Independent Variables
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Author(s):  
Samsul Alam ◽  
Md. Minhajur Rahman

Popular social media Facebook-oriented social commerce (S-commerce), commonly known as Facebook commerce (F-commerce) has progressed towards a bevy business in Bangladesh. Many young people, especially at the age of 20-28, are now in this industry. The pandemic situation due to coronavirus disease 2019 (COVID-19) forces people to buy more from the online market because of the safety issue. People are getting more interested in the new trend of buying from an online store. The current study aims to explore the impact of COVID-19 on F-commerce, particularly in Bangladesh. It uses the non-probability purposive sampling method and collects 181 usable responses through an online questionnaire. A research model is developed following the social commerce acceptance model (SCAM), and structural equation model partial least square (SEM-PLS) using SmartPLS 3.0 is applied to find out and justify the result. Likert five-point scale for determining the independent variables, including COVID-19 awareness (CA), consumer behavior (CB), and purchase intention (PI), is used. The study result confirms that these three variables have a positive impact on F-commerce. The survey covers other measurable items that indicate some assumptions, which reflect F-commerce consumers’ behavior. The researchers recommend that F-commerce businesspeople must emphasize on mitigating trust issues and provide enhanced home delivery service.


Author(s):  
Greeshma Aarya

Abstract: Response surface methodology is an efficient and powerful tool which is widely applied for casting optimization. In this research aluminum alloy wheel hub casting is done by using BOXBEHNKEN design, three level of each parameter were taken. Solid modeling of casting and gating system is done by CAD. Simulation of Aluminium Alloy (6061 T6) casting were perform in PRO-cast (2009.1) the simulation result indicates that selected parameters significantly affect the quality of casting. ANOVA is employed to examine the relationship between the factors. Input parameter namely flow rate, pouring temperature and runner size were taken to reduce the volume of shrinkage porosity. Experimental Design consist 15 experimental trials and output data obtained from simulation will be optimized through minitab-18. Result indicates that selected independent variables are significantly influence the response. ANOVA gives the optimized value of selected factors which reduces the porosity volume up to 30cm³. Keywords: Sand casting, Shrinkage porosity, Simulation, DOE, Response surface method.


Author(s):  
Y. Gevrekçi ◽  
Ö.İ. Güneri ◽  
Ç. Takma ◽  
A. Yeşilova

Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model. Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p less than 0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.


Author(s):  
Suraya Masrom ◽  
◽  
Norhayati Baharun ◽  
Nor Faezah Mohamad Razi ◽  
Rahayu Abdul Rahman ◽  
...  

Particle Swarm Optimization is a metaheuristics algorithm widely used for optimization problems. This paper presents the research design and implementation of using Particle Swarm Optimization to automate the features selections in the machine learning models for Airbnb price prediction. Today, Airbnb is changing the business models of the hospitality industry globally. While a bigger impact has been given by the Airbnb community to the local economic development of each country, there has been very little effort that investigates on Airbnb pricing issue with machine learning techniques. Focusing on Airbnb Singapore, the main problem on the dataset is the low correlation of the independent variables to the hospitality price. Choosing the best combination of the independent variables is essential, which can be achieved through features selection optimization. Particle Swarm Optimization is useful to optimize the best variables combination for automating the features selection in machine learning models. By comparing the magnitude of change of the R squared values before and after the use of PSO feature selection, the result showed that the automated features selection has improved the results of all the machine learning algorithms mainly in the linear-based machine learning (Linear Regression, Lasso, Ridge). Keywords—Machine Learning, Automated Features Selection, Particle Swarm Optimization, Airbnb


2022 ◽  
Vol 19 (1) ◽  
pp. 69-72
Author(s):  
Sanjeeva Dhakal ◽  
Prabha Kharel

Introduction:  The uncontrolled spread of COVID-19 worldwide has confined millions of people to their homes. In addition to being a public physical health emergency, COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological distress and impacts. The career oriented professional students are away from their academic environment. Aims: This study aims to assess the psychological distress impact of the COVID-19 pandemic among the Proficiency Certificate Level Nursing of  Nepalgunj Nursing Campus, Kohalpur, Banke, Nepal. Methods: The online survey with a link directed to students of Proficiency Certificate Level (PCL) Nursing of Nepalgunj Nursing Campus, Kohalpur, Banke, Nepal  conducted during lockdown (July 16th –July 21st 2020) which was open for 6 days. Sociodemographic characteristics are the independent variables. Psychological distress was constructed using the Kessler Psychological Distress Scale (K10) Scale as a dependent variables. Data were analyzed using Microsoft Excel. Results: The evidence of the survey showed that in total 80.2, % (severely distressed - 30.7%, moderately distressed -29.7%, mildly distressed- 19.8%) of the Proficiency Certificate Level Nursing students of Nepalgunj Nursing Campus, were having psychological distress during COVID-19 pandemic and lockdown assessed by using K10 scale. Conclusion: The present study showed that Proficiency Certificate Level Nursing students were moderately and severely distressed during lockdown of Covid-19 pandemic.


2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Binyam Fekadu ◽  
Ismael Ali ◽  
Zergu Tafesse ◽  
Hailemariam Segni

Abstract Background Essential newborn care (ENC) is a package of interventions which should be provided for every newborn baby regardless of body size or place of delivery immediately after birth and should be continued for at least the seven days that follows. Even though Ethiopia has endorsed the implementation of ENC, as other many counties, it has been challenged. This study was conducted to measure the level of essential newborn care practice and identify health facility level attributes for consistent delivery of ENC services by health care providers. Methods This study employed a retrospective cross-sectional study design in 425 facilities. Descriptive statistics were formulated and presented in tables. Binary logistic regression was employed to assess the statistical association between the outcome variable and the independent variables. All variables with p < 0.2 in the bivariate analysis were identified as candidate variables. Then, multiple logistic regression analysis was performed using candidate variables to determine statistically significant predictors of the consistent delivery of ENC by adjusting for possible confounders. Results A total of 273, (64.2%), of facilities demonstrated consistent delivery of ENC. Five factors—availability of essential obstetrics drugs in delivery rooms, high community score card (CSC) performances, availability of maternity waiting homes, consistent partograph use, and availability of women-friendly delivery services were included in the model. The strongest predictor of consistent delivery of essential newborn care (CD-ENC) was consistent partograph use, recording an odds ratio of 2.66 (AOR = 2.66, 95%CI: 1.71, 4.13). Similarly, providing women-friendly services was strongly associated with increased likelihood of exhibiting CD-ENC. Furthermore, facilities with essential obstetric drugs had 1.88 (AOR = 1.88, 95%CI: 1.15, 3.08) times higher odds of exhibiting consistent delivery of ENC. Conclusion The delivery of essential newborn care depends on both health provider and facility manager actions and availability of platforms to streamline relationships between the clients and health facility management.


2022 ◽  
Author(s):  
Arkady Poliakovsky

We investigate Lorentzian structures in the four-dimensionalspace-time, supplemented either by a covector field of thetime-direction or by a scalar field of the global time. Furthermore,we propose a new metrizable model of the gravity. In contrast to theusual Theory of General Relativity where all ten components of thesymmetric pseudo-metrics are independent variables, the presentedhere model of the gravity essentially depend only on singlefour-covector field, restricted to have only three-independentcomponents. However, we prove that the Gravitational field, ruled bythe proposed model and generated by some massive body, resting andspherically symmetric in some coordinate system, is given by apseudo-metrics, which coincides with thewell known Schwarzschild metric from the General Relativity. TheMaxwell equations and Electrodynamics are also investigated in theframes of the proposed model. In particular, we derive the covariantformulation of Electrodynamics of moving dielectrics andpara/diamagnetic mediums.


2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Karsum Usman ◽  
Usman Moonti ◽  
Sri Endang Saleh

This study aims to determine the effect of price, land area and production costs on the income of rice farmers in North Toto Village, Tilongkabila District, Bone Bolango Regency. Data collection techniques used in this study were observation, interviews, questionnaires, and documentation. With a total sample of 44 farmers in North Toto Village. This research method uses a quantitative approach with multiple linear regression model analysis. The results showed that the price had a negative and insignificant effect on the income of rice farmers in North Toto Village. This means that every 1% increase in price can reduce income by 0.237. Land area has a positive and significant effect on the income of rice farmers in North Toto Village. This means that every 1% increase in land area can increase income by 0.682. Production costs have a negative and significant effect on the income of rice farmers in North Toto Village. This means that every 1% increase can reduce income by -0.254. The coefficient of determination (R Square) is 0.596, this shows that the percentage of rice farmers' income variation which is explained by the variation of the independent variables, namely price, land area and production costs is 59.6% for the remaining 40.4% influenced by other variables.


2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Ali Farki ◽  
Reza Baradaran Kazemzadeh ◽  
Elham Akhondzadeh Noughabi

Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting pulse transit time (PTT), PPG intensity ratio (PIR), and heart rate (HR) features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting systolic blood pressure (SBP) and diastolic blood pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster. The method was implemented using the MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36).


2022 ◽  
Vol 14 (2) ◽  
pp. 36
Author(s):  
Emanuel Arnoni Costa ◽  
Cristine Tagliapietra Schons ◽  
César Augusto Guimarães Finger ◽  
André Felipe Hess

Improving volumetric quantification of Parana pine (Araucaria angustifolia) in Mixed Ombrophilous Forest is a constant need in order to provide accurate and timely information on current and future growing stock to ensure forest management. Thus, the present study aimed to evaluate and compare the volume estimates obtained through Nonlinear Regression (NR), Genetic Algorithm (GA) and Simulated Annealing (SA) in order to generate accurate volume estimates. Volumetric equations were developed including the independent variables diameter at breast height (dbh), total height (h) and crown rate (cr) and from the fit through the NR, GA and SA approaches. The GA and SA approaches evaluated proved to be a reliable optimization strategy for parameter estimation in Parana pine volumetric modelling, however, no significant differences were found in comparison with the NR approach. This study therefore contributes through the generation of robust equations that could be used for accurate estimates of the volume of the Parana pine in southern Brazil, thus supporting the planning and establishment of management and conservation actions.


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