Multiple Independent Variables Research Articles

Maximizing Organizational Performance: The Synergy of AI and BI

Objective: This paper focuses on ways artificial intelligence and business intelligence technologies can be utilized to improve the efficiency of an organization, this paper aims to propose a framework for organizational use to leverage AI and its performance in organizations. Theoretical Framework: In this topic, the author proposes a framework for integrating AI and BI, which includes the following components: data collection and statistical analysis, machine learning algorithms, visualization, and integration with BI systems. Method: This study will employ a statistical methodology called multiple regression, the analysis helps to examine the relationship between a dependent variable (organizational performance) and multiple independent variables (extent of BI integration, extent of AI integration, and AI and BI integration). Results and Discussion: Combining AI and BI in business processes improves decision-making, increases efficiency, and enhances customer experiences. AI automates complex processes, detects patterns, and predicts based on vast data, enhancing BI systems. AI-powered BI systems give businesses a competitive edge by improving decision-making, performance, and customer engagement. Integrating AI and BI leads to improved organizational performance. Research Implications: This paper stresses the significance of evaluating organizational structure before implementing AI-based systems. It guides infrastructure requirements for successful AI implementation in business, emphasizing security implications when making decisions based on AI-generated data. Originality/Value: Integrating AI and BI can lead to faster and more efficient business processes, resulting in higher performance. AI technology can revolutionize decision-making across industries, including business. Practical recommendations are available on how to use AI to improve decision-making in organizations.

Generalized data thinning using sufficient statistics

Our goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural exponential families, X can be thinned into independent random variables X ( 1 ) , … , X ( K ) , such that X = ∑ k = 1 K X ( k ) . These independent random variables can then be used for various model validation and inference tasks, including in contexts where traditional sample splitting fails. In this paper, we generalize their procedure by relaxing this summation requirement and simply asking that some known function of the independent random variables exactly reconstruct X. This generalization of the procedure serves two purposes. First, it greatly expands the families of distributions for which thinning can be performed. Second, it unifies sample splitting and data thinning, which on the surface seem to be very different, as applications of the same principle. This shared principle is sufficiency. We use this insight to perform generalized thinning operations for a diverse set of families.

The Influence of Corporate Social Responsibility, Profitability, Liquidity, and Leverage on Tax Aggressivity

Purpose:The phenomenon that occurred in 2019, PT. Adaro Energy Tbk. Do something avoidance related to payment tax by implementing transfer pricing through the branch. The Company is located in Singapore and is called Coaltrade Services International. From 2009-2017, information via child company pays tax amounting to 125 million USD or Rp. 1.75 trillion ( exchange rate Rp. 14,000) or more small from it should be charged in Indonesia. Study This aims To analyze the influence of corporate social responsibility, Profitability, liquidity and Leverage on aggressiveness tax on IDX30 companies listed on the Indonesian Stock Exchange in 2020-2022.Methodology:This study is based on a deep data study, using secondary data from IDX30 companies listed on the Indonesian Stock Exchange from 2020 to 2022. 13 companies were included in the study and selected through a purposive sampling method. The data was analyzed using multiple linear regression, a statistical technique used to analyze the relationship between multiple independent variables and a dependent variable.Findings:Research results show that corporate social responsibility positively and significantly impacts aggressiveness tax. Implication:On the other hand, Profitability, liquidity, and Leverage are not influential factors in aggressive tax.

Air pollution, cardiorespiratory fitness and biomarkers of oxidative status and inflammation in the 4HAIE study

The aim of this study was to investigate the associations between cardiorespiratory fitness (CRF), long-term air pollution exposure and biochemical markers of oxidative status and inflammation. This is a cross-sectional investigation focusing on biochemical markers of oxidative status and inflammation. Participants were Caucasian (N = 1188; age 18–65 years) who lived for at least 5 years in a high air-polluted (Moravian-Silesian; MS) or low air-polluted (South Bohemia; SB) region of the Czech Republic. Healthy runners and inactive individuals were recruited. A multiple regression analysis was used to explain the relationship between multiple independent variables (CRF, trunk fat mass, sex, socioeconomic status, and region (MS region vs. SB region) and dependent variables (oxidative status, inflammation). CRF, trunk fat mass, age and sex significantly predicted almost all selected markers of oxidative status and inflammation (except GSSG, GSH/GSSG and BDNF). Participants living in the MS region presented significantly higher GPx (by 3.1%) and lower BDNF values (by 4.5%). All other investigated biochemical markers were not significantly influenced by region. We did not find meaningful interactions between long-term air-pollution exposure versus markers of oxidative status and inflammation. However, we showed various significant interactions with sex, age, CRF and body composition. The significant association of living in the high air polluted MS region with the BDNF level warrants further attention.

Impact of Hurricanes, Tropical Storms, and Coastal Extratropical Storms on Indoor Air VOC Concentrations

AbstractUnderstanding vapor intrusion (VI) temporal variability is key for the design of sampling strategies intended to assess reasonable maximum exposure of indoor air concentrations of volatile organic compounds (VOCs) as well as risk evaluation and mitigation planning. VI temporal variability has previously been shown to be dependent on the complex interactions of multiple independent variables—meteorological, hydrogeological, and human behavioral. Several meteorological variables, including barometric pressure, wind speed, and rainfall, are linked during tropical and extratropical storm events. High‐frequency meteorological and indoor VOC data from a series of seven tropical storms and four extratropical storms were collected at a single industrial building with multiple heating, ventilation, and air conditioning (HVAC) zones. The storms and sampling zones showed a variety of effects on trichloroethylene (TCE) concentrations in indoor air. In one zone (supply room), increases in TCE concentrations often, but not always, closely coincided with decreasing barometric pressure, sustained wind speeds over 32 km/h (20 mph), and differential pressures indicating subslab to indoor flow. A second zone, in a restroom, did not show a consistent pattern of temporal correlation between meteorological factors and indoor air concentrations. While peak indoor air concentrations may be associated with the passage of cyclonic storms at some sampling locations, this does not appear to be generalizable to all sampling locations. The observed increase in indoor air concentration potentially attributable to these storms is typically less than an order of magnitude and the duration ranges from a day to a week.

Exploiting Domain Partition in Response Function-Based Dynamic Surrogate Modeling: A Continuous Crystallizer Study

Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity.

Hypertension Medication and Medicare Beneficiaries: Prescription Drug Coverage Satisfaction and Medication Non-Adherence among Older Adults.

Hypertension is so prevalent and requires strict adherence to medications to prevent further disease or death, but there is no study examining factors related to prescription drug non-adherence among 65 years old and older. This study aims to assess the likelihood of medication nonadherence among patients based on factors such as age, race, and socioeconomic status, with the goal of identifying strategies to enhance medication adherence and mitigate associated health risks. Using the 2020 Medicare Current Beneficiary Survey Public Use File to represent nationwide Medicare beneficiaries (unweighted n = 3917, weighted n = 27,134,782), medication non-adherence was related to multiple independent variables (i.e., age, sex, race/ethnicity, socioeconomic status, comorbidities, insurance coverage, and satisfaction with insurance). Cross-tabulations and Wald chi-square tests were used to determine how much each variable was related to non-adherence. Multivariate logistic regression was used to examine the association between medication non-adherence and factors such as prescription drug coverage satisfaction and cost-reducing behavior. Specific trends in medication non-adherence emerged among beneficiaries. Non-adherence was higher in older adults aged 65- to 74-year-olds and those with more chronic conditions (OR = 2.24; 95% CI = 1.74-2.89). If patients were dissatisfied with the medications on the insurance formulary or struggled to find a pharmacy that accepted their medication coverage, they had worse adherence (OR = 2.63; 95% CI = 1.80-3.84). Formulary and coverage must be expanded to improve adherence to antihypertensive medications in Medicare beneficiaries. Older adults aged 65 to 74 years may be less adherent to their medications because they do not see the seriousness of the disease and could benefit from further counseling. Patients with limited activities of daily living and more comorbidities may struggle with complex treatment regimens and should use adherence assistance tools.

SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks

Dynamics of many complex systems, from weather and climate to spread of infectious diseases, can be described by partial differential equations (PDEs). Such PDEs involve unknown function(s), partial derivatives, and typically multiple independent variables. The traditional numerical methods for solving PDEs assume that the data are observed on a regular grid. However, in many applications, for example, weather and air pollution monitoring delivered by the arbitrary located weather stations of the National Weather Services, data records are irregularly spaced. Furthermore, in problems involving prediction analytics such as forecasting wildfire smoke plumes, the primary focus may be on a set of irregular locations associated with urban development. In recent years, deep learning (DL) methods and, in particular, graph neural networks (GNNs) have emerged as a new promising tool that can complement traditional PDE solvers in scenarios of the irregular spaced data, contributing to the newest research trend of physics informed machine learning (PIML). However, most existing PIML methods tend to be limited in their ability to describe higher dimensional structural properties exhibited by real world phenomena, especially, ones that live on manifolds. To address this fundamental challenge, we bring the elements of the Hodge theory and, in particular, simplicial convolution defined on the Hodge Laplacian to the emerging nexus of DL and PDEs. In contrast to conventional Laplacian and the associated convolution operation, the simplicial convolution allows us to rigorously describe diffusion across higher order structures and to better approximate the complex underlying topology and geometry of the data. The new approach, Simplicial Neural Networks for Partial Differential Equations (SNN PDE) offers a computationally efficient yet effective solution for time dependent PDEs. Our studies of a broad range of synthetic data and wildfire processes demonstrate that SNN PDE improves upon state of the art baselines in handling unstructured grids and irregular time intervals of complex physical systems and offers competitive forecasting capabilities for weather and air quality forecasting.

Predicting the equity premium with financial ratios: A comprehensive look over a long period in Korea

This study investigates whether financial ratios, such as the dividend-price, price-to-book, or price-earnings ratios, reliably predict the equity premium in Korea. We construct the longest possible time series data for the stock market index excess returns including dividends and associated financial ratios by collecting data from various sources including data only available in hard copy publications from 1968 to 2021. We also construct the cyclically adjusted price-earnings (CAPE) ratio as a predictor which has not been used in previous studies on the Korean stock market. We estimate the predictive regressions using the IVX method, which is robust to having a persistent independent variable and overlapping data as a dependent variable and can accommodate multiple independent variables. We also assess the economic gains of the predictability by examining the certainty equivalent return for a mean-variance investor. We find that the CAPE ratio reliably predicts the equity premium for horizons ranging from one month to two years, and the results survive various robustness tests.

Maritime decision-makers and cyber security: deck officers’ perception of cyber risks towards IT and OT systems

Through a quantitative study of deck officers’ cyber risk perceptions towards information (IT) and operational (OT) systems, this paper contributes to substantiate the importance of considering human behaviour within maritime cyber security. Using survey data from 293 deck officers working on offshore vessels, statistical analyses were conducted to measure and predict the participants cyber risk perceptions towards IT and OT systems. Performing a Wilcoxon signed-rank test revealed a significant discrepancy in the levels of cyber risk perception between the system categories. Hierarchical regression analyses were conducted to develop statistical models, considering multiple independent variables, including perceived benefit, cyber security training, experience with cyber-attacks, and trust towards various stakeholders. Key findings revealed distinct results for IT and OT systems, and the regression models varied in both predictive power and significance of the independent variables. Perceived benefit positively predicts deck officers cyber risk perception for both IT and OT systems, while trust, which included measures of social trust and confidence, was not found to be significant. Cyber security training and experience with cyber-attacks only influence deck officers’ perception of cyber risks related to operational technology. Practical implications of this work provide actionable recommendations for the maritime industry, including tailored risk communication tools, training programs, reporting systems, and holistic policies.

The Impact of South Korean Work Culture in Improving Working Performance: A Multiple Regression Method Analysis

This study aims to assess the South Korean work culture implemented at WITCON Co., Ltd., its impact on enhancing employee performance, and the extent of its influence. This form of research employs a blend of quantitative and qualitative methodologies, sometimes called mixed methods. WITCON Co., Ltd. is at 35 Seochangnamsunhwan-ro 215beon-gil, Namdong-gu, Incheon, Republic of Korea. The acquired data is primarily classed as independent and dependent variables. Work culture is an independent variable, meaning it can influence other variables. On the other hand, employee performance is classified as a dependent variable, meaning other variables impact it. Both variables were measured using instruments in a questionnaire comprising multiple questions, which were subsequently employed to collect participant data. The poll was administered to the whole foreigner workers at WITCON, comprising 30 people with E-7 and E-9 visas. Hence, the approach employed to examine this study’s findings involves utilizing multiple regression analysis. Multiple linear regression is a regression model that incorporates multiple independent variables. Multiple linear regression analysis is conducted to ascertain the magnitude and direction of the independent variable’s impact on the dependent variable. Furthermore, a Sobel test is conducted to ascertain the statistical significance of the indirect influence of the independent variable on the dependent variable via the mediator variable in a mediation model. The findings indicate that South Korea’s work culture prioritizes the enforcement of discipline in order to attain objectives while still being adaptable and accepting.

Improving drinking water quality through proficiency testing-the impact of testing method and accreditation status on Escherichia coli detection by Canadian environmental testing laboratories.

Water quality testing is crucial for protecting public health, especially considering the number of boil water advisories annually issued across Canada that impact daily life for residents in affected areas. To overcome these challenges, the development of drinking water safety plans and accessibility to regular testing using simple, rapid, and accurate materials are necessary. However, the significance of monitoring the accuracy of environmental microbiology testing laboratories cannot be overlooked. Participation in external quality assessment programs, such as those that include proficiency testing (PT), is a necessary risk management resource that ensures the effectiveness of these testing processes. Proficiency Testing Canada (PTC), in collaboration with the Canadian Microbiological Proficiency Testing (CMPT) program based at the University of British Columbia, have implemented a drinking-water microbiology PT program since 1996. Both PTC and CMPT are ISO/IEC 17043:2010-accredited EQA providers. The drinking water program provided PT challenges to subscribing testing laboratories twice per year. Each challenge consisted of four samples containing unknown concentrations of Escherichia coli (E. coli) and Enterobacter spp. Results from participants were assessed for accuracy based on the method of testing. This cross-sectional study evaluated 150 rural and metropolitan testing sites across Canada between 2016 and 2022. Multivariable logistic regression analysis was conducted to examine the impact of different testing methods and laboratory accreditation status on the proficiency scores. This approach enabled us to assess the association between multiple independent variables and the likelihood of achieving specific proficiency scores, providing insights into how testing methods and accreditation status affect overall performance. After adjusting for rural residence, testing time, and survey year, the membrane filtration method was positively associated with the likelihood of scoring satisfactory results compared to the enzyme-substrate method (OR: 1.75; CI: 1.37-2.24), as well as accreditation status (OR: 1.47; CI: 1.16-1.85). The potential for improvement in environmental laboratory testing performance through the implementation of regulated PT in drinking water safety plans is proposed, along with the need for reliable testing methods applicable to rapid drinking water microbiology testing.

Evaluation of Learning Management Systems for Success Factors

Evaluating an information system for success is key. Evaluating success factors of a Learning Management System (LMS) is essential in perspective of information systems success in a developing country context. eLearning is vital to educational system considering its benefits and impacts, particularly in accessing learning from remote areas, suitable for different learners’ categories, minimal resource utilization in terms of cost and time. Through the literature reviewed on eLearning and Information System, the desire to determine the variables that measure the success factors for information systems continues. Existing Information System (IS) success models do not sufficiently evaluate eLearning in developing country, Uganda inclusive as the current IS success models are generic in nature. This study aimed to describe a model of information system success tailored to eLearning system, Kampala International University as a case study, a Uganda’s context. To address the above objective, a field study was conducted, using questionnaire to determine factors for information systems success in Uganda, a sample size of 370 respondents were used from population size of 5500 using Sloven formula. The respondents comprise of 340 students, 27 lecturers and 4 administrators. The identified success factors are skills & training, infrastructure and management support. Data were cleaned and analyzed using Statistical Package for Social Scientists version 20.0 (SPSS). This study adopted Delone and Mclean’s Information System Success model (2016) and extended it using factors obtained from the field study survey. Analysis was performed to evaluate the model. Results of the study showed that all the independent variables management support, infrastructure, skills and training are positively related to the dependent variable of intention to and use of information system. There exists a strong relationship between the multiple independent variables and the dependent variable. All factors identified, has positive impact in explaining the variation in intention to use and use of the system with r coefficient of 0.343, 0.406 and 0.406 respectively. The results of the study presented a model of success factors for Learning Management System, eLearning specifically. For future research, this study recommends conducting qualitative studies to delve deeper into the nuanced perceptions and experiences of learners and teachers as well as looking into policies that can promote eLearning especially in developing countries.

Physics-Informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media

Simulating solute transport in heterogeneous porous media poses computational challenges due to the high-resolution meshing required for traditional solvers. To overcome these challenges, this study explores a mesh-free method based on deep learning to accelerate solute transport simulation. We employ Physics-informed Neural Networks (PiNN) with a periodic activation function to solve solute transport problems in both homogeneous and heterogeneous porous media governed by the advection-dispersion equation. Unlike traditional neural networks that rely on large training datasets, PiNNs use strong-form mathematical models to constrain the network in the training phase and simultaneously solve for multiple dependent or independent field variables, such as pressure and solute concentration fields. To demonstrate the effectiveness of using PiNNs with a periodic activation function to resolve solute transport in porous media, we construct PiNNs using two activation functions, sin and tanh, for seven case studies, including 1D and 2D scenarios. The accuracy of the PiNNs’ predictions is then evaluated using absolute point error and mean square error metrics and compared to the ground truth solutions obtained analytically or numerically. Our results demonstrate that the PiNN with sin activation function, compared to tanh activation function, is up to two orders of magnitude more accurate and up to two times faster to train, especially in heterogeneous porous media. Moreover, PiNN’s simultaneous predictions of pressure and concentration fields can reduce computational expenses in terms of inference time by three orders of magnitude compared to FEM simulations for two-dimensional cases.

Correlates of frailty in older female cancer survivors

IntroductionCancer survivors are at risk of frailty because of cancer and its treatment. Understanding the factors that increase the risk of frailty is an important aspect of cancer care for the development of interventions to prevent or manage frailty, thus improving cancer survival and overall quality of life of cancer survivors. This study aimed to identify demographic, clinical, and psychosocial correlates of frailty in older, female cancer survivors. Materials and MethodsThis is a sub-study focusing on the exploratory aim of a larger cross-sectional study (NURS-IIR-IUSCC-0748). A total of 213 female cancer survivors aged 59–87 years old were included from the parent study in the current analysis. Frailty, the primary outcome, was measured using the Tilburg Frailty Indicator scale. The independent variables were age, relationship status, clinical stage of cancer, treatment type, comorbidity, depression, affect, optimism, stress, and social support. Stepwise linear regression modeling identified the independent variables that were significantly associated with frailty. ResultsThe final regression model revealed that high patient-reported stress and depression, comorbidity, not being married or living with a partner, and low positive affect were significantly associated with worsening frailty in this population. DiscussionUnderstanding the context of frailty is important for the design of interventions that target factors known to be associated with frailty in older cancer survivors. Further validation with a larger and a more diverse sample from a broad spectrum of sociodemographic and clinical population would fully account for the multiple independent variables influencing frailty in cancer survivors.

개인특성, 가정 및 학교환경 변인이 고등학생의 진로적응력에 미치는 영향

Objectives This study examined the influence of personal characteristics(self-esteem, social withdrawal, de-pression, academic engagement, and cooperation), family environment(parenting attitudes), and school environ-ment(friendship and teacher relationships) on predictors of career adaptability in high school students.
 Methods For this purpose, hierarchical regression analysis was performed on the high school data of 1,213 boys and 1,041 girls from the Korean Children and Youth Panel Survey 2018. Hierarchical regression analysis is a step-by-step method of determining the magnitude of the relative influence of multiple independent variables on the dependent variable.
 Results The results of this study were as follows. First, the factors that influenced high school students' career adaptability were academic engagement and self-esteem, inconsistent parenting, social withdrawal and depres-sion, and friendships. Second, when career adaptability was analyzed by dividing it into career interest, career control, career curiosity, and career confidence, the personal characteristics of academic engagement was found to be the most influential variable. In addition, it was also found that personal characteristics, family environment, and school environment have multi-angular effects on career adaptability and career interest, but school environ-ment has no effect on career control, career curiosity, and career confidence.
 Conclusions Based on this result, implications of this study and suggestions for improving the career adaptability of high school students were discussed.

Pain interference and depressive symptom severity across 10 years in individuals with long-term spinal cord injury

Objective To examine change in pain interference and depression over a 10-year time period in individuals with long-term traumatic spinal cord injury (SCI) and to identify the extent to which changes in pain interference over time predicts change in depressive symptoms. Design Longitudinal analyses of self-report assessment data. Setting Specialty and university hospitals in the Southeastern and Midwestern United States. Participants Adults with a history of traumatic SCI (n = 504) who responded to the three most recent data collection periods of the SCI Longitudinal Aging Study (2008 [Time 1], 2013 [Time 2], and 2018 [Time 3]). The participants averaged 59 years of age and 32 years since injury onset at Time 3. Interventions Not applicable. Main Outcome Measures Patient Health Questionnaire (PHQ-9) assessed depressive symptom severity. The 7-item Pain Interference scale from the Brief Pain Inventory assessed pain interference. Results Over the three study timepoints, the sample averaged moderate levels of pain interference and mild depressive symptom severity. Unconditional linear growth models, reflecting changes in central tendency, indicated that pain interference significantly decreased and depressive symptom severity significantly increased over time. Multiple independent variables random coefficient modeling based on correlations suggested that change in pain interference was positively associated with change in depressive symptom severity over the 10-year study follow-up. Conclusion Average depressive symptom severity worsened over time. Change in pain interference was positively associated with change in depressive symptom severity. These results point to the complexity of aging related changes in depressive symptoms and pain interference. They further support the need for continued assessment of mood and pain experiences, particularly among individuals reaching aging milestones with SCI.

SOLVING PARTIAL DIFFERENTIAL EQUATIONS IN MATLAB

This paper aims to describe the application of educational tools with which it can develop interactivity and help pupils and students to better and more clearly understand mathematics and to understand that it is all around us, that it is our everyday life. The paper will show the ways of creating mathematical educational materials and their use. The calculation of partial differential equations has a wide application. Many problems in scientific research and engineering can be mathematically modeled by partial differential equations. A differential equation containing multiple independent variables is called a PDE (partial differential equation).

The impact of protrusion size on pain, range of motion, functional capacity, and multifidus muscle cross-sectional area in lumbar disc herniation.

To investigate the effect of protrusion amount on pain, lumbar mobility, physical dysfunction, and the cross-sectional area of the multifidus muscle in patients with lumbar disc herniation. 54 male patients aged 20 to 50 years were included in this study in 2 groups. The control group (n = 18) consisted of patients with disc herniation without nerve root compression, and the study group (n = 36) consisted of patients with lumbar disc herniation and nerve root compression in 3 subgroups according to the amount of protrusion. The multifidus muscle cross-sectional area and the amount of protrusion were measured using Magnetic Resonance Imaging. Pain intensity was measured using the Visual Pain Scale, physical dysfunction was measured using the Oswestry Disability Index, flexion-extension was measured with TiltMeter, and trunk rotation was measured with smartphone applications called iPhone Compass. There were no differences in pain levels (P > .05), mean trunk rotation movements of the affected and unaffected sides (P = .001, P = .001, P = .983, and P = .954, respectively), Oswestry Disability Index results (P = .967), or cross-sectional area sizes of the multifidus muscle (P = .866, P = .552, P = .787, respectively). There was a difference between the groups in terms of the mean trunk flexion and extension movement values (P = .001). The regression analysis indicated that there was no significant correlation between the binary variables, and the models exhibited a low explanatory rate for the dependent variable. A relationship has been identified between lumbar disc herniation and low back pain, lumbar movement limitation, and physical functionality. Nevertheless, subsequent to the regression analysis, it became evident that the binary variables did not exhibit a noteworthy relationship, leading to a decrease in the explanatory capacity of the models for the dependent variable. In forthcoming studies, it is advisable to contemplate the augmentation of the subject pool or the incorporation of multiple independent variables into the regression analysis as potential strategies to enhance the model capability in elucidating variations in the dependent variable.

Optimization of constituent proportions for compressive strength of sustainable geopolymer concrete: A statistical approach

Geopolymer concrete (GPC) is an environmentally friendly and sustainable concrete produced through the geopolymerization process, involving the combination of alumino silicate materials with alkaline solutions. It has unique advantage of being carbon-negative which helps reduce carbon dioxide levels in the atmosphere. One challenge with making GPC is the significant variation in compressive strength that arises from differences in constituent proportions, specimen ages, andcuring conditions. There is a need to optimize the GPC constituents to achieve the desired compressive strength. To address this issue, a comprehensive dataset comprising 1242 data points from existing literature on fly ash-based GPC (FA-based GPC) was compiled for the purpose of optimizing the constituent proportions of GPC using statistical analysis. Nine independent variables were selected to develop the predictive models for compressive strength using linear (LRA), multilinear (MRA), and non-linear (NRA) regression analyses. The models were assessed using scatter plots between experimental and predicted responses and adopting the performance criteria such as the statistical significance of the model unknowns, R2 values, root mean square error (RMSE), mean absolute error (MAE), and scatter index (SI). LRA revealed that the compressive strength of GPC is not dependent on any single independent variable. Therefore regression analysis with multiple independent variables was conducted. From MRA, ±15 % error for training dataset and (15–20) % error for validating dataset was estimated. The R2, RMSE, MAE, and SI values were found to be 0.8086, 6.11, 5.35, and 0.21. NRA predicted the compressive strength more efficiently because it provides the error ±10 % for both training and validating datasets with R2, RMSE, MAE, SI values of 0.8321, 5.46, 4.77, and 0.19. Further, curing temperature, specimen age, Na2SiO3/NaOH ratio, I/b ratio, and aggregate content were found as the most significant independent variables. The time and cost-effective design mix for FA based GPC can be prepared using the regression models presented in the current work.