Search Results (2,607)

Surface defects such as depressions, heaving, and irregular undulations frequently develop on aging concrete bridge decks under repeated traffic loading and environmental effects. Accurate and objective identification of such defects is essential for structural serviceability and safety, yet manual inspection remains labor-intensive and subjective. This study develops a systematic framework for surface defect identification through geometric feature augmentation with a streamlined point cloud learning strategy. In practical engineering scenarios, point cloud data of concrete bridge decks can be periodically acquired via vehicle-mounted mobile laser scanning (MLS) systems and subsequently streamlined for analysis. The proposed method heightens defect sensitivity by extracting interpretable geometric descriptors, further integrating multi-scale representations to capture surface defects across varying spatial extents. Evaluated on a public point-level annotated benchmark, the proposed method clearly outperforms the same network trained with geometric coordinates only. To improve result reliability, all experiments were repeated four times with different random seeds, and the performance is reported as mean ± standard deviation. Results show that the proposed method achieves a precision of 0.597 ± 0.021 and an accuracy of 0.933 ± 0.009 under the benchmark protocol. Overall, these results demonstrate a reproducible proof of concept under controlled benchmark conditions for bridge deck surface defect segmentation, while broader cross-site and cross-sensor validation will be pursued in future work. Full article

The mechanisms by which the regional hydroclimate responds to global climate forcing are complex, particularly in geographically heterogeneous countries like China. Focusing on Hunan Province, this study employs the Standardized Precipitation Index (SPI) derived from long-term precipitation records at 87 meteorological stations to delineate climatic sub-regions with coherent dry–wet variability. Using rotated empirical orthogonal function analysis, we systematically characterize the spatiotemporal patterns of SPI components and quantify their teleconnections with global ocean–atmosphere circulation modes. The analysis of multi-timescale SPI reveals four distinct sub-regions and a pronounced northwest–southeast dipole in long-term trends. Despite an overall reduction in annual drought, the northwestern sub-region experienced intensification. Seasonally, a pattern of spring/autumn drying versus summer/winter wetting emerged. Wavelet analysis identified dominant interannual (2–7 years) and interdecadal (13–71 months) oscillations. These periodicities are significantly teleconnected to large-scale circulation indices (e.g., Southern Oscillation and Pacific Decadal Oscillation), with influences peaking at 16–64-month and 2–5-year scales. Importantly, the primary circulating driver differs by sub-region, revealing a complex teleconnection landscape. The findings delineate region-specific atmospheric pathways, offering insights to bolster drought preparedness and optimize water allocation, thereby enhancing climate resilience in vulnerable monsoon transition zones. Full article

The Jiaodong Peninsula hosts one of the largest gold provinces in the world. The Xincheng gold deposit, located within the Jiaojia gold metallogenic belt, is the largest deposit in this belt and represents a super-large fractured alteration-type gold deposit hosted in fracture zones with relatively well-preserved conditions. Mineralization and hydrothermal alteration are controlled by the Jiaojia Fault zone and its subsidiary faults. The Jiaojia Fault (JJF) serves as the principal ore-hosting structure of the Xincheng deposit, and its multi-stage activity has governed the mineralization, subsequent modification, and preservation of the deposit. However, the post-mineralization cooling, uplift, and exhumation history of the deposit remains poorly constrained. In this study, zircon and apatite fission-track thermochronology analyses were conducted, and inverse thermal history modeling of apatite was performed to reconstruct the tectonic-metallogenic evolution of the Xincheng gold deposit. The zircon fission-track ages range from 90.0 ± 4.0 to 118.0 ± 5.2 Ma, which are younger than the mineralization age (~120 Ma), indicating that the region experienced widespread cooling during the Late Early Cretaceous. This cooling event was likely related to crustal uplift and exhumation triggered by a transformation of the tectonic regime. The apatite fission-track ages range from 15 ± 1.8 to 38 ± 2.7 Ma, recording the Cenozoic cooling and uplift history after mineralization. The inverse thermal history modeling results show that the post-mineralization cooling process can be divided into three stages. The first stage, from 42 ± 5 to 30 ± 4 Ma, is characterized by rapid cooling, with an average cooling rate of 4.23 °C/Myr. The second stage, from 30 ± 4 to 12 Ma, represents a period of slow cooling, with an average cooling rate of 0.98 °C/Myr. Since 12 Ma, the third stage has been marked by renewed rapid cooling, with an average cooling rate of 4.17 °C/Myr. Variations in cooling rates among different stages reflect adjustments in the regional tectonic stress field and the influence of activity along the JJF. Based on the fission track thermochronological data and a reasonable estimate of the geothermal gradient, the total amount of exhumation since 120 Ma is calculated to be approximately 8.22 km. Integration of these results indicates that the shallow portion of the deposit has undergone a certain degree of erosion; however, the overall preservation conditions remain favorable, and significant exploration potential persists at depth and along strike. This study constrains the post-mineralization cooling and erosion history of the Xincheng gold deposit, reveals the controlling role of multi-stage tectonic activity on deposit preservation, and provides new temporal constraints and a scientific basis for preservation assessment and deep exploration of gold deposits in the Jiaodong Peninsula and in regions with similar tectonic settings. Full article

Modern organizations increasingly rely on heterogeneous database environments that combine relational, document-oriented, and key-value storage systems to optimize performance for diverse application requirements. However, this technological diversity creates significant challenges for implementing consistent data governance policies, regulatory compliance, and access control across disparate systems. Traditional governance approaches that operate within individual database silos fail to provide unified policy enforcement and create compliance gaps that expose organizations to regulatory and operational risks. This paper presents a novel API-driven architecture that enables unified data governance across heterogeneous database environments without requiring database-specific modifications or vendor lock-in. The proposed framework implements a centralized governance layer that coordinates policy enforcement across PostgreSQL, MongoDB, and Amazon DynamoDB systems through RESTful API interfaces. Key architectural components include differentiated access control through hierarchical API key management, automated compliance workflows for regulatory requirements such as GDPR, real-time audit trail generation, and comprehensive data quality monitoring with automated improvement mechanisms. Comprehensive experimental evaluation demonstrates the framework’s effectiveness across multiple operational dimensions. The system achieved 95.2% accuracy in access control enforcement across different data classification levels, while automated GDPR compliance workflows demonstrated 98.6% success rates with average processing times of 2.9 h. Performance evaluation reveals acceptable overhead characteristics with linear scaling patterns for PostgreSQL operations (R² = 0.89), consistent sub-20ms response times for MongoDB logging operations, and sustained throughput rates ranging from 38.9 to 142.7 requests per second across the integrated system. Data quality improvements ranged from 16.1% to 34.3% across accuracy, completeness, consistency, and timeliness dimensions over a 12-week monitoring period, with accuracy improving by 17.8 percentage points, completeness by 13.2 percentage points, consistency by 19.7 percentage points, and timeliness by 24.5 percentage points. The duplicate detection system achieved 94.6% precision and 95.6% recall across various duplicate types, including cross-database redundancy identification. The results demonstrate that API-driven governance architectures can effectively address the persistent challenges of policy fragmentation in multi-database environments while maintaining operational performance and enabling measurable improvements in data quality and regulatory compliance. The framework provides a practical migration path for organizations seeking to implement comprehensive governance capabilities without replacing existing database infrastructure investments. Full article

Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions. Full article

This study aims to evaluate the impact of agricultural land property rights system reform on Agricultural Green Total Factor Productivity (AGTFP) and to uncover its underlying mechanisms. Treating the nationwide rollout of the Three Rights Separation Reform (TRSR) as a quasi-natural experiment, we employ provincial panel data from 2011 to 2023. The Super-SBM model is applied to measure AGTFP, followed by a multi-period Difference-in-Differences framework to identify the causal effects. The results indicate that the TRSR significantly enhances AGTFP, yielding an average improvement of 0.112 units. Mechanism analyses reveal that this gain is achieved through three distinct channels: promoting labor-saving technological progress, optimizing factor allocation efficiency, and facilitating agricultural green transformation. Heterogeneity analyses further demonstrate that the positive effects are more pronounced in plains regions, areas with lower rural per capita income, and jurisdictions with higher agricultural fiscal expenditure. These findings remain robust after a series of robustness and endogeneity tests. This study provides novel institutional evidence on the drivers of AGTFP and offers policy-relevant insights for advancing sustainable agricultural transformation in developing economies. Full article

Despite extensive research on queueing models in flexible manufacturing systems (FMS), few studies have simultaneously considered the heterogeneity of servers and the impact of synchronous working vacations on system performance. To fill this research gap, this study proposes a novel multi-server queueing model that uniquely integrates two-phase heterogeneous services with synchronous working vacation interruptions. The innovation lies in capturing the complex task processing mechanism where the two-phase service is provided by different servers, and they conduct working vacations synchronously when the system is empty and terminate vacations once the system population reaches a specified threshold. Based on the matrix-analytic approach, this research investigates the stability condition and the stationary distribution of the system. A key numerical finding is the diminishing marginal returns of server numbers, where exceeding an optimal count increases congestion, thereby degrading overall performance. The vacation interruption threshold l is also shown to significantly influence server state allocation. A comparative analysis of three different system configurations, demonstrates that dynamic server number or a vacation interruption threshold adjustment effectively mitigates congestion; and in order to enhance the system clearing capability, when vacations are longer, opting for multiple servers is preferable, whereas a single server is more suitable during shorter vacation periods. For cost optimization, three algorithms (CPSO, JAYA, MLS-JAYA) consistently converge to the same robust optimal solution for server count and vacation rate. Furthermore, to simultaneously minimize waiting time, we apply the MOEA/D algorithm to study the bi-objective optimization problem. Full article

Precise spatiotemporal monitoring of soil moisture is fundamental to the efficient regulation and sustainable utilization of agricultural water resources in arid and semi-arid irrigation districts. This study focuses on the Yichang Irrigation District within the Hetao Irrigation Area to elucidate the spatiotemporal dynamics of surface soil moisture during the crop growing season. Multi-year Landsat 8/9 remote sensing imagery (2022–2024) was integrated with the Temperature Vegetation Dryness Index (TVDI) framework to construct two feature spaces, namely Normalized Difference Vegetation Index–Land Surface Temperature (NDVI–LST) and Enhanced Vegetation Index–Land Surface Temperature (EVI–LST). A dual-index complementary inversion strategy was applied for soil moisture estimation, and the outputs were validated against Soil Moisture Active Passive (SMAP) soil moisture products and MOD16 evapotranspiration products. Results indicated that the dry edges of the feature spaces derived from both vegetation indices exhibited double-inflection-point characteristics, with optimal fitting intervals located between the inflection points. The inflection point positions shifted dynamically with variations in crop coverage. During bare-soil and low-vegetation-coverage periods (May, June, and September), the minimum thresholds for low NDVI and EVI values were 0.07 and 0.06, respectively, whereas during high-vegetation-coverage periods in July and August, the minimum thresholds for both indices increased to 0.15. NDVI demonstrated superior performance during May, June, and September, whereas EVI exhibited greater advantages during active crop growth periods in July–August. The optimized model achieved robust inversion accuracy, with a validation R² of 0.81 for the measured soil moisture in the 0–20 cm layer on 12 May 2024. The inversion results exhibited strong correlations with the SMAP soil moisture products (R² = 0.663 during low crop coverage; R² = 0.625 during high crop coverage) and MOD16 evapotranspiration data (R = 0.751). The spatiotemporal patterns of soil moisture were distinctly discerned. Following spring irrigation in May, abundant moisture in certain areas resulted in bimodal distribution patterns in the inversion results. June exhibited the lowest soil moisture content across the study area, with arid zones making up 36.67% of the total area. From July to August, concentrated precipitation coupled with summer irrigation reduced the proportion of extremely arid zones to below 0.98%. Full article

Accurate prediction of landslide displacement is crucial for hazard prevention. However, recurrent neural network (RNN) models have limitations in simultaneously capturing lag time and feature importance, and their black-box nature limits their interpretability. Moreover, the performance of single models varies across different deformation stages, especially during acceleration. To address these challenges, we propose an interpretable deep reinforcement learning-based adaptive ensemble (DRL-AE) framework. The method employs Seasonal and Trend decomposition using Loess to separate cumulative displacement into trend and periodic components. Trend and periodic sequences are predicted using double exponential smoothing and three RNN variants, respectively. An improved Convolutional Block Attention Module (ICBAM) enhances periodic feature extraction and provides temporal–spatial interpretability. The Deep Deterministic Policy Gradient algorithm adaptively integrates multi-model predictions in response to evolving environmental conditions. To validate the DRL-AE, a case study is conducted on the Baijiabao landslide in Zigui County, China. The results indicate that the DRL-AE substantially enhances prediction accuracy. For periodic displacement, it reduces MAE by 10.02% and RMSE by 6.65%, and increases R² by 4.27% compared with the ICBAM-GRU model. The results also confirm the effectiveness of ICBAM in feature extraction, and the generated heatmaps provide intuitive interpretability of the relevant triggering factors. Full article

To achieve the “dual carbon” goals, the management and control of the construction sector’s embodied carbon is crucial, as it is a key field of carbon emissions. This study focuses on the entire process of building structural design, construction and procurement, and building material production and trading. Based on the principles of system dynamics, it constructs a building embodied carbon analysis model consisting of three subsystems: building structural design, production, and building material market. The core elements of each subsystem and their interaction relationships are clarified, and the model variables and parameters are defined. Through multi-scenario simulation analysis, the influence mechanisms of key factors such as different building heights, seismic influence coefficients, expected project costs, and carbon reduction policies on building embodied carbon are explored. The results show that building height and seismic influence coefficients have significant impacts on material consumption during the building structural design stage, with building height exerting a more prominent driving effect; increasing the prefabrication rate can improve construction efficiency, shorten the construction period, reduce construction carbon emissions, and simultaneously balance the current pressure of rising labor costs; and carbon reduction policies guide market demand, prompting low-carbon building material manufacturers to expand R&D investment and production capacity, forming a positive cycle of “demand growth—cost reduction—market expansion”. In contrast, conventional building materials are affected by tightened carbon quotas and rising carbon prices, leading to a continuous shrinkage of their market share and gradual withdrawal from the market, ultimately realizing overall carbon reduction in the industry. The system dynamics model constructed in this study provides a scientific analysis framework for the full-process management and control of building embodied carbon, reveals the key influencing factors and evolution laws, and offers theoretical support and practical reference for the precise management and control of building embodied carbon and the formulation of carbon reduction pathways. Full article

Public cultural services are traditionally viewed as welfare provisions. However, this perspective overlooks their productive externalities as critical social infrastructure. This study treats China’s National Public Cultural Service System Demonstration Zone program as a quasi-natural experiment to examine its economic performance. The analysis utilizes panel data from 280 prefecture-level cities between 2008 and 2021 and employs a multi-period difference-in-differences model. Results show that the policy successfully increased employment in the cultural sector. This was achieved by enabling flexible labor opportunities through digital platforms and government procurement, rather than through significant growth in formal enterprises. We term this structural divergence De-organized Growth. Mechanism analysis confirms that Fiscal-Digital Synergy drives this phenomenon. Effective collaboration between government funding and digital technology activates cultural consumption on the demand side and facilitates disintermediation on the supply side. Crucially, we identify a nonlinear Digital Exclusion Trap. In this trap, fiscal support is ineffective or even counterproductive in regions falling below a critical digital infrastructure threshold. The findings suggest that the equalized provision of public culture serves as a productive input for achieving UN Sustainable Development Goal 8 regarding decent work. We advocate for a shift in governance paradigms from traditional administration to a strategic purchaser role. This role leverages digital platforms to foster a more inclusive labor market. Full article

Background/Objectives: This prospective study aimed to investigate asymptomatic microscopic changes in the myocardium following postoperative radiation therapy (RT) in patients with left breast cancer using multi-medical assessment techniques. Methods: This study included 16 left-sided breast cancer patients who received postoperative RT between January 2021 and December 2022 at our institution. Cardiac examinations were performed before RT and at 1, 12, 24, and 48 weeks after RT. We conducted comparative analyses between pre-RT and various post-RT time points, exploring correlations between changes in hematologic biomarkers, global longitudinal strain (GLS), and myocardial metabolism. Results: Inflammatory biomarkers such as the neutrophil–lymphocyte, platelet–lymphocyte, and lymphocyte–monocyte ratios changed between the pre- and post-RT periods but returned to normal levels after several months. However, troponin T and soluble suppression of tumorigenicity 2 showed sustained changes during the 1-year follow-up period. Among echocardiographic parameters, GLS_LAX showed a significant difference between pre-RT and post-RT assessments. Additionally, irradiated and non-irradiated myocardial metabolic ratios on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography differed between pre-RT and post-RT and remained altered up to one year after treatment. Conclusions: These findings suggest that subclinical myocardial changes may persist following RT, although the clinical significance of subclinical myocardial changes remains uncertain and warrants further investigation. Full article

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty. Full article

Addressing the challenges of high dimensionality, strong nonlinearity, and multiple constraints in multi-UAV cooperative path planning, this paper proposes a Behavior-Adaptive Aquila Optimizer (EAO) achieved by enhancing Aquila Optimizer (AO). EAO constructs a multi-strategy cooperative framework that integrates a periodic diversity maintenance mechanism, a diversity-based dynamic neighborhood guidance mechanism, a narrowed exploitation behavior based on neighborhood differential evolution, and a search-state-aware adaptive behavior selection mechanism. Through dynamic behavior adjustment during the search process, the proposed algorithm improves search performance and stability. To validate its effectiveness, EAO was systematically evaluated on the CEC2017 and CEC2020 benchmark suites and compared with the original AO and 13 representative high-performance optimization algorithms. Parameter sensitivity analysis, an ablation study, and an exploration–exploitation experiment were also conducted. The results show that EAO achieves the best overall performance ranking. Furthermore, EAO was applied to multi-UAV cooperative path-planning simulations in complex environments that considered UAV dynamic constraints. Comparative experiments with five competitive algorithms demonstrate that EAO achieves superior performance in terms of path-planning fitness, number of effective trajectories, and runtime. Compared with AO, EAO improves the average fitness by 80.42%, 81.25%, 81.34%, and 84.84% across different map environments, confirming its feasibility and effectiveness for multi-UAV cooperative path planning. Full article

This study identifies and extracts two typical drought characteristics, drought frequency and drought severity, based on the Meteorological Drought Composite Index (MCI), and systematically analyzes their spatiotemporal evolution in Inner Mongolia. Using a two-stage geographical detector approach, the dominant factors of drought characteristics and their spatial variations are quantitatively identified across different drought grades and subregions, and the weights of drought indicators are determined accordingly. Finally, a multi-level drought hazard assessment is conducted using a drought hazard index model, providing scientific support for drought risk management and disaster prevention in Inner Mongolia. The results indicate that (1) drought characteristics exhibit significant spatial heterogeneity. Drought frequency presents a distinct east–high to west–low gradient, while high values of drought severity are concentrated in the central and southwestern regions. Temporally, drought frequency shows an increasing trend, whereas drought severity demonstrates periodic fluctuations and relative stability. (2) Results from factor and interaction detection reveal that light, moderate, and extreme drought levels are primarily influenced by the combined effects of regions with extremely high drought frequency and drought severity. In contrast, severe drought is mainly driven by regions with extremely high frequency and high severity. Moreover, the interaction between multiple factors significantly enhances the explanatory power for drought severity levels compared to individual factors. (3) The drought hazard assessment shows that high-hazard areas are mainly concentrated in Alxa League, Tongliao City, and other regions. The spatial distribution of hazard levels is highly consistent with historical drought statistics, thereby validating the rationality and practical applicability of the proposed model. Full article