Search Results (9,608)

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

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 antifoaming performance of natural gas desulfurization solvents is critical for maintaining product gas quality and ensuring the safe operation of processing units. Hydrocarbon impurities can enter amine solutions through feed-gas entrainment, wellhead flowback carryover, and leakage of equipment lubricants. These contaminants may gradually accumulate in the solvent system and become a significant contributor to foaming. To address the industrial demand for rapid quantitative determination of hydrocarbon contaminants in desulfurization solvents, this study investigates in-service UDS-series solvents and representative samples collected from a natural gas purification plant in western Sichuan. NMR spectroscopy and GC-MS analyses reveal that the impurities are predominantly n-alkanes in the C₁₃-C₁₈ range, based on which a corresponding reference standard oil was prepared. COSMO-RS calculations combined with molecular interaction analysis identify n-hexane as the optimal extraction solvent. The ultraviolet spectrophotometric method commonly used to determine hydrocarbons in environmental water samples shows limited sensitivity to long-chain n-alkanes and requires strong acid pretreatment that disrupts the amine solvent matrix, rendering it unsuitable for UDS solvents. In contrast, the n-hexane extraction-GC-FID method showed good linearity, precision, and accuracy, meeting engineering analytical requirements for monitoring hydrocarbon contamination in MDEA-based UDS desulfurization solvents. Full article

To realize the real-time structural health and operational safety monitoring of military and industrial devices, such as hypersonic vehicles, aero-engine blades, and thermal power plant boilers, at operating temperatures up to and beyond 1400 °C, this study presents a miniaturised, integrated, high-thermal-stability wireless sensor device. This study investigated the influence of temperature on the interdigital electrodes (IDEs) of surface acoustic wave (SAW) temperature sensors for three configurations: bare electrode, single-layer protective film, and multilayer composite film. While the exposed electrode exhibited thermal stability at 1000 °C, it underwent structural failure at 1250 °C. To achieve health monitoring at temperatures exceeding 1400 °C, an Al₂O₃/AlN/Al₂O₃ multilayer protective architecture was developed. The device demonstrated functionality up to 1400 °C with a temperature coefficient of frequency (TCF) of −40.03 ppm/°C, yielding a sensitivity of 12.0 kHz/°C at a center frequency of ~300 MHz. The electrode protection structure elevated the maximum operating temperature. A hybrid heterogeneous integration of high-temperature co-fired ceramic (HTCC) inverted-F antenna and a Langasite (LGS) SAW device with a multilayer composite film was realised. The wireless device maintained functionality from room temperature to 1400 °C and withstood 1400 °C for 2 h, exhibiting a maximum repeatability error of 12.67% (corresponding to a temperature measurement error of ~177.4 °C at 1400 °C). This integrated design enables the miniaturization of high-temperature wireless sensors, making them suitable for harsh environments. Full article

Anomaly detection performance in sensor data is highly sensitive to model hyperparameters, which is central to reliable monitoring in mobile Internet security and industrial IoT (IIoT) scenarios. We propose an IWOA-LightGBM-based anomaly detection method for sensor data. For machine learning-based anomaly detection methods, hyperparameter selection often determines model performance, so we propose an Improved Whale Optimization Algorithm (IWOA) and further use it to optimize the hyperparameters of the LightGBM algorithm. To avoid falling into local optima and accelerate algorithm convergence, the WOA is improved by integrating nonlinear convergence factor, adaptive inertia weight factor and stochastic differential mutation strategy. Experimental results show that during hyperparameter optimization for LightGBM model training, the IWOA achieves faster convergence and higher computational efficiency compared to the Whale Optimization Algorithm (WOA), with anomaly detection accuracy exceeding 90%. Full article

Under green mine construction and efficient resource utilization, non-pillar mining has been increasingly applied. However, surrounding rock control remains difficult in traditional gob-side entry retaining under large mining height conditions. To address this problem, a cooperative control method combining roof cutting and pressure relief with a pre-placed backfill strip for coal pillar replacement is proposed. Taking the 15,108 and 15,110 working faces of Wangzhuang Coal Industry as the engineering background, a mechanical model and FLAC3D simulations were used to analyze the effects of roof cutting height and backfill strip width. The results show that roof cutting shortens the goaf-side suspended roof, weakens lateral abutment pressure, and improves the stress state of the strip. When the roof cutting height increases from 11 m to 13 m, the peak vertical stress of the strip decreases from 16.2 MPa to 13.9 MPa, with a reduction of 14.2%. When the strip width increases from 1.0 m to 1.5 m, the peak stress decreases by about 12.0%. Thus, the reasonable roof cutting height and strip width are determined to be 13 m and 1.5 m. Field monitoring shows maximum roof-to-floor and rib-to-rib convergences of 178.5 mm and 143.5 mm, respectively, with no obvious strip instability. Full article

Infrared imaging technology has been widely adopted for industrial gas leak detection due to its capability for large field-of-view, long-range, and dynamic monitoring. However, in practical applications, natural object interference within the scene, together with the blurred contours and low contrast of infrared images, severely degrades the performance of gas detection and leakage region segmentation. To address these challenges, this paper proposes a gas leak detection method that integrates gas characteristics with spatiotemporal information. Specifically, the non-solid characteristics of gas are incorporated to constrain the foreground extraction process of the Gaussian Mixture Model (GMM), thereby suppressing interfering moving objects. Furthermore, by exploiting the spatiotemporal information in infrared image sequences, a multi-scale cross-attention fusion model is designed to fuse multi-scale and global feature representations, improving the accuracy of foreground detection. Finally, density-based clustering is employed to achieve complete segmentation of gas regions with irregular shapes. Experimental results demonstrate that the proposed method effectively suppresses interference from solid objects, accurately detects gas leakage, and successfully segments the diffusion regions. Compared with existing approaches, the proposed method shows significant advantages and provides a valuable reference for research on infrared imaging-based gas leak detection. Full article

Water is fundamental to urban sustainability, structuring the urban water cycle from supply to wastewater treatment and discharge. Basic sanitation services are a core component of this system, directly influencing sustainable water use and environmental quality. Sanitation 4.0 applies Industry 4.0 technologies to enable real-time monitoring, data-driven management, and process optimization. This study investigates how the implementation of Industry 4.0 technologies transforms the management of basic sanitation services. A systematic literature review (SLR) was conducted to provide a theoretical foundation and identify research gaps. Articles were selected using a structured and reproducible method, and qualitative data were coded and analyzed with NVivo software. The results indicate that Sanitation 4.0 encompasses diverse applications, with artificial intelligence (AI), big data and data analytics, and internet of things (IoT) emerging as the most frequently implemented technologies in water distribution, wastewater treatment, and service management. IoT demonstrated broad versatility, while robots and augmented reality remain underexplored. Data security emerged as the area most in need of attention. This research concludes that Industry 4.0 technologies are reshaping the management and delivery of sanitation services, supporting innovation and progress toward universal access. Full article

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a physics-informed predictive digital twin framework operating in an offline or near-real-time predictive configuration for vibration prediction and surface roughness modeling in milling processes. Impact hammer testing was conducted to extract the dominant modal properties of the spindle–tool assembly, which were embedded into a Simulink-based dynamic framework to predict tool vibration under varying cutting conditions. Full-immersion slot milling experiments on AL6061 were performed for validation. Within all datasets, including training phase and validation phase, the predicted vibration amplitudes exhibit a coefficient of determination $R^2=0.94$ with measured values. The overall MAPE and RMSE are about 10.39% and 0.234, respectively. Power-law regression-based surface roughness prediction models were subsequently established using cutting parameters and both measured and DT-predicted vibration features through logarithmic transformation and least-squares fitting. The results show that the roughness prediction model using vibration features predicted by the digital twin model achieved a correlation coefficient of approximately $R^2=0.84$, with MAPE = 9.57% and RMSE = 0.16 μm, which is comparable to the predictive model based on experimentally measured vibration. These results indicate that, within the investigated machining conditions, the digital twin can provide vibration features suitable for surface roughness prediction, demonstrating its potential as a virtual sensing approach. This work advances digital twin applications from process monitoring toward predictive, quality-oriented machining systems and provides a foundation for adaptive parameter updating in intelligent manufacturing environments. Full article

Fine particulate matter (PM_2.5) composition, rather than mass alone, plays a critical role in determining toxicity and health impact. This study examined short-term associations between daily PM_2.5 constituents—black carbon (BC), nitrate (NO₃⁻), ammonium (NH₄⁺), and trace elements—and cardiopulmonary hospital admissions in Makkah, Saudi Arabia. Twelve months of constituent data from the Alharam monitoring site were linked to Herra hospital admissions for cardiovascular (CVD) and pulmonary diseases, stratified by visit type, age, and sex. Negative-binomial generalized linear models estimated adjusted relative risks (aRRs) per interquartile range increase in each constituent, controlling for meteorology, seasonality, and temporal trends. Mean PM_2.5 was 113.6 µg/m³; BC, sulfur, NO₃⁻, and NH₄⁺ dominated the fine fraction. Crustal elements were strongly intercorrelated (r > 0.9), while BC, lead (Pb), and nickel (Ni) showed moderate correlations (r ≈ 0.4–0.6), suggesting shared anthropogenic origins. BC increased CVD emergency/outpatient visits by 18% (aRR = 1.18; 95% CI: 1.08–1.29) and inpatient admissions by 25% (aRR = 1.25; 95% CI: 1.07–1.46). Ni and sulfur were also significant predictors; crustal elements were not. Multi-pollutant models confirmed BC and Pb as independent predictors (aRR = 1.19; 95% CI: 1.02–1.38). Effects were strongest among older adults aged 45–65 at lag 0–2 days. These findings highlight the need for emission controls targeting traffic and industrial combustion sources. Full article

This study characterized the physicochemical, rheological, and sensory parameters of five commercial natural-flavored yogurt brands available in the Peruvian highland’s region. The methodology included proximate composition, pH, titratable acidity, soluble solids, color (CIELab*), flow properties, viscoelastic behavior, and consumer sensory analysis using CATA questions and an acceptability test. The results revealed high variability among samples: soluble solids ranged from 7.24 to 16.27 °Brix, acidity from 0.68 to 1.03%, and two distinct rheological groups were identified: firm yogurts (G′ > 190 Pa) and soft yogurts (G′ < 30 Pa). Sensory attributes such as “pleasant texture,” “creamy,” and “milky flavor” positively influenced acceptability, whereas “acidic” and “watery” attributes negatively affected it. The sample with a balanced sensory profile (moderately sweet and creamy) achieved the highest acceptability score (7.8/9). It is concluded that yogurt quality in the highlands market is heterogeneous and that consumer acceptability depends more on sensory balance than on firmness alone. It is recommended that the dairy industry standardize fermentation processes to control acidity and optimize texture, prioritizing creaminess and homogeneity, and that regulatory authorities strengthen monitoring of these critical parameters to ensure safe and consistent products. Full article

Digital twin technology is rapidly gaining traction in the semiconductor industry for its ability to model manufacturing processes, including packaging engineering, to monitor and optimise performance cost-effectively. This paper focuses on two key areas of development. The first part explores the potential of digital design and additive manufacturing to produce high-performance, compact thermal management solutions that significantly reduce device junction temperatures and enhance operational efficiency. The second part presents the development of surrogate models to predict junction temperatures of electronic packages under varying operating and geometrical conditions. These models, trained using deep learning, were integrated into a user-friendly COMSOL Multiphysics application builder version 6.3. The proposed digital twin framework enables fast and accurate full-thermal field predictions in comparison to conventional 3D finite element simulations. Full article