Search Results (26,376)

The role of digital–real integration in urban green transition remains underexplored, particularly regarding whether green patent and AI serve as mediators or moderators. This study investigates this question within the context of the Yangtze River Economic Belt, a critical region for China’s sustainability efforts. Using panel data from 84 prefecture-level cities in the Yangtze River Economic Belt (2010–2022), we examine the impact mechanisms and spatial spillovers of digital–real integration on urban green transition, with explicit attention to two green factors: green patent and AI. We find that digital–real integration significantly promotes urban green transition. Both factors function as moderators rather than mediators: green patent positively condition the effect of digital–real integration, while AI negatively conditions it, and formal mediation tests yield no significant indirect effects. Spatial Durbin Model estimates further confirm that the green benefits of digital–real integration are predominantly localized, with no significant complementary spillovers detected across most spatial weight specifications. While a competitive spillover emerges under geographic adjacency, the overall evidence points to the localized nature of digital–real integration’s environmental effects. Full article

Endocannabinoids are lipid mediators consisting of esters, amides and ethers of long-chain polyunsaturated fatty acids. In this work, attention was focused on N-arachidonoyl tyrosine (NA-Tyr) and N-arachidonoyl dopamine (NADA), the amides of arachidonic acid with tyrosine and dopamine, respectively. NADA is an endogenous ligand of both type 1 cannabinoid receptors and type 1 vanilloid channel receptors. NADA is considered an endogenous compound with capsaicin-like activity and is distributed in several brain areas. The metabolic fate of endocannabinoids involves numerous enzymatic activities, which are only partially characterized. In particular, the biological activity of these biomolecules is terminated by enzymes with hydrolytic or oxygenase/oxidase activity. As part of this problem, we studied the oxidation of NADA and NA-Tyr mediated by mushroom tyrosinase. Our experimental data show that tyrosinase can oxidize both NADA and NA-Tyr. The oxidation of these biomolecules was also carried out in the presence of cysteine, allowing us to observe the formation of endocannabinoid/endovanilloid adducts with cysteine. These results were derived from chromatographic analyses and mass spectral experiments. During the tyrosinase-mediated oxidation in the presence of cysteine, it was possible to observe the production of a melanin-like pigment. The spectral characteristics of this pigment are consistent with those of pheomelanin, the pigment that contributes to the structure of neuromelanin. While mushroom tyrosinase serves here as a convenient biomimetic model to investigate the oxidative susceptibility of NADA and NA-Tyr, extrapolating these in vitro findings to mammalian physiology requires caution. Nevertheless, considering the neuronal distribution of these precursors and the documented, albeit debated, presence of tyrosinase-like activity in the central nervous system (CNS), these results offer a chemical rationale to further investigate whether similar oxidative pathways occur in vivo and potentially contribute to neurodegenerative mechanisms. Full article

Entropy regularization is a recurring mechanism in reinforcement learning (RL), but its meaning changes across algorithmic settings. In classical online RL, entropy encourages exploration and smooths policy improvement; in inverse RL and imitation learning, maximum-entropy resolves ambiguity among expert-consistent behaviors; in offline RL, entropy must be balanced against data support; in generative policies, entropy becomes a tractability problem; and in reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs), token entropy is tied to reasoning diversity, calibration, and collapse. This review organizes these developments into a unified taxonomy. We first summarize the mathematical foundations of maximum-entropy RL, soft Bellman equations, policy-gradient entropy dynamics, and Kullback–Leibler (KL)-constrained mirror descent. We then review entropy in imitation learning, offline RL, intrinsic motivation, diffusion and flow-based policy classes, and RLVR. Particular attention is given to recent work on entropy collapse in reasoning LLMs, entropy-based advantage shaping, covariance-based control, positive-advantage reweighting, and ordinary differential equation (ODE)-based flow-matching policies with tractable entropy. The review emphasizes that entropy is not universally beneficial: useful exploration, support preservation, multimodality, calibration, and reasoning diversity require different entropy objects and different control mechanisms. Full article

Pruritus is a common and burdensome symptom in inflammatory, autoimmune, and connective tissue skin diseases, significantly impairing quality of life, sleep, and psychological well-being. Pruritus arises from a complex interplay between skin barrier dysfunction, immune activation, and neuronal sensitization involving cytokines, alarmins, neuropeptides, and sensory pathways. Increasing evidence indicates that both biological sex and gender-related factors influence itch perception, severity, and clinical expression, although these differences remain insufficiently explored. This review provides a comprehensive analysis of current evidence on sex- and gender-related differences in pruritus across dermatological diseases, with particular attention to the neuroimmune mechanisms underlying chronic itch. Available studies suggest that women more frequently report greater itch intensity, enhanced psychological burden, and higher impairment in daily activities and sleep, whereas men may exhibit different clinical and sensory profiles. However, findings remain heterogeneous because of methodological limitations, small cohorts, and the lack of standardized itch assessment tools. In addition to biological determinants, psychosocial and behavioral factors likely contribute to sex- and gender-specific differences in chronic pruritus. Overall, the available evidence highlights the need for more standardized and sex-informed research approaches to improve the understanding and management of pruritus in dermatological diseases. Full article

To address the challenges of extremely small object scales, weak texture information, and severe background interference in aerial remote sensing images, we propose MambaIR-YOLO, a feature-guided lightweight state-space framework for aerial small-object detection. Based on the YOLOv5 architecture, this method introduces systematic improvements in three key areas—fine-grained feature modeling, long-range dependency learning, and high-resolution spatial information preservation—while ensuring real-time performance. Specifically, a feature-level MambaIR_SR (feature-level Mamba-based super-resolution guidance) training auxiliary branch is designed to generate high-resolution detail-guided information by fusing shallow-level detail features with deep-level semantic features, improving the representation of small-object edges and textures during the training phase. In the main network, we introduce the Object Detail State-Space Block (ODSSBlock), driven by Lightweight Mamba. Through a channel-compressed state-space modeling mechanism, the ODSSBlock unifies local detail preservation and long-range context modeling with low computational overhead. Concurrently, a Feature Modulation Block (FMB) is constructed at the shallow feature level to enhance the representation of high-frequency structural information, thereby mitigating the irreversible detail degradation caused by multiple downsampling steps. In the feature fusion and detection stages, we introduce a Coordinate and Channel Attention (C3CA) attention enhancement module and construct a lightweight decoupled detection head based on a Lightweight Decoupled Head (LADH). This performs single-scale dense prediction on high-resolution features, improving small-object localization and reducing information loss. Notably, the MambaIR_SR branch is exclusively utilized for optimization during the training phase and is entirely discarded during inference, introducing no additional computational overhead. Experimental results on the VEDAI dataset demonstrate that the proposed method achieves an average mAP50 of 84.19 ± 0.03% over three independent runs, with only 4.46 M parameters and 19.97 GFLOPs, outperforming the baseline methods while maintaining low computational cost. Full article

Complex-product manufacturing workshops are characterized by diverse process routes, heterogeneous machine capabilities, tight coupling between processing and assembly, and stringent parent–child kitting constraints. These characteristics make fixed-priority rules insufficient for coordinating job release, machine competition, and assembly waiting. To address scheduling in a production process that connects front-end processing with back-end fixed-position assembly, this study proposes an attention-based proximal policy optimization method. First, the manufacturing process is formulated as a staged model comprising a front-end hybrid flow-shop processing stage and a back-end fixed-position assembly stage. The model captures operation precedence, machine heterogeneity, stage transitions, and kitting constraints. Next, a reinforcement-learning scheduling framework is established by defining the state space, action space, and dynamic action mask, thereby incorporating operation sequences, machine eligibility, resource occupancy, and assembly release constraints into sequential decision making. Furthermore, an A-PPO policy that combines local operation attention with machine-competition attention is designed to select feasible job–equipment-unit matching actions. An industrial engineering case shows that A-PPO achieves a makespan of 3823.7, outperforming six fixed-priority rules (Rule 2–Rule 7) and a genetic algorithm (GA) baseline. GA achieves a makespan of 3875.3, whereas the best rule-based methods achieve 3986.4. Compared with GA, A-PPO reduces the makespan by 1.33%; compared with Rule 6 and Rule 7, it reduces the makespan by 4.08%; and compared with the average of the six rules, it reduces the makespan by 18.95%. The results demonstrate that the proposed method shortens order completion cycles and supports intelligent scheduling in complex-product manufacturing workshops. The proposed method is currently applicable to phase-oriented complex-product manufacturing workshops with structured process routes, equipment capabilities, and parent–child assembly constraints; its transferability across layouts and enterprises requires further validation. Full article

This study proposes a hybrid deep learning model named LSTM-CBAM-Time2Vec-KAN based on symmetry awareness and optimized multi-module collaborative features, aiming to improve the accuracy and stability of stock price prediction. To address common shortcomings in traditional forecasting models such as insufficient feature extraction, difficulties in parameter optimization, and inadequate utilization of temporal characteristics, the research innovatively exploits the symmetry inherent in financial time series, particularly their temporal periodicity and cross-dimensional feature consistency, to construct an intelligent prediction framework that integrates multiple modules. First, wavelet transform is applied to perform multi-scale decomposition and signal reconstruction on the raw stock price sequence, effectively extracting high signal-to-noise ratio features. Second, the Northern Goshawk Optimization (NGO) algorithm is employed to jointly optimize key hyperparameters of the model, including the LSTM hidden layer dimension and CBAM compression ratio, thereby resolving the challenge of parameter coupling across modules. Third, the CBAM attention mechanism enhances the importance of temporal features extracted by LSTM through a dual mechanism of channel and spatial attention, enabling the model to focus on critical price movement points. Meanwhile, Time2Vec encoding transforms temporal information into embedding representations with periodic properties, effectively capturing cyclical patterns at daily, weekly, and monthly trading intervals. Finally, the Kolmogorov–Arnold network (KAN) fuses multimodal features and produces precise predictive outputs. Experimental results show that the proposed model significantly outperforms all baseline models in four evaluation metrics, namely mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²), which verifies its superior prediction accuracy and robustness. Furthermore, analyses of stock price forecasting under different time spans and simulated trading performance under various trading strategies further demonstrate that this study provides a feasible and effective technical solution for financial time-series forecasting, with important theoretical research value and practical application value. Full article

Accurate prediction of the Remaining Useful Life (RUL) of nuclear power plant systems can support more informed maintenance strategies and help reduce unplanned outages, motivating continued research into reliable, physically grounded prognostic models. This study evaluates machine-learning-based prognostic models, focusing on their ability to learn degradation patterns directly from operational data. Baseline Feedforward Neural Networks (FNNs), Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), and a hybrid GNN–Transformer architecture are assessed using data generated from the ASHERAH dynamic Pressurized Water Reactor simulator, which incorporates realistic degradation mechanisms, including condenser fouling and pump head loss. The proposed hybrid model integrates graph-based representations of component interactions with Transformer-based temporal attention to capture both system-level dependencies and long-term degradation dynamics. Model performance is evaluated using error-based and tolerance-based metrics, including Prediction Within Bounds Accuracy ($PWB{A}_{20\%}$), alongside uncertainty calibration via Prediction Interval Coverage Probability (PICP), with uncertainty quantified through deep ensembles. Results show that baseline NNs exhibit limited predictive accuracy and poor uncertainty calibration, while models incorporating temporal modeling and system topology achieve substantial improvements. The GNN–Transformer attains the strongest performance, yielding the highest $PWB{A}_{20\% }$ (87.3%) and significantly improved uncertainty calibration, with an average PICP of 90.0%. These findings demonstrate the effectiveness of topology-aware, attention-based architectures for robust and reliable RUL prediction in complex nuclear systems. Full article

The intelligent transformation of ports has become a critical pathway for advancing sustainable regional development in the context of rapid globalization and digitalization. While existing studies primarily focus on evaluating port intelligence, limited attention has been given to its role in fostering regional economic sustainability. To address this gap, this study examines the Yangtze River Delta (YRD) region as a representative case to explore the mechanisms through which port intelligent upgrading influences regional economic systems. An integrated analytical framework is developed in which the Analytic Hierarchy Process (AHP) is used to construct a composite index of port intelligence, and Principal Component Analysis (PCA) is employed to measure regional economic development. Furthermore, a Panel Vector Autoregression (PVAR) model is applied to capture the dynamic interactions between these two systems. The results indicate that port intelligence has increased significantly across the sampled ports, with the Port of Shanghai and the Port of Ningbo–Zhoushan exhibiting particularly notable growth, rising by 28% and 44%, respectively, over the period of 2011–2021. The PVAR results further reveal a sustained upward trend in regional economic development, suggesting a positive and persistent association between port intelligence and regional economic performance. These findings provide both theoretical and empirical support for understanding the role of port intelligent transformation in promoting regional economic sustainability and offer policy-relevant insights for fostering coordinated development between port systems and regional economies. Full article

Background: Cardiotocography (CTG) represents a cornerstone in modern intrapartum fetal surveillance, allowing continuous assessment of fetal well-being through the analysis of fetal heart rate patterns in relation to uterine contractions. Despite its widespread clinical use, the interpretation of CTG tracings remains complex and is often associated with high interobserver variability and limited specificity in predicting adverse outcomes. In recent years, increasing attention has been directed toward placental pathology as a key determinant in the pathophysiology of adverse perinatal events. The placenta, as a dynamic organ mediating maternal–fetal exchange, plays a crucial role in fetal oxygenation and nutrient supply. Alterations in its structure and function may contribute to both chronic and acute fetal compromise. This retrospective study aimed to investigate the relationship between pathological intrapartum CTG tracings and maternal–fetal outcomes, with a particular focus on correlating these findings with macroscopic and microscopic placental abnormalities, compared to a control group of uncomplicated pregnancies with normal CTG patterns. Material and methods: We evaluated maternal, fetal, and placental histopathological data from 85 patients who exhibited pathological intrapartum cardiotocographic tracings. All deliveries occurred between January 2023 and March 2025 at the Obstetrics and Gynecology Departments of the ‘A. Gemelli’ University Hospital and Careggi University Hospital. A control group consisting of 50 women with normal CTG patterns and uncomplicated term singleton pregnancies, delivered at the same institutions, was used for comparison. Results: Cases with pathological CTG showed poorer neonatal outcomes compared with those with normal CTG. Infants in the pathological CTG group had lower Apgar scores, more frequent NICU admissions, and one case of hypoxic–ischemic encephalopathy. Mode of delivery, Apgar scores, NICU admission, and arterial pH were all significantly associated with CTG classification. A higher birth weight-to-placental weight ratio in the pathological CTG group suggested possible uteroplacental insufficiency. Macroscopic and histological placental findings, including hypercoiled umbilical cords and intervillous thrombosis, were also significantly more frequent in cases with pathological CTG. Conclusions: The findings of this study suggest that pathological CTG reflects not an isolated event but rather a multifactorial dysfunction of the feto-placental unit. Its associations with low Apgar scores, reduced arterial pH, abnormal BW/PW ratio, intervillous thrombi, and umbilical cord hypercoiling are consistent with existing evidence and support the interplay of chronic placental abnormalities and acute mechanical factors in the development of suspected fetal hypoxia. The future development of multivariate predictive models integrating clinical, biochemical, and anatomo-pathological variables may improve the early identification of pregnancies at risk and optimize intrapartum management, thereby reducing adverse perinatal outcomes. Full article

With the rapid advancement of the IoT society and growing awareness of environmental issues, thermoelectric conversion technology—which directly converts waste heat into electricity—is gaining attention as a self-powered, autonomous power source capable of driving countless devices. While currently mainstream metal-based inorganic thermoelectric materials demonstrate high performance, their high rigidity and brittleness, as well as their frequent inclusion of toxic heavy metals, have limited their application in biological systems and on curved surfaces. As a next-generation alternative, single-walled carbon nanotubes (SWCNTs)—which possess excellent flexibility, electrical conductivity, and mechanical strength while being low in toxicity—are garnering significant attention. However, n-type SWCNT materials, which are essential for thermoelectric module fabrication, have faced two major barriers to practical application: low atmospheric stability (they easily revert to p-type upon exposure to atmospheric oxygen and moisture) and thermoelectric performance that falls short of inorganic materials. This review comprehensively outlines the latest composite approaches designed to overcome these critical challenges and achieve both extreme atmospheric stability and high thermoelectric performance in n-type SWCNT materials, along with the flexibility required to withstand severe deformation. Three main strategies are discussed. The first is the organic/polymer approach, which involves doping with organic small molecules that control the LUMO level or bicyclic organic superbases with strong electron-donating properties, as well as polymer coating, to achieve long-term stable n-type characteristics and high power output even in air or under severe high-temperature conditions. The second is the inorganic hybrid strategy, which involves nanoscale compositing with inorganic materials such as Bi₂Te₃ and Cu₂O; this reduces thermal conductivity through phonon scattering via interface control, while the inorganic layer physically blocks oxygen to ensure long-term atmospheric stability. The third approach involves ultra-long-term stabilization techniques, such as bulk encapsulation using cationic or gemini surfactants, and environmentally friendly aqueous processes utilizing natural amino acids. Furthermore, we discuss the latest developments in imparting practical-level toughness (flexibility) capable of withstanding thousands of bending cycles and high tensile stress through the introduction of dynamic covalent network polymers and elastomers. The conformal flexible thermoelectric power generation modules created through the integration of composite optimization, low-environmental-impact processes, and doping techniques will serve as a crucial foundational technology for realizing a sustainable next-generation electronics society, including future wearable devices, artificial skin, and smart sensor networks. Full article

Smart-city governance increasingly relies on data infrastructures to connect public agencies, digital platforms, and urban services; however, complex emergencies continue to expose fragmentation in information sharing, administrative responsibilities, and cross-boundary coordination. A key unresolved question is whether data-driven policy experimentation can strengthen the institutional foundations of emergency collaboration. In this study, we examine China’s National Big Data Comprehensive Pilot Zones as a systemic governance intervention and apply a multi-period difference-in-differences model to estimate the effect of pilot-zone construction on the policy-text-based institutionalization of emergency collaboration, using provincial panel data for 30 Chinese provinces from 2010 to 2022. The results show that pilot-zone construction significantly strengthens institutionalized emergency collaboration in provincial policy systems. Mechanism tests provide evidence consistent with two complementary pathways: emergency-related technological innovation, measured by granted patents screened through IPC/CPC classifications and title–abstract keywords, supports task-specific capacities such as risk sensing, early warning, emergency communication, command support, and resource allocation; digital government strengthens administrative interoperability, data sharing, platform-based coordination, and standardized interdepartmental procedures. Heterogeneity analyses show stronger effects in eastern, middle-income, and severely aging regions, suggesting that policy effectiveness depends on implementation capacity, absorptive capacity, and emergency-service demand. This study contributes to systems governance and smart-city research by showing how data-driven policy experimentation can shape the formal institutionalization of emergency collaboration. The findings should be interpreted as evidence of institutionalized policy attention and formal collaborative arrangements rather than direct evidence of field-level emergency response performance. Full article

Dynamic consent ecosystems have become increasingly complex due to the widespread adoption of Consent Management Platforms (CMPs), multi-layer preference interfaces, asynchronous rendering architectures, and adaptive interaction workflows. Existing privacy-auditing approaches primarily rely on static interface inspection and therefore provide limited support for reconstructing and evaluating dynamic consent interactions. To address these limitations, this study proposes a computational measurement framework for the automated reconstruction and analysis of dynamic consent ecosystems. The framework integrates five computational layers for browser-based acquisition, interaction sensing, multi-layer synchronization, consent-state verification, and Adaptive Cognitive Load Dark Pattern (ACL-DP) operationalization. The proposed methodology combines asynchronous browser automation, interaction workflow reconstruction, multi-source evidence synchronization, backend consent verification, and rule-based mechanism scoring to transform complex consent interactions into reproducible quantitative representations. Evaluation across 18,665 consent ecosystems generated 59 synchronized variables spanning interface, interaction, textual, and consent-state dimensions. Workflow reconstruction successfully recovered interaction trajectories for 99.6% of observable consent environments, while backend verification identified consent mismatches in 77.6% of environments with complete frontend–backend aligned evidence, revealing substantial divergence between observable consent decisions and backend consent behavior. The ACL-DP framework operationalizes four mechanism families, Effort Engineering, Attention Engineering, Cognitive Load Amplification, and Algorithmic Adaptivity, the latter capturing observable runtime, session-dependent, context-sensitive, and backend-mediated variation in consent behavior. The results revealed persistent procedural and attentional asymmetries, recurrent hidden rejection mechanisms, and widespread frontend–backend consent inconsistencies, with Effort Engineering emerging as the dominant manipulation strategy. Validation through reproducibility analysis, sensitivity analysis, statistical uncertainty assessment, and a human benchmark of 100 independently annotated websites demonstrated high inter-run consistency and moderate-to-substantial inter-rater agreement, supporting the framework’s reliability and validity. This work provides a reproducible foundation for large-scale privacy interaction analysis and evidence-based evaluation of dynamic consent ecosystems. Full article

Intricate natural wood textures and diversified defect morphologies hinder high-precision recognition of visible surface defects on sawn lumber. Six common types of surface defects exist on sawn lumber, including dry knots, edge knots, small knots, sound knots, wavy defects, and splits. Among these defect types, edge knots, small knots, wavy defects, and splits bring great difficulties to detection due to their tiny areas, slender geometric outlines and indistinct boundaries. To accurately identify the above defects, a customized You Only Look Once version 8 medium (YOLOv8m)-based framework was developed for lumber surface inspection. First, the Cross-Stage Partial Bottleneck with Two Convolutions embedded with Efficient Channel Attention (C2f-ECA) and Space-to-Depth Convolution (SPD-Conv) are introduced into the backbone to enhance channel-wise feature representation and preserve fine spatial details during downsampling, while C2f with Deformable Convolution (C2f-DCN) is embedded in the deep feature extraction branch to improve the geometric modeling of irregular defects. Second, a C2f-DCN with Exponential Moving Average Attention (C2f-DCN-EMA) module and dynamic upsampling (DySample) are integrated in the feature-fusion stage to refine multi-scale features and reconstruct local edges. Third, Scaled Intersection over Union (SIoU) loss is used to improve bounding-box regression for defects with extreme aspect ratios. Experiments show that the proposed model achieves 91.8% mean Average Precision at IoU 0.5 (mAP@50) and 69.3% mean Average Precision across IoU thresholds of 0.5–0.95 (mAP@50-95), exceeding the YOLOv8m baseline by 1.0 and 1.5 percentage points, respectively. Full article

Accurate dynamic cost prediction is essential for budget optimization and risk mitigation in State Grid projects. However, traditional models and even recent deep learning approaches fall short, as they treat cost drivers independently, adopt simplistic concatenation that destroys sourcewise structure, or fail to handle irregularly sampled and partially missing multi-source data. This paper proposes a novel data-driven framework that integrates multi-source business data through a hierarchical tensor fusion mechanism and a hybrid spatiotemporal architecture. The problem is formalized as multivariate time-series prediction with irregular sampling and missing modalities. The framework comprises three synergistic innovations: a differentiable low-rank CANDECOMP/PARAFAC (CP) decomposition layer with adaptive attention weights that preserves cross-source structure while enabling compact dimensionality reduction; a spatiotemporal attention-based bidirectional gated recurrent unit (Bi-GRU) that captures long-range temporal dependencies; and a graph convolutional network (GCN) that explicitly learns interrelations among cost drivers, a capability absent in most existing forecasting methods. The entire system is trained end to end with a customized loss combining mean squared error, quantile loss, and temporal consistency regularization. Extensive experiments on three State Grid substation projects demonstrate that the proposed method outperforms state-of-the-art baselines by 12.7–18.4% in MAPE and maintains robust performance with up to 40% of data missing. These results confirm that explicitly modeling both temporal evolution and driver interdependencies within a unified fusion framework is the key to reliable cost forecasting in large-scale infrastructure projects. Full article