Search Results (882)

In reconfigurable radio frequency (RF) microsystems, the interconnect structure critically affects high-frequency signal integrity, and the accuracy of electromagnetic (EM) modeling directly determines the overall system performance. Conventional neural network-based surrogate models mainly focus on minimizing numerical errors, while neglecting essential physical constraints, such as causality and passivity, thereby limiting their applicability in both time and frequency domains. This paper proposes a physics-constrained Neuro-Transfer surrogate model with a broadband output architecture to directly predict S-parameters over the 1–50 GHz range. Causality and passivity are enforced through dedicated regularization terms during training. Furthermore, a particle swarm optimization (PSO)-based multi-objective intelligent optimization framework is developed, incorporating fixed-weight normalization and a linearly decreasing inertia weight strategy to simultaneously optimize the S11, S21, and S22 performance of a quasi-coaxial TSV composite structure. Target values are set to −25 dB, −0.54 dB, and −24 dB, respectively. The optimized structural parameters yield prediction-to-simulation deviations below 1 dB, with an average prediction error of 2.11% on the test set. 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: Healthcare systems globally face complex challenges including rising costs, increasing chronic disease burden, and fragmentation of care. Systems-based models represent promising approaches to healthcare transformation, yet their implementation remains incompletely understood. Objective: To critically analyze the Saudi model of Care (MoC) as a case study of systems-based healthcare transformation, examining its conceptual framework, implementation strategies, and projected health outcomes. Methods: We conducted a narrative review synthesizing publicly available official documents on the Saudi MoC, primarily the 2017 overview and 2025 revision, identified through targeted searches of Ministry of Health websites and grey literature portals (no date restrictions); formal quality appraisal was not applied as sources were official policy documents, with bias mitigated through cross-verification and critical analysis. Results: The Saudi MoC exemplifies systems-based transformation through its multi-layered framework organized around six patient-centered systems of care spanning the lifecycle. Key innovations include: (1) an architectural approach integrating activated individuals, healthy communities, virtual care, and traditional clinical settings; (2) a comprehensive intervention taxonomy with 42 specific initiatives; (3) explicit contextual adaptations for diverse settings; and (4) a phased implementation approach with detailed performance metrics. National indicators improved during the reform period, including life expectancy and maternal and child health. These are national trends observed during the period of health reforms. Causal attribution to the Model of Care requires a counterfactual evaluation. Conclusions: This analysis of the Saudi MoC contributes to the literature on systems-based healthcare transformation by illuminating how theoretical principles can be operationalized at national scale. The model’s patient-centered design, comprehensive intervention taxonomy, and attention to implementation factors offer valuable insights for other healthcare systems pursuing transformation. Further research should examine actual implementation outcomes as the model matures. Full article

Metabolic reprogramming has recently been recognized as related to immune disorders in ulcerative colitis (UC), but the specific metabolic pathways and genes involved remain unclear. Here, Mendelian randomization confirmed that mannose and mannonate exhibited a negative causal relationship with UC, and that the immune cell phenotype HLA DR on CD33dim HLA DR+ CD11b− mediated the effect of mannonate on UC. Bulk RNA sequencing data revealed that mannose metabolism abnormity is critical for driving the innate and acquired immune response. A well-performing diagnostic model related to mannose metabolism was constructed using SVM analysis, achieving an AUC-ROC value of 0.987 in the training set and an AUC-ROC value of 0.899 in the validation set. Single-cell analysis revealed that epithelial cells in which the mannose metabolism pathway was inactivated demonstrated increased intercell communication with myeloid cells, T cells, and B cells. In vitro experiments confirmed that KHK and AKR1B10 were suppressed under inflammatory stimulation, which may hinder mannose-related metabolism. This study elucidates the protective role of mannose metabolism in UC and provides a novel gene signature for diagnosis and treatment. Full article

The integration of artificial intelligence (AI) in the manufacturing industry is increasingly prevalent, presenting both ongoing opportunities and challenges for organizations while also significantly impacting worker behavior and psychology. Drawing on data from 405 workers in China, this study employs hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) to investigate the influence mechanism of AI-enabled job characteristics on work-related flow. Key findings reveal that: AI-enabled job characteristics positively predict work-related flow by increasing perceived challenge stress, yet simultaneously exert a negative influence by exacerbating perceived hindrance stress; techno-efficacy significantly alleviates the relationship between AI-enabled job characteristics and perceived hindrance stress but does not moderate the path via perceived challenge stress; fsQCA identifies four distinct causal configurations of antecedents leading to high work-related flow. This research elucidates the complexities of AI-enabled job characteristics and their dual-faceted impact on work-related flow. By integrating AI into the study of worker psychology and behavior, it extends the contextual scope of job characteristics research. Furthermore, the application of fsQCA provides novel insights into the antecedent conditions and configurational pathways for achieving work-related flow, offering significant theoretical and practical implications. Full article

Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails to identify temporal change in the risk-set composition due to treatment assignment and unobserved factors among multiple contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose abandoning the baseline hazard and using kernel-based machine learning to explicitly model the change in the risk set with or without latent variables. For this framework, we clarify the context in which hazard ratios can be causally interpreted, then develop a method based on Neyman orthogonality to compute debiased maximum likelihood estimators of hazard ratios, proving necessary convergence results. Numerical simulations confirm that the proposed method identifies the true hazard ratios with minimal bias. These results lay the foundation for the development of a useful alternative method for causal inference with uncontrolled, observational data in modern epidemiology. Full article

Founder CEOs lead a significant number of public U.S. firms, and these firms often differ from other firms led by non-founder CEOs in terms of various important firm characteristics. In our paper, we investigate the financial choices of founder-CEO-led firms and non-founder-CEO firms in a utility industry setting within the context of the U.S. Our results show that founder CEO status has a significant positive influence on financial choices (cash holdings, investment ratio, equity ratio, and interest coverage) of utility companies. After addressing potential causality and performing additional robust measures, our findings still hold and suggest that CEO origin is important for explaining variation in financial choices of utility companies. Overall, our findings make a valuable contribution to the literature on utility firms, founder CEOs, and CEO characteristics by connecting them through an angle that is previously unexplored. Full article

Atopic dermatitis (AD) and autoimmune diseases exhibit epidemiological comorbidity, yet the shared genetic architecture remains incompletely understood. We investigated the genetic overlap between AD and three autoimmune disorders including inflammatory bowel disease (IBD), rheumatoid arthritis (RA), and vitiligo, leveraging genome-wide association data. Despite modest evidence for global genetic correlations, we found 113 independent pleiotropic loci shared among AD and autoimmune diseases, with 11 displaying a concordant effect across all 3 pairwise comparisons. Gene-set and tissue enrichment analyses evidenced the inflammatory background of pleiotropic associations. Multi-trait colocalization analysis prioritized 22 loci, linking the tissue-specific expression of DOK2, GPR132, RERE, RERE-AS1, SUOX, TNFRSF11A, and TRAF1 pleiotropic genes with AD risk. Mendelian randomization revealed no causal effect of genetic liability to AD on autoimmune diseases. Nevertheless, genetic liability to IBD increased AD risk, while vitiligo exhibited a protective effect post outlier correction. Our findings provide mechanistic insights into the multimorbidity of atopic dermatitis (AD) and autoimmune diseases, offering additional evidence for the pleiotropic genetic architecture of AD that contributes to systemic immune dysregulation across multiple organ systems. Full article

Violence against women and girls (VAWG) remains a critical problem within Brazilian higher education institutions, where deep-rooted cultural norms and institutional shortcomings continue to foster unsafe environments for female students. Although national and international bodies have raised concerns, few studies have thoroughly examined the layered causes of VAWG in academic settings using comprehensive analytical methods. This study aims to explore the causal factors of VAWG within Brazilian universities by applying a structured survey and analyzing the responses using the Fuzzy Synthetic Evaluation (FSE) approach. This method allows for a nuanced interpretation of the collected data by assigning weighted values to various contributing factors. The research assessed five major dimensions—individual, interpersonal, institutional, community and societal causal factors. The findings reveal that societal and institutional causes significantly contribute to VAWG, while individual factors play a comparatively minor role. These insights point to the structural and systemic nature of VAWG in academic settings, emphasizing the need for broad reforms. Based on the results, practical recommendations, including cultural reorientation, stricter institutional policies, and gender-sensitive training are recommended. By applying FSE in this context, the study offers a novel approach to evaluating and addressing gender-based violence (GBV) in higher education, contributing to a valuable model for future research and institutional policymaking. The results offer critical insights that can guide interventions to foster safer and more inclusive university environments in Brazil. Full article

Psychedelic drugs show promise in treating a wide range of mental health conditions. These drugs are beneficial in part because they disrupt prior ideas and patterns of behavior and because they increase neurogenesis and neuroplasticity. It is important therefore to consider the causal impact of the social context not just during but also following psychedelic experiences. Modern cultural or social contexts might thwart or discourage spiritual integration, but local integration support groups are shown to be helpful, especially for those seeking to reflect the meaning of spiritual or religious themes. These groups might be offered within Christianity, which can provide (1) a connection to a community or a social context at the local level together with (2) a set of theological beliefs as an interpretive context that supports spiritual growth in general and psychedelic spiritual integration in particular. Full article

This paper presents ALMODES, a discrete-time modeling approach for social systems that uses matrix algebra and directed graphs. The method bridges the gap between static network analysis and continuous System Dynamics, offering a transparent framework that reduces data requirements. The method enables clear causal mapping, rapid simulation, straightforward sensitivity analysis, and natural hybridization with agent-based or discrete-event models. Two case studies illustrate its utility for sustainable-development strategy: in an urban public-health setting, modernization and sanitation policies drive sustained declines in disease despite growth, whereas reversing the population-to-modernization link triggers a morbidity rebound that can be prevented by strengthening the modernization-to-sanitation pathway; in a high-tech services Balanced Scorecard model, a baseline backlog spike depresses customer satisfaction, aggressive hiring shortens the spike but erodes income, and coordinated boosts to training and incentives (about twelve percent productivity gain) remove the backlog early, stabilize customers, and improve income, highlighting human-capital policy as a robust lever. ALMODES thus supports pragmatic policy design under limited, expert-elicited parameters. Future research will address uncertainty quantification, time-varying structures and shocks, automated calibration and empirical validation at scale, optimization and control design, richer integration with hybrid simulation, participatory interfaces for stakeholders, and standardized benchmarks across domains. Full article

This study explores the relationship between and within Hausa and English letter sound knowledge and word decoding skills among children studying in early years settings in northern Nigeria. There is a lack of correlational studies as well as causal evidence in the African context to indicate any transfer of language skills from L1 and L2 and vice versa. Test scores from 851 children studying in 158 government provided pre-primary schools took tests in letter sound (phoneme) and reading (word) decoding skills. Through bivariate correlations and a just-identified feedback path model, the results support Cummins’ interdependence hypothesis. Hausa and English word scores are bidirectionally associated, and the data reveal very strong significant positive correlations between Hausa and English letter sound scores and Hausa and English word scores. With the language policy set to change in Nigeria concerning the use of the language of the immediate community becoming a possible medium of instruction, these results, supporting bidirectionality and linguistic interdependence, provide evidence for the teaching of L1 and L2 in pre-primary settings in northern Nigeria. Full article

We present a new measure of emotion perception called PAGE (Perceiving AI Generated Emotions). The test includes 20 emotions, expressed by ethnically diverse faces, spanning a wide range of ages. We created stimuli with generative AI, illustrating a method to build customizable assessments of emotional intelligence at relatively low cost. Study 1 describes the validation of the image set and test construction. Study 2 reports the psychometric properties of the test, including convergent validity and relatively strong reliability. Study 3 explores predictive validity using a lab experiment in which we causally identify the contributions managers make to teams. PAGE scores predict managers’ causal contributions to group success, a finding which is robust to controlling for personality and demographic characteristics. We discuss the potential of generative AI to automate development of non-cognitive skill assessments. Full article

Tower crane structural failures remain a major safety concern on construction sites. To improve accident prevention, this study proposes an intelligent framework that combines an improved Transformer model with a Directional Interest Score (DIS) Apriori algorithm and complex-network analysis. A corpus of 535 tower crane accident reports (2002–2024) was compiled and annotated with causal and accident entities according to system–safety theory. Segment embeddings were introduced to the Transformer to reinforce boundary detection, enabling accurate extraction of causative factors and relation triples. The DIS-Apriori algorithm was then used to mine both positive and negative association rules while aggressively pruning irrelevant item sets. Eventually, causative factors were mapped into a weighted, directed complex network where edge weights reflect the absolute frequency difference between positive and negative rules, and edge directions correspond to their signs. Experiments show that the Transformer achieves higher precision and recall than baseline models, and DIS-Apriori substantially reduces unnecessary item-set complexity while preserving critical rules. Network analysis revealed five critical causal links and a closed-loop causal link that warrant priority intervention. The proposed method delivers a data-driven, explainable tool for pinpointing key risk sources and designing targeted mitigation strategies, offering practical value for intelligent safety management of tower cranes. Full article

Understanding visitors’ outdoor activities in urban public spaces and their relationship with the physical environment is essential for improving the precision of public space design. This study, set in the context of Yangzhou, China, focuses on physical activity and other wellbeing behaviors in meso-scale waterfront public spaces, aiming to explore the characteristics of visitor behavior. A professional behavioral observation protocol was employed, combined with object detection and multi-object tracking algorithms, to systematically code visitor activities in the waterfront area. Subsequently, agent-based modeling (ABM) and three-dimensional isovist analysis (3D isovist) were introduced to construct a quantitative framework for assessing visual accessibility. The results reveal a significant positive correlation between facade Visual Exposure Time (seen from the observer) and isovist field area (seen from the object), providing strong evidence that visual accessibility is a primary causal driver of pedestrian behavior—independent of other causality. Based on these findings, this study proposes actionable design guidelines: “Prioritize small-scale, high-density waterfront building facade layouts to maximize visual efficiency” and “Leverage topographical variation along the waterfront by introducing cross-river visual corridors at intervals of ≤45 m”. The integrated analytical toolkit developed in this study—combining behavioral simulation with spatial–visual analysis—provides not only a theoretical foundation but also clear practical guidance for the fine-grained renewal and design of waterfront public spaces. Full article