Search Results (15,625)

To mitigate the entanglement, agglomeration, and unstable conveying of high-moisture rice residues during stubble crushing for field incorporation, a discrete element method (DEM)-based modeling and optimization framework was developed to enhance the performance of a stubble-crushing device under wet paddy-field conditions. The device structure and kinematics were first analyzed, and the physical and mechanical properties of the residues were obtained through field measurements. A hollow wet–flexible straw model was then proposed to account for both mechanical breakage and moisture-induced adhesive interactions. Key contact and material parameters were calibrated using DEM simulations coupled with laboratory shear and three-point bending tests, showing good agreement with experimental trends. The validated model was subsequently extended to the device scale to characterize the cyclic capture–acceleration–throwing behavior of residues inside the crushing chamber. The individual and interactive effects of rotor speed, forward speed, and throwing-chamber clearance on comminution efficiency and conveying stability were investigated. A multi-objective response surface optimization identified an optimal parameter combination of 2000 rpm rotor speed, 0.87 m s⁻¹ forward speed, and 10.5 cm clearance. Under these conditions, the comminution rate reached 96.94%, and the coefficient of variation in throwing uniformity was 8.71%. Field validation further confirmed the reliability of the simulation results, with relative errors below 6%. Overall, the proposed framework provides an effective tool for the design optimization and parameter selection of wet-residue comminution equipment. Full article

Sugar beet (Beta vulgaris L.) is an economically important crop that accounts for approximately 20% of global sugar production. The success of future breeding programs depends on the effective utilization of existing genetic resources. The aim of this study was to assess the genetic diversity and population structure of 192 sugar beet (Beta vulgaris L.) genotypes, including commercial cultivars and accessions obtained from the USDA gene bank, using SCoT and ISSR molecular markers, and to identify potential genetic resources for sugar beet breeding programs. In this study, a total of 192 sugar beet genotypes, including 187 accessions from the USDA (U.S. Department of Agriculture) gene bank and 5 commercial cultivars, were evaluated for genetic diversity using Start Codon Targeted (SCoT) and Inter Simple Sequence Repeat (ISSR) markers. A total of 68 scorable bands were obtained from five SCoT and three ISSR primers, and all bands were found to be polymorphic (100% polymorphism). Parameters such as polymorphic information content (PIC), Nei’s genetic diversity, and Shannon’s index indicated a high level of variation within the gene pool, with SCoT markers being more informative than ISSR markers. Dendrogram analyses based on Nei’s genetic distance revealed that the populations were separated into two main groups, while the sub-clusterings contained broad genetic variation. STRUCTURE analysis identified four (K = 4) populations for the SCoT data and three (K = 3) populations for the ISSR data; the inclusion of a high number of individuals in the admixture population indicated extensive gene flow. Principal component analysis (PCA) revealed both homogeneous groups and differentiated genotypes contributing to within-population diversity. The results demonstrate that the combined use of SCoT and ISSR markers provides powerful and complementary tools for assessing genetic diversity in sugar beet. The findings provide a solid scientific basis for the development of new, high-yielding and high-quality sugar beet cultivars as well as for the conservation of existing genetic resources. Molecular data constitute an important reference for guiding sugar beet breeding programs and for the effective utilization of genetic resources. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article

In September 2021, significant changes in the geophysical and geochemical parameters on Vulcano Island were recorded by the surveillance network activities and periodic surveys. Between October 2021 and June 2024, additional surveys were conducted to acquire LIDAR, thermal, and RGB datasets for the generation of Digital Terrain Models (DTMs), orthophotos, and fumarole field maps. These data were collected using DJI Matrice 300 UAS platforms. Precision positioning was ensured through a POS/NAV RTK georeferencing approach. The instrumentation included Genius R-Fans-16 and DJI Zenmuse L1 laser scanners for structural mapping, alongside Zenmuse H20T infrared cameras for the thermal detection of potential instabilities on the volcano flanks, focused on the northern area and summit of Gran Cratere La Fossa, and these were subsequently repeated in May 2022, October 2022, October 2023, and June 2024. Additionally, 3D reconstruction targeted morphological variations in unstable areas like the cone top, Forgia Vecchia, and the 1988 landslide site. In May 2022, anomalous degassing in the Eastern Bay led to increased gas and hydrothermal fluid emissions, causing water whitening in front of Baia di Levante. Optical-thermal monitoring, both on land and at sea, detected multiple hydrothermal gas streams, aiding in assessing the magnitude and areal extension of fumarolic fields. These findings contribute to establishing a comprehensive monitoring approach for understanding the volcanic unrest evolution cost-effectively and safely. Full article

To address the low application accuracy and poor spreading uniformity of conventional lime spreaders, an electromagnetic vibration-assisted variable-rate lime spreader integrating a shaftless screw metering mechanism was developed. The overall configuration and operating principle are presented. Considering the physicochemical characteristics of lime powder, including fine particle size, strong drift tendency, and poor flowability, a shaftless screw metering unit was designed to improve discharge stability and metering accuracy. To enhance dispersion uniformity, a vertical electromagnetic vibration device was developed, and its key parameters were determined through a theoretical analysis of vibration frequency and amplitude. In addition, the structure and kinematic parameters of the spreading disc were optimized by analyzing particle trajectories and outlet distribution patterns. A closed-loop feedback control strategy was implemented to enable precise variable-rate application. Static bench tests demonstrated a metering accuracy of 96.42%, and the dispersion uniformity was at least 84.14% at an electromagnetic vibration frequency of 10 to 18 Hz. Field evaluations further showed that the coefficient of variation for transverse uniformity was no more than 17.88%, while the maximum coefficient of variation for longitudinal stability was 18.09%. These results indicate that the proposed spreader satisfies the operational requirements for accurate and uniform variable-rate application of lime powder. Full article

With the increasing demand for perception in complex road environments in intelligent driving, rapid and accurate identification of paved-road obstacles has become a critical prerequisite for driving safety and comfort. Various types of road obstacles can significantly affect vehicle stability and ride quality. To address this challenge, a lightweight and efficient vision-based obstacle detection framework, termed GroupScale-YOLO, is proposed, in which detection accuracy and computational efficiency are jointly enhanced through the collaborative design of multiple novel modules. First, a dedicated dataset targeting common paved-road obstacles is constructed, and six data augmentation strategies are employed to mitigate the adverse effects of road surface undulations and illumination variations on visual perception. Second, to overcome the limitations of YOLOv11n in paved-road obstacle detection tasks, targeted optimizations are introduced to the backbone network, convolutional blocks, and detection head. Experimental results indicate that GroupScale-YOLO achieves a 29.95% reduction in model parameters while simultaneously increasing mAP@0.5 by 0.6% on the self-built dataset, demonstrating its suitability for deployment in resource-constrained scenarios. Furthermore, real-vehicle road tests confirm that the proposed method maintains stable and accurate obstacle detection performance under practical driving conditions, offering a reliable solution for intelligent vehicle environmental perception. Full article

Accurate and real-time vehicle detection is a fundamental requirement for smart urban traffic monitoring, particularly in densely populated cities where heterogeneous traffic, frequent occlusion, and severe scale variation challenge lightweight vision systems deployed at the edge. To address these issues, this paper proposes BDNet, a lightweight YOLOv12-based vehicle detection framework designed to enhance feature preservation, contextual modeling, and multi-scale representation for intelligent transportation systems. BDNet integrates three complementary architectural components: (i) HyDASE, a hybrid detail-preserving downsampling module that mitigates information loss during resolution reduction; (ii) C3k2_MogaBlock, which strengthens long-range contextual interactions through multi-order gated aggregation; and (iii) an A2C2f_FRFN neck, which refines multi-scale features by suppressing redundancy and emphasizing discriminative responses. To support evaluation under realistic developing-region traffic conditions, we introduce the Bangladeshi Road Vehicle Dataset (BRVD), comprising 10,200 annotated images across 13 native vehicle categories captured under diverse urban scenarios, including daytime, nighttime, fog, and rain. On BRVD, BDNet achieves 85.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 67.3% ${m\mathcal{A}\mathcal{P}}_{50-95}$, outperforming YOLOv12n by +1.4 and +0.7 percentage points, respectively, while maintaining a compact footprint of 2.5 M parameters, 6.0 GFLOPs, and a real-time inference speed of 285.7 FPS. Cross-dataset evaluation on VisDrone-DET2019, using models trained exclusively on BRVD, further demonstrates improved generalization, achieving 31.9% ${m\mathcal{A}\mathcal{P}}_{50}$ and 17.9% ${m\mathcal{A}\mathcal{P}}_{50-95}$. These results indicate that BDNet provides an effective and resource-efficient vehicle detection solution for smart city–scale urban traffic monitoring. Full article

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

Urban forests serve as representations of nature within city landscapes. Green Forest, spanning 5,198,412 square meters, has been incorporated into the Municipality of Timișoara’s public domain and designated as a forest park. This fact increased green space per capita and enriched biodiversity within Timișoara’s landscape architecture. This study explores the diversity of Green Forest trees and highlights their contribution to the urban landscape. Statistical methods, including comparative and linear relationships analyses, were employed to assess significant variations in the dendrometric parameters of the analyzed tree species: mean tree height, mean trunk diameter at breast height (DBH), tree age, and stand density. Principal Component Analysis (PCA) and cluster analysis were applied to uncover underlying patterns in the data. Using ArchiCAD and Lumion, high-quality 3D visual representations were developed for an ecological education area, an active recreation region, and a passive recreation area within Green Forest. Due to their morphological characteristics and phenotypic traits, the predominant tree species include Quercus robur, Quercus cerris, Quercus rubra, Fraxinus excelsior, Acer platanoides, Acer pseudoplatanus, Ulmus campestris, and Robinia pseudoacacia, which contribute to Timișoara’s urban aesthetic. Moreover, the results of the dendrometric analysis provide a foundation for further research in urban ecology. A key practical application of this study is landscape design renderings, which provide detailed and realistic visualizations to effectively communicate the design and functionality of Green Forest’s spaces. If implemented, these developments will encourage public engagement with nature, promoting mental and physical well-being within the community. Full article

Conventional material balance methods, typically based on single- or dual-porosity models solvable via single-step linearization, are inadequate for hydraulically fractured shale oil reservoirs due to their pronounced heterogeneity and contrasting interzonal connectivity. Specifically, dual-zone models fail to represent the realistic characteristics of shale oil reservoirs because they treat artificially created hydraulic fractures and natural fractures as equivalent, despite their substantially different properties. To address this gap, this paper proposes a novel three-zone conceptual model, segmenting the reservoir into the matrix zone (MZ), the Weakly Stimulated Zone (WSZ, low-conductivity zone), and the Strongly Stimulated Zone (SSZ, high-conductivity zone). A corresponding three-zone gas injection replenishment material balance model is developed. This model explicitly captures interactions between injected gas and formation fluids and incorporates dynamic variations in pore volume and fluid saturation induced by imbibition. To solve the complexities introduced by the triple-porosity system, a dedicated two-step linearization solution procedure is proposed. Utilizing conventional production performance and basic PVT data, the method enables simultaneous estimation of zone-specific developed reserves and prediction of the Estimated Ultimate Recovery (EUR) through a least squares algorithm. Validation against actual well cases and multi-well statistics confirms that the method provides stable and reliable zonal reserve characterization and EUR forecasting. The results indicate that the MZ contributes the majority of the geological reserves, accounting for >70%. The WSZ contributes approximately 29.5% of the reserves and serves as the primary source for energy replenishment in the shale oil reservoir. In contrast, the SSZ contributes less than 0.5% of the reserves but acts as the dominant channel for flow convergence, controlling the main fluid production pathways. The proposed framework not only offers a practical tool for refined reserve assessment in shale oil reservoirs but also provides a computational basis and decision support for the design and injection parameter optimization of pre-pad CO₂ energy storage fracturing schemes. Full article

The reliability of photovoltaic (PV) equipment is vital for ensuring the safe and stable operation of power systems. While multimodal large language models (MLLMs) open up promising avenues for intelligent fault diagnosis, they often falter when confronted with the heterogeneity of PV data—where visual observations come from different sensor modalities (e.g., visible, infrared, and thermal) and display strong domain-dependent variations. Conventional Low-Rank Adaptation (LoRA) is not expressive enough to model such modality-aware differences, which can result in insufficient exploitation of informative patterns. To overcome this limitation, we propose PV-FaultExpert, a domain-adaptive MLLM designed specifically for PV equipment fault analysis. PV-FaultExpert is built upon DyLoRA (Dynamic Expert Routing with LoRA), a dynamic routing strategy that reformulates standard LoRA into a shared low-rank component coupled with multiple expert-specific adapters. A routing module then selects expert paths according to input characteristics, allowing the model to adapt to diverse modalities while maintaining parameter efficiency. Moreover, we construct a PVfault diagnosis dataset via ChatGPT-4o-assisted chain-of-thought reasoning and subsequent expert verification, which both supports model training and enables rigorous evaluation of our method. Extensive experiments demonstrate that PV-FaultExpert consistently surpasses strong baselines, including GPT-4 and Claude-3, across multiple evaluation criteria, producing fault analysis reports that are accurate, interpretable, and aligned with safety-critical requirements. Full article

In arid and semi-arid regions, the hydro-sedimentary processes responsible for reservoir siltation remain insufficiently studied. This study focuses on the Taskourt Dam, one of the major reservoirs in the Marrakech-Safi region in central Morocco. A 450 cm thick sediment core was collected from the reservoir to assess the impact of extreme flood variability on sediment dynamic. A multi-approach analysis was conducted, including sequence analysis, grain-size and bulk and clay mineralogy of the sediments. In addition, hydrological parameters, instantaneous discharge, historical variations in daily water volumes in the reservoir, spillway discharge volumes, and siltation rates were determined through bathymetric surveys. The aim is to identify and evaluate the dynamics of sedimentation evolution within the reservoir. The results highlight two major phases in the siltation history of the Taskourt reservoir. (1) From 2011 to 2016, the siltation rate experienced rapid growth, marked by several major flood events. This intense sedimentary dynamic is illustrated by an accumulation of 418 cm of sediments. The floods of 2014 and 2016 strongly contributed to the intensification of flow energy and to a significant sediment load during this period. (2) From 2017 to 2023, the siltation significantly slowed down, associated with a prolonged drought period. This trend is recorded by a limited sedimentary deposit of 32 cm in thickness. This study provides valuable insights for the development of integrated sediment management strategies, supporting sustainable reservoir operation and effective planning, particularly in similar contexts worldwide. Full article

To accurately characterize the complex coupling and nonlinear interactions between meteorological and oceanic variables in offshore wind power scenarios, this study proposes a novel forecasting model based on a “multi-scale fusion-decomposition-reconstruction-optimization-prediction” framework. This model integrates Variational Modal Decomposition (VMD) with the feature-interaction Informer framework, employing an enhanced Honey Badger Algorithm (HBA) for the collaborative optimization of their key parameters. The enhanced HBA integrates cubic chaotic mapping, random perturbation strategy, elite tangent search, and differential mutation operations to strengthen its global optimization capability and convergence efficiency. The model construction process proceeds as follows: First, sample entropy (SE) is applied to evaluate the entropy values and reconstruct sequences of the modal components obtained from VMD. Subsequently, the dynamic adjustment of the maximum information coefficient (DE-MIC) is employed to select key input variables from multi-source features. Subsequently, the feature interaction-probabilistic sparse attention mechanism (FI-ProbSparse-AM) unique to the feature interaction-based Informer is employed to construct an attention architecture capable of explicitly modeling dependencies among multidimensional variables, thereby effectively capturing the spatiotemporal latent correlations between wind power output and multi-source features. Experiments based on real offshore wind farm data demonstrate that the MAPE values are reduced by approximately 11% compared to existing benchmark models. The proposed method demonstrates significant advantages in both prediction accuracy and stability. Full article

This paper proposes chattering reduction using various switching functions in the sliding mode control (SMC) method for permanent magnet synchronous motor (PMSM) drives. In general, a PI controller is used in PMSM control systems. However, the PI controller has limitations due to the linear control structure when dealing with nonlinearities and uncertainties, and its low robustness to parameter variations and disturbances can lead to performance degradation when operating conditions change. To overcome these limitations, the SMC method has been researched. However, the traditional SMC method uses the signum function as the switching function, and the discontinuity of the signum function causes chattering. Therefore, this paper uses various improved continuous switching functions to improve the responsiveness without chattering. Simulation and experimental results confirm that these various switching functions enhance both the responsiveness and robustness against disturbances in PMSM drives. Full article

This study examines how aggregate financial risk tolerance (FRT), measured from repeated survey responses, co-evolves with stock-market dynamics over time. The observed FRT index is treated as a noisy preference signal containing both gradual drift and episodic deviations, and its market relevance is evaluated under time variation, frequency components, and stress regimes. Using monthly data that align the survey-based FRT index with market returns and risk measures, a three-part econometric design is implemented. First, a time-varying parameter VAR (TVP-VAR) characterizes bidirectional, non-constant linkages between FRT and market outcomes. Second, signal-extraction methods decompose FRT into a smooth “normal” component and a high-frequency “abnormal” component (with robustness to alternative filters) to test whether short-run deviations contain distinct information for volatility and downside risk. Third, a Markov-switching specification assesses state dependence by testing whether the FRT–market relationship differs between low-stress and high-stress regimes. Across specifications, the FRT–market linkage is strongly state dependent: the sign and magnitude of FRT effects drift over time and differ across regimes, with high-frequency FRT deviations aligning more closely with risk dynamics than the smooth component. Predictive validation is provided via out-of-sample forecasting of next-month market risk using elastic net and gradient boosting relative to an AR(1) benchmark; explainability analysis (SHAP) indicates that abnormal FRT contributes incremental predictive content beyond standard market-state variables. Overall, the framework offers a mathematically transparent approach to modeling survey-based preference signals in markets and supports regime-aware forecasting and risk-management applications. Full article