Search Results (33,413)

Dust generated during coal mining and transportation poses serious threats to miners’ health, operational safety, and the surrounding environment. However, comprehensive review studies on dust suppression in coal mines remain limited, particularly those integrating dust movement laws with numerical simulation approaches. This review presents a systematic and reproducible analysis of dust control methods in coal mines with a particular focus on numerical simulation. Current research progress and development trends are summarized from three aspects: structural optimization of dust suppression devices, optimization of operating conditions, and ventilation system design. Existing studies indicate that structural improvements mainly concentrate on nozzle geometry, diameter, installation position, and spraying distance, while operating condition optimization primarily involves pressure regulation. Due to the complexity and high cost of full-scale experimental platforms, ventilation system optimization is largely achieved through numerical simulation, supplemented by field measurements. Studies based purely on numerical simulations remain limited in addressing the chemical modification of dust removers; however, with the advancement of molecular dynamics techniques, this area may represent a promising direction for future research. Full article

The rising cost of conventional protein sources such as soybean meal has prompted the search for sustainable and economical alternatives in aquafeeds. Sugar beet pulp (SBP), an abundant by-product of the sugar industry, possesses nutritional potential but is limited by its high fiber and anti-nutritional factors. Solid-state fermentation (SSF) offers a promising approach to enhance its nutritive value and functional properties. This study evaluated the effects of dietary inclusion of mixed microbial solid-state fermented beet pulp (FBP) on the growth, systemic health and intestinal function of juvenile yellow catfish (Pelteobagrus fulvidraco). First, orthogonal optimization determined Lactiplantibacillus plantarum:Saccharomycopsis fibuligera:Bacillus subtilis = 1:3:3 as the optimal ratio, significantly improving the nutritional profile of FBP. Based on this optimized FBP, an 8-week feeding trial, five isonitrogenous and isolipidic diets were formulated by replacing 0–12% soybean meal with FBP. The results demonstrated that 9% FBP inclusion yielded optimal growth performance and significantly improved muscle texture. At the systemic level, FBP supplementation reduced serum lipid markers and liver enzyme activities while enhancing antioxidant capacity. At the intestinal level, FBP promoted intestinal health by increasing key digestive enzyme (lipase, trypsin, amylase) activities, stimulating villus development, and improving intestinal antioxidant status. Furthermore, gut microbiota analysis revealed that dietary FBP supplementation significantly modulated intestinal microbial composition, with notable enrichment of genera such as Leucobacter. In conclusion, FBP is a multi-functional ingredient that enhances growth, product quality, systemic physiology, and intestinal health in yellow catfish aquaculture. These findings provide a viable strategy for the sustainable utilization of agricultural by-products in aquafeeds. Full article

The freshwater snail Bellamya purificata is both ecologically and economically significant, exhibiting notable sexual dimorphism in growth and nutritional traits that underscore the importance of breeding of monosex stocks. However, the genetic basis of sex determination remains unclear. Herein, genome-wide association studies (GWASs) combined with transcriptomic analysis were conducted to identify sex-linked markers and candidate genes for this species. GWAS generated 571 significantly sex-associated SNPs and 1853 InDels, corresponding to 44 candidate genes. Multiple significant SNP peaks were detected on chromosomes 1 and 2, with mrc2 and mis18bp1 as key candidate genes. A sex-linked InDel marker located within mis18bp1 can distinguish males and females cost-effectively. Genotype analysis of the sex-associated loci revealed that most females were homozygous while males were heterozygous, suggesting that B. purificata has a primarily XX/XY sex determination system. Comparative gonadal transcriptome analyses identified 2996 female-biased and 4281 male-biased genes. Among them, sry, sox8, dmrt1 and dmrt2 may be critical in male sex differentiation, while β-catenin, foxl2, esr1 and nr5a2 may be important in female sex differentiation. Integration of GWAS and transcriptomic data highlighted four pronounced sex-associated candidate genes, including mis18bp1, rnf216, tbx1 and mrc2. These results provide a valuable foundation for elucidating the genetic mechanisms underlying sex determination and for the development of monosex stocks in B. purificata. Full article

Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is presented that integrates a multi-objective fuzzy optimization model into an adaptive digital twin along with an agentic AI-based dynamic goal reset mechanism. The main methodological innovation of this study is not in the separate development of each of these components but in their structured integration in the form of a self-regulating decision-making loop in which the priority of goals is dynamically adjusted based on the current state of the system. Computational results based on real and simulated data show that the proposed framework reduces the total logistics cost by about 4–5% and reduces product waste by about 13% while simultaneously improving the service level by about 4%. Resilience analysis shows faster performance recovery in the face of operational disruptions, and scalability results confirm the controlled growth of computational time with increasing problem size. These findings demonstrate the effectiveness of integrating adaptive digital twins and agentic AI in a multi-objective fuzzy optimization environment for intelligent and resilient food logistics management. Full article

Although the transition from the Waterfall model to Agile practices has accelerated software delivery, it has often weakened documentation practices, contributing to persistent information asymmetry between Product Managers and Developers. This study introduces an LLM-based reverse engineering framework designed to assist product management workflows by analyzing source code and generating enriched development tickets. The proposed Interactive Product Management Assistant leverages the long-context capabilities of Gemini 1.5 Pro together with a context-caching mechanism to analyze large codebases, identify ambiguities in product requests, highlight potential edge cases, detect possible cascading dependencies (“domino effects”), and generate code pointers that guide developers to relevant implementation areas. The framework was evaluated through case studies on several open-source projects, including WordPress, ERPNext, Ghost, and Odoo. The results suggest that the system can support requirement clarification, improve visibility of potential implementation impacts, and reduce exploratory effort during code analysis. In addition, the implemented preprocessing and caching mechanisms reduce analysis costs and improve operational efficiency during iterative interactions. Rather than providing a large-scale quantitative before-and-after comparison, this paper presents a qualitative case study and a proof-of-concept implementation to demonstrate the feasibility of the proposed approach. Overall, the findings demonstrate the feasibility of using LLM-assisted reverse engineering to support requirements analysis and product–developer collaboration, highlighting the potential of AI-based tools to complement traditional requirements engineering practices in complex software projects. Full article

This study addresses the dual challenges of low copper recovery and persistent arsenic pollution in the bioleaching of low-grade, high-arsenic copper ores containing enargite (Cu₃AsS₄). Through integrated electrochemical, chemical, and biological investigations, a selective and environmentally sustainable two-stage hybrid leaching process was developed. Electrochemical analysis identified a critical oxidation threshold of ~750 mV governing enargite dissolution. Chemical leaching and X-ray Photoelectron Spectroscopy (XPS) analysis revealed a temperature-dependent sulfur transformation pathway, enabling a staged thermal strategy: flotation below 40 °C to maximize hydrophobic elemental sulfur (S⁰) formation, and bioleaching at 40–55 °C to promote complete sulfur oxidation to sulfate. Optimization produced a two-stage process comprising 10-day chemical pre-leaching with FeSO₄ (10.0 g/L Fe²⁺) followed by bioleaching, achieving 78.3% copper extraction while suppressing arsenic dissolution to approximately 10%. The use of FeSO₄ instead of Fe₂(SO₄)₃ reduces reagent costs by ~70%, saving an estimated CNY 47,250 daily at 1000 t/d scale. Leaching toxicity tests confirm residue As < 0.10 mg/L, meeting non-hazardous waste standards (GB5085.3-2007). This work provides the first integrated demonstration of electrochemical threshold control combined with temperature-dependent sulfur speciation for selective copper extraction from arsenic-bearing enargite ores, offering a scalable, reagent-economical, and environmentally sustainable metallurgical route. Full article

Forests are important food granaries. The accessibility of a healthy diet is the key factor in food and health equity. However, there is a lack of research focusing on its influence on locals at different levels of development. China’s population comprises various groups of farmers, allowing for the comparison of influence pathways across different economic levels of farmers. This research explores the topic with an empirical study conducted in Jiangxi Province, China, using data from 1939 valid responses collected across 216 villages. The analysis was performed using a mixed-effects ordered logistic model and a mediation effect model. The results of the baseline and mediation effect analyses reveal that there are four influence pathways. First, farmers’ forest resource endowments play a significant role in improving farmers’ perception of healthy diet accessibility (direct access type). Second, farmers’ forest resource endowments increase the accessibility of healthy diets by reducing the perceived costs of healthy diets (cost-relieving type). Third, farmers’ forest resource endowments increase the accessibility of a healthy diet by enhancing the perceived quality of the natural environment (quality scarcity type). Fourth, farmers’ forest resource endowments increase the perceived environmental quality, decrease the perceived costs of healthy diets, and affect the perception of healthy diets’ accessibility (cost-reducing type). The results of heterogeneity analysis based on the independent variables (health-related information, age, education level, disposable income, household size, communication and transportation convenience) reveal that for disadvantaged groups, the effect type tends to be the “direct access type” and “cost-relieving type”, and for advantaged groups, the effect type tends to be the “quality scarcity type”. Through empirical analysis, this study explains how forest resource endowments of different farmer groups influence their access to healthy diets, which lays a foundation for better understanding the association and formulating relevant policies. Decision makers should recognize the distinct influence of forest resource endowments on different farmer groups and develop policies related to forest resource management and healthy diets for farmers. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article

This paper presents the design and analysis of solar systems for agricultural applications and the sustainable energy supply of villages, based on a case study of a rural settlement comprising 30 households. The village energy demand is quantified through a detailed assessment of hourly load profiles for daytime and nighttime operation, identifying peak loads and total daily energy consumption. Energy usage patterns are established for residential buildings, agricultural water pumping, public lighting, healthcare facilities, and commercial services. To meet these energy requirements sustainably, a 60 kW photovoltaic (PV) system is proposed in combination with a solar thermal water heating system designed to supply domestic and agricultural hot water. This study details the design methodology and simulation of the solar thermal system, including heat transfer modeling and system dimensioning. MATLAB (V.22b) simulations are conducted to evaluate system performance, covering PV energy generation, battery charge–discharge cycles, and thermal behavior over a 24 h period. Comparative analyses of standalone PV, hybrid PV/T, and combined PV and solar thermal configurations demonstrate that separate PV and thermal systems provide superior cost-effectiveness, operational reliability, and reduced maintenance requirements. The results confirm the technical feasibility, economic viability, and environmental benefits of solar-based solutions for rural electrification and agricultural applications. The results indicate that the analyzed rural settlement has an estimated daily electricity demand of approximately 590 kWh. Based on this demand, a 60 kW photovoltaic system was selected to ensure sufficient daytime electricity production while also allowing battery charging for nighttime consumption. In addition, the solar thermal system can increase the water temperature from approximately 10 °C to 55–80 °C, depending on solar irradiance conditions. The combined PV and solar thermal configuration demonstrates the potential to provide a reliable and sustainable energy solution for rural off-grid communities. Full article

The increasing frequency of torrential rainfall due to global warming has resulted in a significant rise in urban flooding and river overflows. Rainwater pumping stations, typically located near rivers, serve as buffers between sewer systems and receiving water bodies, helping to mitigate flood risks. A primary challenge in operating these stations is optimizing pump performance to prevent flooding while minimizing energy consumption and costs. Various computational methods, including meta-heuristics and deep learning, have been proposed to tackle this optimization problem. However, most studies either overlook or inadequately address pump maintenance costs, which are essential for long-term operational efficiency. This gap stems from the lack of a comprehensive model that accurately captures the full spectrum of costs involved in pump operation. This paper introduces a cost estimation model that integrates both deterministic and probabilistic elements to enhance the energy-efficient operation of rainwater pumping stations. The model focuses on pumps with capacities of 100 m³/min and 170 m³/min, which are commonly used. It takes into account electricity consumption costs as well as maintenance costs arising from frequent on/off cycles and dry-run events. Predictions of failures due to these operational stresses are modeled using the Crow–AMSAA non-homogeneous Poisson process (NHPP) and Weibull distributions—probabilistic models widely used in mechanical failure analysis. To evaluate the proposed model, simulations were conducted using the Storm Water Management Model (SWMM), comparing a deep reinforcement learning-based control strategy with the current operational method at the Gasan Pumping Station in Seoul, South Korea. The pump operating costs associated with each method were calculated and analyzed using the proposed model, demonstrating its potential for ensuring cost-effective and reliable pump operation. Full article

Hydrogen energy storage serves as a pivotal technology for integrating high proportions of renewable energy, yet its development faces constraints due to substantial investment requirements and imperfect market mechanisms. Green Asset-Backed Notes (ABNs) offer potential to alleviate financing constraints; however, their synergistic effects with hydrogen storage market strategies remain unexplored. This paper constructs a two-layer Stackelberg game model integrating ABN financing with day-ahead trading. Multi-scenario analysis reveals that ABN financing costs significantly influence the operational economics of energy storage: low-cost financing enhances hydrogen storage’s price responsiveness and arbitrage capabilities, whereas high costs suppress its market participation. The research provides quantitative evidence for leveraging financial instruments to enhance hydrogen storage competitiveness. Full article

Under conditions of limited irrigation and excessive fertilizer application in the arid regions of Xinjiang, it is essential to adopt well-coordinated strategies to improve yield and crop water productivity (WP). In this study, a comparative experiment was conducted with three irrigation levels, T1 (100% crop evapotranspiration, ET_c), T2 (75% ET_c), and T3 (50% ET_c), combined with three potassium application rates, K1 (540 kg ha⁻¹), K2 (360 kg ha⁻¹), and K3 (180 kg ha⁻¹). The objective was to investigate their effects on the yield, quality, and economic benefits of jujube trees. Limited irrigation amounts significantly affected the photosynthetic characteristics, growth parameters, and ET_c of jujube trees, whereas potassium fertilizer levels primarily regulated fruit development and yield formation. Compared with full irrigation, mild deficit irrigation caused a moderate yield reduction but significantly enhanced economic returns due to the improved water productivity and fruit quality. In contrast, severe water deficit led to substantial decreases in growth parameters and economic benefits by 12.87–45.70% and 81.69%, respectively. Potassium application demonstrated a significant threshold effect, with the K2 treatment showing greater improvements in fruit quality indices, including reducing sugars, vitamin C, and other key quality parameters, compared to the K3 treatment. Based on hierarchical–grey relational analysis, the combination of 75% ET_c and 300 kg K ha⁻¹ was identified as the optimal water–potassium management strategy. The net profit was 29,199 CNY. The benefit–cost ratio increased to 3.63, and the WP improved by 16.17% compared to full irrigation. Thus, this study provides an important theoretical basis and technical support for water-saving and quality-improving cultivation of jujube trees in arid regions. Full article

This study introduces a portable piezoelectric thin-film microbalance platform that combines acoustic signal analysis with deep learning for point-of-care mass detection. The system employs a flexible polyvinylidene fluoride sensor, a smartphone for acoustic signal acquisition, and three deep learning models: convolutional neural network, long short-term memory network, and Transformer. Experimental findings indicate that the Transformer achieves the highest classification accuracy of 99.5%, outperforming the convolutional neural network at 96.9% and the long short-term memory network at 97.3%, attributed to its enhanced capability to capture long-range temporal dependencies. The platform facilitates real-time, label-free detection without the necessity for bulky instrumentation, providing a cost-effective and scalable solution for decentralized diagnostics. This research establishes a foundational framework for intelligent portable micro-mass sensing with significant potential applications in precision medicine, environmental monitoring, and personalized healthcare. Full article

This paper presents the analysis and results of the numerical simulation of the biofuel combustion process: namely, the volumetric mixture of diesel oil (ON) and camelina seed oil methyl ester (CSME) in a diesel engine. The mathematical model used in the simulation is based on a four-stroke diesel engine acting as a power generator. To enable simulations depending on the type of biofuel, a model algorithm was developed in the MATLAB/Simulink environment that allowed for the conditions and parameters to be adjusted according to specific test requirements. The numerical simulation was built on the basis of a real stand, in order to confirm the results of previous research both theoretically and in real applications. The calculation approach starts with the elemental composition of the fuel and goes through the intake, compression, combustion, and expansion stages, culminating in the thermal balance of the engine. The mathematical model confirmed the obtained results, which are comparable to the results from the research station. The obtained results confirm the legitimacy of using CSME as an additive to diesel and show its impact on engine performance that can be optimized to achieve the desired results. The use of pure CSME (100%) resulted in an increase in engine power and torque, probably due to the oxygen content of the biofuel molecules and its higher cetane number, which improves its ignition characteristics. However, an increase in unit fuel consumption has been observed, indicating lower energy efficiency compared to clean diesel, which is partially offset by the higher density of biofuel. The model takes into account the physicochemical properties of the fuel, such as the viscosity, cetane number and density, which significantly affect the fuel injection and atomization processes. Although the simulation is based on simplified assumptions, its results highlight the potential of biofuels in heavy transport and their cost-effectiveness as an alternative to fossil fuels. The developed model is used not only to evaluate the engine performance, but also as a tool for assessing the thermal efficiency, and optimizing the composition of the fuel mixture. Full article

With the continuous elevation of aviation safety standards, accurate monitoring of ground support vehicles in aircraft stand areas has become a critical task for enhancing overall aircraft stand operational safety. Given the limitations of existing surface movement radar and multi-camera surveillance systems in terms of cost, deployment complexity, and coverage, this paper proposes a lightweight vision-based framework for vehicle state perception and spatiotemporal behavior analysis oriented toward aircraft stand safety. Leveraging existing fixed monocular monitoring resources in the stand area, the framework first establishes a precise mapping from image pixel coordinates to the physical plane through self-calibration and homography transformation utilizing scene line features, thereby achieving unified spatial measurement of vehicle targets. Subsequently, it integrates an improved lightweight YOLO detector (incorporating Ghost modules and CBAM for noise suppression) with the ByteTrack tracking algorithm to enable stable extraction of vehicle trajectories under complex occlusion conditions. Finally, by combining functional zone division within the stand, a semantic map is constructed, and a behavior analysis method based on a spatiotemporal finite state machine is proposed. This method performs joint reasoning by fusing multi-dimensional constraints including position, zone, and time, enabling automatic detection of abnormal behaviors such as “intrusion into restricted areas” and “abnormal stop.” Quantitative evaluations demonstrate the framework’s efficacy: it achieves an average physical localization error (RMSE) of 0.32 m, and the improved detection model reaches an accuracy (mAP@50) of 90.4% for ground support vehicles. In tests simulating typical violation scenarios, the system achieved high recall (96.0%) and precision (95.8%) rates in detecting ‘area intrusion’ and ‘abnormal stop’ violations, respectively. These results, achieved using only existing surveillance cameras, validate its potential as a cost-effective and easily deployable tool to augment existing safety monitoring systems for airport ground operations. Full article