Aerosol Characteristics Over an Eastern Indian City Using Sun Photometer Observations and Machine Learning Frameworks

Aerosol Robotic Network (AERONET) Sun photometer observations were carried out at Ouarzazate, Morocco, during the Saharan Mineral Dust Experiment (SAMUM) 2006. Data from one measurement day, 19 May 2006, are used to derive particle optical and microphysical parameters with AERONET's latest version of light‐scattering model for non‐spherical particle geometry. In our analysis we also make use of a novel measurement channel at 1638 nm wavelength. We compare the results to data products obtained by airborne high‐spectral‐resolution lidar, several ground‐based Raman lidar, and airborne and ground‐based in situ measurement platforms. We chose that specific measurement day because the dust plume was vertically well mixed. Extinction coefficients from AERONET Sun photometer and lidar observations and in situ measurements agree well. Ångström exponents from Sun photometer and lidar are in close agreement, too. Airborne in situ measurements of dust particle size distributions show larger effective radii than inferred from the AERONET data. Complex refractive indices that are derived with the AERONET algorithm differ from the values obtained with different independent techniques employed in our study. The single‐scattering albedo was derived from the airborne observations of particle size distributions and complex refractive indices. Single‐scattering albedo differs to the value inferred from the AERONET data. The differences may be attributed to the different effective radii that we obtained from the various techniques. The differences between the data products from the various measurement platforms, however, cannot be generalized, as we could only test data for one measurement day. An analysis of additional measurements is under way.

Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

Background and purposeThis study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.Materials and methodsNPC patients treated with IMRT between Jan. 2019 and Dec. 2021 were randomly assigned to a training cohort (n = 328) and a validation cohort (n = 141) at a ratio of 7:3. A total of 33 clinical and dose-volume variables were collected. Significant variables (p < 0.05) were identified through univariate Cox analysis and further refined using a 101-combination machine learning (ML) framework to develop a robust predictive model. The model was subsequently simplified through multivariate Cox analysis and a nomogram. Finally, the performance of the model was evaluated using the C-index, calibration plots, and decision curve analysis.ResultsUsing a 101-combination ML framework, we developed a predictive model for RIHT in NPC. The Coxboost + RSF method with 11 predictors achieved the best performance (C-index: 0.91 [training], 0.71 [validation]). A simplified five-variable model (pre-treatment TSH, TSH-to-thyroid-volume ratio, age, V45, V20) was created via multi-cox regression, with a C-index of 0.80 [training] and 0.71 [validation]. High-risk patients had significantly higher three-year RIHT incidences (72.3% vs. 18.6%, p < 0.0001) in the training cohort, and 67.9% versus 24.4% (p < 0.0001) in the validation cohort. The model showed strong calibration and confirmed clinical utility through decision curve analysis, supporting its use in personalized treatment planning.ConclusionWe developed a ML framework to identify key predictive factors for RIHT, which was simplified into a five-variable model for clinical use, offering a robust tool for predicting RIHT risk in decision-making.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13014-025-02725-5.

Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs. The aim of this study was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia. A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection. All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient's length of stay, Charlson Index, operation length and intensive care unit stay were common predictors. Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance. We have successfully developed a "real-time" risk prediction model, where patient risk scores are calculated and reviewed daily.

Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e., non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.

Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning (ML) frameworks. This integration has not only deepened our understanding of blood diseases such as leukemia and lymphoma, but also enhanced diagnostic accuracy and personalized treatment strategies. By applying ML algorithms to analyze large-scale biological data, researchers can more effectively identify disease patterns, predict treatment responses, and provide new perspectives for the diagnosis and treatment of hematologic disorders. Here, we provide an overview of the current landscape of biological data resources and the application of ML frameworks pertinent to hematology research.

Introduction: Heart failure (HF) in critical care settings exhibits complex interactions with glycemic dysregulation. Traditional metrics fail to capture dynamic glucose fluctuations linked to poor outcomes. This study defines ICU-specific glycemic targets and establishes a machine learning (ML) framework integrating novel glucose indicators to predict 28-day mortality. Methods: Cohort: 18,211 ICU HF patients (MIMIC-IV, 2008-2019). Analysis: 1. Glycemic Metrics: Calculated time-weighted average glucose (TWABG), time-in-range (TIR), time-below-range (TBR), time-above-range (TAR) 2. Association Studies: Restricted cubic splines (RCS) to identify optimal glycemic ranges. Multivariable Cox regression with adjustment and subgroup analysis with interaction testing. 3. ML Modeling: Dual-stage feature selection: Lasso regression and Boruta algorithm. Model development: Developed models using 14 algorithms; SHAP interpretation applied to best-performing model (highest ROC-AUC). Validation: SHAP analysis of an exemplar case demonstrated individualized mortality risk interpretation without formal model validation. Results: 1. Optimal Glycemic Ranges: Patients within the optimal TWABG range (85–133 mg/dL) demonstrated the lowest mortality (15.09%,P< 0.001), with improved survival associated with both higher time-in-range (TIR) proportion and absence of glycemic excursions (time-below-range [TBR] or time-above-range [TAR] exposure) 2. ML Prediction: Feature selection via Lasso regression identified 30 predictors. Boruta algorithm confirmed 29 significant features. The optimized XGBoost model predicted 28-day mortality with AUC 0.779 (0.762-0.796) Conclusions: Maintaining blood glucose within TWABG 85–133 mg/dL and maximizing TIR significantly reduces 28-day mortality in ICU HF patients, while avoiding excursions independently improves survival. Our ML framework, integrating dynamic glucose metrics with clinically selected features, accurately predicted mortality risk (AUC 0.779). Findings advocate ICU-specific glucose management and demonstrate ML’s potential for risk stratification and personalized interventions.

Occupant behavior prediction has become a significant part of smart building technologies. This research emphasizes on HVAC (Heating Ventilation and Air Conditioning) system as its operation is crucial in terms of both energy usage and user comfort. In previous researches, comfort prediction is developed using the filled questionnaires from occupants. The prediction output is employed to control the mechanical components of the system. On/Off control is mostly developed using rule-based distance triggering that turns on/off the system when occupants enter/exit a specific perimeter. This article proposes a plug-and-play HVAC control system with an adaptive cloud machine learning (ML) framework that utilizes room current ambient conditions together with the historical adjustment log to automatically adjust room temperature according to the user comfort using Support Vector Classification (SVC). This system can also turn on air conditioner automatically using predictive control which implements GPS location and the occupant's data on the Artificial Neural Network (ANN). Furthermore, cloud deployment can solve problems in processing power and storage of an end device, also provide scalability for future development. Findings show that an adaptive algorithm in the ML framework can perform well, even when the occupant's behavior changes slowly.

Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor nodes responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework, and the applicability of artificial intelligence and ML to a practical engineering problem, i.,e, data-driven failure prediction in brittle materials.

This study focuses on the optimization process of crash box design. The design optimization process is resource‐intensive and requires multiple dynamic simulations. Numerous design parameters can be varied to satisfy the crashworthiness objectives. Therefore an intelligent and robust machine learning (ML) framework has been developed. This framework can be employed to assist in the optimization of various crashworthiness components with different crashworthiness objectives.Here, a reinforcement learning‐based (RL) machine learning framework is developed. It consists of a finite element method (FEM) surrogate and RL environment. The FEM surrogate is trained using data from FEM simulations as well as synthetic data generated by a Generative Adversarial Network (GAN). An inverse problem is solved for optimizing the geometrical parameters of the boundary value problem while the RL is receiving input from the FEM surrogate. RL has proven to be accurate in many fields due to its ability to explore and exploit the learned dynamics. Conventional algorithms require numerous iterations and tuned functions to achieve appropriate results and need to be initialized after a slight change in the problem. Due to an optimization of input parameters in an inverse problem, RL is chosen for the present investigation.For the simulation data needed, the crash box is designed with linear first‐order accurate four nodal shell elements. Also, bilinear elastoplastic material along with geometrical nonlinearity is used to simulate the dynamic deformation process.The RL agents learn to vary the crash box design parameters and search for the optimal parameters. The optimal parameters are based on the user‐defined crashworthiness objectives.

Yield Curve and Recession Forecasting in a Machine Learning Framework

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R2, RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R2 from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications.

This study presents a machine learning (ML) framework for predicting heat transfer in magnetohydrodynamic (MHD) mixed convection within a complex V-shaped cavity filled with a nano-enhanced phase change material (NEPCM) suspension. Accurate computational fluid dynamics (CFD) simulations are essential for understanding heat transfer mechanisms in such systems, but generating comprehensive data through high-fidelity models remains computationally expensive. To address this challenge, we develop an integrated ML approach that combines synthetic data generation, physics-informed feature engineering, and optimized ensemble boosting. The methodology first augments a limited 34-sample CFD dataset to 2034 samples using Latin Hypercube Sampling with Radial Basis Function interpolation. Next, 14 physics-based features are engineered to encode the underlying physical phenomena. Finally, hyperparameters of three gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)–are optimized via cross-validation. The framework predicts the average Nusselt number ( N u avg ) and average kinetic energy ( K E avg ) from seven geometric and operational inputs. CatBoost achieved optimal performance for N u avg ( R 2 = 0 . 9745 , mean absolute percentage error = 1.27%), while XGBoost excelled for K E avg ( R 2 = 0 . 9920 , mean absolute percentage error = 1.04%). The novelty of this work lies in its ability to generalize across different output variables and significantly reduce computational cost, enabling rapid design optimization and in-depth parametric analysis. This generalizable approach reduces computation time from hours to milliseconds, facilitating efficient design optimization and in-depth parametric studies. • ML framework predicts heat transfer in MHD mixed convection of NEPCM suspension. • Combines physics-informed feature engineering with optimized ensemble boosting. • CatBoost best for Nusselt number (R 2 = 0.9745), XGBoost for kinetic energy (R 2 = 0.9920). • Synthetic data generation expands limited CFD dataset from 34 to 2034 samples. • Reduces simulation time from hours to milliseconds for efficient design exploration.

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.