Coefficient Of Determination R2 Research Articles

Assessment of Rainfall-Induced Shallow Landslides in Kavre District, Nepal

Steep slopes and fragile geology make Nepal a landslide-prone country. Kavre district experiences a large number of shallow landslides in the monsoon season. This research aims to establish rainfall intensity relation with rainfall duration along with rainfall threshold for the initiation of shallow landslides in the Kavre district, Nepal. The six rain gauge stations' rainfall data in the Kavre district from 1987-2016 and landslide data from 1987-2013 were used for establishing rainfall thresholds. The rainfall that risks the landslide event was sorted and the regression analysis of the rainfall and landslide yielded a relationship I =86.738 *D-1.066 having a coefficient of determination R2 = 0.1257. The analysis also incorporates the drawing of the lower boundary which is the 48-h rainfall depth that indicates the rainfall threshold for the initiation of landslide in the Kavre district. The 48-hour rainfall intensity and rainfall depth at which the risk of landslide initiation is high in the Kavre district is 1.4 mm/h. and 67.18mm. Analysis of the antecedent rainfall and the landslide occurrence event indicated that the daily rainfall at failure conditions is better correlated with 3 days of cumulative rainfall at failure than with 5, 7, 10, 15, and 30 days. The study is significant for rainfall-induced landslide disaster management.

The first LC-MS/MS stereoselective bioanalytical methods to quantitatevely detect 9R- and 9S-hexahydrocannabinols and their metabolites in human blood, oral fluid and urine

A sensitive LC-MS/MS method for the simultaneous quantification of the (9R)- and (9S)- hexahydrocannabinols (HHCs), and their metabolites, in human urine, oral fluid (OF) and blood samples were developed, validated and used to the biological samples of volunteers. The analytes were extracted from 100µL human samples. An isocratic elution mode with methanol was used for chromatographic separation of (9R)- and (9S)-HHC on an immobilized amylose tris(3-chloro-5-methylphenylcarbamate)-based chiral column Lux i-Amylose-3. The flow-rate of the mobile phase was 0.5mL/min. An isocratic elution mode of methanol and water (80/20, v/v) was used for chromatographic separation of metabolites of (9R)- and (9S)-HHC on a Lux AMP chiral column (with a proprietary chiral selector) at a flow rate of 0.5mL/min. MS/MS analysis was performed in positive ionization mode for HHC epimers, while in negative ionization mode was used for metabolites of HHCs. The calibration curves for HHCs and their metabolites in human samples ranged from 0.25- 240ngmL-1 and 1 - 100ngmL-1, respectively, with determination coefficients (r2) of ≥ 0.99. All analytes were stable at room temperature, 4 °C, in the autosampler (+10°C) and −20 °C for 24h, after three freeze/thaw cycles, and when stored at −20 °C up to one week after quality control (QC) sample preparation (concentration differences less than 20% with respect to time zero response), in blood, urine and OF.

Contribution of socio-economic determinants to the management of sustainable performance: case study of Moroccan companies with Corporate Social Responsibility label

In a dynamic environment marked by significant shifts in production and marketing systems across various industries, companies are compelled to adhere to national and international standards to meet evolving demands from both internal and external stakeholders. These emerging requirements have spurred companies to adopt strategic initiatives, such as emphasizing Corporate Social Responsibility (CSR), as part of their managerial actions. The primary aim is to integrate two key management science themes: indicators derived from management control systems and the management of sustainable performance. Consequently, our focus lies in the socio-economic approach, particularly the socio-economic cost-value of activities, which influences the diversity of indicators on one hand and the management of sustainable performance on the other hand, mediated through their variety. This underscores the importance of managing sustainable performance by utilizing measures and data tied to socio-economic fundamentals, prompting us to formulate our research question as follows: "To what extent does the cost-value of activities impact the management of sustainable performance?" The empirical investigation transitions from simply describing variables within our sample companies to conducting factor analysis via Principal Component Analysis-PCA. This aims to establish a robust factorial structure and subject several constructs to testing, assessing the internal consistency of the model, the quality and reliability of the sample, and the validity of research hypotheses. Our findings align with prior research, indicating that diverse indicators can inform managers about social and environmental practices, fostering positive effects on sustainable firm performance. However, excessive reliance on these indicators may lead to confusion and dispersion within companies. Hence, it's imperative to tailor indicators to the specific context and objectives of each firm, considering industry-specific nuances. Our study demonstrates that leveraging a diverse range of indicators is pivotal for enhancing sustainable performance dimensions and managing socio-economic factors amidst external pressures. Evaluating the measurement model is crucial for validating the reliability and validity of measurement instruments, as well as assessing the structural model's overall quality, which includes tests like coefficient of determination-R², Goodness of fit-GoF, and others. It's important to note that our measurement model encompasses both reflective and formative constructs. The structural model's quality is gauged by its explanatory power, evaluated through R², indicating explained variance in the variables under scrutiny. To address this issue, we chose to examine the connections between key socio-economic factors and the management of sustainable performance in Moroccan companies certified with CSR labels. Through structural equation modeling, we found that effectively managing the cost-value of activities using diverse indicators plays a significant role in enhancing sustainable performance within CSR-certified companies.

Pengaruh Implementasi Media Pembelajaran Terhadap Hasil Belajar PKn Siswa SMP Bina Jaya Banguntapan Yogyakarta

Learning media is a tool used in learning and has an important role in increasing teacher efficiency and effectiveness in teaching. This research aims to determine the effect of learning media on Civics learning outcomes. This research uses a quantitative descriptive approach. The sampling technique used was Stratified Random Sampling, while data collection was carried out through questionnaires, observation and documentation techniques. The results of the regression analysis in this study show the equation Y = 112.537+(-0.686X). This equation means the coefficient value. In addition, it is also known that the coefficient of determination R2 (RSquare) is 0.102, which means the magnitude of the influence of the independent variable (use of learning media (x) on the dependent variable (civic learning outcomes (y) is 10.2%, while the remainder is (89 .8%) is influenced by other variables outside of learning variables that do not use learning media.

Heterogenous factors modulate social cognition across diverse settings in brain health and disease

AbstractBackgroundSocial cognition dysfunction in neurodegenerative diseases such as the behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s Disease (AD) is typically ascribed to the underlying pathophysiological processes spanning specific atrophy and connectivity alterations. However, social cognition performance might be modulated by a set of more heterogenous, non‐specific factors, including demographics (sex, age, education, socioeconomic status), general cognitive and executive abilities, and behavior inside the scanner (i.e., movements), but their relative contribution is poorly understood. Here, we develop a machine learning approach to characterize multimodal factors that explain the inter‐individual heterogeneity of social cognition in a diverse multicentric sample.MethodA total of 998 individuals > 50 years from international research networks in high‐ and middle‐income countries participated in this study: 102 with bvFTD, 339 with AD, 96 with mild cognitive impairment (MCI), and 461 with preserved cognition (with and without cognitive complaints). Social cognition was assessed with the Mini Social Cognition and Emotional Assessment (MiniSEA) battery, which includes tests of facial emotion recognition and theory of mind. After data harmonization, we implemented support vector regressions combined with bootstrapping to predict social cognition outcomes from multimodal features, including diagnosis, demographics, cognitive and executive functions, grey matter volume, fMRI resting‐state network connectivity, and movement artifacts, and assessed their relative contribution to performance (Figure 1). Models were assessed with the coefficient of determination R², Fisher’s F test, Cohen’s F², 95% confidence intervals, and p‐values.ResultGeneral cognitive and executive functions and years of education consistently emerged among the top predictive features of social cognition performance and explained more variance than diagnosis. Other features making significant contributions were: sex, the gray matter volume of relevant regions, and in‐scanner movements. Age and resting‐state network connectivity did not make any significant contribution to performance heterogeneity (Figure 2).ConclusionSocial cognition performance was more strongly predicted by a set of heterogenous individual differences than by neurodegenerative diagnoses and associated brain signatures. Diagnoses might relate to complex profiles. Results highlight a critical modulator role of heterogeneity on social cognition, with implications for the neuropsychological assessment in brain health and disease across diverse, multi‐setting populations.

Harga Diri dan Schadenfreude pada Karyawan

Employees are required to build harmonious relationships between workers in order to create work productivity. However, in reality conflicts between employees still occur frequently. This study aims to determine the relationship between self-esteem and Schadenfreude in empoyees. The hypothesis in this study is that there is a relationship between self-esteem and Schadenfreude. The subjects in this study were employees who worked in an agency or company with an age range of 18 years and over and working for more than 1 year. Data collection techniques will use the self-esteem scale and Schadenfreude scale. The data were analyzed using pearson's correlation product moment in the SPSS version 26 software program. Based on the results of the analysis, the correlation coefficient values was -0.624 and p=0.000. (p≤0.050). These results indicate a significant negative relationship to self-esteem and Schadenfreude. The coefficient of determination R2 is 0.390, which means that self-esteem has a relationship of 39% to Schadenfreude while the remaining 61% is influenced by other factors.

Pengaruh Inovasi Produk dan Keunggulan Bersaing terhadap Keberhasilan Usaha pada UMKM Industri Makanan dan Minuman di Kota Medan

This research aims to determine the influence of product innovation and competitive advantage on business success in Food Industry MSMEs in Medan City. The MSME Food and Beverage Industry is one of the businesses most in demand by young entrepreneurs. However, there are also risks in running this business due to extensive competition in the Food and Beverage Industry, where many MSMEs in the Food and Beverage Industry experience failure due to extensive competition and lack of innovation in the products offered. The research focuses on Medan City MSMEs because these MSMEs are one of the largest provinces producing MSMEs in the Food and Beverage Industry, with a population of 1,828 MSMEs. The sample in the research was 95 MSMEs in Medan City which were taken using the Slovin formula. The data collection technique used in this research was carried out by distributing structured questionnaires using the SPSS v.28 application. The analysis techniques used in this research are multiple linear regression, partial test (t test), simultaneous significance test (F test) and coefficient of determination R2. The research results show that there is a positive and significant influence of product innovation on business success, there is a positive and significant influence of competitive advantage on business success and there is a simultaneous positive and significant influence of product innovation and competitive advantage on the success of MSME businesses in Medan City.

Laboratory Investigation and Machine Learning Modeling of Road Pavement Asphalt Mixtures Prepared with Construction and Demolition Waste and RAP

Due to the decreasing availability of virgin materials coupled with an increased awareness of environmental sustainability issues, many researchers have focused their efforts on investigating innovative technological solutions in the civil engineering domain. This paper aims to evaluate the suitability of construction and demolition waste (C and DW) and reclaimed asphalt pavement (RAP) reused within asphalt mixtures (AMs) prepared for the binder layer of road pavements. Both hot and cold mixing methodologies were investigated. The technical assessment was based on the volumetric and mechanical suitability, according to saturated surface dry voids (SSDV) and indirect tensile strength (ITS) tests carried out at 10 °C, respectively. Laboratory findings showed that all the hot AMs matched the desired target SSDV at the design gyrations number at different optimum bitumen content levels, alternatively showing a non-significant variation or a significant increase in ITS compared to conventional hot mix asphalt. Conversely, the cold AMs with cement and emulsion bitumen showed a greater volume of voids and moisture sensitivity, and lower temperature susceptibility compared to hot AMs, reaching, on average, 11% lower ITS when using coarse C and DW aggregates and 43% lower ITS when using filler from C and DW. These volumetric and mechanical properties were modeled by means of support vector machines and categorical boosting (CatBoost) machine learning algorithms. The results proved to be satisfactory, with CatBoost determination coefficients R2 referring to SSDV and ITS equal to 0.8678 and 0.9916, respectively. This allowed for the mechanical performance of these sustainable mixtures to be predicted with high accuracy and implemented within conventional mix design procedures.

MATLAB-Based Algorithm and Software for Analysis of Wavy Collagen Fibers.

Knowledge of soft tissue fiber structure is necessary for accurate characterization and modeling of their mechanical response. Fiber configuration and structure informs both our understanding of healthy tissue physiology and of pathological processes resulting from diseased states. This study develops an automatic algorithm to simultaneously estimate fiber global orientation, abundance, and waviness in an investigated image. To our best knowledge, this is the first validated algorithm which can reliably separate fiber waviness from its global orientation for considerably wavy fibers. This is much needed feature for biological tissue characterization. The algorithm is based on incremental movement of local regions of interest (ROI) and analyzes two-dimensional images. Pixels belonging to the fiber are identified in the ROI, and ROI movement is determined according to local orientation of fiber within the ROI. The algorithm is validated with artificial images and ten images of porcine trachea containing wavy fibers. In each image, 80-120 fibers were tracked manually to serve as verification. The coefficient of determination R2 between curve lengths and histograms documenting the fiber waviness and global orientation were used as metrics for analysis. Verification-confirmed results were independent of image rotation and degree of fiber waviness, with curve length accuracy demonstrated to be below 1% of fiber curved length. Validation-confirmed median and interquartile range of R2, respectively, were 0.90 and 0.05 for curved length, 0.92 and 0.07 for waviness, and 0.96 and 0.04 for global orientation histograms. Software constructed from the proposed algorithm was able to track one fiber in about 1.1 s using a typical office computer. The proposed algorithm can reliably and accurately estimate fiber waviness, curve length, and global orientation simultaneously, moving beyond the limitations of prior methods.

El Niño’s Effects on Southern African Agriculture in 2023/24 and Anticipatory Action Strategies to Reduce the Impacts in Zimbabwe

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino’s effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers’ livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures.

Analysis of Non-Cash Transactions, Debit Cards, Credit Cards, and Electronic Money, on the Money Supply in Indonesia

This study was conducted to determine the effect between the independent variable, namely non-cash transactions (debit card transactions, credit card transactions, and electronic money transactions) on the dependent variable, namely the Money Supply (M1) in Indonesia. The research was conducted using a quantitative approach and the type of research used secondary data in the form of time series data from 2012-2021 obtained from Bank Indonesia and the Central Bureau of Statistics. The purpose of this study is to determine the effect of non-cash transactions on the money supply in Indonesia during 2012-2021. The analysis model used is multiple linear regression using SPSS. This study also uses the statistical t test, statistical F test, and the coefficient of determination R2. Based on the results of the research that has been done, it shows that debit card transactions have a positive effect on the amount of money in circulation and credit card transactions have a negative effect on the money supply in 2012-2021. Meanwhile, electronic money transactions have no effect on the money supply in 2012-2021

Modelo de predicción para cuantificar la fracción de eyección recuperada del ventrículo izquierdo en taquimiocardiopatía

Introduction and objectivesTachycardiomyopathy is a frequent cause of reversible ventricular dysfunction, whose predictors of recovery are not properly identified. Their individual estimate would be useful to carry out the corresponding invasive procedures. MethodsThis is an observational, retrospective and unicentral study. Cases diagnosed with tachycardiomyopathy were collected between September 2015 and January 2023. The sample was split into 2 study groups: the first one was used for the construction of the linear regression model by performing a multivariate analysis based on evaluating all possible equations (sample 1, 70%); whereas the second one was intended for the validation of the model (sample 2, 30%). ResultsA total of 134 patients were gathered and left ventricular ejection fraction (LVEF) at recovery (the difference between final and original LVEF) was calculated in all of them. Within sample 1, setting parameters of 16,383 models were estimated. The model selected, based on Mallows’ Cp index, was the composed of the following variables: sex, arterial hypertension (HT), LVEF at diagnosis, achievement of rhythm control, and ablation. The selected model explains about half of the individual variability of the recovered LVEF (R2 0.493). Afterwards, individual LVEF at recovery predicted by the model was compared with the observed LVEF in sample 2. No overall significant differences were observed in the R2 coefficient of determination. ConclusionsSex, arterial hypertension, LVEF at diagnosis, achievement of rhythm control, and ablation were considered as predictors of recovered LVEF in patients with tachycardiomyopathy. This has made possible a quantitative estimate when integrating them within the model.

Thermally activated adsorbent over chemically modified Carica papaya tree adsorbents for removal of chromium from tannery wastewater

This work investigated on the adsorption efficacies of contrasting Carica papaya tree adsorbents in removing chromium from the tannery wastewater. The convenience of thermally activated adsorbent was evaluated over chemically modified adsorbents developed employing ZnCl2, KOH, and HNO3 individually followed by calcination (600 °C) for 3 h. Chromium removal efficiencies obtained for ZnCl2, KOH, and HNO3 modified and thermally modified Carica papaya tree adsorbents were 71.33 %, 73.58 %, 76.25 %, and 99.89 %, respectively. Chromium adsorption on different Carica papaya tree adsorbents was assessed by FTIR, EDX, TEM, and BET surface area analysis. The maximum chromium removal efficiency of thermally modified adsorbent was 99.88 % at 0.25-g/25 mL of adsorbent dose for a 20 min stirring time. Both the Pseudo-second-order equation and Freundlich isotherm showed the best fitting with determination coefficients R2 = 0.9997 and R2 = 0.9999, respectively. Freundlich isotherm and Pseudo-second-order kinetic revealed the adsorption reaction as a chemisorption reaction with stronger interaction between adsorbent and chromium.

Improving the Estimation of the Diffuse Component of Photosynthetically Active Radiation (PAR)

AbstractMost weather forecasting models are not able to accurately reproduce the great variability existing in the measurements of the diffuse component of photosynthetically active radiation (PAR; 400–700 nm) under all sky conditions. Based on the well‐known relationship between the diffuse fraction (k) and the clearness index (kt), this study addresses improvements in estimations by proposing adaptations of previous models, which were previously applied only to the total solar irradiance (TSI; 280–3,000 nm). In order to reproduce this variability, additional parameters were introduced. The models were tested employing a multisite database gathered at the Mediterranean basin. Since Artificial Neural Network (ANN) models are not limited to fixed coefficients to predict the diffuse fraction of PAR (kPAR), these types of models are more accurate than empirical ones, reaching determination coefficients (r2) up to 0.998. However, the simpler linear model proposed by Foyo‐Moreno et al. (2018), https://doi.org/10.1016/j.atmosres.2017.12.012 shows a similar performance to the ANN models, directly predicting the diffuse component of PAR (PARDiffuse) from TSIDiffuse, with a r2 up to 0.997. Results obtained here also determine that the most important variables for estimating PARDiffuse are kt or kt,PAR, and the apparent solar time (AST). Therefore, PARDiffuse can be modeled using TSI measured in most radiometric stations, reaching r2 up to 0.858 for empirical models and 0.970 for ANN models. This modified approach will allow for the very accurate construction of long‐term data series of PARDiffuse in regions where continuous measurements of PAR are not available.

Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus.

Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

Developed of Taper Equation and Volume for Eucalyptus camaldulensis Dem. in the Nineveh Region

This study aimed to estimate a taper equation for eucalyptus trees growing in Nineveh Governorate, it is used in forest inventory or management planning systems, giving information about the diameter at any point along the main stem of the tree. Data were collected from three naturally growing trees for diameter at breast height, total height, and diameter at different levels, starting from 0.5 m above ground level, with distances of half a meter along the main stem, using computer and regression methods. different. Linear and non-linear. In the preparation of the taper equation, as well as the use of statistical measures, namely, the coefficient of determination R2, the standard error attributed to the S.E rate, in the test of the best taper equation, and through the comparison between equations through statistical measures, we were able to obtain the following taper equation: d = 14.2299 * hi -0.1873 And by taking the integration of the taper equation, we got an equation for estimating the size of eucalyptus trees, which is:V1 = 0.02542(hi)0.6254.

Prediction of ethylene permeance through perforated packaging films using a dimensionless correlation

Ethylene is a ripening hormone biosynthesized by fruit and vegetables that can accumulate inside the package and accelerate ripening in fresh produce, which, in turn, affects its shelf-life. Perforating the fruit package material can facilitate the effective removal of ethylene gas through the transmission process. Excessive perforations will allow ethylene to completely escape, but will then invalidate the possibility of modified atmosphere packaging (MAP). In order to design MAP for climacteric fruits, it is therefore essential to quantify ethylene transfer through the package, and not only oxygen and carbon dioxide. While there is ample information on the transmission of water vapour, oxygen, and carbon dioxide in perforated MAP, little research has focused on ethylene. The objective of this study was to develop a dimensionless correlation for predicting ethylene permeance through a perforated film. The ethylene permeance ranged from 1.70 to 4.06 cm/s per perforation, depending on film thickness (0.002–0.003 cm), perforation diameter (0.05–0.095 cm), cooling air velocity (1–18 cm/s), and temperature (4–24 °C). The developed dimensionless correlation relates Stanton, Peclet and Schmidt numbers. The coefficient of determination (R2) of the dimensionless correlation developed with a first set of 36 data points was 99.62%. The dimensionless correlation developed was then validated with 2 sets of 12 data points each, with the model predictions fitting very well the experimental data of these validation trials (coefficient of determination R2 = 97.69%). The correlation is thus validated for axial Peclet numbers in the 0.01–0.35 range and radial Peclet numbers in the 0.4–11.1 range, while the Schmidt number barely varies in MAP conditions (being around 0.98). The perforation diameter was the most influential parameter on ethylene permeance, while cooling air velocity and temperature had a moderate impact, and film thickness had little effect.

Prediction of wood drying process based on artificial neural network

Taking the conventional drying process of Pinus sylvestris square wood with pith as the research material, based on the Back Propagation (BP) neural network algorithm, a model was constructed using the real-time online-measurement data. Softening treatment time and temperature, variable treatment time and temperature, initial moisture content of wood, and position of wood core and sapwood were used as model inputs. Wood drying rate and longitudinal cracking degree were used as outputs to indicate wood drying quality. The results showed that with a suitable model structure of 6-9-2 (input layer-hidden layer-output layer), the coefficient of determination R2 and mean square error of the test samples were 0.96, 0.99, and 0.00605, respectively, indicating that the neural network model has good generalization ability. Compared with the experimental value, the predicted value basically conforms to the change law and size of the experimental value, and the error distribution is approximately 2%. This shows that the BP neural network model can simulate the drying rate and longitudinal cracking degree in the drying process and realize the prediction of the drying process.

Analisis Tingkat Pengetahuan, Lokasi dan Literasi Keuangan Syariah terhadap Minat Masyarakat Menggunakan Produk Bank Syariah Indonesia Cabang Kabupaten Belitung

Sharia banking is an implementation of a larger sharia economic system. This research aims to find out whether the level of knowledge, location and sharia financial literacy influence people's decisions to use Bank Syariah Indonesia products at the Belitung Regency branch. This research uses a descriptive quantitative approach. The techniques used in sampling are non-probability sampling and convenience sampling and the data analysis technique used is multiple linear regression. The results of the analysis using the t test illustrate that the level of knowledge influences people's interest with a t value of 4.461 which is greater than t table 1.991. Location has no influence on people's interest in using Bank Syariah Indonesia products based on a tcount value of 6.797 which is greater than ttable 1.991. Meanwhile, sharia financial literacy influences people's interest in using Bank Syariah Indonesia products with a tcount value of 6.797 which is greater than ttable 1.991. Furthermore, based on multiple linear regression testing, the coefficient of determination R2 is 0.641 or 64.1%, which means that the variables of level of knowledge, location and sharia financial literacy influence 64.1% of people's interest in using Bank Syariah Indonesia products and the remaining 35.9% is explained by other variables outside the variables studied in the research such as government policy and public demand.

CFD modelling of the microclimate of a cultivated greenhouse: A validation study between experimental and numerical results

In this work, we present the validation of a numerical model of a greenhouse thermally in-sulated on three sides with a tomato crop. A CFD software (Ansys-Fluent) was used to solve the numerical model. The discrete ordinate model was included to solve the radiative trans-fer equation. The results of the numerical model were compared with the values of air tem-perature observations at different points in the greenhouse. Good agreement was obtained between the simulated and measured values, with coefficients of determination R2 = 0.77, R2 = 0.84, R2 = 0.99, and R2 = 0.89 for the temperatures of the points 10 cm, 80 cm, and 210 cm above the ground and the average temperature in the greenhouse, respectively. A third-order polynomial curve was drawn between the simulated and measured values of relative humidity in the greenhouse. These R2 values are 0.9786 and 0.7165, the simulated and measured relative humidity, respectively. The simulation results showed low velocity values with an average of 0.525 m/s located between 1.5 m and 2 m from the ground.