Residual Value Research Articles

Anomalous Nernst effect and thermal conductivity in Co3Sn2S2 polycrystals

A polycrystalline sample of Co3Sn2S2 shandite was synthesized using spark plasma sintering for its densification. The unit cell parameters, a = 5.3688(1) Å and c = 13.1797(1) Å, refined from room temperature X-ray diffraction data, confirm the R-3m shandite structure. The frustrated nature of the Co magnetic network is invoked to explain the low effective paramagnetic moment of Co, μeff = 1.04μB/Co, whereas the ferromagnetic Curie temperature, TC = 175K, determined from T-dependent magnetic susceptibility corresponds to the metal to metal transition at that temperature. Interestingly, the residual resistivity ratio and resistivity values indicate that the electronic transport is driven by the in-plane contribution. This behaviour close to that of ab-plane transport of Co3Sn2S2 crystal fits well with the measurement of an anomalous Nernst effect (ANE). The latter reaches at T = 100K a maximum value Sxy = +1.5 μV K−1, i.e. a reduction by a factor of ≈2 as compared to crystal, explained by the reduction for the magnetization, M, of the former. This is also demonstrated by the H-dependent Sxy and M loops that match very well. Similarly, a hysteresis is found for the thermal conductivity demonstrating the existence of an anomalous thermal Hall effect of about 5 % of the total thermal conductivity. This study shows that ANE can be also measured in ceramics of Weyl ferromagnets.

Comparing the modeling functions of hybrid nano-lubricant containing CuO and MWCNTs with standard quality measurement criteria to introduce the most optimal correlation function

One of the important properties of fluids is viscosity. In this research, the viscosity modeling of nano-lubricant (NLB) was done using RSM. Using the response surface methodology (RSM), several models including Cubic, Quartic, 2FI and Quadratic, models were designed and the relevant statistical parameters were presented. The selected model after checking the evaluation parameters R-squared, Adjusted R2, Predicted R2 and Std. Dev. is selected and the values are equal to the values of 0.9993, 0.9992, 0.9989 and 4.62, respectively. Statistical charts including residual values, normal distribution, Box-Cox and predicted values in terms of real values also introduce the best model. The trend of viscosity changes of the selected model was evaluated according to the basic parameters T, solid volume fraction and shear rate , and the temperature parameter was introduced as the most influential parameter.

Cross-cultural adaptation, reliability, and validity study of the Turkish version of the “Spinal Cord Injury-Falls Concern Scale”

Purpose This study aims to conduct a Turkish validity and reliability study of the Spinal Cord Injury-Falls Concern Scale (SCI-FCS). Methods The study involved 134 volunteer patients with spinal cord injury. Various validity analyses, including content validity analysis, Confirmatory Factor Analysis (CFA), Pearson product-moment correlation coefficient, and Cronbach’s alpha value based on a single application for reliability, were employed. The Falls Efficacy Scale-International (FES-I) was used for criterion validity. Results All the standardized regression coefficients for the single factor measurement model established with 16 items in the scale are above 0.84. The CFA model, also with a single factor and 16 items, yields the following fit indices: Root-Mean-Square Error of Approximation value of 0.15, Standardized Root Mean Residual Squares value of 0.06, Chi-square value of 2.20, and Comparative Fit Index and Non-Normed Fit Index values of 0.98. The scores they received from the SCI-FCS and the scores they received from the FES-I scale have a statistically significant positive correlation (p < 0.01). The test-retest reliability coefficient value is 0.81, and the Cronbach Alpha value is 0.97. Conclusion The Turkish version of the SCI-FCS is a valid and reliable tool for determining fall concerns for patients with spinal cord injury.

Socioeconomic inequalities in uptake of HIV testing during antenatal care: evidence from Sub-Saharan Africa

BackgroundMeasuring socioeconomic inequalities in healthcare usage represents a critical step towards promoting health equity, in alignment with the principles of universal health coverage and the United Nations’ Sustainable Development Goals. In this study, we assessed the socioeconomic inequalities in HIV testing during antenatal care (ANC) in sub-Saharan Africa.MethodsSub-Saharan Africa was the focus of this study. Benin, Burundi, Cameroon, Ethiopia, Gambia, Guinea, Liberia, Malawi, Mali, Mauritania, Mozambique, Rwanda, Sierra Leone, Uganda, Zambia, and Zimbabwe were the countries included in the study. This study used current Demographic and Health Surveys data spanning from 2015 to 2022. A total of 70,028 women who tested for HIV as part of antenatal contacts formed the sample for analysis. We utilized the standard concentration index and curve to understand the socioeconomic inequalities in HIV testing during antenatal care among women. Additionally, a decomposition analysis of the concentration index was ran to ascertain the contributions of each factor to the inequality.ResultsOverall, 73.9% of women in sub-Saharan Africa tested for HIV during ANC. The countries with the highest proportions were Malawi, Rwanda, Zambia, and Zimbabwe. Mali Benin, Guinea, Mali, and Mauritania were the countries with the lowest proportions of HIV testing. Being among the richer [AOR 1.10, 95% CI: 1.02,1.18] and richest [AOR 1.41, 95% CI:1.30, 1.54] wealth quintiles increased the odds of HIV testing during ANC. The concentration value of 0.03 and the curve show that HIV testing is more concentrated among women in the highest wealth quintile. Hence, wealthy women are advantaged in terms of HIV testing. As the model’s residual value is negative (-0.057), the model overestimates the level of inequality in the outcome variable (HIV during ANC), which means that the model’s explanatory factors can account for higher concentration than is the case.ConclusionWe found that there is substantial wealth index-related inequalities in HIV testing, with women of the poorest wealth index disadvantaged in relation to the HIV testing. This emphasizes the necessity for sub-Saharan Africa public health programs to think about concentrating their limited resources on focused initiatives to grasp women from these socioeconomic circumstances. To increase women’s access to HIV testing, maternal and child health programs in sub-Saharan Africa should attempt to minimize female illiteracy and poverty. Consequently, health education may be required to provide women with comprehensive HIV knowledge and decrease the number of lost opportunities for women to get tested for HIV. Given the link between knowledge of HIV and HIV testing, it is important to focus on community education and sensitization about HIV and the need to know one’s status.

Investigation on effects of thermo-mechanical coupling on residual stress in lithium niobate in surface acoustic wave device

Surface acoustic wave (SAW) devices are subject to thermomechanical coupling during operation, and when the temperature rises sharply to 180 °C, the surface of the lithium niobate crystal substrate generates nonuniform thermal stresses, thus leaving large residual stresses after complete cooling, which seriously influences its service performance and life. In this study, the thermal residual stresses of lithium niobate wafers were tested by nanoindentation tests, and the nanoindentation test process was simulated. The inverse calculation of the parameters of the lithium niobate constitutive model was carried out by comparing the load-displacement curves obtained from the finite element calculations with the experimental results. A multiscale model of the SAW device was established, its operation process was simulated, and then the generation law of residual stress was further discussed. The nanoindentation test results show that the thermal residual stress is positively correlated with the heating time under the effect of the same temperature; however, at the heating temperature up to about 180 °C, the lithium niobate crystal enters the high-temperature hyperelastic interval, and the yield stress of the crystal increases significantly at this stage. The constitutive model curves obtained by finite element calculations are in good agreement with the experimental results, and the thermal residual stress value of lithium niobate can reach 2/3 of its yield stress outside the high-temperature hyperelastic interval by comparison tests. The calculation results of the multi-scale model of the SAW device indicate that excessive thermal stress is the main cause of damage during the thermo-mechanical coupling of the SAW device. The polarization direction of lithium niobate crystals may cause the development of damage cracks, increasing the heat dissipation can effectively reduce the thermal stress to which it is subjected, and better extend the service life of the SAW device.

Glass flakes for enhancing mechanical properties of glass/epoxy composites

The purpose of this study is to improve composite mechanical properties by reducing the degradation induced by seawater in glass fibre/epoxy composites. To do this, glass flakes (50 µm in size) were placed on the surfaces of the glass fibre/epoxy composite material using various ratios (5%–15%). Glass flakes were introduced into composite materials that were exposed to seawater for a period ranging from 0 to 12 months, and changes in bending, tensile, impact, compression, and compression after impact strengths were assessed. Flexural tests revealed that the gel coat and glass flakes increased their loads by up to 24% and 23% after 6 and 12 months of water immersion, respectively. Tensile tests indicated that lower tensile strength loss occurred as the glass flake ratio rose. As the immersion duration increased, the effect of glass flakes became more evident in terms of tensile strength. Composites containing glass flakes showed greater impact strength load values in both 20 and 30 J impacts compared to the control sample. The use of glass flakes improved the residual strength values because it prevented water penetration. The control sample retained up to 50% of its compressive strength, while the 15% glass-flake reinforcement promoted retention to 56%.

Optimizing the printing parameters for dimensional accuracy of distal femur bone by using Taguchi’s method

BackgroundFused deposition modelling (FDM) is a popular additive manufacturing technique with capability of producing complex and integrate shapes. One of the critical aspects of FDM is the dimensional accuracy of 3D (three-dimension) printed model, especially in medical science applications, as proper fit and function with human body can prevent patient’s discomfort, complication or even harm.ObjectiveIn this research work, the optimisation of print parameters: layer height, nozzle temperature, printing speed, infill pattern and infill density for improving the dimensional accuracy of distal femur bone, an irregular and complex shaped geometry is carried out using Taguchi’s method and to study its influence using ANOVA (analysis of variance).Methodology3D CAD (computer-aided design) model of the distal femur bone is generated from a CT (computerized tomography) scan using 3D slicer and its associated errors are corrected using Ansys SpaceClaim. The model is prepared for printing using Ultimaker Cura as per L16 orthogonal array experimental layout where TEA (trans epicondylar axis), which is the distance between the most prominent point of the lateral and medial epicondyle, is set at 45° from X-axis in XY plane, i.e. diagonally on the plane of printing bed. It is then printed with PLA (polylactic acid) filament. Length along TEA is compared accordingly with 3D CAD model. Taguchi’s method of ‘smaller the better’ is applied for reducing deviation. Further, ANOVA analysis is done on the data set and a linear regression model is also developed.ResultThrough Taguchi’s method, the optimum parameters were found to be triangle for infill pattern, 200 °C for nozzle temperature, 30 mm/s for nozzle speed, 0.1 mm for layer height and 40% for infill density. ANOVA analysis shows that all parameters contribute significantly with layer height being the most influential parameter, followed by infill pattern, nozzle speed, nozzle temperature and infill density. Mathematical model through multiple linear regression method was developed with determination of coefficient value of 96.91% and standard residual value is within the acceptable range of ± 2 indicating that there is no outliner in the data.

Development of 2-dimensional and 3-dimensional QSAR models of Indazole derivatives as TTK inhibitors having Anticancer potential

The study aimed to explore the anticancer efficacy of indazole pharmacophore by analyzing a series of 109 derivatives of indazole as Tyrosine Threonine Kinase (TTK) inhibitors through quantitative activity relationship analysis using 2D and 3D QSAR techniques. The best 2D-QSAR model was generated by the MLR method, showing a high correlation coefficient (r2) of 0.9512, and good internal (q2), and external (pred_r2) cross-validation regression coefficients of 0.8998, and 0.8661, respectively. The residual values were modest, indicating good agreement between the observed and predicted pIC50 values, which suggested that the chosen model was predictably accurate. The 3D QSAR model, built using the SWF kNN approach, displayed a high internal cross-validation regression coefficient (q2) of 0.9132. Essential structural features/considerations in developing indazole as prospective anticancer medicines have been suggested. The study provides a reliable and predictive model for the prediction of anticancer activity of indazole derivatives. The identified essential structural features/considerations may be useful for the development of prospective anticancer medicines.

Development of the GSTARIMA(1,1,1) model order for climate data forecasting

The space-time model combines spatial and temporal elements. One example is the Generalized Space-Time Autoregressive (GSTAR) Model, which improves the Space-Time Autoregressive (STAR) model. The GSTAR model assumes that each location has heterogeneity characteristics, and that the data is stationary. In this research, the moving average component is calculated by involving the relationship between variable values at a certain time and residual values at a previous time, and it is assumed that the data is not stationary, so the model used is the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Model. The model order for GSTARIMA is determined through the Space-Time Autocorrelation Function (STACF) and Space-Time Partial Autocorrelation Function (STPACF) to ensure accurate forecasting. Previous research only discussed the GSTARIMA(1,1,1) model, so in this research, the GSTARIMA(3,1,1) model will be addressed as a form of development of the GSTARIMA(1,1,1) model and applied to climate data. The climate data used in this research is sourced from NASA POWER and consists of rainfall variables with large data sizes, requiring the use of the data analytics lifecycle method to analyse Big Data. The lifecycle includes six phases: discovery, data preparation, model planning, model building, communicating results, and operationalization. Based on the data processing results with Python software, the GSTARIMA(3,1,1) model has a MAPE value of 9% for out-sample data and 11% for in-sample data. In contrast, the GSTARIMA(1,1,1) model has a MAPE value of 11% for out-sample data and 12% for in-sample data. So the GSTARIMA(3,1,1) model provides more accurate forecasting results. Therefore, selecting the correct model order is crucial for accurate forecasting.

Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening

The industry's demand for intricate geometries has spurred research into additive manufacturing (AM). Customising material properties, including surface roughness, integrity and porosity reduction, are the key industrial goals. This necessitates a holistic approach integrating AM, laser shock peening (LSP) and non-planar geometry considerations. In this study, machine learning and neural networks offer a novel way to create intricate, abstract models capable of discerning complex process relationships. Our focus is on leveraging the certain range of laser parameters (energy, spot area, overlap) to identify optimal residual stress, average surface roughness, and porosity values. Confirmatory experiments demonstrate close agreement, with an 8% discrepancy between modelled and actual residual stress values. This approach's viability is evident even with limited datasets, provided proper precautions are taken.

Electrochemical capacitive dengue aptasensor using NS1 in undiluted human serum.

Non-structural 1 (NS1) is a protein biomarker that can be found in blood in the early stages of dengue and related infections (Zika and Chikungunya). This study aims to develop a biosensor to selectively quantify NS1 using DNA aptamer co-immobilized on gold electrodes with 6-(ferrocenyl)hexanethiol (FCH) using electrochemical capacitive spectroscopy. This technique uses a redox probe (FCH) immobilized on the self-assembled monolayer to convert impedance into capacitance information. The developed platform was blocked with bovine serum albumin before NS1 exposure and the ratio between aptamers and FCH was optimized. Theaptasensor was tested using commercial NS1 serotype 4 in phosphate-buffered saline and commercial undiluted human serum. Using the optimum applied potential provideshigh sensitivity (3 and 4 nF per decade) and low limit of detection (30.9 and 41.8fg/mL) with a large linear range (10pg to 1µg/mL and 10pg to 100ng/mL, respectively). Both results exhibit a residual standard deviation value < 1%. The results suggested that this aptasensor was capable of detecting NS1 in the clinical range and can be applied to any other specific aptamer with FCH, opening the path for label-free miniaturized point-of-care devices with high sensitivity and specificity.

Diagnostic Value of Echocardiography Combined with Residual Cholesterol for Asymptomatic Myocardial Ischaemia in Coronary Artery Disease.

This study aimed to investigate the diagnostic value of combined echocardiography and residual cholesterol in asymptomatic myocardial ischaemia. One hundred and fifty-seven patients were seen at Hefei BOE Hospital from 2019 to 2022. The patients were divided into two groups, the observation group (n=90, confirmed asymptomatic myocardial ischaemia) and the control group (n=67, negative diagnosis), based on coronary angiography. The observation group had significantly higher residual cholesterol levels (p=0.001). A combined approach of echocardiography and serum residual cholesterol values showed statistically higher accuracy (p<0.05), with ROC curve analysis supporting the superiority of this method [AUC 0.788 (0.711-0.865), Yoden index 0.576]. It also demonstrated higher sensitivity (88.9%) and specificity (68.7%). The study concluded that combined echocardiography and serum residual cholesterol testing offer superior diagnostic efficacy and practicality for asymptomatic myocardial ischaemia, recommending it for the clinical use. Key Words: Echocardiography, Residual cholesterol, Asymptomatic myocardial ischaemia, Diagnosis.

Effects of forming techniques on residual stresses in stiffening ribs of sandwich panels

This study investigates residual stresses in stiffening ribs of composite materials formed by stamping in a punch-die system. Using two-dimensional X-ray diffraction (XRD), we measured residual stresses on both the anterior and posterior sides. Three LITECOR® composite types, with core thicknesses of 0.8, 1.25, and 1.6 mm, were examined. Results indicate that as core thickness increases, residual stress values decrease on both sides. Notably, anterior side stresses on the y-axis are relatively higher (1027–1199 MPa) compared to the x-axis, exceeding posterior side values (998–1083 MPa) at 0.8 mm core thickness. Moreover, the study compares these values with Single Point Incremental Forming (SPIF) and finds that SPIF generally yields lower stress values for all core thicknesses. These findings suggest that SPIF is favourable for achieving minimal residual stress in LITECOR® composites. Addressing residual stresses is crucial for enhancing structural integrity and extending the service life of sandwich panels and composite materials.

Effect of sand coating on durability and micro-structural deterioration of GFRP rebar under alkaline environment attack

The objective of this study is to investigate the consequences of the sand-coated layer on the durability of glass fiber reinforced polymer (GFRP) rebars under four alkali solution concentrations and three temperatures. A total of 464 GFRP rebars specimens were manufactured to undergo accelerated corrosion experiments and mechanical performance test. Among them, 438 specimens were used to test the residual tensile strength of GFRP rebars after 120 days of immersion; 26 specimens were applied for surface observation and microscopic testing to analyze the degradation and damage process of GFRP rebars. SEM and digital camera were used to examine the micro-structure of samples either with or without sand layer covering. The unidirectional tensile strength experiment was conducted to test the remaining tensile strength of GFRP rebars after corrosion. The experimental results indicate that sand coating provides better protection and restraint in GFRP rebar durability reduction, resulting in a lower rate of fiber loosening and resin cracking and flaking when exposed to a solution over a pH of 12.5. As the failure of GFRP rebars ranges from local damage to cracking of the peripheral sand protective layer, the pH value of 12.5 is considered a critical alkali solution concentration. Based on experimental results, an improved model was proposed to predict the short-term and long-term residual strength values of GFRP rebars. The calculation results indicate that the predict values are in good agreement with the experimental ones, and the proposed model can be used to predict the residual strength of GFRP rebars.

Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil.

Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Analisis Faktor Konfirmatori untuk Mengidentifikasi Peubah Indikator Utama dalam Pengukuran Peubah Laten

Research on attitudes, preferences and behavior are research that involves latent variables. Latent variables are measured by observing some indicator variables. Indicator variables that measure a latent variable are selected based on the researcher's perspective, however, it is necessary to consider the previous of related research. Indicator variables need to have theoretical meaning, be valid and reliable in measuring latent variables. Confirmatory factor analysis (CFA) is a statistical method that can be used to determine the validity of the indicator variables. In this research, CFA is used to determine the main indicator variables that characterize the reasons for choosing a study program at a university by high school graduates (or equivalent). There are 20 indicator variables chosen to represent several latent factors such as image, job prospects, interests and campus facilities. The results indicate that the latent factors of image, job prospects, interest and facilities can be respectively represented by the majority of alumni occupying strategic positions in their careers, the easiness of alumni for getting a job, a large number of individuals in surroundings who are working as a data analysts and representative library. The findings also reveals that all the selected indicators are significant so that no indicators need to be excluded. Evaluation of the model shows that the specified model fits the analyzed data. This is indicated by the Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) values ​​which reach good fit criteria.

Numerical and experimental study of residual stress in multi-pass laser welded 5A06 alloy ultra-thick plate

The laser welding technology of ultra-thick plates has been widely applied in many crucial engineering structures, where its safety and stability still suffer from the residual stress brought by the welding temperature. In this work, experimental and numerical studies are carried out to reveal the evolution and distribution of the residual stress. A 5A06 aluminum alloy plate with 30 mm thickness is used to conduct the laser welding experiment, and the short-wavelength X-ray diffractometer (SWXRD) is employed to measure the residual stress. Furthermore, a multi-pass laser welding numerical model is developed to predict the residual stress during the welding process. The simulation results of the weld pool geometry and residual stress agree well with the experimental data, which gives a good validation of the numerical model. Based on the proposed numerical model, the influence of the welding parameters, including inter-pass cooling time and welding rate are also investigated. It is discovered that the reduction of the inter-pass cooling time results in weakness effect of the temperature gradient, which hinders the generation of plastic deformation and leads to a lower residual stress value. Meanwhile, the simulation results also show that the increase in welding rate promotes the generation of thermal stress, which brings a higher residual stress value appeared in the structure.

PEMODELAN JUMLAH PENUMPANG PESAWAT DI BANDARA SOEKARNO-HATTA MENGGUNAKAN MODEL HYBRiD SARIMA-FTSMC

The aim of this research is to model the number of airplane passengers at Soelkarno-Hatta airport using a model hybrid SARIMA-FTSMC. The data used in this research is secondary data in the form of data on the number of airplane passengers at Soekarno-Hatta airport from January 2010 to May 2023 which was obtained via the websitebps.go.id. After analyzing the data, the best model was obtained in the SARIMA model, namely SARIMA(1,1,1)(0,1,1)12. Then based on the residual value from the processed data SARIMA will be modeled using the FTSMC model. Next, the residual value of SARIMA(1,1,1)(0,1,1)12 which has been modeled using the FTSMC model is added to the modeling value from SARIMA (1,1,1)(0,1,1)12 known as a modelhybrid SARIMA-FTSMC, with the aim of obtaining a higher level of accuracy or smaller error compared to the SARIMA model. After that, the accuracy level of the model is calculatedhybrid SARIMA-FTSMC uses MAD, MSE, and MAPE. In this research, the level of accuracy of the model was very good, this was shown by the MAPE value being less than 20%.

Point on Wave Energization Strategy and Sequential Phase Shifting for Sympathetic Inrush Current Mitigation in Three-Phase Transformer - Measurement

Inrush currents generated when an electrical transformer is energized can achieve great values and cause abnormal difficulties in the power grid. The sympathetic inrush current phenomenon is resulted in the power system when a transformer is energized with the existence of other transformer already energized. The main objective of this article is the attenuation of sympathetic inrush current in a three-phase transformer. The control technique is carried out by the consideration of the residual flux value when de-energizing the transformer as well as respecting the three-phase shifting. Moreover, an experimental setup to measure the inrush current will be presented. Furthermore, a technique for controlling the circuit breaker to energize a 2 kVA three-phase transformer which allows the sympathetic inrush current to be attenuated was also applied and tested in the experimental. The contribution of this paper is that this technique is applied in measurements with an in-depth estimation of the residual flux. The proposed technique made it possible to completely eliminate the sympathetic inrush current.

THE EFFECT OF AUTOMOTIVE LABORATORY INFRASTRUCTURE ON STUDENT LEARNING SATISFACTION IN DIESEL MOTOR PRACTICUM COURSE IN MECHANICAL ENGINEERING STUDY PROGRAM PALANGKA RAYA UNIVERSITY

In this study, regression analysis was used to examine the relationship between the availability of laboratory facilities and student satisfaction in the Mechanical Engineering Education program at UPR. The basic assumptions of the regression model were met, which included normality of residual distribution, homoscedasticity, and no autocorrelation. The test results showed that there was no heteroscedasticity and autocorrelation in this regression model, and the residual values had a normal distribution. In regression analysis, it is important to evaluate the significance of independent variables on the dependent variable. The test results showed that the predictor variable (availability of laboratory facilities) had a significant effect on the dependent variable (student satisfaction). An increase in laboratory facilities availability would contribute to a 1.162 increase in student satisfaction. The R-Square value of 0.637 or 63.7% indicates that 63.7% of the variation in student satisfaction can be explained by the availability of laboratory facilities, and the remaining is influenced by other variables not included in the model. This shows that the availability of laboratory facilities has a significant effect on student satisfaction. In conclusion, this study shows that the availability of laboratory facilities has a significant positive effect on student satisfaction in the Mechanical Engineering Education program at UPR. These findings can be used as input for the university to improve the availability of laboratory facilities to provide a better learning experience for students.