Search Results (1,792)

The photocatalytic reduction of CO₂ into high-value chemicals utilizing solar energy represents a sustainable approach to mitigating greenhouse gas emissions and advancing renewable chemical production. Recently, copper-based metal–organic frameworks (Cu-MOFs) have been extensively researched for their potential in photocatalytic CO₂ reduction, due to their high affinity for capturing CO₂, the presence of unsaturated Cu sites, and their advantageous photochemical properties. In this review, we first provide an overview of Cu active sites in the secondary building units (SBUs) of MOFs, focusing on their selective adsorption of CO₂ gas and analyzing the mechanisms of the multi-electron transfer processes involved in Cu-based photocatalytic reduction of CO₂. Ultimately, this article outlines the existing obstacles and suggests potential avenues for future research. Full article

China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture. Full article

With strategic mineral exploration extending to deep and complex geological settings, traditional methods increasingly struggle to dissect metallogenic systems and locate ore bodies precisely. This synthesis of current progress in muon imaging (a technology leveraging cosmic ray muons’ high penetration) aims to address these exploration challenges. Muon imaging operates by exploiting the energy attenuation of cosmic ray muons when penetrating earth media. It records muon transmission trajectories via high-precision detector arrays and constructs detailed subsurface density distribution images through advanced 3D inversion algorithms, enabling non-invasive detection of deep ore bodies. This review is organized into four thematic sections: (1) technical principles of muon imaging; (2) practical applications and advantages in ore exploration; (3) current challenges in deployment; (4) optimization strategies and future prospects. In practical applications, muon imaging has demonstrated unique advantages: it penetrates thick overburden and high-resistance rock masses to delineate blind ore bodies, with simultaneous gains in exploration efficiency and cost reduction. Optimized data acquisition and processing further allow it to capture dynamic changes in rock mass structure over hours to days, supporting proactive mine safety management. However, challenges remain, including complex muon event analysis, long data acquisition cycles, and limited distinguishability for low-density-contrast formations. It discusses solutions via multi-source geophysical data integration, optimized acquisition strategies, detector performance improvements, and intelligent data processing algorithms to enhance practicality and reliability. Future advancements in muon imaging are expected to drive breakthroughs in ultra-deep ore-forming system exploration, positioning it as a key force in innovating strategic mineral resource exploration technologies. Full article

Vertical alliances within agricultural supply chains serve as critical institutional vehicles for deepening triple-sector integration (primary–secondary–tertiary) in rural economies, driving agricultural modernization, and advancing rural revitalization. However, sustaining alliance stability constitutes a complex dynamic process wherein inadequate stakeholder engagement and collaborative failures frequently precipitate alliance instability or even dissolution. Existing scholarship exhibits limited systematic examination of the micro-mechanisms and regulatory pathways through which multi-agent strategic interactions affect alliance stability from a dynamic evolutionary perspective. To address this gap, this research focuses on China’s core agricultural innovation vehicle—the Agricultural Industrialization Consortium—and examines the tripartite structure of “Leading Enterprise–Family Farm–Integrated Agricultural Service Providers.” We construct a tripartite evolutionary game model to systematically analyze (1) the influence mechanisms governing cooperative strategy selection, and (2) the regulatory effects of key parameters on consortium stability through strategic stability analysis and multi-scenario simulations. Our key findings are as follows: Four strategic equilibrium scenarios emerge under specific conditions, with synergistic parameter optimization constituting the fundamental driver of alliance stability. Specific mechanisms are as follows: (i) compensation mechanisms effectively mobilize leading enterprises under widespread defection, though excessive penalties erode reciprocity principles; (ii) strategic reductions in benefit sharing ratios coupled with moderate factor value-added coefficients are critical for reversing leading enterprises’ defection; (iii) dual adjustment of cost sharing and benefit sharing coefficients is necessary to resolve bilateral defection dilemmas; and (iv) synchronized optimization of compensation, cost sharing, benefit sharing, and value-added parameters represents the sole pathway to achieving stable (1,1,1) full-cooperation equilibrium. Critical barriers include threshold effects in benefit sharing ratios (defection triggers when shared benefits > cooperative benefits) and the inherent trade-off between penalty intensity and alliance resilience. Consequently, policy interventions must balance immediate constraints with long-term cooperative sustainability. This study extends the application of evolutionary game theory in agricultural organization research by revealing the micro-level mechanisms underlying alliance stability and providing a novel analytical framework for addressing the ‘strategy–equilibrium’ paradox in multi-agent cooperation. Our work not only offers new theoretical perspectives and methodological support for understanding the dynamic stability mechanisms of agricultural vertical alliances but also establishes a substantive theoretical foundation for optimizing consortium governance and promoting long-term alliance stability. Full article

For aquaculture systems, maximizing feed efficiency is a major challenge since it directly affects growth rates and economic sustainability. Feed is one of the largest costs in aquaculture, and feed waste is a significant environmental issue that requires effective management strategies. This paper suggests a novel approach for optimal fish feeding in pond aquaculture systems that integrates vision language models (VLMs), optical flow, and advanced image processing techniques to enhance feed management strategies. The system allows for the precise assessment of fish needs in connection to their feeding habits by integrating real-time data on biomass estimates and water quality conditions. By combining these data sources, the system makes informed decisions about when to activate automated feeders, optimizing feed distribution and cutting waste. A case study was conducted at a profit-driven tilapia farm where the system had been operational for over half a year. The results indicate significant improvements in feed conversion ratios (FCR) and a 28% reduction in feed waste. Our study found that, under controlled conditions, an average of 135 kg of feed was saved daily, resulting in a cost savings of approximately $1800 over the course of the study. The VLM-based fish feeding behavior recognition system proved effective in recognizing a range of feeding behaviors within a complex dataset in a series of tests conducted in a controlled pond aquaculture setting, with an F1-score of 0.95, accuracy of 92%, precision of 0.90, and recall of 0.85. Because it offers a scalable framework for enhancing aquaculture resource use and promoting sustainable practices, this study has significant implications. Our study demonstrates how combining language models and image processing could transform feeding practices, ultimately improving aquaculture’s environmental stewardship and profitability. Full article

This study presents new developments in novel drag reduction devices for road vehicles, focusing on the use of inflatable and alternative material rear drag reduction devices that employ both a single- and multi-cavity approach. The effectiveness of these devices is assessed through on-road testing using constant power measurements to evaluate the resulting drag reductions. Surface pressure measurements collected during testing are compared with CFD predictions, using both the RANS and HLES methods to evaluate how accurately pressure changes are modelled when the devices are fitted to the test vehicles. A novel method for analysing vehicle surface flow in real-world conditions is also introduced, involving the capture and processing of video-recorded tuft imagery to determine appropriate means and standard deviations for the surface flow behaviour. Additionally, the study presents the latest advancements in multi-cavity drag reduction device design, along with considerations on how such devices can significantly enhance the benefits of vehicle platooning. Full article

Bio-based surfactants have demonstrated significant potential as economically viable and environmentally sustainable alternatives to petroleum-derived surfactants, with the global biosurfactant market expanding from USD 4.41 billion in 2023 to a projected USD 6.71 billion by 2032, representing a compound annual growth rate of 5.4%. While conventional surfactants such as alkyl aryl sulfates and alkyl benzene sulfonates exhibit extremely high aquatic toxicity and impose substantial ecological costs, biosurfactants including lipopeptides (surfactin, iturin, fengycin, lichenysin) produced by Bacillus species and glycolipids (rhamnolipids, sophorolipids, trehalose lipids, mannosylerythritol lipids) from Pseudomonas demonstrate superior biodegradability. However, current biosurfactant production costs, ranging from 5 to20 USD/kg, cannot compete effectively with synthetic surfactants, averaging approximately 2 USD/kg, necessitating comprehensive process improvements to achieve commercial viability. The utilization of renewable agricultural feedstocks containing 65–70% carbohydrates, including corn stover, sugarcane bagasse, rice bran, and palm oil mill effluent, has achieved production costs as low as 3.8 USD/kg through advanced optimized pretreatment technologies, enzyme catalysis, simultaneous saccharification and fermentation (SSF), and downstream processes, resulting in cost reductions compared to conventional methods. The implementation of artificial intelligence and machine learning algorithms for bioprocess optimization enables simultaneous optimization of genetic engineering, metabolic pathways, and fermentation parameters, achieving yield improvements and cost reductions, with projections indicating production costs below 2.50 USD/kg being needed in the next decade to achieve cost parity with synthetic surfactants, maintaining economic viability. Full article

The effective removal of oxygen (O), nitrogen (N), sulfur (S), and oxide inclusions from superalloy reverts is crucial for enhancing service life and achieving cost efficiency. However, refining DZ125 superalloy presents particular challenges, as conventional processes prove ineffective against hafnium (Hf) oxides. This study introduces an innovative purification method combining droplet electron-beam melting (EBM) with centrifugal directional solidification. Through this advanced EBM technique, we successfully produced ultrapure DZ125 superalloy with nitrogen content reduced below 5 ppm and total O + N + S content below 10 ppm. Most significantly, the process nearly eliminated Hf oxides from the reverts, meeting the stringent purity standards for DZ125 superalloy. We conducted a comprehensive analysis of inclusion morphology and composition in three distinct regions: the top slag layer, final solidification zone, and interior section of the ingot processed at varying EBM power levels. Our findings reveal that MC-type carbides at the slag–crucible interface were formed. There are HfO₂, TaC, and Al₂O₃ in the final solidification zone, with notable encapsulation of HfO₂ particulates within Al₂O₃ particles; and few HfO₂ and Al₂O₃ inclusions exist in the ingot interior. It is also found that increasing EBM power from 36 kW to 46 kW significantly improved impurity removal efficiency, as evidenced by substantial reductions in both inclusion quantity and size. This enhanced purification stems from two primary mechanisms: (1) flotation of inclusions during EBM melting, facilitated by Marangoni convection, droplet stirring effects, and centrifugal forces generated by ingot rotation; and (2) decomposition of stable oxides enabled by the high-energy density characteristic of EBM and high-vacuum processing environment. This combined approach demonstrates superior capability in overcoming the limitations of traditional refining methods, particularly for challenging Hf oxide removal, while establishing an effective pathway for superalloy revert recycling. Full article

Driven by the growing demand for sustainable materials, spent coffee grounds have emerged as a promising bio-based reinforcement in polymer composites, particularly for additive manufacturing applications. As a readily available byproduct of the coffee industry, spent coffee grounds contain cellulose, hemicellulose, lignin, proteins, and oils, making them attractive fillers for both thermoplastic and thermoset matrices. Incorporating spent coffee grounds into composites supports waste valorization, cost reduction, and environmental sustainability by transforming organic waste into functional materials. This review first examines the issue of spent coffee ground waste, addressing its environmental footprint and disposal challenges. It then explores the composition and properties of spent coffee grounds. The paper provides a comprehensive overview of composites based on spent coffee grounds for 3D printing, covering processing methods, potential applications, and current challenges in additive manufacturing. Special attention is given to the preparation and processing of these composites, including key steps such as drying, grinding, sieving, and surface modification to enhance compatibility with polymer matrices. Various additive manufacturing techniques influence the printability, processability, and mechanical performance of such composites. While spent coffee grounds offer notable sustainability advantages, challenges such as weak interfacial adhesion, moisture sensitivity, and reduced mechanical properties necessitate optimized processing conditions, surface treatments, and tailored material formulations. This review highlights recent advancements and outlines future research directions, emphasizing the need for stronger interactions between spent coffee grounds and polymer matrices, improved recyclability, and scalable additive manufacturing solutions to establish spent coffee grounds as a viable and eco-friendly alternative for 3D printing applications. Full article

This study incorporates a nonlinear seepage relationship into Biot’s consolidation theory and simulates the consolidation of a single vertical drain under vacuum preloading using the finite element method. The model, simplified via the equal-strain assumption, is validated against theoretical predictions. Under the axisymmetric Biot’s framework, consolidation behavior is analyzed in detail. The results show that in the early stages of consolidation, excess pore water pressure in the vicinity of the prefabricated vertical drain (PVD) does not fully dissipate and may even increase, indicating the occurrence of the Mandel–Cryer effect. As the consolidation process advances, the consolidation front gradually extends outward, and the void ratio near the PVD decreases rapidly, leading to the formation of a clogging zone. In contrast, the reduction in the void ratio in the non-clogging region is relatively slow. The progressive development of the clogging zone significantly impedes the overall consolidation rate. Furthermore, this study explores the influence of key parameters—including the compression index, permeability coefficient, well diameter ratio, smear effect, and well resistance—on the formation of the clogging zone and the Mandel–Cryer effect. Full article

The undifferentiated nasopharyngeal cancer (NPC) is a multifactorial disease mainly due to Epstein-Barr Virus (EBV) infection. The transmembrane tyrosine kinase ‘EphA2’ and the protease ‘Furin’ are implicated in the EBV entry into epithelial cells and other physiological processes. To gain insights into the association of single-nucleotide polymorphisms (SNPs) rs4702 and rs6603883 (FURIN and EPHA2 genes, respectively) with the risk and prognosis of the NPC, the genotypes of 471 individuals (228 cases and 243 controls) were assessed alongside risk cofactors (sex, tobacco, alcohol, occupation, and recurrent Ear, Nose and Throat infections) and prognosis cofactors (Tumor stage, local invasion, lymph node involvement, and metastasis) using multivariable logistic regression. We found that only the rs4702 AG/GG genotypes were statistically significantly associated with a reduced risk of cancer, both in the overall population and in men (approximately 50% reduction). The rs4702 GG genotype was also associated with a low frequency of local tumor invasion in the whole population (OR = 0.382, p = 0.017, co-dominant model, and OR = 0.409, p = 0.02, recessive model), but heterozygous women were associated with a higher lymph node involvement (OR = 3.53, p = 0.031, co-dominant model, and OR = 3.62, p = 0.02, overdominant model). The rs6603883 GG genotype was associated, in the dominant model, with distant metastasis in the whole population (OR = 2.5, p = 0.024), with advanced clinical stage in men (OR = 2.22, p = 0.034), and with advanced clinical stage and distant metastasis in patients under 49 years (OR = 3.13, p = 0.009, and OR = 5.15, p = 0.011, respectively). Additionally, men having the rs6603883 GA genotype were associated with lymph node invasion (OR = 2.22, p = 0.027, overdominant model). Our study is the first to demonstrate that FURIN and EPHA2 germline gene polymorphisms are associated with NPC risk (for rs4702) and prognosis (for both rs4702 and rs6603883), with sex-specific differences. These results need to be replicated and further investigated in other populations. Full article

The development of medical question-answering systems (QASs) encounters substantial challenges due to the complexities of medical terminologies and the lack of reliable datasets. The shortcomings of traditional artificial intelligence (AI) driven QAS lead to generating outcomes with a higher rate of hallucinations. In order to overcome these limitations, there is a demand for a reliable QAS to understand and process complex medical queries and validate the quality and relevance of its outcomes. In this study, we develop a medical QAS by integrating SPARQL, retrieval-augmented generation (RAG), and generative pre-trained transformer (GPT)-Neo models. Using this strategy, we generate a synthetic dataset to train and validate the proposed model, addressing the limitations of the existing QASs. The proposed QAS was generalized on the MEDQA dataset. The findings revealed that the model achieves a generalization accuracy of 87.26% with a minimal hallucination rate of 0.16. The model outperformed the existing models by leveraging deep learning techniques to handle complex medical queries. The dynamic responsive capability of the proposed model enables it to maintain the accuracy of medical information in a rapidly evolving healthcare environment. Employing advanced hallucination reduction and query refinement techniques can fine-tune the model’s performance. Full article

As China advances toward its 2060 carbon neutrality goal, the electrification of inland waterway shipping has emerged as a strategic pathway for reducing emissions. This study constructs a 2025–2060 dynamic material flow analysis framework that integrates three core dimensions: (1) all-electric ships (AES) diffusion, estimated via a GDP-elasticity model and carbon emission accounting; (2) battery technology evolution, including lithium iron phosphate and solid-state batteries; and (3) recycling system improvements, incorporating direct recycling, cascade utilization, and metallurgical processes. The research sets up three AES penetration scenarios, two battery technologies, and three recycling technology improvement scenarios, resulting in seven combination scenarios for analysis. Through multi-scenario simulations, it reveals synergistic pathways for resource security and decarbonization goals. Key findings include that to meet carbon reduction targets, AES penetration in inland shipping must reach 25.36% by 2060, corresponding to cumulative new ship constructions of 51.5–79.9k units, with total lithium demand ranging from 49.1–95.9 kt, and recycling potential reaching 5.4–25.2 kt. Results also reveal that under current allocation assumptions, the AES sector may face lithium shortages between 2047 and 2057 unless recycling rates improve or electrification pathways are optimized. The work innovatively links battery tech dynamics and recycling optimization for China’s inland shipping and provides actionable guidance for balancing decarbonization and lithium resource security. Full article

Accurate prediction of slab temperature during the continuous casting and rolling process is essential for optimizing reheating furnace scheduling and achieving energy savings and emission reductions in steel production. However, because of the dynamic boundary conditions caused by the complex transport processes, obtaining precise temperature data for slabs remains challenging. These difficulties lead to issues such as low hot charging rates, mixing of hot and cold slabs in reheating furnaces, and excessive heat loss from slabs after cutting. To address these challenges, this study develops a mathematical model to calculate slab temperatures during the continuous casting and rolling process, providing a foundation for production scheduling optimization. The model accounts for the coupled heat transfer effects induced by dynamic slab stacking and the stacking heat transfer effects resulting from slabs with varying cross-sectional dimensions. Validation against experimental data demonstrated the model’s accuracy and reliability. Key findings highlighted that neglecting dynamic stacking effects or simplifying slab dimensions introduces errors. These results enhance slab temperature tracking in complex processes and advance related theoretical understanding. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article