scholarly journals A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

2022 ◽  
pp. 179-186
Author(s):  
Yuvraj Praveen Soni ◽  
Eugene Fernandez
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Solar Irradiance ◽  
Harmonic Distortion ◽  
System Control ◽  
Solar Pv ◽  
Pv System ◽  
Simulink Model ◽  
The Impact

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

Cognition predicts days-alive-out-of-hospital after LVAD implantation

2021 ◽  
pp. 039139882110184
Author(s):  
Marykay A Pavol ◽  
Amelia K Boehme ◽  
Melana Yuzefpolskaya ◽  
Mathew S Maurer ◽  
Jesus Casida ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Left Ventricular ◽  
Motor Speed ◽  
Ventricular Assist ◽  
Hospitalization Rates ◽  
Left Ventricular Assist ◽  
Patient Groups ◽  
The Impact

Objective: Cognition influences hospitalization rates for a variety of patient groups but this association has not been examined in heart failure (HF) patients undergoing left ventricular assist device (LVAD) implantation. We used cognition to predict days-alive-out-of-hospital (DAOH) in patients after LVAD surgery. Methods: We retrospectively identified 59 HF patients with cognitive assessment prior to LVAD. Cognitive tests of attention, memory, language, and visual motor speed were averaged into one score. DAOH was converted to a percentage based on total days from LVAD surgery to either heart transplant or 900 days post-LVAD. Variables significantly associated with DAOH in univariate analyses were included in a linear regression model to predict DAOH. Results: A linear regression model including LVAD type (continuous or pulsatile flow) and cognition significantly predicted DAOH (F(2,54) = 6.44, p = 0.003, R2 = .19). Inspection of each variable revealed that cognition was a significant predictor in the model (β = .11, SE = .04, p = 0.007) but LVAD type was not ( p = 0.08). Conclusions: Cognitive performance assessed prior to LVAD implantation predicted how much time patients spent out of the hospital following surgery. Further studies are warranted to identify the impact of pre-LVAD cognition on post-LVAD hospitalization.

scholarly journals A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

2020 ◽  
Vol 12 (18) ◽  
pp. 3099
Author(s):  
Jean-François Léon ◽  
Nadège Martiny ◽  
Sébastien Merlet
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Linear Regression Model ◽  
Population Exposure ◽  
Aerosol Layer ◽  
Multi Linear Regression ◽  
Sun Photometer ◽  
The Impact

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.

scholarly journals Influence of the key account manager in the provisioning management: Evidence from staple companies during the events of COVID-19

Accounting ◽  
2022 ◽  
Vol 8 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Luis-Ricardo Flores-Vilcapoma ◽  
Cynthia-Paola A lbengrin-Mendoza ◽  
Gabriela-Briggite Gomez-Rojas ◽  
Yuri Sánchez-Solis ◽  
Wagner Vicente-Ramos
Keyword(s):  
Experimental Design ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Cross Sectional ◽  
Key Account ◽  
The Impact

The purpose of this research was to evaluate the degree of influence exercised by the Key Account Manager in the provisioning management in the main companies called Staple in Peru, during the events of COVID-19. The research was of type quantitative, cross-sectional and temporal, with a non-experimental design, using a multiple linear regression model and correlation analysis to determine the impact that exists between the variables. The data belongs to the Industrias San Miguel company, distributed in a weekly period from June 2019 to March 2021, which gives 88 observations. The results allow us to conclude that the Key Account Manager is an important manager of the supply of goods during the crisis caused by COVID-19 in staple companies.

scholarly journals Extreme return, extreme volatility and investor sentiment

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 3949-3961 ◽  
Author(s):  
Xu Gong ◽  
Fenghua Wen ◽  
Zhifang He ◽  
Jia Yang ◽  
Xiaoguang Yang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Investor Sentiment ◽  
Generalized Least Squares ◽  
Regression Methods ◽  
Leading Role ◽  
The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

scholarly journals Distribusi Belanja Desa dan Capaian Status Pembangunan Desa di Provinsi Riau

2021 ◽  
Vol 5 (2) ◽  
pp. 407
Author(s):  
Bakti Kharisma ◽  
Werry Darta Taifur ◽  
Fajri Muharja
Keyword(s):  
Linear Regression ◽  
Rural Development ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Development Process ◽  
Multiple Linear Regression Model ◽  
Urban Bias ◽  
The Impact ◽  
The Village

The Village Law has become one of the berakhthroughs in overcoming the impact of development that tends to be urban bias. Village is no longer only an object of development but the main actor in rural development process. The source of the budget for the implamentation of rural development has increased significantly with the village fund policy. This study aims to analyze the impact of village budgets and village typology on the achievement of village status in Riau Province. Multiple linear regression model was used to analyze the impact of village budget and village typology has a significant impact on the increase in the developing village index in Riau Province.

scholarly journals Direct admission to a surgical service reduces hospital length of stay for patients with biliary disease

2019 ◽  
Vol 8 (3) ◽  
pp. 1
Author(s):  
Gregory S. Corwin ◽  
Rebecca Reif ◽  
Kevin W. Sexton
Keyword(s):  
Linear Regression ◽  
Length Of Stay ◽  
Regression Model ◽  
Linear Regression Model ◽  
Medical Service ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Biliary Disease ◽  
Surgical Service ◽  
The Impact

Background: Biliary tract disease is a common condition often necessitating surgical intervention. It has been suggested that categorically admitting these patients to a surgical service rather than a medical service may improve patient outcomes. Our objective was to assess the impact of a protocol change that mandated preferentially admitting patients with biliary disease to a surgical service.Methods: This is a retrospective observational study of patients presenting with biliary disease to a single institution before and after a protocol change that mandated admitting these patients directly to a surgical service. A generalized linear regression model was conducted to analyze the effect of practice change on length of stay, which was primary studied outcome.Results: A total of 3,389 patients were included in the study (n = 1,866 for pre, and n=1,523 for post). There was no difference in hospital length of stay between pre and post groups for non-operative patients (1.9 days ± 4.3 versus 1.9 days ±5.2, p = .972).  However, for operative patients, length of stay was shorter for the post group (4.1 days ± 6.1 vs 6.3 days ± 14.0, p = .066). The linear regression model found that operative patients had an increased probability of having a longer length of stay (coefficient, 0.21; 95% CI, 0.14, 0.29; p < .001).Conclusion: Admission of patients with biliary disease to a surgical service rather than a medical service is associated with shorter length of stay for patients who undergo an operative intervention. An approach of admitting all patients presenting with biliary disease to a surgical service has the potential to significantly reduce hospital costs. Our study supports primary responsibility for surgeons in the care of patients with potentially operative conditions.

scholarly journals Em Busca do Orientador de Formação Ideal

2015 ◽  
Vol 28 (4) ◽  
pp. 486
Author(s):  
Ana Pinheiro Sá ◽  
Cristina Teixeira-Pinto ◽  
Rafaela Veríssimo ◽  
Andreia Vilas-Boas ◽  
João Firmino-Machado
Keyword(s):  
Internal Medicine ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Learning Process ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Clinical Teacher ◽  
Clinical Teachers ◽  
The Impact

<strong>Introduction:</strong> The authors established the profile of the Internal Medicine clinical teachers in Portugal aiming to define a future interventional strategy plan as adequate as possible to the target group and to the problems identified by the residents.<br /><strong>Material and Methods:</strong> Observational, transversal, analytic study. An online anonymous questionnaire was defined, evaluating the demographic characteristics of the clinical teachers, their path in Internal Medicine and their involvement in the residents learning process.<br /><strong>Results:</strong> We collected 213 valid questionnaires, making for an estimated response rate of 28.4%. Median global satisfaction with the clinical teacher was 4.52 (± 1.33 points) and the classification of the relationship between resident and clinical teacher was 4.86 ± 1.04 points. The perfect clinical teacher is defined by high standards of dedication and responsibility (4.9 ± 1.37 points), practical (4.8 ± 1.12 points) and theoretical skills (4.8 ± 1.07 points). The multiple linear regression model allowed to determine predictors of the resident’s satisfaction with their clinical teacher, justifying 82,5% of the variation of satisfaction with the clinical teacher (R2 = 0.83; R2 a = 0.82).<br /><strong>Discussion:</strong> Postgraduate medical education consists of an interaction between several areas of knowledge and intervening variables in the learning process having the clinical teacher in the central role. Overall, the pedagogical abilities were the most valued by the Internal Medicine residents regarding their clinical teacher, as determinants of a quality residentship.<br /><strong>Conclusion:</strong> This study demonstrates the critical relevance of the clinical teacher in the satisfaction of residents with their residentship. The established multiple linear regression model highlights the impact of the clinical and pedagogical relantionship with the clinical teacher in a relevant increase in the satisfaction with the latter.

Research on the impact of home country patent level on outward foreign direct investment: Empirical analysis via equal part linear regression model and Grey Computing

2020 ◽  
pp. 002072092092251
Author(s):  
Yuanbing Zhu ◽  
Xueying Chen ◽  
Gang Wang ◽  
Zuchang Zhong ◽  
Meier Zhuang
Keyword(s):  
Foreign Direct Investment ◽  
Linear Regression ◽  
Regression Model ◽  
Empirical Analysis ◽  
Linear Regression Model ◽  
Direct Investment ◽  
Outward Foreign Direct Investment ◽  
Equal Part ◽  
Patent Applications ◽  
The Impact

From the practice of developed countries, countries with higher patent applications and PCT patent applications (such as the United States, China, Japan, the United Kingdom, Germany, etc.) have relatively higher outward foreign direct investment, and the actual data of provinces in China also show that with the improvement of the patent level in various provinces and cities, the intensity of outward foreign direct investment in each province and city has also increased. At present, there are relatively few research data and the research method is relatively single. Therefore, collecting panel data on China’s 31 provinces from 2003 to 2016, this paper conducts an empirical analysis on the influence of patent level on outward foreign direct investment via analytical method of equal part linear regression and Grey Computing. By comparing analysis results with the model and the results with conventional linear regression model, the difference of different regression models is observed. Furthermore, the impact of China’s patent level on China’s inter-provincial outward foreign direct investment is further analyzed.

The Impact of Climate Change on Agriculture in Morocco: An Econometric Study

2020 ◽  
Vol 1 (2) ◽  
pp. 19-28
Author(s):  
Faycel Tazigh
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

This paper aims to analyze the relationship that may exist between climate change and cereal yield in Morocco. In order to study this correlation between variables, we used the most common form of regression model which is the multiple linear regression model. There are two main uses of multiple linear regression model. The first one is to quantify the weight of impact that the independent variables had on the dependent variable. The second use is to predict not only the relationship that may found between variables but also their impacts. In our case, we have chosen temperature and precipitation as an independent variables and cereal yield as dependent variable.

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