Search results for: lasso regression

Authors: Ferry Kurniawan

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In this paper, we nowcast quarterly output growth in Indonesia by exploiting higher frequency data (monthly indicators) using a mixed-frequency factor model and exploiting both quarterly and monthly data. Nowcasting quarterly GDP in Indonesia is particularly relevant for the central bank of Indonesia which set the policy rate in the monthly Board of Governors Meeting; whereby one of the important step is the assessment of the current state of the economy. Thus, having an accurate and up-to-date quarterly GDP nowcast every time new monthly information becomes available would clearly be of interest for central bank of Indonesia, for example, as the initial assessment of the current state of the economy -including nowcast- will be used as input for longer term forecast. We consider a small scale mixed-frequency factor model to produce nowcasts. In particular, we specify variables as year-on-year growth rates thus the relation between quarterly and monthly data is expressed in year-on-year growth rates. To assess the performance of the model, we compare the nowcasts with two other approaches: autoregressive model –which is often difficult when forecasting output growth- and Mixed Data Sampling (MIDAS) regression. In particular, both mixed frequency factor model and MIDAS nowcasts are produced by exploiting the same set of monthly indicators. Hence, we compare the nowcasts performance of the two approaches directly. To preview the results, we find that by exploiting monthly indicators using mixed-frequency factor model and MIDAS regression we improve the nowcast accuracy over a benchmark simple autoregressive model that uses only quarterly frequency data. However, it is not clear whether the MIDAS or mixed-frequency factor model is better. Neither set of nowcasts encompasses the other; suggesting that both nowcasts are valuable in nowcasting GDP but neither is sufficient. By combining the two individual nowcasts, we find that the nowcast combination not only increases the accuracy - relative to individual nowcasts- but also lowers the risk of the worst performance of the individual nowcasts.

Keywords: nowcasting, mixed-frequency data, factor model, nowcasts combination

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Klary Geisler, Dalit Lev-Arey, Yael Hacohen

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It is a well-known fact that exercise has favorable effects on people's physical health, as well as mental well-being. However, as for as excessive exercise, it may likely elevate negative consequences (e.g., physical injuries, negligence of everyday responsibilities such as work, family life). Lately, there is a growing interest in exercise addiction, sometimes referred to as exercise dependence, which is defined as a craving for physical activity that results in extreme work-out sessions and generates negative physiological and psychological symptoms (e.g., withdrawal symptoms, tolerance, social conflict). Exercise addiction is considered a behavioral addiction, yet it was not included in the latest editions of the diagnostic and statistical manual of mental disorders (DSM-IV), due to lack of significant research. Specifically, there is scarce research on the relationship between exercise addiction and personality dimensions. The purpose of the current research was to examine the relationship between primary exercise addiction symptoms and the big five dimensions, perfectionism (high performance expectations and self-critical performance evaluations) and subjective affect. participants were 152 trainees on a variety of aerobic sports activities (running, cycling, swimming) that were recruited through sports groups and trainers. 88% of participants trained for at least 5 hours per week, 24% of the participants trained above 10 hours per week. To test the predictive ability of the IVs a hierarchical linear regression with forced block entry was performed. It was found that Neuroticism significantly predicted exercise addiction symptoms (20% of the variance, p<0.001), while consciousness was negatively correlated with exercise addiction symptoms (14% of variance p<0.05); both had a unique contribution. Other dimensions of the big five (agreeableness, openness and extraversion) did not have any contribution to the dependent. Moreover, maladaptive perfectionism (self-critical performance evaluations) significantly predicted exercise addiction symptoms as well (10% of the variance P < 0.05). The overall regression model explained 54% of variance.

Keywords: big five, consciousness, excessive exercise, exercise addiction, neuroticism, perfectionism, personality

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Authors: Nima Safaei, Arezoo Masoud, Babak Safaei

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In recent years, there has been a growing emphasis on the role of urban planning in walking capability and the effects of individual and socioeconomic factors on the physical activity levels of city dwellers. Although considerable number of studies are conducted about walkability and for identifying the effective factors in walking mode choice in developed countries, to our best knowledge, literature lacks in the study of factors affecting choice of walking mode in developing countries. Due to the high importance of health aspects of human societies and in order to make insights and incentives for reducing traffic during rush hours, many researchers and policy makers in the field of transportation planning have devoted much attention to walkability studies; they have tried to improve the effective factors in the choice of walking mode in city neighborhoods. In this study, effective factors in walkability that have proven to have significant impact on the choice of walking mode, are studied at the same time in work trips. The data for the study is collected from the employees in their workplaces by well-instructed people using questionnaires; the statistical population of the study consists of 117 employed people who commute daily from work to home in Rasht city of Iran during the beginning of spring 2015. Results of the study which are found through the linear regression modeling, show that people who do not have freedom of choice for choosing their living locations and need to be present at their workplaces in certain hours have lower levels of walking. Additionally, unlike some of the previous studies which were conducted in developed countries, coincidental effects of Body Mass Index (BMI) and the income level of employees, do not have a significant effect on the walking level in work travels.

Keywords: BMI, linear regression, transportation, walking, work trips

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Authors: Muhammad Farooq, Muhammad Idrees

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The present study has been designed to explore the causes and effects of Youth Bulge in Pakistan. However, youth bulge is a part of population segment which create problem for the whole society. The youth bulge is a common phenomenon in many developing countries, and in particular, in the least developed countries. It is often due to a stage of development where a country achieves success in reducing infant mortality but mothers still have a high fertility rate. The result is that a large share of the population is comprised of children and young adults, and today’s children are tomorrow’s young adults. Youth often play a prominent role in political violence and the existence of a “youth bulge” has been associated with times of political crisis. The population pyramid of Pakistan represents a large youth proportion and our government did not use that youth in positive way and did not provide them opportunity for development, this situation creates frustration in youth that leads them towards conflict, unrest and violence. This study will be focus on the opportunity and motives of the youth bulge situation in Pakistan in the lens of youth bulge theory. Moreover, it will give some suggestions to utilize youth in the development activities and avoid youth bulge situation in Pakistan. The present research was conducted in the metropolitan entities of Punjab, Pakistan. A sample of 300 respondents was taken from three randomly selected metropolitan entities (Faisalabad, Lahore and Rawalpindi) of Punjab Province of Pakistan. Information regarding demography, household, locality and other socio-cultural variables related to causes and effects of youth bulge in the state was collected through a well structured interview schedule. Mean, Standard Deviation and frequency distribution were used to check the measure of central tendency. Multiple linear regression was also applied to measure the influence of various independent variables on the response variable.

Keywords: youth bulge, violence, conflict, social unrest, crime, metropolitan entities, mean, standard deviation, multiple linear regression

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Authors: Caren Hilger, Silke Burkert, Friederike Kendel

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Men with localized prostate cancer (PCa) have to choose among different treatment options, such as active surveillance (AS) and radical prostatectomy (RP). All available treatment options may be accompanied by specific psychological or physiological side effects. Depending on the nature and extent of these side effects, patients are more or less likely to be satisfied or to struggle with their treatment decision in the long term. Therefore, the aim of this study was to assess and explain decisional regret in men with localized PCa. The role of erectile functioning as one of the main physiological side effects of invasive PCa treatment, depressive symptoms as a common psychological side effect, and the association of erectile functioning and depressive symptoms with decisional regret were investigated. Men with localized PCa initially managed with AS or RP (N=292) were matched according to length of therapy (mean 47.9±15.4 months). Subjects completed mailed questionnaires assessing decisional regret, changes in erectile functioning, depressive symptoms, and sociodemographic variables. Clinical data were obtained from case report forms. Differences among the two treatment groups (AS and RP) were calculated using t-tests and χ²-tests, relationships of decisional regret with erectile functioning and depressive symptoms were computed using multiple regression. Men were on average 70±7.2 years old. The two treatment groups differed markedly regarding decisional regret (p<.001, d=.50), changes in erectile functioning (p<.001, d=1.2), and depressive symptoms (p=.01, d=.30), with men after RP reporting higher values, respectively. Regression analyses showed that after adjustment for age, tumor risk category, and changes in erectile functioning, depressive symptoms were still significantly associated with decisional regret (B=0.52, p<.001). Additionally, when predicting decisional regret, the interaction of changes in erectile functioning and depressive symptoms reached significance for men after RP (B=0.52, p<.001), but not for men under AS (B=-0.16, p=.14). With increased changes in erectile functioning, the association of depressive symptoms with decisional regret became stronger in men after RP. Decisional regret is a phenomenon more prominent in men after RP than in men under AS. Erectile functioning and depressive symptoms interact in their prediction of decisional regret. Screening and treating depressive symptoms might constitute a starting point for interventions aiming to reduce decisional regret in this target group.

Keywords: active surveillance, decisional regret, depressive symptoms, erectile functioning, prostate cancer, radical prostatectomy

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Authors: Rukhsana Kausar, Issam Malki

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The integration and assimilation of ethnic minority immigrants (EMIs) and their subsequent generations remains a serious unsettled issue in most of the host countries. This study conducts the labour market gender analysis to investigate specifically whether second generation of ethnic minority immigrants in the UK is gaining access to professional and managerial employment and advantaged occupational positions on par with their native counterparts. The data used to examine the labour market achievements of EMIs is taken from Labour Force Survey (LFS) for the period 2014-2018. We apply a multivalued treatment under ignorability as proposed by Cattaneo (2010), which refers to treatment effects under the assumptions of (i) selection – on – observables and (ii) common support. We report estimates of Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATET), and Potential Outcomes Means (POM) using three estimators, including the Regression Adjustment (RA), Augmented Inverse Probability Weighting (AIPW) and Inverse Probability Weighting- Regression Adjustment (IPWRA). We consider two cases: the case with four categories where the first-generation natives are the base category, the second case combine all natives as a base group. Our findings suggest the following. Under Case 1, the estimated probabilities and differences across groups are consistently similar and highly significant. As expected, first generation natives have the highest probability for higher career attainment among both men and women. The findings also suggest that first generation immigrants perform better than the remaining two groups, including the second-generation natives and immigrants. Furthermore, second generation immigrants have higher probability to attain higher professional career, while this is lower for a managerial career. Similar conclusions are reached under Case 2. That is to say that both first – generation and second – generation immigrants have a lower probability for higher career and managerial attainment. First – generation immigrants are found to perform better than second – generation immigrants.

Keywords: immigrnats, second generation, occupational attainment, ethnicity

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Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

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Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

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Authors: Mahmoud T. Alwidyan, Ahmad Alrawashdeh, Alaa O. Oteir

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Background: Emergency medical service (EMS) providers, oftentimes, use the lights and sirens (L&S) of their ambulances to warn road users, navigate through traffic, and expedite transport to save lives of ill and injured patients. Despite the contribution of road users in the effectiveness of reducing transport time of EMS ambulances using L&S, there is a lack of empirical assessments exploring the road user’s attitude and behavior in such situations. This study, therefore, aimed to assess the attitude and behavior of road users in response to EMS ambulances with warning L&S in use. Methods: This was a cross-sectional survey developed and distributed to adult road users in Northern Jordan. The questionnaire included 20 items addressing demographics, attitudes, and behavior toward emergency ambulances. We described the participants’ responses and assessed the association between demographics and attitude statements using logistic regression. Results: A total of 1302 questionnaires were complete and appropriate for analysis. The mean age was 34.2 (SD± 11.4) years, and the majority were males (72.6%). About half of road users (47.9%) in our sample would perform inappropriate action in response to EMS ambulances with L&S in use. The multivariate logistic regression model show that being female (OR, 0.63; 95% CI = 0.48-0.81), more educated (OR, 0.68; 95% CI = 0.53-0.86), or public transport driver (OR, 0.55; 95% CI = 0.34-0.90) is significantly associated with inappropriate response to EMS ambulances. Additionally, a significant proportion of road users may perform inappropriate and lawless driving practices such as crossing red traffic lights or following the passing by EMS ambulances, which would, in turn, increase the risk on ambulances and other road users. Conclusions: A large proportion of road users in Jordan may respond inappropriately to the EMS ambulances, and many engage in risky driving behaviors due perhaps to the lack of procedural knowledge. Policy-related interventions and educational programs are crucially needed to increase public awareness of the traffic law concerning EMS ambulances and to enhance appropriate driving behavior, which, in turn, improves the efficiency of ambulance services.

Keywords: EMS ambulances, lights and sirens, road users, attitude and behavior

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Authors: Yavuz Erdoğan, Hidayet Çiftçi

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Cyberbullying, a phenomenon among adolescents, is defined as actions that use information and communication technologies such as social media to support deliberate, repeated, and hostile behaviour by an individual or group. With the advancement in communication and information technology, cyberbullying has expanded its boundaries among students in schools. Thus, parents, psychologists, educators, and lawmakers must become aware of the potential risks of this phenomenon. In the light of these perspectives, this study aims to investigate the relationships between cyberbullying perception and Facebook attitudes of Turkish students. A survey method was used for the study and the data were collected by “Cyberbullying Perception Scale”, “Facebook Attitude Scale” and “Personal Information Form”. For this purpose, study has been conducted during 2014-2015 academic year, with a total of 748 students with 493 male (%65.9) and 255 female (%34.1) from randomly selected high schools. In the analysis of data Pearson correlation and multiple regression analysis, multivariate analysis of variance (MANOVA) and Scheffe post hoc test has been used. At the end of the study, the results displayed a negative correlation between Turkish students’ Facebook attitudes and cyberbullying perception (r=-.210; p<0.05). In order to identify the predictors of students’ cyberbullying perception, multiple regression analysis was used. As a result, significant relations were detected between cyberbullying perception and independent variables (F=5.102; p<0.05). Independent variables together explain 11.0% of the total variance in cyberbullying scores. The variables that significantly predict the students’ cyberbullying perception are Facebook attitudes (t=-5.875; p<0.05), and gender (t=3.035; p<0.05). In order to calculate the effects of independent variables on students’ Facebook attitudes and cyberbullying perception MANOVA was conducted. The results of the MANOVA indicate that the Facebook attitudes and cyberbullying perception were significantly differed according to students’ gender, age, educational attainment of the mother, educational attainment of the father, income of the family and daily usage of internet.

Keywords: facebook, cyberbullying, attitude, internet usage

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Authors: Jasbir Arora, Indu Talwar, Daisy Sahni, Vidya Rattan

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Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians.

Keywords: forensic odontology, age estimation, North India, teeth

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Authors: Richard Okyere Boadu, Godwin Adzakpah, Nathan Kumasenu Mensah, Kwame Adu Okyere Boadu, Jonathan Kissi, Christiana Dziyaba, Rosemary Bermaa Abrefa

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Information and communication technology (ICT) has significantly advanced global healthcare, with electronic health (e-Health) applications improving health records and delivery. These innovations, including electronic health records, strengthen healthcare systems. The study investigates healthcare professionals' perceptions of health information applications and their associated factors in the Cape Coast Metropolis of Ghana's health facilities. Methods: We used a descriptive cross-sectional study design to collect data from 632 healthcare professionals (HCPs), in the three purposively selected health facilities in the Cape Coast municipality of Ghana in July 2022. Shapiro-Wilk test was used to check the normality of dependent variables. Descriptive statistics were used to report means with corresponding standard deviations for continuous variables. Proportions were also reported for categorical variables. Bivariate regression analysis was conducted to determine the factors influencing the Benefits of Information Technology (BoIT); Barriers to Information Technology Use (BITU); and Motives of Information Technology Use (MoITU) in healthcare delivery. Stata SE version 15 was used for the analysis. A p-value of less than 0.05 served as the basis for considering a statistically significant accepting hypothesis. Results: Healthcare professionals (HCPs) generally perceived moderate benefits (Mean score (M)=5.67) from information technology (IT) in healthcare. However, they slightly agreed that barriers like insufficient computers (M=5.11), frequent system downtime (M=5.09), low system performance (M=5.04), and inadequate staff training (M=4.88) hindered IT utilization. Respondents slightly agreed that training (M=5.56), technical support (M=5.46), and changes in work procedures (M=5.10) motivated their IT use. Bivariate regression analysis revealed significant influences of education, working experience, healthcare profession, and IT training on attitudes towards IT utilization in healthcare delivery (BoIT, BITU, and MoITU). Additionally, the age of healthcare providers, education, and working experience significantly influenced BITU. Ultimately, age, education, working experience, healthcare profession, and IT training significantly influenced MoITU in healthcare delivery. Conclusions: Healthcare professionals acknowledge moderate benefits of IT in healthcare but encounter barriers like inadequate resources and training. Motives for IT use include staff training and support. Bivariate regression analysis shows education, working experience, profession, and IT training significantly influence attitudes toward IT adoption. Targeted interventions and policies can enhance IT utilization in the Cape Coast Metropolis, Ghana.

Keywords: health information application, utilization of information application, information technology use, healthcare

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Authors: Yiseyon Sunday Hosu, Motebang Dominic Vincent Nakin, Elphina N. Cishe

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Universal access to clean and safe drinking water and basic sanitation is one of the targets of the 6th Sustainable Development Goals (SDGs). This paper explores the evidence-based indicators of Water Rights Acts (2013) among households in the rural communities in the Mthatha River catchment of OR Tambo District Municipality of South Africa. Daily access to minimum 25 litres/person and the factors influencing clean water access were investigated in the catchment. A total number of 420 households were surveyed in the upper, peri-urban, lower and coastal regions of Mthatha Rivier catchment. Descriptive and logistic regression analyses were conducted on the data collected from the households to elicit vital information on domestic water security among rural community dwellers. The results show that approximately 68 percent of total households surveyed have access to the required minimum 25 litre/person/day, with 66.3 percent in upper region, 76 per cent in the peri-urban, 1.1 percent in the lower and 2.3 percent in the coastal regions. Only 30 percent among the total surveyed households had access to piped water either in the house or public taps. The logistic regression showed that access to clean water was influenced by lack of water infrastructure, proximity to urban regions, daily flow of pipe-borne water, household size and distance to public taps. This paper recommends that viable integrated rural community-based water infrastructure provision strategies between NGOs and local authority and the promotion of point of use (POU) technologies to enhance better access to clean water.

Keywords: domestic water, household technology, water security, rural community

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Authors: Hyuk-Hoon Kim, Young Gi Min

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Background: In carbon monoxide (CO) intoxication, the pathophysiology of delayed neurological sequelae (DNS) is very complex and remains poorly understood. And predicting whether patients who exhibit resolved acute symptoms have escaped or will experience DNS represents a very important clinical issue. Brain magnetic resonance (MR) imaging has been conducted to assess the severity of brain damage as an objective method to predict prognosis. And co-ingestion of a second poison in patients with intentional CO poisoning occurs in almost one-half of patients. Among patients with co-ingestions, 66% ingested ethanol. We assessed the effects of ethanol on neurologic sequelae prevalence in acute CO intoxication by means of abnormal lesion in brain MR. Method: This study was conducted retrospectively by collecting data for patients who visited an emergency medical center during a period of 5 years. The enrollment criteria were diagnosis of acute CO poisoning and the measurement of the serum ethanol level and history of taking a brain MR during admission period. Official readout data by radiologist are used to decide whether abnormal lesion is existed or not. The enrolled patients were divided into two groups: patients with abnormal lesion and without abnormal lesion in Brain MR. A standardized extraction using medical record was performed; Mann Whitney U test and logistic regression analysis were performed. Result: A total of 112 patients were enrolled, and 68 patients presented abnormal brain lesion on MR. The abnormal brain lesion group had lower serum ethanol level (mean, 20.14 vs 46.71 mg/dL) (p-value<0.001). In addition, univariate logistic regression analysis showed the serum ethanol level (OR, 0.99; 95% CI, 0.98 -1.00) was independently associated with the development of abnormal lesion in brain MR. Conclusion: Ethanol could have neuroprotective effect in acute CO intoxication by sedative effect in stressful situation and mitigative effect in neuro-inflammatory reaction.

Keywords: carbon monoxide, delayed neuropsychological sequelae, ethanol, intoxication, magnetic resonance

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Authors: Shan-Hong Ying, Ying-Fang Wang

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A surrogate approach was deployed for assessing exposures of multiple chemicals at the selected working area of coating processes and applied to assess the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. For the selected area, 6 to 12 portable photoionization detector (PID) were placed uniformly in its workplace to measure its total VOCs concentrations (CT-VOCs) for 6 randomly selected workshifts. Simultaneously, one sampling strain was placed beside one of these portable PIDs, and the collected air sample was analyzed for individual concentration (CVOCi) of 5 VOCs (xylene, butanone, toluene, butyl acetate, and dimethylformamide). Predictive models were established by relating the CT-VOCs to CVOCi of each individual compound via simple regression analysis. The established predictive models were employed to predict each CVOCi based on the measured CT-VOC for each the similar working area using the same portable PID. Results show that predictive models obtained from simple linear regression analyses were found with an R2 = 0.83~0.99 indicating that CT-VOCs were adequate for predicting CVOCi. In order to verify the validity of the exposure prediction model, the sampling analysis of the above chemical substances was further carried out and the correlation between the measured value (Cm) and the predicted value (Cp) was analyzed. It was found that there is a good correction between the predicted value and measured value of each measured chemical substance (R2=0.83~0.98). Therefore, the surrogate approach could be assessed the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. However, it is recommended to establish the prediction model between the chemical substances belonging to each coater and the direct-reading PID, which is more representative of reality exposure situation and more accurately to estimate the long-term exposure concentration of operators.

Keywords: exposure assessment, exposure prediction model, surrogate approach, TVOC

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Authors: Azadeh Shahbazi

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This study tries to find out the determinants of capital structure in hotel industry in 7 important hotel chains in USA within the period of 12 years of 2000 to 2012. The study is used a panel pooled regression to realize the relation among different variables. Results show that the variables which could make changes in the capital structure of firms are Non-Debt Tax Shield and Tangibility.

Keywords: capital structure, service industry, hospitality, finance

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Authors: M. Towhidur Rahman, Nasim Zaman Piyas, M. Sadiqul Baree, Shahnewaz Ahmed

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The design of high-speed craft has recently become one of the most active areas of naval architecture. Speed increase makes these vehicles more efficient and useful for military, economic or leisure purpose. The planing hull is designed specifically to achieve relatively high speed on the surface of the water. Speed on the water surface is closely related to the size of the vessel and the installed power. The Savitsky method was first presented in 1964 for application to non-monohedric hulls and for application to stepped hulls. This method is well known as a reliable comparative to CFD analysis of hull resistance. A computer program based on Savitsky’s method has been developed using MATLAB. The power of high-speed vessels has been computed in this research. At first, the program reads some principal parameters such as displacement, LCG, Speed, Deadrise angle, inclination of thrust line with respect to keel line etc. and calculates the resistance of the hull using empirical planning equations of Savitsky. However, some functions used in the empirical equations are available only in the graphical form, which is not suitable for the automatic computation. We use digital plotting system to extract data from nomogram. As a result, value of wetted length-beam ratio and trim angle can be determined directly from the input of initial variables, which makes the power calculation automated without manually plotting of secondary variables such as p/b and other coefficients and the regression equations of those functions are derived by using data from different charts. Finally, the trim angle, mean wetted length-beam ratio, frictional coefficient, resistance, and power are computed and compared with the results of Savitsky and good agreement has been observed.

Keywords: nomogram, planing hull, principal parameters, regression

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Authors: Julius Steinkopf, Eric Rost, Aike Hessel, Sonia Lippke

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Objective: This study examined possible determinants of return to work (RTW) in individuals who participated in a medical rehabilitation program longitudinally over a time period of six months. Design/methodology/approach: N=1,044 rehabilitants were included in the baseline measurement in terms of completing a questionnaire during their medical rehabilitation. About 30% (n=350) have remained in the study in terms of participating in computer-assisted telephone interviewing (CATI) six months later. Frequency analyses and Regression analyses were run. Findings: About 70% of the rehabilitants returned to work six months after rehabilitation. Regression analyses revealed that the RTW rates were significantly predicted by gender (OR=0.12, men were more likely to return), perceived social support (OR=3.01) and current physical functioning (OR=1.25). Furthermore RTW motives, like expected monetary rewards (OR=25.2) and feelings of being needed (OR=0.18) same as motives for not returning to work (nRTW), like the wish to stop working in order to spend time with the spouse (OR=0.13) or a lack of enjoyment of work (OR=3.81), significantly predicted return to work rates. Life satisfaction, self-efficacy beliefs, mental health, current income, educational background or age did not significantly increase explained variance (all ps > .05). Practical implications: Taking theses predictors into account provides options to increase the effectiveness of interventions aiming at increasing RTW: Medical rehabilitations should not only aim at improving the physical functioning but also to enhance beneficial motives and social support as well as support women specifically in order to improve the effectiveness of medical rehabilitation and public health interventions. Originality/value: Illness-caused work absences are related to high financial costs and individual burden. Despite of the public health and societal implications, this is one of the very few studies investigating systematically fitness and health for the return to work.

Keywords: gender, fitness, health, physical functioning

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Yibeltal T. Bayou, Yohana S. Mashalla, Gloria Thupayagale-Tshweneagae

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Background: According to the World Health Organization, caesarean section delivery rates of more than 10-15% caesarean section deliveries in any specific geographic region in the world are not justifiable. The aim of the study was to describe the level and analyse determinants of caesarean section delivery in Addis Ababa. Methods: Data was collected in Addis Ababa using a structured questionnaire administered to 901 women aged 15-49 years through a stratified two-stage cluster sampling technique. Binary logistic regression model was employed to identify predictors of caesarean section delivery. Results: Among the 835 women who delivered their last birth at healthcare facilities, 19.2% of them gave birth by caesarean section. About 9.0% of the caesarean section births were due to mother’s request or service provider’s influence without any medical indication. The caesarean section delivery rate was much higher than the recommended rate particularly among the non-slum residents (27.2%); clients of private healthcare facilities (41.1%); currently married women (20.6%); women with secondary (22.2%) and tertiary (33.6%) level of education; and women belonging to the highest wealth quintile household (28.2%). The majority (65.8%) of the caesarean section clients were not informed about the consequences of caesarean section delivery by service providers. The logistic regression model shows that older age (30-49), secondary and above education, non-slum residence, high-risk pregnancy and receiving adequate antenatal care were significantly positively associated with caesarean section delivery. Conclusion: Despite the unreserved effort towards achieving MDG 5 through safe skilled delivery assistance among others, the high caesarean section rate beyond the recommend limit, and the finding that caesarean sections done without medical indications were also alarming. The government and city administration should take appropriate measures before the problems become setbacks in healthcare provision. Further investigations should focus on the effect of caesarean section delivery on maternal and child health outcomes in the study area.

Keywords: Addis Ababa, caesarean section, mode of delivery, slum residence

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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: Hepinda Fajari Nuharini, Sugiarti A. Musabiq

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Background: Various and continuous pressures that exceed individual resources can cause first-year undergraduate college students to experience psychological distress. Psychological distress can occur when individuals use rumination as cognitive coping strategies. Rumination is one of the cognitive coping strategies that can be used by individuals to respond to psychological distress that causes individuals to think about the causes and consequences of events that have occurred. Rumination had two subtypes, such as brooding and reflective. Therefore, the purpose of this study was determining the role of brooding and reflective as subtypes of rumination toward psychological distress in University of Indonesia first-year undergraduate students. Methods: Participants of this study were 403 University of Indonesia first-year undergraduate students aged between 18 and 21 years old. Psychological distress measured using self reporting questionnaire (SRQ-20) and brooding and reflective as subtypes of rumination measured using Ruminative Response Scale - Short Version (RRS - Short Version). Results: Binary logistic regression analyses showed that 22.8% of the variation in psychological distress could be explained by the brooding and reflective as subtypes of rumination, while 77.2% of the variation in psychological distress could be explained by other factors (Nagelkerke R² = 0,228). The results of the binary logistic regression analysis also showed rumination subtype brooding is a significant predictor of psychological distress (b = 0,306; p < 0.05), whereas rumination subtype reflective is not a significant predictor of psychological distress (b = 0,073; p > 0.05). Conclusion: The findings of this study showed a positive relationship between brooding and psychological distress indicates that a higher level of brooding will predict higher psychological distress. Meanwhile, a negative relationship between reflective and psychological distress indicates a higher level of reflective will predict lower psychological distress in University of Indonesia first-year undergraduate students. Added Values: The psychological distress among first-year undergraduate students would then have an impact on student academic performance. Therefore, the results of this study can be used as a reference for making preventive action to reduce the percentage and impact of psychological distress among first-year undergraduate students.

Keywords: brooding as subtypes of rumination, first-year undergraduate students, psychological distress, reflective as subtypes of rumination

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Authors: Debasis Sarkar, Jatan Talati

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This paper is an attempt to develop an Integrated Mass Rapid Transit System (MRTS) for a smart city project in Western India. Integrated transportation is one of the enablers of smart transportation for providing a seamless intercity as well as regional level transportation experience. The success of a smart city project at the city level for transportation is providing proper integration to different mass rapid transit modes by way of integrating information, physical, network of routes fares, etc. The methodology adopted for this study was primary data research through questionnaire survey. The respondents of the questionnaire survey have responded on the issues about their perceptions on the ways and means to improve public transport services in urban cities. The respondents were also required to identify the factors and attributes which might motivate more people to shift towards the public mode. Also, the respondents were questioned about the factors which they feel might restrain the integration of various modes of MRTS. Furthermore, this study also focuses on developing a utility equation for respondents with the help of multiple linear regression analysis and its probability to shift to public transport for certain factors listed in the questionnaire. It has been observed that for shifting to public transport, the most important factors that need to be considered were travel time saving and comfort rating. Also, an Integrated MRTS can be obtained by combining metro rail with BRTS, metro rail with monorail, monorail with BRTS and metro rail with Indian railways. Providing a common smart card to transport users for accessing all the different available modes would be a pragmatic solution towards integration of the available modes of MRTS.

Keywords: mass rapid transit systems, smart city, metro rail, bus rapid transit system, multiple linear regression, smart card, automated fare collection system

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Authors: Ahmed Atwa, Ashraf Hafez, Mohamed Abdelhameed, Adel Nabeeh, Mohamed Dawaba, Tamer Helmy

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Introduction and Objective: Tumour staging and grading do not usually reflect the future behavior of Wilms' tumor (WT) regarding mortality. Therefore, in this study, P53, Ki67 and cyclin A immunohistochemistry were used in a trial to predict WT cancer-specific survival (CSS). Methods: In this nonconcurrent cohort study, patients' archived data, including age at presentation, gender, history, clinical examination and radiological investigations, were retrieved then the patients were reviewed at the outpatient clinic of a tertiary care center by history-taking, clinical examination and radiological investigations to detect the oncological outcome. Cases that received preoperative chemotherapy or died due to causes other than WT were excluded. Formalin-fixed, paraffin-embedded specimens obtained from the previously preserved blocks at the pathology laboratory were taken on positively charged slides for IHC with p53, Ki67 and cyclin A. All specimens were examined by an experienced histopathologist devoted to the urological practice and blinded to the patient's clinical findings. P53 and cyclin A staining were scored as 0 (no nuclear staining),1 (<10% nuclear staining), 2 (10-50% nuclear staining) and 3 (>50% nuclear staining). Ki67 proliferation index (PI) was graded as low, borderline and high. Results: Of the 75 cases, 40 (53.3%) were males and 35 (46.7%) were females, and the median age was 36 months (2-216). With a mean follow-up of 78.6±31 months, cancer-specific mortality (CSM) occurred in 15 (20%) and 11 (14.7%) patients, respectively. Kaplan-Meier curve was used for survival analysis, and groups were compared using the Log-rank test. Multivariate logistic regression and Cox regression were not used because only one variable (cyclin A) had shown statistical significance (P=.02), whereas the other significant factor (residual tumor) had few cases. Conclusions: Cyclin A IHC should be considered as a marker for the prediction of WT CSS. Prospective studies with a larger sample size are needed.

Keywords: wilms’ tumour, nephroblastoma, urology, survival

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Authors: David A. Olasehinde, Peter I. Olasehinde, Segun M. A. Adelana, Dapo O. Olasehinde

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An investigation was carried out on underground water system dynamics within Ilorin metropolis to monitor the subsurface flow and its corresponding pollution. Africa population growth rate is the highest among the regions of the world, especially in urban areas. A corresponding increase in waste generation and a change in waste composition from predominantly organic to non-organic waste has also been observed. Percolation of leachate from non-engineered landfills, the chief means of waste disposal in many of its cities, constitutes a threat to the underground water bodies. Ilorin city, a transboundary town in southwestern Nigeria, is a ready microcosm of Africa’s unique challenge. In spite of the fact that groundwater is naturally protected from common contaminants such as bacteria as the subsurface provides natural attenuation process, groundwater samples have been noted to however possesses relatively higher dissolved chemical contaminants such as bicarbonate, sodium, and chloride which poses a great threat to environmental receptors and human consumption. The Geographic Information System (GIS) was used as a tool to illustrate, subsurface dynamics and the corresponding pollutant indicators. Forty-four sampling points were selected around known groundwater pollutant, major old dumpsites without landfill liners. The results of the groundwater flow directions and the corresponding contaminant transport were presented using expert geospatial software. The experimental results were subjected to four descriptive statistical analyses, namely: principal component analysis, Pearson correlation analysis, scree plot analysis, and Ward cluster analysis. Regression model was also developed aimed at finding functional relationships that can adequately relate or describe the behaviour of water qualities and the hypothetical factors landfill characteristics that may influence them namely; distance of source of water body from dumpsites, static water level of groundwater, subsurface permeability (inferred from hydraulic gradient), and soil infiltration. The regression equations developed were validated using the graphical approach. Underground water seems to flow from the northern portion of Ilorin metropolis down southwards transporting contaminants. Pollution pattern in the study area generally assumed a bimodal pattern with the major concentration of the chemical pollutants in the underground watershed and the recharge. The correlation between contaminant concentrations and the spread of pollution indicates that areas of lower subsurface permeability display a higher concentration of dissolved chemical content. The principal component analysis showed that conductivity, suspended solids, calcium hardness, total dissolved solids, total coliforms, and coliforms were the chief contaminant indicators in the underground water system in the study area. Pearson correlation revealed a high correlation of electrical conductivity for many parameters analyzed. In the same vein, the regression models suggest that the heavier the molecular weight of a chemical contaminant of a pollutant from a point source, the greater the pollution of the underground water system at a short distance. The study concludes that the associative properties of landfill have a significant effect on groundwater quality in the study area.

Keywords: dumpsite, leachate, groundwater pollution, linear regression, principal component

Procedia PDF Downloads 115

Authors: Eric D. Olson

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The lesbian/gay/bisexual/transgender/queer/questioning tourist (LGBTQ+) travels more frequently, spends more money on travel, and is more likely to travel internationally compared to their straight/heterosexual counterparts. For Vietnam, this represents a huge opportunity to increase international tourism, considering social advancements and recognition of the LGBTQ+ have greatly increased in the past few years in Vietnam. For example, Vietnam’s Health Ministry confirmed in 2022 that same-sex attraction and being transgender is not a mental health condition. A robust hospitality ecosystem of LGBTQ+ tourism suppliers already exists in Vietnam catering to LGBTQ+ tourists (e.g., Gay Hanoi Tours, VietPride). Vietnam is a safe and welcoming destination with incredible nature, cosmopolitan cities, and friendly people; however, there is a dearth of academic and industry research that has examined how LGBTQ+ international tourists perceive Vietnam as an LGBTQ+ friendly destination. To rectify this gap, this research examines Vietnam as an LGBTQ+ destination in order to provide government officials, destination marketers, and industry practitioners with insight into this increasingly visible tourist market segment. A self-administered survey instrument was administered to n=375 international LGBTQ+ tourists to examine their perceptions of Vietnam. A factor analysis found three categories of LGBTQ+ factors of visitation to Vietnam: safety and security (Eigenvalue = 4.12, variance = 32.45, α = .82); LGBTQ+ attractions (Eigenvalue = 3.65 variance = 24.23, α = .75); and friendly interactions (Eigenvalue = 3.71, variance = 10.45, α = .96). Multiple regression was used to examine LGBTQ+ visitation factors and intention to visit Vietnam, F=12.20 (2, 127), p < .001, R2 = .56. Safety and security (β = 0.42, p < .001), LGBTQ+ attractions (β = 0.61, p < .001) and friendly interactions (β = 0.42, p < .001) are predictors to visit Vietnam. Results are consistent with previous research that highlight safety/security is of utmost importance to the community when traveling. Attractions, such as LGBTQ+ tours, suppliers, and festivals can also be used as a pull factor in encouraging tourism. Implications/limitations will be discussed.

Keywords: tourism, LGBTQ, vietnam, regression

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Authors: H. Hajimolahoseini, J. Hashemi, D. Redfearn

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Background: Catheter ablation is an effective therapy for symptomatic atrial fibrillation (AF). The intracardiac electrocardiogram (IEGM) collected during this procedure contains precious information that has not been explored to its full capacity. Novel processing techniques allow looking at these recordings from different perspectives which can lead to improved therapeutic approaches. In our previous study, we showed that variation in amplitude measured through Shannon Entropy could be used as an AF recurrence risk stratification factor in patients who received Ibutilide before the electrograms were recorded. The aim of this study is to further investigate the effect of Ibutilide on characteristics of the recorded signals from the left atrium (LA) of a patient with persistent AF before and after administration of the drug. Methods: The IEGMs collected from different intra-atrial sites of 12 patients were studied and compared before and after Ibutilide administration. First, the before and after Ibutilide IEGMs that were recorded within a Euclidian distance of 3 mm in LA were selected as pairs for comparison. For every selected pair of IEGMs, the Probability Distribution Function (PDF) of the amplitude in time domain and magnitude in frequency domain was estimated using the regression analysis. The PDF represents the relative likelihood of a variable falling within a specific range of values. Results: Our observations showed that in time domain, the PDF of amplitudes was fitted to a Gaussian distribution while in frequency domain, it was fitted to a Rayleigh distribution. Our observations also revealed that after Ibutilide administration, the IEGMs would have significantly narrower short-tailed PDFs both in time and frequency domains. Conclusion: This study shows that the PDFs of the IEGMs before and after administration of Ibutilide represents significantly different properties, both in time and frequency domains. Hence, by fitting the PDF of IEGMs in time domain to a Gaussian distribution or in frequency domain to a Rayleigh distribution, the effect of Ibutilide can easily be tracked using the statistics of their PDF (e.g., standard deviation) while this is difficult through the waveform of IEGMs itself.

Keywords: atrial fibrillation, catheter ablation, probability distribution function, time-frequency characteristics

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Authors: Elizabeth Malebogo Mosepele

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Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation.

Keywords: Chi squared, geographic information system, multinomial logistic regression, remote sensing, road side vegetation

Procedia PDF Downloads 432

Authors: Li-hsing Shih, Tzu-hsun Yen

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Because of the long-term commitment to net zero carbon emission, utility companies include more renewable energy supply, which generates electricity with time and weather restrictions. This leads to time-of-use electricity pricing to reflect the actual cost of energy supply. From an end-user point of view, better residential energy management is needed to incorporate the time-of-use prices and assist end users in scheduling their daily use of electricity. This study uses bi-criteria optimization methods to schedule daily energy consumption by minimizing the electricity cost and maximizing the comfort of end users. Different from most previous research, this study schedules users’ activities rather than household appliances to have better measures of users’ comfort/satisfaction. The relation between each activity and the use of different appliances could be defined by users. The comfort level is at the highest when the time and duration of an activity completely meet the user’s expectation, and the comfort level decreases when the time and duration do not meet expectations. A questionnaire survey was conducted to collect data for establishing regression models that describe users’ comfort levels when the execution time and duration of activities are different from user expectations. Six regression models representing the comfort levels for six types of activities were established using the responses to the questionnaire survey. A computer program is developed to evaluate electricity cost and the comfort level for each feasible schedule and then find the non-dominated schedules. The Epsilon constraint method is used to find the optimal schedule out of the non-dominated schedules. A hypothetical case is presented to demonstrate the effectiveness of the proposed approach and the computer program. Using the program, users can obtain the optimal schedule of daily energy consumption by inputting the intended time and duration of activities and the given time-of-use electricity prices.

Keywords: bi-criteria optimization, energy consumption, time-of-use price, scheduling

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Authors: Kedar Vishnu, Parmod Kumar

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India is becoming most favored retail destination in the world. The organized retail has presented many opportunities to farmers to increase income by shifting cropping pattern from food grains to commercial crops. Previous research revealed potential benefits for farmers by supplying fruits and vegetables to organized retail channels. However the supply of fruits and vegetables from small and marginal farmers remain low than expected. The main objective of this paper is to identify the factors determining market participation of smallholder farmers in modern organized retail chains. Attempt is also made to find out factors influencing the choice of participation in particular organized retail collection centers as compared to other organized retail. The paper was based on primary survey of 40 Beans and Tomato farmers who supply to organized retail collection centers from Karnataka, India. Multiple regression technique is used to identify the factors determining quantity sold at collection centers. The regression result, show that area under vegetables, yield, and price from modern collection center and having access to technical help were found significantly affecting quantity sold into modern organized retail channels. On the opposite, increased rejection rates and vegetable prices at APMC were found influencing farmers decision into the reverse side. Empirical result of the multinomial logit model show that Reliance fresh has tendency to prefer large farmers who can supply more quality and better quantity compared with TESCO and More collection centers. The negative sign of area, having access to technical help, transportation cost, and number of bore wells led to higher probability of farmers to participate in Reliance Fresh collection centers as compared with More and TESCO.

Keywords: fruits, vegetables, organized retail markets, multinomial logit model

Procedia PDF Downloads 344