Search Results (4,205)

Although new AI systems have been developed based on the integration of information from multiple sources under conditions of uncertainty, classical probabilistic models are unable to provide structured, interpretable, and reliable representations in the presence of contextual and order effects. Specifically, the fundamental principles of Kolmogorov’s assumptions underlying the modeling overlook certain common violations in real-world decision-making processes, such as non-commutativity, contextual dependence among agents, and interaction effects between information sources characterized by experiential heterogeneity. A Positive Operator-Valued Measure (POVM) formalism defined on a Hilbert space of latent states forms the basis of this article’s structured epistemic representation framework to support the reliability and interpretability of the black box in AI. The resulting model generalizes the classical epistemic quadruplet: Probability, Plausibility, Credibility, and Possibility within a single geometric framework in which three essential non-classical effect mechanisms emerge—(i) the non-commutativity of information acquisition, (ii) the contextuality arising from incompatible observational frameworks, and (iii) the interference interactions between information acquisition channels. We propose the concept of a quantum-inspired fusion operator (QI-Happenability), which introduces symmetric and antisymmetric feedback interaction terms based on the estimation of ordered residuals. An analysis of the proposed framework is then performed using real data, validating the model on the Efron et al. diabetes regression dataset (N = 442; ten standardized physiological predictors; continuous disease progression target), available in scikit-learn, which showed a 17.4% reduction in mean absolute error (MAE) compared to traditional models and significant improvements over polynomial machine learning baselines, as well as ensemble machine learning methods, within a rigorous cross-validation protocol. Contextual analysis indicates that 68% of cases violate classical bounds (CHSH inequality, p < 0.001), empirically confirming the non-classical structured representation in multi-source data. The results confirm the proposed approach as a simpler, more interpretable, and more reliable alternative to black-box models: this work demonstrates how the use of structured epistemic representations in reasoning under uncertainty preserves formal interpretability while retaining useful semantic information. By linking quantum cognition and applied AI, this work could help lay the groundwork for a new generation of interpretable and reliable decision-making systems. Full article

With the rapid development of edge AI chips and autonomous driving algorithms, autonomous driving perception systems are evolving toward higher accuracy, lower latency, and lightweight deployment. This trend places greater demands on real-time instance segmentation algorithms in terms of multi-scale feature representation, spatial detail modeling, and edge deployment efficiency. To address these challenges, this paper proposes PMS-Net (Progressive Multi-Scale Network), a network designed for real-time instance segmentation. PMS-Net adopts a progressive multi-scale feature modeling mechanism that progressively enlarges the receptive field while integrating semantic and fine-grained spatial information across different scales. This enables efficient collaboration between local features and global contextual information, thereby enhancing scale-awareness and feature representation while maintaining a lightweight architecture. In addition, efficient feature encoding and dynamic feature reconstruction are incorporated to further improve spatial alignment, boundary recovery, and semantic continuity for complex scene modeling. Experimental results show that PMS-Net achieves 36.7% Mask mAP50 and 175 FPS on the Cityscapes dataset, outperforming the baseline by 2.9%. Deployment experiments on the NVIDIA Jetson Orin NX platform further demonstrate that PMS-Net achieves 35.2% Mask mAP50 and 98 FPS, improving the baseline by 3.7% and 14.0%, respectively. These results validate the effectiveness and practicality of PMS-Net for real-time edge-deployed autonomous driving applications. Full article

Geographical indication (GI) products are often understood through terroir, certification, and branding, yet less attention has been paid to the feedback dynamics through which their market value is amplified and destabilized. This paper applies systems thinking to the post-harvest value–trust subsystem of Xinhui Chenpi, a traditional Chinese aged citrus pericarp product with GI protection, to examine how value amplification and systemic vulnerability emerge through interacting feedback mechanisms. Drawing on 28 semi-structured interviews conducted between September 2023 and December 2024, supplemented by participant observation, policy and standard documents, field-based market observations, and contextual media materials, the study develops a preliminary causal loop diagram (CLD) with 15 endogenous feedback variables, 3 boundary value-input variables, 4 exogenous contextual inputs, and 21 causal links (18 endogenous and 3 value-input). The model identifies two reinforcing loops and two balancing loops through which price expectations, holding incentives, credible circulation supply, perceived scarcity, misrepresentation, and trust erosion interact. The trust-erosion loop shows how premium-driven misrepresentation increases claim uncertainty, weakens open-market consumer trust, reduces credible open-market liquidity, and further contracts credible circulation supply. Buyer exit and delayed supply response operate as limited balancing mechanisms because their effects are segment-dependent and constrained by aging and verification delays. The proposed CLD suggests that high-value mechanisms in aging-dependent GI products may also generate structural vulnerabilities, with implications for managing consumer trust, claim verification, and credible circulation in premium markets. Full article

The judgment of the Court of Justice of the European Union in Case C-713/23, Wojewoda Mazowiecki, constitutes a significant development in the Court’s case law concerning the recognition of personal status acquired in another Member State. While the decision has primarily been discussed in the context of civil registration and the free movement of Union citizens, its implications for cross-border succession have received little scholarly attention. This article examines the consequences of the judgment for the application of Regulation (EU) No. 650/2012 on succession, with particular emphasis on Slovak succession law, which neither recognises same-sex marriage nor registered partnerships. Using doctrinal legal analysis and a contextual interpretation of the Court’s jurisprudence, the article argues that the judgment should be understood through the concept of functional recognition. Although Member States remain competent to regulate marriage, they may be required to recognise the legal effects of a same-sex marriage lawfully concluded in another Member State where such recognition is necessary to ensure the effectiveness of EU law. The article demonstrates that this approach may influence the determination of heirs, the status of the surviving spouse, matrimonial property settlement, and the legal effects of the European Certificate of Succession. It concludes that Slovak authorities should apply a functional, case-by-case assessment that reconciles national constitutional identity with the effective protection of rights guaranteed by EU succession law, thereby contributing to the broader debate on the interaction between national family law and European private international law. Full article

Women with polycystic ovary syndrome (PCOS) have a substantially increased risk of endometrial cancer (EC), yet the biological mechanisms underpinning this association, to support future prevention and therapeutics, remain incompletely understood. Insulin-like growth factor (IGF)-associated signaling has been implicated, but existing evidence is conflicting. In this pilot translational study, serum IGF1 and IGFBP-3 were measured in women with PCOS (n = 12 for IGF1 and n = 6 for IGFBP-3) and controls (n = 24 for IGF1 and n = 7 for IGFBP-3). Pooled serum, stratified by IGF bioactivity, was applied to human EC cells to further assess effects on cell viability, cell cycle distribution, and downstream signaling. Computational analysis of publicly available endometrial cancer datasets was used to contextualize experimental findings. Serum IGF1 and IGFBP-3 levels did not differ significantly between PCOS and control groups. However, pooled PCOS serum was associated with increased EC cell viability, altered cell cycle progression and PI3K/AKT/mTOR signaling compared with control serum in this exploratory model. Pharmacological inhibition of IGF1R partially attenuated these effects, suggesting that IGF-associated pathways may contribute but are unlikely to act in isolation. In silico analysis identified frequent alterations in PI3K/AKT/mTOR-related genes in EC, consistent with pathway-level vulnerability rather than IGF1-specific dependence. These findings suggest that PCOS serum contains factors that are associated with increased EC cell viability and altered signaling pathways with partial involvement of IGF signaling; however, these findings should be interpreted cautiously given the exploratory pooled-serum design, small subgroup sizes, and use of a single EC cell line. However, multiple metabolic and hormonal pathways are likely to contribute. Larger, better-controlled studies incorporating insulin, sex steroids, and multiple EC models are required before causal inferences can be made. Full article

Albumin administration in sepsis and septic shock remains controversial because of uncertain clinical benefits and complex acid–base effects. Unlike crystalloids, albumin influences acid–base equilibrium through effects on chloride balance, strong ion difference (SID), buffering systems, and weak acid concentration. These effects may be interpreted differently by the traditional bicarbonate-centered framework and Stewart’s physicochemical approach. In the traditional paradigm, albumin acts mainly as a non-bicarbonate plasma buffer influencing base excess and anion gap interpretation. In the Stewart framework, albumin is a weak non-volatile acid contributing to total weak acids (Atot), thereby influencing hydrogen ion dissociation and pH regulation. This narrative review compares these two approaches to albumin-related acid–base physiology in sepsis, with emphasis on chloride balance, strong ion difference, hypoalbuminemia, dilutional effects, and cardiorespiratory interactions during mechanical ventilation and septic shock. We also contextualize these mechanisms within major randomized trials of albumin and resuscitation fluids and present illustrative conceptual simulations illustrating the directional acid–base effects of different fluid compositions. The narrative review focuses on masked acidosis in hypoalbuminemic patients and on the interpretation of pH changes after albumin administration. Finally, we propose an integrated bedside framework combining traditional, physicochemical, respiratory, and hemodynamic variables for acid–base interpretation during fluid resuscitation. Full article

Public sector organizations face persistent challenges in workforce retention, particularly in institutionally complex and resource-constrained environments. Although leadership is widely recognized as a key factor, research remains fragmented across the public administration and human resource management literature. This article develops a governance-sensitive framework to explain how leadership influences workforce retention in public service organizations. Drawing on a PRISMA 2020-guided systematic review of 30 studies, it integrates insights to examine the relationships between people-centered leadership, organizational commitment, and turnover intentions. The synthesis suggests that leadership effects are primarily indirect, operating through affective organizational commitment. Administrative burden (red tape) and working conditions emerge as key contextual factors shaping these relationships. The study contributes by offering an integrated framework and identifying implications for managing workforce retention in public healthcare organizations and comparable institutionally dense public service settings. The article proposes an integrated conceptual framework for future empirical research. Full article

Background/Objectives: The paper aimed to synthesize the diagnostic performance of artificial intelligence (AI) methods for classifying breast lesions on contrast-enhanced breast MRI and to estimate a pooled area under the receiver operating characteristic curve (AUC). Methods: This targeted evidence synthesis and meta-analysis was informed by PRISMA 2020 reporting principles where applicable. Eligible studies were drawn from an investigator-supplied corpus of 12 primary manuscripts and assessed against predefined criteria. MEDLINE/PubMed, Embase, and Web of Science were consulted through October 2025 to contextualize the literature and verify bibliographic and study details; additional database records were not screened for eligibility. We included studies applying machine learning or deep learning to contrast-enhanced breast MRI for benign-versus-malignant lesion classification and reporting an AUC on an independent test set, external validation set, or patient-wise cross-validation. AUCs were pooled on the logit scale using an inverse-variance DerSimonian–Laird random-effects model, and heterogeneity was quantified using I². Results: Nine studies met the criteria for quantitative synthesis (evaluation-set sizes, 60–3936). The pooled random-effects AUC was 0.898 (95% CI, 0.875–0.918), with substantial heterogeneity (I² = 88.1%) and a 95% prediction interval of 0.824–0.943, indicating that performance may vary meaningfully across settings. Conclusions: AI models showed promising discriminative performance within this targeted corpus, but substantial heterogeneity, differences in unit of analysis, approximated variance estimates, and limited external institutional validation temper confidence in generalizability. The pooled AUC should be interpreted descriptively, and future studies should prioritize rigorous multi-institutional external validation, transparent reporting, and prospective reader- or workflow-impact evaluation before routine clinical deployment. Full article

Road sign detection is crucial for highway maintenance but often suffers from sign loss, occlusion, and spatial misjudgments such as repeated local detections or mapping errors. To address these issues, this study proposes YOLO-DeepSort, a tracking and detection framework integrating a novel Spatial Multivariate Clustering Algorithm with GPS information (SCMCg). The YOLOv9 detector is augmented using Mixed Local Channel Attention (MLCA) and DualConv modules to enhance image feature extraction and contextual awareness while compressing the theoretical model volume. In the tracking phase, DeepSort combined with SCMCg employs Delaunay triangulation and hierarchical GPS constraints to refine spatial clustering and data association. Validation was conducted on a mixed dataset comprising the CCTSDB and self-collected images from a Ningxia national road. Experimental results indicate that the proposed model operates efficiently at 21.4 M parameters, achieving a precision of 97.8%, a mean Average Precision (mAP) of 91.2%, and a tracking Success Rate of 97.3%. Compared to the baseline YOLOv8-DeepSort, absolute improvements of 4.60% in precision and 4.20% in mAP were observed. The integrated framework effectively mitigates occlusion and tracking spatial errors, providing a robust and lightweight methodology for the automated condition assessment of intelligent transportation infrastructure. Full article

Background/Objectives: Substance use disorders remain a significant public health concern, particularly when vulnerability emerges during adolescence and young adulthood. Although much of the literature has focused on treatment and recovery, prevention-oriented scholarship increasingly points to the value of strengths-based approaches that reduce risk by cultivating protective processes. This narrative review examines resilience as a protective factor in substance use prevention across adolescence and young adulthood, with particular attention to health promotion, developmental context, and multilevel prevention systems. Methods: A narrative review approach was used to synthesize interdisciplinary literature from psychology, public health, education, addiction studies, and prevention science. The review focused on peer-reviewed scholarship addressing resilience, protective factors, and prevention-related processes relevant to adolescent and young adult substance use. Literature was analyzed thematically across major domains including conceptualizations of resilience, developmental relevance, protective factors, prevention strategies, diverse and at-risk populations, and conceptual and methodological challenges. Results: The literature suggests that resilience is most useful for substance use prevention when conceptualized as a dynamic and ecological process rather than a fixed individual trait. Protective processes associated with lower substance use risk include emotional regulation, adaptive coping, family support, peer connectedness, school engagement, community cohesion, and culturally meaningful sources of identity and belonging. Prevention strategies appear most promising when they are multilevel, relational, developmentally responsive, and integrated with broader mental health and health promotion efforts. At the same time, the literature remains limited by definitional inconsistency, measurement variability, heterogeneous outcome indicators, a predominance of cross-sectional evidence, inconsistent or null protective effects for some factors and outcomes, and insufficiently contextualized uses of resilience. Conclusions: Resilience offers a valuable framework for substance use prevention across adolescence and young adulthood when it is grounded in developmental, ecological, and equity-aware perspectives. Prevention may be strengthened by moving beyond deficit-focused models and intentionally cultivating the supportive conditions under which young people can adapt, connect, and thrive without turning to substances as a means of coping. Full article

Multimodal object detection leveraging RGB and infrared imagery has become essential for robust all-weather perception in unmanned aerial vehicle (UAV) applications. However, existing methods still struggle with effective cross-modal feature fusion, spatial misalignment between modalities, and scale variation of objects in aerial views. In this paper, we propose CMGFDet, a Cross-Modal Gated Fusion Network with Multi-Receptive Field Aggregation designed for RGB–infrared aerial object detection. Our framework introduces three coordinated modules: (1) a Cross-Modal Feature Fusion Network (CMFFN) that employs a gated attention mechanism to selectively aggregate complementary information from both modalities during encoding; (2) a Global–Local Attention Module (GLAM) that performs hierarchical cross-modal feature alignment by jointly modelling global channel statistics and local spatial correlations in the decoder; and (3) a Multi-Receptive Field Aggregation Network (MRFAN) that captures multi-scale contextual information through parallel depthwise convolutions with diverse kernel sizes. Additionally, we incorporate a deep supervision strategy and a composite loss function to enhance training efficiency. Extensive experiments on four public benchmarks (DroneVehicle, RGBTDronePerson, VEDAI, and VTUAV) show that CMGFDet improves the previous best mAP@0.5 by 1.6%, 2.2%, 1.9%, and 2.2%, respectively. The implementation code will be released upon acceptance to support reproducibility. Full article

Fabric defect detection is a critical task in automated textile quality inspection, where both high localization accuracy and fast inference are required, especially for small and irregular defects. Although recent YOLO-based detectors offer a favorable balance of speed and accuracy, their convolutional architectures primarily capture local features and are less effective at detecting defects that require broader contextual understanding. Transformer-based detectors improve global context modeling but typically introduce higher computational complexity. To balance this trade-off, this study proposes a lightweight hybrid detector, YOLOv11-Transformer, which integrates multi-head self-attention (MHSA) modules into the backbone and neck of YOLOv11. The model was evaluated on a fabric defect dataset of 4375 images covering four defect categories: holes, cuts, thread errors, and metallic contamination. Comparative experiments were conducted against YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLOv11, YOLOv11-DETR, and RT-DETR under unified training conditions, with additional efficiency benchmarks against YOLOv6 and YOLOv7-Tiny. The proposed model achieved the best mAP50-95 of 94.50%, outperforming the baseline YOLOv11 by 2.40 percentage points, while maintaining efficient inference at 14.2 ms per image (approximately 70 FPS) on an NVIDIA RTX 4060 GPU. These results indicate that lightweight attention integration can improve fine-grained defect localization while preserving a practical accuracy-efficiency trade-off for automated textile inspection. Full article

Background/Objectives: Postural control depends on the integration and reweighting of visual, somatosensory, vestibular, and contextual sensory information. Stable auditory cues may support balance, whereas complex musical stimulation may impose additional sensory-cognitive demand during multisensory conflict. This study examined the acute effects of relative device-output music intensity on virtual-reality-based postural control in trained athletes and explored whether responses differed by sport background. Methods: Forty-eight athletes from tennis, combat sports, swimming, football, and volleyball completed the Clinical Test of Sensory Interaction in Balance delivered through virtual reality (CTSIB-VR) and Limits of Stability (LOS) assessments under four auditory conditions: routine/no sound and low (+10 dB), moderate (+20 dB), and high (+30 dB) relative device-output increments. Linear mixed-effects models included sport, auditory condition, and their interaction as fixed effects and participant-specific random intercepts and random linear condition slopes. Model-based estimated marginal means, Bonferroni-adjusted contrasts, 1.5×IQR sensitivity analyses, and robust generalized estimating equations were calculated. Results: Auditory condition affected all five CTSIB-VR outcomes (Wald χ²(3) = 16.773–94.404, all p < 0.001). The routine condition exceeded the high-intensity condition for composite score (adjusted mean difference = 6.05, 95% CI 3.99–8.10; Bonferroni-adjusted p < 0.001) and somatosensory score (8.62, 95% CI 6.78–10.46; adjusted p < 0.001). Sport × condition interactions were significant for all CTSIB-VR outcomes (χ²(12) = 54.869–98.953, all p < 0.001), but sport-stratified findings were exploratory. For LOS, auditory-condition effects were detected for endpoint excursion (p = 0.004), maximum excursion (p < 0.001), and directional control (p = 0.002), whereas reaction time (p = 0.648) and movement velocity (p = 0.056) did not show clear main effects. Sensitivity analyses supported the endpoint-excursion, maximum-excursion, and directional-control findings; movement-velocity inference was method-sensitive. Conclusions: Relative device-output music intensity was associated with consistent changes in CTSIB-VR sensory-organization measures and outcome-specific changes in LOS performance. Sport-related patterns require confirmation in adequately powered, balanced samples. Full article

Background/Objectives: Anaemia is a critical public health challenge in Sub-Saharan Africa (SSA), particularly among adolescents. The objective was to estimate the pooled prevalence of anaemia among adolescents in SSA and synthesise evidence on its nutritional and contextual determinants. Methods: A systematic search of databases including PubMed, CINAHL and Web of Science was conducted for studies published from January 2000 to October 2025. The methodological approach was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pooled prevalence estimates were computed using random-effects models, and heterogeneity was assessed using the I² statistic and Cochran’s Q test. Subgroup and meta-regression analyses were performed to explore sources of variation. Determinants were synthesised as pooled odds ratios with 95% confidence intervals. Publication bias was evaluated through funnel plot asymmetry and Egger’s regression test. Results: Forty-eight (n = 48) studies involving 76,596 adolescents met the inclusion criteria. The pooled prevalence of anaemia was 33% (95% CI: 0.28–0.39), with substantial heterogeneity across studies. Factors associated with higher odds of anaemia included low dietary diversity (OR 1.74, 95% CI: 1.10–2.74) and poor iron or micronutrient supplementation adherence or lack of supplementation (OR 1.94, 95% CI: 1.25–3.01). Infection-related exposures (OR 3.03, 95% CI: 1.99–4.61) and environmental determinants (OR 2.23, 95% CI: 1.41–3.55) were also significantly associated with anaemia. Socio-demographic factors associated with anaemia included menstrual factors among girls (OR 2.51, 95% CI: 1.58–3.99), household size (OR 3.32, 95% CI: 1.61–6.85), awareness (OR 1.71, 95% CI: 1.08–2.70), and age group (OR 2.37, 95% CI: 1.47–3.83). Conclusions: Anaemia among adolescents in SSA remains an important public health concern, although the pooled estimates should be interpreted cautiously because of substantial heterogeneity and methodological differences across studies. The findings suggest that adolescent anaemia is associated with multiple nutritional and contextual factors, but more consistent and context-specific research is needed to refine regional estimates and guide interventions. Full article

This article presents the use of autoencoders as part of a dimensionality-reduction method in the task of crime clustering. The study was conducted on a real-world crime dataset from the city of Chicago, based on publicly available police records. The initial data processing involved selecting and extracting variables, aggregating the selected variables, and converting crime categories and incident locations into contextual embeddings. The data prepared in this way was used to train various autoencoder architectures, including Vanilla, convolutional, denoising and variational models. The representations obtained from the latent layer of the encoder were then used as input data for clustering methods, such as k-means, Gaussian mixture model, and spectral clustering. The experimental results showed that the use of autoencoders in the clustering process enabled the identification of distinct groups of offences, with the best results (measured using the ARI and NMI metrics) obtained for Vanilla autoencoders combined with k-means and GMM, particularly with intermediate latent-space dimensions. The results confirm the potential of autoencoders as effective tools for dimensionality reduction and feature extraction in crime data analysis, as well as their usefulness in the exploratory analysis of complex urban data. Full article