Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

Search Results (297)

Search Parameters:
Keywords = Gradient-Boosting Decision Tree (GBDT)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
22 pages, 8682 KiB  
Article
Predicting EGFRL858R/T790M/C797S Inhibitory Effect of Osimertinib Derivatives by Mixed Kernel SVM Enhanced with CLPSO
by Shaokang Li, Wenzhe Dong and Aili Qu
Pharmaceuticals 2025, 18(8), 1092; https://doi.org/10.3390/ph18081092 - 23 Jul 2025
Viewed by 156
Abstract
Background/Objectives: The resistance mutations EGFRL858R/T790M/C797S in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims [...] Read more.
Background/Objectives: The resistance mutations EGFRL858R/T790M/C797S in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFRL858R/T790M/C797S mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation (QLOO2), 5-fold cross validation (Q5fold2) and concordance correlation coefficient (CCC), QF12, and QF22. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: R2 and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; QLOO2, Q5fold2, CCC, QF12, and QF22 are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC50 values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFRL858R/T790M/C797S inhibitors. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Drug Design and Discovery)
Show Figures

Graphical abstract

21 pages, 8521 KiB  
Article
Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
by Jintong Ren, Lizhi Liu, You Wu, Lijian Ouyang and Zhenyu Yu
Forests 2025, 16(7), 1198; https://doi.org/10.3390/f16071198 - 20 Jul 2025
Viewed by 261
Abstract
Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in [...] Read more.
Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in the Liuchong River Basin, Bijie City, Guizhou Province, China. The proposed model incorporates multiscale residual blocks and channel attention mechanisms to improve spatial feature extraction and spectral dependency modeling. A dataset of 150 ground inventory plots was employed for supervised training and validation. Comparative experiments with Random Forest, Gradient Boosting Decision Trees (GBDT), and Vision Transformer (ViT) demonstrate that the enhanced ResNet achieves the best performance, with a root mean square error (RMSE) of 23.02 Mg/ha and a coefficient of determination (R2) of 0.773 on the test set. Spatial mapping results further reveal that the model effectively captures fine-scale carbon stock variations across mountainous forested landscapes. These findings underscore the potential of combining multispectral remote sensing and advanced neural architectures for scalable, high-resolution forest carbon estimation in complex terrain. Full article
(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)
Show Figures

Figure 1

18 pages, 6810 KiB  
Article
The Impact of the Built Environment on Innovation Output in High-Density Urban Centres at the Micro-Scale: A Case Study of the G60 S&T Innovation Valley, China
by Lie Wang and Lingyue Li
Buildings 2025, 15(14), 2528; https://doi.org/10.3390/buildings15142528 - 18 Jul 2025
Viewed by 165
Abstract
The micro-scale interplay between the built environment and innovation has attracted increasing scholarly attention. However, discussions on how such microdynamics operate and vary across high-density cities remain insufficient. This study focuses on nine high-density urban centres along the G60 S&T Innovation Valley and [...] Read more.
The micro-scale interplay between the built environment and innovation has attracted increasing scholarly attention. However, discussions on how such microdynamics operate and vary across high-density cities remain insufficient. This study focuses on nine high-density urban centres along the G60 S&T Innovation Valley and employs a fine-grained grid unit, viz. 1 km × 1 km, combined with the gradient boosting decision tree (GBDT) model to address these issues. Results show that urban construction density-related variables, including the building density, floor area ratio, and transportation network density, generally rank higher than the amenity density and proximity-related variables. The former contributes 50.90% of the total relative importance in predicting invention patent application density (IPAD), while the latter two contribute 13.64% and 35.46%, respectively. Threshold effect analysis identifies optimal levels for enhancing IPAD. Specifically, the optimal building density is approximately 20%, floor area ratio is 5, and transportation network density is 8 km/km2. Optimal distances to universities, city centres, and transportation hubs are around 1 km, 17 km, and 9 km, respectively. Furthermore, significant city-level heterogeneity was observed: most density-related variables consistently have an overall positive association with IPAD, with metropolitan cities (e.g., Hangzhou and Suzhou) exhibiting notably higher optimal values compared to medium and small cities (e.g., Xuancheng and Huzhou). In contrast, the threshold effects of proximity-related variables on IPAD are more complex and diverse. These findings offer empirical support for enhancing innovation in high-density urban environments. Full article
Show Figures

Figure 1

26 pages, 6730 KiB  
Article
Construction and Application of Carbon Emissions Estimation Model for China Based on Gradient Boosting Algorithm
by Dongjie Guan, Yitong Shi, Lilei Zhou, Xusen Zhu, Demei Zhao, Guochuan Peng and Xiujuan He
Remote Sens. 2025, 17(14), 2383; https://doi.org/10.3390/rs17142383 - 10 Jul 2025
Viewed by 302
Abstract
Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. [...] Read more.
Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. To overcome these limitations, this study develops and validates a high-resolution predictive model using advanced gradient boosting algorithms—Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—based on socioeconomic, industrial, and environmental data from 2732 Chinese counties during 2008–2017. Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R2 = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. Spatial–temporal analyses revealed that while national emissions are slowing, the eastern region is approaching stabilization, whereas emissions in central and western regions are projected to continue rising through 2027. Furthermore, SHapley Additive exPlanations (SHAP) were applied to interpret the marginal and interaction effects of key variables. The results indicate that GDP, energy intensity, and nighttime lights exert the greatest influence on model predictions, while ecological indicators such as NDVI exhibit negative associations. SHAP dependence plots further reveal nonlinear relationships and regional heterogeneity among factors. The key innovation of this study lies in constructing a scalable and interpretable county-level carbon emissions model that integrates gradient boosting with SHAP-based variable attribution, overcoming limitations in spatial resolution and model transparency. Full article
Show Figures

Figure 1

23 pages, 7709 KiB  
Article
Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin
by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li and Xiaoqiong Jiang
Sensors 2025, 25(14), 4314; https://doi.org/10.3390/s25144314 - 10 Jul 2025
Viewed by 182
Abstract
Land use change plays a pivotal role in understanding surface processes and environmental dynamics, exerting considerable influence on regional ecosystem management. Traditional monitoring approaches, which often rely on manual sampling and single spectral features, exhibit limitations in efficiency and accuracy. This study proposes [...] Read more.
Land use change plays a pivotal role in understanding surface processes and environmental dynamics, exerting considerable influence on regional ecosystem management. Traditional monitoring approaches, which often rely on manual sampling and single spectral features, exhibit limitations in efficiency and accuracy. This study proposes an innovative technical framework that integrates automated sample generation, multi-feature optimization, and classification model refinement to enhance the accuracy of land use classification and enable detailed spatiotemporal analysis in the Ebinur Lake Basin. By integrating Landsat data with multi-temporal European Space Agency (ESA) products, we acquired 14,000 pixels of 2021 land use samples, with multi-temporal spectral features enabling robust sample transfer to 12028 pixels in 2011 and 10,997 pixels in 2001. Multi-temporal composite data were reorganized and reconstructed to form annual and monthly feature spaces that integrate spectral bands, indices, terrain, and texture information. Feature selection based on the Gini coefficient and Out-Of-Bag Error (OOBE) reduced the original 48 features to 23. In addition, an object-oriented Gradient Boosting Decision Tree (GBDT) model was employed to perform accurate land use classification. A systematic evaluation confirmed the effectiveness of the proposed framework, achieving an overall accuracy of 93.17% and a Kappa coefficient of 92.03%, while significantly reducing noise in the classification maps. Based on land use classification results from three different periods, the spatial distribution and pattern changes of major land use types in the region over the past two decades were investigated through analyses of ellipses, centroid shifts, area changes, and transition matrices. This automated framework effectively enhances automation, offering technical support for accurate large-area land use classification. Full article
(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)
Show Figures

Figure 1

21 pages, 34246 KiB  
Article
A Multi-Epiphysiological Indicator Dog Emotion Classification System Integrating Skin and Muscle Potential Signals
by Wenqi Jia, Yanzhi Hu, Zimeng Wang, Kai Song and Boyan Huang
Animals 2025, 15(13), 1984; https://doi.org/10.3390/ani15131984 - 5 Jul 2025
Viewed by 291
Abstract
This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling [...] Read more.
This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling physiological signals from dogs exposed to four fundamental emotional states: happiness, sadness, fear, and anger. Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. Notably, this is the first study to integrate a fusion of two complementary electrophysiological indicators—skin and muscle potentials—into a multi-modal dataset for canine emotion recognition. Further interpretability analysis using Shapley Additive exPlanations (SHAP) revealed skin potential and voice pattern features as the most contributive to model performance. The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies. Full article
(This article belongs to the Special Issue Animal–Computer Interaction: New Horizons in Animal Welfare)
Show Figures

Figure 1

23 pages, 5897 KiB  
Article
Dynamic Strength Prediction of Brittle Engineering Materials via Stacked Multi-Model Ensemble Learning and Interpretability-Driven Feature Analysis
by Xin Cai, Yunmin Wang, Yihan Zhao, Liye Chen, Peiyu Wang, Zhongkang Wang and Jianguo Li
Materials 2025, 18(13), 3054; https://doi.org/10.3390/ma18133054 - 27 Jun 2025
Viewed by 587
Abstract
Accurate prediction of the dynamic compressive strength of brittle engineering materials is of significant theoretical and engineering importance for underground engineering design, safety assessment, and dynamic hazard prevention. To enhance prediction accuracy and model interpretability, this study proposes a novel framework integrating stacking [...] Read more.
Accurate prediction of the dynamic compressive strength of brittle engineering materials is of significant theoretical and engineering importance for underground engineering design, safety assessment, and dynamic hazard prevention. To enhance prediction accuracy and model interpretability, this study proposes a novel framework integrating stacking ensemble learning with SHapley Additive exPlanations (SHAP) for dynamic strength prediction. Leveraging multidimensional input variables, including static strength, strain rate, P-wave velocity, bulk density, and specimen geometry parameters, we constructed six machine learning regression models: K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), LightGBM, XGBoost, and Multilayer Perceptron Neural Network (MLPNN). Through comparative performance evaluation, optimal base models were selected for stacking ensemble training. Results demonstrate that the proposed stacking model outperforms individual models in prediction accuracy, stability, and generalization capability. Further SHAP-based interpretability analysis reveals that strain rate dominates the prediction outcomes, with its SHAP values exhibiting a characteristic nonlinear response trend. Additionally, structural and mechanical variables such as static strength, P-wave velocity, and bulk density demonstrate significant positive contributions to model outputs. This framework provides a robust tool for intelligent prediction and mechanistic interpretation of the dynamic strength of brittle materials. Full article
Show Figures

Figure 1

27 pages, 3666 KiB  
Article
A LightGBM-Based Power Grid Frequency Prediction Method with Dynamic Significance–Correlation Feature Weighting
by Jie Zhou, Xiangqian Tong, Shixian Bai and Jing Zhou
Energies 2025, 18(13), 3308; https://doi.org/10.3390/en18133308 - 24 Jun 2025
Viewed by 314
Abstract
Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance [...] Read more.
Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance efficiency and accuracy. In this paper, we propose a Dynamic Significance–Correlation Weighting (D-SCW) method, which generates dynamic weight coefficients that evolve over time. This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. The results show that the D-SCW-LightGBM framework reduces the root mean squared error (RMSE) by 5.2% to 10.4% and shortens the dynamic response delay by 52% compared with the benchmark method in high renewable penetration scenarios, confirming its effectiveness in both prediction accuracy and computational efficiency. Full article
Show Figures

Figure 1

28 pages, 6197 KiB  
Systematic Review
Risk Assessment of Microplastics in Humans: Distribution, Exposure, and Toxicological Effects
by Yifei Li, Wei Ling, Jian Yang and Yi Xing
Polymers 2025, 17(12), 1699; https://doi.org/10.3390/polym17121699 - 18 Jun 2025
Viewed by 1003
Abstract
Microplastics are widely present in the environment, and their potential risks to human health have attracted increasing attention. Research on microplastics has exhibited exponential growth since 2014, with a fast-growing focus on human health risks. Keyword co-occurrence networks indicate a research shift from [...] Read more.
Microplastics are widely present in the environment, and their potential risks to human health have attracted increasing attention. Research on microplastics has exhibited exponential growth since 2014, with a fast-growing focus on human health risks. Keyword co-occurrence networks indicate a research shift from environmental pollution toward human exposure and health effects. Additionally, Trend Factor analysis reveals emerging research topics such as reproductive toxicity, neurotoxicity, and impacts on gut microbiota. This meta-analysis included 125 studies comprising 2977 data samples. The results demonstrated that cytotoxicity in experimental systems was primarily concentrated in Grade I (non-toxic, 62.8%) and Grade II (mildly toxic, 27.6%). Notably, inhibitory effects on cells were significantly enhanced when microplastic concentrations exceeded 40 μg/mL or particle sizes were smaller than 0.02 μm. The Gradient Boosting Decision Tree (GBDT) model was applied to predict cell viability, achieving an R2 value of 0.737 for the test set and a classification accuracy of 81.5%. Furthermore, reproductive- and circulatory-system cells exhibited the highest sensitivity to microplastics, whereas connective-tissue cells had the lowest survival rates. The study also identified an overuse of polystyrene (PS) polymers and spherical particles in experimental designs, deviating from realistic exposure scenarios. Full article
(This article belongs to the Section Polymer Applications)
Show Figures

Graphical abstract

20 pages, 3916 KiB  
Article
Bridging the Gap: Limitations of Machine Learning in Real-World Prediction of Heavy Metal Accumulation in Rice in Hunan Province
by Qing-Qian Peng, Xia Zhou, Hang Zhou, Ye Liao, Zi-Yu Han, Lu Hu, Peng Zeng, Jiao-Feng Gu and Rong Zhang
Agronomy 2025, 15(6), 1478; https://doi.org/10.3390/agronomy15061478 - 18 Jun 2025
Viewed by 501
Abstract
Cadmium (Cd) pollution poses a severe threat to rice safety and human health, while traditional linear models exhibit significant limitations in predicting rice Cd accumulation due to environmental complexities. This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision [...] Read more.
Cadmium (Cd) pollution poses a severe threat to rice safety and human health, while traditional linear models exhibit significant limitations in predicting rice Cd accumulation due to environmental complexities. This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. The results showed that the RF model performed best on the test set (R2 = 0.62), with the dominant features being soil’s available Cd (contributing 9.74%) and precipitation during the rice-filling stage (joint contribution of 15.96%). However, the model’s predictive performance experienced a sharp decline on the independent 2023 validation set comprising 393 samples from Yizhang County and Lengshuitan District, with R2 values ranging from −0.12 to −0.31. This highlighted the fundamental limitations of static data-driven paradigms. Agronomic management measures, simplified by heterogeneous data and binary encoding, failed to effectively represent the actual intervention intensity. The study demonstrated that while machine learning models captured nonlinear relationships in laboratory environments, they struggled to adapt to the dynamic interactions and spatiotemporal heterogeneity of farmland systems. Future efforts should focus on developing hybrid models guided by mechanistic insights, integrating dynamic environmental processes and real-time data, and promoting localized “one model per region” strategies to enhance predictive robustness. This study provides methodological insights for the technological transformation of agricultural artificial intelligence, emphasizing that the deep integration of data-driven approaches and mechanistic understanding is crucial for overcoming the “last mile” challenge. Full article
Show Figures

Graphical abstract

20 pages, 4858 KiB  
Article
Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis
by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu and Hongtao Jiang
Remote Sens. 2025, 17(12), 2055; https://doi.org/10.3390/rs17122055 - 14 Jun 2025
Viewed by 415
Abstract
Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, [...] Read more.
Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, respectively, implementing a multi-gradient fertilization design with 39 plots and 810 sampling grids. Multispectral imagery was acquired by unmanned aerial vehicles (UAVs) during five critical growth stages: mid-tillering (T1), late-tillering (T2), mid-elongation (T3), late-elongation (T4), and maturation (T5). Following rigorous image preprocessing (including stitching, geometric correction, and radiometric correction), 16 VIs were extracted. To identify yield-sensitive vegetation indices (VIs), a spectral feature selection criterion combining gray relational analysis and correlation analysis (GRD-r) was proposed. Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. Results demonstrated that multi-stage models consistently outperformed their single-stage counterparts. Among the single-stage models, the RF model using T3-stage features achieved the highest accuracy (R2 = 0.78, RMSEV = 7.47 t/hm2). The best performance among multi-stage models was obtained using a GBDT model constructed from a combination of DVI (T1), NDVI (T2), TDVI (T3), NDVI (T4), and SRPI (T5), yielding R2 = 0.83 and RMSEV = 6.63 t/hm2. This study highlights the advantages of integrating multi-temporal spectral features and advanced machine learning techniques for improving sugarcane yield prediction, providing a theoretical foundation and practical guidance for precision agriculture and harvest logistics. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
Show Figures

Figure 1

27 pages, 9628 KiB  
Article
Exploring the Nonlinear Impacts of Built Environment on Urban Vitality from a Spatiotemporal Perspective at the Block Scale in Chongqing
by Jiayu Yang and Enxu Wang
ISPRS Int. J. Geo-Inf. 2025, 14(6), 225; https://doi.org/10.3390/ijgi14060225 - 7 Jun 2025
Viewed by 616
Abstract
Examining the relationship between built environment (BE) and urban vitality (UV) is beneficial for promoting urban planning, as it deepens the understanding of how spatial design shapes urban life and activity patterns. However, the nonlinear effects of BE on UV from a spatiotemporal [...] Read more.
Examining the relationship between built environment (BE) and urban vitality (UV) is beneficial for promoting urban planning, as it deepens the understanding of how spatial design shapes urban life and activity patterns. However, the nonlinear effects of BE on UV from a spatiotemporal perspective have not been fully explored. In this study, the central urban area of Chongqing at the block scale is selected as a research case. The Gradient Boosting Decision Tree with SHapley Additive exPlanations (GBDT-SHAP) model is used to examine the nonlinear impacts of BE on UV. The results show the following: (1) The BE has a stronger overall impact on UV during holidays. Road intersection density (RID) has the greatest impact on UV on weekdays and holidays, building density (BD) has the greatest impact on weekend mornings, cultural and leisure accessibility (CLA) has the greatest impact on weekend afternoons, and commercial accessibility (CA) has the most significant impact on weekend evenings; (2) the impacts of the BE on UV exhibit significant nonlinear characteristics, with BD and park and square accessibility (PSA) showing a first increasing and then inhibiting effect on UV; lower CA, CLA, and MSA have inhibitory effects on UV, with higher normalized difference vegetation index (NDVI) values similarly demonstrating such effects; building height (BH), bus stop density (BSD), road network density (RD), and RID have enhancing effects on UV; functional mix degree (FMD) and water proximity index (WPI) show different trends in different time periods; (3) there are significant interactive effects among BE such as BD and BH, CA; RD and WPI, MSA; FMD and BH, PSA; PSA and CLA. A comprehensive understanding of these interactive relationships is crucial for optimizing the BE to enhance UV. This study provides a theoretical basis for urban planners to develop more effective, time-sensitive strategies. Future research should explore these nonlinear and interactive effects across different cities and scales to further generalize the findings. Full article
Show Figures

Figure 1

23 pages, 2180 KiB  
Article
Leveraging Advanced Mathematical Methods in Artificial Intelligence to Explore Heterogeneity and Asymmetry in Cross-Border Travel Satisfaction
by Yan Xu, Huajie Yang, Zibin Ye, Xiaobo Ma, Lei Tong and Xinyi Yu
Mathematics 2025, 13(11), 1896; https://doi.org/10.3390/math13111896 - 5 Jun 2025
Viewed by 401
Abstract
The cross-border port serves as a crucial cross-border travel connecting mainland China with Hong Kong and Macau, directly impacting the overall satisfaction of cross-border travel. While previous studies on neighborhoods, communities, and other areas have thoroughly examined the heterogeneity and asymmetry in satisfaction, [...] Read more.
The cross-border port serves as a crucial cross-border travel connecting mainland China with Hong Kong and Macau, directly impacting the overall satisfaction of cross-border travel. While previous studies on neighborhoods, communities, and other areas have thoroughly examined the heterogeneity and asymmetry in satisfaction, research on the satisfaction of cross-border travel at ports remains notably limited. This paper explores the heterogeneity and asymmetry of cross-border travel satisfaction using gradient boosted decision trees (GBDT) and k-means cluster analysis under the framework of three-factor theory, aiming to demonstrate the latest scientific research results on the fundamental theories and applications of artificial intelligence. The results show prevalent asymmetric relationships between factors and cross-border travel satisfaction, with the factor structure exhibiting heterogeneity across different groups. High-income individuals were more likely to prioritize the reliability of cross-border travel, whereas low-income individuals tended to emphasize the convenience of travel. Finally, this paper proposes improvement priorities for different types of passengers, reflecting the practical application of advanced mathematical methods in artificial intelligence to drive intelligent decision-making. Full article
Show Figures

Figure 1

21 pages, 3453 KiB  
Article
Explaining Urban Vitality Through Interpretable Machine Learning: A Big Data Approach Using Street View Images and Environmental Factors
by Dong Li, Houzeng Han, Jian Wang and Xingxing Xiao
Sustainability 2025, 17(11), 4926; https://doi.org/10.3390/su17114926 - 27 May 2025
Viewed by 679
Abstract
Urban vitality (UV) is a critical indicator for measuring the level of sustainable urban development, closely associated with environmental factors such as population density, economic activity, and spatial utilization efficiency. However, traditional methods face significant limitations in capturing the heterogeneity and nonlinear relationships [...] Read more.
Urban vitality (UV) is a critical indicator for measuring the level of sustainable urban development, closely associated with environmental factors such as population density, economic activity, and spatial utilization efficiency. However, traditional methods face significant limitations in capturing the heterogeneity and nonlinear relationships between urban vitality and its influencing factors. This study suggests an interpretable machine learning framework to address the aforementioned issues. It combines a gradient boosting decision tree (GBDT) model with the SHapley Additive exPlanation (SHAP) framework to examine the urban vitality distribution characteristics and factors that influence them in Beijing’s fifth ring road. The main findings include the following: Urban vitality within Beijing’s fifth ring road exhibits significant spatial clustering and positive correlations, with clear spatial heterogeneity. The plot ratio (PR) exerts a notable positive influence on urban vitality, while green space accessibility (DG) demonstrates the strongest negative impact. The building density (BD), in contrast, shows a strong negative correlation with urban vitality. Variables such as the NDVI, average housing price (AHP), and road network density (RND) contribute significantly to urban vitality, reflecting the combined effects of vegetation coverage, economic conditions, and transportation layout. The findings provide a quantitative analytical tool for urban planning, facilitating resource optimization, improving urban vitality, and supporting scientific and rational decision-making. Full article
(This article belongs to the Special Issue Socially Sustainable Urban and Architectural Design)
Show Figures

Figure 1

21 pages, 1720 KiB  
Article
An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network
by Zhiling Liu, Ye Chen, Xinrui Dong and Jing Liu
Brain Sci. 2025, 15(6), 569; https://doi.org/10.3390/brainsci15060569 - 25 May 2025
Viewed by 578
Abstract
(1) Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Functional MRI (fMRI) has been widely applied to investigate brain functional abnormalities associated with ASD, yet challenges remain due to complex data characteristics and limited [...] Read more.
(1) Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Functional MRI (fMRI) has been widely applied to investigate brain functional abnormalities associated with ASD, yet challenges remain due to complex data characteristics and limited spatiotemporal information capture. This study aims to improve the ability to capture spatiotemporal dynamics of brain activity by proposing an advanced framework. (2) Methods: This study proposes an ASD recognition method that combines 3D Convolutional Neural Networks (3D-CNNs) and segmented temporal decision networks. The method first uses the 3D-CNN to automatically extract high-dimensional spatial features directly from the raw 4D fMRI data. It then captures temporal dynamic properties through a designed segmented Long Short-Term Memory (LSTM) network. The concatenated spatiotemporal features are classified using Gradient Boosting Decision Trees (GBDTs), and finally, a voting mechanism is applied to determine whether the subject belongs to the ASD group based on the prediction results. This approach not only enhances the efficiency of spatiotemporal feature extraction but also improves the model’s ability to learn complex brain activity patterns. (3) Results: The proposed method was evaluated on the ABIDE dataset, which includes 1035 subjects from 17 different brain imaging centers. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches, achieving an average accuracy of 0.85. (4) Conclusions: Our method provides a new solution for ASD classification by leveraging the spatiotemporal information of 4D fMRI data, achieving a significant improvement in classification performance. These results not only offer a new computational tool for ASD diagnosis but also provide important insights into understanding its neurobiological mechanisms. Full article
Show Figures

Figure 1

Back to TopTop