Optimized machine learning approaches for identifying vertical temperature gradient on ballastless track in natural environments

Machine learning simulation of one-dimensional deterministic water wave propagation

Electroless plating is a chemical process commonly used in semiconductor manufacturing that deposits a uniform metal coating onto a substrate through an auto-catalytic reduction reaction without using electricity. High quality deposition hinges on the precise control of bath composition and operating conditions, which in turn requires accurate and accessible monitoring of the bath chemical concentration. While chemical analyzers present an effective monitoring technique, they are generally limited by high costs, complexity, and maintenance. Time-delayed measurements also impose challenges on real-time operations, and any reduction to the monitoring lag would be highly beneficial. We introduce a data-driven machine learning (ML) approach able to achieve fast concentration predictions directly from operating conditions, and able to compute the prediction uncertainty that is valuable for subsequent robust control and optimization and for improving transparency and trustworthiness of ML tools in manufacturing settings. Notably, our ML procedure overcomes challenges of asynchronous time-series training data with missing values, and where available data are often noisy and sparse. Our ML approach begins with a data preprocessing step to handle asynchronous measurements and missing values by engineering features rooted in the underlying physical processes. Notably, a systematic feature selection is performed to down select features that are most correlated with the prediction target, thereby reduce model overfitting. We then compare a number of regression model architectures for capturing the sequential data relationships, including linear models, random forest, extreme gradient boosting, and fully connected, long short-term memory, and transformer neural networks. We quantify the model uncertainty by training them in a Bayesian manner using scalable Stein variational gradient descent, to compute the posterior probability distributions conditioned on the training data that reflect the uncertainty in the models induced by the quality and quantity of the available observations. We demonstrate the overall ML approach on a real-world dataset from a semiconductor manufacturing plant that exhibits the aforementioned data challenges. The best performing models exhibited 1, 5, 12% accuracy (defined as achieving within a 3% margin) improvements on predicting the metal ion, alkali, reductant concentrations, respectively, over a naive feature extraction technique. It also achieved 13, 10, 32% accuracy improvements over an autoregressive integrated moving average (ARIMA) baseline. These results show the approach’s effectiveness in providing reliable predictions with quantified uncertainty.

BackgroundMalaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters.MethodsWe obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers.ResultsThe multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location.ConclusionThe study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.

Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication.

Smartphone mechanocardiography (sMCG) is a new technique that allows patients to record a cardiac rhythm strip using a smartphone built-in tri-axial accelerometer and gyroscope. In this study, we investigated how a self-applied sMCG can reliably contribute to the differentiation of atrial fibrillation (AFib) from the sinus rhythm (SR). A study sample of 300 elderly adults including 150 AFib cases with persistent and paroxysmal AFib was recruited. Among the 300 subjects, 182 subjects (82 AFib) completed two recordings, one physician-applied and one self-applied. The remaining patients (n = 118) were nervous, in a quite poor condition, and not interested in or capable of concentrating on using a smartphone in the acute setting of a hospital in order to perform the self-applied recording. Two data processing frameworks were used, knowledge-based learning (KL) which is a rule-based algorithm and machine learning (ML) which is an automated classification technique. For the ML approach, we considered four classifiers, namely random forest (RF), extreme gradient boosting (XGB), support vector machines (SVM), and artificial neural network (NN). For evaluation, a leave-one-subject-out cross-validation was adopted for the ML approach. Compared to physician-interpreted ECG-derived labels, the KL approach predicted AFib with sensitivity values of 0.963 and 0.976, specificity values of 0.980 and 0.929, and F-measure values of 0.972 and 0.952 for the physician- and self-applied measurements, respectively. Similarly, NN which was the best ML classifier according to the F-measure values, demonstrated, on average, sensitivity values of 0.976 and 0.938, specificity values of 0.962 and 0.936, and F-measure values of 0.969 and 0.937, respectively. All other classifiers delivered quite similar results. The sMCG technology for AFib detection, supported by the KL and ML approaches, can accurately differentiate AFib from SR in both physician- and self-applied recording scenarios. This new technology can help to screen patients with episodic or undiagnosed AFib and also be used as a home-based self-applied monitoring technique.

Introduction Spine trauma represents a major cause of disability and substantial healthcare costs. Despite classification systems and risk scores, outcome prediction following spine trauma remains challenging. Machine learning (ML) approaches offer multidimensionality but vary widely in methodology, performance, and clinical application. This systematic review aims to characterize the use of ML for outcome prediction and prognostication in spine trauma. Methods A systematic search of PubMed, Embase, Scopus, Web of Science, and Cochrane Library was performed for publications from January 2015 to May 2025. Studies reporting ML for predicting clinical outcomes in spine trauma patients were identified using pre-defined criteria. Primary outcomes were types and performance metrics of ML models for spine trauma prognostication. Secondary outcomes included cost-effectiveness, clinical utility and implementation feasibility. Results Twenty-seven studies were included, of which 26 were published from 2020 onwards. Only one study was prospective, while the rest were retrospective. Commonly used ML algorithms for spine trauma prognosis were logistic regression (LR), extreme gradient boosting (XGBoost), Random Forest (RF), light gradient boosting machine (LightGBM), and support vector machine (SVM). Outcomes predicted include mortality, neurological function or recovery, ambulation and walking, self-care and independence, complications, treatment failure, length of stay, and discharge destination. Most studies employed internal testing and validation; only four reported external validation. Seven studies reported direct comparison with non-ML models or risk scores, demonstrating comparable or superior performance of ML models. Performance metrics include area under the curve (AUC), accuracy, sensitivity, specificity, precision, F1-score, Brier score, and others. The highest AUC was reported by a nomogram constructed using consensus clustering to predict postoperative pulmonary infection in patients with acute cervical spinal cord injury (internal validation; AUC 0.993; 95% CI 0.981–1.000). Quality of evidence varied between studies, but risk of bias was a concern. Conclusions Existing evidence on ML for spine trauma prognosis is characterized by retrospective study designs, homogeneous cohorts, and lack of external validation, limiting their generalizability and real-world application. While ML approaches demonstrate encouraging predictive performance for spine trauma outcomes, their clinical adoption is limited by methodological heterogeneity. More robust validation, standardized reporting, and comparison with traditional tools are needed before ML models can reliably inform clinical decision-making.

Modeling of necking area reduction of carbon steel in hydrogen environment using machine learning approach

Background: Adolescent depression is highly prevalent in low- and middle-income countries (LMICs). Identifying top key risk factors is necessary to inform effective prevention program design. Machine learning (ML) offers a powerful approach to analyze complex multidomain of data to identify the most relevant predictors and estimate risks for mental health problems. This paper applies ML to study risks for adolescent depression to enhance adolescent depression prevention efforts in LMICs. Methods: Six ML approaches (e.g., Explainable Boosting Machine, random forests, and XGBoost) were applied to study the risks of depression. Data were drawn from a digital health intervention study conducted in Kenya (year 2024–2025, n = 269). Multiple domains of childhood and adolescent adversity and stress experiences were used to predict adolescent depression (using PHQ9-A). Findings: We found that ML was a valuable approach in the early identification of adolescents at risk for depression. Among the six ML approaches applied, the random forest approach outperformed other ML approaches, especially when multiple domains of risks were included. We also found that childhood adversity or home adversity alone were not strong predictors for depression. Adding adolescent stress experiences and community school adversity experiences significantly improves the accuracy and predictability of depression. Using the top-15 and top-20 ranking factors, we achieved 74.8% and 75.1% accuracy in depression prediction, which was similar to the accuracy when all 49 adverse/stress factors were included in the predictive model (78.3%). Conclusions: Innovative ML and modern predictive modeling approaches have the potential to transform modern preventive mental health care by better utilizing multidomain data to identify individuals at risk for developing depression early and identify top risk factors (for targeted individuals and/or populations). Findings from ML can inform tailored intervention design to better mitigate risks in order to prevent depression problem development. They can also inform the better utilization of resources to target high-need cases and key determinants, which is particularly useful for LMICs and low-resource settings. This paper illustrates an example of how to move toward this direction. Future research is needed to validate the approach.

Pharmaceutical contamination of aquatic systems poses an increasing environmental concern due to the persistence, bioactivity, and incomplete removal of these compounds by conventional wastewater treatment processes. This study investigates the adsorptive removal of three structurally distinct pharmaceuticals: ketotifen fumarate (KF), doxycycline hyclate (DXC), and nystatin (Nyst), using raw bentonite (RB). By combining batch experiments with an interpretable machine learning (ML) framework, adsorption kinetics, equilibrium, and thermodynamics were evaluated. Additionally, four Ant Lion Optimizer (ALO)-optimized models: Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were employed to predict adsorption capacity under diverse conditions. RB exhibited high adsorption capacities: 178.86 ± 1.26 mg/g for KF, 222.91 ± 2.02 mg/g for DXC, and 190.25 ± 2.86 mg/g for Nyst. Adsorption equilibrium was best described by the Freundlich isotherm, indicating multilayer adsorption on a heterogeneous surface. Thermodynamic and spectroscopic analyses revealed a dual mechanism involving electrostatic attraction, hydrogen bonding, cation exchange, and van der Waals interactions, with DXC and Nyst adsorption being endothermic and KF adsorption exothermic. SHAP (SHapley Additive exPlanations) analysis identified adsorbent dosage, initial concentration, and pH as dominant operational factors, while the molecular descriptor nC (number of carbon atoms) emerged as key to differentiating pharmaceuticals, linking larger molecular size to stronger adsorption. The XGBoost model achieved the highest accuracy (R2 = 0.972, RMSE = 0.1225), demonstrating robust generalizability. These findings highlight RB as a low-cost, scalable adsorbent and establish an interpretable ML approach capable of linking molecular structure to adsorption behavior.

Commentary: Machine learning and cardiac surgery risk prediction

As artificial intelligence techniques become more sophisticated, we anticipate that robots collaborating with humans will develop their own intentions, leading to potential conflicts in interaction. This development calls for advanced conflict resolution strategies in physical human–robot interaction (pHRI), a key focus of our research. We use a machine learning (ML) classifier to detect conflicts during co-manipulation tasks to adapt the robot’s behavior accordingly using an admittance controller. In our approach, we focus on two groups of interactions, namely “harmonious” and “conflicting,” corresponding respectively to the cases of the human and the robot working in harmony to transport an object when they aim for the same target, and human and robot are in conflict when human changes the manipulation plan, e.g. due to a change in the direction of movement or parking location of the object. Co-manipulation scenarios were designed to investigate the efficacy of the proposed ML approach, involving 20 participants. Task performance achieved by the ML approach was compared against three alternative approaches: (a) a rule-based (RB) Approach, where interaction behaviors were rule-derived from statistical distributions of haptic features; (b) an unyielding robot that is proactive during harmonious interactions but does not resolve conflicts otherwise, and (c) a passive robot which always follows the human partner. This mode of cooperation is known as “hand guidance” in pHRI literature and is frequently used in industrial settings for so-called “teaching” a trajectory to a collaborative robot. The results show that the proposed ML approach is superior to the others in task performance. However, a detailed questionnaire administered after the experiments, which contains several metrics, covering a spectrum of dimensions to measure the subjective opinion of the participants, reveals that the most preferred mode of interaction with the robot is surprisingly passive. This preference indicates a strong inclination toward an interaction mode that gives more control to humans and offers less demanding interaction, even if it is not the most efficient in task performance. Hence, there is a clear trade-off between task performance and the preferred mode of interaction of humans with a robot, and a well-balanced approach is necessary for designing effective pHRI systems in the future.

Machine learning approach for delamination detection with feature missing and noise polluted vibration characteristics

Removal of congo red from water by adsorption onto activated carbon derived from waste black cardamom peels and machine learning modeling

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

Chlorophyll is widely used to assess the level of eutrophication, which is recognized as one of the major causes of deterioration in water quality, especially across Irish inland waters. The use of optical remote sensing for chlorophyll monitoring has already been shown to be able to complement existing in-situ chlorophyll. However, as cloud cover impacts all optical sensors this limits the usefulness in many locations to develop long term records. A potential solution is to combine information from multiple satellites using a machine learning approach. In this study, we develop long-term time series of chlorophyll concentrations for lakes in Ireland using four different remote sensing platforms: Landsat-8, MODIS, Sentinel-2, and Sentinel-3. Several machine learning approaches have been tested, including K-nearest neighbourhood, random forest (RF), XGBoost (Extreme Gradient Boosting), artificial neutral network (ANN), and support vector regression (SVR).Initial result indicate that a machine learning model utilising all four platforms and in-situ observation is effective in developing long-term chlorophyll concentrations. While the methods are tested and validated for Irish lakes, the methodology has the potential to be applied in a global context.