Coefficient Of Determination Values Research Articles

Pengaruh Kompensasi, Kesehatan Dan Keselamatan Kerja Terhadap Kinerja Karyawan Pada PT. Sinar Jaya Inti Mulya Kota Metro

The workforce plays a very big role in carrying out company activities, the existence ofhuman resources in a company plays a very important role. This research aims to analyze theinfluence of Compensation, Occupational Health and Safety on Employee Performance. Thisresearch was conducted at PT. Sinar Jaya Inti Mulya Metro City, Lampung. This researchuses quantitative methods. The data used is primary data obtained using a questionnairemethod for a sample using the Slovin formula, obtained as many as 121 employees at PT.Sinar Jaya Inti Mulya Metro City. The analysis tool used is Multiple Linear Regression withthe help of the SPSS 20.0 program. The research results show that 1) Compensation has apositive and significant influence on employee performance. 2) Health has a positive andsignificant influence on employee performance. 3) Work safety has a positive and significantinfluence on purchasing decisions. 4) Compensation, health and work safety have a positiveand significant influence together on employee performance. The coefficient of determinationvalue is 0.67, which means that the dependent variable can be explained by the independentvariable by 67%. This means that the independent variables include compensation,occupational health and safety at 67% while the remaining 33% is influenced by othervariables not examined in this research.

Bagaimana Tingkat Kesejahteraan Masyarakat, Pendidikan, dan Layanan Kesehatan Berdampak terhadap Penyebaran Kasus Positif dan Kematian Covid-19 di Jawa Bali?

Many factors cause the distribution of positive cases and deaths of Covid-19 in Indonesia. This study aims to determine the effect of welfare, Education, and health services on positive cases and fatalities of COVID-19 from 128 regencies and municipalities in Java and Bali islands. Data were collected from BPS and kawalcovid19.id. This study applied the Ordinary Least Squared method in two models. The variables are converted into Natural Logarithmic. The results of the regression of Model I show a high determination coefficient value of 0.718 with the value of the Regional GDP income per capita of (-0.389), Regional GDP (0.739), and Education (2.139) affect the distribution of the positive cases of Covid-19, except variable of Poverty (0.045) whose results are not significant. Meanwhile, Model II shows a regression result of Positive Covid-19 cases (0.999), Regional GDP income per capita (1.052), Regional GDP (-0.955) can affect the death cases of Covid-19 (0.151), Education (0.-431), and Health (0.-165) with nonsignificant results. Moreover, in this Model, the independent variable can affect the spread of death cases of Covid-19 by 59.3%.

Mathematical models to forecast temporal variations of power law shape parameters of a PV module working in real weather conditions: Prediction of maximum power and comparison with single-diode model

In this study, an innovative approach has been proposed to generate real time current–voltage characteristics and forecast peak power of photovoltaic (PV) modules working outdoors beneath real meteorological conditions. Power-Law Model (PLM) including four unknown parameters, two PV metrics (ISC, VOC), and two profile parameters (γ, m), has been used for fast, accurate and reliable modelling of PV modules. Explicit expressions giving γ and m parameters as functions of cardinal point coordinates (IMPP,VMPP, ISC and VOC) have been established. A daily monitoring of PV quantities, throughout one reference day, has been used to derive novel mathematical models of γ and m depending on module temperature and solar irradiance. To describe PV module behavior under changing meteorological conditions during an arbitrary day, mathematical models have been called to reproduce dynamical values of PLM shape parameters γ and m. Reliability of the approach has been assessed by comparing current–voltage characteristics and maximum power coming from PLM and Single-Diode Model (SDM) to experimental data of twenty-two PV modules measured at NREL. Comparison also reveals that I-V characteristics generated and predicted separately by PLM and SDM parameters are very close. In some cases, I-V characteristics generated by PLM parameters are more accurate. Furthermore, NRMSE values for generated and predicted I-V characteristics for all kinds of PV modules evaluated for different working conditions do not exceed 4% for both PLM and SDM, and determination coefficient values for long-term prediction of maximum power are close to one and annual average value is greater than 0,95.

New insight into biodegradation mechanism of phenylurea herbicides by cytochrome P450 enzymes: Successive N-demethylation mechanism

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure − activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

Pineapple Peel Flours: Drying Kinetics, Thermodynamic Properties, and Physicochemical Characterization

Pineapple is a widely cultivated, consumed, and processed fruit by the industry. However, only 22.5% of the whole fruit is used, which constitutes economic waste and environmental impact. The objective was to determine the drying kinetics and characterize the residual peel flours of two pineapple varieties at four drying temperatures. Jupi and Pérola pineapple peels were dried at temperatures of 50, 60, 70, and 80 °C in a thin layer. Ten mathematical models were adjusted to the experimental data to characterize the drying process. Fresh samples and flours were characterized according to their physicochemical properties (water content, ash, water activity, total sugars, reducers, pH, acidity, proteins, lipids, carbohydrates, and total energy value—TEV). The Midilli model was chosen because it best represents the drying process with high values of determination coefficients (R2) and low mean squared deviations (MSD), Chi-square (χ2), and estimated mean error (EME). The increase in temperature led to an increase in the effective diffusivity coefficient and consequent reduction in drying time. The activation energy obtained from the Arrhenius equation was 24.59 and 26.25 kJ/mol for Jupi and Pérola, respectively. Differences were reported in the enthalpy and entropy decrease with the increasing temperature, contrary to the Gibbs free energy. The flours produced had good characteristics for conservation, being acidic with low water content and low water activity. High levels of total and reducing sugars, carbohydrates, and total energy value were observed, in addition to good protein content.

Effects of Flow Unsteadiness on the Transport of Bimodal Bed Material

The grain size distribution of the transported bed load was experimentally investigated under unsteady flow conditions with bimodal mixture of sand and gravel in a laboratory flume. Five various triangular hydrographs were generated. A clockwise behavior for the total bed load versus shear velocity was observed meaning that the bed load during rising limb was higher than that of falling limb. It was found that the percent finer at the plateau of bimodal sediment size distribution curve had higher values during the initial and final phases compared to those obtained during the peak time. At all plateaus, the percent finer values related to the hydrograph peak discharge were in the same order of magnitude with that of the bed material. The sand content of the transported bed material initially decreased, then maintained a constant value during a certain time interval and finally returned to its original value. The sand percent of the bed load decreased in the falling limb showing a counterclockwise loop and the duration of the hydrograph did not affect the results considerably. The greater the peak flow rate of the hydrograph, the greater was the hysteresis. The bimodality index was calculated for all transported sediment samples and it was revealed that its initial and final values were less than that of the bed material but it was approximately the same elsewhere. The 5% finer sediment amount was nearly equal during rising and falling limbs. It was revealed that D50 value of the bed load decreased in the rising limb showing a clockwise loop. The hysteresis was not considerably changed according to the hydrograph characteristics. The clockwise type hysteresis was also observed for the size group of D95. The lag increased as the peak flow rate increased. A strong relation was found between the dimensionless total bed load Wt* and the total work index Wk as well as Wk and the ratio WR/WF. The correlations between the dimensionless total bed load and the unsteadiness parameters P, and Pmod were very weak, whereas a high value of determination coefficient was obtained with the unsteadiness parameter Pgt, implying an appreciable interdependence.

Estimating residential buildings’ energy usage utilising a combination of Teaching–Learning–Based Optimization (TLBO) method with conventional prediction techniques

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R2 value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R2 (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings’ cooling load.

Prediction and optimization of frying conditions of air fried pork rind using a direct linking model

The properties of puffed food change significantly, primarily due to temperature-induced changes in water vaporization during frying. This study aimed to establish a linking model that compiles data from moisture content and air temperature to determine the optimal frying conditions for making hot air-fried pork rind. The kinetics of pork rind drying were observed using a simple first-order reaction model to match the experimental data, indicating a decrease in moisture content during air frying. A model that considers mass and heat transfer during air frying was developed to predict the moisture content, water activity, puffing ratio, breaking force, appearance, and the optimized frying time for pork rind. The precision of the predictive model was examined by comparing simulated and measured values of the physical properties of fried pork rind during air frying. The simulation of the physical qualities of fried pork rind during air frying. The simulation of the physical qualities of fried pork rind could be directly associated with moisture content and air temperature. The minimum breaking force of the pork rind was observed at a frying temperature of 180 °C. Furthermore, the time required to reach this minimum breaking force was found to be 10.0, 3.5, 2.5, and 1.5 min as the moisture rate constant increased to 4.50 × 10−3, 1.20 × 10−2, 1.95 × 10−2, and 2.70 × 10−2 1/s, respectively. Based on the high determination coefficient values, the direct linking model accurately predicted the properties of fried pork rind. Thus, we successfully developed a method to optimize the frying time and moisture rate constant. It is evident that the optimized time decreases as the moisture rate constant increases. Consequently, a frying optimization strategy was developed for predicting the frying performance of pork rind, using optimized time, for industrial-scale production.

Efficient wastewater treatment in petroleum refineries: Hybrid electro-fenton and photocatalysis (UV/ZnO) process

In this study, a new hybrid system composed of a photo-catalysis (UV/ZnO) process combined with an electro-Fenton (EF) process equipped with a micro porous graphite air diffusion cathode (MPGADC) was used to treat petroleum refinery wastewater (PRW). The hybrid system was operated in a batch recycle mode and its performance was investigated and optimized using a response surface methodology (RSM). Three operating parameters affecting on the removal efficiency (RE%) of chemical oxygen demand (COD) were investigated including current density (2–10 mA/cm2), ZnO dosage (0.05–0.35 g/L), and Fe2+concentration (0.05–0.15 mM). Results showed that the optimum operating conditions can be achieved using a current density of 6.44 mA/cm2, with ZnO dosage of 0.35 g/L, and concentration of Fe2+ ions at 0.05 mM in which RE% of 90.15% was obtained with requiring a total electrical energy consumption (EECT) of 19.102KWh/m3. Furthermore, analysis of variance (ANOVA) exhibited a high value of determination coefficient (R2) close to 98.24% indicating a satisfactory fit between the obtained regression model and the experimental results. Moreover, ANOVA results revealed that current density has the most significant effect on RE% with a contribution of 52.41% followed by ZnO dosage and Fe2+ concentration both of them with a contribution of 11.64%. The comparison between the individual photo-catalysis process and hybrid process showed that RE% increases from 77.83% to 90.15% making an enhancement by 15.83% in RE% while EECT decreased from 27.171to 19.102KWh/m3 making a reduction by 29.7% in EECT. Hence, combining EF with photo-catalysis process improves the efficiency and reduced the required energy in comparison with the individual photo-catalysis process. Therefore, the combined process could be considered as an alternative, sustainable, green and cost-effective method for treatment of refinery wastewaters.

Bio-impedance measurements for meat quality determination of pork loins under repeated freeze-thaw treatments

Evaluation of key quality traits in the cold chain is an important factor in ensuring meat safety. To meet the demands of consumers, development of non-destructive detection methods is essential for quality assessment of meat products. Electrical impedance spectroscopy (EIS) has emerged as one promising non-destructive technique in the food industry. In this study, EIS was developed to investigate the effects of freeze-thawing on quality parameters of porcine meat. Bioimpedance from 20 Hz to 200 kHz was collected from frozen-thawed meat, and the experimental impedance data were fitted to a Cole impedance model. The model fitting resulted in corresponding determination coefficient values of the resistance R and the reactance X, for needle electrodes (R2R=0.9683; R2X =0.9063) and plate electrodes (R2R=0.9442; R2X =0.8347). The Cole-impedance model well represented the collected bioimpedance datasets with standard deviations of 0.9611 Ω for needle electrodes and 0.9272 Ω for plate electrodes. Furthermore, there were significant differences in the Cole-impedance model parameters, which were attributed to the formation of ice crystals that implied cell membrane degeneration during freeze-thawing. Our results highlight the potential of bio-impedance measurements, gaining better insight on quality defects assessing in freeze-thawed meat.

A genetic programming approach for the prediction of solar chimney power plant power

This study empirically examines the effectiveness of the solar chimney power plants (SCPP), which is an innovative method for the use of solar energy, in the middle latitude region (37.88° N) in the northern hemisphere. Experimental measurement parameters of temperature, velocity, and radiation obtained from four cardinal directions (north, south, east, and west) and from 26 different locations were recorded throughout the day (08:00 am-07:30 pm). These measurements were used to calculate and estimate the power values used to measure the energy efficiency of the solar chimney. In the study, the genetic programming method was used to estimate the power value. In the five different forecast models developed, 15 different parameters were used diagonally. The obtained power estimation equations were cross-checked using statistical methods. Determination coefficient value is higher than >0.90 in all equations and is close to the ideal. In the comparison of all statistical models, the Global Performance Indicator (GPI) was used, with the best result obtained from Model 2. The results of this model are MAPE:6.5233, MBE: −0.0124, RMSE:1.1036 ve t-stat:0.0374, which is quite close to zero. In addition, the power values obtained from both the calculated and developed models were dimensioned and compared over the power-time factor of eight different studies in the literature. The mean relative error value nof the values calculated as a result of this comparison varies between 2.3% and 21.3%. This study provides a model for the different geometries of solar chimney prototypes and provides an example for measuring the design and performance of solar chimney systems to be installed in the middle latitudes and in regions with “Csa” climate characteristics, and is recommended for academic and industrial users. The mathematical models developed can be considered as a step to increase the efficiency of the solar chimney power plants.

The impact of thermal insulating materials in heat loss control in smart green buildings using experimental and swarm intelligent analysis.

The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and promote sustainability. The characteristics of non-stationary and non-linear heat loss through environmentally friendly materials make it challenging to anticipate accurately. At the same time, many of the industry's presently accessible computational models have been created with this in mind; the majority call for powerful computers and time-consuming computations. The artificial neural network (ANN) has been utilized for prediction, and ground-breaking research has shown the viability of this strategy. This research proposes an artificial neural network (ANN) prototype to estimate construction cooling load usage. ANN is integrated with the vortex search algorithm (VS), stochastic fractal search (SFS), and multi-verse optimizer (MVO) models to compare the three models' outcomes and suggest a more accurate strategy. These techniques make a linear mapping among the output and input parameters, often utilized for modeling and regression. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.9464, 0.99827, and 0.99522 for VS-MLP, SFS-MLP, and MVO-MLP, respectively, with an unknown dataset which is acceptable. The training RMSE amounts for VS-MLP, SFS-MLP, and MVO-MLP are 0.06433, 0.00619, and 0.01028 for the unknown dataset, which is acceptable. According to the MAE values of 0.0082902, 0.0047834, and 0.0076534 in the training phase for VS-MLP, SFS-MLP, and MVO-MLP approaches and the values of testing MAE error of 0.029107, 0.018167, and 0.029212 for VS-MLP, SFS-MLP, and MVO-MLP approaches, respectively, it is obtained that the SFS-MLP has a lower MAE value. The lowest RMSE value and the higher R2 value indicate the favorable accuracy of the SFS-MLP technique.

Geographically Weighted Panel Regression Modelling of Dengue Hemorrhagic Fever Data Using Exponential Kernel Function

Geographically Weighted Panel Regression (GWPR) model is a panel regression model applied to spatial data. This research takes the Fixed Effect Model (FEM) panel regression as the global model and GWPR as the local model for dengue hemorrhagic fever (DHF) in East Kalimantan Province data over the years 2018-2020. DHF is a disease that has the potential to become an extraordinary event which is accompanied by death. In comparison to Indonesia, East Kalimantan Province's DHF Incident Rate (IR) was high in 2020. East Kalimantan's IR is 60.6 per 100,000 population, compared to Indonesia's IR of 40.0 per 100,000 population. This research aims to obtain the GWPR model, as well as to acquire factors that affect DHF in East Kalimantan Province over the years 2018-2020 based GWPR model. The parameter of the GWPR model was estimated on each observation location using the Weighted Least Square (WLS) method, which is an Ordinary Least Square (OLS) with the addition of spatial weighting. The spatial weighting on the GWPR model was determined by the best weighting function between fixed exponential and adaptive exponential. The optimum weighting function with a minimum cross-validation (CV) value of 1.7317×106 is adaptive exponential. Based on GWPR parameter testing, factors that affect DHF are local and diverse in each 10 regencies/municipalities in East Kalimantan Province. These factors are population density, number of health facilities, percentage of proper sanitation use in the household, percentage of household with qualified drinking water sources, and percentage of health services. The coefficient of determination of the GWPR model obtains a higher value than the FEM, which is 95.33%. Based on the measurement of goodness using the coefficient of determination value, it can be concluded that GWPR is the best method to model the DHF data rather than the FEM.

Selectivity comparison of acetonitrile-methanol-water ternary mobile phases on an octadecylsiloxane-bonded silica stationary phase.

The solvation parameter model was used in this study to investigate various intermolecular interactions that influence retention on the standard C18 stationary phase for the solvent system acetonitrile:methanol (ACN:MeOH, 1:1). In comparison to the organic mobile phase modifiers acetonitrile, acetone, methanol, 2-propanol, and tetrahydrofuran, the solvent strength for the ACN:MeOH (1:1) solvent system was evaluated. To facilitate the interpretation of various intermolecular interactions that contribute to retention on a standard C18 stationary phase for the solvent system ACN:MeOH (1:1), system maps were constructed and compared with those of acetone, tetrahydrofuran, acetonitrile, 2-propanol, and methanol. The solvation parameter models were constructed for the ternary solvent system ACN:MeOH (1:1)-water, and in the models constructed, the coefficient of determination values were from 0.998 to 0.999, the Fisher statistic values for the models were from 1687 to 4015, and the standard error of the estimate values ranged from 0.022 to 0.029. The solvent system ACN:MeOH (1:1) has retention properties more similar to methanol than acetonitrile, indicating methanol's influence is more dominant.

PENGARUH DISIPLIN KERJA DAN LINGKUNGAN KERJA TERHADAP KINERJA KARYAWAN PADA PT PRIMA SOEKA BUANA BEKASI

The aim of this research is to determine the influence of work discipline and work environment on employee performance at PT. Prima Soeaka Buana. The research method used was quantitative. The sampling technique used was saturated sampling, and a total of 60 respondents were obtained as the sample. Data analysis included validity test, reliability test, classical assumption test, regression analysis, correlation coefficient, determination coefficient, and hypothesis testing. Work discipline has a significant effect on employee performance with a simple linear regression equation of Y = 23.668 + 0.465X1, and a correlation coefficient of 0.457. The determination coefficient value is 20.9%, and the hypothesis test resulted in a t-value > t-table, or (3.914 > 1.671). Furthermore, the work environment also has a significant effect on employee performance with a simple linear regression equation of Y = 15.170 + 0.661X2, and a correlation coefficient of 0.723. The determination coefficient value is 52.3%, and the hypothesis test resulted in a t-value > t-table, or (7.968 > 1.671). Simultaneously, work discipline and work environment significantly influence employee performance with a multiple linear regression equation of Y = 3.697 + 0.338X1 + 0.603X2, and a correlation coefficient of 0.793. The determination coefficient value is 62.9%, while the remaining 37.1% is influenced by other factors. The hypothesis test resulted in an F-value > F-table, or (48.302 > 3.159).

Implementation of state business support programs: current situation, analysis and proposals

Object: To determine the factors affecting the development of medium and small businesses, as well as to investigate the degree of influence of state support on entrepreneurship in the Republic of Kazakhstan.Methods: The study used statistical methods of analysis, including multiple correlation-regression modeling.Findings: The authors put forward and tested hypotheses about the presence of a relationship between the dependent variable “Share of SMEs in GDP” and such factors as “The amount of funds for support for SMEs”, “Employed in the SME”, “Number of able-bodied population”, “Index of prices for food products”, “GDP per capita”, “Level of youth unemployment”. To prove or refute these hypotheses, a correlation and regression analysis was carried out. A check of the variable model for multicollinearity has been checked and the determination coefficient is calculated. The value of the r-quadratic determination coefficient for the dependent variable “SME share in GDP” is of high value (0.96), which suggests that the factors included in the model are well described and have a high degree of influence on it. In general, the regression analysis showed that the resulting model is adequate and reliable. Statistically significant coefficients were determined for the variables “Able-bodied population”, “GDP per capita”, “Youth unemployment rate”, that is, the listed indicators play a significant role in changing the “Share of SMEs in GDP”. At the same time, the factors “Number of working-age population” and “Unemployment rate among youth” have an inverse relationship with the indicator “Share of SMEs in GDP”.Conclusions: regression analysis showed that in Kazakhstan there are certain problems in the implementation of state policy in relation to the development of entrepreneurship. This fact is confirmed by the results of the study. The main hypothesis about the impact of the “Amount of funds to support SMEs” on the indicator “Share of SMEs in GDP” was not confirmed. The presence of problems in the distribution of state resources requires constructive state measures to solve the existing problems.

Pengaruh Trait Big Five Personality dan Dukungan Sosial Terhadap Flourishing Mahasiswa Menjalani Perkuliahan Selama Masa Pandemi

Empirically, this study aims to look at the Effect of Big Five Personality Traits and Social Support on Flourishing Students Undergoing Lectures During the Pandemic Period. The population in this study were 150 students and 150 students were the sample where the sample was taken using random sampling. Data collection techniques, namely questionnaires and documentation, data analysis was carried out by multiple regression, hypothesis testing t test, F test, and determination test. The results showed that the main effect of each of the Big Five dimensions of Personality and Social Support p: (1) a. There is a significant positive effect between Extraversion and Flourishing personality types as seen from the significance value where the coefficient tx2ay = 9.529 with p = 0.000, means p <0.05, (2) b. There is no significant positive effect on Agreeableness Fluorishing, where the coefficient tx2by 1.165 with p=0.282, means p> 0.05, (3) c. There is a significant positive influence between Counsicientiousness and flourishing personality types where the coefficient tx2cy = 10.099 with p = 0.000, means p <0.05, (4) d. There is no significant positive effect between Nerotic and Fluorishing personality types, where the coefficient of tx2dy is 0.896 with p = 0.372, meaning p>0.05, (5) e. There is no significant positive effect between Openness to experience and Fluorishing personality types, where the tx2ey coefficient is 0.098 with p = 0.922, meaning p> 0.05. Social support has a significant positive effect on Floursihing where the value of the coefficient tx1y = 8.161 with p = 0.000 means p <0.05. For the Interaction effect, (1).a there is a significant influence of social support and personality type a (Extraversion) simultaneously on Fluorishing where the coefficient F = 45.410; with p = 0.000 means p < 0.050. (2) b. There is significant effect of social support and personality type b (Agreebleness) simultaneously on Fluorishing where the coefficient F = 34.853; with p = 0.000 means p < 0.050. (3) c. There is a significant influence of social support and personality type c (Counscientiousness) simultaneously on Fluorishing where the coefficient F = 78.855; with p = 0.000 means p < 0.050. (4) d. There is a significant effect of social support and personality type d (Nerotic) simultaneously on Fluorishing where the coefficient F = 33,497; with p = 0.000 means p < 0.050. (5) e. There is a significant effect of social support and personality type e (Opennes to experience) simultaneously on Fluorishing where the coefficient F = 33.080; with p = 0.000 means p < 0.050.Of the Big Five Personality and Social Support variables, only the Counsicientiousness and Extraversion dimensions have the most significant determination coefficient values, where Counsicientiousness (R2) = 0.518 or 51.80%, while Extraversion (R2) = 0.382 or 38, 20% (2) Thus it can be concluded that of the five dimensions of Big Five Personality there are two dimensions that have a significant influence, namely Counsicientiousness and Extraversion between Big Five Personality and Social support with Flourishing in third semester students of the law faculty of North Sumatra University in undergoing college during their tenure pandemic.

Some aspects of reproductive biology of gangetic ailia, \(\textit{Ailia coila}\) (Hamilton, 1822) in Bangladesh

The present study was conducted by Bangladesh Fisheries Research Institute, Floodplain Sub-station to investigate the reproductive biology of Ailia coila in the northern region of Bangladesh. A total number of 100 fish samples were collected on a monthly basis from the Atrai River and Jamuna River during the period from January 2022 to December 2022. The highest mean value of the gonado-somatic index (GSI) was recorded in July (8.78 ± 1.95%), whereas the lowest was found in December (0.15 ± 0.09%). The highest individual fecundity (2,450 ± 570) and ova diameter (0.38 ± 0.07 mm) was also observed in July. From the histological observation of the ovary, the highest percentages (75%) of mature oocytes were observed during the month of July. Based on the GSI, fecundity and gonadal histology, the breeding season of A. coila was observed from June to August with a remarkable peak in July. In the case of length weight relationship (LWR), the coefficient of determination value (r2) was found 0.96 and the slope was found b = 1.50 which indicated the pattern of negative allometric growth of this species as b < 3. In contrast, an increase was recorded in the fecundity associated with the rise of total length, body weight and gonad weight showed a significant linear relationship. This study would assist in the development of induced breeding techniques and provide valuable information for the sustainable management of this population in the inland open ecosystem.

What is the effect of learning models and interests on study results?

This study aims to determine whether there is a significant influence between the learning model (NHT and STAD) and student interest on learning outcomes . This study uses a mixed methods approach with sequential explanatory. Sequential explanatory is a research where the initial data collection is quantitative which is then followed by qualitative data. The data collection instrument is in the form of a response questionnaire for the STAD and NHT learning models with 18 statements , student interest questionnaires of 1 1 statement, and learning outcomes test sheets containing 7 questions. Furthermore, the data were analyzed by descriptive statistics to find the average value and using inferential statistics in the form of multiple regression test and ANOVA test to see quantitative data, while for qualitative data using Miles Huberman, namely data reduction, data display, and conclusions. This study uses 327 students of SMK 1 and SMK 4 in Jambi City by using a total sampling technique, namely the entire population is used as the research sample . Then interviewed 20 students who were willing to conduct interviews. The results of this study indicate that Student responses to the STAD learning model have a good category, which is 60.2% (197 of 327) and student responses to the NHT learning model of teacher pedagogic competence also have a low category. good that is equal to 58.7% (192 of 225). Student interest in learning also has a good category, which is 60.9% (199 out of 327. And there is a significant influence between learning models, interests and student learning outcomes with the determination coefficient value is (R2) 0.729 . This means the contribution of the model learning and student interest in student learning outcomes by 72.9 %, while the remaining 27.1 % is influenced by other variables.

Pengaruh ROA, DAR dan NPM terhadap Harga Saham Perusahaan


 The aims of this research is to determine the effect of ROA, DAR and NPM on stock prices. The population of this research are manufacturing companies in the Cosmetics and Household sub-sector which are listed on the Indonesia Stock Exchange (BEI) in 2018-2022. Purposive sampling appliied as the sampling method, and according to the established criteria, there were fives companies that met the criterias to be sampled. Quantitative methods with multiple linear regression analysis tools applied in this research. This research showed that Return On Assets (ROA) partially had a positive and significant effect on stock prices. Meanwhile, the Debt to Assets Ratio (DAR) and Net Profit Margin (NPM) partially have a negative and insignificant effect on stock prices. The Coefficient of Determination value (R2) is 0.566 which indicates that stock price as a variable Y is influenced by Return On Assets (X1), Debt to Asset Ratio (X2), and Net Profit Margin (X3) of 56.6%, while the other 43.4% is influenced by other variables outside the variables studied.
 Keywords: Return on Assets, Debt to Assets Ratio, Net Profit Margin, Stock Prices, Cosmetics & Household.