Traditional Regression Models Research Articles

Research on efficiency simulation model of pumping stations based on data-driven methods

The operational efficiency error of a pumping station unit is affected by several factors, resulting in a large error between the theoretical and actual pumping efficiencies. To solve the problem of accurately simulating the operating efficiency of the pumping station unit, this study conducted research on efficiency simulation models of pumping based on data-driven from three aspects: ①model algorithm, ②feature input and ③response output. In this study, eight machine learning models were introduced to establish pumping unit efficiency simulation models and they were compared with the traditional polynomial regression models, and the models with better performance were selected. Then the experiment proposed using “upstream water level + downstream water level” (UWL + DWL) instead of the traditional “head” (H) as feature input to train the models, and discussed the effects on efficiency simulation accuracy with direct fitting efficiency and fitting power then calculate the efficiency through formula. An example analysis was carried out using the historical data of eight units at Pizhou Station and Suining 2nd Station on the East Line of the South-to-North Water Diversion Project. The experimental results show that ① among all methods, the models based on Gaussian Process Regression (GPR) has the most accurate in ERMS, EMA, R², and EMI indicators, R² is close to 0.92. ②After UWL + DWL was used to train the models, all the test set indicators of the GPR models improved, and the EMA indicator decreased from an average of 0.39–0.26 %. ③ The study found that the fitting efficiency directly is better than the calculation efficiency after fitting the power, but the R² of fitting the power is better than the efficiency, which may provide another idea for the optimal goal of economic operation of pumping stations. ④ On the whole, trainning the GPR models with UWL + DWL to simulate efficiency has the highest precision, for example the EMA and EMI indicators of No.4 unit in Pizhou station can be reduced from 16.49 % and 20.40–0.18 % and 1.55 % respectively. The research results have practical significance and can provide strong support for the economical operation of pump stations.

A knowledge-transfer-based approach for combining ordinal regression and medical scoring system in the early prediction of sepsis with electronic health records

Objective:The prediction of sepsis, especially early diagnosis, has received a significant attention in biomedical research. In order to improve current medical scoring system and overcome the limitations of class imbalance and sample size of local EHR (electronic health records), we propose a novel knowledge-transfer-based approach, which combines a medical scoring system and an ordinal logistic regression model. Materials and Methods:Medical scoring systems (i.e. NEWS, SIRS and QSOFA) are generally robust and useful for sepsis diagnosis. With local EHR, machine-learning-based methods have been widely used for building prediction models/methods, but they are often impacted by class imbalance and sample size. Knowledge distillation and knowledge transfer have recently been proposed as a combination approach for improving the prediction performance and model generalization. In this study, we developed a novel knowledge-transfer-based method for combining a medical scoring system (after a proposed score transformation) and an ordinal logistic regression model. We mathematically confirmed that it was equivalent to a specific form of the weighted regression. Furthermore, we theoretically explored its effectiveness in the scenario of class imbalance. Results:For the local dataset and the MIMIC-IV dataset, the VUS (the volume under the multi-dimensional ROC surface, a generalization measure of AUC-ROC for ordinal categories) of the knowledge-transfer-based model (ORNEWS) based on the NEWS scoring system were 0.384 and 0.339, respectively, while the VUS of the traditional ordinal regression model (OR) were 0.352 and 0.322, respectively. Consistent analysis results were also observed for the knowledge-transfer-based models based on the SIRS/QSOFA scoring systems in the ordinal scenarios. Additionally, the predicted probabilities and the binary classification ROC curves of the knowledge-transfer-based models indicated that this approach enhanced the predicted probabilities for the minority classes while reducing the predicted probabilities for the majority classes, which improved AUCs/VUSs on imbalanced data. Discussion:Knowledge transfer, which combines a medical scoring system and a machine-learning-based model, improves the prediction performance for early diagnosis of sepsis, especially in the scenarios of class imbalance and limited sample size.

The development and validation of a prognostic prediction modeling study in acute myocardial infarction patients after percutaneous coronary intervention: hemorrhea and major cardiovascular adverse events.

Percutaneous coronary intervention (PCI) is one of the most important diagnostic and therapeutic techniques in cardiology. At present, the traditional prediction models for postoperative events after PCI are ineffective, but machine learning has great potential in identification and prediction of risk. Machine learning can reduce overfitting through regularization techniques, cross-validation and ensemble learning, making the model more accurate in predicting large amounts of complex unknown data. This study sought to identify the risk of hemorrhea and major adverse cardiovascular events (MACEs) in patients after PCI through machine learning. The entire study population consisted of 7,931 individual patients who underwent PCI at Jiangsu Provincial Hospital and The Affiliated Wuxi Second People's Hospital from January 2007 to January 2022. The risk of postoperative hemorrhea and MACE (including cardiac death and in-stent restenosis) was predicted by 53 clinical features after admission. The population was assigned to the training set and the validation set in a specific ratio by simple randomization. Different machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), random forest (RF), and deep learning neural network (DNN), were trained to build prediction models. A 5-fold cross-validation was applied to correct errors. Several evaluation indexes, including the area under the receiver operating characteristic (ROC) curve (AUC), accuracy (Acc), sensitivity (Sens), specificity (Spec), and net reclassification improvement (NRI), were used to compare the predictive performance. To improve the interpretability of the model and identify risk factors individually, SHapley Additive exPlanation (SHAP) was introduced. In this study, 306 patients (3.9%) experienced hemorrhea, 107 patients (1.3%) experienced cardiac death, and 218 patients (2.7%) developed in-stent restenosis. In the training set and validation set, except for previous PCI and statins, there were no significant differences. XGBoost was observed to be the best predictor of every event, namely hemorrhea [AUC: 0.921, 95% confidence interval (CI): 0.864-0.978, Acc: 0.845, Sens: 0.851, Spec: 0.837 and NRI: 0.140], cardiac death (AUC: 0.939, 95% CI: 0.903-0.975, Acc: 0.914, Sens: 0.950, Spec: 0.800 and NRI: 0.148), and in-stent restenosis (AUC: 0.915; 95% CI: 0.863-0.967, Acc: 0.834, Sens: 0.778, Spec: 0.902 and NRI: 0.077). SHAP showed that the number of stents had the greatest influence on hemorrhea, while age and drug-coated balloon were the main factors in cardiogenic death and stent restenosis (all P<0.05). The XGBoost model (machine learning) performed better than the traditional logistic regression model in identifying hemorrhea and MACE after PCI. Machine learning models can be used as a tool for risk prediction. The machine learning model described in this study can personalize the prediction of hemorrhea and MACE after PCI for specific patients, helping clinicians adjust intervenable features.

Variational quantum algorithm for forecasting drugs for corrosion inhibitor

This study explores the development and evaluation of a Variational Quantum Algorithm (VQA) for predicting a drug as a corrosion inhibitor, highlighting its advantages over traditional regression models. The VQA leverages quantum-enhanced feature mapping and optimization techniques to capture complex, non-linear relationships within the data. Comparative analysis with AutoRegressive with exogenous inputs (ARX) and Gradient Boosting (GB) models demonstrate the superior performance of VQA across key metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Deviation (MAD). The VQA achieved the lowest RMSE (4.40), MAE (3.33), and MAD (3.17) values, indicating enhanced predictive accuracy and stability. These results underscore the potential of quantum machine learning techniques in advancing predictive modeling capabilities, offering significant improvements in accuracy and consistency over classical methods. The findings suggest that VQA is a promising approach for applications requiring high precision and reliability, paving the way for broader adoption of quantum-enhanced models in material science and beyond.

AI-assisted ultrasonic wave analysis for automated classification of steel corrosion-induced concrete damage

Early detection of cracks in reinforced concrete caused by chloride intrusion is crucial for effective maintenance. This paper develops and compares AI-assisted models that use ultrasonic pulse waves to automatically assess early-stage concrete damage, particularly cracks caused by steel corrosion. Data were collected from 108 concrete cubes with various mixture designs, cover depths, and steel corrosion levels induced by the impressed current technique. The AI models, particularly convolutional neural networks (CNNs), outperformed traditional regression models, achieving up to 84% classification accuracy. The systematic and automated features of the AI-assisted algorithm enable reliable, consistent and rapid ultrasonic wave data analysis, which is especially beneficial for the condition assessment of large infrastructure systems. This advancement inspires further research into integrating automated IoT and robotics-assisted systems for comprehensive infrastructure monitoring.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Predicting GDP with Machine Learning Technique

This paper proposes a method for reducing model errors in regressions when modelling macroeconomic variables by using machine learning algorithms and traditional time series regression models. In this paper, machine learning models are subjected to repeated k-fold cross validation and hyperparameter tuning. The linear model uses repeated k-fold cross validation, on the other hand, the traditional time series model Mixed Data Sampling Auto Regressive Distribution Lag model is run without repeated k-fold cross validation and hyperparameter tuning. The results show that integrating repeated k-fold cross validation with hyperparameter tuning increases the overall performance of machine learning algorithms and each model records the average outcome from all folds and runs. These findings demonstrate how machine learning models outperform the traditional time series model.

Continuous Kidney replacement Therapy Dosage and Mortality in Critically Ill Patients: a Retrospective Cohort Study using Marginal Structural Model.

Continuous kidney replacement therapy (CKRT) is a crucial intervention for hemodynamically unstable patients with acute kidney injury (AKI). Despite the recommendations to offer a CKRT dose of 20-25 mL/kg/h, the optimal CKRT dose remains uncertain, especially whether low-dose CKRT is associated with poor outcomes. This study investigated the association between low CKRT dosage and 90-day mortality using a marginal structural model (MSM). Using the MIMIC-IV database, adult patients who received CKRT for more than 24 hours were included. Data on time-fixed and time-dependent variables were collected. Patients were categorized based on CKRT dose thresholds of 13 and 20 ml/kg/h. Among the 1,329 patients, the 90-day mortality rate was 49.6%. The median age of the patients was 62 years (IQR: 52-72). Changes in CKRT dosing during treatment were frequent. Patients with a reduced delivered CKRT dose (<20 and < 13 ml/kg/h) generally exhibited low values during the initial days of CKRT, with an increase in the delivered CKRT dose. After adjusting only for baseline variables (traditional Cox regression model), patients receiving CKRT doses <13 ml/kg/h had significantly greater 90-day mortality (HR: 1.70, 95% CI 1.16-2.49) than those receiving CKRT doses ≥13 ml/kg/h. However, after adjusting for time-dependent variables, the CKRT dose was not significantly associated with mortality at either the 13 or 20 ml/kg/h threshold. Additionally, there were no significant associations between the delivered CKRT dose and 90-day mortality within the range of 5 to 40 ml/kg/h. This study highlights the impact of methodological approaches on the association between CKRT dose and mortality and that with personalized adjustments, there may not be a lower limit of the unsafe CKRT dose. However, lower CKRT doses were initially associated with higher mortality, and adjusting for time-dependent variables nullified this association.

Hybrid model-based early diagnosis of esophageal disorders using convolutional neural network and refined logistic regression

Accurate diagnosis of the stage of esophageal disorders is crucial in the treatment planning for patients with esophageal cancer and in improving the 5-year survival rate. The progression of esophageal cancer typically begins with precancerous esophageal disorders such as gastroesophageal reflux disease (GERD), esophagitis, and non-dysplasia Barrett’s esophagus, eventually advancing to low- and high-dysplasia Barrett’s esophagus and ultimately to esophageal adenocarcinoma (EAC). The majority of prior research efforts have primarily focused on the identification of general gastrointestinal (GI) tract diseases and the detection of esophageal cancer, with limited attention to the diverse spectrum of esophageal disorders. To address this gap, an innovative framework called Hybrid Model-Based Esophageal Disorder Diagnosis (HMEDD) is developed in this work. The primary goal of HMEDD is to enable early diagnosis of various esophageal disorders using gastroscopic images. HMEDD combines the feature extraction capabilities of an Esophageal Convolutional Neural Network (EsoNet) with the high classification accuracy of a Refined Logistic Regression (RLR) model. EsoNet comprises 14 weight layers and kernels 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left(3\ imes 3\\right)$$\\end{document} used for high-level deep feature learning. Esophageal disorders are classified using the RLR model, which is developed by fine-tuning hyperparameters in the traditional Logistic Regression (LR) model using Random Search Cross-Validation (RandomizedSearchCV). HMEDD is extensively validated using a data set containing numerous esophageal abnormalities captured through gastroscopic images. The results of this work demonstrate the effectiveness of HMEDD in accurately classifying different esophageal disorders, with an impressive accuracy of 92.15%. These findings will assist physicians in the accurate early diagnosis of esophageal disorders, ultimately preventing their progression to cancer.

Prenatal air pollution and children's autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods.

Maternal nutrient intake may moderate associations between environmental exposures and children's neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009-2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 μm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children's autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

Radial basis function neural network and overlay sampling uniform design toward polylactic acid molecular weight prediction

Polylactic acid (PLA) is a potential polymer material as a substitute for traditional plastics, and the accurate molecular weight distribution range of PLA is strictly required in practical applications. Therefore, exploring the relationship between synthetic conditions and PLA molecular weight is crucially important. In this work, direct polycondensation combined with overlay sampling uniform design (OSUD) was applied to synthesize the low molecular weight PLA. Then a multiple regression model and two artificial neural network models on PLA molecular weight versus reaction temperature, reaction time, and catalyst dosage were developed for PLA molecular weight prediction. The characterization results indicated that the low molecular weight PLA was efficiently synthesized under this method. Meanwhile, the experimental dataset acquired from OSUD successfully established three predictive models for PLA molecular weight. Among them, both artificial neural network models had significantly better predictive performance than the regression model. Notably, the radial basis function neural network model had the best predictive accuracy with only 11.9% of mean relative error on the validation dataset, which improved by 67.7% compared with the traditional multiple regression model. This work successfully predicted PLA molecular weight in a direct polycondensation process using artificial neural network models combined with OSUD, which provided guidance for the future implementation of molecular weight-controlled polymer’ synthesis.

Startup drift compensation of RLG based on monotone constrained RBF neural network

Serious startup drift of the Ring Laser Gyroscope (RLG) is observed during cold startup process, which will dramatically degrade the performances of the corresponding Inertial Navigation System (INS). In this paper, correlation analysis method, which analyzes the relationship between the startup drift of the RLG and the temperature change, is used to determine the significant temperature-related terms during gyroscope startup. Based on the significant temperature-related terms and the startup time length, a startup drift compensation model for RLG based on monotonicity-constrained Radial Basis Function (RBF) neural network is proposed and validated. Compared with the raw RLG data without compensation, the standard deviation of the RLG output with the proposed constrained RBF network model is decreased by more than 46%, and the peak-to-peak value is decreased by more than 35%. Compared with the traditional multiple regression model, the standard deviation and peak-to-peak value of the RLG output are decreased by more than 10% and 6%, respectively. Compared with the common RBF network model, the standard deviation and peak-to-peak value of the RLG output are decreased by more than 8% and 3%, respectively. Navigation experiments also validate the effectiveness of the compensation model.

Predicting recurrent gestational diabetes mellitus using artificial intelligence models: a retrospective cohort study.

We aimed to develop novel artificial intelligence (AI) models based on early pregnancy features to forecast the likelihood of recurrent gestational diabetes mellitus (GDM) before 14weeks of gestation in subsequent pregnancies. This study involved a cohort of 588 women who had two consecutive singleton deliveries and were diagnosed with GDM during the index pregnancy. The least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. 5 AI algorithms, namely support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting (LGB), decision tree classifier (DTC), and random forest (RF) classifier, and traditional multivariate logistic regression (LR) model, were employed to construct predictive models for recurrent GDM. 326 (55.4%) experienced GDM recurrence in subsequent pregnancy. In the training set (67% of the study sample), 13 features were selected for AI models construction. In the testing set (33% of the study sample), the AI models (LGB, RF, and XGB) exhibited outstanding discrimination, with AUROC values of 0.942, 0.936, and 0.924, respectively. The traditional LR model showed moderate discrimination (AUROC = 0.696). LGB, RF, and XGB models also demonstrated excellent calibration, while other models indicated a lack of fit. All AI models showed superior overall net benefits, with LGB, RF, and XGB outperforming the others. The proposed LGB model demonstrated exceptional accuracy, excellent calibration, and superior overall net benefits. These advancements have the potential to assist healthcare professionals in advising women with a history of GDM and in developing preventive strategies to mitigate the adverse effects on maternal and fetal well-being.

Prediction and analysis of sea surface temperature based on LSTM-transformer model

This paper introduces a novel method for predicting sea surface temperature (SST) using a hybrid LSTM-Transformer model. The study utilizes the ERA5 dataset for SST prediction at six specific locations near China. The LSTM-Transformer model combines the temporal processing capability of LSTM with the efficient data processing power of Transformer, showing superior performance in reducing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), as well as improving the R-squared (R²) value, compared to standalone LSTM, Transformer, and traditional Logistic Regression (LR) models. This is particularly evident in spring and autumn, indicating its robustness to seasonal changes. The model's performance varies across different geographic locations, with lower prediction errors in low-latitude and open sea areas, attributed to the less complex environmental dynamics compared to continental shelf areas. Overall, the LSTM-Transformer hybrid model presents a significant advancement in SST prediction, providing important implications for fisheries, meteorology, and climate change research.

Comparative Analysis with Statistical and Machine Learning for Modeling Overall and High Salinity along the Scheldt Estuary

Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations.

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney donation programs. We aimed to develop a prediction model for post-donation renal function following living kidney donation using machine learning. This retrospective cohort study was conducted with 823 living kidney donors between 2009 and 2020. The dataset was randomly split into training (80%) and test sets (20%). The main outcome was the post-donation estimated glomerular filtration rate (eGFR) 12months after nephrectomy. We compared the performance of machine learning techniques, traditional regression models, and models from previous studies. The best-performing model was selected based on the mean absolute error (MAE) and root mean square error (RMSE). The mean age of the participants was 45.2 ± 12.3years, and 48.4% were males. The mean pre-donation and post-donation eGFRs were 101.3 ± 13.0and 68.8 ± 12.7mL/min/1.73m2, respectively. The XGBoost model with the eGFR, age, serum creatinine, 24-h urine creatinine, 24-h urine sodium, creatinine clearance, cystatin C, cystatin C-based eGFR, computed tomography volume of the remaining kidney/body weight, normalized GFR of the remaining kidney measured through a diethylenetriaminepentaacetic acid scan, and sex, showed the best performance with a mean absolute error of 6.23 and root mean square error of 8.06. An easy-to-use web application titled Kidney Donation with Nephrologic Intelligence (KDNI) was developed. The prediction model using XGBoost accurately predicted the post-donation eGFR after living kidney donation. This model can be applied in clinical practice using KDNI, the developed web application.

Determinates of investor opinion gap around IPOs: A machine learning approach

The current study examines the factors influencing investor opinions on issues related to listed firms during the first day of Initial Public Offerings (IPOs), focusing on a sample of 350 fixed-priced IPOs listed on the Malaysian stock exchange (Bursa Malaysia) from 2004 to 2021. This research contributes to existing literature by employing various machine learning methods, which address the limitations of traditional linear regression models commonly used in previous studies. Specifically, five methods—extra tree regressor (ETR), single feature selection (SFS), reverse single feature (RSF), recursive feature elimination (RFE), and sequential modelling feature adding (SMFA)—are utilized to assess the importance of features in predicting the investor opinion gap within the dataset.The study's experiments indicate that these methods effectively mitigate noisy data, enhancing their reliability for this type of analysis. The findings provide valuable insights for regulators regarding safeguarding investors' rights to information disclosed in prospectuses.

Risk of stroke admission after long-term exposure to PM1: Evidence from a large cohort in South China

BackgroundLimited attention has been paid to the health effects of long-term PM1 exposure on stroke admission. Current investigations exploring the long-term PM exposure effect are largely based on observational studies, and PM generally is not allocated randomly to participants. Using traditional regression models might confuse messaging and hinder policy recommendations for pollution control and disease prevention policies. MethodsWe conducted a cohort study among 36,271 adults from one of the largest cities in China in 2015 and followed up through 2020. Hazard ratios of stroke admissions following long-term PM1 exposure were estimated via a causal inference approach, marginal structural time-varying Cox proportional hazard model, accounting for multiple confounders. Additionally, several sensitivity analyses and impact modification analyses were carried out. Results and discussionAssociations with 1 μg/m3 increase in long-term PM1 were identified for total (HR, 1.079; 95 %CI, 1.012–1.151) and ischemic stroke admissions (HR, 1.092; 95 %CI, 1.018–1.171). The harmful associations varied with exposure duration, initially increasing and then decreasing. The 2–3 years cumulative exposure was associated with a 3.3–5.4 % raised risk for total stroke. For every 1 μg/m³ increase in long-term PM1 exposure, females exhibited a higher risk of both total and ischemic stroke (13 % and 16 %) than men (4 % and 5 %). Low-exposure individuals (whose annual PM1 concentrations were under the third quartile among the annual concentrations for all the participants) exhibited greater sensitivity to PM1 effects (total stroke: 1.079 vs. 1.107; ischemic stroke: 1.092 vs. 1.116). The results underline the importance of safeguarding low-exposed people in highly polluted areas and suggest that long-term PM1 exposure may increase stroke admission risk, warranting attention to vulnerable groups.

Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood

PurposeTo achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.Design/methodology/approachA robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.FindingsThis paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.Originality/valueThis study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning

In the domain of supervised learning, interpolation and extrapolation serve as crucial methodologies for predicting data points within and beyond the confines of a given dataset, respectively. The efficacy of these methods is closely linked to the nature of the dataset, with increased challenges when multivariate feature vectors are handled. This paper introduces a novel prediction framework that integrates interpolation and extrapolation techniques. Central to this method are two main innovations: an optimization model that effectively classifies new multivariate data points as either interior or exterior to the known dataset, and a hybrid prediction system that combines k-nearest neighbor (kNN) and linear regression. Tested on the port state control (PSC) inspection dataset at the port of Hong Kong, our framework generally demonstrates superior precision in predictive outcomes than traditional kNN and linear regression models. This research enriches the literature by illustrating the enhanced capability of combining interpolation and extrapolation techniques in supervised learning.