Regression Coefficients Research Articles

Principal Component Analysis and Near-Infrared Spectroscopy as Noninvasive Blood Glucose Assay Methods.

In this paper, a new model is presented for estimation of the blood glucose level from the measured near-infrared absorbance. The model has been developed in such a way that the regression coefficients of this linear relation have been approximated by considering only the molar absorptivity of the glucose and the obtained coefficients have been utilized to estimate the blood glucose levels from the measured absorbances. The estimation of the blood glucose concentrations by this blind approach exhibited an acceptable accuracy in comparison to the more accurate principal components regression method. The blood sample absorbances have been measured using a Fourier transform infrared device while the blood glucose levels have been determined by a commercial finger-prick glucometer device.

Study on the Response of Net Primary Productivity to Vegetation Phenology and Its Influencing Factors in Karst Ecologically Fragile Regions

Net primary productivity (NPP) is a crucial indicator of ecosystem function and sustainability. Quantifying the response of NPP to phenological dynamics is essential for understanding the impact of climate change on ecosystem processes. In this study, vegetation phenology data for Guizhou Province were extracted from the MCD12Q2 dataset, and NPP was estimated using the Normalized Difference Vegetation Index (NDVI) combined with meteorological data. Linear regression, trend analysis, and structural equation modeling were employed to clarify the spatiotemporal patterns of NPP and phenology as a basis for exploring the role of climatic factors in the NPP’s response to phenological changes. The results indicate that 72.15% of Guizhou Province shows an increasing trend in vegetation NPP (slope = 5.0981, p = 0.002). The start of the growing season (measured as SOS) tends to advance (slope = −0.4004, p = 0.0528), while the end of the growing season (measured as EOS) tends to delay (slope = 0.2747, p = 0.1011), resulting in an overall extension of the increasing length of the season (LOS) (slope = 0.64549, p = 0.0065). The spatiotemporal patterns of SOS, EOS, LOS, and NPP varied with elevation changes. For every 500 m increase in altitude, NPP decreased by 25.3 gC/m2, SOS was delayed by 7.1 days, EOS advanced by 1.25 days, and LOS decreased by 8.36 days. These findings suggest that the response of NPP to phenological changes is primarily controlled by local climatic and topographical conditions. Additionally, the indirect effects of climate on NPP through phenological changes were more significant than the direct effects. Climatic factors play varying roles in the NPP response to phenological dynamics, highlighting the profound influence of climate in regulating the mechanisms by which NPP responds to phenological changes.

Utilizing Robotic Process Automation and Artificial Intelligence in Auditing to Mitigate Audit Risks

Automating repetitive tasks significantly increases the efficiency of operations by identifying repetitive tasks, then choosing the appropriate tools for automation, designing successful workflows, testing, iteration, monitoring the performance of operations continuously, and making the necessary adjustments, which helps auditors accomplish their mission. This research aims to highlight the urgent need to develop the skills of accountants and auditors to maintain alignment with the swift advancements in digital technology in the accounting field to reduce fraud risks and protect stakeholders. The study relied on the applied aspect of 157 questionnaires distributed to a sample of auditors working in auditing firms and Federal Financial Supervision Bureau auditors. We used a five-point Likert scale to answer the questions on the questionnaire and the SPSS V.25 statistical package to look at the data and test our research hypotheses. We used Pearson correlation, simple regression, the coefficient of determination and interpretation R2, and the standard coefficient of regression B. Our research showed that using robotic process automation and artificial intelligence in auditing would make audited financial statements more reliable. Adding auditors' skills, artificial intelligence, and process automation will also significantly mitigate inherent control and detection risks. Furthermore, enhancing the quality of auditing

Use of thymidine kinase 1 autoantibody, thymidine kinase antigen, extracellular protein kinase A autoantibody, and C-reactive protein for early detection of malignant tumors in dogs.

Although the importance of early diagnosis of malignant tumors is a major concern, an easily accessible in-house method has not been established. To investigate the most optimal model by combining thymidine kinase 1 (TK1) autoantibody, extracellular protein kinase A (ECPKA) autoantibody, TK1 antigen, C-reactive protein (CRP), age, breed, and sex. Serum samples from 1702 dogs were collected from local animal hospitals and referral animal medical centers in Korea. TK1 autoantibody, ECPKA autoantibody, and CRP were measured using LFIA methods in serum samples of dog to design a new neoplastic index (NI) for early detection of malignant tumors in dogs. AUC of TK1 autoantibody model with TK1 antigen, CRP, age, and breed in multiple logistic regression analysis was 0.966 (TK1 autoantibody, P = .0005; TK1 antigen, P = .0003), and when the cutoff value was 0.417, the specificity was 87.1%, and sensitivity was 96.4%. Regression coefficients were 24.4, 20.5, 24.8, 10.6, respectively in TK1 autoantibody, TK1 antigen, CRP, and age, and the effect by breed (regression coefficient 2.1, 3.0) was the lowest. The same multiple logistic regression analysis on dogs with lymphoma, and AUC of TK1 autoantibody model was 0.981 (all P < .0001, TK1 antigen P = .09), when the cutoff value was 0.352, the specificity was 92.9%, and sensitivity was 93.7%. The NI including TK1 autoantibody could be useful in the screening test for both lymphoma and other tumors.

Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT

Estimating progression of acute ischemic brain lesions – or biological lesion age - holds huge practical importance for hyperacute stroke management. The current best method for determining lesion age from non-contrast computerised tomography (NCCT), measures Relative Intensity (RI), termed Net Water Uptake (NWU). We optimised lesion age estimation from NCCT using a convolutional neural network – radiomics (CNN-R) model trained upon chronometric lesion age (Onset Time to Scan: OTS), while validating against chronometric and biological lesion age in external datasets (N = 1945). Coefficients of determination (R2) for OTS prediction, using CNN-R, and RI models were 0.58 and 0.32 respectively; while CNN-R estimated OTS showed stronger associations with ischemic core:penumbra ratio, than RI and chronometric, OTS (ρ2 = 0.37, 0.19, 0.11); and with early lesion expansion (regression coefficients >2x for CNN-R versus others) (all comparisons: p < 0.05). Concluding, deep-learning analytics of NCCT lesions is approximately twice as accurate as NWU for estimating chronometric and biological lesion ages.

Differences in oral hypofunction prevalence and category measures across age groups and sex in Japan: a pilot study

ObjectivesThe deterioration of oral function to a state of oral hypofunction (OHF) has been associated with malnutrition and frailty. This cross-sectional pilot study investigated for differences in OHF prevalence and its category measures across age groups and sex, as well as their associations with physical function.MethodsA total of 155 healthy adults (median age: 55 years, range: 22–89 years) participated in this study after providing informed consent. The cohort was age and sex matched. Seven oral function measures based on the concept of OHF were assessed: oral hygiene (Hygiene), oral dryness, maximum occlusal force (MOF), lip-tongue motor function (LTMF), maximum tongue pressure (MTP), masticatory function, and swallowing function. Hand grip strength (HGS) was also measured. The participants were divided into the young (20–39 years), middle (40–64 years), and old (≥ 65 years) age groups for linear regression analysis of differences in oral and physical function. Differences in OHF prevalence were tested by the chi-square test.ResultsThe prevalence of OHF was significantly higher in the old group than in the young and middle groups. Such OHF measures as Hygiene, MTP, LTMF, and MOF were significantly worse in the old group as well. The measures of Hygiene and LTMF showed a moderate correlation with age in the multiple regression model (standardized partial regression coefficient, β = 0.24 and -0.19, respectively). HGS was significantly associated with MTP (β = 0.58, p < 0.001) and LTMF (β = 0.38, p = 0.002) in both male and female participants.ConclusionsOur findings suggest that OHF prevalence increases with age, especially after 65 years old. However, the pathological route may vary among OHF categories.

Assessing the Accuracy of Cardiovascular Disease Prediction Using Female-Specific Risk Factors in Women Aged 45 to 69 Years in the UK Biobank Study.

Cardiovascular disease (CVD) is the leading cause of mortality in women. We aimed to assess whether adding female-specific risk factors to traditional factors could improve CVD risk prediction. We used a cohort of women from the UK Biobank Study aged 45 to 69 years, free of CVD at baseline (2006-2010) followed until the end of 2019. We developed Cox proportional hazards models using the risk factors included in 3 contemporary CVD risk calculators: Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT. We added each of the following female-specific risk factors, individually and all together, to determine if these improved measures of discrimination and calibration for predicting CVD: early menarche (<11 years), endometriosis, excessive, frequent or irregular menstruation, miscarriage, number of miscarriages, number of stillbirths, infertility, preeclampsia or eclampsia, gestational diabetes (without subsequent type 2 diabetes), premature menopause (<40 years), early menopause (<45 years), and natural or surgical early menopause (menopause <45 years or timing of menopause reported as unknown and oophorectomy reported at age <45). In the model of 135 142 women (mean age, 57.5 years; SD, 6.8) using risk factors from Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, CVD incidence was 5.3 per 1000 person-years. The c-indices for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models were 0.710, 0.713, and 0.718, respectively. Adding each of the female-specific risk factors did not improve the c-index, the net reclassification index, the integrated discrimination index, the slope of the regression line for predicted versus observed events, and the Brier score or plots of calibration. Adding all female-specific risk factors simultaneously increased the c-index for the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease, Qrisk2, and PREDICT models to 0.712, 0.715, and 0.720, respectively. Although several female-specific factors have been shown to be early indicators of CVD risk, these factors should not be used to reclassify risk in women aged 45 to 69 years when considering whether to commence a blood pressure or lipid-lowering medication.

Price Volatility Mitigation and the Securities Market Financial Performance of Listed Insurance Firms at Nairobi Securities Exchange.

The performance of securities market globally plays an important role in both local and international markets. The high rise of such markets has given an increase in the number or risks associated with firms registered at the stock market. This study therefore sought to examine the effect of price volatility mitigation on the securities market financial performance of listed insurance firms in Kenya. The study adopted a descriptive research design and a quantitative research approach. The target population was 548 staff working in finance, investment, risk, actuarial and operations departments in the six insurance firms listed in Nairobi Securities Exchange. The sample size was determined using Yamane's Formula and stratified random sampling was used in the selection of the sample size. The study made use of both primary and secondary data. The study used a data extraction tool to collect secondary data from the annual reports and financial statements of the insurance companies. The study made use of structured questionnaires to collect primary data. The questionnaires generated quantitative data. Descriptive and inferential statistics was used in analyzing quantitative data with the help of the Statistical Package for Social Sciences (SPSS version 24) statistical software. Descriptive statistics included frequency distribution, percentages, mean, and standard deviation. Inferential data analysis was done using Pearson correlation coefficient and linear regression analysis. The study found that price volatility mitigation has a positive and significant effect on securities market financial performance of listed insurance firms in Kenya. The study recommends that insurance firms should focus on implementing strategies to effectively manage supply and demand dynamics within the securities market. This includes closely monitoring market trends, analyzing supply-demand imbalances, and adjusting pricing and stock clearance strategies accordingly. By actively managing supply-demand dynamics, firms can mitigate price volatility risks and enhance securities market financial performance

Effect of corporate voluntary disclosure on corporate performance: Evidence from a transitional economy

Corporate performance is one of the core pillars of accelerating the economic growth of any economy. There are some direct and indirect components of corporate performance. This study is conducted to measure the impact of indirect factors, such as corporate voluntary disclosures, on corporate performance. A sample of 872 annual reports is investigated. A content analysis has been conducted to measure voluntary disclosure. The study employs the short panel data, fixed model regression, and Panel Corrected Standard Error (PCSE). The analysis indicates that corporate voluntary disclosure has influenced the return on assets with a regression coefficient of 0.0724 at p ≤ 0.01. Corporate voluntary disclosure in the transitional economy significantly influences corporate performance. However, the volume of corporate voluntary reporting in energy-related disclosure and climate-related disclosure is minimal, as the mean is only 2.21% and 2.31%. This implies that the government of the transitional economy may focus on persuading corporations to disclose additional information besides mandatory disclosure. In addition, indirect determinant of corporate performance, like corporate voluntary disclosure, brings sustainability to corporate performance, which further assists the transitional economy in shifting its economic status from one dimension to another. AcknowledgmentThe authors are very thankful to the Institute of Advanced Research, United International University, Bangladesh, for providing partial funds to complete this empirical study.This project is partially funded by the Institute for Advanced Research Publication Grant of United International University, Ref. No.: IAR-2024-Pub-069.

Relationship between evaluation factors and star ratings for Japanese community healthcare institutions in electronic word-of-mouth reviews: an observational study

BackgroundInternet reviews have become increasingly crucial for both healthcare providers and patients. Electronic word-of-mouth (eWOM) reviews on internet sites often comprise textual content with numerical ratings. In this study, we aimed to identify the evaluation factors of community healthcare institutions regarding eWOM reviews and the impact of each evaluation factor on the institution’s ratings.MethodsAn observational study was conducted to qualitatively and quantitatively analyse eWOM data posted on Google for randomly selected healthcare institutions in Hirosaki, Japan from September to October 2022. For qualitative data, the authors repeatedly read the eWOM text, coded it, and categorised related sections. For quantitative analysis, a multivariate analysis using a linear regression model was conducted with the categorised factors from the qualitative analysis as explanatory variables and eWOM ratings as response variables.ResultsTwenty medical institutions (two hospitals and 18 clinics) were randomly extracted from the registry. A total of 147 eWOM texts from each institution were analysed, and coding was performed for 474 segments in the texts. In the qualitative analysis, the evaluated factors in eWOM texts for medical institutions were categorised as communication (evaluation factor for communication with healthcare providers), clinical practice (evaluation factor for the clinical practice of healthcare providers), and medical institution (evaluation factor for the characteristics of medical institutions). According to the multiple regression analysis, the partial regression coefficients for the explanatory variables of positive communication, clinical practice, and medical institution evaluations were 1.04 (95% confidence interval 0.70 to 1.39), 0.65 (95% confidence interval 0.27 to 1.04), and 0.75 (95% confidence interval 0.35 to 1.15), respectively, with the number of ratings as the response variable. Partial regression coefficients for the explanatory variables of negative communication, clinical practice, and medical institution evaluations were − 1.52 (95% confidence interval − 1.88 to -1.16), -0.90 (95% confidence interval − 1.28 to -0.52), and − 0.24 (95% confidence interval − 0.60 to 0.12), respectively.ConclusionWe quantitatively and qualitatively analysed the eWOM reviews and ratings of healthcare institutions posted on Google. Three evaluation factors were identified: communication, clinical practice, and medical institution. Our study revealed that communication significantly impacts ratings.

Understanding preoperative health-related quality of life in rotator cuff tear patients: role of patients’ characteristics

BackgroundAssessing health-related quality of life (HRQoL) is a widely employed method in orthopedics to evaluate patient well-being and measure the effectiveness of surgical interventions. Understanding the relationship between specific baseline factors and preoperative HRQoL can help clinicians identify patients at risk for low quality of life and thus, develop preventive strategies that adequately address individual patient needs. The objective was to assess associations between baseline factors and preoperative HRQoL in patients undergoing arthroscopic rotator cuff repair (ARCR).MethodsThis study is part of a multicenter prospective Swiss cohort study that included patients undergoing ARCR between June 2020 and November 2021. Data were derived from baseline and surgery forms. HRQoL was assessed using the European Quality of Life 5-Dimension 5-Level (EQ-5D-5L) questionnaire to calculate EQ-5D index and EQ VAS scores. Univariable and multivariable linear regression models examined associations between the 21 factors and preoperative HRQoL. Final models were determined using stepwise backward regression.ResultsA total of 973 included patients (mean age [SD], 57.3 [9.4] years; 611 men [63%]) had a mean [SD] EQ-5D index and EQ VAS of 0.70 [0.23] and 68.7 [19.8], respectively. Being male (regression coefficient (β), 0.05; 95% CI [0.02–0.08]), having a higher age (β, 0.02; 95% CI [0.01–0.03]) and higher education levels (e.g., university, college: β, 0.11; 95% CI [0.06–0.16]) were associated with a higher EQ-5D index. Increased body mass index (β, -0.02; 95% CI [-0.04 to -0.01]) and worse sleep quality (β, -0.03; 95% CI [-0.04 to -0.03]) were associated with a lower EQ-5D index. Factors negatively associated with overall EQ VAS health were depression (e.g., moderate: β, -12.70; 95% CI [-16.18 to -9.21]), presence of at least one comorbidity (β, -3.71; 95% CI [-5.91 to -1.52]), and pain (β, -1.81; 95% CI [-2.36 to -1.26]).ConclusionOur results suggest that preoperative HRQoL is highly associated with sociodemographic and patient-related factors. Specifically addressing these factors may improve orthopedic care.

A robust and efficient change point detection method for high-dimensional linear models

In the context of linear models, a key problem of interest is to estimate the regression coefficient. Nevertheless, in certain instances, the vector of unknown coefficient parameters in a linear regression model differs from one segment to another. In this paper, when the dimension of covariates is high, a new method is proposed to examine a linear model in which the regression coefficient of two subpopulations may be different. To achieve robustness and efficiency, we introduce modal linear regression as a means of estimating the unknown coefficient parameters. Furthermore, our proposed method is capable of selecting variables and checking change points. Under certain mild assumptions, the limiting behavior of our proposed method can be established. Additionally, an estimation algorithm based on kick-one-off and SCAD approach is developed to implement in practice. For illustration, simulation studies and a real data are considered to assess the performance of our proposed method.

Unraveling the variability and geoclimatic drivers of daily precipitation concentration over a Western Himalayan Province of India.

A sound understanding of daily precipitation concentration provides critical insights about extreme precipitation and associated hazards such as rainstorms, flash floods, cloud bursts, and soil erosion. Therefore, this study examines the spatiotemporal variability and geoclimatic drivers of daily precipitation concentration using the concentration index (CI), precipitation concentration degree (PCD), Q Index (QI), and Theil index (TI) over a Western Himalayan Province, namely Himachal Pradesh, India. For this, the daily precipitation data pertaining to 111 grids (0.25° × 0.25° resolution), encompassing entire province, have been downloaded from India Meteorological Department (IMD), Pune, for the period 1951 to 2020. The trends in concentration indices have been detected using Mann-Kendall (MK) and Sen's slope methods, whereas their relationship with geoclimatic drivers have been assessed using Pearson's correlation coefficient (physiographic) and random forest regression model (climatic) at annual and monsoon season time scales. The results have revealed a high correlation between CI-TI (> 0.95), followed by QI-TI (> 0.84), QI-PCD (> 0.80), TI-PCD (> 0.78), QI-CI (> 0.65), and CI-PCD (> 0.60), representing nearly similar precipitation concentration distribution. Among the concentration indices, QI has shown maximum sensitivity to long-term changes followed by TI, CI, and PCD at both time scales. Similarly, physiographic factors have maximum effect on QI, showing its greatest sensitivity to latitude, longitude and elevation. While considering the impact of climatic drivers, Atlantic Meridian Oscillation (AMO) and Sunspot activity have exerted maximum influence on annual variability of CI, PCD, QI, and TI, whereas monsoon variability of these indices has been modulated by Pacific Decadal Oscillation (PDO), especially over northern parts of Himachal Pradesh. These findings will assist soil and water resource managers, disaster executives, agricultural stakeholders, and urban planners in understanding the unexplored aspects of daily precipitation concentration and making informed decisions over the mountainous regions of western Himalaya.

Image retrievable encryption based on linear fitting and orthogonal transformation

Abstract With the development of cloud computing, an increasing number of resource-constrained image owners tend to store their images in the cloud and rely on image retrieval services to obtain the images they desire. However, the security of the cloud cannot be fully guaranteed. To ensure image security while achieving good retrieval performance, we have designed a retrievable image encryption algorithm based on linear fitting and orthogonal transformation. This algorithm first generates encryption and feature extraction domains through orthogonal decomposition, and then applies a modified ResNet50 network for feature extraction in the feature extraction domain. The encryption process employs an improved affine transformation based on linear fitting, where part of the fitting values comes from the original image data and the other part comes from data generated by a chaotic system. Additionally, to simplify the measurement of feature similarity in the cloud, we have designed a hierarchical feature index tree to narrow the retrieval scope, thereby reducing retrieval complexity. Experimental results show that the proposed algorithm effectively protects image privacy and achieves high retrieval accuracy. The F-score reached 6.7634% on the Ghim10k dataset and 25.514% on the Corel 1K dataset, significantly improving upon traditional methods. This algorithm has potential application value in the fields of secure image storage and efficient retrieval in the cloud.

Integration of Neural Networks and First-Principles Model for Optimizing l-Lactide Branched Polymerization.

Addressing the growing demand for sustainable materials, this research paves the way for the efficient consumption and sustainable production of branched polylactide (PLA). A novel hybrid modeling approach combines first-principles (FP) model with artificial neural network (ANN) for ring-opening polymerization (ROP). The hybrid ANN, trained with FP model data, demonstrated optimal performance with a hidden layer of 20 neurons, achieving a root mean square error (RMSE) of 0.004 and a regression coefficient (R2) of 0.99. The hybrid model accurately predicted key polymer properties, including average molecular weights (Mn and Mw), polydispersity index (PDI), degree of branching (DB), monomer conversion, and polymerization time. Validation was performed on various branched PLA compositions (PLLH80, PLLH94, and PLLH97). Multiobjective optimization (MOO) using NSGA-II showed strong agreement between FP model and hybrid ANN across six case studies, highlighting their effectiveness in predicting polymerization outcomes.

Sustainable Cycling: Boosting Commuting and Tourism Opportunities in Istria

This study explores the evolution of cycling in Istria, a region located in parts of Slovenia and Croatia along the Northern Adriatic coast, recognised for its vibrant tourism sector. The research aims to evaluate the potential of cycling for both daily commuting and tourism while examining its economic, environmental, and social impacts. The study uses secondary data from 2010 to 2023, econometric methods, and interviews to assess how cycling contributes to economic growth, regional collaboration, employment rates, and greenhouse gas (GHG) emissions. In Slovenia, regression analysis indicates that bicycle imports positively correlate with tourist arrivals, which positively influences GDP. However, VAR analysis shows that employment has a negative impact on GHG emissions. In contrast, the increase in bicycles contributes to higher GHG emissions, suggesting the possibility of greenwashing or double pollution. In Croatia, regression coefficients were found to be insignificant, aligning with insights gained from interviews. Nonetheless, VAR analysis reveals that tourist arrivals and GDP positively reinforce each other, while GHG emissions negatively affect employment. Bicycles are shown to reduce GHG emissions, and tourist arrivals are also associated with lowered GHG emissions. The study concludes that cycling holds significant potential for fostering sustainable tourism and regional development; however, the differing impacts observed in Slovenia and Croatia necessitate tailored policy interventions. The hypothesis is partially confirmed in Slovenia, where bicycle imports positively impact tourist arrivals and GDP but are challenged by increased GHG emissions, while in Croatia, the hypothesis is not supported by direct economic gains from cycling. However, bicycles contribute to reducing GHG emissions.

Morphological data of the superior vena cava predicted by multiple linear regression equations

This study explores the main surface markers in the hemodialysis puncture catheterization pathway and the relative spatial position of venous vessels in the puncture results, providing data support for venous puncture. Seventy cadavers were studied; angles of vertical axis with superior vena cava, left and right brachiocephalic vein at the confluence and circumference of superior vena cava were the primary parameters. Bivariate correlation analysis was used to determine in relation to other body parameters, and a regression equation was also obtained. The angle of the vertical axis with the superior vena cava at the confluence was linearly correlated with the distances between the coracoid processes and the jugular notch to coracoid processes. The angle of the vertical axis with left brachiocephalic vein was linearly correlated with the thickness of thorax and the length from the upper pericardial margin to the confluence. The angle of the vertical axis with right brachiocephalic vein was correlated with clavicular midline length, acromial distance, intersection point of clavicle to clavicular midline, confluence to clavicular midline, superior edge of the blood vessels to superior edge of clavicle at the right intersection, and superior edge of pericardium to confluence. There is a linear relationship between the superior vena cava circumference and the distance from the superior vena cava to the aorta. A number of parameters influence angles of the vertical axis with superior vena cava, left and right brachiocephalic veins and the superior vena cava circumference which can provide guidance for intubation.

Machine learning and regression in the management of runoff in bauxite mines under rehabilitation.

Accurate and reliable forecasting of monthly runoff considering several years of rehabilitation helps in planning and managing the water resources system of bauxite mining areas. A combination of linear regression models and artificial intelligence was proposed and applied to predict runoff from mined areas. The ideal number of input variables was chosen considering a correction threshold (correlation above 95%), stepwise selection, and according to the recursive feature elimination (RFE). The linear regression equations were divided into explanatory and predictive, with the predictive ones being trained and validated with all possible combinations of annual data periods. Five machine learning models (linear model (LM), K-nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), and Cubist) were trained and used as a prediction instrument on the test set of each subsequent year. Precipitation, accumulated precipitation duration, maximum precipitation intensity, and number of events were the most explanatory variables in most linear regression and machine learning equations. The linear regression model approach presented satisfactory performance indices in predicting surface runoff in a mined bauxite area. The Cubist and RF models were the best predictors for the greatest number of years of rehabilitation, with a coefficient of determination varying with maximum values between 0.52 and 0.88 in validation. There was no best model that consistently presented the best result in all years of rehabilitation, and it is recommended to use the annual model for the respective year of rehabilitation. The linear regression equations and algorithms were useful tools in predicting runoff series and, therefore, promising monthly runoff prediction. The presented approach becomes a starting point in the search to optimize the hydrological planning of surface bauxite mines.

Prenatal Tobacco Exposure, Brain Subcortical Volumes, and Gray-White Matter Contrast.

Maternal tobacco use during pregnancy (MTDP) remains a major public health challenge. However, the complete spectrum of effects of MTDP is not fully understood. To examine the longitudinal associations of MTDP and children's brain morphometric subcortical volume and gray-white matter contrast (GWC) development. Cohort study of children aged 9 to 10 years at wave 1 (October 2016 to October 2018) and at a 2-year follow-up (wave 2; August 2018 to January 2021; aged 11-12 years) across 21 US sites in the Adolescent Brain Cognitive Development (ABCD) Study. Data were analyzed from October 2023 to October 2024. MTDP. Morphometric brain measures of subcortical volume and GWC. Among the 11 448 children (51.5% male; 13.1% Black; 24.0% Hispanic; and 52.9% White) at wave 1, 1607 (16.6%; 95% CI, 13.0%-20.2%) were identified with MTDP exposure. At wave 1, children with MTDP exposure (vs no exposure) exhibited lower GWC in widespread brain regions primarily located in the frontal (eg, superior frontal; regression coefficient [B] = -0.0019; SE, 0.0006; P = .004), parietal (eg, supramarginal; B = -0.0021; SE, 0.0007; P = .002) and temporal lobes (eg, middle temporal; B = -0.0024; SE, 0.0007; P < .001). These differences in GWC continued to be significant at wave 2. In regard to subcortical volume, children with MTDP exposure demonstrated smaller volume of the lateral ventricle (B = -257.5; SE, 78.6; P = .001) and caudate (B = -37.7; SE, 14.0; P = .01) in the left hemisphere at wave 1, and lower volume of the caudate in both left (B = -48.7; SE, 15.9; P = .002) and right hemisphere (B = -45.5; SE, 16.1; P = .01) at wave 2. This cohort study found that MTDP exposure was associated with lower GWC across the whole cortex and smaller caudate nuclei volume compared with no exposure, signifying the importance of preventing MTDP and necessitating further research on this topic.

CineECG Repolarization Gradients Predict Acute Hemodynamic Response in CRT Patients.

A variable proportion of non-responders to cardiac resynchronization therapy (CRT) warrants the search for new approaches to optimize the position of the left ventricular (LV) lead and the CRT device programming. CineECG is a novel ECG modality proposed for the spatial visualization and quantification of myocardial depolarization and repolarization sequences. The present study aimed to evaluate CineECG-derived parameters in different pacing modes and to test their associations with acute hemodynamic responses in CRT patients. CineECG was used to construct the average electrical path within the cardiac anatomy from the 12-lead ECG. CineECG and LV dP/dt max were tested in 15 patients with nonischemic dilated cardiomyopathy and left bundle branch block (QRS: 170 ± 17 ms; LVEF: 26 ± 5.5%) under pacing protocols with different LV lead localizations. The CineECG-derived path directions were computed for the QRS and ST-T intervals for the anteroposterior (Xh), interventricular (Yh), and apicobasal (Zh) axes. In a multivariate linear regression analysis with adjustment for the pacing protocol type, the ST-T path direction Yh was independently associated with the increase in dP/dt max during CRT, [regression coefficient 639.4 (95% confidence interval: 187.9-1090.9), p = 0.006]. In ROC curve analysis, the ST-T path direction Yh was associated with the achievement of a 10% increase in dP/dt max (AUC: 0.779, p = 0.002) with the optimal cut-off > 0.084 (left-to-right direction) with sensitivity 0.67 and specificity 0.92. The acute hemodynamic response in CRT patients was associated with specific CineECG repolarization sequence parameters, warranting their further testing as potential predictors of clinical outcomes.