Search Results (1,604)

Among the Central Asian republics, Uzbekistan is unique in that approximately 80% of its territory lies within a plain, characterized by an arid geographic zone and dry climate. Agricultural production in these regions is possible only through artificial irrigation. In recent years, global climate change and challenges related to transboundary water use have led to a reduction in water availability. The average annual water allocation to Uzbekistan is estimated at 51–53 billion m³, of which 90–91% is consumed by the agricultural sector. Due to the uneven distribution of water resources and the complex topography of irrigated lands, water supply is supported by numerous pumping stations operated by the state, water users associations, farms, and clusters. Additionally, well-based pumping systems are employed to maintain groundwater levels and ensure irrigation. On average, these facilities consume around 8.0 billion kWh of electricity annually. The agricultural sector faces several critical challenges, including crop water deficits caused by water shortages, slow adoption of water-saving technologies, and limited implementation of drip irrigation on household plots, dachas, and greenhouses that play a key role in food supply. Moreover, the delivery of water to fertile lands situated far from main power lines and water sources remains problematic. This article aims to explore the integration of solar energy solutions to support drip irrigation in both large-scale agricultural lands (ω = 1.0–100.0 ha and above) and small-scale areas such as homestead plots, dachas, and greenhouses (ω = 0.01–1.0 ha), as well as their application in small- to medium-sized pumping stations. Based on the research and experimental design work carried out, three mobile photovoltaic units—MPPU-8-500-4000, MPPU-2-550-1100, and MPPU-4-500-2000—were developed and implemented to address water and energy shortages in agriculture. Full article

This paper analyzes the potential of hydrogen technologies in transport, placing it within the context of global environmental and energy challenges. Its primary purpose is to evaluate the prospects for the implementation of these technologies at international and national levels, including Poland. This study utilizes a literature review and an analysis of the results of a highly limited, exploratory pilot survey measuring public perception of hydrogen technology in transport. It is critical to note that the survey was conducted on a small, non-representative sample and exhibited a strong geographical bias, primarily collecting responses from Europe (50 people) and North America (30 people). This study also details hydrogen vehicle types (FCEV, HICE) and the essential infrastructure required (HRS). Despite solid technological foundations, the development of hydrogen technology heavily relies on non-technical factors, such as infrastructure development, support policy, and social acceptance. Globally, the number of vehicles and stations is growing but remains limited, with the pace of development correlating with the involvement of countries. The pilot survey revealed a generally positive perception of the technology (mainly due to environmental benefits) but highlighted three key barriers: limited availability of refueling infrastructure—51.5% of respondents strongly agreed on this obstacle, high purchase and maintenance costs, and insufficient public awareness. Infrastructure subsidies and tax breaks were identified as effective incentives. Hydrogen technology offers a potentially competitive and sustainable transport solution, but it demands significant systemic support, intensive investment in large-scale infrastructure expansion, and comprehensive educational activities. Further governmental engagement is crucial. The severe limitations resulting from the pilot nature of the survey should be rigorously taken into account during interpretation. Full article

Real-time object detection in Unmanned Aerial Vehicle (UAV) imagery is critical yet challenging, requiring high accuracy amidst complex scenes with multi-scale and small objects, under stringent onboard computational constraints. While existing methods struggle to balance accuracy and efficiency, we propose RTUAV-YOLO, a family of lightweight models based on YOLOv11 tailored for UAV real-time object detection. First, to mitigate the feature imbalance and progressive information degradation of small objects in current architectures multi-scale processing, we developed a Multi-Scale Feature Adaptive Modulation module (MSFAM) that enhances small-target feature extraction capabilities through adaptive weight generation mechanisms and dual-pathway heterogeneous feature aggregation. Second, to overcome the limitations in contextual information acquisition exhibited by current architectures in complex scene analysis, we propose a Progressive Dilated Separable Convolution Module (PDSCM) that achieves effective aggregation of multi-scale target contextual information through continuous receptive field expansion. Third, to preserve fine-grained spatial information of small objects during feature map downsampling operations, we engineered a Lightweight DownSampling Module (LDSM) to replace the traditional convolutional module. Finally, to rectify the insensitivity of current Intersection over Union (IoU) metrics toward small objects, we introduce the Minimum Point Distance Wise IoU (MPDWIoU) loss function, which enhances small-target localization precision through the integration of distance-aware penalty terms and adaptive weighting mechanisms. Comprehensive experiments on the VisDrone2019 dataset show that RTUAV-YOLO achieves an average improvement of 3.4% and 2.4% in mAP50 and mAP50-95, respectively, compared to the baseline model, while reducing the number of parameters by 65.3%. Its generalization capability for UAV object detection is further validated on the UAVDT and UAVVaste datasets. The proposed model is deployed on a typical airborne platform, Jetson Orin Nano, providing an effective solution for real-time object detection scenarios in actual UAVs. Full article

Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. Full article

Small-scale wind turbines are becoming increasingly important in renewable energy systems due to their ability to operate in low-wind-speed environments and adapt to various installation locations, especially in areas with energy shortages. This paper presents the design, analysis and development of a Helical Vertical Axis type Wind Turbine (H-VAWT) using uPVC pipe as the blade material, offering a lightweight, low-cost, and corrosion resistant solution. The blade structure is optimized for use in residential and off-grid areas with unstable wind conditions. Structural analysis is conducted in ANSYS, including static load analysis (deformation, equivalent stress, shear stress, maximum stress), torsional and bending stress, and modal analysis to assess mechanical performance and vibrational stability. Three blade designs are initially considered, and the helical model (0–45° twist) is selected based on simulation results. The prototype is successfully fabricated and tested under different wind speeds, showing effective power generation, with favorable results in power output, power coefficient, tip-speed ratio (TSR), and relative velocity. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

To address the challenges of strong nonlinearity, stringent terminal constraints, and the trade-off between computational efficiency and accuracy in the midcourse guidance trajectory optimization problem of aerodynamically controlled interceptors, this paper proposes a variable-discrete-scale sequential convex programming (SCP) method. Firstly, a dynamic model is established by introducing the range domain to replace the traditional time domain, thereby reducing the approximation error of the planned trajectory. Second, to overcome the critical issues of solution space restriction and trajectory divergence caused by terminal equality constraints, a terminal error-proportional relaxation approach is proposed. Subsequently, an improved second-order cone programming (SOCP) formulation is developed through systematic integration of three key techniques: terminal error-proportional relaxation, variable trust region, and path normalization. Finally, an initial trajectory generation algorithm is proposed, upon which a variable-discrete-scale optimization framework is constructed. This framework incorporates a residual-driven discrete-scale adaptation mechanism, which balances discretization errors and computational load. Numerical simulation results indicate that under large discretization scales, the computation time required by the improved SOCP is only about 5.4% of that of GPOPS-II. For small-discretization-scale optimization, the SCP method with the variable discretization framework demonstrates high efficiency, achieving comparable accuracy to GPOPS-II while reducing the computation time to approximately 7.4% of that required by GPOPS-II. Full article

The information of sediment flux (Q_s) from hydrometric data-scarce small coastal watersheds is an important supplement for interpreting the sedimentary records of continental shelf sedimentary systems. This paper proposes a solution to estimate their values based upon the empirical formula of small and medium-sized coastal watersheds in adjacent regions, taking the 25 small rivers in Hainan Island as example. Three categories of methods were applied to calculate the Q_s. The first category involves the direct application of global empirical formulas, while the second and third categories utilizes empirical formulas that have been calibrated with regional characteristic data. The Q_s calculation accuracy the above methods was validated by the observed values of typical rivers. Key findings include: (1) The area values of watersheds extracted from SRTM (Shuttle Radar Topography Mission) data exhibit a high correlation with actual values, confirmed the reliability and applicability of SRTM data; (2) The Global equation significantly overestimates Q_s for the validation rivers (average relative error of 18.73), while employing the pristine-modified and disturbed-modified equations effectively improves the calculation accuracy (average relative errors of 0.72 and 1.64, respectively); (3) By averaging the results of different models, the Q_s for the major rivers in Hainan Island was calculated as 6.07 Mt/a before large-scale human activities and 4.56 Mt/a after. This study demonstrates that modification not only needs to be considered to adjust global empirical formulas but also to differentiate between the scenarios of before and after large-scale human activities in small coastal watersheds. Full article

In recent years, drone-based wildfire detection technology has advanced rapidly, yet existing methods still encounter numerous challenges. For instance, high background complexity leads to frequent false positives and false negatives in models, which struggle to accurately identify both small-scale fire points and large-scale wildfires simultaneously. Furthermore, the complex model architecture and substantial parameter count hinder lightweight deployment requirements for drone platforms. To this end, this paper presents a lightweight drone-based wildfire detection model, DFE-YOLO. This model utilizes dynamic frequency domain enhancement technology to resolve the aforementioned challenges. Specifically, this study enhances small object detection capabilities through a four-tier detection mechanism; improves feature representation and robustness against interference by incorporating a Dynamic Frequency Domain Enhancement Module (DFDEM) and a Target Feature Enhancement Module (C2f_CBAM); and significantly reduces parameter count via a multi-scale sparse sampling module (MS3) to address resource constraints on drones. Experimental results demonstrate that DFE-YOLO achieves mAP50 scores of 88.4% and 88.0% on the Multiple lighting levels and Multiple wildfire objects Synthetic Forest Wildfire Dataset (M4SFWD) and Fire-detection datasets, respectively, whilst reducing parameters by 23.1%. Concurrently, mAP50-95 reaches 50.6% and 63.7%. Comprehensive results demonstrate that DFE-YOLO surpasses existing mainstream detection models in both accuracy and efficiency, providing a reliable solution for wildfire monitoring via unmanned aerial vehicles. Full article

Object detection in airport surface surveillance presents significant challenges, primarily due to the extreme variation in object scales and the critical need for contextual information. To address these issues, we propose a novel deep learning architecture that integrates two specialized modules: the Poly Kernel Inception (PKI) module and the Context Anchor Attention (CAA) module. The PKI module is designed to effectively capture multi-scale features, enabling the accurate detection of objects ranging from large aircraft to small staff members. Concurrently, the CAA module leverages long-range contextual information, which significantly enhances the model’s ability to precisely localize and identify targets within complex scenes. The synergistic integration of these two modules demonstrates a substantial improvement in feature extraction performance, leading to enhanced detection accuracy on our publicly available ASS dataset. This work provides a robust and effective solution for the challenging task of airport surface object detection, establishing a strong foundation for future research in this domain. Full article

This work addresses the challenges of airline planning, which requires the integration of flight scheduling, aircraft availability, and maintenance to ensure both airworthiness and profitability. Current solutions, often developed by human experts, are susceptible to bias and may yield suboptimal results due to the inherent complexity of the problem. Furthermore, existing state-of-the-art approaches often inadequately address critical factors, such as maintenance, variable flight numbers, discrete time slots, and potential flight repetition. This paper presents a novel approach to aircraft routing optimization using a model that incorporates critical constraints, including path connectivity, flight duration, maintenance requirements, turnaround times, and closed routes. The proposed solution employs a simulated annealing algorithm enhanced with specialized perturbation operators and constraint-handling techniques. The main contributions are twofold: the development of an optimization model tailored to small airlines and the design of operators capable of efficiently solving large-scale, realistic scenarios. The method is validated using established benchmarks from the literature and a real case study from a Mexican commercial airline, demonstrating its ability to generate feasible and competitive routing configurations. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

Riverbank erosion in the Vietnamese Mekong Delta (VMD) poses a serious threat to agricultural lands, infrastructure, and local communities. Conventional protective measures, such as synthetic geotextiles and concrete revetments, are often costly and environmentally disruptive. This study investigates the potential of Eichhornia crassipes, a widely available invasive species, commonly known as water hyacinth (WH), to produce biodegradable geotextiles as a low-cost, nature-based solution (NbS) for small-scale riverbank protection. It is the first to test minimally processed WH mats under simulated tidal conditions in the VMD. Laboratory experiments were conducted to evaluate the geotextile’s (1) sediment retention capacity, (2) wave energy reduction, and (3) mechanical durability under wet–dry cycles. Results show that the WH geotextile effectively reduced sediment resuspension, decreasing turbidity levels from 800 FTU (unprotected scenario) to below 50 FTU. The geotextile also attenuated wave energy, reducing significant wave heights by approximately 35–40%. Mechanical testing revealed that the fish bone weaving pattern with adhesive coating achieved the highest tensile strength (8.36 kN/m after 12 wet–dry cycles), while uncoated samples demonstrated higher elongation (up to 61.67%), providing greater flexibility. These demonstrate the feasibility of WH geotextiles as a scalable nature-based solution for erosion-prone tropical deltas. Future studies should focus on field-scale validation, biodegradation rates, and performance optimization for long-term applications. Full article

Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants. Full article

Drones offer a promising solution for automating distribution tower inspection, but real-time defect detection remains challenging due to limited computational resources and the small size of critical defects. This paper proposes TDD-YOLO, an optimized model based on YOLOv11n, which enhances small defect detection through four key improvements: (1) SPD-Conv preserves fine-grained details, (2) CBAM amplifies defect salience, (3) BiFPN enables efficient multi-scale fusion, and (4) a dedicated high-resolution detection head improves localization precision. Evaluated on a custom dataset, TDD-YOLO achieves an mAP@0.5 of 0.873, outperforming the baseline by 3.9%. When deployed on a Jetson Orin Nano at 640 × 640 resolution, the system achieves an average frame rate of 28 FPS, demonstrating its practical viability for real-time autonomous inspection. Full article