Search Results (682)

Drought stress significantly limits the function and stability of desert ecosystems. This research examines the distribution characteristics of soil moisture across different microtopographic types in the semi-fixed dunes located at the southeastern edge of the Tengger Desert. We constructed a path model to examine the direct and indirect impacts of topography, shrub vegetation, and herbaceous vegetation. The data encompassed soil moisture, topography, and vegetation variables, which were collected from field experiments to ensure their accuracy and relevance. Furthermore, SHAP models based on machine learning algorithms were utilized to elucidate the specific mechanisms through which key factors influence soil moisture. The results of the descriptive statistics indicate the highest surface soil moisture content, recorded at 1.21%, was observed at the bottom of the dunes, while the leeward slopes demonstrated elevated moisture levels in the middle and deep soil layers, with measurements of 2.25% and 2.43%, respectively. Soil moisture at different depths initially decreases and then increases with greater herbaceous cover and slope direction, while surface soil moisture follows a similar trend in terms of height difference, with 3 m serving as the boundary for trend changes. Middle and deep soil moistures initially increase and then decrease with greater biomass and shrub coverage, with 30 g and 40% serving as the boundary for trend changes respectively. This study elucidates the spatial distribution patterns and influencing factors of soil moisture in semi-fixed dunes, offering valuable references for the establishment of sand-stabilizing vegetation in desert regions. Full article

The Karst region is a critical ecological barrier and functional zone in China. Understanding the spatiotemporal evolution of its ecosystem services and its relationship with human activities and climate change is of importance for achieving regional ecological protection and high-quality development. In this study, we used the InVEST model and CASA model to evaluate the spatiotemporal evolution pattern of ecosystem services in the study area from 2000 to 2020. The XGBoost–SHAP model was used to reveal the key indicators and thresholds of changes in major ecosystem services in the study area due to climate change and human activities. The results showed significant land use changes in the study area from 2000 to 2020, particularly the conversion of cropland to construction land, which was more intense in economically developed areas. The areas of forest and grassland increased initially but later decreased due to the impact of human activities and natural factors. Habitat quality (HQ) showed an overall declining trend, while soil retention (SR) and water yield (WY) services exhibited significant interannual variations due to climate change. The changes in rainfall had a particularly notable impact on these services; in years with excessive rainfall, soil erosion intensified, leading to a decline in SR services, whereas in years with moderate rainfall, SR and WY services improved. Carbon fixation (CF) services were enhanced with the expansion of forest areas. The XGBoost–SHAP model further revealed that the effects of rainfall and sunshine duration on ecosystem services were nonlinear, while population density and the proportion of construction land had a significant negative impact on habitat quality and soil retention. The expansion of construction land had the most significant negative impact on habitat quality, whereas the increase in forest land significantly improved carbon fixation and the soil retention capacity. By revealing the mechanisms of the impact of climate change and human activities on ecosystem services, we aimed to provide support for the promotion of ecological conservation and sustainable development strategies in the study area, as well as to provide an important reference for areas with geographic similarities to the study area. Full article

With the rapid advancement of the Internet of Things, network security has garnered increasing attention from researchers. Applying deep learning (DL) has significantly enhanced the performance of Network Intrusion Detection Systems (NIDSs). However, due to its complexity and “black box” problem, deploying DL-based NIDS models in practical scenarios poses several challenges, including model interpretability and being lightweight. Feature selection (FS) in DL models plays a crucial role in minimizing model parameters and decreasing computational overheads while enhancing NIDS performance. Hence, selecting effective features remains a pivotal concern for NIDSs. In light of this, this paper proposes an interpretable feature selection method for encrypted traffic intrusion detection based on SHAP and causality principles. This approach utilizes the results of model interpretation for feature selection to reduce feature count while ensuring model reliability. We evaluate and validate our proposed method on two public network traffic datasets, CICIDS2017 and NSL-KDD, employing both a CNN and a random forest (RF). Experimental results demonstrate superior performance achieved by our proposed method. Full article

This study is focused on the punching strength of fiber-reinforced polymer (FRP) concrete slabs. The mechanical properties of reinforced concrete slabs are often constrained by their punching shear strength at the column connection regions. Researchers have explored the use of fiber-reinforced polymer reinforcement as an alternative to traditional steel reinforcement to address this limitation. However, current codes poorly calculate the punching shear strength of FRP-reinforced concrete slabs. The aim of this study was to create a robust model that can accurately predict its punching shear strength, thus improving the analysis and design of composite structures with FRP-reinforced concrete slabs. In this study, 189 sets of experimental data were collected, and six machine learning models, including linear regression, support vector machine, BP neural network, decision tree, random forest, and eXtreme Gradient Boosting, were constructed and evaluated based on goodness of fit, standard deviation, and root-mean-square error in order to select the most suitable model for this study. The optimal model obtained was compared with the models proposed by codes and the researchers. Finally, a model explainability study was conducted using SHapley Additive exPlanations (SHAP). The results showed that random forests performed best among all machine learning models and outperformed existing models suggested by codes and researchers. The effective depth of the FRP-reinforced concrete slabs was the most important and proportional to the punching shear strength. This study not only provides guidance on the design of FRP-reinforced concrete slabs but also informs future engineering practice. Full article

This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from the Empatica E4 smart watch, the Dexcom Continuous Glucose Monitor (CGM) measuring IG, and a food log was utilized. The raw data were processed into features, which were then used to train different ML models. This study evaluates the performance of decision tree (DT), support vector machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), lasso cross-validation (LassoCV), Ridge, Elastic Net, and XGBoost models. For classification, IG labels were categorized into high, standard, and low, and the performance of the ML models was assessed using accuracy (40–78%), precision (41–78%), recall (39–77%), F1-score (0.31–0.77), and receiver operating characteristic (ROC) curves. Regression models predicting IG values were evaluated based on R-squared values (−7.84–0.84), mean absolute error (5.54–60.84 mg/dL), root mean square error (9.04–68.07 mg/dL), and visual methods like residual and QQ plots. To assess whether the differences between models were statistically significant, the Friedman test was carried out and was interpreted using the Nemenyi post hoc test. Tree-based models, particularly RF and DT, demonstrated superior accuracy for classification tasks in comparison to other models. For regression, the RF model achieved the lowest RMSE of 9.04 mg/dL with an R-squared value of 0.84, while the GNB model performed the worst, with an RMSE of 68.07 mg/dL. A SHAP analysis identified time from midnight as the most significant predictor. Partial dependence plots revealed complex feature interactions in the RF model, contrasting with the simpler interactions captured by LDA. Full article

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model’s performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings. Full article

Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision. Full article

Fiber-reinforced polymers (FRPs) are increasingly being used as a composite material in concrete slabs due to their high strength-to-weight ratio and resistance to corrosion. However, FRP-reinforced concrete slabs, similar to traditional systems, are susceptible to punching shear failure, a critical design concern. Existing empirical models and design provisions for predicting the punching shear strength of FRP-reinforced concrete slabs often exhibit significant bias and dispersion. These errors highlight the need for more reliable predictive models. This study aims to develop gradient-boosted regression tree (GBRT) models to accurately predict the shear strength of FRP-reinforced concrete panels and to address the limitations of existing empirical models. A comprehensive database of 238 sets of experimental results for FRP-reinforced concrete slabs has been compiled from the literature. Different machine learning algorithms were considered, and the performance of GBRT models was evaluated against these algorithms. The dataset was divided into training and testing sets to verify the accuracy of the model. The results indicated that the GBRT model achieved the highest prediction accuracy, with root mean square error (RMSE) of 64.85, mean absolute error (MAE) of 42.89, and coefficient of determination (R²) of 0.955. Comparative analysis with existing experimental models showed that the GBRT model outperformed these traditional approaches. The SHapley Additive exPlanation (SHAP) method was used to interpret the GBRT model, providing insight into the contribution of each input variable to the prediction of punching shear strength. The analysis emphasized the importance of variables such as slab thickness, FRP reinforcement ratio, and critical section perimeter. This study demonstrates the effectiveness of the GBRT model in predicting the punching shear strength of FRP-reinforced concrete slabs with high accuracy. SHAP analysis elucidates key factors that influence model predictions and provides valuable insights for future research and design improvements. Full article

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil–water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclination angles, water cut rates, and daily flow rates were the most significant features contributing to the predictions. This study confirms the efficacy and superiority of the Bayesian-optimised XGBoost (BO-XGBoost) algorithm in predicting oil–water two-phase flow regimes, offering a robust and effective methodology for investigating complex subsurface fluid dynamics. The research outcomes are crucial in improving the accuracy of oil–water two-phase flow predictions and introducing innovative technical approaches within the domain of petroleum engineering. This work lays a foundational stone for the advancement and application of multiphase flow studies. Full article

This study investigates the complex relationships between process parameters and material properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We examine the effects of key parameters, including biochar content (BC), layer thickness (LT), raster angle (RA), infill pattern (IP), and infill density (ID), on the tensile, flexural, and impact strengths of FDM-printed pure PLA and biochar-reinforced PLA composites. Mechanical testing was used to measure the ultimate tensile strength (UTS), flexural strength (FS), and impact strength (IS) of the 3D-printed samples. The extreme gradient boosting (XGB) algorithm was used to build a predictive model based on the data collected from mechanical testing. Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Partial Dependence Plot (PDP) techniques were implemented to understand the effects of the interactions of key parameters on mechanical properties such as UTS, FS, and IS. Prediction by XGB was accurate for UTS, FS, and IS, with R-squared values of 0.96, 0.95, and 0.85, respectively. The explanation showed that infill density has the most significant influence on UTS and FS, with SHAP values of +2.75 and +5.8, respectively. BC has the most significant influence on IS, with a SHAP value of +2.69. PDP reveals that using 0.3 mm LT and 30° RA enhances mechanical properties. This study contributes to the field of the application of artificial intelligence in additive manufacturing. A novel approach is presented in which machine learning and XAI techniques such as SHAP, LIME, and PDP are combined and used not only for optimization but also to provide valuable insights about the interaction of the process parameters with mechanical properties. Full article

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones. Full article

COVID-19 vaccination status has become a significant factor influencing carbon emissions in recent years. This paper explores the relationship between vaccination programs and CO₂ emissions to provide scientific support for future emergency management. The study utilizes daily carbon emissions data and daily vaccination program data from six sectors within the European Union. It compares the accuracy of various machine learning models by incorporating 11 economic control variables. Additionally, it quantitatively decomposes the contribution of each variable to carbon emissions during the pandemic using SHAP values. The findings indicate that the LightGBM model predicts carbon emissions much more accurately than other models. Furthermore, COVID-19-related variables, such as daily vaccination volumes and cumulative vaccination totals, are identified as significant factors affecting carbon emissions. Full article

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability. Full article

Investigating urban vitality and comprehending the influence mechanisms of the built environment is essential for achieving sustainable urban growth and improving the quality of life for residents. Current research has rarely addressed the nonlinear relationships and synergistic effects between urban vitality and the built environment at the neighborhood scale. This oversight may overlook the influence of key neighborhoods and overestimate or underestimate the influence of different factors on urban vitality. Using Guangzhou’s central urban area as a case study, this research develops a comprehensive urban vitality assessment system that includes economic, social, cultural, and ecological dimensions, utilizing multi-source data such as POI, Dazhong Dianping, Baidu heatmap, and NDVI. Additionally, the XGBoost-SHAP model is applied to uncover the nonlinear impacts of different built environment factors on neighborhood vitality. The findings reveal that: (1) urban vitality diminishes progressively from the center to the periphery; (2) proximity to Zhujiang New Town is the most critical factor for neighborhood vitality (with a contribution of 0.039), while functional diversity and public facility accessibility are also significant (with contributions ranging from 0.033 to 0.009); (3) built environment factors exert nonlinear influences on neighborhood vitality, notably with a threshold effect for subway station accessibility (feature value of 0.1); (4) there are notable synergistic effects among different built environment dimensions. For example, neighborhoods close to Zhujiang New Town (feature value below 0.12) with high POI density (feature value above 0.04) experience significant positive synergistic effects. These findings can inform targeted policy recommendations for precise urban planning. Full article

Measuring human perception of environmental safety and quantifying the street view elements that affect human perception of environmental safety are of great significance for improving the urban environment and residents’ safety perception. However, domestic large-scale quantitative research on the safety perception of Chinese local cities needs to be deepened. Therefore, this paper chooses Chaoyang District in Beijing as the research area. Firstly, the network safety perception distribution of Chaoyang District is calculated and presented through the CNN model trained based on the perception dataset constructed by Chinese local cities. Then, the street view elements are extracted from the street view images using image semantic segmentation and target detection technology. Finally, the street view elements that affect the road safety perception are identified and analyzed based on LightGBM and SHAP interpretation framework. The results show the following: (1) the overall safety perception level of Chaoyang District in Beijing is high; (2) the number of motor vehicles and the proportion of the area of roads, skies, and sidewalks are the four factors that have the greatest impact on environmental safety perception; (3) there is an interaction between different street view elements on safety perception, and the proportion and number of street view elements have interaction on safety perception; (4) in the sections with the lowest, moderate, and highest levels of safety perception, the influence of street view elements on safety perception is inconsistent. Finally, this paper summarizes the results and points out the shortcomings of the research. Full article