). The study identifies a dual challenge of systemic shortcomings and behavioural inertia, suggesting that regulatory action alone is insufficient without farmer engagement and education. Policy and extension programmes should prioritise collection infrastructure, association-based training, and Integrated Pest Management to promote sustainable pesticide waste management. These insights advance understanding of pesticide disposal behaviour and offer actionable guidance for environmental governance in low- and middle-income agricultural contexts. Full article

Silicone-based implants have been widely used in breast reconstruction but have also been associated with poorly understood complications, including pathologic foreign body responses such as capsular contracture. In this study, we leveraged 3D-printing technology to generate silicone-based implants in a novel, anatomically relevant, prepectoral rat model. We used this model to evaluate the response to an extracellular matrix-based product: ovine-derived reinforced tissue matrix (RTM). Two-piece negative molds were developed through computer-aided design and 3D-printed. The molds were filled with various polydimethylsiloxane mixtures and dip-coated to fabricate implants. Implant material characterization revealed that the implants retained the original 3D-printed mold shape and qualitatively demonstrated a shell with an inner solid gel-like structure. Fabricated implants had smooth surfaces, as well as tunable features including implant stiffness (storage modulus). From initial studies in our rat model, placement of bilateral prepectoral implants allowed assessment of both muscle- and skin-facing capsules and were well-tolerated for at least 12 weeks. Comparison of the foreign body response between RTM-covered and uncovered (control) implants in this model revealed that the capsule thickness did not differ between groups at the 12-week endpoint. However, RTM reduced contractile fibroblasts (alpha-smooth muscle actin) and macrophages (Iba1) compared to the control. Our findings suggested that RTM may improve capsule quality by attenuating cells involved in fibrosis, even when total capsule thickness remains unchanged. However, these changes to cells involved in fibrosis were only observed at this early endpoint and may not predict long-term clinical outcomes. Full article

Seismic vulnerability assessment of reinforced concrete (RC) structures is crucial in earthquake-prone regions to mitigate risks to life and property. This study proposes a systematic three-phase framework for enhanced seismic risk assessment: (1) Automation, (2) Evaluation, and (3) Predictive Modeling. For the Automation Phase, a web-based tool was developed to digitize and streamline the Turkish Rapid Visual Screening (RVS) procedure, eliminating manual calculation errors while improving efficiency. During the Evaluation Phase, we applied this tool to assess 600 buildings, classifying them into four distinct risk categories (no, low, moderate, and high risk) through standardized scoring. Finally, in the Predictive Modeling Phase we conducted correlation analysis to identify key seismic risk factors (e.g., building height showing a strong negative correlation, while soft-story mechanisms and short columns emerged as critical vulnerabilities) and implemented three machine learning models (XGBoost, Random Forest, and AdaBoost) for risk prediction, with XGBoost achieving superior accuracy. The framework’s validation confirmed the web tool’s reliability relative to conventional methods while revealing most buildings as low-risk, demonstrating how this integrated approach—combining automated screening, large-scale assessment, and data-driven prediction—provides a scalable solution for seismic risk mitigation in vulnerable regions. Full article

Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level density estimation and prediction-error heuristics often become unreliable. In this work, we propose an information-theoretic framework for intrinsically motivated reinforcement learning grounded in the Information Bottleneck principle. Our approach learns compact latent state representations by explicitly balancing the compression of observations and the preservation of predictive information about future state transitions. Within this bottlenecked latent space, intrinsic rewards are defined through information-theoretic quantities that characterize the novelty of state–action transitions in terms of mutual information, rather than raw observation dissimilarity. To enable scalable estimation in continuous and high-dimensional settings, we employ neural mutual information estimators that avoid explicit density modeling and contrastive objectives based on the construction of positive–negative pairs. We evaluate the proposed method on two representative combinatorial routing problems, the Travelling Salesman Problem and the Split Delivery Vehicle Routing Problem, formulated as Markov decision processes with sparse terminal rewards. These problems serve as controlled testbeds for studying exploration and representation learning under long-horizon decision making. Experimental results demonstrate that the proposed information bottleneck-driven intrinsic motivation improves exploration efficiency, training stability, and solution quality compared to standard reinforcement learning baselines. Full article

This study investigates the demographic transformation of Spain’s settlement system from 2000 to the present, driven by intersecting forces of rural depopulation, metropolitan concentration, immigration, and welfare-state dynamics. Building on an integrated theoretical framework that combines Maslow’s hierarchy of needs, demographic accounting, territorial carrying capacity, and spatial centrality, the research aims to (1) identify the mechanisms governing population redistribution across Spanish municipalities, and (2) simulate future demographic trajectories under current policy regimes. Key findings reveal that all net population growth since 2000 stems exclusively from immigration and its demographic sequelae, while the native Spanish cohort has experienced a net decline of 5.5 million due to negative natural change. The analysis further uncovers a self-reinforcing “demographic trap,” wherein welfare eligibility tied to household size incentivizes higher fertility among economically vulnerable immigrant groups, even as native families delay childbearing due to economic precarity. These dynamics are accelerating a process of “territorial transmutation,” projected to culminate in a shift in de facto governance by 2045. The study concludes that immigration alone cannot reverse rural depopulation or ensure fiscal sustainability without structural reforms to welfare design, territorial incentives, and demographic foresight. Full article

Bacterial infections and the lack of bioactivity of titanium implants and their alloys remain critical challenges for the long-term performance and clinical success of these devices. These issues arise from the undesirable combination of early microbial adhesion and the limited ability of metallic surfaces to form a bioactive interface capable of supporting osseointegration. To address these limitations simultaneously, this study employed electrical discharge machining (EDM), which enables surface topography modification and in situ incorporation of bioactive ions from the dielectric fluid. Ti-6Al-4V ELI surfaces were modified using two dielectric fluids, a fluorine/phosphorus-based solution (DF1-F) and a calcium/phosphorus-based solution (DF2-Ca), under positive and negative polarities. The recast layer was characterized by SEM and EDS, while bioactivity was evaluated through immersion in simulated body fluid (SBF) for up to 21 days. Antibacterial performance was assessed against Staphylococcus aureus at 6 h and 24 h of incubation. The results demonstrated that dielectric composition and polarity strongly influenced ionic incorporation and the structural stability of the modified layers. The DF2-Ca(+) condition exhibited the most favorable bioactive response, with Ca/P ratios closer to hydroxyapatite and surface morphologies typical of mineralized coatings. In antibacterial assays, Ca/P-containing surfaces significantly decreased S. aureus attachment (>80–90%). Overall, EDM with Ca/P-containing dielectrics enables the fabrication of Ti-6Al-4V surfaces with enhanced mineralization capacity and anti-adhesive effects against Gram-positive bacteria, reinforcing their potential for multifunctional biomedical applications. Full article

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from $42.3\%$ to $97.1\%$ on MMM2, from $0.0\%$ to $57.5\%$ on 6h vs. 8z, and from $58.0\%$ to $96.6\%$ on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered. Full article

In this article, the authors address the Serbian media narrative about the EU’s communication on lithium mining in Serbia. In an effort to answer the question of how this narrative can influence the positioning of the EU on Serbia as a candidate country for EU membership, the authors have made a research based on a quantitative–qualitative analysis of media coverage, drawing on a sample of 192 articles (N = 192) published by four Serbian online news portals (RTS, N1, B92, and Blic). The analysis leads to two main conclusions: (1) It indicates an inversion in the general approach to foreign policy orientation across the analyzed media platforms. The customary discourses on Serbia’s foreign policy trajectory temporarily diverged from established patterns—specifically, the fervently pro-Western orientation characteristic of anti-government platforms and the ostensibly West-sceptical orientation typical of pro-government media. This reinforces the argument that the primary structuring line of media discourse in Serbia lies in the division between pro-regime and anti-regime orientations. (2) Media repositioning has exerted a pronounced negative effect on pro-European segments of the Serbian public, reactivating the thesis of “stabilocracy”, conceptualized as the dynamic relationship between authoritarian regimes in the Balkans and their external supporters. According to the authors, the EU’s inability to anticipate the drastic negative shift in public sentiment toward it—particularly among those segments of Serbian society that had been most supportive—or, alternatively, its decision to continue pursuing its own economic interests despite such awareness, underscores the profound flaws in the political communication it employed in this case. Full article