Search Results (148,721)

The biotechnological conversion of CO₂ to biomethane represents an energy-efficient, environmentally friendly, and sustainable approach within the waste-to-energy cycle. This process, in which CO₂ and H₂ are converted to biomethane in anaerobic bioreactors, is referred to as hydrogenotrophic biomethane production. While several studies have investigated hydrogenotrophic biomethane production, there is a lack of research comparing flocculent and granular sludge inoculum in continuously operated systems fed with a gas substrate. Both granular and flocculent sludge possess distinct advantages: granular sludge offers higher density, stronger microbial cohesion, and superior settling performance, whereas flocculent sludge provides faster substrate accessibility and more rapid initial microbial activity. In this study, two UASB (Upflow Anaerobic Sludge Blanket) reactors operated under mesophilic conditions were continuously fed with synthetic off-gas composed of pure H₂ and CO₂ in a 4:1 ratio and were compared in terms of microbial community shifts and their effects on hydrogenotrophic biomethane production. Biomethane production reached 75 ± 2% in the granular sludge reactor, significantly higher than the 64 ± 1.3% obtained with flocculent sludge. Although hydrogen consumption did not differ significantly, the granular sludge reactor exhibited higher CO₂ removal efficiency. Microbial analyses further revealed that granular sludge was more effective in supporting methanogenic archaea under conditions of gas substrate feeding. These findings offer advantageous suggestions for improving biogas production, enhancing waste gas management, and advancing sustainable energy generation. Full article

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction. Full article

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition. Full article

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article

The Centralized Teacher with Decentralized Student (CTDS) framework is a multi-agent reinforcement learning (MARL) approach that utilizes knowledge distillation within the Centralized Training with Decentralized Execution (CTDE) paradigm. In this framework, a teacher module learns optimal Q-values using global observations and distills this knowledge to a student module that operates with only local information. However, CTDS has limitations including inefficient knowledge distillation processes and performance gaps between teacher and student modules. This paper proposes the evolutionary sampling method that employs genetic algorithms to optimize selective knowledge distillation in CTDS frameworks. Our approach utilizes a selective sampling strategy that focuses on samples with large Q-value differences between teacher and student models. The genetic algorithm optimizes adaptive sampling ratios through evolutionary processes, where the chromosome represent sampling ratio sequences. This evolutionary optimization discovers optimal adaptive sampling sequences that minimize teacher–student performance gaps. Experimental validation in the $StarCraft$ Multi-Agent Challenge (SMAC) environment confirms that our method achieved superior performance compared to the existing CTDS methods. This approach addresses the inefficiency in knowledge distillation and performance gap issues while improving overall performance through the genetic algorithm. Full article

Farmers’ land transfer practices optimize the allocation of agricultural resources by transferring them to more efficient operators. This enhances agricultural productivity and advances rural revitalization. However, due to the lack of financial institution outlets in rural areas, the availability of financial services in rural areas is limited, which in turn hinders the transfer of rural land. This study examines the impact of digital financial inclusion, characterized by the deep integration of internet technology and financial services, on farmers’ land transfer behavior in China. The study uses data from the China Family Panel Studies (2012–2022) and provincial digital financial inclusion data. The results show that digital financial inclusion significantly promotes rural land transfer-out. The mechanisms reveal two pathways: (1) digital financial inclusion expands non-agricultural entrepreneurship by easing credit constraints and reducing reliance on land livelihoods; (2) it increases participation in commercial insurance, mitigating risks of land abandonment. Heterogeneity analysis reveals stronger effects in eastern China and among educated households. Theoretically, the study identifies the dual role of financial technology in reshaping rural land markets through credit access and risk management. Practically, it reveals how DFI influences land transfer behavior, providing a basis for the government to formulate policies that combine the two, ultimately enhancing the production capacity, operational efficiency, and market competitiveness of smallholder farmers. The findings offer global insights for developing countries that are leveraging digital finance to activate rural land markets and achieve digital financial inclusion. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30°C to 125°C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

Orthogonal Frequency Division Multiplexing (OFDM) modulation currently dominates the physical layer design in modern transmission systems. Its primary advantage is the simple reconstruction of channel frequency response (CFR). However, the Least Squares (LS) algorithm commonly used here is prone to significant estimation errors due to noise interference. A promising and relatively simple alternative is a DFT-based strategy that uses a pre-computed refinement/correction matrix to improve estimation performance. This paper investigates two implementation approaches for CFR reconstruction with pre-computed matrices. Focusing on multiplication operations, a threshold number of active subcarriers was identified at which these two implementations exhibit comparable numerical complexity. A numerical performance factor was defined and a detailed performance analysis was carried out for different guard interval (GI) lengths and the number of active subcarriers in the OFDM signal. Additionally, to maintain channel estimation quality irrespective of GI length, a channel impulse response (CIR) energy detection procedure was introduced. This procedure refines the results of the symbol synchronization process and, by using the circular shift property, preserves constant values of the precomputed matrix coefficients without system performance loss, as measured by Bit Error Rate (BER) and Mean Square Error (MSE) metrics. Full article

Driven by the urgent need to decarbonize the logistics sector—where conventional vehicles exhibit high energy consumption and emissions, posing significant environmental sustainability challenges—electrification represents a pivotal strategy for reducing emissions and achieving sustainable urban freight transport. Despite rising global electric vehicle sales, the penetration rate of electric logistics vehicles (ELVs) remains comparatively low, impeding progress toward sustainable logistics objectives. Battery-swapping mode (BSM) has emerged as a potential solution to enhance operational efficiency and economic viability, thereby accelerating sustainable adoption. This model improves ELV operational efficiency through rapid battery swaps at centralized stations. This study constructs a tripartite evolutionary game model involving government, consumers, and BSM-ELV manufacturers to analyze market dynamics under diverse strategies. Key considerations include market scale, government environmental benefits, battery leasing/purchasing costs, lifecycle cost analysis (via discount rates), and resource efficiency (reserve battery ratio $\lambda$). MATLAB-2021b-based simulations predict participant strategy evolution paths. Findings reveal that market size and manufacturer expectations significantly influence governmental and manufacturing strategies. Crucially, incorporating discount rates demonstrates that battery leasing reduces consumer enterprises’ initial investment, enhancing economic sustainability and cash flow while offering superior total cost of ownership. Furthermore, gradual reduction of government subsidies effectively stimulates market self-regulation, incentivizes leasing adoption, and bolsters long-term economic/operational sustainability. Market feedback can guide policy adjustments toward fiscally sustainable support mechanisms. This study proposes the following management implications for advancing sustainable logistics: 1. Governments should phase out subsidies systematically to foster market resilience; 2. Manufacturers must invest in BSM R&D to improve efficiency and resource circularity; 3. Consumer enterprises can achieve economic benefits and emission reductions by adopting BSM-ELVs. Full article

Understory-cultivated Panax ginseng possesses high pharmacological and economic value; however, its visual quality grading predominantly relies on subjective manual assessment, constraining industrial scalability. To address challenges including fine-grained morphological variations, boundary ambiguity, and complex natural backgrounds, this study proposes Ginseng-YOLO, a lightweight and deployment-friendly object detection model for automated ginseng grade classification. The model is built on the YOLOv11n (You Only Look Once11n) framework and integrates three complementary components: (1) C2-LWA, a cross-stage local window attention module that enhances discrimination of key visual features, such as primary root contours and fibrous textures; (2) ADown, a non-parametric downsampling mechanism that substitutes convolution operations with parallel pooling, markedly reducing computational complexity; and (3) Slide Loss, a piecewise IoU-weighted loss function designed to emphasize learning from samples with ambiguous or irregular boundaries. Experimental results on a curated multi-grade ginseng dataset indicate that Ginseng-YOLO achieves a Precision of 84.9%, a Recall of 83.9%, and an mAP@50 of 88.7%, outperforming YOLOv11n and other state-of-the-art variants. The model maintains a compact footprint, with 2.0 M parameters, 5.3 GFLOPs, and 4.6 MB model size, supporting real-time deployment on edge devices. Ablation studies further confirm the synergistic contributions of the proposed modules in enhancing feature representation, architectural efficiency, and training robustness. Successful deployment on the NVIDIA Jetson Nano demonstrates practical real-time inference capability under limited computational resources. This work provides a scalable approach for intelligent grading of forest-grown ginseng and offers methodological insights for the design of lightweight models in medicinal plants and agricultural applications. Full article

Compared with conventional gas reservoirs, deep shale gas reservoirs are characterized by developed faults and fractures, strong heterogeneity, high stress sensitivity, and complex in situ stress distribution. To address traditional 3D static models’ inability to predict in situ stress changes in strongly heterogeneous reservoirs during fracturing, this study takes the deep shale gas in the Zigong block of the Sichuan Basin as an example. By comprehensively considering the heterogeneity and anisotropy of geomechanical parameters and natural fractures in shale gas reservoirs, a 4D in situ stress multi-physics coupling model for shale gas reservoirs based on geology–engineering integration is established. Through coupling geomechanical parameters with fracturing operation data, the dynamic evolution laws of multi-scale stress fields from single-stage to platform-scale during large-scale fracturing of horizontal wells in deep shale gas reservoirs are systematically studied. The research results show the following: (1) The fracturing process has a significant impact on the magnitude and direction of the stress field. With the injection of fracturing fluid, both the minimum and maximum horizontal principal stresses increase, with the minimum horizontal principal stress rising by 1.8–6.4 MPa and the maximum horizontal principal stress by 1.1–3.2 MPa; near the wellbore, there is an obvious deflection in the direction of in situ stress. (2) As the number of fracturing stages increases, the minimum horizontal principal stress shows an obvious cumulative growth trend, with a more significant increase in the later stages, and there is a phenomenon of stress accumulation along the wellbore, with the stress difference decreasing from 15 MPa to 11 MPa. (3) The on-site adoption of the fracturing operation method featuring overall flush advancement and inter-well staggered fracture placement has achieved good stress balance; comparative analysis shows that the stress communication degree of the 400 m well spacing is weaker than that of the 300 m well spacing. This study provides a more reasonable simulation method for large-scale fracturing development of deep shale gas, which can more accurately predict and evaluate the dynamic stress field changes during fracturing, thereby guiding fracturing operations in actual production. Full article

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

As a consequence of the gradual exhaustion of apatite ore reserves, intensive comminution has been implemented in mineral processing operations to enhance phosphorus liberation. Consequently, improving the flotation efficiency of fine particles has remained a persistent challenge within the phosphate industry. The performance of flotation columns is strongly affected by the interaction between gas (bubble) and particle. The present research was designed to evaluate how certain process variables and chemical dosages influence gas holdup and its correlation with the column flotation performance of fine particles derived from a low-grade apatite ore. Column flotation experiments were conducted employing a factorial experimental approach to evaluate the effects of air flow rate, surfactant concentration, collector dosage, and depressant dosage on gas holdup, P₂O₅ grade, and recovery. The results made it possible to identify the levels of gas holdup that lead to appropriate values of P₂O₅ grade and recovery simultaneously, and their relation with the operating variables and reagent dosage. Gas holdup values higher than 23.5% led to the desired values of P₂O₅ grade (>30%) and recovery (>60%) simultaneously. Statistical models were developed with high correlation coefficients (R² > 0.98) to predict P₂O₅ grade and recovery as functions of the operating variables. This research provides a comprehensive framework of the gas holdup effect on column flotation systems, offering significant potential for improving the economic viability of low-grade phosphate ore processing. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article