Search Results (130,192)

Biochar-based fertilizers (BBFs), including formulations enriched with additives, are sustainable alternatives to conventional fertilizers, promoting waste reuse and controlled nutrient release. This study performed a global meta-analysis to evaluate nutrient dynamics (release and leaching in water and soil) and the agronomic performance of additive-enhanced BBFs compared with unfertilized and/or conventionally fertilized controls. Thirty studies were selected, with 264 experimental pairs extracted from the Web of Science and Scopus databases, and analyzed using a random-effects model. The results indicated that BBFs enriched with natural mineral additives promoted an average increase of 204.3% in nutrient release in water (p < 0.001), whereas in soil biotechnological additives showed the greatest increase, with 109.8% (p < 0.001). Leaching was reduced by up to 74.4% with BBFs enhanced with agricultural residue additives and by 46.9% with industrial additives, indicating greater nutrient retention and greater nutrient-use efficiency. In terms of agronomic performance, additive-enhanced BBFs resulted in average increases of 49.3% in plant height, 232.3% in aboveground biomass, 60.8% in root biomass, and 11.2% in grain yield, compared to unfertilized soil. Overall, the effectiveness of BBFs depends on both the type of additive and the application method, with industrial and mineral additives being the most promising for controlled nutrient release and increased crop productivity. Full article

Against the backdrop of environmental governance systems transitioning from command-and-control to multi-stakeholder collaboration, elucidating the mechanisms and pathways through which voluntary environmental regulations influence green technological innovation in heavily polluting enterprises holds significant implications for advancing green innovation and high-quality development. This paper systematically examines the synergistic mechanisms of command-and-control versus voluntary environmental regulations on green technological innovation in heavily polluting enterprises, utilising data from listed companies in China’s high-pollution industries between 2008 and 2024. Unlike previous studies predominantly focused on the impact of a single regulatory type, this study reveals an interactive effect between the two: moderate command-and-control regulation provides essential institutional support for voluntary environmental regulation, such as ISO 14001 certification, thereby generating a complementary enhancement effect. However, overly stringent command-and-control regulation diverts innovation resources from enterprises, thereby suppressing the incentive effect of voluntary regulation. This conclusion transcends the traditional analytical paradigm within environmental regulation theory that treats command-and-control and voluntary regulations as mutually exclusive opposites, revealing instead a dynamic relationship where both synergistic and constraining effects coexist. This discovery provides crucial theoretical underpinnings and empirical evidence for constructing an environmental governance system that combines command-and-control constraints with flexible incentives, ensuring compatibility between policy objectives and corporate behaviour. Full article

In industrial quality control, the precise identification of severe structural weld defects is paramount. Traditional 2D image-based detection methods are susceptible to illumination and texture interference, while high-precision 3D laser scanning solutions are costly and impractical for large-scale deployment. To achieve reliable geometric defect detection at low cost, this paper proposes a detection framework based on monocular depth estimation and a dual-attention point cloud network. First, YOLOv8 is employed for rapid region of interest extraction, and an advanced monocular depth estimation model generates 3D pseudo-point clouds containing geometric information. Secondly, addressing the challenge of distinct spatial orientation features in missed weld defects that are prone to confusion, this paper introduces a dual-attention-enhanced point cloud classification network named DA-PointNet++. This model embeds dual-attention modules within the PointNet++ backbone network, enhancing key feature representation in both the channel and spatial dimensions. Experimental results demonstrate that this approach achieves an accuracy of 93.67% and a recall rate of 90.51% in a unified binary classification task for general weld defect detection, effectively identifying both normal welds and complex missed weld defects. Compared to PointConv, Dynamic Graph Convolutional Neural Network (DGCNN), and mainstream Point Cloud Transformer, this method significantly reduces false negative rates while maintaining low computational costs, offering a cost-effective solution for industrial automation. Full article

Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., “Parsha (Scab),” “Brown spotting,” “White-Gray spotting,” and “Rust,” which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. Full article

Radiation detection technology is critical in medical diagnosis, high-energy physics experiments, nuclear environmental monitoring, and radiation safety protection. Its technological iteration stems from innovations in high-performance radiation detection materials. Traditional materials often have narrow dose–response intervals, insufficient high-precision measurement capability, low spatial resolution, and poor stability, failing to meet high-precision detection requirements. Ag-doped phosphate glass (Ag-PG), based on radio-photoluminescence (RPL), effectively addresses these limitations with its comprehensive advantages: high radiation sensitivity, a wide linear dose–response range, submicron spatial resolution for radiation imaging, write-erase-rewrite capability, and visualized dose monitoring potential, and it also boasts significant fundamental research value and engineering application prospects. Specifically, while existing RPL reviews mainly provide a comprehensive analysis from the perspective of RPL and present typical RPL material systems, this paper systematically analyzes the structural characteristics of the Ag-PG matrix and the coordination configuration and site occupation of Ag ions. It clarifies RPL luminescence properties, dose–response mechanisms, and the evolution of luminescence centers, while reviewing advancements in applications such as radiation dose detection and high-resolution X-ray imaging. By summarizing the current research status, technical advantages and existing challenges of Ag-PG, this study provides theoretical references and conceptual insights to promote breakthroughs in its fundamental research and practical applications in high-precision radiation dose detection, advanced medical imaging, micro-nano-scale radiation detection, and nuclear industry non-destructive testing. Full article

Grain-oriented (GO) silicon steel cores in low-voltage current transformers suffer magnetic degradation from residual stress and increased dislocation density during slitting and winding. This study addresses the gap in systematic optimization of secondary annealing on assembled toroidal cores using a 3² full-factorial design varying temperature (650, 850, 1050 °C) and holding time (60, 90, 120 min) on M4 grade cores. Results showed temperature is the dominant factor, while holding time exhibits a synergistic non-linear effect. The optimal condition (850 °C, 90 min) reduced specific losses from 0.85 W/kg to 0.43 W/kg (49% reduction). Mechanistic analysis confirmed this improvement is driven by complete primary recrystallization (equiaxed grains ~50–60 µm), dislocation annihilation (~10 HV hardness reduction), and reinforcement of the Goss texture ({110} <001>). SEM, EDS, and ICP-OES demonstrated that the Carlite coating remained dimensionally (1.67–1.83 µm) and chemically stable, with beneficial decarburization. Temperatures above 850 °C caused magnetic deterioration due to excessive grain growth. These results provide a validated, industrial framework for recovering magnetic efficiency in wound toroidal cores without compromising coating integrity. Full article

Chunmee tea quality is critical to its grading, and accurate identification is essential for quality evaluation and market valuation. However, traditional machine learning relies on manual feature extraction and causes spectral information loss, while conventional one-dimensional convolutional neural networks (1D-CNNs) are restricted by fixed kernels and narrow receptive fields, making multi-scale feature capture difficult. In this study, an improved DeepSpectra model integrated with the Inception module and residual connections was proposed for end-to-end automatic grading of Chunmee tea. A total of 360 samples across six grades (60 samples per grade) were collected using an Antaris II near-infrared spectrometer and preprocessed by multiplicative scatter correction (MSC). The proposed model was compared with other models. Results showed that under a 7:1:2 train–validation–test split, the proposed DeepSpectra achieved an average test accuracy of 96.39 ± 1.63% across ten random sample divisions, significantly outperforming the other models (p < 0.05). The model also exhibited excellent stability in five-fold cross-validation and superior generalization in small-sample scenarios, and a lightweight structure with low inference latency of 2.2 ms, which is suitable for real-time industrial applications. This work provides a reliable, efficient, and end-to-end method for grading Chunmee tea and offers a promising strategy for intelligent and rapid quality control of green tea. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

Brush seals, as a fundamental dynamic sealing technology in the aerospace and energy propulsion industries, require performance enhancement through instantaneous adjustment of the friction coefficient and force analysis of brush filaments. This paper establishes an instantaneous friction coefficient correction method based on the open volume between bristles and the backing plate. The downstream section of the double-row brush wire (2.6 mm) was quantitatively identified as the maximum leakage point, and it was found that the vortex characteristic length in the downstream area is approximately 1–3 times the bristle gap, with an increasing pressure ratio enhancing downstream turbulence and reducing gas leakage. A cantilever beam structural model was developed to assess the motion, force, and hysteresis properties of a single filament. Additionally, a porous medium model was utilized to elucidate the flow field and temperature distribution within the seal. The results suggest that the lag angle increases linearly over the first one-third of the brush wire’s length from the free end to the fixed end and is directly proportional to the pressure difference ΔP, reaching a maximum of 10.18°. The viscous drag causes the radial force y-component F_xy to increase and then decrease near the free end. The rear baffle contact force, F_b, shows variable peaks at two-thirds of the filament length. The displacement at the brush filament’s free end, the deflection angle, and the bending moment are directly proportional to the pressure differential. As pressure increases, the deformed region propagates toward the fixed end, and the maximum displacement at the free end of the brush wire reaches 13.04 mm. The leakage rate increases nearly linearly with ΔP and its deformation, reaching a maximum of 0.00849 m²/s. The pressure gradient growth rates of 164%, 73%, and 29% at the front baffle corner demonstrate that adding pressure chambers on front and rear baffles is optimal for high-pressure scenarios (ΔP > 0.3 MPa), while the formation of vortices between bristles and rotor reduces tip friction force and front-row turbulent disturbance, providing design guidance for extending seal service life. Full article

Flotation is a fundamental unit operation in mineral processing; however, achieving high selectivity while reducing the environmental impact of reagents remains a major challenge in phosphate ore beneficiation. Conventional depressants often exhibit limited selectivity and may pose environmental concerns, highlighting the need for sustainable alternatives. This study reports, for the first time, the application of starch nanostructures derived from potato pulp processing residues as a depressant in phosphate flotation, representing an innovative and eco-friendly approach. An exploratory and experimental methodology was adopted, including nanostarch synthesis via acid hydrolysis followed by centrifugation and sonication, as well as comprehensive physicochemical characterization. The primary objective was to evaluate the selective depressant performance of the nanomaterial in apatite–calcite flotation systems. The synthesized nanostructures exhibited particle diameters ranging from 179 to 443.6 nm. Microflotation tests conducted in a Hallimond tube using pure mineral samples under alkaline conditions (pH ≈ 9), at a depressant dosage of 500 mg/L and in combination with a plant-based fatty acid collector, revealed a pronounced selectivity window, resulting in an approximately 77% difference in flotation recovery between apatite and calcite. These findings demonstrate that nanostarch derived from agro-industrial residues is a promising, biodegradable, and sustainable depressant capable of enhancing selectivity in phosphate flotation. The results contribute to the advancement of greener mineral processing Technologies, although Further studies are required to elucidate the underlying interaction mechanisms. Full article

Contaminated gas flows are encountered in most industrial processes and require efficient removal of fine dispersed particles of various types and characteristics. Conventional cyclones are widely used under harsh operating conditions; however, their separation efficiency for fine particulate fractions remains relatively low. In this study, next-generation cyclones with a multichannel design featuring cylindrical and spiral casings are investigated, enabling particle collection efficiencies of approximately 90% for particles with a diameter of 2 µm. Under harsh microclimatic conditions—particularly at high humidity levels of 70% or higher and elevated temperatures of 50 to 200 °C—such technology is prone to clogging, necessitating complex regeneration procedures. Recent research has focused on improved channel geometries incorporating secondary peripheral flows, adapted for gas cleaning in harsh environments. Experimental results demonstrate effective removal of fine-dispersed glass and clay particles up to 20 µm in size at initial concentrations of 0.5–15 g/m³. The theoretical assessment of the influence of harsh gas flow conditions includes analyses of critical flow characteristics and the mechanical forces acting on fine particles under varying temperature and humidity conditions. The results indicate a maximum purification efficiency of up to 87.3% with an aerodynamic pressure drop of 440 Pa. The impact of harsh microclimatic conditions is most pronounced in the magnitudes of the centrifugal and drag forces: with an increase in the gas flow temperature by every 50 °C within the range from 0 to 200 °C, these forces increase by factors of 7.3–32.7 and 4–6.3, respectively. Full article

The rapid development of the aquaculture industry has led to increasing discharges of hypersaline and nutrient-enriched maricultural wastewater. Functional ceramic membranes have garnered significant advantages due to their exceptional chemical stability and high tailorability through surface and interface engineering. This research reviewed recent advances including the functionalization of ceramic membranes and hybrid systems coupled with advanced oxidation processes (AOPs) for enhancing degradations of nutrients and organics in maricultural wastewater treatment. Catalytic ceramic membranes enhanced removal of micropollutants including antibiotics and heavy metals. This review further systematically classified categorization of established functional ceramic membranes and synthesizes cutting-edge modification approaches for membrane fouling mitigation. Finally, this review evaluated the application prospects, challenges for scaled implementation, and proposed future research directions of functional ceramic membranes in the treatment of maricultural wastewater. Full article

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

The mechanism by which Sb influences the hot ductility and fracture behavior of corrosion-resistant steel within the temperature range of 650–1200 °C was systematically investigated using scanning electron microscopy (SEM) and electron probe microanalysis (EPMA). The temperature interval of the ductility trough and the underlying mechanisms responsible for its occurrence were elucidated. The results indicated that ductility troughs for the 0.09Sb and 0.15Sb steels occurred at 726–949 °C and 736–995 °C, respectively. Increasing Sb content broadened the ductility trough temperature range and shifted the minimum ductility temperature to higher values. The ductility trough was attributed to the combined effects of grain boundary ferrite films, coarse precipitates, and non-equilibrium grain boundary segregation of Sb. During deformation in the austenite–ferrite two-phase region at 800 °C, the hot ductility is primarily governed by the thickness of the grain boundary ferrite film. These ferrite films are prone to stress concentration, thereby reducing the hot ductility of both the 0.09Sb steel and the 0.15Sb steel. In the single-phase austenite region at 900 °C, coarse Ti(C,N) and MnS precipitates readily act as crack initiation sites, leading to intergranular fracture in the 0.15Sb steel. Non-equilibrium Sb grain boundary segregation further weakens grain boundary cohesion, thereby deteriorating the hot ductility of the steel. Moreover, increasing Sb content enhanced the magnitude of non-equilibrium grain boundary segregation and elevated its peak temperature, thereby raising the minimum ductility temperature. This work provides a theoretical basis and technical guidance for optimizing the continuous casting of Sb-containing corrosion-resistant steel in industrial production, thereby contributing to improved surface quality of continuously cast slabs. Full article