Search Results (8,021)

The presence of understory vegetation not only influences slope-scale soil and water conservation but also exerts a profound effect on hydrodynamic characteristics and the processes of runoff and sediment production. Therefore, in this study, different vegetation types and vegetation coverages (bare land, 30%, 60%, and 90%) were set up by simulating rainfall (45, 60, 90, and 120 mm·h⁻¹) to evaluate the runoff-sediment process and the response characteristics of hydrodynamic parameters. The results showed that increasing vegetation cover significantly reduced soil erosion on forest slopes (p < 0.05). When the vegetation cover ranged from 60% to 90%, vegetation pattern C and pattern D were the most effective in suppressing erosion, where increased cover improved runoff stability. Under low-cover conditions, overland flow tended toward turbulent and rapid regimes, whereas under high cover conditions, flow was primarily laminar and slow. Patterns C and D significantly reduced flow velocity and water depth (p < 0.05). Structural equation patterning revealed that, under different vegetation patterns, the runoff power (ω), Reynolds number (Re), and resistance coefficient (f) more effectively characterized the erosion process. Among these, the Reynolds number and runoff power were the dominant factors driving erosion on red soil slopes. By contrast, runoff shear stress was significantly reduced under high-cover conditions and showed weak correlation with sediment yield, suggesting that it was unsuitable as an indicator of slope erosion. Segmental vegetation arrangements and increasing vegetation cover near runoff outlets—especially at 60–90% coverage—effectively reduced soil erosion. These findings provide scientific insight into the hydrodynamic mechanisms of vegetation cover on slopes and offer theoretical support for optimizing soil and water conservation strategies on hilly terrain. Full article

The electric arc furnace (EAF) is a key technology in the steel production industry, particularly for recycling scrap iron. It plays a crucial role in the shift to low-carbon metallurgy, responding to the growing demand for more sustainable production methods. Alongside its environmental and energy benefits, the EAF process generates significant amounts of solid by-products, including dust (EAFD) and slag (EAFS). These wastes are not only rich in base metals but also contain critical elements, which have attracted increasing scientific and industrial interest. Depending on the waste type, key metals such as zinc (from EAFD) and chromium, vanadium, and titanium (from EAFS) are targeted for recovery. This review examines the chemical and phase compositions of these wastes, various leaching techniques (often combined with pretreatment stages), and methods for final metal recovery, either in their pure form or as compounds. Key challenges in hydrometallurgical routes include chloride contamination, the dissolution of refractory zinc ferrite, and impurity management. Despite current limited industrial adoption, hydrometallurgical approaches show significant promise as efficient and environmentally friendly solutions for resource recycling, offering high-purity metal recovery. Full article

The accurate classification of staple crops is of great significance for scientifically promoting food production. Crop classification methods based on deep learning models or medium/low-resolution images have been applied in plain areas. However, existing methods perform poorly in complex mountainous scenes with rugged terrain, diverse planting structures, and fragmented farmland. This study introduces the Complex Scene Crop Classification U-Net+ (CSCCU+), designed to improve staple crop classification accuracy in intricate landscapes by integrating supplementary spectral information through an additional branch input. CSCCU+ employs a multi-branch architecture comprising three distinct pathways: the primary branch, auxiliary branch, and supplementary branch. The model utilizes a multi-level feature fusion architecture, including layered integration via the Shallow Feature Fusion (SFF) and Deep Feature Fusion (DFF) modules, alongside a balance parameter for adaptive feature importance calibration. This design optimizes feature learning and enhances model performance. Experimental validation using GaoFen-2 (GF-2) imagery in Xifeng County, Guizhou Province, China, involved a dataset of 2000 image patches (256 × 256 pixels) spanning seven categories. The method achieved corn and rice classification accuracies of 89.16% and 88.32%, respectively, with a mean intersection over union (mIoU) of 87.04%, outperforming comparative models (U-Net, DeeplabV3+, and CSCCU). This research paves the way for staple crop classification in complex land surfaces using high-resolution imagery, enabling accurate crop mapping and providing robust data support for smart agricultural applications. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article

The transition to sustainable energy systems in the agroindustrial sector requires rigorous methodologies that enable a comprehensive and quantitative assessment of the technical and economic viability and environmental impact of photovoltaic integration. This study develops and validates a hybrid multi-criteria methodology structured in three phases: (i) analytical modeling of the load profile and preliminary sizing, (ii) advanced energy simulation using PVsyst for operational optimization and validation against empirical data, and (iii) environmental assessment using life cycle analysis (LCA) under ISO 14040/44 standards. The methodology is applied to a Cuban agroindustrial plant with an annual electricity demand of 290,870 kWh, resulting in the design of a 200 kWp photovoltaic system capable of supplying 291,513 kWh/year, thereby achieving total coverage of the electricity demand. The economic analysis yields an LCOE of 0.064 USD/kWh and an NPV of USD 139,408, while the environmental component allows for a mitigation of 113 t CO₂-eq/year. The robustness of the model is validated by comparison with historical records, yielding an MBE of $-0.65\%$, an RMSE of $2.87\%$, an MAPE of $2.62\%$, and an R² of $0.98$. This comprehensive approach demonstrates its superiority over previous methodologies by effectively integrating the three pillars of sustainability in an agroindustrial context, thus offering a scientifically sound, replicable, and adaptable tool for decision-making in advanced energy projects. The results position this methodology as a benchmark for future research and applications in emerging production scales. Full article

The design of automatic control systems is critical for ensuring safety in gas field surface engineering production. However, over-reliance on standardized design approaches within the context of automation technology can compromise system flexibility and neglect individualized cost-effectiveness considerations. This paper identifies a comprehensive evaluation method as the preferred approach for assessing station control systems by comparing the advantages and disadvantages of various common evaluation techniques. We propose an integrated semi-quantitative and quantitative evaluation method designed to comprehensively and accurately assess the effectiveness of station automatic control systems. For the semi-quantitative framework, we first establish a specific indicator system for the control system and employ the Analytic Hierarchy Process (AHP) to determine indicator weights tailored to different station types, achieving a scientific quantification of evaluation criteria. Additionally, we utilize quantitative calculation methods, specifically reliability and availability analyses, to evaluate the station’s automatic control system. Differential research is conducted to customize the evaluation based on the distinct process characteristics of various gas field stations. Differential design calculations and analyses were performed for a single station, improving the economy and adaptability of the automatic control system design. The proposed comprehensive evaluation method ensures the safe and stable operation of control system designs and provides a new approach for the automation and intelligent transformation of gas field surface engineering. Full article

This study comprehensively analysed the scientific production of the extraction, characterisation, and toxicological risk of resistant starches obtained from agro-industrial by-products. Articles indexed in the Scopus database between 2015 and 2025 were analysed. The results showed a progressive increase in publications led by Chinese institutions, the most notable being Jiangnan University. Most of the studies were published in high-impact journals, with the International Journal of Biological Macromolecules standing out, followed by Carbohydrate Polymers and Food Chemistry, all in the first quartile. Most studies focused on extraction methods (physical, chemical, and mechanical) and starch characterisation (morphological, structural, molecular, physicochemical, and functional). Emerging trends are directed towards innovative applications such as functional foods. However, the risks associated with contaminants in reusing agro-industrial by-products have not been adequately explored, showing an important gap in the current scientific literature. In this context, future research should focus on evaluating toxicological risks derived from these processes, considering the presence and behaviour of heavy metals, pesticides, and mycotoxins, as well as the possible migration of chemical compounds generated during extraction. Full article

Gastrointestinal (GI) cancers represent a major global health burden. Among them, colorectal cancer (CRC) is the most common type, followed by esophagus, stomach, liver, and pancreatic cancer. Since disturbance of the gut microbiota has been directly associated with the development of severe health issues, including cancer, probiotic administration may induce dysbiosis reversion and ameliorate carcinogenesis. Therefore, manipulation of the gut microbiota composition based on probiotic utilization has gradually attained scientific interest as a potent therapeutic modality for GI cancers. This review aims to synthesize the current in vitro and in vivo evidence on probiotics’ effectiveness in GI cancer chemoprevention and treatment. It also provides a classification of the fundamental anticancer features of probiotics, including antiproliferation and cell death induction, anticarcinogenic compound production, reduction in chemotherapy-related toxicity, gut microbiota modulation, intestinal barrier improvement, antioxidant activity, immunomodulatory/anti-inflammatory effects, and carcinogen detoxification. Finally, it underscores the future perspectives and challenges of probiotic administration to individuals. In this regard, it emphasizes the exploitation of advanced encapsulation techniques and the development of novel genetically engineered probiotics and next-generation probiotics as feasible ways to improve their bioavailability, ensure their targeted delivery, and eliminate their mild side effects to the host’s health. Full article

The complex trade-offs and synergies among ecosystem services (ESs) in arid regions influence the stability and sustainable development of regional ecosystems. As a representative oasis–desert transition zone, the Kashgar region requires quantifying the key drivers and thresholds influencing ecosystem services, which is crucial for regional management. This study examines the spatio-temporal changes and interactions of five types of ES (grain production, water yield, soil retention, carbon storage, and habitat quality) and employs Restricted Cubic Splines to quantify the nonlinear changes and threshold effects of natural and social drivers. The results indicate the following: (1) During the period from 2000 to 2020, supply services (grain production) and regulatory services (water yield and soil retention) showed growth, while support services (carbon storage and habitat quality) declined slightly; (2) the synergistic effects of ecological services improved across the entire region, but trade-off effects emerged in certain local areas; and (3) the NDVI is the core natural factor driving the spatio-temporal differentiation of ESs. In 2020, when the NDVI exceeded 0.35, it had an adverse impact on habitat quality and carbon storage. Among social factors, water yield and habitat quality exhibit the highest threshold points with land use development intensity. An increase in land development intensity significantly impacts the trade-off and synergistic relationships among ESs, leading to local imbalances in ES resource supply and demand. These findings enhance our understanding of the nonlinear characteristics and potential mechanisms of ecosystems in arid regions, providing a scientific basis for ecosystem management in these areas. Full article

Consumer demand for high-quality products, combined with expectations for more sustainable production systems and animal welfare, is driving major changes in livestock farming practices. It is known that space allowance plays a central role in pig welfare, promoting resting and reducing the incidence of injuries and stress-related behaviors; however, there is little scientific evidence on the effect that available space has on the carcass and meat quality. In this study, space allowances were compared, in both an indoor conventional system (1.15, 1.9 and 3 m²/pig) and an indoor organic system with outdoor access (1.4 + 1, 2.6 + 2 and 3.9 + 3 m²/pig). The increase in space available for pigs had no effect on pig performance, carcass and meat quality characteristics, such as pH, drip and cooking loss. However, lowering stocking density in the conventional indoor housing system improved meat tenderness, as assessed by the Slice Shear Force test, while no difference was found between meat tenderness in organic pigs raised with three different stocking densities. Increased space allowance per pig reduced n-3 fatty acids in pig loins from both housing systems and n-6 fatty acids and PUFAs in loins from pigs reared in the organic housing system with both indoor and outdoor space. Full article

The construction industry is responsible for 50% of mineral resource extraction and 35% of greenhouse gas (GHG) emissions. In this context, concrete stands out as one of the most consumed materials in the world. More than 30 billion tons of this material are produced annually, resulting in the extraction of around 19.4 billion tons of aggregates (mainly sand and gravel) per year. Therefore, it is urgent to develop strategies that aim to minimize the environmental impacts arising from this production chain. Currently, one of the most widely adopted solutions is the production of concrete through the reuse of construction and demolition waste. Thus, the objective of this research is to conduct a systematic literature review (SLR) on the use of recycled aggregates in concrete production, aiming to increase urban resilience by reducing the consumption of natural aggregates. An extensive search was performed in one of the most respected scientific databases (Scopus), and after a careful selection process, the main articles related to the topic were considered eligible through the PRISMA protocol. The selected manuscripts were then subjected to bibliographic and bibliometric analyses, allowing us to reach the state-of-the-art on the subject. The results obtained on the replacement rates of natural aggregate by recycled aggregate indicate that the recommendations vary from 20 to 60%, and these rates may be higher as long as the recycled aggregate is characterized, and may reach up to 100% as long as the matric concrete has a minimum compressive strength of 60 MPa. The specific gravity of most recycled aggregates ranges from 1.91 to 2.70, maintaining an average density of 2.32 g/cm³. Residual mortar adhered to recycled aggregates ranges from 20 to 56%. The water absorption process of recycled aggregate can vary from 2 to 15%. The mechanical strength of mixtures with recycled aggregates is significantly reduced due to the amount of mortar adhered to the aggregates. The use of recycled aggregates results in a compressive strength approximately 2.6 to 43% lower than that of concrete with natural aggregates, depending on the replacement rate. The same behavior was identified in relation to tensile strength. The modulus of elasticity showed a reduction of 25%, and the flexural strength was reduced by up to 15%. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article

Biomass is one of the most important sources of renewable energy, generating large amounts of ash. This increases the amount of waste, landfill, and air pollution. This work focuses on the sustainable disposal of this ash by producing an innovative binder. The mechanical and microstructural properties of alkali-activated biomass fly ash (BFA) and diatomite (DT) mixtures are currently insufficiently studied. New scientific knowledge of these properties is needed. This study presents the possibility of using BFA and diatomite as aluminosilicate precursors for the production of an alkaline-activated binder. It was found that the reactivity of BFA is relatively low. Based on XRD analysis, the mineral composition of BFA is dominated by quartz and calcite, both of which are non-reactive minerals. Therefore, mixtures with DT were created as precursors. According to Rietveld analysis data, an amorphous part was found in both precursor materials, BFA and DT. Comparing the chemical composition of BFA and DT using XRF and Rietveld analysis data, it was found that the amorphous part of BFA consists of CaO, while the amorphous part of DT consists of SiO₂. Thus, the combination of these precursors should complement each other during the geopolymerisation process. After 28 days of curing, the strength of the binders was dependent on the amount of DT, and the highest strength values, such as 16.4 MPa and 15.3 MPa, were obtained when DT contents were 10% and 30%, respectively. After geopolymerisation, XRD analysis showed that calcium silicate hydrate, hydrotalcite, and calcium aluminium silicate hydrate (zeolite A type) were formed. SEM analysis confirmed the XRD results and showed that DT additives (10% and 30% by weight) improved the microstructure of alkali-activated BFA, which is closely related to compressive strength values. The proposed binder will be useful in the preparation of concrete, which could be used for artificial aggregates or small architectural elements. Full article

Electric heating furnaces are widely used in industrial production and scientific research, where the quality of temperature control directly affects product performance and operational safety. However, precise control remains challenging due to the system’s nonlinear behaviour, time-varying characteristics, and significant time delays. To overcome these issues, this paper proposes a composite control method that integrates an auto-encoder-based prediction model with fuzzy PI control. Specifically, a discrete-time temperature model is constructed, in which the auto-encoder learns the system dynamics and predicts future temperatures, while the fuzzy controller adaptively tunes the PI parameters in real time. This approach improves both modelling accuracy and the adaptability of the control system. The simulation results on the MATLAB/Simulink platform show that the proposed method maintains the temperature overshoot within 2% under various disturbances, including a maximum delay of 243 s, ±2 °C measurement noise, 10% voltage fluctuation, and abrupt 10% gain variation. These results demonstrate the method’s strong robustness and indicate its suitability for advanced control design in complex industrial environments. Full article