Search Results (9,269)

Yellow River Delta National Nature Reserve plays a critical role in ecological conservation, and assessing its ecosystem service value (ESV) is essential for guiding sustainable management strategies that harmonize development and preservation. This study was motivated by the need to generate actionable insights for adaptive conservation planning in this vulnerable coastal region. We evaluated the spatiotemporal dynamics of ESV from 2000 to 2020 using a combination of remote sensing, geographic information system analyses, and statistical modeling. Primary drivers influencing the spatial heterogeneity of ecosystem service value were identified through geographical detector analysis, and future trends were projected based on historical patterns. The results revealed that (1) ESV showed a clear spatial gradient, with higher values in coastal zones, moderate values along river channels, and lower values inland, and exhibited an overall significant increase over the two decades, primarily driven by improvements in regulating services; (2) wetland area and precipitation were the most influential factors, though socio-economic elements and environmental conditions also contributed to ESV distribution; and (3) future ESV is expected to follow current trends, reinforcing the importance of current management practices. Given that the continuous increase in ESV from 2000 to 2020 was predominantly attributed to water body expansion, future conservation strategies should prioritize the protection and restoration of these water resources. Full article

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

Monitoring process stability and tool condition is essential for ensuring machining quality and efficiency. This study investigates the influence of sensorized tool holders on dynamic properties and machining results. Three clamping conditions, one conventional and two different sensor-integrated tool holders (equipped with strain gauges or piezoelectric force sensors), are compared. Experimental modal analyses are carried out to determine the frequency-dependent dynamic compliance of the systems. Machining tests using a developed reference workpiece enable the investigation of process forces, wear development, and the surface quality achieved under real conditions. The results show that the dynamic behavior of the tools varies significantly depending on the respective excitation frequency, whereby the different structural properties of the tool holders have a clearly measurable influence on their dynamic properties, particularly near process-relevant excitation frequencies. However, no clear deterioration in terms of process stability or machining performance can be determined. In some cases, the sensorized tool holders can contribute to reduced tool wear and improved process stability. These findings emphasize that sensorized tool holders do not necessarily worsen the machining results and can be applied without negative effects when aligned with the system’s modal characteristics. Full article

The widespread integration of Internet-connected devices into industrial environments has enhanced connectivity and automation but has also increased the exposure of industrial cyber–physical systems to security threats. Detecting anomalies is essential for ensuring operational continuity and safeguarding critical assets, yet the dynamic, real-time nature of such data poses challenges for developing effective defenses. This paper introduces DataSense, a comprehensive dataset designed to advance security research in industrial networked environments. DataSense contains synchronized sensor and network stream data, capturing interactions among diverse industrial sensors, commonly used connected devices, and network equipment, enabling vulnerability studies across heterogeneous industrial setups. The dataset was generated through the controlled execution of 50 realistic attacks spanning seven major categories: reconnaissance, denial of service, distributed denial of service, web exploitation, man-in-the-middle, brute force, and malware. This process produced a balanced mix of benign and malicious traffic that reflects real-world conditions. To enhance its utility, we introduce an original feature selection approach that identifies features most relevant to improving detection rates while minimizing resource usage. Comprehensive experiments with a broad spectrum of machine learning and deep learning models validate the dataset’s applicability, making DataSense a valuable resource for developing robust systems for detecting anomalies and preventing intrusions in real time within industrial environments. Full article

High-speed impact is a critical mechanism for structural damage. The infrared signatures generated during fragment formation provide essential data for damage assessment, protective system design, and target identification. This study investigated an aluminum alloy blunt projectile penetrating a target plate by employing smoothed particle hydrodynamics to simulate the debris ejection thermal and infrared properties. The infrared signatures of the debris clouds were calculated using Mie scattering theory under a spherical particle approximation. The reverse Monte Carlo methodology was applied to solve the radiative transfer equations and quantify the infrared emission characteristics. The infrared radiation characteristics of the debris cloud were investigated for projectile impact velocities of 800, 1000, and 1200 m/s. The results showed that the anterior debris regions reached peak temperatures of approximately 1200 K, with a temperature rise of 150–200 K per 200 m/s velocity increase behind the target. The medium-wave (3–5 μm) infrared intensity of the debris cloud was higher than the long-wave (8–12 μm) infrared intensity. The development of debris clouds enhanced the dispersion effect and slowed the increase in infrared radiation intensity in the same time interval. This study provides theoretical foundations for the dynamic infrared radiation characteristics of fragments generated by high-velocity projectile impacts. The infrared radiation characteristics within typical spectral bands can be utilized to assess hit probability and kill effectiveness. Full article

The Qinling Mountains, situated in the climatic transition zone between northern and southern China, represent a critical region for climate and ecological studies due to their unique transitional characteristics and the rising frequency of extreme climate events. As net primary productivity (NPP) is a key indicator of ecosystem stability, clarifying its response to extreme climate events is essential for understanding ecological resilience in this region. In this study, daily observational data from 123 meteorological stations (1960–2023) were used to derive eight extreme temperature and precipitation indices. Combined with MODIS NPP data (2001–2023), we applied Theil–Sen slope estimation, Mann–Kendall significance testing, ridge regression, Pearson correlation analysis, and Moran’s I spatial autocorrelation to systematically investigate the spatiotemporal dynamics and driving mechanisms of NPP. The main findings are as follows: (1) From 2001 to 2023, the mean annual NPP in the Qinling region was 558.43 ± 134.27 gC·m⁻²·year⁻¹, showing a significant increasing trend of 5.44 gC·m⁻²·year⁻¹ (p < 0.05). (2) Extreme temperature indices exhibited significant changes, whereas among the precipitation indices, only the number of days with daily precipitation ≥ 20 mm (R20) showed a significant trend, suggesting that extreme temperatures exert a stronger influence in the region. (3) Correlation analysis indicated that temperature-related indices were generally positively correlated, precipitation-related indices displayed even stronger associations, and covariation existed among extreme precipitation events of varying intensities. Moreover, precipitation indices demonstrated relatively stable spatial distributions, while temperature indices fluctuated considerably. (4) Absolute contribution analysis further revealed that the number of days with daily minimum temperature below the 10th percentile (TN10p) contributed up to 3.53 gC·m⁻²·year⁻¹ to annual NPP variation in the Henan subregion, whereas maximum rainfall over five consecutive days (Rx5day) exerted an overall negative effect on NPP (−0.77 gC·m⁻²·year⁻¹). By integrating long-term meteorological observations with remote sensing products, this study quantitatively evaluates the differential impacts of extreme climate events on vegetation within a climatic transition zone, offering important implications for ecological conservation and adaptive management in the Qinling Mountains. Full article

Understanding the spatial patterns of ethnic inter-embeddedness is essential for promoting sustainable development in multi-ethnic regions. This study develops a novel “Clustered-Boundary-Cellular” typological model to interpret the spatial differentiation of traditional villages in China’s Hehuang region. Using an integrated approach that combines GIS spatial analysis (Kernel Density Estimation, Ripley’s K-function, and Standard Deviational Ellipse), spatial statistics (Global Moran’s I), and other statistical tests (Kruskal–Wallis tests and multinomial logistic regression), we categorized and analyzed 153 nationally designated traditional villages. The results indicate the following: (1) The villages exhibit significant spatial differentiation, falling into three distinct scenarios. Clustered–Isolation villages (107/153, 69.9%) are predominantly located in topographically constrained areas and display strong spatial clustering; Boundary–Permeation villages (24/153, 15.7%) are distributed along transport corridors and show the highest road density (0.55 km/km²); Cellular–Symbiosis villages (22/153, 14.4%) occur in multi-ethnic cores areas and exhibit a relatively random spatial distribution. (2) This differentiation results from the synergistic effects of multidimensional drivers: natural environmental constraints (notably elevation and proximity to rivers), religious–cultural adaptation (Global Moran’s I analysis confirms the strong clustering of Tibetan and Salar groups, reflecting distinct religious spatial logics), and economic transition dynamics (transportation infrastructure serves as a key catalyst). This study demonstrates the value of the proposed model as an analytical tool for diagnosing ethnic spatial relations. The findings offer important insights and spatial guidance for formulating context-sensitive strategies for sustainable governance, cultural heritage preservation, and ethnic integration. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation. Full article

Despite the widespread use of Porcine circovirus type 2 (PCV2) vaccination, subclinical infection persists and remains a concern due to its economic impact. Therefore, continuous herd-level monitoring is essential to assess the dynamics of this infection on farms and minimize its impact. This study evaluated the applicability of a loop-mediated isothermal amplification (LAMP) assay for PCV2 detection in serum, air, and surface samples collected under field conditions. In addition, a simplified Direct LAMP protocol, omitting DNA extraction, was compared with quantitative PCR (qPCR) as the reference method. A total of 360 samples from PCV2 vaccinated and unvaccinated fattening farms were analyzed. Diagnostic performance was assessed in terms of sensitivity, specificity, predictive values, and concordance with qPCR, using Cohen’s kappa coefficient (κ). LAMP showed higher agreement with qPCR (κ = 0.52) than Direct LAMP (κ = 0.16). Serum samples provided the most reliable results when DNA extraction was performed, reaching substantial agreement with qPCR (κ = 0.76). However, Direct LAMP applied directly to serum was negatively affected by inhibitory substances, resulting in a significant drop in sensitivity. In contrast both air and surface samples yielded comparable results between LAMP and Direct LAMP, without the need for DNA extraction. Notably, LAMP-based assays detected PCV2 circulation earlier than qPCR, particularly in environmental samples. These findings demonstrate the potential of LAMP as a practical alternative to qPCR for PCV2 monitoring. While DNA extraction remains essential for reliable detection in serum, Direct LAMP represents a promising strategy for environmental surveillance, enabling rapid, low-cost, and on-farm diagnostics. Full article

Antimicrobial resistance (AMR) is widely recognized as a major global public health threat, yet its origins and implications extend beyond the simple misuse or overuse of antibiotics. This study explores AMR as a complex, multifactorial phenomenon shaped by biological, clinical, dental, environmental, and social dynamics, with particular attention to the emerging role of dentistry. A narrative literature review was performed, drawing from textbooks, peer-reviewed articles, and official World Health Organization (WHO) reports, with emphasis on recent findings on periodontal biofilms as reservoirs of resistance genes. The analysis shows that AMR develops through bacterial mutations, horizontal gene transfer, environmental contamination, healthcare-associated practices, and patient behaviors, all of which interact to sustain its spread. Within dentistry, subgingival microresistances are gaining relevance, complicating treatment strategies and underscoring the need for more conscious clinical decision-making. The findings suggest that reducing antibiotic prescriptions or developing new drugs alone will not suffice; instead, a systemic, interdisciplinary approach is required, integrating microbiology, clinical practice, public health, and institutional responsibility. Such awareness is essential to confront the significant clinical, economic, and social implications of AMR and to foster strategies capable of addressing its complex and evolving nature. Full article

This study investigates the use of thermally activated composite panels (TACP) as an energy-efficient retrofitting solution for existing buildings. By utilizing low-grade heat sources, TACP offers significant energy efficiency and carbon emission reduction. To address the challenges posed by high embodied carbon, exterior envelope delamination, and fire safety risks associated with conventional insulation materials, three TACP configurations were proposed: external insulation with externally embedded pipes (EE-TACP), external insulation with internally embedded pipes (EI-TACP), and internal insulation with internally embedded pipes (II-TACP). These were proposed for adapting TACP-walls to the diverse climate zones of Gansu Province. To clarify the thermal performance, energy-saving potential, and reduction effect on conventional insulation thickness of TACP-walls under different climatic conditions and heat source temperatures, dynamic heat transfer models of the TACP-wall were developed and validated. Three energy-saving rate (ε) indicators were introduced to analyze the dynamic thermal behavior in typical cities across the three climate zones of Gansu Province. A comparison was made among the three configurations in terms of instantaneous thermal response characteristics, cumulative internal surface heat load, and ε, relative to both non-insulated walls (NI-walls) and conventional insulated energy-saving walls (CE-walls). The results demonstrate that the application of TACP-walls significantly enhances thermal performance under all three climatic conditions. Irrespective of operating conditions, the ε remains essentially constant corresponding to the four insulation thicknesses, indicating that traditional thickness can be drastically curtailed. EE-TACP, EI-TACP, and II-TACP achieve pronounced ε values of 7.57/7.97/7.50 relative to NI-wall and CE-wall at inlet temperatures of 28–36 °C. Among the three configurations, EI-TACP performs most prominently across all climate zones. The findings provide a valuable reference for the application and optimization of thermally activated technology in energy-efficient retrofits of existing buildings. Full article

As offshore wind power moves into deeper waters, large-scale electrical platforms are key to efficient power transmission. However, their heavy topside modules create major installation challenges. As traditional lifting methods are inadequate, the float-over method has become a viable solution for installing topside modules, but it is essential to study the structural responses to collisions during the process to ensure construction and equipment safety. This study establishes a physical model of the offshore converter station at a 1:65 scale based on the elastic force-gravity similarity principle. Assuming the barge carrying the topside module descends at a constant speed, the study investigates the dynamic response of the platform during the float-over mating process. Float-over collision tests are conducted to obtain the platform’s acceleration, strain, and displacement responses and to analyze the effects of collision speed, offset position, and Leg Mating Unit (LMU) stiffness on the dynamic structural response characteristics. The results show that as collision speed increases from 10 mm/s to 50 mm/s, the topside acceleration response increases up to 5.7 times. Beam strain remains mostly unchanged, and displacement increases first, then decreases. Under fixed descent velocity, x-offset increases jacket strain and converter valve acceleration, while y-offset raises platform acceleration and reduces valve acceleration by approximately 20 percent. At 50 mm/s, higher LMU stiffness causes the acceleration response to first drop, then rise. These findings support safe float-over installation. Full article

Airports are among the most energy-intensive infrastructures, and the decarbonization of ground operations is essential to achieving sustainable aviation goals. Vehicle-integrated photovoltaic (VIPV) offers a promising strategy to complement electrification by enabling on-board renewable generation. While previous studies have mainly focused on fixed PV installations such as rooftops or carports, the potential of VIPV in airports has largely been overlooked, and no structured methodology has been established to investigate it. This study addresses this gap by proposing a two-scenario framework for assessing VIPV performance. The first scenario, named the Generalized Approach, estimates annual energy production based on irradiance data, vehicle surface area, and driving-to-standby ratios. The second scenario, named the Data-Driven Approach, incorporates detailed GPS-based driving data to capture the dynamic effects of orientation, speed, and operating conditions. Applied to European and Middle Eastern airports, the framework showed that VIPV could cover 1700–5500 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for buses, 650–5000 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for minibuses, and 840–6180 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for luggage tractors, with avoided emissions strongly influenced by local grid intensity. Grid parity analysis indicated favorable conditions in sunny, high-cost electricity markets. The framework is transferable to other VIPV applications and provides a practical tool for evaluating their technical, environmental, and economic potential. Full article

Mercury, a global pollutant with both persistence and high toxicity, has remained a focal point in environmental science research over the past half-century. As a key pathway in the terrestrial mercury cycle, plants actively assimilate gaseous elemental mercury (Hg⁰) through leaf stomata, constituting a critical pathway for terrestrial mercury cycling. The litterfall mercury concentration serves as a biological indicator to quantify vegetation’s mercury interception capacity, providing essential data for global mercury cycle modelling. To investigate this, 15 sampling sites throughout the country were selected, and litterfall was collected monthly for 12 consecutive months to determine the litterfall amount, composition, and leaf mercury dynamics. The results revealed that annual litterfall production ranged from 1.10–8.56 t·hm⁻², with leaf components dominating (45.58%–89.11%). Furthermore, three seasonal litterfall patterns emerged: unimodal, bimodal, and irregular. Regarding mercury, the mercury concentration in leaf litter exhibited a certain seasonal variation trend, with the mercury content in leaves in most areas being higher in autumn and winter. Specifically, the mercury concentration in litterfall showed a significant negative correlation with latitude and a significant positive correlation with air temperature, precipitation, and litterfall amount (p < 0.05). Additionally, the concentration of Hg in dying leaves exhibited some geographical variations. Full article

In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with the principle of justifiable granularity (PJG) adopted as the specific implementation. Time series data are first granulated using PJG to extract compact features that preserve local dynamics. The KPCA model, equipped with a radial basis function kernel, is then applied to capture nonlinear correlations and construct monitoring statistics including $T^2$ and SPE. Thresholds are derived from training data and used for online anomaly detection. The method is evaluated on the Tennessee Eastman process and Continuous Stirred Tank Reactor datasets, covering various types of faults. Experimental results demonstrate that the proposed method achieves a near-zero false alarm rate below 1% and maintains a missed detection rate under 6%, highlighting its effectiveness and robustness across different fault scenarios and industrial datasets. Full article