Search Results (3,163)

A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a “morphology generation–performance evaluation–morphology optimization” framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a “morphology–performance” dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor–area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume–Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization. Full article

The coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive to climate variability, particularly extreme events that affect productivity. This study evaluated the impacts of extreme climatic events on the productivity of dwarf green coconut in northeastern Pará, analyzing rainy (PC—December to July) and less rainy (PMC—August to November) periods between 2015 and 2023. Meteorological and experimental data were used, including extreme climate variables such as maximum temperature (HT) and precipitation (HEP), defined by the 90th percentiles, and low precipitation (LP, 10th percentile). Predictive models, such as Multiple Linear Regression (MLR) and Random Forest (RF), were developed. RF showed better performance, with an RMSE equivalent to 20% of the average productivity, while that of MLR exceeded 50%. However, RF struggled with generalization in the test set, likely due to overfitting. The inclusion of lagged productivity (productivity t-1) highlighted its significant influence. During the PC, extreme high precipitation (HEP) events and excessive water surplus (HE) occurring after the fifth month of inflorescence development contributed to increased productivity, whereas during the PMC, low-precipitation (LP) events led to productivity reductions. Notably, under certain circumstances, elevated precipitation can mitigate the negative impacts of low water availability. These findings underscore the need for adaptive management strategies to mitigate climatic impacts and promote stability in dwarf green coconut production. Full article

The Cerrado domain, one of the richest on Earth, is among the most threatened in South America due to human activities, resulting in biodiversity loss, altered fire dynamics, water pollution, and other environmental impacts. Monitoring this domain is crucial for preserving its biodiversity and ecosystem services. This study aimed to apply machine learning techniques to classify the main vegetation formations of the Cerrado within the IBGE Ecological Reserve, a protected area in Brazil, using high-resolution PlanetScope imagery from 2021 to 2024. Three machine learning methods were evaluated: Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). A post-processing process was applied to avoid misclassification of forest in areas of savanna. After performance evaluation, the SVM method achieved the highest classification accuracy (overall accuracy of 97.51%, kappa coefficient of 0.9649) among the evaluated models. This study identified five main classes: grassland (GRA), savanna (SAV), bare soil (BS), samambaião (SAM, representing the superdominant species Pteridium esculentum), and forest (FOR). Over the three-year period (2021–2024), SAV and GRA formations were dominant in the reserve, reflecting the typical physiognomies of the Cerrado. This study successfully delineated areas occupied by the superdominant species P. esculentum, which was concentrated near gallery forests. The generated maps provide valuable insights into the vegetation dynamics within a protected area, aiding in monitoring efforts and suggesting potential new areas for protection in light of imminent anthropogenic threats. This study demonstrates the effectiveness of combining high-resolution satellite imagery with machine learning techniques for detailed vegetation mapping and monitoring in the Cerrado domain. Full article

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunodeficiencies, and cancers. The vaccinia virus Tiantan strain (VVTT) has been instrumental as an irreplaceable vaccine strain in the eradication of smallpox in China; however, it still presents significant adverse toxic effects. After the WHO recommended that routine smallpox vaccination be discontinued, the Chinese government stopped the national smallpox vaccination program in 1981. The outbreak of monkeypox in 2022 has focused people’s attention on the Orthopoxvirus. However, there are limited reports on the safety and toxic side effects of VVTT. In this study, we employed a combination of transcriptomic analysis and machine learning-based feature selection to identify key genes implicated in the VVTT infection process. We utilized four machine learning algorithms, including random forest (RF), minimum redundancy maximum relevance (MRMR), eXtreme Gradient Boosting (XGB), and least absolute shrinkage and selection operator cross-validation (LASSOCV), for feature selection. Among these, XGB was found to be the most effective and was used for further screening, resulting in an optimal model with an ROC curve of 0.98. Our analysis revealed the involvement of pathways such as spinocerebellar ataxia and the p53 signaling pathway. Additionally, we identified three critical targets during VVTT infection—ARC, JUNB, and EGR2—and further validated these targets using qPCR. Our research elucidates the mechanism by which VVTT infects cells, enhancing our understanding of the smallpox vaccine. This knowledge not only facilitates the development of new and more effective vaccines but also contributes to a deeper comprehension of viral pathogenesis. By advancing our understanding of the molecular mechanisms underlying VVTT infection, this study lays the foundation for the further development of VVTT. Such insights are crucial for strengthening global health security and ensuring a resilient response to future pandemics. Full article

The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to identify key geotechnical parameters influencing Vs predictions. Additionally, a Bayesian GLM is developed to explicitly account for uncertainties arising from geotechnical variability. The effectiveness and predictive performance of the proposed models were validated through comparison with real case scenarios. The results highlight the unique advantages of each model. The XGBoost model demonstrates good predictive performance, achieving high coefficient of determination (R2), index of agreement (IA), Kling–Gupta efficiency (KGE) values, and low error values while effectively explaining the impact of input parameters on Vs. In contrast, the Bayesian GLM provides probabilistic predictions with 95% credible intervals, capturing the uncertainty associated with the predictions. The integration of these two approaches creates a comprehensive framework that combines the strengths of high-accuracy ML predictions with the uncertainty quantification of Bayesian inference. This hybrid methodology offers a powerful and interpretable tool for Vs prediction, providing engineers with the confidence to make informed decisions. Full article

In this study, an Improved Zebra Optimization Algorithm (ZOA) is proposed based on the search mechanism of the Sparrow Optimization Algorithm (SSA), the perturbation mechanism of the Particle Swarm Algorithm (PSO), and the adaptive function. Then, Improved Zebra Optimization Algorithm (IZOA) was used to optimize the Deep Hybrid Kernel Extreme Learning Machine Model (DHKELM), and the IZOA-DHKELM was obtained. The model has been used to predict the color of heat-treated wood for different species, temperatures, times, media, and profile types. In this article, the original DHKELM and the ZOA-DHKELM were compared to verify the validity and accuracy of the model. The results indicated that the IZOA-DHKELM decreased the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) by 56.2%, 67.4%, and 34.2%, respectively, while enhancing the coefficient of determination, R2, to 0.9952 compared to the ZOA-DHKELM. This demonstrated that the model was significantly optimized, with improved generalization ability and prediction accuracy. It can better meet the actual engineering needs. Full article

Mineral grade estimation is a vital phase in mine planning and design, as well as in the mining project’s economic assessment. In mining, commonly accepted methods of ore grade estimation include geometrical approaches and geostatistical techniques such as kriging, which effectively capture the spatial grade variation within a deposit. The application of machine-learning (ML) techniques has been explored in the estimation of mineral resources, where complex correlations need to be captured. In this paper, the authors developed four machine-learning regression models, i.e., support vector regression (SVR), random forest regression (RFR), k-nearest neighbour (KNN) regression, and extreme gradient boost (XGBoost) regression, using a geological database to predict the grade in an Indian iron ore deposit. When compared with ordinary kriging (R² = 0.74; RMSE = 2.09), the RFR (R² = 0.74; RMSE = 2.06), XGBoost (R² = 0.73; RMSE = 2.12), and KNN (R² = 0.73; RMSE = 2.11) regression models produced similar results. The block model predictions generated using the RFR, XGBoost, and KNN models show comparable accuracy and spatial trends to those of ordinary kriging, whereas SVR was identified as less effective. When integrated with geological methods, these models demonstrate significant potential for enhancing and optimizing mine planning and design processes in similar iron ore deposits. Full article

This paper presents a comprehensive study of the mechanical properties of lime-based mortar in an acidic environment, employing both experimental analysis and machine learning to model techniques. Despite the extensive use of lime-based mortar in construction, particularly for the strengthening of structures as externally bonded materials, its behavior under acidic conditions remains poorly understood in the literature. This study aims to address this gap by investigating the mechanical performance of lime-based mortar under prolonged exposure to acidic environments, laying the groundwork for further research in this critical area. In the experimental phase, a commercial hydraulic lime-based mortar was subjected to varying environmental conditions, including acidic solution immersion with a pH of 3.0, distilled water immersion, and dry storage. Subsequently, the specimens were tested under flexure following exposure durations of 1000, 3000, and 5000 h. In the modeling phase, the extreme gradient boosting (XGBoost) algorithm was deployed to predict the mechanical properties of the lime-based mortar by 1000, 3000, and 5000 h of exposure. Using the experimental data, the machine learning models were trained to capture the complex relationships between the stress-displacement curve (as the output) and various environmental and mechanical properties, including density, corrosion, moisture, and exposure duration (as input features). The predictive models demonstrated remarkable accuracy and generalization (using a 4-fold cross-validation approach) capabilities (R² = 0.984 and RMSE = 0.116, for testing dataset), offering a reliable tool for estimating the mortar’s behavior over extended periods in an acidic environment. The comparative analysis demonstrated that mortar samples exposed to an acidic environment reached peak values at 3000 h of exposure, followed by a decrease in the mechanical properties with prolonged acidic exposure. In contrast, specimens exposed to distilled water and dry conditions exhibited an earlier onset of strength increase, indicating different material responses under varying environmental conditions. Full article

Identifying the tectonic setting of rocks is essential for gaining insights into the geological contexts in which these rocks were formed, aiding in tectonic plate reconstruction and enhancing our comprehensive understanding of the Earth’s history. The application of machine learning algorithms helps identify complex patterns and relationships between big data that may be overlooked by binary or ternary tectonomagmatic discrimination diagrams based on basalt compositions. In this study, three machine learning algorithms, i.e., Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were employed to classify the basalts from seven diverse settings, including intraplate basalts, island arc basalts, ocean island basalts, mid-ocean ridge basalts, back-arc basin basalts, oceanic flood basalts, and continental flood basalts. Specifically, for altered and fresh basalt samples, we utilized 22 immobile elements and 35 major and trace elements, respectively, to construct discrimination models. The results indicate that XGBoost demonstrates the best performance in discriminating basalts into seven tectonic settings, achieving accuracies of 85% and 89% for the altered and fresh basalt samples, respectively. A key innovation of our newly developed tectonic discrimination model is the establishment of tailored models for altered and fresh basalts. Moreover, by omitting isotopic features during model construction, the new models offer broader applicability in predicting a wider range of basalt samples in practical scenarios. The classification models were applied to investigate the Carboniferous to Permian evolution in the Western Tianshan Orogen (WTO), revealing that the subduction of Tianshan Ocean ceased at the end of Carboniferous and the WTO evolved into a post-collisional orogenesis during the Permian. Full article

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tuberculosis), remains a leading cause of death from infectious diseases globally. The treatment of active TB relies on first- and second-line drugs, however, the emergence of drug resistance poses a significant challenge to global TB control efforts. Recent advances in whole-genome sequencing combined with machine learning have shown promise in predicting drug resistance. This study aimed to evaluate the performance of four machine learning models in classifying resistance to ethambutol, isoniazid, and rifampicin in M. tuberculosis isolates. Methods: Four machine learning models—Extreme Gradient Boosting Classifier (XGBC), Logistic Gradient Boosting Classifier (LGBC), Gradient Boosting Classifier (GBC), and an Artificial Neural Network (ANN)—were trained using a Variant Call Format (VCF) dataset preprocessed by the CRyPTIC consortium. Three datasets were used: the original dataset, a principal component analysis (PCA)-reduced dataset, and a dataset prioritizing significant mutations identified by the XGBC model. The models were trained and tested across these datasets, and their performance was compared using sensitivity, specificity, Precision, F1-scores and Accuracy. Results: All models were applied to the PCA-reduced dataset, while the XGBC model was also evaluated using the mutation-prioritized dataset. The XGBC model trained on the original dataset outperformed the others, achieving sensitivity values of 0.97, 0.90, and 0.94; specificity values of 0.97, 0.99, and 0.96; and F1-scores of 0.93, 0.94, and 0.92 for ethambutol, isoniazid, and rifampicin, respectively. These results demonstrate the superior accuracy of the XGBC model in classifying drug resistance. Conclusions: The study highlights the effectiveness of using a binary representation of mutations to train the XGBC model for predicting resistance and susceptibility to key TB drugs. The XGBC model trained on the original dataset demonstrated the highest performance among the evaluated models, suggesting its potential for clinical application in combating drug-resistant tuberculosis. Further research is needed to validate and expand these findings for broader implementation in TB diagnostics. Full article

This paper presents a machine learning-based approach to grade engine health and generate a respective score ranging from 0 to 100 for tuned high-performance vehicles. It integrates the technical intricacies of automotive engineering with machine learning practices in a clear and sequential process. Data are collected from sensors monitoring revolutions per minute, boost, rail pressure, timing, and temperature. The data are processed for supervised learning and analyzed using visualizations such as a heatmap and t-SNE plots. Models are trained, innovatively tuned through hyperparameter optimization, and tested for their ability to grade new data logs. The results highlight K-Neighbors, Extra Trees, and Extreme Gradient Boosting as exceptional regressors for this task. The automated grading of engine health and performance enhances objectivity and efficiency in the tuning process and potentially serves as a basis for a digital twin. The developed methodology is discussed in the context of health evaluation for any sensor-based system, with practical applications extending across various domains and industries. Full article

Grassland ecosystems provide a range of services in semi-arid and arid regions. However, they have significantly declined due to overgrazing and desertification. In the current study, we employed Landsat time series data (TM, OLI, OLI-2) spanning from 1990 to 2024, combined with vegetation indices such as NDVI and SAVI, along with NDWI and digital elevation models (DEMs), to analyze land cover dynamics in the Ugii Lake watershed area, Mongolia. By integrating multisource remote sensing data into the advanced XGBoost (extreme gradient boosting) machine learning algorithm, we achieved high classification accuracy, with overall accuracies exceeding 94% and Kappa coefficients greater than 0.92. The results revealed a decline in montane grasslands (−6.2%) and an increase in other grassland types, suggesting ecosystem redistribution influenced by climatic and anthropogenic factors. Cropland exhibited resilience, recovering from a significant decline in the 1990s to moderate growth by 2024. Our findings highlight the stability of barren land and underscore pressures from ecological degradation and human activities. This study provides up-to-date statistical data to support decision-making in the conservation and sustainable management of grassland ecosystems in Mongolia under changing climatic conditions. Full article

Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal–organic frameworks (MOFs), with their unique structural properties, show significant potential for HD adsorption applications. Due to the extreme hazards of HD, most experimental studies focus on its simulants, but molecular simulation research on these simulants remains limited. Simulation analyses of simulants can uncover structure–performance relationships and enable experimental validation, optimizing methods, and improving material design and performance predictions. This study integrates molecular simulations, machine learning (ML), and molecular fingerprinting (MFs) to identify MOFs with high adsorption performance for the HD simulant diethyl sulfide (DES), followed by in-depth structural analysis and comparison. First, MOFs are categorized into Top, Middle, and Bottom materials based on their adsorption efficiency. Univariate analysis, machine learning, and molecular fingerprinting are then used to identify and compare the distinguishing features and fingerprints of each category. Univariate analysis helps identify the optimal structural ranges of Top and Bottom materials, providing a reference for initial material screening. Machine learning feature importance analysis, combined with SHAP methods, identifies the key features that most significantly influence model predictions across categories, offering valuable insights for future material design. Molecular fingerprint analysis reveals critical fingerprint combinations, showing that adsorption performance is optimized when features such as metal oxides, nitrogen-containing heterocycles, six-membered rings, and C=C double bonds co-exist. The integrated analysis using HTCS, ML, and MFs provides new perspectives for designing high-performance MOFs and demonstrates significant potential for developing materials for the adsorption of CWAs and their simulants. Full article

This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series. Full article