Search Results (18,795)

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges. Full article

Enterprise systems are widely adopted in organizations, yet user acceptance remains a major challenge due to the complex interplay of cognitive, social, motivational, and innovation-related factors. Existing technology acceptance models often provide fragmented explanations by focusing on limited determinants. This study proposes TAM 4, an exploratory framework integrating constructs from the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Hedonic-Motivation System Adoption Model (HMSAM), and Diffusion of Innovation (DOI). The study was conducted in the context of enterprise application usage and professional enterprise system training environments involving organizational users, trainees, and practitioners. Data were collected from 115 enterprise system users (trainees and practitioners). To consolidate overlapping indicators and strengthen construct definition, principal component analysis (PCA) was applied, yielding seven higher-order constructs that explain 81.642% of cumulative variance. The framework was validated using PLS-SEM with three scenario-based structural models (full mediation, partial mediation, and direct effects). The results show that Model 3 provides the best fit and predictive performance (SRMR = 0.048; NFI = 0.786), indicating that enterprise system adoption is better explained through a direct effect structure rather than a purely mediated TAM pathway. The novelty of this study lies in introducing TAM 4 as a PCA-driven multi-theory acceptance model and evaluating its explanatory robustness through multi-scenario model comparison, offering practical insights for improving enterprise system implementation strategies. Full article

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

Rural e-commerce is reshaping agricultural markets, yet its environmental consequences remain insufficiently understood. This study examines how the Rural E-commerce Comprehensive Demonstration (RECD) program affects agricultural carbon outcomes in China. Using a balanced panel of 2152 counties from 2010 to 2022, we employ a multi-period difference-in-differences (DID) model to identify the effect of the RECD policy. The results show that the RECD policy significantly increases total agricultural carbon emissions. Evidence for production expansion and production restructuring suggests that improved market access and stronger price incentives encourage output expansion and a shift toward more market-oriented production, thereby raising aggregate emissions. At the same time, the RECD policy significantly reduces the carbon emission intensity and improves the carbon emission efficiency, indicating better carbon performance per unit of agricultural output. Further analysis shows that this dual result reflects the coexistence of efficiency gains and scale expansion, with the scale effect dominating the technical effect at the current stage. The emission-increasing effect is more pronounced in balanced agricultural areas, poverty-designated counties, counties with weaker initial e-commerce foundations, and counties with higher initial emission levels, while stronger environmental regulation and green technological innovation significantly mitigate this effect. In addition, the RECD policy generates spillover effects on neighboring counties within 50 km. These findings provide empirical evidence on the effects of the RECD policy on agricultural carbon emissions and offer policy guidance for integrating rural e-commerce policies with low-carbon agricultural transformation. Full article

With the rapid deployment of deep neural networks (DNNs) on edge devices, traditional hardware accelerators face significant challenges in terms of data security, computational redundancy caused by sparsity, and uneven utilization of on-chip resources. This paper proposes SaE-FPGA, a secure and efficient DNN accelerator designed specifically for edge FPGA platforms. The architecture introduces three core innovations: (1) Hash-Bypass Processing Unit (HBPU): Integrating a high-speed SHA-256 hardware engine with a hash-sparse bitmap mechanism, it enables real-time data integrity verification within a single clock cycle while skipping computations for redundant zero-value data. (2) Flexible Mixed-Precision Processing Element (FMP): By reconfiguring idle BRAM and LUT resources into an active lookup table multiplication engine, it overcomes the physical bit-width limitations of DSP blocks and supports INT8/INT6/INT4 mixed-precision multiplication. (3) Multi-mode Reconfigurable Streaming Frame (MRSF): A sparse-aware, elastic load balancing and data routing mechanism designed to mask long memory access latencies and ensure high hardware resource utilization. Experimental results on the Zynq 7045 platform demonstrate that SaE-FPGA reduces redundant computations by 23.2% while maintaining high precision and minimizing precision loss. The system effectively mitigates the risk of physical tampering. When tested on ResNet-50, it achieved a 27.2% improvement in energy efficiency and a 2.97× speedup compared to DSP-based FPGA solutions. Furthermore, by fully exploiting the hybrid BRAM-LUT and DSP configuration, the proposed accelerator achieves a remarkable peak throughput of 782.4 GOPS. Full article

Cement–soil mixing piles commonly face the problem of insufficient pile quality during on-site construction, and traditional measures such as increasing grouting pressure or enhancing mixing intensity are difficult to resolve effectively. The development of flowable solidified soil technology offers a new path for innovating soil pile reinforcement techniques. Based on an in situ test, this research proposes and introduces a new technology for pre-mixed fluid-solidified soil piles (PSPs). This technique effectively improves pile quality and significantly enhances pile bearing capacity by pre-mixing flowable solidified soil and then grouting it after pre-drilling holes with a screw drill. The results show that reinforcement of soil piles using the pre-mixed flowable solidified soil and pre-drilled grouting process has significantly improved pile quality, with better core sample integrity and uniformity. The results indicate that the characteristic bearing capacity of the uniform-section PSP is 252 kPa, meeting the design requirement of 130 kPa. The ultimate bearing capacity of the uniform-section PSP is 177% higher than that of the uniform-section CMP. In addition, the ultimate bearing capacity of the PSP after variable-section treatment is 153% higher than that of the uniform-section PSP. Finally, new design recommendations have been proposed, specifically calculation formulas for the load-bearing capacity and settlement of composite foundations based on current standards. Full article

Diatom nanotechnology offers significant potential for the development of innovative diatom-based nanocarriers for drug delivery and bioimaging, with promising implications for the treatment and diagnosis of diverse diseases. However, clinical translation of these nanocarriers remains limited due to an incomplete understanding of their in vivo fate. Current studies on the biodistribution, intracellular behavior, biodegradation, and clearance of diatom-based nanocarriers are inadequate and often lack systematic evaluation, leaving critical knowledge gaps. A comprehensive understanding of how these nanocarriers traverse biological barriers, interact with cellular components, and are ultimately eliminated from the body is essential for their rational design and safe clinical implementation. This perspective critically examines the in vivo fate of diatom-based nanocarriers, highlighting recent advances while identifying key challenges and unresolved questions. By integrating insights into their biodistribution, intracellular interactions, toxicological profile, biodegradation, and clearance mechanisms, this article provides a framework to guide the development of more effective and clinically relevant diatom-based nanoplatforms. Furthermore, it outlines future research directions and design strategies for next-generation nanoformulations, aiming to accelerate their translation from bench to the bedside. Full article

This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is the Visibility and Line-of-Sight Path Simplification (VLoSPS) algorithm, an algorithm-independent post-processing method that removes redundant waypoints through long-range axis-aligned visibility analysis while preserving path feasibility. VLoSPS is integrated with the Direction-Aware Path Planning Approach (DAPPA) to reduce angular deviations and improve directional continuity. The proposed framework is applicable to standard algorithms, including A*, Dijkstra, Breadth-First Search (BFS), and Depth-First Search (DFS), without modifying their internal search mechanisms. The main academic contributions comprise the formulation of a generalized post-processing architecture for UAV-derived occupancy maps, the introduction of a visibility-aware waypoint reduction strategy, and extensive validation using two synthetic maze datasets and three UAV-derived semantically segmented real-world datasets. On the Göttingen Maze Dataset, the VLoSPS and DAPPA pipeline reduced the average path lengths of A*, Dijkstra, BFS, and DFS by 5.42%, 9.46%, 10.44%, and 86.00%, respectively. The consistent improvements across real-world datasets demonstrate the effectiveness, computational feasibility, and general applicability of the proposed framework for UAV-assisted UGV path planning. The implementation code and benchmark resources developed in this study are publicly released to promote reproducibility and facilitate future research. Full article

Coal spontaneous combustion in gob areas is a major disaster endangering safe production in underground coal mines, and accurate prediction of carbon monoxide (CO), the core signature gas of coal oxidation, is critical for early warning and targeted prevention of mine fire disasters. However, CO concentration in gob areas is governed by complex gas–solid thermal–chemical multi-field coupling, presenting strong nonlinear characteristics. Traditional numerical methods suffer from prohibitive computational cost, purely data-driven models have inherent black-box defects, and conventional Physics-Informed Neural Networks (PINNs) require explicit full governing equations, which are hard to establish for such complex systems. This paper first proposes a Physics-Informed Modified Kolmogorov–Arnold Network (PIM-KAN), which deeply integrates domain physical knowledge with KAN architecture via a physics encoding layer, a residual-modified KAN layer, a multi-physics attention mechanism, and a multi-term physical consistency constraint framework. Experiments on 3125 real coal mine field samples show that the PIM-KAN achieves R² = 0.9965 and RMSE = 0.9290 ppm, reducing RMSE by 19.5% compared with MLP, and outperforming all baseline models. Ablation studies confirm the significant contribution of each innovation module, and attention weight analysis is highly consistent with Arrhenius reaction kinetics, verifying its superior prediction accuracy, physical consistency and intrinsic interpretability. Full article

As the “fifth major factor of production,” data plays a crucial role in fostering China’s tourism industry, advancing high-quality economic development, and gaining competitive market advantages. Serving as institutional infrastructure for data factor rights confirmation, pricing, trading, and value conversion, data trading platforms are central to the market-based allocation of data factors. The efficient flow and value realization of data elements have paved the way for the rapid development of digital tourism; new forms of digital tourism represent a profound transformation of the industry resulting from integration and innovation with other sectors. Based on the platform ecosystem theory, we select the panel data of 297 Chinese cities from 2012 to 2024 and innovatively use the Double/Debiased Machine Learning (DDML) model to empirically test the impact of data trading platforms on the new forms of digital tourism and its mechanisms. It is found that the construction of data trading platforms effectively empowers the development of new forms of digital tourism, and this conclusion still holds after a series of robustness tests, such as changing the sample split ratio, replacing the machine learning algorithm, and the instrumental variables method. Mechanism analysis indicates that data trading platforms significantly promote new forms of digital tourism through dual pathways of talent agglomeration and technological innovation, an effect further strengthened by increased government support. Heterogeneity analysis found that the empowerment effect is more significant in cities with lower resource endowment and common administrative level and historical cities, which can be effectively transformed into an employment support effect. Spatial effect analysis reveals that the establishment of data trading platforms exerts a positive pull effect on new forms of tourism in surrounding cities within a 30 km core zone. However, this effect gradually weakens with increasing distance, turning into a significant negative siphon effect beyond 60 km. The findings provide theoretical basis and empirical support for regionally differentiated digital tourism development policies. Full article

The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future. Full article

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

Ball vegetables (such as cabbage, Chinese cabbage, broccoli, etc.) hold an important position in the vegetable industry due to their unique morphology and diverse applications and are widely favored by both consumers and the market. However, the harvesting of Ball vegetables poses significant challenges to agricultural production and market supply. Traditional manual harvesting struggles to meet the rapid demands of large-scale cultivation, primarily due to its high labor intensity and time-consuming nature, compounded by the increasingly prominent issues of aging and shortage of agricultural labor in recent years. As an alternative, intelligent harvesting robot technology, through integration with optimized cropping practices, innovations in preservation techniques, and improvements in processing workflows, offers an effective solution for expanding market planting areas and enhancing production efficiency. However, such harvesting robots still require further optimization and improvement in terms of adaptability, operational efficiency, and damage control. To systematically review the research progress and current status of this field, this study employs a bibliometric analysis approach to evaluate the current performance characteristics of various types of heading vegetable harvesting robots, aiming to provide a reference for future technological developments. This review analyzes solutions suitable for low-damage, high-quality harvesting of Ball vegetables in modern agriculture from five dimensions: identification and localization, row-following mechanisms, cutting mechanisms, pulling and conveying mechanisms, and leaf-removal mechanisms. It also summarizes the main challenges currently facing harvesting equipment, including the complexity of harvest targets, diversification of crop varieties and cultivation patterns, and harvest-induced damage to Ball vegetables. Finally, this review provides a future outlook on heading vegetable harvesting from four perspectives: research on the characteristics of Ball vegetables, investigation into harvest-induced damage mechanisms, improvement in machinery adaptability, and enhancement in equipment versatility and intelligence. Full article

Given the accelerated growth of built-up land, optimizing land-use patterns is a practical strategy for reducing urban carbon emissions. While previous studies have concentrated on landscape patterns, the association between the morphological spatial pattern (MSPA) of built-up land and carbon emissions remains unknown. The MSPA not only captures the fine-scale characteristics of land use but also provides direct guidance for urban planning. To fill this gap, we took China, the world’s largest carbon-emitting country, as a case study. First, the MSPA of built-up land was identified from multitemporal land-use data for 2005, 2010, 2015, and 2018. Next, a covariance analysis was conducted to identify the control variables that are significantly associated with carbon emissions. Finally, we innovatively integrated the MSPA with machine learning techniques to explore the association between the MSPA of built-up land and carbon emissions, thereby overcoming the limitations of traditional landscape indices. The results demonstrate an increasingly evident decoupling effect between carbon emissions and socioeconomic growth in China, while the MSPA factors played increasingly significant roles. In particular, a “network” configuration of built-up land is more conducive to low-carbon city planning than compact development. Additionally, the merging of “islets” into “cores” should be avoided. Our findings highlight the growing importance of the MSPA in carbon reduction and can shed light on the spatial design of built-up land. Full article

In the frame of the final analysis of the HERFUSE activities a life cycle assessment (LCA) has been planned to support the performance evaluation of the new Clean Aviation (CA) architectural concepts. The HERFUSE project is focused on designing innovative fuselage and empennages suitable for the future Hybrid-Electric Regional Aircraft (HER) that will contribute to the overall target to reduce greenhouse gas (GHG) emissions. HERFUSE will study the challenges in fuselage and empennage layout, material, components, manufacturing and assembly derived from the integration of the relevant fuselage systems for HER as defined in the strategic research and innovation agenda SRIA for a Hybrid-Electric Regional Aircraft and in HER-01 topic. Full article