Regression Coefficients Research Articles

Development and validation of a HPLC-UV method for the quantification of major phospholipids in milk

Phospholipids have gained increased interest in the past decades because of their numerous health benefits. Due to the amphiphilic nature of phospholipids, their quantitative analysis requires lengthy procedures. Hydrophilic interaction liquid chromatography (HILIC) with UV detector was used in the present study to effectively separate the major classes of phospholipids found in milk. Phospholipids were effectively separated on a diol-HILIC column using a gradient elution method using acetonitrile and 10 mM ammonium acetate (pH 5 adjusted using acetic acid). Good chromatographic separation of major phospholipid classes: phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM) was possible at 203 nm. The developed method was validated for both phospholipid standards and milk fat. The correlation coefficient for phospholipids using a linear regression equation for 1–50 μg/mL standard solutions and milk fat were >0.99 and >0.98, respectively. The relative standard deviation for intra-day and inter-day repeatability of phospholipids were less than 6 %, and recoveries fell within the 75-120% range. The developed method was applied for the quantification of phospholipids (PC, PE, and SM) in milk from indigenous and crossbred cattle. Gir, Sahiwal, and Karan Fries cows had no significant difference in PE and PC, while Gir cows exhibited significantly higher levels of SM.

Analyzing Treatment Effect by Integrating Existing Propensity Score and Outcome Regressions with Heterogeneous Covariate Sets

Analyzing treatment or exposure effect is a major research theme in scientific studies. In the current big-data era where multiple sources of data are available, it is of interest to perform a synthesized analysis of treatment effects by integrating information from different data sources or studies. However, studies may contain heterogeneous and incomplete covariate sets, and individual data therein may not be accessible. We apply and extend the generalized meta-analysis method to integrate summary results (e.g., regression coefficients) of outcome and treatment (propensity score, PS) regression analyses across different datasets that may contain heterogeneous covariate sets. The proposed integrated analysis utilizes a reference dataset, which contains data on the complete set of covariates. The asymptotic distribution for the proposed integrated estimator is established. Simulations reveal that the proposed estimator performs well. We apply the proposed method to obtain the causal effect of waist circumference on hypertension by integrating two existing outcomes and PS regression analyses with different sets of covariates.

Interpretation of georadar data based on machine learning technologies

In the laboratory analysis of geological data, a number of problems arise due to the insufficient accuracy of the results. For example, different laboratories may provide different end results for the same samples, which creates a problem. This can lead to unreliable results, which can ultimately reduce the quality of the assessment. Machine learning allows to speed up the processing of radar data, as well as avoid the above-mentioned "misunderstandings". The problem of conducting scientific research at specialized landfills for a comprehensive assessment of the possibilities of using computer technology in the interpretation of georadar profiles is urgent. This makes it possible to objectively evaluate the result of automatic interpretation of georadar data. The several machine-learning algorithms described in the article are designing to improve the analysis and interpretation of data by incorporating various methods for optimizing georadar data processing processes. These methods include regression, classification and clustering. By incorporating these methods of optimizing the processing of georadar data into several machine-learning algorithms, the software can provide a comprehensive analysis and interpretation of the data obtained. This allows for a better understanding of the relationships, patterns and trends in the data, which ultimately leads to more informed decision-making and improved understanding. To improve the understanding of the results, the following quantitative indicators were obtained: correlation coefficient – 0.7072, determination coefficient – 0.5001, all these indicators correspond to these models. The deviation from the regression line is on average 22.37 units. Based on the classification results, the soil was determined to be wet. Errors in the sets do not exceed 1 %

Accuracy of Instrumented Mouthguards During Direct Jaw Impacts Seen in Boxing.

Measuring head kinematics data is important to understand and develop methods and standards to mitigate head injuries in contact sports. Instrumented mouthguards (iMGs) have been developed to address coupling issues with previous sensors. Although validated with anthropomorphic test devices (ATDs), there is limited post-mortem human subjects (PMHS) data which provides more accurate soft tissue responses. This study evaluated two iMGs (Prevent Biometrics (PRE) and Diversified Technical Systems (DTS) in response to direct jaw impacts. Three unembalmed male cadaver heads were properly fitted with two different boil-and-bite iMGs and impacted with hook (4m/s) and uppercut (3m/s) punches. A reference sensor (REF) was rigidly attached to the base of the skull, impact kinematics were transformed to the head center of gravity and linear and angular kinematic data were compared to the iMGs including Peak Linear Acceleration, Peak Angular Acceleration, Peak Angular Velocity, Head Injury Criterion (HIC), HIC duration, and Brain Injury Criterion. Compared to the REF sensor, the PRE iMG underpredicted most of the kinematic data with slopes of the validation regression line between 0.72 and 1.04 and the DTS overpredicted all the kinematic data with slopes of the regression line between 1.4 and 8.7. While the PRE iMG was closer to the REF sensor compared to the DTS iMG, the results did not support the previous findings reported with use of ATDs. Hence, our study highlights the benefits of using PMHS for validating the accuracy of iMGs since they closely mimic the human body compared to any ATD's mandible.

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

PENGARUH KECEMASAN DI PERPUSTAKAAN TERHADAP PEMENUHAN KEBUTUHAN INFORMASI PEMUSTAKA PADA PERPUSTAKAAN UIN SUNAN KALIJAGA YOGYAKARTA

This research examines how anxiety influences fulfilling users' information needs at the UIN Sunan Kalijaga Yogyakarta Library. This research uses a quantitative method using a sample of 100 active student respondents at UIN Sunan Kalijaga Yogyakarta with a purposive sampling technique. The analysis results show that the anxiety variable's coefficient of determination on meeting needs is 0.642. These results show that the influence of anxiety on fulfilling information needs in the UIN Sunan Kalijaga Yogyakarta Library is 64.2%. The nature of the impact of these two variables is positive, where the simple linear regression equation is Y = 4.203 – 0.334X. The results of hypothesis testing show a rcalculate of 13.268. These results show results that are greater than. This research concludes that library anxiety significantly and positively influences meeting information needs.

Air-assisted solidified floating organic drop microextraction method based on green supramolecular solvent for arsenic and selenium qantification in water and food samples

A supramolecular solvent (SUPRAS) was prepared from undecanoic acid and tetrabutylammonium hydroxide and used as an extraction solvent. A fast microextraction approach based on air-assisted solidified floating organic drop (AA-SFODME) coupled to a hydride generation atomic absorption spectrometer (HG-AAS) for arsenic and selenium traces determination in water and food samples was developed. The air bubbles enhanced the extraction efficiency by reducing both the time and the required chemical amounts. The 2-((4-iodophenyl) diazenyl)-8-hydroxyquinoline (IPHQ) was used as a chelation reagent. The experimental factors that impact extraction efficiency were improved and optimized. The limits of detection (LODs) for arsenic and selenium were 0.09 and 0.07 µg L−1, respectively. Also, the preconcentration factors (PFs) and enhancement factors (EFs) were found to be 100 and 103 for both arsenic and selenium, respectively. The linearity of both two calibration curves ranged from 0.10 to 700 µg L−1, with linear regression coefficients (R2) of 0.9986 and 0.9978, respectively. Finally, the greenness score was determined using two tools: the analytical greenness calculator (AGREE) and the Blue Applicability Grade Index (BAGI). The high greenness scores from these tools can make this method even more environmentally friendly and practical.

Multi-instrument spectroscopic study for authentication of curcumin content in commercial turmeric powders using machine learning algorithms

Adulteration during processing of turmeric powder not only causes health risks for the consumers but also affects its quality. There is a need for rapid and non-invasive analysis of its active ingredient, curcumin, during the supply-chain. In the present study a total six IR instruments ranging from hand-held (NIR), portable (NIR) and standalone (FTNIR and FTIR) were used to obtain spectral data of 160 different turmeric samples. The curcumin content quantified using HPLC procedure was used as the response variable for analytical model using machine learning tools. Real coded genetic algorithm (RCGA) as the variable selection procedure provided most critical variables in the sets of 10, 20, 30 and 40 variables. Sensitivity analysis has revealed the most critical fingerprint(s) in authenticating curcumin across all the instruments. The hand-held (NIR) device with only 20 spectral variables resulted in 93 % accuracy using SVM classifier, and RP (regression co-efficient of prediction) values of 0.970 and 0.997 using RF and XGBoost, respectively. In case of FTNIR and FTIR instruments 100 % classification accuracy was achieved using SVM, whereas RF and XGBoost resulted in RP values greater than 0.93. This study enables classification and quantification of curcumin content in commercial turmeric powders using non-destructive methodology.

Overcoming Time-Varying Confounding in Self-Controlled Case Series with Active Comparators: Application and Recommendations.

Confounding by indication is a key challenge for pharmacoepidemiologists. Although self-controlled study designs address time-invariant confounding, indications sometimes vary over time. For example, infection might act as a time-varying confounder in a study of antibiotics and uveitis, because it is time-limited and a direct cause both of receiving antibiotics and uveitis. Methods for incorporating active comparators in self-controlled studies to address such time-varying confounding by indication have only recently been developed. In this paper we formalize these methods, and provide a detailed description for how the active comparator rate ratio can be derived in a self-controlled case series (SCCS): either by explicitly comparing the regression coefficients for a drug of interest and an active comparator under certain circumstances using a simple ratio approach, or through the use of a nested regression model. The approaches are compared in two case studies, one examining the association between thiazolidinediones and fractures, and one examining the association between fluoroquinolones and uveitis using the UK Clinical Practice Research DataLink. Finally, we provide recommendations for the use of these methods, which we hope will support the design, execution and interpretation of SCCS using active comparators and thereby increase the robustness of pharmacoepidemiological studies.

Effects of artificial intelligence learning environments on engineers’ intercultural communication competence: mediating role of learning adaptability

ABSTRACT The purpose of this study is to investigate the impact of artificial intelligence learning environments on the intercultural communication competence of engineers. Engineering students at Chinese higher education institutions were invited to participate, and 990 students (510 males and 480 females) from different school types, majors, and grades completed an online survey. After analyses with multivariate linear regression and structural equation modelling, the results show that different dimensions of AI learning environments – including personalized learning, use of technology, and cooperation and interaction – have significant positive impacts on engineers’ intercultural communication competence and learning adaptability. At the same time, learning adaptability plays a mediating role between AI learning environments and intercultural communication competence. This study suggests that AI learning environments may have the potential to (i) enhance engineers’ intercultural communication competence and learning adaptability, an (ii) indirectly improve engineers’ intercultural communication competence through enhancements in their learning adaptability.

Temporal recovery and prognostic factors for dysphagia following cardiovascular surgery: Retrospective analysis and development of predictive score

ObjectiveDysphagia is a common complication associated with cardiovascular surgery (CVS). This study sought to better understand recovery timelines, identify risk factors, and create a prognostic model for oral intake restoration. MethodsThis retrospective study included 134 patients who had CVS between April 2022 and March 2024. We assessed swallowing ability through fiberoptic endoscopic evaluation of swallowing (FEES). We randomly divided the patients’ data into a training dataset and a test dataset in a ratio of 70/30 and Kaplan–Meier analyses and Cox regression were used to assess predictors of total oral intake. We also created a scoring system using the estimated regression coefficients. ResultsMost patients with CVS achieved total oral intake in 7–11 days after extubation. Over 168 h of intubation, the presence of penetration or aspiration, a poor FEES score (score > 6), and perioperative complications were significant risk factors for delayed total oral intake. The predicting score was calculated by adding the scores for each individual factor, including FEES score, penetration/aspiration, and preoperative complications. Scores ranged 0–8, categorizing patients into 0–2, 3–5, and 6–8 groups, clearly demonstrating that the higher the predicting score, the longer the time to total oral intake in both the training and the test dataset. ConclusionsAll risk factors for unsuccessful or delayed total oral intake were intubation for more than a week, poor swallowing ability, and the presence of perioperative complications. The scoring system accurately predicts the restoration of oral intake following CVS.

Media Shaping Heroes: An Analysis of Chinese Public’s Media Consumption and Heroic Identification

Heroism serves as the banner showcasing the sublime spirit of the Chinese nation, and constructing a societal identity rooted in heroism is a crucial reference for the successful continuation of heroism in the new era. Moreover, media consumption plays a pivotal role in influencing public identification with heroism. From the perspective of cultivation theory, this study surveyed 511 respondents nationwide, utilizing quantitative measurements and empirical analysis through linear regression equations. The results indicate that there is a significant positive correlation between the media usage of the Chinese public and their social identification with heroism, impacting cognitive, emotional, and behavioral dimensions differently. Additionally, traditional media is the main force in shaping this identification. Concurrently, there are evident generational differences in the public’s heroic social identity, with the memory of heroes in the new media era showing a trend of "intergenerational decline." Future research should explore how to effectively use media to transmit the spirit of heroism and promote value integration. This includes optimizing media content, formats, and communication strategies to meet the needs of different audiences and enhancing the public’s deep understanding and recognition of heroism.

Relationship Between Health Literacy Level and the Adoption of Preventive Behaviors Against Coronavirus Disease

Background: Health literacy skills play a pivotal role in adopting preventive behaviors. The COVID-19 pandemic has brought renewed emphasis on health-oriented behaviors, and health literacy is considered one of the most influential indicators affecting health-centric behaviors. Objectives: This study was conducted to determine the relationship between the level of health literacy and the adoption of preventive behaviors against COVID-19 among students at Jundishapur University of Medical Sciences in Ahvaz. Methods: In this study, the research population comprised 223 newly enrolled students from seven main faculties at Jundishapur University of Medical Sciences in Ahvaz in the year 2022. Participants were randomly selected, and data collection instruments included a COVID-19 prevention questionnaire and a health literacy questionnaire for adults. The questionnaire links and study participation consent forms were sent to the participants. Inclusion criteria consisted of willingness and consent to participate in the study, and enrollment in the first year of study (either the first or second semester) in various academic levels at Ahvaz University of Medical Sciences. The exit criteria were defined as failure to respond to more than 5% of the questions. After data collection, descriptive statistics, correlation coefficient tests, and regression analysis were employed using SPSS software for data analysis. Results: The findings revealed that women, married individuals, employed participants, and those with underlying health conditions obtained higher scores in preventive behaviors against COVID-19 and health literacy. Regression coefficients indicated that a one-unit increase in health literacy scores could lead to a 0.439-unit increase in preventive behaviors against COVID-19. According to correlation analysis, a positive, direct, and significant correlation existed among all dimensions of health literacy and preventive behaviors against COVID-19 (P < 0.05), with the behavioral dimension being the only significant predictor of preventive behaviors. Specifically, a one-unit increase in the behavioral dimension score could result in a 0.373-unit increase in preventive behaviors against COVID-19 (P < 0.001). Conclusions: The results showed that health literacy is directly related to COVID-19 preventive behaviors. Therefore, to promote preventive behaviors against infectious diseases, special attention should be paid to health literacy and its influential dimensions. Recognizing the importance of advancing health literacy across various dimensions can significantly contribute to the control and prevention of communicable diseases.

Modelling to predict performance and emission parameters of hydrogen, additive and nano-particles blended fuel in a dual-fuel diesel engine through artificial neural network

The depleted state of fossil fuels has led to the need to find an alternate solution for the utilization of transportation vehicles. The use of hydrogen, fuel additives, and nanoparticles has been part of the research in the recent past. Additionally, the use of artificial neural network was applied to anticipate the results for the optimization of the work and output, which has also been a study of interest recently. In the current work, different blends of diesel with hydrogen, TGME as a fuel additive, and Al2O3 nanoparticles were utilized to check for the best performance and the least emissions. The best performance was seen with the inclusion of TGME in diesel along with hydrogen, while the use of nanoparticles increased CO formation. An improvement in the BTE was also found with the blends of TGME and nanoparticles in diesel along with hydrogen. The usage of TGME along with H2 performed the best in terms of BTE (15.8%) and while the least emission of NOx, HC, and CO was found with the combination of diesel and H2. The artificial neural network was applied afterwards to predict the results using the obtained data from the experimental results. It was found that the values of regression coefficients for BTE and ITE were close to 1 (0.99 for BTE and 0.98 for ITE). In the above cases, the value of the mean square error was also observed to be least. Furthermore, the results for the regression coefficient of emissions were close to 1 for NOx (0.94), HC (0.89), and CO (0.93) with minimum possible mean square errors in all the cases. The results showed that they were in line with the predicted data as well as the available literature for the anticipation of the obtained results.

Pengaruh Lingkungan Kerja dan Motivasi Kerja Terhadap Kinerja Karyawan Pada PT. HM Sampoerna TBK Di DKI Jakarta

The purpose of this research is to determine and analyze the influence of the work environment and work motivation on employee performance. The research method used was quantitative research method, where variables were measured using a Likert scale. Data collection methods include questionnaires, interviews and documentation. The research was conducted using a validity and reliability sample of 30 people taken from a total population of 385 people, while the research sample was 196 people using the Slovin method. Data processing uses SPSS version 26 software. The data analysis techniques use classical assumption tests, simple linear regression analysis, hypothesis testing, and coefficient of determination. The research results show that partially working environment variables has a significant and positive effect on employee performance. Partially, the work motivation variable has a significant and positive effect on employee performance.

Assessing the Impact of Financial Characteristics on the Performance of Ghana Club 100 Companies

After a decade of reforming policy, building and developing the multi-national market in Ghana, the Ghana Investment Promotion Centre (GIPC) introduced Ghana Club 100 to recognize the top 100 performing companies in terms of their competitiveness and contribution to economic growth. We adopted a quantitative research methodology involving correlational and casual research design to assess the impact of financial characteristics on the performance of 80 listed Ghana Club 100 firms. Financial characteristics represent the independent variables measured using liquidity, financial leverage and business activity, whiles company performance represents measured using return on asset. SPSS Pearson's correlation coefficient and multiple regression were used to analyze the data gathered from the 80 listed companies. The data analysis reveals four key findings: 1/ a statistically significant positive relationship exists between leverage and performance; 2/ no statistically significant relationship exists between liquidity and performance; 3/ a statistically significant positive relationship exists between business activity and performance; and 4/ leverage, liquidity and business activity had statistically significant positive impact on firm performance. These findings contribute to knowledge by critically explaining the casual relationship between financial characteristics and firm performance with specific reference to Ghana Club 100 companies. The findings have strategic implications for the Ghanaian businesses and economy in terms of enhancing firm’s financial performance. Due to limited access to scarce resources, the scope of the study focused on Club 100 firms which represent all the companies in Ghana. Future research can be conducted on the effect of financial characteristics on the performance of non-listed companies in Ghana.

The Influence of Celebrity Endorsement on Customer Loyalty: A Case of Econet Wireless Company

This research investigates the influence of celebrity endorsement on customer loyalty, using the case of Econet Company. Most literature sources are available on developed countries, with a few on developing countries. A pragmatic philosophical approach involving the use of the exploratory, descriptive and explanatory research design was adopted to fully understand the research questions. Quantitative results were complemented by qualitative data obtained from interviews. In conducting the survey, a sample of 130 respondents was selected using the Krejice and Morgan (1970) sample estimation tables. The targeted respondents included the Econet Company customers. A Likert scale structured questionnaire was used to collect data. Spearman coefficient and multiple regression analysis were used to analyse data with the aid of the Social Package for Social Sciences (SPSS). The Cronbach Alpha test was performed, and all the values were greater than 0.7, which was therefore accepted (Nunnally and Bernstein, 1994). It was revealed that independent variables (celebrity reliability, celebrity expertise) and the mediating variables (customer satisfaction and customer trustworthiness) have a positive influence on customer loyalty according to the Spearman Correlation test, which yielded a minimum correlation coefficient of 0.58 and a maximum of 0.78 leading to the rejection of the proposed null hypothesis. The study recommended that Econet Company select celebrity endorsers based on the celebrity’s reliability, attractiveness, and expertise, as this contributes significantly to customer satisfaction and trustworthiness, which will result in customer loyalty. Furthermore, Econet Company must perform a well-thought screening and choose wisely the celebrity endorsers who align correctly with the image and values of the company’s brands or products. It must also avoid using celebrities with any kind of negative publicity. The study contributed not only to the literature on celebrity endorsements but also to filling the gap in the literature, providing immense usage for these techniques. Finally, the conclusion suggests that further research be carried out on a larger sample size and done on another

Roles of Family Functioning and Cognitive Failure in Predicting High-risk Behaviors Among Students with a History of Learning Disability

Background: Students with learning disabilities may face challenges associated with their condition, which could contribute to engaging in risky behaviors. Objectives: This study aimed to investigate the effects of family functioning and cognitive failure in predicting high-risk behaviors among students with a history of learning disabilities. Methods: The statistical population of this descriptive-correlational study included all high school students with a history of learning disabilities in Ahvaz, Iran, in 2023. Convenience sampling with informed consent was employed to select 158 high school students with a history of learning disabilities as the research sample. Data were collected using the High-Risk Behavior Scale, Family Functioning Questionnaire, and Cognitive Failure Questionnaire. Pearson’s correlation coefficient and stepwise regression were used for data analysis. Results: The results indicated a significant negative relationship between family functioning and high-risk behaviors (r = -0.41, P < 0.001). Conversely, there was a significant positive relationship between cognitive failure and high-risk behaviors (r = 0.49, P < 0.001). Family functioning and cognitive failure together predicted 29.4% of the variance in students' high-risk behaviors. Conclusions: These findings suggest that family functioning and cognitive abilities are important factors to consider in understanding high-risk behaviors. Further research using longitudinal or experimental designs is needed to explore the causal relationships between these variables.

Assessment of pain intensity and analgesic requirements in patients with diabetes mellitus after STEMI

Abstract Introduction Patients with diabetes encounter a significantly higher risk of myocardial infarction, heart failure, and mortality following acute cardiac events. Examining pain intensity and analgesic needs in patients with diabetes can provide a comprehensive understanding of the pain experienced and individual analgesic needs in patients with diabetes. Aim of the study The aim was to assess pain intensity and analgesic requirements in patients with diabetes mellitus after STEMI. Material and Methods The study involved 104 participants (58 female and 46 male, with a mean age of approximately 72 years, M±SD= 11 yeas). The study was conducted at the Heart Institute, in the Intensive Cardiology Unit, and data were collected from patients following STEMI Participants were divided into two groups: 54 people with diabetes and 50 people without diabetes. The influence of factors such as gender, age, existing diseases, haemoglobin levels and the presence of diabetes on pain intensity was analysed. In the diabetes group, the effects of treatment method, duration of illness and diabetes compliance on pain intensity were also considered. A visual analogue scale (VAS) was used to assess pain, and analgesia requirement was assessed using an analgesic ladder. The level of significance wasp < 0.05. Results The analysis indicated the effect of diabetes on pain intensity in patients with myocardial infarction. Those with diabetes experienced significantly less pain, however glycated HbA1c levels above 7.0 mg/dl predisposed to experiencing more pain. The b standardized regression coefficient indicated for diabetes ,a strong negative correlation (β=-0.77 ,p = 0,008), and for HbA1c level ,a strong positive correlation (β=0.88 ,p =0,889). Comparative analysis of analgesics revealed lower usage in the diabetes group(p=0.002). Diabetes Patients, the most frequent use of first-line drugs while the group of patients without diabetes needed second-level analgesic treatment. Those with diabetes rated their pain level as low ,while the non-diabetic group was dominated by respondents with an intermediate pain level. Non-adherence to diabetic recommendations correlated with higher pain intensity , with a strong positive correlation (β coefficient=0.90, p = 0,010 ). No significant impact was observed based on gender, age, the presence of comorbidities, or the influence of diabetes treatment methods and duration on the intensity of perceived pain. Conclusions The analysis indicated the effect of diabetes on pain intensity in patients with myocardial infarction. Those with diabetes experienced significantly less pain than non-diabetes patients. Diabetes patients required a lower level of the analgesics ladder.

Achieving Net-Zero Energy Buildings: Analyzing and Optimizing Strategies Using Sensitivity Analysis

Enhancing building energy efficiency and incorporating renewable energy technologies are crucial for combating climate change and promoting sustainability. This study employed a sensitivity analysis to evaluate the influence of various parameters on building costs and energy consumption. Standardized Regression Coefficients (SRC) were used to measure the sensitivity of each variable. The type of glass, wall construction, window-to-wall ratios, and shading elements were identified as having the most significant impact on building costs and electricity usage. SRC values provided a numerical representation of the strength and direction of the relationship between these factors and the output variables. Furthermore, the study quantified the energy savings achieved through optimization methods. The data indicated an average reduction in consumption of 22%, with variations between 21% and 23% across different floors. These results highlighted the effectiveness of optimizing variables and applying energy-efficient design principles. The findings of this investigation enhance our understanding of the role of sensitivity analysis in optimizing building energy efficiency. They can serve as a reference for making decisions on integrating renewable energy technologies into buildings and designing them. These strategies can help reduce environmental impact, promote sustainable construction practices, and achieve significant energy savings.