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Unveiling the nonlinear relationships and co-mitigation effects of green and blue space landscapes on PM2.5 exposure through explainable machine learning

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SUSTAINABLE CITIES AND SOCIETY 2025年 122卷

作者： Cao, Wei Wang, Liyan Li, Rui Zhou, Wen Zhang, Deshun Yangzhou Univ Coll Hort & Landscape Architecture Yangzhou 225009 Jiangsu Provinc Peoples R China Tongji Univ Coll Architecture & Urban Planning Dept Landscape Architecture Shanghai 200092 Peoples R China

Green-blue spaces are nature-based solutions to mitigate particulate matter pollution. However, the individual and co-mitigation effects of green-blue space landscapes on PM2.5 exposure risk remain poorly understood. This study employed an explainable machine learning framework to investigate the nonlinear relationships, interaction effects, and heterogeneity of green-blue space landscape patterns on population-weighted PM2.5 exposure (PWP) in the Yangtze River Delta, China. Our findings highlight that (1) Greenspace coverage (G_PLAND), mean greenspace patch size (G_AREA_MN), blue space patch contiguity (W_CONTIG_MN), and mean distance between blue space patches (W_ENN_MN) are the four most influential landscape indicators. (2) G_PLAND and G_AREA_MN negatively influence PWP with thresholds of 40 % and 50 ha, respectively. W_CONTIG_MN (> 0.26) and W_ENN_MN (< 400 m) positively impact PWP. (3) Effects of green-blue space landscapes on PWP vary with different exposure levels: high (blue space is more important), medium (green and blue space are equally important), and low (green-blue spaces are not important). (4) Interactions of green and blue spaces can reinforce PWP mitigation under certain conditions (G_PLAND > 40 %, G_AREA_MN < 12 ha, W_ENN_MN and W_CONTIG_MN with thresholds of 200 m and 0.31, respectively). The findings can facilitate comprehensive planning and optimization of regional green-blue spaces to mitigate PWP.

关键词： PM2.5 exposure Green and blue spaces Landscape pattern explainable machine learning Interaction effects

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Application of explainable machine learning in the production of pullulan by Aureobasidium pullulans CGMCCNO.7055

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2025年第Pt 2期308卷 142374页

作者： Chen, Shiwei Li, Wenmin Zhao, Xiaowen Li, Miaoxin Zhao, Tingbin Zheng, Guobao Cao, Weifeng Qiao, Changsheng Tianjin Univ Sci & Technol State Key Lab Biobased Fiber Mat Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Key Lab Ind Fermentat Microbiol Minist Educ Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Tianjin Engn Res Ctr Microbial Metab & Fermentat P Sch Biotechnol Tianjin 300457 Peoples R China Tianjin Huizhi Biotrans Bioengn Co Ltd Tianjin 300457 Peoples R China Ningxia Acad Agr & Forestry Sci Inst Forestry Sci Agr Biotechnol Res Ctr Yinchuan 750002 Peoples R China

The application of machine learning in pullulan biofermentation has demonstrated significant potential. explainable machine learning enhances model transparency and interpretability by revealing the relationships between variables. In this study, we compared the predictive performance of six machine learning models. The Categorical Boosting (CatBoost) model demonstrated the best fit for biomass and pullulan molecular weight, while eXtreme Gradient Boosting (XGBoost) excelled in predicting pullulan production. Additionally, feature importance and SHapley Additive exPlanations (SHAP) analyses visualized the complex relationships between medium conditions and objectives. Yeast extract emerged as the most influential factor for all three targets. Meanwhile, NaCl and initial pH showed potential in regulating pullulan production and molecular weight, respectively. Finally, optimal medium conditions for maximizing biomass, pullulan production, and molecular weight were determined using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm, achieving a maximum integrated optimization rate of 275.08 % (calculated as the average of improvements across the three objectives). This study effectively expands the application of the NSGA-III algorithm in multi-objective optimization for pullulan production. These findings contribute to advancing the application of explainable machine learning and advanced intelligent algorithms in the field of pullulan production.

关键词： Pullulan explainable machine learning SHAP NSGA-III

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Modeling the fasting blood glucose response to basal insulin adjustment in type 2 diabetes: An explainable machine learning approach on real-world data

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INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2025年 195卷 105758-105758页

作者： Thomsen, Camilla Heisel Nyholm Kronborg, Thomas Hangaard, Stine Vestergaard, Peter Hejlesen, Ole Jensen, Morten Hasselstrom Aalborg Univ Dept Hlth Sci & Technol Aalborg Denmark Steno Diabet Ctr North Denmark Aalborg Denmark Aalborg Univ Hosp Dept Endocrinol Aalborg Denmark Novo Nordisk A S Data Sci Soborg Denmark

Introduction: Optimal basal insulin titration for people with type 2 diabetes is vital to effectively reducing the risk of complications. However, a sizeable proportion of people (30-50 %) remain in suboptimal glycemic control six months post-initiation of basal insulin. This indicates a clear need for novel titration methods that account for individual patient variability in real-world settings. Objective: This study aims to investigate the use of real-world data and explainable machine learning in modeling fasting glucose responses to basal insulin adjustments, focusing on identifying factors influencing fasting glucose variability. Methods: A three-step explanatory approach was used to develop models using multiple linear regression, forward feature selection, and three-fold cross-validation. The models were built progressively, starting with a baseline model incorporating fasting blood glucose and insulin dose adjustments, followed by iterative models that in turn included biometric data, social factors, and biochemistry data, and lastly, a comprehensive model without constraints on the feature pool. Results: The baseline model yielded an average root mean squared error (RMSE) of 1.52 [95% CI: 1.33-1.71]. The iterative models resulted in an average RMSE of 1.49 [95% CI: 1.35-1.62] (biometric data), 1.47 [95% CI: 1.36-1.58] (social factors), and 1.52 [95% CI: 1.34-1.70] (biochemistry data). The comprehensive model yielded an average RMSE of 1.44 [95% CI: 1.41-1.48]. Conclusion: Developing explainable machine learning models using real-world data is possible for basal insulin titration. However, model performance is influenced by data's ability to capture everyday behavior, underscoring the need for incorporating more detailed behavioral and social data to optimize future titration models.

关键词： Type 2 diabetes Basal insulin titration explainable machine learning Fasting blood glucose response Basal insulin dose adjustment

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Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, explainable machine learning and Experimental Validation

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JOURNAL OF INFLAMMATION RESEARCH 2025年 18卷 637-652页

作者： Dong, Qiu Wu, Jiayang Zhang, Huaguo Chen, Xinhui Xu, Xi Chen, Jifeng Shi, Changzheng Luo, Liangping Zhang, Dong Jinan Univ Affiliated Hosp 1 Dept Bone & Joint Surg Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Med Imaging Ctr Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Guangzhou Key Lab Mol & Funct Imaging Clin Transla Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 1 Dept Ultrasonog Guangzhou Guangdong Peoples R China Jinan Univ Affiliated Hosp 5 Med Imaging Ctr Heyuan Guangdong Peoples R China

Purpose: Immunometabolism is pivotal in rheumatoid arthritis (RA) pathogenesis, yet the intricacies of its pathological regulatory mechanisms remain poorly understood. This study explores the complex immunometabolic landscape of RA to identify potential therapeutic targets. Patients and Methods: We integrated genome-wide association study (GWAS) data involving 1,400 plasma metabolites, 731 immune cell traits, and RA outcomes from over 58,000 participants. Mendelian randomization (MR) and mediation analyses were applied to evaluate causal relationships among plasma metabolites, immune cells, and RA. We further analyzed single-cell and bulk transcriptomes to investigate differential gene expression, immune cell interactions, and relevant biological processes. machine learning models identified hub genes, which were validated via quantitative real-time PCR (qRT-PCR). Then, potential small- molecule drugs were screened using the Connectivity Map (CMAP) and molecular docking. Finally, a phenome-wide association study (PheWAS) was conducted to evaluate potential side effects of drugs targeting the hub genes. Results: Causalities were found between six plasma metabolites, five immune cells and RA in genetically determined levels. Notably, DC mediated 18% of the protective effect of PE on RA. Autophagy-related scores were elevated in both RA and DC subsets in PE- associated biological processes. Through observation in the functional differences in cellular interactions between the identified clusters, DCs with high autophagy scores may process such as necroptosis and the activation of the Jak-STAT signaling pathway in contributing the pathogenesis of RA. explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). CMAP, molecular docking, and PheWAS analysis further highlighted vismodegib as a promising therapeutic candidate. Conclusion: This study clarifies the key immunometabolic mechanisms in RA, pinpoi

关键词： rheumatoid arthritis dendritic cells single-cell sequencing bulk transcriptome explainable machine learning drug repositioning

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Enhancing biogas production from municipal wastewater sludge and grease trap waste: explainable machine learning models for prediction and parameter identification

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FUEL 2025年 391卷

作者： Yalcinkaya, Sedat Yucel, Ozgun Marmara Univ Fac Engn Dept Environm Engn Istanbul Turkiye Gebze Tech Univ Fac Engn Dept Chem Engn Kocaeli Turkiye

This research investigates the use of explainable machine learning (ML) models to predict biogas production and identify critical parameters in the anaerobic co-digestion (AcoD) of municipal wastewater sludge (MSS) and grease trap waste (GTW). While traditional ML models offer high accuracy, they often lack transparency, making it difficult to understand the underlying relationships between input variables and outputs. This study addresses this gap by employing various explainable ML approaches. GTW ratio, TS reactor, pH feed, and alkalinity reactor were identified as the most influential parameters for biogas production using SHAP, a superior feature importance method. Sequential backward, F test and Increase in node purity methods selected a larger set of variables with reduced interpretability. Ensemble Least Squares Boosting (LSBoost) with all variables achieved the highest R2 value (0.9555), but Gaussian Process (GP) with SHAP-selected variables showed better performance (R2 = 0.9577) with a simpler model. SHAP effectively identified key parameters, leading to a transparent and generalizable model. This approach balances prediction accuracy with model explainability, aiding in process optimization and control. Overall, this study demonstrates the effectiveness of explainable ML in AcoD modeling. By combining accurate prediction with interpretability, these models offer valuable insights into the complex dynamics of the process and pave the way for improved biogas production and process stability.

关键词： Anaerobic digestion machine learning explainable machine learning Feature importance Feature selection Biogas

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Internet of things-driven approach integrated with explainable machine learning models for ship fuel consumption prediction

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ALEXANDRIA ENGINEERING JOURNAL 2025年 118卷 664-680页

作者： Nguyen, Van Nhanh Chung, Nghia Balaji, G. N. Rudzki, Krzysztof Hoang, Anh Tuan HUTECH Univ Inst Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Transport Inst Maritime Ho Chi Minh City Vietnam Vellore Inst Technol Sch Comp Sci & Engn Vellore India Gdyn Maritime Univ Fac Marine Engn Gdynia Poland Dong Nai Technol Univ Fac Engn Bien Hoa City Vietnam Korea Univ Grad Sch Energy & Environm 145 Anam ro Seoul 02841 South Korea

The International Maritime Organization has proposed several operational policies and measures to lower ships' specific fuel consumption (SFC) and associated emissions toward the sustainability of maritime activities, showing the need for creating exact predictive models based on actual operational conditions. Modern combined and integrated techniques between highly precise sensors, the Internet of Things (IoT), and advanced machine learning (ML) can help in accurate real-time data collection and robust prediction model building. In this work, an IoT-driven approach combined with explainable ML models was developed to predict the SFC of ships based on data collected from high-quality sensors. Indeed, five different MLs were employed including linear regression, decision tree, random forest, XGBoost, and Gradient Boosting Regression. Resultantly, XGBoost emerged as the best model for predicting SFC with the highest R2 (Train: 0.997, Test: 0.95), lowest MSE (Train: 1.052, Test: 16.791), and minimal MAPE (Train: 0.08 %, Test: 0.23 %). Moreover, the interpretability analysis identified "Main engine shaft power" as the most significant predictor with a mean SHAP value of around 3.5. More importantly, these findings highlighted the importance of engine power, torque, and speed in driving model predictions for ship SFC, thus helping in a comprehensive understanding of the black-box model.

关键词： Ship fuel consumption Maritime industry Internet of things explainable machine learning SHAP values Model prediction

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A novel algal bloom risk assessment framework by integrating environmental factors based on explainable machine learning

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ECOLOGICAL INFORMATICS 2025年 87卷

作者： Gao, Lingfang Shangguan, Yulin Sun, Zhong Shen, Qiaohui Zhou, Lianqing Zhejiang Univ Inst Agr Remote Sensing & Informat Technol Applica Coll Environm & Resource Sci Hangzhou 310058 Peoples R China Zhejiang Ecol & Environm Monitoring Ctr Hangzhou 310012 Peoples R China

In recent years, the algal blooms have intensified, posing mounting threats to aquatic ecosystems and water security. However, most previous studies merely detected algal blooms according to the characteristics of the water body at the time of algal bloom occurrence, overlooking the influence of environmental factors on algae proliferation. This study proposes a novel algal bloom risk assessment framework that integrates explainable machine learning with multivariate environmental analysis. Specifically, the Shapley Additive Explanations (SHAP) effect values were used to separately explore the relationship between chlorophyll a (Chla) and six factors, namely the total phosphorus (TP), total nitrogen (TN), TN: TP ratio (RNP), dissolved oxygen (DO), temperature, and precipitation, across riverine and lacustrine ecosystems. Results identified TP and temperature as dominant regulators, accounting for the first two in lakes and the second and third positions in rivers. The thermal effect varies between different ecosystems: Chla decreases after reaching a peak in lakes, while Chla increases linearly with temperature in rivers. In addition, DO played an important role in rivers. The Chla concentration was estimated using Random Forest and thresholds for bloom identification were adjusted (25 mu g/ L for lakes and 40 mu g/L for rivers), reflecting hydrodynamic and optical disparities. The risk framework was applied to the Qiantang River Basin (2020-2022), and results showed low annual risk (mean Algal Bloom Risk Index <0.5) but identified spring susceptibility related to nutrient resuspension and thermal stratification. By quantifying the impact of environmental factors on algal blooms, this study improves algal bloom risk assessment in rivers and lakes, which advances proactive bloom management in mixed river-lake basins under intensifying anthropogenic and climatic pressures.

关键词： Algal bloom risk Influencing factors explainable machine learning Qiantang River basin

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Decoding spatial patterns of urban thermal comfort: explainable machine learning reveals drivers of thermal perception

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ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 2025年 114卷

作者： Hu, Chunguang Zeng, Hui Peking Univ Sch Urban Planning & Design Shenzhen 518055 Peoples R China

Thermal comfort (TC) is a pivotal indicator of urban quality of life and influences public health, productivity, and satisfaction. This study leverages remote sensing data from 2019 to 2023 to construct a national-scale TC framework using the Modified Temperature and Humidity Index (MTHI). This analysis reveals the spatial heterogeneity of TCs across China and their key driving mechanisms. The findings show a northwest-southeast gradient in the TC, with a decreasing contrast in this direction and a north-south disparity alongside a northward shift in heat discomfort centers. High-comfort zones are found in the western plateaus, northeastern regions, and southern mountains;conversely, low-comfort zones are concentrated in the northwest, North China Plain, and southern basins, particularly in the densely urbanized eastern coastal cities and central urban clusters. Coastal areas show high internal variability, whereas inland plateaus are more stable. Natural environmental factors have emerged as the primary drivers of TC. Shapley additive explanations (SHAP) values show a continuous upward trend, underscoring the crucial role of enhanced NDVI in improving TC. Although socioeconomic factors show increased SHAP values, their adverse impacts persist as urbanization and rising building density exacerbate TC deterioration. Landscape factors exert complex effects on TC, with water body landscapes displaying an optimal regulatory range. Interactions among driving factors, characterized by direct and complex trade-offs, further modulate and intensify the effects of TC. The proposed multiscale optimization framework provides strategic insights for managing China's urban thermal environment and offers guidance for other regions with similar climate challenges.

关键词： Thermal comfort Spatial heterogeneity explainable machine learning Driving mechanisms Landscape patterns

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Analyzing Key Factors Influencing Human Mobility Before and During COVID-19 With explainable machine learning

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TRANSACTIONS IN GIS 2025年第1期29卷

作者： Wang, Pingping Yuan, Yihong Texas State Univ Dept Geog & Environm Studies San Marcos TX 78666 USA

The COVID-19 pandemic highlighted and worsened social inequalities in the United States. This study uses mobile phone location data at the Census Block Group level and explainable machine learning methods to examine the relationships between various factors and human mobility in ten Metropolitan Statistical Areas to uncover how different factors influenced mobility disparities. Our key findings show significant reductions in all mobility indices during the summer of 2020 compared to 2019, mainly due to stay-at-home orders, business closures, and health concerns. Median household income was a consistent positive driver of mobility, while the minority rate negatively impacted movement, exacerbating existing inequalities. Remote work significantly affected full-time and part-time job mobility. These findings highlight the need for fair and resilient mobility policies that consider decentralized commuting, flexible work models, and sustainable transportation. These insights can help policymakers and urban planners address inequalities, support economic recovery, and build inclusive urban environments.

关键词： automated machine learning COVID-19 explainable machine learning human mobility inequity

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Non-linear Phillips Curve for India: Evidence from explainable machine learning

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COMPUTATIONAL ECONOMICS 2025年 1-44页

作者： Pratap, Bhanu Pawar, Amit Sengupta, Shovon Reserve Bank India Mumbai India Fidel Investments Boston MA USA BITS Pilani Dept Econ & Finance Hyderabad India Sorbonne Univ Abu Dhabi SAFIR Abu Dhabi U Arab Emirates

The conventional, linear Phillips curve model-while a useful guide for policymaking-falls short in terms of forecasting power amidst structural breaks and inherent non-linearities. This paper addresses these shortcomings by applying machine learning (ML) methods within a New Keynesian Phillips Curve framework to forecast and explain headline inflation in India, a large emerging economy. Our forecasting analysis suggests that ML-based methods provide significant gains in forecasting accuracy over standard linear models. Further, using explainable ML techniques, we empirically show that the Phillips curve relationship in India is a highly non-linear process which is efficiently captured by ML models. While headline inflation is found to be most strongly influenced by inflation expectations followed by past inflation and output gap, the relationship exhibits non-linearities in the form of thresholds and interaction effects between covariates. Supply shocks, except rainfall, seem to have a marginal impact on headline inflation. ML models, therefore, not only enhance forecast accuracy but also help uncover complex, non-linear relationships in the data in a flexible manner.

关键词： Phillips curve Inflation forecasting machine learning Shapley values explainable machine learning Conformal prediction intervals C32 C45 C52 C53 E31

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