Search results for: multinomial logistic regression model

Authors: P. Kazymbet, G. Abildinova, K.Makhambetov, M. Bakhtin, D. Rybalkina, K. Zhumadilov

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Conducted cytogenetic research in workers Stepnogorsk Mining-Chemical Combine (Akmola region) with the study of 26341 chromosomal metaphase. Using a regression analysis with program DataFit, version 5.0, dependence between exposure dose and the following cytogenetic exponents has been studied: frequency of aberrant cells, frequency of chromosomal aberrations, frequency of the amounts of dicentric chromosomes, and centric rings. Experimental data on calibration curves "dose-effect" enabled the development of a mathematical model, allowing on data of the frequency of aberrant cells, chromosome aberrations, the amounts of dicentric chromosomes and centric rings calculate the absorbed dose at the time of the study. In the dose range of 0.1 Gy to 5.0 Gy dependence cytogenetic parameters on the dose had the following equation: Y = 0,0067е^0,3307х (R2 = 0,8206) – for frequency of chromosomal aberrations; Y = 0,0057е^0,3161х (R2 = 0,8832) –for frequency of cells with chromosomal aberrations; Y =5 Е-0,5е^0,6383 (R2 = 0,6321) – or frequency of the amounts of dicentric chromosomes and centric rings on cells. On the basis of cytogenetic parameters and regression equations calculated absorbed dose in workers of uranium production at the time of the study did not exceed 0.3 Gy.

Keywords: Stepnogorsk, mathematical modeling, cytogenetic, dicentric chromosomes

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Authors: Pirakorn Sukonthaman, Paphitchaya Temphattharachok, Warangkana Thammasanya, Kraichart Tantrakarnarpa, Tanongson Tientavorn

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Agriculture is the main occupation in Thailand. Excessive amount of pesticides are used to increase the products but are toxic to human body. In 2009, Bureau of Epidemiology received 1,691 cases reported with pesticides toxicity (2.66:100,000) which 10.61 % of them is caused by Organophosphate. The purposes are to find factors associated with pesticides used and plasma cholinesterase level and other emerging issues that previous studies did not explain among agricultural workers in Baan Na Yao, Chachoengsao, Thailand. This research was an exploratory mixed method study. Qualitative interviews and quantitative questionnaires were used together in order to gather information from the agricultural workers (mainly cassava and rice farming) directly exposed to pesticides within 2 months simultaneously. Qualitative participants were selected by purposive sampling and a total survey for quantitative ones. The quantitative data was statistically analyzed by using multiple logistic regression model. Qualitative data was transcribed verbatim and thematically analyzed. For qualitative study, 15 participants were interviewed and 300/323 participants (92.88%) were given questionnaires, of which were 175 male and 125 female and 113 among them were spraymen. The prevalence of abnormal plasma cholinesterase level was 92.28% (Safe 7.72% Risky 49.33% and Unsafe 42.95%). Participants with inappropriate behaviors during spraying had a significant association with plasma cholinesterase level (95%CI=1.399-14.858) but other factors such as age, gender, education, attitude and knowledge had no association. They also had encountered various symptoms from pesticides such as fatigue (61%), vertigo (59.67%) and headache (58.86%), etc. Although they had high knowledge and attitude they still had poor behaviors. Moreover, our qualitative component showed that though they had worn the personal protective equipment (PPE) regularly, their PPE was not standard. Not only substandard PPE, but also there were obstacles of wearing such as the hot climate and inconvenience. They misunderstood their symptoms from using pesticides as allergy. Therefore, they did not seek for proper medical check-ups and treatment. This research revealed almost all of the participants have abnormal levels of plasma cholinesterase related especially those with poor behaviors. They also wore PPE but inadequately and misunderstood the symptoms produced by organophosphate use as allergy. Therefore, they did not seek for medical treatment. Occupation health education, modification of PPE and periodic medical checking are ways to make agricultural workers concern and know if there is any progression in a long term.

Keywords: pesticides, plasma cholinesterase level, spraymen, agricultural workers

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Ivan Poza Martínez

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The international energy landscape has been significantly affected by the Covid-19 pandemic and te conflict in Ukraine. The Vaca Muerta sedimentary formation in Argentina´s Neuquén province has become a crucial area for energy production, specifically in the shale gas ad shale oil sectors. The massive investment required for theexploitation of this reserve make it essential to understand te determinants of the investment in the upstream sector at both local ad international levels. The aim of this study is to identify the qualitative and quantitative determinants of investment in Vaca Muerta. The research methodolody employs both quantiative ( econometrics ) and qualitative approaches. A linear regression model is used to analyze the impact in non-conventional hydrocarbons. The study highlights that, in addition to quantitative factors, qualitative variables, particularly the design of a regulatory framework, significantly influence the level of the investment in Vaca Muerta. The analysis reveals the importance of attracting both domestic and foreign capital investment. This research contributes to understanding the factors influencing investment inthe Vaca Muerta regioncomapred to other published studies. It emphasizes to role of qualitative varibles, such as regulatory frameworks, in the development of the shale gas and oil sectors. The study uses a combination ofquantitative data , such a investment figures, and qualitative data, such a regulatory frameworks. The data is collected from various rpeorts and industry publications. The linear regression model is used to analyze the relationship between the variables and the investment in Vaca Muerta. The research addresses the question of what factors drive investment in the Vaca Muerta region, both from a quantitative and qualitative perspective. The study concludes that a combination of quantitative and qualitative factors, including the design of a regulatory framework, plays a significant role in attracting investment in Vaca Muerta. It highlights the importance of these determinants in the developmentof the local energy sector and the potential economic benefits for Argentina and the Southern Cone region.

Keywords: vaca muerta, FDI, shale gas, shale oil, YPF

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Authors: Asish Subedi, Krishna Pokharel, Birendra Prasad Sah, Pashupati Chaudhary

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Objectives: To evaluate an association between preoperative Nepali pain catastrophizing scale (N-PCS) scores and postoperative pain intensity and total opioid consumption. Methods: In this prospective cohort study we enrolled 135 patients with an American Society of Anaesthesiologists physical status I or II, aged between 18 and 65 years, and scheduled for surgery for lower-extremity fracture under spinal anaesthesia. Maximum postoperative pain reported during the 24 h was classified into two groups, no-mild pain group (Numeric rating scale [NRS] scores 1 to 3) and a moderate-severe pain group (NRS 4-10). The Spearman correlation coefficient was used to compare the association between the baseline N-PCS scores and outcome variables, i.e., the maximum NRS pain score and the total tramadol consumption within the first 24 h after surgery. Logistic regression models were used to identify the predictors for the intensity of postoperative pain. Results: As four patients violated the protocol, the data of 131 patients were analysed. Mean N-PCS scores reported by the moderate-severe pain group was 27.39 ±9.50 compared to 18.64 ±10 mean N-PCS scores by the no-mild pain group (p<0.001). Preoperative PCS scores correlated positively with postoperative pain intensity (r =0.39, [95% CI 0.23-0.52], p<0.001) and total tramadol consumption (r =0.32, [95% CI 0.16-0.47], p<0.001). An increase in catastrophizing scores was associated with postoperative moderate-severe pain (odds ratio, 1.08 [95% confidence interval, 1.02-1.15], p=0.006) after adjusting for gender, ethnicity and preoperative anxiety. Conclusion: Patients who reported higher pain catastrophizing preoperatively were at increased risk of experiencing moderate-severe postoperative pain.

Keywords: nepali, pain catastrophizing, postoperative pain, trauma

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Authors: Idit Kalisher, Israel Luski

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One result of the growing dynamicity of labor markets in recent decades is a wider scope of employment mobility – i.e., transitions between employers, either within or between careers. Employment mobility decisions are primarily affected by the current employment status of the worker, which is reflected in wage and tenure. Using 34,328 observations from the National Longitudinal Survey of Youth 1979 (NLS79), which were derived from the USA population between 1990 and 2012, this paper aims to investigate the effects of wage and tenure over employment mobility choices, and additionally to examine the effects of other personal characteristics, individual labor market characteristics and macroeconomic factors. The estimation strategy was designed to address two challenges that arise from the combination of the model and the data: (a) endogeneity of the wage and the tenure in the choice equation; and (b) unobserved heterogeneity, as the data of this research is longitudinal. To address (a), estimation was performed using two-stage limited dependent variable procedure (2SLDV); and to address (b), the second stage was estimated using femlogit – an implementation of the multinomial logit model with fixed effects. Among workers who have experienced at least one turnover, the wage was found to have a main effect on career turnover likelihood of all workers, whereas the wage effect on job turnover likelihood was found to be dependent on individual characteristics. The wage was found to negatively affect the turnover likelihood and the effect was found to vary across wage level: high-wage workers were more affected compared to low-wage workers. Tenure was found to have a main positive effect on both turnover types’ likelihoods, though the effect was moderated by the wage. The findings also reveal that as their wage increases, women are more likely to turnover than men, and academically educated workers are more likely to turnover within careers. Minorities were found to be as likely as Caucasians to turnover post wage-increase, but less likely to turnover with each additional tenure year. The wage and the tenure effects were found to vary also between careers. The difference in attitude towards money, labor market opportunities and risk aversion could explain these findings. Additionally, the likelihood of a turnover was found to be affected by previous unemployment spells, age, and other labor market and personal characteristics. The results of this research could assist policymakers as well as business owners and employers. The former may be able to encourage women and older workers’ employment by considering the effects of gender and age on the probability of a turnover, and the latter may be able to assess their employees’ likelihood of a turnover by considering the effects of their personal characteristics.

Keywords: employment mobility, endogeneity, femlogit, turnover

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Authors: Luz del Carmen Rubí Félix Peña, Jose Adan Felix-Ortiz, Ely Sara Lopez-Alvarez, Wenceslao Valenzuela-Quiñonez

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On a global scale, Mexico is the sixth largest producer of farmed white shrimp (Penaeus vannamei). The activity suffered significant economic losses due to acute hepatopancreatic necrosis (AHPND) caused by a strain of Vibrio parahaemolyticus. For control, the first option is the application of antibiotics in food, causing changes in the environment and bacterial communities, which has produced greater virulence and resistance of pathogenic bacteria. An alternative treatment is silver nanoparticles (AgNPs) generated by green synthesis, which have shown an antibacterial capacity by destroying the cell membrane or denaturing the cell. However, the doses at which these are effective are still unknown. The aim is to calculate the minimum inhibitory concentration (MIC) using the Gompertz, Richard, and Logistic model of biosynthesized AgNPs against a strain of V. parahaemolyticus. Through the testing of different formulations of AgNPs synthesized from Euphorbia prostrate (Ep) extracts against V. parahaemolyticus causing AHPND in white shrimp. Aqueous and ethanol extracts were obtained, and the concentration of phenols and flavonoids was quantified. In the antibiograms, AgNPs were formulated in ethanol extracts of Ep (20 and 30%). The inhibition halo at well dilution test were 18±1.7 and 17.67±2.1 mm against V. parahaemolyticus. A broth microdilution was performed with the inhibitory agents (aqueous and ethanolic extracts and AgNPs) and 20 μL of the inoculum of V. parahaemolyticus. The MIC for AgNPs was 6.2-9.3 μg/mL and for ethanol extract of 49-73 mg/mL. The Akaike index (AIC) was used to choose the Gompertz model for ethanol extracts of Ep as the best data descriptor (AIC=204.8, 10%; 45.5, 20%, and 204.8, 30%). The Richards model was at AgNPs ethanol extract with AIC=-9.3 (10%), -17.5 (20 and 30%). The MIC calculated for EP extracts with the modified Gompertz model were 20 mg/mL (10% and 20% extract) and 40 mg/mL at 30%, while Richard was winner for AgNPs-synthesized it was 5 μg/mL (10% and 20%) and 8 μg/mL (30%). The solver tool Excel was used for the calculations of the models and inhibition curves against V.parahaemolyticus.

Keywords: green synthesis, euphorbia prostata, phenols, flavonoids, bactericide

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Authors: Ivan Balachin, Geanette Polanco, Jiang Xingliang, Hu Qin

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The problem of global warming raised up interest towards renewable energy sources. To reduce cost of wind energy is a challenge. Before building of wind park conditions such as: average wind speed, direction, time for each wind, probability of icing, must be considered in the design phase. Operation values used on the setting of control systems also will depend on mentioned variables. Here it is proposed a procedure to be include in the evaluation of the performance of a wind turbine, based on the amplitude of wind changes, the number of changes and their duration. A generic study case based on actual data is presented. Data analysing techniques were applied to model the power required for yaw system based on amplitude and data amount of wind changes. A theoretical model between time, amplitude of wind changes and angular speed of nacelle rotation was identified.

Keywords: field data processing, regression determination, wind turbine performance, wind turbine placing, yaw system losses

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Authors: Muhamad Hafiz Ismail, Ahmad H., Nelfianty M. R.

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The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future.

Keywords: soccer talent identification, fitness and physical test, soccer skills test, psychological test

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Authors: Tsion Tekalegn

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In Ethiopia, improved potato varieties are essential for food security, but smallholders' adoption of improved technologies limits their productivity. For this study, data was collected based on a structured questionnaire randomly collected from the 329 sample farmers (158 adopters and 171 non-adopters). We estimate the adoption of improved variety and causal impact using Endogenous Switching Regression (ESR), and a propensity Score Matching (PSM) was used to test the treatment effect. This helps us estimate the effect of improved potato variety on smallholder farmer income by controlling for the role of the selection bias problem stemming from both observed and unobserved heterogeneity. According to the result, key determinants influencing adoption include livestock ownership, access to extension services, and farming experience, which positively affect the likelihood of adopting improved varieties. In contrast, access to irrigation negatively correlates with adoption, suggesting that farmers with reliable water sources perceive less need for improved varieties. The ESR model result confirmed that improved potato variety adoption increases the smallholder farmer income with an estimated gain of 8.77%. Thus, to improve the potato variety of the farming households, the government should give due emphasis to potato production, and the extension services need to be strengthened.

Keywords: adoption, improved potato varieties, endogenous switching regression, Ethiopia

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Authors: Daphine Namara, Jeremy I. Schwartz, Andrew K. Tusubira, Willi McFarland, Caroline Birungi, Fred C. Semitala, Martin Muddu

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Emerging evidence suggests a possible association between hyperglycemia and dolutegravir (DTG), a preferred first-line antiretroviral agent in sub-Saharan Africa (SSA). There is a need for rigorous studies to validate this association in the face of increasing DTG use and the burden of non-communicable diseases among people living with HIV (PLHIV). We conducted a case-control study to assess the risk of hyperglycemia associated with the use of DTG among PLHIV attending Mulago ISS Clinic in Kampala. Cases had hyperglycemia, while controls had no hyperglycemia, as confirmed by fasting plasma glucose and oral glucose tolerance tests. Demographic, laboratory, and clinical data were collected using interviewer-administered questionnaires and medical record abstraction. The analysis compared cases and controls on DTG use prior to diagnosis of hyperglycemia while controlling for potential confounders using multivariable logistic regression. We included 204 cases and 231 controls. In multivariable analysis, patients with prior DTG use had seven times greater odds of subsequent diagnosis of hyperglycemia compared to those who had non-DTG-based regimens (adjusted odds ratio [aOR] 7.01, 95% CI 1.96-25.09). The odds of hyperglycemia also increased with age (56 years and above vs. 18-35, aOR 12.38, 95% CI 3.79-40.50) and hypertension (aOR 5.78, 95% CI 2.53-13.21). Our study demonstrates a strong association between prior DTG exposure and subsequent diagnosis of hyperglycemia. Given the benefits of DTG, wide-scale use, and the growing burden of diabetes mellitus (DM) in SSA, there is a need for systematic screening for hyperglycemia and consideration of alternate regimens for those at risk for DM.

Keywords: HIV, hyperglycemia, doluteravir, diabetes

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Himayatullah Khan, Alena Fedorova

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The present paper analyses the income-consumption relationships in Pakistan using annual time series data from 1980-81 to 2010-1. The paper uses the Augmented Dickey-Fuller test to check the unit root and stationarity in these two time series. The paper finds that the two time series are nonstationary but stationary at their first difference levels. The Augmented Engle-Granger test and the Cointegrating Regression Durbin-Watson test imply that the two time series of consumption and income are cointegrated and that long-run marginal propensity to consume is 0.88 which is given by the estimated (static) equilibrium relation. The paper also used the error correction mechanism to find out to model dynamic relationship. The purpose of the ECM is to indicate the speed of adjustment from the short-run equilibrium to the long-run equilibrium state. The results show that MPC is equal to 0.93 and is highly significant. The coefficient of Engle-Granger residuals is negative but insignificant. Statistically, the equilibrium error term is zero, which suggests that consumption adjusts to changes in GDP in the same period. The short-run changes in GDP have a positive impact on short-run changes in consumption. The paper concludes that we may interpret 0.93 as the short-run MPC. The pair-wise Granger Causality test shows that both GDP and consumption Granger cause each other.

Keywords: cointegrating regression, Augmented Dickey Fuller test, Augmented Engle-Granger test, Granger causality, error correction mechanism

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: M. Capocelli, A. Caputo, M. De Falco, D. Mazzei, V. Piemonte

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This paper presents the experimental results and the related modeling of a thermal energy storage (TES) facility, ideated and realized by ENEA and realizing the thermocline with an innovative geometry. Firstly, the thermal energy exchange model of an equivalent shell & tube heat exchanger is described and tested to reproduce the performance of the spiral exchanger installed in the TES. Through the regression of the experimental data, a first-order thermocline model was also validated to provide an analytical function of the thermocline, useful for the performance evaluation and the comparison with other systems and implementation in simulations of integrated systems (e.g. power plants). The experimental data obtained from the plant start-up and the short-cut modeling of the system can be useful for the process analysis, for the scale-up of the thermal storage system and to investigate the feasibility of its implementation in actual case-studies.

Keywords: CSP plants, thermal energy storage, thermocline, mathematical modelling, experimental data

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Authors: Kalandi Charan Pradhan

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In this study, we investigate does weather variation determine temporary labour migration using 210 sample households from six Semi-Arid Tropic (SAT) villages for the period of 2005-2014 in India. The study has made an attempt to examine how households use temporary labour migration as a coping mechanism to minimise the risk rather than maximize the utility of the households. The study employs panel Logit regression model to predict the probability of household having at least one temporary labour migrant. As per as econometrics result, it is found that along with demographic and socioeconomic factors; weather variation plays an important role to determine the decision of migration at household level. In order to capture the weather variation, the study uses mean crop yield deviation over the study periods. Based on the random effect logit regression result, the study found that there is a concave relationship between weather variation and decision of temporary labour migration. This argument supports the theory of New Economics of Labour Migration (NELM), which highlights the decision of labour migration not only maximise the households’ utility but it helps to minimise the risks.

Keywords: temporary migration, socioeconomic factors, weather variation, crop yield, logit estimation

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Authors: My Ha, Suwei Lin, Huiying Zou

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This study aimed at investigating the association between teacher support and academic resilience in a developing country. Using the data from PISA 2018 Student Questionnaire and Cognitive Tests, the study provided evidence of the significant impact teacher support had on reading literacy among 15-year-old students from low socio-economic status (SES) homes in Vietnam. From a total of 5773 Vietnamese participants from all backgrounds, a sample of 1765 disadvantaged students was drawn for analysis. As a result, 32 percent of the low SES sample was identified as resilient. Through their response to the PISA items regarding the frequency of support they received from teachers, the result of Latent Class Analysis (LCA) divides children into three subgroups: High Support (74.6%), Fair Support (21.6%), and Low Support (3.8%). The high support group reported the highest proportion of resilient students. Meanwhile, the low support group scored the lowest mean on reading test and had the lowest rate of resilience. Also, as the level of support increases, reading achievement becomes less dependent on socioeconomic status, reflected by the decrease in both the slope and magnitude of their correlation. Logistic regression revealed that 1 unit increase in standardized teacher support would lead to an increase of 29.1 percent in the odds of a student becoming resilient. The study emphasizes the role of supportive teachers in promoting resilience, as well as lowering educational inequity in general.

Keywords: academic resilience, disadvantaged students, teacher support, inequity, PISA

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Authors: Maha Zouaoui Khalifa, Hakim Ben Othman, Hussaney Khaled

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Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC.

Keywords: unconditional conservatism, conditional conservatism, cost of equity capital, OLS, quantile regression, emerging markets, MENA countries

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Authors: B.S. Roopa

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Objectives: To investigate the prevalence of concomitant use of GPDs in patients treated with NSAIDs and GPDs in recommended dose and frequency as prophylaxis. And also to know the association between risk factors and prescription of GPDs in patients treated with NSAIDs. Methods: Study was a prospective, observational, cross-sectional survey. Data from patients with prescription of NSAIDs at the out-patient departments of secondary care Hospital, Nilgiris, India were collected in a specially designed proforma for a period of 45 days. Analysis using χ2 tests for discrete variables. Factors that might be associated with prescription of GPD with NSIADs were assessed in multiple logistic regression models. Results: Three hundred and three patients were included in this study, and the rate of GPD prescription was 89.1%. Most of the patients received H2-receptor antagonist, and, to a lesser degree, antacid and proton pump inhibitor. Patients with history of GI ulcer/bleeding were much more likely to be co-prescribed GPD than those who had no history of GI disorders .Compared with patients who were managed in general outpatient clinic, those managed in Secondary care hospital in Nilgrisis, India were more likely to receive GPD. Conclusions: The prescription rate of GPD with NSAIDs is high. Patients were prescribed with H2RA with dose of 150mg twice daily, which are not effective in reducing the risk of NSAIDs induced gastric ulcer. Only the frequency of NSAIDs prescription was considered significant determinant for the co-prescription with GPAs in patients who are < 65 years and ≥ 65 years old.

Keywords: gastro protective agents, non steridol anti inlfammatory agents

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Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee

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In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.

Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences

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Authors: Sashank Nyapati, Laura Lorente-Prieto, Maria Peiro

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The general objective of this study is to analyse the mediating role of meaningfulness in the relationships between job autonomy and job satisfaction and supportive organizational climate and job satisfaction. Theories such as the Job Characteristics Model, Conservation of Resources theory, as well as the Job Demands-Resources theory were used as theoretical framework. Data was obtained from the 5th European Working Conditions Survey (EWCS), and sample was composed of 1005 and 1000 workers from Spain and Portugal respectively. The analysis was conducted using the SOBEL Macro for SPSS (A multiple regression mediation model) developed by Preacher and Hayes in 2003. Results indicated that Meaningfulness partially mediates both the Job Autonomy-Job Satisfaction as well as the Supportive Organizational Climate-Job Satisfaction relationships. However, the percentages are large enough to draw substantial conclusions, especially that Job Meaningfulness plays an essential – if indirect – role in the amount of Satisfaction that one experiences at work. Some theoretical and practical implications are discussed.

Keywords: meaningfulness, job autonomy, supportive organizational climate, job satisfaction

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Authors: Saeed Eskandari, Seyed Rasoul Mehdikhani

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Radiation sources have been used in many industries, such as gamma sources in medical imaging. These waves have destructive effects on humans and the environment. It is very important to detect and find the source of these waves because these sources cannot be seen by the eye. A portable robot has been designed and built with the purpose of revealing radiation sources that are able to scan the place from 5 to 20 meters away and shows the location of the sources according to the intensity of the waves on a two-dimensional digital image. The operation of the robot is done by measuring the pixels separately. By increasing the image measurement resolution, we will have a more accurate scan of the environment, and more points will be detected. But this causes a lot of time to be spent on scanning. In this paper, to overcome this challenge, we designed a method that can optimize this time. In this method, a small number of important points of the environment are measured. Hence the remaining pixels are predicted and estimated by regression algorithms in machine learning. The research method is based on comparing the actual values of all pixels. These steps have been repeated with several other radiation sources. The obtained results of the study show that the values estimated by the regression method are very close to the real values.

Keywords: regression, machine learning, scan radiation, robot

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Authors: Strahinja Kovacevic, Milica Karadzic Banjac, Jasmina Vitas, Stefan Vukmanovic, Radomir Malbasa, Lidija Jevric, Sanja Podunavac-Kuzmanovic

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The present study deals with chemometric regression analysis of quality parameters and the radical scavenging ability of kombucha fermented kefir-like products obtained with winter savory (WS), peppermint (P), stinging nettle (SN) and wild thyme tea (WT) kombucha inoculums. Each analyzed sample was described by milk fat content (MF, %), total unsaturated fatty acids content (TUFA, %), monounsaturated fatty acids content (MUFA, %), polyunsaturated fatty acids content (PUFA, %), the ability of free radicals scavenging (RSA Dₚₚₕ, % and RSA.ₒₕ, %) and pH values measured after each hour from the start until the end of fermentation. The aim of the conducted regression analysis was to establish chemometric models which can predict the radical scavenging ability (RSA Dₚₚₕ, % and RSA.ₒₕ, %) of the samples by correlating it with the MF, TUFA, MUFA, PUFA and the pH value at the beginning, in the middle and at the end of fermentation process which lasted between 11 and 17 hours, until pH value of 4.5 was reached. The analysis was carried out applying univariate linear (ULR) and multiple linear regression (MLR) methods on the raw data and the data standardized by the min-max normalization method. The obtained models were characterized by very limited prediction power (poor cross-validation parameters) and weak statistical characteristics. Based on the conducted analysis it can be concluded that the resulting radical scavenging ability cannot be precisely predicted only on the basis of MF, TUFA, MUFA, PUFA content, and pH values, however, other quality parameters should be considered and included in the further modeling. This study is based upon work from project: Kombucha beverages production using alternative substrates from the territory of the Autonomous Province of Vojvodina, 142-451-2400/2019-03, supported by Provincial Secretariat for Higher Education and Scientific Research of AP Vojvodina.

Keywords: chemometrics, regression analysis, kombucha, quality control

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Authors: Aljohara M. Alquaiz, Tawfik A. M. Khoja, Abdullah Alsharif, Ambreen Kazi, Ashry Gad Mohamed, Hamad Al Mane, Abdullah Aldiris, Shaffi Ahamed Shaikh

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Objective: To determine the prevalence and correlates of anemia in male and female adolescents in Riyadh, Kingdom of Saudi Arabia. Design: A cross-sectional community based study setting: Five primary health care centers in Riyadh. Subjects: We invited 203 male and 292 female adolescents aged 13-18 years for interview, anthropometric measurements and complete blood count. Blood hemoglobin was measured with coulter cellular analysis system using light scatter method. Results: Using the WHO cut-off of Hb < 12gms/dl, 16.7%(34) males and 34%(100) females were suffering from anemia. The mean Hb (±SD) in males and females was 13.5(±1.4) and 12.3(±1.2) mg/dl, respectively. Mean(±SD) MCV, MCH, MCHC and RDW in male and female adolescents were 77.8(±6.2) vs76.4(±10.3)fL, 26.1(±2.7) vs25.5(±2.6)pg, 32.7(±2.4) vs32.2(±2.6)g/dL, 13.9(±1.4) vs13.6(±1.3)%, respectively. Multivariate logistic regression revealed that positive family history of iron deficiency anemia(IDA)(OR 4.7,95%CI 1.7–12.2), infrequent intake (OR 3.7,95%CI 1.3–10.0) and never intake of fresh juices(OR 3.5,95%CI 1.4–9.5), 13 to 14 years age (OR 3.1,95%CI 1.2–9.3) were significantly associated with anemia in male adolescents; whereas in females: family history of IDA (OR 3.4, 95%CI 1.5–7.6), being over-weight(OR 3.0,95%CI 1.4–6.1), no intake of fresh juice (OR 2.6,95%CI 1.4–5.1), living in an apartment (OR 2.0, 95%CI 1.1-3.8) or living in small house (OR 2.5, 95%CI 1.2-5.3) were significantly associated with anemia. Conclusion: Anemia is more prevalent among Saudi female adolescents as compared to males. Important factors like positive family history of IDA, overweight, lack of fresh juice intake and low socioeconomic status are significantly associated with anemia in adolescents.

Keywords: adolescents, anemia, correlates, obesity

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Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

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Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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Authors: Getachew Adugna

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Background: Chronic energy malnutrition and human immune deficiency virus among pregnant women are highly prevalent in Sub-Saharan Africa, and they are interrelated in a vicious cycle. However, the prevalence of chronic energy malnutrition and its determinant factors among human immune deficiency virus-positive pregnant women is not well studied in Ethiopia and Addis Ababa in particular. Objective: To determine the prevalence & associated factors of chronic energy malnutrition among human immune deficiency virus-positive pregnant women in health centres of Addis Ababa Ethiopia. Methods: An institution-based cross-sectional study was conducted and a systematic random sampling technique was used to select study subjects. A total of 253 study subjects were enrolled in the study—a structured and pre-tested questionnaire collected sociodemographic, maternal health-related, and nutritional-related variables. MUAC measurements were taken and medical charts were reviewed. Bi-variable and multi-variable logistic regression analyses were used to assess the effect of different factors on chronic energy malnutrition. Result: The overall prevalence of chronic energy malnutrition was 32.0%. It was significantly associated with dietary counselling (AOR: 0.062; 95%CI: 0.007, 0.549), CD4 level (AOR: 0.219; 95%CI: 0.025, 1.908), and clinical stage (AOR: 0.127; 95%CI: 0.053, 0.305). Conclusions: The prevalence of chronic energy malnutrition among Human Immune deficiency virus-infected pregnant women in Addis Ababa was high and Nutritional Intervention should be an integral part of the HIV care program.

Keywords: chronic energy malnutrition, HIV, MUAC, Addis Ababa

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Authors: Khalifa Maha, Ben Othman Hakim, Khaled Hussainey

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Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC.

Keywords: unconditional conservatism, conditional conservatism, cost of equity capital, OLS, quantile regression, emerging markets, MENA countries

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Elena Chernyak, Ryan Ceresola

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Intimate partner violence is a serious social problem that affects a million women worldwide and impacts their health and wellbeing. Some risk factors for intimate partner violence against women (e.g., disobeying or arguing with a partner, women’s age, education, and employment) are similar in many countries, both developed and developing. However, one of the principal and most significant contributors to women’s vulnerability to violence perpetrated by their intimate partners is the witnessing of interparental aggression in the family of origin. Witnessing interparental violence may lead to acceptance of intimate partner violence as a normal way to resolve conflicts. Thus, utilization of violence becomes the behavioral model: men who witnessed the parental violence are more likely to employ physical violence against their female partners whereas women who observed their fathers beating their mothers learn to tolerate aggressive behavior and become victims of domestic violence themselves. Taking into consideration the importance of this subject matter, the association between witnessing intimate partner violence in family-of-origin and experience of intimate partner violence in adulthood requires further attention. The objective of this research is to analyze and compare the prevalence of intimate partner violence in ten developing countries in different regions, namely: Mali, Haiti, Jordan, Peru, the Philippines, Pakistan, Cambodia, Egypt, the Dominican Republic and Nigeria. Specifically, this research asks whether witnessing interparental violence in a family of origin is associated with the woman’s experience of intimate partner violence during adulthood and to what extent this factor varies among the countries under investigation. This study contributes to the literature on domestic violence against women, prevalence and experience of intimate partner violence against women in developing countries, and the risk factors, using recently collected, nationally representative population-based data from above-mentioned countries. The data used in this research are derived from the demographic and health surveys conducted in the ten mentioned above countries from 2013-2016. These surveys are cross-sectional, nationally representative surveys of ever-married or cohabitating women of reproductive age and the good source of high quality and comprehensive information about women, their children, partners, and households. To complete this analysis, multivariate logistic regression was run for each of the countries, and the results are presented with odds ratios, in order to highlight the effect of witnessing intimate partner violence controlling for other factors. The results of this study indicated that having witnessed partner violence in a family of origin significantly (by 50-500%) increases the likelihood of experiencing later abuse for respondents in all countries. This finding provides robust support for the intergenerational transmission of violence theory that explains the link between interparental aggression and intimate partner violence in subsequent relationships in adulthood as a result of a learned model of behavior observed in childhood. Furthermore, it was found that some of the control variables (e.g., education, number of children, and wealth) are associated with intimate partner violence in some countries under investigation while are not associated with male partner’s abusive behavior in some other, which may be explained by specific cultural and economic factors.

Keywords: intimate partner violence, domestic violence against women, developing countries, demographic and health surveys, risk factors

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Authors: Khalid Kaddoura, Ahmed Assad, Tarek Zayed

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Sewer pipelines are prone to deterioration over-time. In fact, their deterioration does not follow a fixed downward pattern. This is in fact due to the defects that propagate through their service life. Sewer pipeline defects are categorized into distinct groups. However, the main two groups are the structural and operational defects. By definition, the structural defects influence the structural integrity of the sewer pipelines such as deformation, cracks, fractures, holes, etc. However, the operational defects are the ones that affect the flow of the sewer medium in the pipelines such as: roots, debris, attached deposits, infiltration, etc. Yet, the process for each defect to emerge follows a cause and effect relationship. Deformation, which is the change of the sewer pipeline geometry, is one type of an influencing defect that could be found in many sewer pipelines due to many surrounding factors. This defect could lead to collapse if the percentage exceeds 15%. Therefore, it is essential to predict the deformation percentage before confronting such a situation. Accordingly, this study will predict the percentage of the deformation defect in sewer pipelines adopting the multiple regression analysis. Several factors will be considered in establishing the model, which are expected to influence the defamation defect severity. Besides, this study will construct a time-based curve to understand how the defect would evolve overtime. Thus, this study is expected to be an asset for decision-makers as it will provide informative conclusions about the deformation defect severity. As a result, inspections will be minimized and so the budgets.

Keywords: deformation, prediction, regression analysis, sewer pipelines

Procedia PDF Downloads 183