Multiple Regression Method Research Articles

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.

Mechanical mechanism analysis of rockburst in deep-buried tunnel with high in-situ stress

The Qinling water conveyance tunnel has a large buried depth and high in-situ stress level, and rockburst disasters frequently occurred during excavation. In order to find out the mechanical mechanism of rockburst, the research work in this paper is as follows: (1) In-situ three-dimensional hydraulic fracturing method was used to measure the in-situ stress of the deep buried tunnel crossing the ridge. (2) Based on the measured in-situ stress results, the stress distribution characteristics of the tunnel crossing the ridge were obtained by the multiple linear regression method, and the rockburst tendency during construction was predicted. (3) A three-dimensional numerical model of tunnel excavation was established to analyze the dynamic adjustment characteristics of the surrounding rock stress and elastic strain energy during TBM excavation, and to clarify the mechanical mechanism of rockburst. The research results show that the maximum principal stress of the deep-buried tunnel crossing the ridge of Qinling is 40–66 MPa, which belongs to extremely high in-situ stress level, and medium-strong rockburst may occur during excavation. In the process of TBM excavation, the stress of the surrounding rock in the range of 2.6 times the diameter of the tunnel before and after the working face is adjusted violently, and the concentrated zones after the stress redistribution are mainly distributed in the arch roof and arch bottom, and the stress concentration coefficient can reach 2.06. The arch roof, arch waist, and arch bottom are susceptible to immediate rockburst due to stress transient unloading at the moment of excavation. After the elastic strain energy of the surrounding rock at the arch roof and the arch bottom is released and accumulated, it is easy to cause time delayed rockburst, and the depth of the rockburst pit can reach 3.5 m, which is consistent with the rockburst phenomenon in the field.

Pengaruh Current Ratio, Debt to Asset Ratio, dan Total Asset Turn Over terhadap Net Profit Margin

This study aims to determine the effect of Current Ratio, Debt to Asset Ratio and Total Asset Turn Over on Net Profit Margin. The data used in this study is secondary data with a population of retail sub-sector companies listed on the Indonesia Stock Exchange with the observation year 2019 - 2022, totaling 31 companies. The sampling technique used in this study was purposive sampling method with data analysis using multiple linear regression methods and using the Statistical Package for the Social Sciences (SPSS) version 22 program and Microsoft Excel for Windows 2019. The results of this study indicate that Current Ratio, Debt to Asset Ratio and Total Asset Turn Over affected Net Profit Margin with a coefficient of determination (R2) of 83.3% and the rest is influenced by other variables not studied in this study.

Pengaruh Green Accounting, Intellectual Capital terhadap Kinerja Keuangan Perusahaan Sektor Kesehatan di Indonesia

This study's primary goal is to examine how intellectual capital and green accounting affect the financial performance of health sector businesses. The value added intellectual coefficient (VAIC) is used to quantify intellectual capital, and environmental expenses and eco-friendly products are used to measure green accounting. The analyzed data consists of secondary data from the annual reports of healthcare companies listed on the Indonesia Stock Exchange (IDX) for the period from 2019 to 2022. Multiple linear regression method was employed in the data analysis. The study's conclusions suggest that while green products and environmental cost have no effect on a company's financial performance, intellectual capital has a favorable effect on the financial performance of businesses in the health sector. These results underscore the importance of effective management of intellectual capital to enhance financial success in the healthcare sector.

Pengaruh Profitabilitas, Leverage, dan Umur Perusahaan Terhadap Praktik Tax Avoidance: Studi Kasus Perusahaan Sektor Energi yang Terdaftar di Bursa Efek Indonesia Tahun 2020 – 2023

This research aims to investigate the influence of profitability, leverage, and company age on tax avoidance practices in energy sector companies in Indonesia that are listed on the Indonesia Stock Exchange. The data were analyzed using multiple regression methods on a sample of 46 financial reports from the years 2020 to 2023. The results of the analysis show that profitability does not have a significant effect on tax avoidance, while leverage also does not have a significant effect. However, company age has been proven to have a significant effect on tax avoidance. Nonetheless, these three variables have a simultaneous effect on tax avoidance. This research is relevant for improving tax policies and reducing tax avoidance practices that are detrimental to the state.

Enhancing Nursing Competence in China: The Interplay of Resilience, Mindfulness and Social Support

ObjectivesTo address the nursing crisis, it is imperative to comprehend the factors that influence nursing competencies, which are crucial for the delivery of quality patient care. BackgroundFacing demographic shifts and increasingly complex healthcare demands, China's nursing sector struggles with workforce shortages and the need to enhance core competencies. This research explores the interplay of social support, psychological resilience, mindfulness and nursing competencies in various hospital environments in China. MethodsThrough a cross-sectional survey, 941 nurses across tertiary, secondary and private healthcare settings completed self-assessment questionnaires. The analysis included multiple linear regression and comparative methods to assess how psychological resilience, mindfulness and social support have an impact on nursing competencies. ResultsFindings revealed a strong relationship between psychological resilience and nursing competencies, with resilience being a key predictor. Mindfulness and social support also significantly contributed to competency levels. Nurses in tertiary hospitals showed greater competencies than those in secondary or private facilities. ConclusionEnhancing nursing competencies requires targeted interventions focusing on professional development and supportive workplace cultures. Incorporating psychological resilience, social support and mindfulness into nurse training is crucial for improving practice and policy.

Combining Bourgoyne and Young Equations by Bagging Tree Regression to Predict Rate of Penetration in a Southern Iraqi Field, Case Study

Abstract Achieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain knowledge and capturing non-linear relationships. Its ensemble nature also mitigates the impact of outliers. This approach combines physics-based equations with machine learning to enable more accurate ROP predictions in drilling operations. It enhances drilling efficiency, reduces expenses, and improves decision-making in the oil and gas sector. Extensive testing on actual drilling datasets has demonstrated outstanding performance compared to the multiple linear regression (MLR) method. With the increased R2 and zero P-value. positive findings show that this tool can benefit precise future ROP prediction in southern Iraqi oil well drilling.

Correlation analysis of occupational stress and metabolic syndrome among employees of a power grid enterprise in China.

Being in a state of high occupational stress may disrupt the metabolic balance of the body, thus increasing the risk of metabolic diseases. However, the evidence about the relationship between occupational stress and metabolic syndrome was limited. To explore the association between occupational stress and metabolic syndrome (MetS) in employees of a power grid enterprise. A total of 1091 employees were recruited from a power grid enterprise in China. Excluding those who failed to complete the questionnaire and those who had incomplete health check-ups, 945 subjects were included in the study. Assessment of occupational stress was used by job demand-control (JDC) and effort-reward imbalance (ERI) questionnaires, respectively. The information on body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected. The levels of high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and fasting blood glucose (FBG) in the fasting venous blood samples were measured. Logistic regression analysis and multiple linear regression methods were used to analyze the correlation between JDC and ERI models of occupational stress, metabolic syndrome, and its components, respectively. The prevalence of MetS was 8.4% and 9.9% in JDC and ERI model high occupational stress employees, respectively. ERI model occupational stress and smoking are significantly associated with the risk of MetS. ERI ratio was significantly associated with lower HDL-C levels. Gender, age, marital status, smoking, high-temperature and high-altitude work were significantly associated with metabolic component levels. Our study revealed a high detection rate of occupational stress in both JDC and ERI models among employees of a power grid enterprise. ERI model occupational stress, demanding more attention, was associated with the risk of MetS as well as its components such as HDL-C.

Structure and microwave dielectric characteristics of (Ba,Sr,Ca)HfO3 ceramics

In the present work, the structure and microwave dielectric properties of (Ba,Sr,Ca)HfO3 ceramics were systematically investigated to understand the general mechanism of tuning the temperature coefficient of resonant frequency, τf in perovskite ceramics. Ba1–xSrxHfO3 and Sr1–yCayHfO3 could form continuous solid solutions while the solid solubility of Ca in Ba1–yCayHfO3 was about 20% (in mole). τf changed nonlinearly with increasing tolerance factor as the result of competition between the increase in the restoring force on the ions and the increase in polarizability. Under the guidance of three microscopic mechanisms affecting τf, a preliminary attempt was made to explore the suitable parameters to predict the variation trend of τf. Normalized ionic radii and the τf values of the end members were selected as independent variables, and τf was calculated by using multiple linear regression method. For Ba1–xSrxHfO3 and Sr1–yCayHfO3 with orthorhombic structures, the root mean square error between the calculated and measured τf was only 6.8×10–6 °C–1. The good agreement between the calculated τf values and the measured ones in Ba1–x–ySrxCayHfO3 ceramics confirmed its validity where three elements jointly occupy A-site, but it only works when there is no structural phase transition. Progresses in this research field would not only deepen our understanding of mechanism of regulating properties in multi-ion solid solutions, but also boost the developments of closely related fields such as machine learning-assisted design and high-entropy ceramics. Finally, a good combination of microwave dielectric properties was achieved in Sr0.15Ca0.85Hf0.96Ti0.040O3: εr = 27.8, Qf = 36,470 GHz, τf = +5×10–6 °C–1.

Prediction of the level of crime cases using multiple linear regression in the city of Pontianak

This study aims to develop a predictive model for the crime rate in the Police Resort Area of Kota (POLRESTA) Pontianak using the Multiple Linear Regression method based on secondary data obtained from the Criminal Investigation Unit of POLRESTA Pontianak. The utilization of descriptive statistical techniques and data visualization aids in identifying relevant features that enrich the information within the model. The evaluation results indicate that this model performs well in both modeling and predicting crime rates in Kota Pontianak. Despite the variations in error rates between training and testing data, the model still demonstrates its proficiency in predicting known data. The testing results also reveal that the Mean Absolute Percentage Error (MAPE) values for each crime category exhibit variations in the testing dataset, with MAPE for "Berat" increasing to 12.91%, MAPE for "Sedang" increasing to 30.11%, and MAPE for "Ringan" increasing to 26.59%. Consequently, this study concludes that the Multiple Linear Regression method holds potential as an effective tool for decision-making and the development of strategies to combat criminal activities in Kota Pontianak

Relationship between family function and anxiety among nurses during the COVID-19 pandemic: a mediating role of expressive suppression

BackgroundThe aims of the present study were to investigate the incidence of nurses who suffered anxiety during the COVID-19 pandemic and to explore how expressive suppression influences the relationship between family function and anxiety.MethodsThis study used cross-sectional research and simple random sampling. A total of 300 questionnaires were distributed and 254 questionnaires were qualified after invalid questionnaires were proposed, thus a total of 254 female nurses from a tertiary hospital were included in this study. The measurement included General demographic questionnaires, the Self-Scale Anxiety, Scale (SAS), Emotion Regulation Strategies Questionnaire (ERQ), and Family Function Assessment (FAD). T-test, nonparametric Wilcoxon or Kruskal-Wallis test, χ2 test, Pearson or Spearman correlation analysis, multiple stepwise regression and bootstrap methods was performed to analyze the data.ResultsIn this study, 22.4% of the nurses exhibited anxiety symptoms, with 17.7% eliciting mild anxiety symptoms, 4.3% showing moderate anxiety symptoms and 0.4% with severe anxiety symptoms. Family function and expressive suppression were positively associated with anxiety severity. And family function influenced anxiety among nurses via direct and indirect (Expressive suppression -mediated) pathways.ConclusionsExpressive suppression partially mediated the influence of family function on anxiety symptom. To this end, nurse administrators should establish a robust mental health support system encompassing psychological counseling and emotional support groups. Furthermore, nurse administrators should consistently inquire about nurses’ family situations, encourage nurses to articulate their emotions and needs candidly, both within the domestic sphere and the workplace, while refraining from excessive self-repression.

The Influence of Technology Literacy and the Use of Artificial Intelligence (AI) By Hasanuddin University Students on the Change of Habits in Completing Academic Tasks

With the rapid advancement of technology and the integration of artificial intelligence in various aspects of life, students' approach to academic tasks is evolving. This study aims to analyze the effect of using AI on changes in habits in completing academic assignments. The type of research used is descriptive quantitative with multiple Linear Regression method. In this study, the sample was drawn with cluster random sampling of 400 people who had used AI which was divided into 16 faculties at Hasanuddin University. The data collection method in this study uses a questionnaire distributed online via kuesio.id which contains statements, interviews and documentation by searching the internet. In testing data analysis techniques, namely validity and reliability tests calculated using the SPSS (Statistical Product and Service Solutions) program with Cronbach Alpha (α) above 0.60, so the overall statement is declared reliable. The results of this study indicate that the literacy level of Hasanuddin University students is categorized as good with a respondent achievement rate (TCR) of 78.3% and the use of AI that is often used from the 15 applications listed by researchers, namely ChatGPT by 26.7%, Perplexity 12.7%, and Grammarly 12.6%. The results of data analysis show that there is a positive correlation between technological literacy and the ability to use AI with changes in habits in completing academic tasks. Students who have high literacy and are skilled in utilizing AI tend to be more efficient and effective in completing their tasks.

Pengaruh Kompetensi dan Lingkungan Kerja terhadap Kinerja Pegawai

The study of employee performance continues to be a major focus of human resource experts and practitioners. Various influential factors have been analyzed in relation to efforts to improve performance. Various aspects have been explored, both from the organizational and individual side, with various perspectives resulting in diverse findings. The focus of the study aimed to understand the impact of competencies and working conditions on employee outcomes. The approach in the study uses quantitative correlation which examines cause-and-effect relationships. The study population consisted of 96 employees of the Maluku Province Bawaslu, which was also used as the research sample (saturated sample). The instrument used has gone through validity and reliability testing, which then the data is analyzed using multiple linear regression methods. The study findings show that competence and work environment have a positive and significant impact on employee performance. This suggests that employees who are more competent and work in better working conditions tend to have better performance. The path analysis and discussion conducted in the study also supported these findings, concluding that competence working conditions have a positive and meaningful impact on employee performance, thus reinforcing the conclusion that these two factors are important in improving employee performance.

Prediction of cyber-attacks in air transport using neural networks

This article addresses the topic of cyber-attacks in air transport, which is crucial for ensuring the safety and reliability of airports and air transport operations. The aim of the article was to present a new method for predicting cyber-attacks in air transport based on neural networks. The task of the neural network was to determine the multiple regression function based on which the probability of a cyberattack occurring at a specified hour and on a specific day of the week is predicted. The probability, depending on the time of the cyberattack occurrence, was determined using theoretical distributions. The method was verified with real data. Verification of the method confirmed its high effectiveness, determined at the level of 92%. The study examined the effectiveness of using the classical multiple regression method in predicting cyber-attacks in air transport. The classical multiple regression model covered only 0.14 of the input data, while the regression model generated by the neural network covered 0.99, indicating the high efficiency of the developed neural network.

Estimasi Laju Pertumbuhan Penduduk menggunakan Metode Regresi Linier Berganda di Kabupaten Batang

Population growth in an area is very important for development and as a benchmark for development and progress. One way to estimate/estimate population growth is to use Data Mining techniques. Data mining is able to analyze data into information. This research will discuss the population growth of Batang Regency, Central Java in 2024. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make estimates/predictions by utilizing past data regarding population growth so that it can produce a relationship pattern. This Multiple Linear Regression method aims to produce the best predictions. The research data used is population data in Batang Regency in 2019-2023. Based on research that has been carried out, the results show a population growth of 853,800 people in 2024. This figure shows a population increase of 24,917 people compared to 2023 of 828,883 people with an increase percentage of 3%. The benefits of this research can be conveyed to the Batang Regency Government as material for decision and policy making in the year.

Resilience and social support as protective factors against suicidal ideation among tertiary students during COVID-19: a cross-sectional study

BackgroundSuicidal ideation is a depression symptom which represents a key (cognitive) component of suicidality and plays an important role in suicide risk detection, intervention, and prevention. Despite existing research showing the importance of certain factors of depression symptoms and suicidal ideation, less is known about the interaction between the various risk and protective factors. The aim of the study was to examine whether living conditions characteristics and personal circumstances during the COVID-19 pandemic predicted the presence of depression symptoms and suicidal ideation among tertiary students and whether resilience and social support can mitigate the detrimental effects of difficult life circumstances.MethodA large online cross-sectional study was conducted in March 2021 among 4,645 Slovenian tertiary students. Hierarchical multiple regression and hierarchical logistic regression methods were used to assess and compare the effect of life circumstances variables, as opposed to resilience and social support, on depression symptoms and suicidal ideation.ResultsFemale gender, single relationship status, living alone, a higher degree of household conflict, having a history of mental illness and chronic disease diagnosis were significant predictors of depression scores. All but gender were also predictors of suicidal ideation. Household conflict and a history of mental illness were the factors showing the strongest effect in both cases. On the other hand, social support and, in particular, resilience proved to be strong protective factors against depression symptoms and suicidal ideation. After accounting for one’s resilience and social support, the explained variance in depression scores was more than doubled, while the harmful effect of household conflict and history of mental illness significantly decreased.ConclusionsThe findings stress the importance of one’s resilience and social support and explain why some people manage to maintain mental well-being despite finding themselves in difficult life circumstances, which was the case for many tertiary students during the COVID-19 pandemic. These insights may inform preventive efforts against developing suicidal ideation and may be used as support for the design and implementation of interventions for improving resilience and social support from childhood onward.

THE IMPACT OF TRANSFORMATIONAL LEADERSHIP, ORGANIZATIONAL CULTURE, AND WORK MOTIVATION ON EMPLOYEE PERFORMANCE AT LPP RRI JEMBER

This study was conducted at LPP RRI Jember which aims to determine the effect of transformational leadership style, organizational culture and work motivation on employee performance at LPP RRI Jember. The analysis was conducted on the performance of 83 employees of LPP RRI Jember using saturated sampling techniques and data collection techniques were carried out by means of observation and distributing questionnaires to all employees of LPP RRI Jember about various information / data needed using a Likert scale while data analysis used multiple regression methods with the help of the SPSS program. Company leaders should be able to provide motivation to subordinates both in moral and material form to improve employee performance and foster an attitude of loyalty to the company. Leaders are also required to pay more attention to the policies taken in leading the company, and determine a good leadership strategy or style in order to create a conducive work atmosphere between superiors and subordinates.

Implementation of data mining to estimate the need for toast bread supply at junction cafe using the multiple linear regression method

Implementation of data mining to estimate the need for toast bread supply at junction cafe using the multiple linear regression method

Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.

Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.

Financial Literacy and Credit Accessibility for Small Scale Business Sustainable Growth

This study is based on financial literacy and credit accessibility for small scale business sustainable growth Owo, Ondo State. The specific objectives of this study were; to examine the impact of financial behaviour, financial attitudes on the performance of small and medium scale enterprises in Owo, Ondo state. The population of the study was 218 respondents of owners and employees of SMEs in Owo, Ondo state. The study used primary data which was collected using questionnaire which comprised of both open ended and close ended questions each addressing the study's objectives. 218 copies questionnaire were administered out of which 181 were retrieved and used for data analysis. Analysis on the demographic features of the respondents were carry out with multiple regression method was used to test for two hypotheses. The result of the study indicates that; financial behaviour has a positive significant on the performance of SMEs in Owo, Ondo State, that financial attitude has positive and significant relationship with the performance of SMEs in Owo, Ondo State. The findings led to the conclusion that financial behaviour and financial attitude have a significant effect on SMEs performance and can directly determine the organization growth and increase in business assets. The study therefore recommended that owners and employees of SMEs should take a conscious effort to improve upon their financial behaviour and financial attitude by attending workshops, conferences and short courses on financial literacy.