Search Results (321)

Loess, a typical soil widely distributed in China, exhibits engineering properties that are highly sensitive to environmental changes, leading to increased erosion and the development of surface cracks. This article examines the influence of initial moisture content, dry density, and thickness on crack formation in compacted loess subjected to wet–dry cycles, using both laboratory experiments and numerical simulation analysis. It quantitatively analyzes the process of crack evolution using digital image processing technology. The experimental results indicate that wet–dry cycles can cause cumulative damage to the soil, significantly encouraging the initiation and expansion of secondary cracks. New cracks often branch out and extend along the existing crack network, demonstrating that the initial crack morphology has a controlling effect over the final crack distribution pattern. Numerical simulations based on MultiFracS software further revealed that soil samples with a thickness of 0.5 cm exhibited more pronounced surface cracking characteristics than those with a thickness of 2 cm, with thinner layers of soil tending to form a more complex network of cracks. The simulation results align closely with the indoor test data, confirming the reliability of the established model in predicting fracture dynamics. The study provides theoretical underpinnings and practical guidance for evaluating the stability of engineering slopes and for managing and mitigating fissure hazards in loess. Full article

Efficient decentralized power management is crucial for enhancing the reliability, resilience, responsiveness, and sustainability of secondary power distribution systems, thereby preventing major power outages and providing rapid responses. However, existing secondary power distribution networks are prone to failures, thus compromising their operational trustworthiness and efficiency. This work proposes an intelligent, decentralized control system with distributed processing capabilities. The proposed system is designed to automate fault detection and rectification along with optimized power management at secondary distribution nodes. The system enables rapid fault detection (line-to-line, line-to-ground, and overload) and initiates a fault-based response to isolate the load through controlled relays. Additionally, an intelligent power management system automatically rectifies surge faults (short-lived faults) and reports non-surge faults (persistent faults) to the control center. It continuously updates the status of real-time power parameters to the database using a Global System for Mobile Communications (GSM)-based communication system with a frequency of 60 s per sample for power management. The Proteus-based simulation and a scaled-down model validate the efficiency and supremacy of the proposed system over the existing control system for power distribution nodes. The results demonstrate that our model detects critical faults and initiates the response within 100 and 200 milliseconds, respectively. Surge faults are automatically rectified within 90 s, while non-surge faults are reported to the database after 90 s. This approach significantly reduces downtime, enables energy accountability, and supports sustainable energy management through a decentralized and distributed control system. Full article

Near-Infrared Spectroscopy (NIRS) analysis technology faces numerous challenges in industrial applications. Firstly, the generalization capability of models is significantly affected by instrumental heterogeneity, environmental interference, and sample diversity. Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. As a result, the large amount of previously accumulated NIRS and reference data cannot be effectively utilized in modeling for new instruments or new varieties, thereby limiting improvements in modeling efficiency and prediction accuracy. To address these limitations, this study proposes a novel transfer learning framework integrating multi-scale network architecture with Balanced Distribution Adaptation (BDA) to enhance cross-instrument compatibility. The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. Experimental results on open corn and pharmaceutical datasets demonstrate that BDSER-InceptionNet achieves state-of-the-art performance on primary instruments. Notably, the proposed Method 6 successfully enables NIRS model sharing from primary to secondary instruments, effectively mitigating spectral discrepancies and significantly improving transfer efficacy. Full article

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO₂ emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO₂ emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies. Full article

Gelatin is a commonly used protein-based hydrogel. However, the thermo-reversible nature of gelatin makes it unstable at physiological and higher temperatures. Therefore, this study adopted phosphates and glutaminase transaminase (TG) to modify gelation and studied the effects of combining sodium hexametaphosphate (SHP) and TG on the structure and gel properties of TG-crosslinked gelatin. This study focused on the effects of different SHP concentrations (0, 0.4, 0.8, 1.2, 1.6, 2.0, 2.4, 2.8 mmol/L) on the water distribution, textural properties, rheological properties, and microstructure of the TG-crosslinked gelatin gels. Results showed that the free water content in the TG-crosslinked gelatin gel declined with the increasing SHP addition when the concentration of SHP was kept below 2.0 mmol/L. The gel of TG-crosslinked gelatin at the SHP concentration of 1.6 mmol/L exhibited the highest hardness (304.258 g), chewiness (366.916 g) and η₅₀. All the TG-crosslinked gelatin gels with SHP modification were non-Newtonian pseudoplastic fluids. The G′ and G″ of TG-crosslinked gelatin increased before the SHP concentration reached 1.6 mmol/L, and the TG-crosslinked gelatin with 1.6 mmol/L SHP exhibited the largest G″ and G′. The fluorescence intensity of TG-crosslinked gelatin with SHP concentration above 1.6 mmol/L decreased with the increasing SHP concentration. SHP modified the secondary structure of TG-crosslinked gelatin gels. The gel of TG-crosslinked gelatin with the SHP concentration of 1.6 mmol/L exhibited a porous, smooth, and dense network structure. This research provides references for modifying gelatin and the application of gels in the encapsulation of bioactive ingredients and probiotics. Full article

Modular multilevel converter (MMC)-based power electronic transformers (PETs) present a promising solution for connecting AC/DC microgrids to facilitate renewable energy access. However, the capacitor ripple voltage in MMC-based PET submodules hinders volume optimization and power density enhancement, significantly limiting their application in distribution networks. To address this issue, this study introduces an optimized method for suppressing the submodule capacitor ripple voltage in MMC-based PET systems under normal and grid fault conditions. First, an MMC–PET topology featuring upper and lower arm coupling is proposed. Subsequently, a double-frequency circulating current injection strategy is incorporated on the MMC side to eliminate the double-frequency ripple voltage of the submodule capacitor. Furthermore, a phase-shifting control strategy is applied in the isolation stage of the dual-active bridge (DAB) to transfer the submodule capacitor selective ripple voltages to the isolation stage coupling link, effectively eliminating the fundamental frequency ripple voltage. The optimized approach successfully suppresses capacitor ripples without increasing current stress on the isolated-stage DAB switches, even under grid fault conditions, which are not addressed by existing ripple suppression methods, thereby reducing device size and cost while ensuring reliable operation. Specifically, the peak-to-peak submodule capacitor ripple voltage is reduced from 232 V to 10 V, and the peak current of the isolation-stage secondary-side switch is limited to ±90 A. The second harmonic ripple voltage on the LVDC bus can be decreased from ±5 V to ±1 V with the proposed method under the asymmetric grid voltage condition. Subsequently, a system simulation model is developed in MATLAB/Simulink. The simulation results validated the accuracy of the theoretical analysis and demonstrated the effectiveness of the proposed method. Full article

Multiband spectrum sensing in a cooperative environment is a novel solution for efficient spectrum resource management under the cognitive radio networks (CRNs) paradigm. This paper presents a distinctive framework where a central entity collects power spectral density data from multiple geographically distributed secondary users and applies the Gramian angular field (GAF) summation method to transform the time-series data into image representations. A major contribution of this work is the integration of these GAF images with a convolutional neural network (CNN), enabling precise and real-time detection of primary user activity and spectrum occupancy. The proposed approach achieves 99.6% accuracy in determining spectrum occupancy, significantly outperforming traditional sensing techniques. The main contributions of this study are (i) the introduction of GAF-based image representations for cooperative spectrum sensing in CRNs; (ii) the development of a CNN-based classification framework for enhanced spectrum occupancy detection; and (iii) the demonstration of superior detection performance in dynamic, real-time environments. Full article

Human action recognition (HAR) based on Wi-Fi signals has become a research hotspot due to its advantages of privacy protection, a comfortable experience, and a reliable recognition effect. However, the performance of existing Wi-Fi-based HAR systems is vulnerable to changes in environments and shows poor system generalization capabilities. In this paper, we propose a cross-environment HAR system (CHARS) based on the channel state information (CSI) of Wi-Fi signals for the recognition of human activities in different indoor environments. To achieve good performance for cross-environment HAR, a two-stage action recognition method is proposed. In the first stage, an HAR adversarial network is designed to extract robust action features independent of environments. Through the maximum–minimum learning scheme, the aim is to narrow the distribution gap between action features extracted from the source and the target (i.e., new) environments without using any label information from the target environment, which is beneficial for the generalization of the cross-environment HAR system. In the second stage, a self-training strategy is introduced to further extract action recognition information from the target environment and perform secondary optimization, enhancing the overall performance of the cross-environment HAR system. The results of experiments show that the proposed system achieves more reliable performance in target environments, demonstrating the generalization ability of the proposed CHARS to environmental changes. Full article

Moyamoya disease (MMD) is a rare yet clinically significant cerebrovascular disorder characterized by progressive stenosis of the distal internal carotid artery and/or its principal branches, accompanied by the development of characteristic collateral vessel networks. This disease demonstrates a complex multifactorial etiology with strong genetic determinants, as evidenced by its distinct geographical distribution patterns and familial clustering. Recent genetic researches have identified multiple pathogenic mutations contributing to MMD development through three principal mechanisms: progressive vascular stenosis, abnormal angiogenesis, and dysregulated inflammatory responses. Furthermore, moyamoya syndrome frequently occurs as a secondary vascular complication in various monogenic disorders. This review provides a comprehensive analysis of recent genetic advances in MMD in view of diverse pathogenic pathways, offering valuable perspectives on the molecular mechanisms underlying disease development and potential therapeutic targets. Full article

Limited labels and detailed changed land-cover interpretation requirements pose challenges for time-series PolSAR change monitoring research. Accurate labels and supervised models are difficult to reuse between massive unlabeled time-series PolSAR data due to the complex distribution shifts caused by different imaging parameters, scene changes, and random noises. Moreover, many related methods can only detect binary changes in PolSAR images and struggle to track the detailed land cover changes. In this study, an unsupervised cross-domain method based on limited-label transfer learning and a vision transformer (LLTL-ViT) is proposed for PolSAR land-cover change monitoring, which effectively alleviates the problem of difficult label reuse caused by domain shift in time-series SAR data, significantly improves the efficiency of label reuse, and provides a new paradigm for the transfer learning of time-series polarimetric SAR. Firstly, based on the polarimetric scattering characteristics and manifold-embedded distribution alignment transfer learning, LLTL-ViT transfers the limited labeled samples of source-domain PolSAR data to unlabeled target-domain PolSAR time-series for initial classification. Secondly, the accurate samples of target domains are further selected based on the initial transfer classification results, and the deep learning network ViT is applied to classify the time-series PolSAR images accurately. Thirdly, with the reliable secondary classification results of time-series PolSAR images, the detailed changes in land cover can be accurately tracked. Four groups of cross-domain change monitoring experiments were conducted on the Radarsat-2, Sentinel-1, and UAVSAR datasets, with about 10% labeled samples from the source-domain PolSAR. LLTL-ViT can reuse the samples between unlabeled target-domain time-series and leads to a change detection accuracy and specific land-cover change tracking accuracy of 85.22–96.36% and 72.18–88.06%, respectively. Full article

Islanded microgrids commonly use droop control methods for autonomous power distribution; however, this approach causes system frequency deviation when common loads change. This deviation can be eliminated using secondary control methods, but the core of this approach is to generate compensation values equal to the offset amount to add to the controller, thereby eliminating deviations from rated values. Such a mechanism can actually achieve the same effect by setting power reference values within the droop control method. The power references within the controller need to be adjusted dynamically, and they are associated with common load variations. Therefore, establishing a fitting relationship between the adjustment of power reference and changes in common loads can achieve better frequency regulation, keeping the system frequency operating within rated frequency ranges. These two types of data are correlated, however, due to physical parameters, the fitting between them is not strictly fixed in a mathematical sense. Thus, to find their interconnected relationships, using intelligent methods becomes crucial. This paper proposes a shallow neural network-based method to achieve fitting relationships. Moreover, to address power inputs with zero values, an input enhancement method is proposed to prevent potential gradient vanishing and ineffective learning problems. Thus, through precise matching between power commands and load consumption, the system frequency can be maintained near rated values. Various simulation scenarios demonstrate the feasibility and effectiveness of the proposed method. Full article

Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development. However, the diverse shapes and scales of WWTPs and their key facilities pose challenges for traditional detection methods. This study employs a Multi-Attention Network (MANet) for WWTP extraction, integrating channel and spatial feature attention. Additionally, a Global-Local Feature Modeling Network (GLFMN) is introduced to segment key facilities, specifically sedimentation and secondary sedimentation tanks. The approach is applied to Beijing, utilizing geographic data such as WWTP locations, treatment capacities, and surrounding residential and water distributions. Results indicate that MANet achieves 80.1% accuracy with a 90.4% recall rate, while GLFMN significantly improves the extraction of key facilities compared to traditional methods. The spatial analysis reveals WWTP distribution characteristics, offering insights into treatment capacity and geographic influences. These findings contribute to emission regulation, water quality supervision, and enterprise management of WWTPs in Beijing. This research provides a valuable reference for optimizing wastewater treatment infrastructure and supports decision-making in environmental governance and sustainable urban development. Full article

To fully utilize the reactive power resources of distributed photovoltaic (PV) systems, this study proposes a coordinated var-voltage control strategy for the main distribution network, incorporating the reactive power regulation capability of distributed PV. Firstly, the Automatic Voltage Control (AVC) tertiary and secondary voltage control methods and optimization models in the main and distribution networks area are analyzed, and the physical equivalence of the reactive power compensation equipment involved is carried out. In this study, a coordinated local var-voltage control method is proposed, which integrates AVC primary voltage control and divides the control scheme into feeder and station areas, respectively. Through the analysis of actual operation cases in a regional power grid, the results demonstrate a reduction in network loss by 171.14 kW through voltage adjustment, validating the effectiveness of the proposed strategy. This method fully leverages the reactive power regulation capability of distributed renewable energy sources, reduces the operational frequency of reactive power equipment in substations, and synergizes with the AVC system to achieve optimal power grid operation. Full article

The aim of this study was to expand the monitoring network and evaluate the accuracy of inexpensive WoMaster ES-104 sensors for monitoring particulate matter (PM) in temperate latitudes, using the example of the Southern Baikal region. The research methods included continuous measurements of PM_2.5 and PM₁₀ concentrations, temperature, and humidity at three stations (Listvyanka, Patrony, and Tankhoy) from October 2023 to October 2024, using the LCS WoMaster ES-104. ERA5-Land reanalysis data and the HYSPLIT model were used to analyze meteorological conditions and air mass trajectories. The results of this study showed a high correlation between the WoMaster ES-104 and the DustTrak 8533; the correlation coefficient was 0.94 (R² = 0.85) for both fractions. The seasonal dynamics of PM_2.5 and PM₁₀ were characterized by a dual-mode distribution with maxima in summer (secondary aerosols, high humidity) and winter (anthropogenic emissions, inversions). The diurnal cycles showed morning/evening peaks associated with transport activity and atmospheric stratification. Extreme concentrations were recorded in anticyclonal weather (weak north-westerly winds, stable atmosphere). This study confirms the suitability of the LCS WoMaster ES-104 for real-time monitoring of PM_2.5 and PM₁₀, which contributes to sustainable development by increasing the availability of air quality data for ecologically significant regions such as Lake Baikal. Full article

The development of vehicle-to-grid (V2G) technique and the growth of battery swapping stations are expected to enhance the resilience of power networks. However, V2G battery swapping stations exhibit inconsistencies among internal battery packs, where the power capacity is significantly affected by the battery swapping behavior of electric vehicle (EV) users. To address this issue, this paper proposes a secondary frequency control strategy for V2G battery swapping stations that accounts for battery pack heterogeneity. First, a user behavioral model is developed through quantitative analysis of key factors such as economic incentives, time costs, and battery degradation, which is then used to optimize the operation of V2G battery swapping stations. Moreover, active balancing of EV battery energy levels is achieved by incorporating penalty terms into the objective function. Finally, a distributed secondary frequency control strategy based on the consensus algorithm is established to minimize total frequency control loss. Simulation results demonstrate that the proposed strategy effectively meets the secondary frequency control requirements of the power grid. Full article