scholarly journals Debt as a Source of Financial Energy of the Farm—What Causes the Use of External Capital in Financing Agricultural Activity? A Model Approach

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4124
Author(s):  
Danuta Zawadzka ◽  
Agnieszka Strzelecka ◽  
Ewa Szafraniec-Siluta
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Regression Tree ◽  
Agricultural Activity ◽  
Factors Influencing ◽  
External Capital ◽  
Farm Accountancy Data Network ◽  
Using Data ◽  
The One

The aim of this study was to identify and assess the factors influencing the increase in the financial energy of a farm through the use of external capital, taking into account the farmer’s and farm characteristics. For its implementation, a logistic regression model and a classification-regression tree analysis (CRT) were used. The study was conducted on a group of farms in Central Pomerania (Poland) participating in the system of collecting and using data from farms (Farm Accountancy Data Network—FADN). Data on 348 farms were used for the analyses, obtained through a survey conducted in 2020 with the use of a questionnaire. Based on the analysis of the research results presented in the literature to date, it was established that the use of external capital in a farm as a factor increasing financial energy is determined, on the one hand, by the socio-demographic characteristics of the farmer and the characteristics of the farm, and on the other hand, by the availability of external financing sources. Factors relating to the first of these aspects were taken into account in the study. Using the logistic regression model, it was established that the propensity to indebtedness of farms is promoted by the following factors: gender of the head of the household (male, GEND), younger age of the head of the household (AGE), having a successor who will take over the farm in the future (SUC), higher value of generated production (PROD_VALUE), larger farm area (AREA) and multi-directional production of the farm (production diversification), as opposed to targeting plant or animal production only (farm specialization—SPEC). The results of the analysis carried out with the use of classification and regression trees (CRT) showed that the key factors influencing the use of outside capital as a source of financial energy in the agricultural production process are, first of all, features relating to an agricultural holding: the value of generated production (PROD_VALUE), agricultural area (AREA) and production direction (SPEC). The age of the farm manager (AGE) turned out to be of key importance among the farmer’s features favoring the tendency to take debt in order to finance agricultural activity.

scholarly journals An Artificial Neural Network–Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry (Preprint)

2020 ◽  
Author(s):  
Niema Ghanad Poor ◽  
Nicholas C West ◽  
Rama Syamala Sreepada ◽  
Srinivas Murthy ◽  
Matthias Görges
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Mortality Risk ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Single Layer ◽  
Ann Model ◽  
Data Set ◽  
Using Data ◽  
Better Than

BACKGROUND In the pediatric intensive care unit (PICU), quantifying illness severity can be guided by risk models to enable timely identification and appropriate intervention. Logistic regression models, including the pediatric index of mortality 2 (PIM-2) and pediatric risk of mortality III (PRISM-III), produce a mortality risk score using data that are routinely available at PICU admission. Artificial neural networks (ANNs) outperform regression models in some medical fields. OBJECTIVE In light of this potential, we aim to examine ANN performance, compared to that of logistic regression, for mortality risk estimation in the PICU. METHODS The analyzed data set included patients from North American PICUs whose discharge diagnostic codes indicated evidence of infection and included the data used for the PIM-2 and PRISM-III calculations and their corresponding scores. We stratified the data set into training and test sets, with approximately equal mortality rates, in an effort to replicate real-world data. Data preprocessing included imputing missing data through simple substitution and normalizing data into binary variables using PRISM-III thresholds. A 2-layer ANN model was built to predict pediatric mortality, along with a simple logistic regression model for comparison. Both models used the same features required by PIM-2 and PRISM-III. Alternative ANN models using single-layer or unnormalized data were also evaluated. Model performance was compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC) and their empirical 95% CIs. RESULTS Data from 102,945 patients (including 4068 deaths) were included in the analysis. The highest performing ANN (AUROC 0.871, 95% CI 0.862-0.880; AUPRC 0.372, 95% CI 0.345-0.396) that used normalized data performed better than PIM-2 (AUROC 0.805, 95% CI 0.801-0.816; AUPRC 0.234, 95% CI 0.213-0.255) and PRISM-III (AUROC 0.844, 95% CI 0.841-0.855; AUPRC 0.348, 95% CI 0.322-0.367). The performance of this ANN was also significantly better than that of the logistic regression model (AUROC 0.862, 95% CI 0.852-0.872; AUPRC 0.329, 95% CI 0.304-0.351). The performance of the ANN that used unnormalized data (AUROC 0.865, 95% CI 0.856-0.874) was slightly inferior to our highest performing ANN; the single-layer ANN architecture performed poorly and was not investigated further. CONCLUSIONS A simple ANN model performed slightly better than the benchmark PIM-2 and PRISM-III scores and a traditional logistic regression model trained on the same data set. The small performance gains achieved by this two-layer ANN model may not offer clinically significant improvement; however, further research with other or more sophisticated model designs and better imputation of missing data may be warranted. CLINICALTRIAL

scholarly journals An Assessment of Factors Influencing Forest Harvesting in Smallholder Tobacco Production in Hurungwe District, Zimbabwe: An Application of Binary Logistic Regression Model

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chipo Chivuraise ◽  
Tafireyi Chamboko ◽  
Godfrey Chagwiza
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Forest Harvesting ◽  
Natural Forest ◽  
Selling Price ◽  
Binary Logistic Regression Model ◽  
Factors Influencing ◽  
Tobacco Production

Deforestation is one of the major effects posed by the smallholder tobacco farming as the farmers heavily depend on firewood sourced from natural forest for curing tobacco. The research aims at assessing the factors that influence the harvesting of natural forest in the production of tobacco. Data is collected through the structured questionnaire from 60 randomly selected farmers. Binary logistic regression model is used to explain the significance of factors influencing natural forest harvesting. Results show that farmer experience, tobacco selling price, and agricultural training level negatively affect the harvesting of natural forests (to obtain firewood) for curing tobacco significantly (p<0.05). However, gender, size of the household, tobacco yield, and level of education are insignificant (p>0.05) in influencing natural forest harvesting. Though farmers are exploiting the environment and at the same time increasing foreign currency earning through tobacco production, there is therefore a need to put in place policies that encourage sustainable forest product utilization such as gum plantations, subsidizing price of coal, and introducing fees, as well as penalties or taxes to the offenders.

scholarly journals Study of Factors Affecting the Entrepreneurship Behavior of Returned Migrants Using Binary Logistic Regression Model

2019 ◽  
Vol 3 ◽  
pp. 57-68
Author(s):  
Madhav Kumar Bhusal ◽  
Hari Prasad Pandey
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Home Country ◽  
Household Head ◽  
Binary Logistic Regression ◽  
Educational Status ◽  
Categorical Variables ◽  
Binary Logistic Regression Model ◽  
Factors Influencing

Background: Entrepreneurship or business ownership is a significant source of employment and economic growth. Many studies conducted by different researchers have shown that increase in entrepreneurial activities helps to reduce unemployment. Thousands of Nepalese youths exodus for foreign migration every year for employments due to lack of adequate working environment in Nepal. In this context, identification of significant factors influencing the entrepreneurship behavior of returned migrants could be useful for planner, decision makers, and other concerned authorities. Objective: To explore the entrepreneurship status of returned migrants and to ascertain the factors influencing the entrepreneurship behavior of returned migrants. Materials and Methods: This study was based on primary data of 393 returned migrants collected through convenience sampling in Sarawal Rural Municipality of Parasi district, Nepal. People who stayed abroad at least one year and returned during 2010 to 2017 were included in the study. On the basis of Industrial Enterprise Act, 2016a, Nepal, a person who has invested Nepalese rupees five lakh or more in business besides housing and land is considered as an entrepreneur. The response variable is entrepreneurship status and it is defined according to the aforementioned act. Both quantitative and categorical variables were used as predictor variables. Factors associated with entrepreneurship behavior were extracted using Chi-square test and binary logistic regression model. Results: Out of sample of 393 returned migrants, 137 (34.9%) were entrepreneur and rest 256 (65.1%) were non-entrepreneur. Results showed that for main occupation of household head odds ratio (OR) = 4.008 & confidence interval (CI) = 2.396 to 6.703. Similarly, for educational status of returned migrants OR = 2.650 & CI = 1.599 to 4.392. For the covariate skills learnt at abroad OR = 2.750 & CI = 1.654 to 4.573. Conclusion: The study revealed that majority of returned migrants were non-entrepreneur. The factors ‘main occupation of household head’, ‘educational status of returned migrant’, ‘remittance received at home per year’ and ‘skills learnt abroad’ are the major determinants behind the entrepreneurship behavior of returned migrants. It is suggested that higher education and adequate skills should be taken before departing from home country so that the migrants can earn more money which will help to start their own businesses once they get back to their home country.

Modeling the Probability of Hazardous Materials Release in Crashes at Highway–Rail Grade Crossings

2018 ◽  
Vol 2672 (10) ◽  
pp. 28-37 ◽  
Author(s):  
Amirfarrokh Iranitalab ◽  
Yashu Kang ◽  
Aemal Khattak
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Weather Conditions ◽  
Hazardous Materials ◽  
Class I ◽  
Release Probability ◽  
Data Subset ◽  
Grade Crossings ◽  
Using Data

Crashes at Highway–Rail Grade Crossings (HRGCs) that involve a truck or a train carrying hazardous materials (hazmat) expose people and the environment to potentially severe consequences of hazmat release. This research involved statistical modeling of the probability of hazmat release from trucks and/or trains in crashes at HRGCs to identify factors associated with hazmat release. The Federal Railroad Administration (FRA) HRGC crash dataset (2007–2016) yielded two subsets of crashes: 1) those involving hazmat-carrying trucks, and 2) those involving hazmat-carrying trains. Results from a logistic regression model using data subset 1 (crashes involving hazmat-carrying trucks) with hazmat release/no release as the response variable showed that standard flashing signal lights, railroad crossbucks, and railroad classes II and III (relative to railroad class I) were associated with lower hazmat release probability from hazmat-carrying trucks. Hazmat release probability from trucks was higher with freight train involvement. Results from a logistic regression model using data subset 2 (crashes involving hazmat-carrying trains) revealed that hazmat release probability from trains was lower with warmer temperature. However, the probability of release from trains was greater with railroad class II (relative to railroad class I), type of highway user (different types of trucks and motorcycle relative to automobiles), and weather conditions (fog, sleet or snow, relative to clear). A comparison of the results from this study with HRGC crash severity studies highlighted the importance and usefulness of this study.

scholarly journals THETA: Machine-learning Algorithm for Evaluating Hybrid Operation Theater Entry of Trauma Patients

2020 ◽  
Author(s):  
Atsushi Senda ◽  
Akira Endo ◽  
Takahiro Kinoshita ◽  
Yasuhiro Otomo
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Web Application ◽  
Logistic Regression Model ◽  
Learning Algorithm ◽  
Regression Tree ◽  
Operating Rooms ◽  
Classification And Regression Tree ◽  
Trauma Patients

Abstract Background The clinical benefits of hybrid operating rooms are recognized globally. However, appropriate conditions for entry into such rooms must be urgently established, because they exclusively benefit few patients under severe trauma while requiring a significant amount of resources. This paper presents an algorithm to triage trauma patients into a hybrid operating room. Methods This retrospective observational study was conducted using the Japan Trauma Data Bank database comprising information collected between January 2004 and December 2018. A machine-learning-based triage algorithm is developed using the baseline demographics, injury mechanisms, and vital signs obtained from the database. The analysis dataset comprised information regarding 117,771 trauma patients with abbreviated injury scale (AIS) > 3. The performance of the proposed model was compared against those of other statistical models (logistic regression and classification and regression tree [CART] models) while considering the status quo entry condition (systolic blood pressure < 90 mmHg). Results The proposed trauma hybrid-suite entry algorithm (THETA) outperforms other algorithms (PR-AUC: THETA [0.59], logistic regression model [0.22], and CART [0.20]; AUROC: THETA [0.93], logistic regression model [0.88], and CART [0.86]), thereby facilitating appropriate triaging of patients who would potentially benefit from resuscitation performed using angiographic percutaneous techniques and operative resuscitation suites. Conclusions An accurate machine-learning-based algorithm is developed to triage patient entry into hybrid operating rooms via a web application, thereby enabling emergency doctors to utilize limited medical resources more efficiently.

scholarly journals Factors Influencing Household Adoption of Solar Home System in Baso Liben District, Amhara Regional State of Ethiopia

2021 ◽  
Author(s):  
Asmare Mossie Zeru ◽  
Dawit Diriba Guta
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Information Access ◽  
Farm Income ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Regional State ◽  
Solar Home System

Abstract Background: Although solar energy is abundant, accessible, affordable, and ecologically and environmentally friendly, in rural Ethiopia the majority of households are still using pollutant kerosene for lighting. It is important to understand the demand and supply-side factors affecting adoption of technology. For this purpose, this study investigates the factors influencing household adoption of the solar home system (SHS).Methodology: The data used for the econometric model was collected from randomly selected 228 solar home system adopter and 143 non- adopter households in Baso Liben district, Amhara regional state of Ethiopia. The logistic regression model was applied to examine the factors affecting households’ willingness to adopt SHS. Results: The finding of this study shows significant variation in many of the socioeconomic and demographic characteristics between adopters and non-adopters. The result of the binary logistic regression model indicated that as income of a household increase, their propensity to adopt solar home system also increases. Likewise, participation in off-farm income activities, house type, educational status of the head, training access, media access, and prior knowledge of the technology positively correlated with the probability of adoption. On the other hand, the gender of the head (being male) and access to electricity were negatively associated with the adoption of SHS. Conclusion: Therefore, policy measures should create awareness through training, education, and information access or better media availability, and improving the economic status of households through creating lucrative off-farm income-earning opportunities to achieve enhanced adoption of the solar home system.

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