Search Results (1,396)

Climate change has caused considerable uncertainty to oilseed rape production. However, the climatic suitability for oilseed rape cultivation and its future changing trend remain unclear, specifically in Jiangsu Province—a major oilseed rape producing-region in China. Based on the past 50 years (1969–2018) of daily meteorological data from 13 meteorological stations in the province, this study established a climate suitability assessment model for oilseed rape cultivation. Temperature, precipitation, and sunlight were comprehensively analyzed, with suitable zones delineated through GIS spatial analysis and the natural break method. With the incorporation of SSP2-4.5 climatic scenario simulation data, the study projected the evolving trends of oilseed rape cultivation climatic suitability zones from 2024 to 2050 in the province. The findings reveal that over the past five decades, the climatic suitability for oilseed rape planting in the province has demonstrated the following patterns: temperature suitability increased by 0.02 per decade, precipitation suitability declined by −0.01 per decade, sunlight suitability decreased by −0.01 per decade, and comprehensive suitability rose by 0.01 per decade. High climatic suitability with the index of 0.80–1.00 was predominantly clustered in the central region, while moderate suitability zones with the index of 0.50–0.80 were mainly found in its northern and southern regions. Unsuitable zones with the index of 0.00–0.50 were mainly confined to the northern and southern extremities of the province. Under future climate scenarios, oilseed rape planting suitability is projected to improve significantly, with highly suitable zones expanding, particularly into the central and parts of the northern Jiangsu. Moderately suitable zones also will be extended, including potential areas such as the parts of Lianyungang and Wuxi. Unsuitable zones will be reduced, with only limited areas like southern Wuxi retaining lower suitability. Future temperature increases in Lianyungang are expected to be in favor of oilseed rape production. However, excessive precipitation in the southern region will require enhanced drainage measures. Improved temperature and precipitation conditions in Xuzhou are anticipated to boost the climatic suitability. Overall, oilseed rape planting climatic factors in the central and northern regions are projected to improve, enabling production expansion, while the southern region will face the challenge of excessive precipitation in Jiangsu Province. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

In the three-roll planetary rolling process, excessively high surface temperature of the rolls can easily lead to copper adhesion, deterioration of roll surface quality, shortened rolling lifespan, and severely affect the quality of copper tube products as well as production efficiency. To improve the cooling efficiency of the roll cooling system, this study developed a fluid–solid–heat coupled model and validated it experimentally to investigate the effects of nozzle diameter, spray angle, and axial position of the spray ring on the cooling performance of the roll surface. Given the low computational efficiency of finite element simulations, three machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM)—were introduced and evaluated to identify the most suitable predictive model. Subsequently, the Particle Swarm Optimization (PSO) algorithm was employed to optimize the geometric parameters of the spray ring. The results show that the maximum deviation between the coupled model predictions and experimental data was 4.36%, meeting engineering accuracy requirements. Among the three machine learning models, the RF model demonstrated the best performance, achieving RMSE, MAE, and R² values of 1.7336, 1.3203, and 0.9082, respectively, on the test set. The combined RF-PSO optimization approach increased the heat transfer coefficient by 44.72%, providing a robust theoretical foundation for practical process parameter optimization and precision tube manufacturing. Full article

This study presents a data-driven framework for modeling urban heat in a highland region of Quang Ngai Province, Vietnam—an area with limited prior research on heat stress. Using advanced machine learning methods, including Category Boosting (CatBoost) and deep convolutional neural network (CNN), the spatial distribution of urban land surface temperature (LST) is predicted based on topographical, land use/land cover, urban morphological, proximity, and compactness features. Our findings show that incorporating urban compactness metrics significantly enhances prediction accuracy, with CatBoost explaining 89% of LST variance. Based on Shapley Additive Explanations, built-up density, bare land density, distance to river, green space density, and built-up cluster compactness are identified as the most influential factors. Machine learning-based causal analysis further clarifies the direct effects of key urban features on LST. The proposed framework helps reveal distinct characteristics of the study area with respect to urban heat properties. The research findings can support sustainable urban planning and heat stress alleviation in the study area. Full article

The induction quenching–tempering process typically enhances the surface strength and core toughness of alloy steels by utilizing the skin effect. However, the impact of parameters like quenching current and heating time on the microstructure and mechanical property of 34CrNiMo6 steel crankshafts remains unclear. In this work, the microstructure of 34CrNiMo6 steel after induction quenching exhibits three distinct zones: a martensite hardened layer; a transition zone of martensite and tempered sorbite; and a matrix of tempered sorbite. As the induction current (400, 500, and 600 mA) and heating time (3, 5, and 7 s) increase, the hardened layer thickness enhances (up to 3.21 mm). Under the 600 mA and 7 s, the hardened layer reaches peak hardness and residual stress values of 521.48 HV and −330.12 MPa, showing a decreasing trend from surface to core. After tempering at 330 °C for 2 h, the hardened layer mainly consists of tempered martensite, and the surface hardness and residual stress decrease to 417.94 HV and −12.33 MPa. The temperature gradient from quenching balances after tempering, with martensitic phase transformation and stress redistribution reducing hardness and residual stress. Furthermore, the induction quenching–tempering process enhances the toughness of 34CrNiMo6 steel when compared to the untreated specimen, boosting its tensile yield strength, elongation, and tensile strength by 15.3%, 14.9%, and 19.5%, respectively. This work deepens the understanding of induction quenching–tempering process and provides valuable insights for designing alloy steels with excellent mechanical properties. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

This work presents a high-common-mode-rejection-ratio (CMRR) and high-gain FinFET-based bio-potential amplifier with a novel CMRR reduction technique. In this paper, a feedback buffer is used alongside a capacitively coupled chopper-stabilized circuit to reduce the common-mode signal gain, thus boosting the overall CMRR of the circuit. The conventional pseudoresistor in the feedback circuit is replaced with a tunable parallel-cell configuration of pseudoresistors to achieve high linearity. A chopper spike filter is used to mitigate spikes generated by switching activity. The mid-band gain of the chopper-stabilized amplifier is 42.6 dB, with a bandwidth in the range of 6.96 Hz to 621 Hz. The noise efficiency factor (NEF) of the chopper-stabilized amplifier is 6.1, and its power dissipation is 0.92 µW. The linearity of the parallel pseudoresistor cell is tested for different tuning voltages (V_tune) and various numbers of parallel pseudoresistor cells. The simulation results also demonstrate the pseudoresistor cell performance for different process corners and temperature changes. The low cut-off frequency is adjusted by varying the parameters of the parallel pseudoresistor cell. The CMRR of the chopper-stabilized amplifier, with and without the feedback buffer, is 106.9 dB and 100.3 dB, respectively. The feedback buffer also reduces the low cut-off frequency, demonstrating its multi-utility. The proposed circuit is compatible with bio-signal acquisition and processing. Additionally, a machine learning-based arrhythmia diagnosis model is presented using a convolutional neural network (CNN) + Long Short-Term Memory (LSTM) algorithm. For arrhythmia diagnosis using the CNN+LSTM algorithm, an accuracy of 99.12% and a mean square error (MSE) of 0.0273 were achieved. Full article

Oxygen transport membranes (OTMs) have gained a lot of attention for their application in different innovative fields, but the development of new materials able to combine high oxygen permeability and good chemical stability is crucial to boost the exploitation of such membrane-based technologies. Perovskite oxides are widely studied as mixed ionic-electronic conductors for the realization of OTMs. In this article, we focus on _Sr1-xCa_xTi_0.8Fe_0.2O_3-ẟ (SCTF) perovskites and investigate the effect of Ca content on the A-site of the permeation properties, both in single-phase SCTF membranes and in dual-phase membranes obtained by combining SCTF and the ionic conductor Ce_0.8Gd_0.2O₂ (CGO). In single-phase samples, we observed that the substitution of 40% Ca preserves the permeation performances of the non-substituted SrTi_0.8Fe_0.2O_3−ẟ membrane while allowing for a substantial decrease in the sintering temperature, thus facilitating membrane manufacturing. In dual-phase membranes, the increase in the Ca content in the perovskite causes an increase in grain size. The permeation is, at least partially, controlled by the kinetics of the surface exchange reactions. This limitation can be overcome by the addition of an activation layer; however, the permeance of activated CGO-SCTF membranes still remains lower compared to the single-phase parent perovskitic membranes. Full article

In this study, a green and efficient extraction methodology was developed by leveraging the unique properties of chitosan—namely its non-toxicity, biocompatibility, and adhesive nature—to enhance the recovery of bioactive ingredients from Forsythia suspensa leaves. The core mechanism involves the formation of complexes between chitosan and the target bioactive ingredients, which significantly boosts their extraction efficiency. To substantiate this mechanism, comprehensive characterization was performed using Powder X-ray Diffraction (PXRD), Fourier Transform Infrared Spectroscopy (FT-IR), Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), and molecular docking analyses. The results provided robust evidence of a strong interaction between chitosan and the bioactive ingredients, leading to a marked enhancement in both the stability and aqueous solubility of the target compounds. For process optimization, a multi-objective approach was implemented using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously maximize the extraction yields of phillyrin and forsythoside A. The algorithm identified the optimal parameters as a leaf-to-chitosan mass ratio of 10:11.75, a solid-to-liquid ratio of 1:52 g/mL, a temperature of 80 °C, and a duration of 120 min. Under these optimized conditions, the corresponding extraction yields for phillyrin and forsythoside A were 1.68 ± 0.16% and 3.23 ± 0.27%, respectively. These findings collectively indicate that chitosan-assisted extraction represents a highly promising and advanced technology for the sustainable and effective extraction of bioactive ingredients from botanical sources. Full article

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R² values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article

Accurate state-of-health (SOH) estimation of lithium iron phosphate (LiFePO₄) batteries is critical for ensuring the safety and performance of electric vehicles, particularly under extreme operating conditions. This study presents a data-driven SOH prediction framework based on high-frequency electrochemical impedance spectroscopy (EIS) measurements conducted at −5 °C across various states of charge (SOCs). Feature parameters were extracted from the impedance spectra using equivalent circuit modeling. These features were optimized through Bayesian weighting and subsequently fed into three machine learning models: Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). To mitigate SOC-dependent variations, the models were trained, validated, and tested using features from different SOC levels for each aging cycle. This work provides a practical and interpretable approach for battery health monitoring using high-frequency EIS data, even under sub-zero temperature and partial-SOC conditions. The findings offer valuable insights for developing SOC-agnostic SOH estimation models, advancing the reliability of battery management systems in real-world applications. Full article

Bee pollination is an indispensable part of agricultural production and a crucial factor in maintaining ecosystem balance and biodiversity. Understanding foraging behavior and pollination effects is essential for the management of bee pollination. Over a two-year experiment, we evaluated the foraging behavior and pollination effects of bumblebee Bombus terrestris and Chinese honeybee Apis cerana cerana on cherries in orchards. Results showed that all bees exhibited enhanced foraging activity as daytime temperatures rose in early spring. However, the daytime foraging activity of bumblebees differs from that of Chinese honeybees. The number of bumblebees leaving the hive exhibited two peak periods, whereas Chinese honeybees showed only one peak period. Bumblebees had longer working hours and greater pollen-carrying capacity than Chinese honeybees. Undoubtedly, cherries pollinated by bees had higher yields, as indicated by a greater fruit setting rate and yield. Thus, as effective pollinators, their pollination significantly boosts production and presents a viable option for widespread use in cherry cultivation. However, the risk of biological invasion by exotic bumblebees cannot be overlooked before extensive use. Full article

Hollow-fiber water gap membrane distillation (HF-WGMD) modules are gaining attention for desalination applications due to their compact design and high surface-area-to-volume ratio. This study presents a comprehensive CFD model to analyze and compare the performance of two HF-WGMD module configurations: one with a conventional stagnant water gap (WG) and the other incorporating water gap flow circulation. The model was validated against experimental data, showing excellent agreement, and was then used to simulate flow patterns in the feed, water gap, and coolant domains. Results indicate that, at a feed temperature of 80 °C with a stagnant WG, employing a turbulent flow scheme in the feed side increases water flux by 20.7% compared to laminar flow, while increasing coolant flow rate has a minor impact. In contrast, introducing circulation within the water gap significantly enhances performance, boosting water flux by 30.1%. This effect becomes more pronounced with rising feed temperature: increasing from 50 °C to 80 °C leads to a flux increase from 6.74 to 27.89 kg/(m²h) under circulating WG conditions. However, in multistage systems, the energy efficiency trade-off becomes evident. Water gap circulation is more energy-efficient than the stagnant configuration only for systems with fewer than 20 stages. At higher stage counts, the stagnant WG setup proves more efficient. For example, at 80 °C and 50 stages, the stagnant configuration consumes just 793 kWh/m³, representing a 47.3% reduction in energy consumption compared to the circulating WG setup. These findings highlight the performance benefits and energy trade-offs of water gap circulation in HF-WGMD systems, providing valuable guidance for optimization and scalability of high-efficiency desalination module designs. Full article