Search results for: multinomial logistic regression model

Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Roger L. Goodwin

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This paper presents a rank correlation curve. The traditional correlation coefficient is valid for both continuous variables and for integer variables using rank statistics. Since the correlation coefficient has already been established in rank statistics by Spearman, such a calculation can be extended to the correlation curve. This paper presents two survey questions. The survey collected non-continuous variables. We will show weak to moderate correlation. Obviously, one question has a negative effect on the other. A review of the qualitative literature can answer which question and why. The rank correlation curve shows which collection of responses has a positive slope and which collection of responses has a negative slope. Such information is unavailable from the flat, "first-glance" correlation statistics.

Keywords: Bayesian estimation, regression model, rank statistics, correlation, correlation curve

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Authors: Saheed Olakunle Sanusi, Israel Ajibade Adedeji

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The study analysed the effect of microfinance on the profit level of small and medium scale enterprises in Lagos. The data for the study were obtained by simple random sampling, and total of one hundred and fifty (150) small and medium scale enterprises (SMEs) were sampled for the study. Seventy-five (75) each are microfinance users and non-users. Data were analysed using descriptive statistics, logit model, t-test and ordinary least square (OLS) regression. The mean profit of the enterprises using microfinance is ₦16.8m, while for the non-users of microfinance is ₦5.9m. The mean profit of microfinance users is statistically different from the non-users. The result of the logit model specified for the determinant of access to microfinance showed that three of specified variables- educational status of the enterprise head, credit utilisation and volume of business investment are significant at P < 0.01. Enterprises with many years of experience, highly educated enterprise heads and high volume of business investment have more potential access to microfinance. The OLS regression model indicated that three parameters namely number of school years, the volume of business investment and (dummy) participation in microfinance were found to be significant at P < 0.05. These variables are therefore significant determinants of impacts of microfinance on profit level in the study area. The study, therefore, concludes and recommends that to improve the status of small and medium scale enterprises for an increase in profit, the full benefit of access to microfinance can be enhanced through investment in social infrastructure and human capital development. Also, concerted efforts should be made to encouraged non-users of microfinance among SMEs to use it in order to boost their profit.

Keywords: credit utilisation, logit model, microfinance, small and medium enterprises

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Authors: Saleem Z. Ramadan

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In this paper, a model is proposed to determine the life distribution parameters of the useful life region for the PV system utilizing a combination of non-parametric and linear regression analysis for the failure data of these systems. Results showed that this method is dependable for analyzing failure time data for such reliable systems when the data is scarce.

Keywords: masking, bathtub model, reliability, non-parametric analysis, useful life

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Authors: Kamna Solanki, Yudhvir Singh, Sandeep Dalal

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Software testing is a time and cost intensive process. A scrutiny of the code and rigorous testing is required to identify and rectify the putative bugs. The process of bug identification and its consequent correction is continuous in nature and often some of the bugs are removed after the software has been launched in the market. This process of code validation of the altered software during the maintenance phase is termed as Regression testing. Regression testing ubiquitously considers resource constraints; therefore, the deduction of an appropriate set of test cases, from the ensemble of the entire gamut of test cases, is a critical issue for regression test planning. This paper presents a novel method for designing a suitable prioritization process to optimize fault detection rate and performance of regression test on predefined constraints. The proposed method for test case prioritization m-ACO alters the food source selection criteria of natural ants and is basically a modified version of Ant Colony Optimization (ACO). The proposed m-ACO approach has been coded in 'Perl' language and results are validated using three examples by computation of Average Percentage of Faults Detected (APFD) metric.

Keywords: regression testing, software testing, test case prioritization, test suite optimization

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Authors: Lin-Han Chiang Hsieh, Hsiao-Yi Lin

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This study aims to determine the impact of the disclosure of flood potential map on housing prices. The disclosure is supposed to mitigate the market failure by reducing information asymmetry. On the other hand, opponents argue that the official disclosure of simulated results will only create unnecessary disturbances on the housing market. This study identifies the impact of the disclosure of the flood potential map by comparing the hedonic price of flood potential before and after the disclosure. The flood potential map used in this study is published by Taipei municipal government in 2015, which is a result of a comprehensive simulation based on geographical, hydrological, and meteorological factors. The residential property sales data of 2013 to 2016 is used in this study, which is collected from the actual sales price registration system by the Department of Land Administration (DLA). The result shows that the impact of flood potential on residential real estate market is statistically significant both before and after the disclosure. But the trend is clearer after the disclosure, suggesting that the disclosure does have an impact on the market. Also, the result shows that the impact of flood potential differs by the severity and frequency of precipitation. The negative impact for a relatively mild, high frequency flood potential is stronger than that for a heavy, low possibility flood potential. The result indicates that home buyers are of more concern to the frequency, than the intensity of flood. Another contribution of this study is in the methodological perspective. The classic hedonic price analysis with OLS regression suffers from two spatial problems: the endogeneity problem caused by omitted spatial-related variables, and the heterogeneity concern to the presumption that regression coefficients are spatially constant. These two problems are seldom considered in a single model. This study tries to deal with the endogeneity and heterogeneity problem together by combining the spatial fixed-effect model and geographically weighted regression (GWR). A series of literature indicates that the hedonic price of certain environmental assets varies spatially by applying GWR. Since the endogeneity problem is usually not considered in typical GWR models, it is arguable that the omitted spatial-related variables might bias the result of GWR models. By combing the spatial fixed-effect model and GWR, this study concludes that the effect of flood potential map is highly sensitive by location, even after controlling for the spatial autocorrelation at the same time. The main policy application of this result is that it is improper to determine the potential benefit of flood prevention policy by simply multiplying the hedonic price of flood risk by the number of houses. The effect of flood prevention might vary dramatically by location.

Keywords: flood potential, hedonic price analysis, endogeneity, heterogeneity, geographically-weighted regression

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Aayushi Lyngwa, Bimal Kishore Sahoo

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The study discusses the relationship between cognitive ability and the level of education attained by the tribal street entrepreneurs on their financial literacy. It is driven by the objective of examining the effect of cognitive ability on financial ability on the one hand and determining the effect of the same on financial literacy on the other. A field experiment was conducted on 203 tribal street vendors in the north-eastern Indian state of Mizoram. This experiment's calculations are conditioned by providing each question scores like math score (cognitive ability), financial score and debt score (financial ability). After that, categories for each of the variables, like math category (math score), financial category (financial score) and debt category (debt score), are generated to run the regression model. Since the dependent variable is ordinal, an ordered logit regression model was applied. The study shows that street vendors' cognitive and financial abilities are highly correlated. It, therefore, confirms that cognitive ability positively affects the financial literacy of street vendors through the increase in attainment of educational levels. It is also found that concerning the type of street vendors, regular street vendors are more likely to have better cognitive abilities than temporary street vendors. Additionally, street vendors with more cognitive and financial abilities gained better monthly profits and performed habits of bookkeeping. The study attempts to draw a particular focus on a set-up which is economically and socially marginalized in the Indian economy. Its finding contributes to understanding financial literacy in an understudied area and provides policy implications through inclusive financial systems solutions in an economy limited to tribal street vendors.

Keywords: financial literacy, education, street entrepreneurs, tribals, cognitive ability, financial ability, ordered logit regression.

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Authors: Mohammad Akbar Paiman, Yasmin Nadeem Parpio, Naureen Akbarali, Khwaja Mir Islam Saeed, Murad Moosa Khan

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Background: Gender-based violence (GBV) is widely considered a significant public health problem that is associated with acute morbidity and mortality. GBV is commonly understood as a physical, sexual, and mental assault from intimate partners, sexual violence by non-partners, sexual assault of girls, and acts like trafficking women for sex. Objective: This study aimed to determine the factors associated with injuries and trauma among victims of GBV in Afghanistan. Method: We conducted a record-based analysis of the data collected by the Gender Department of the Family Protection Centre nationally between November 2013 and October 2019. Cross-tabulation between different variables such as age, sex, marital status, and type of violence and associations between different types of violence, age, gender, and geographical location was determined using the logistic regression model. Results: During the study period, there were a total of 58,160 GBV in Afghanistan. Most of the victims were women 98% with over three-quarters being adults 78%. Most of the victims were married 76%, followed by single 14%, widowed 5%, and engaged 5%. Over three-quarters of the violence, 73% was observed in the victim’s house while nearly one-quarter of the violence 24 % occurred in the perpetrator’s house. Conclusions: GBV is a significant public health problem in Afghanistan that needs to be addressed at multiple levels including policy, state, and community as well as by raising public awareness and education and a strong code of conduct against GBV by all stakeholders.

Keywords: gender-based violence, physical and psychological violence, injuries, Afghanistan

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: A. Alhusban, M. Alqawasmeh, F. Alfawares

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Introduction: Despite improvements in stroke management, it remains the leading cause of disability worldwide. It has been suggested that enhancing brain angiogenesis after stroke will improve stroke outcome. Promoting post stroke angiogenesis requires the upregulation of angiogenic factors with a simultaneous reduction of anti-angiogenic factors. Thrombospondin-1 is the main anti-angiogenic protein in the living cells. Counterintuitively, it has been shown that animals with Thrombospondin-1 knockdown will have better stroke outcome. Data about the clinical significance of Thrombspondin-1 levels at the time of admission is still lacking. The objective of this work is to assess the association between serum Thrombospondin-1 levels measured at the time of admission and baseline neurologic severity after stroke. Patients and Methods: Blood samples were collected from patients admitted to the King Abdullah University Hospital (KAUH) with ischemic stroke at the time of admission and serum Thrombopsondin-1 levels were measured using ELISA. Patients neurologic severity was evaluated using the National Institute of Health Stroke Scale (NIHSS). Results: Samples from 50 patients admitted between January 2016 and December 2016 were collected. The median age of participants was 68 years and the median NIHSS was 3. Multinomial regression identified serum Thrombospondin-1 as an independent predictor of stroke outcome (p=0.003). Baseline serum Thrombsopondin-1 was negatively associated with NIHSS at the time of admission (spearman rho correlation coefficient=0.272, p=0.032). Conclusion: Serum Thrombospondin-1 at the time of admission may be a useful marker of stroke severity that predicts more severe neurologic severity.

Keywords: thrombospondin, stroke, neuroprotection, biomarkers

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Authors: Gebrehiwot H., Giorgis Bahita

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Firms in Africa experience less financial market in comparison to other emerging and developed countries, thus lagging behind the rest of the world in terms of innovation and growth. Though there are different factors to be considered, underdeveloped financial systems take the lion's share in hindering firm innovation and growth in Africa. Insufficient capacity to innovate is one of the problems facing African businesses. Moreover, a critical challenge faced by firms in Africa is access to finance and the inability of financially constrained firms to grow. Only little is known about how different sources of finance affect firm innovation and growth in Africa, specifically the formal and informal finance effect on firm innovation and growth. This study's aim is to address this gap by using formal and informal finance for working capital and fixed capital and its role in firm innovation and firm growth using firm-level data from the World Bank enterprise survey 2006-2019 with a total of 5661 sample firms from 14 countries based on available data on the selected variables. Additionally, this study examines factors for accessing credit from a formal financial institution. The logit model is used to examine the effect of finance on a firm’s innovation and factors to access formal finance, while the Ordinary List Square (OLS) regression mode is used to investigate the effect of finance on firm growth. 2SLS instrumental variables are used to address the possible endogeneity problem in firm growth and finance-innovation relationships. A result from the logistic regression indicates that both formal and informal finance used for working capital and investment in fixed capital was found to have a significant positive association with product and process innovation. In the case of finance and growth, finding show that positive association of both formal and informal financing to working capital and new investment in fixed capital though the informal has positive relations to firm growth as measured by sale growth but no significant association as measured by employment growth. Formal finance shows more magnitude of effect on innovation and growth when firms use formal finance to finance investment in fixed capital, while informal finance show less compared to formal finance and this confirms previous studies as informal is mainly used for working capital in underdeveloped economies like Africa. The factors that determine credit access: Age, firm size, managerial experience, exporting, gender, and foreign ownership are found to have significant determinant factors in accessing credit from formal and informal sources among the selected sample countries.

Keywords: formal finance, informal finance, innovation, growth

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Authors: Elucir Gir, Myllena Nilce de Freitas Surmano, Laelson Rochelle Milanês Sousa, Mayra Gonçalves Menegueti, Ana Cristina de Oliveira E Silva, Renata Karina Reis

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Objective: To analyze the factors associated with the worsening of the quality of life of health professionals in the Southeast region of Brazil during the COVID-19 pandemic and its associated factors. Method: Analytical cross-sectional study carried out with health professionals from the southeastern region of Brazil. Data collection took place through an online survey with a form stored on the Survey Monkey platform. Bivariate analysis was used, and the chi-square test was adopted, followed by the multiple binary logistic regression model based on the stepwise method. Results: 3,493 health professionals participated in the study. Factors associated with worsening quality of life were: Professional Category (Nursing assistant) [OR 1.851 (95%CI 1.035-3.311) p= 0.038]; types of people who provided care (people in general) [OR 1.445 (95%CI 1.072-1.945) p=0.015]; Supply of good quality PPE by the institution where he works (no) [OR 1.595 (CI 95% 1.144-2.223) p= 0.006] and Supply of good quality PPE by the institution where he works (in part) [OR 1.563 (CI 95% 1.257-1.943) p < 0.001]. Conclusion: The factors associated with the worsening of the quality of life of health professionals during the COVID-19 pandemic were: Professional Category (Nursing assistant); types of people who provided assistance (people in general); Supply of sufficient PPE by the institution where you work (no) and Supply of good quality PPE by the institution where you work (in part). Future studies should investigate to what extent QoL can be improved based on modifiable factors.

Keywords: COVID-19, quality of life, health professionals, respiratory infections

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Authors: Birtukan Atinkut Asmare, Bernhard Freyer, Jim Bingen

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Though it is possible to transform the farming system towards a healthy, sustainable, and toxic-free food system by reducing pesticide use both in the field and postharvest, pesticides, including those that have been banned or severely restricted from use in developed countries, are indiscriminately used in African agriculture. Drawing on social practice theory, this study is about pesticide use practices in smallholder farms and its adverse impacts on women’s health and the environment, with reference to Africa, with an empirical focus on Ethiopia. Data have been collected via integrating diverse quantitative and qualitative approaches such as household surveys (n= 318), focus group discussions (n=6), field observations (n=30), and key informant interviews (n=18), with people along the pesticide value chain, including sellers and extension workers up to women farmers. A binary logistic regression model was used to investigate the factors that influence the adoption of personal protective equipment among female headed households. The findings show that Female-headed households carried out risky and unsafe practices from pesticide purchasing up to disposal, largely motivated by material elements (such as labor, income, time, and the provisioning system) but were notably shaped by competences (skills and knowledge), and meanings (norms, values, rules, and shared ideas). The main meaning or material aspect for pesticide purchasing were the perceptions of efficacy on pests, diseases, and weeds (65%), cost and availability in smaller quantities (60.7%), and a woman’s available time and mobility (58.9%). Pesticide hazards to human health or the environment seem not to be relevant for most female headed households. Unsafe practices of pesticide use among women led to the loss of biodiversity and ecosystem degradation, let alone their and family’s health. As the regression results show, the significant factors that influenced PPE adoption among female headed households were age and retailer information (p < 0.05). In line with the empirical finding, in addition to changing individual competences through advisory services and training, a foundational shift is needed in the sociocultural environment (e.g., policy, advisory), or a change in the meanings (social norms), where women are living and working.

Keywords: biodiversity, competences, ecosystems, ethiopia, female headed households, materials, meanings, pesticide purchasing, pesticide using, social practice theory

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Authors: Ozan Kahraman, Hao Feng

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Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

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Authors: Arjun G. Koppad

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The study was conducted in Joida taluk of Uttara Kannada district, Karnataka, India, to assess the land use land cover (LULC) and forest aboveground biomass using L band SAR data. The study area covered has dense, moderately dense, and sparse forests. The sampled area was 0.01 percent of the forest area with 30 sampling plots which were selected randomly. The point center quadrate (PCQ) method was used to select the tree and collected the tree growth parameters viz., tree height, diameter at breast height (DBH), and diameter at the tree base. The tree crown density was measured with a densitometer. Each sample plot biomass was estimated using the standard formula. In this study, the LULC classification was done using Freeman-Durden, Yamaghuchi and Pauli polarimetric decompositions. It was observed that the Freeman-Durden decomposition showed better LULC classification with an accuracy of 88 percent. An attempt was made to estimate the aboveground biomass using SAR backscatter. The ALOS-2 PALSAR-2 L-band data (HH, HV, VV &VH) fully polarimetric quad-pol SAR data was used. SAR backscatter-based regression model was implemented to retrieve forest aboveground biomass of the study area. Cross-polarization (HV) has shown a good correlation with forest above-ground biomass. The Multi Linear Regression analysis was done to estimate aboveground biomass of the natural forest areas of the Joida taluk. The different polarizations (HH &HV, VV &HH, HV & VH, VV&VH) combination of HH and HV polarization shows a good correlation with field and predicted biomass. The RMSE and value for HH & HV and HH & VV were 78 t/ha and 0.861, 81 t/ha and 0.853, respectively. Hence the model can be recommended for estimating AGB for the dense, moderately dense, and sparse forest.

Keywords: forest, biomass, LULC, back scatter, SAR, regression

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Authors: Asma Ammar, Nabiha Bouafia , Asma BenCheikh, Mohamed Mahjoub, Olfa Ezzi, Wadiaa Bannour, Radhia Helali, Mansour Njah

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Background: The objective of the present study was to identify factors associated with vaccination against Hepatitis B virus (HBV) among healthcare workers (HWs) in the University Hospital Center (UHC) Farhat Hached Sousse, Tunisia. Methods: We conducted a descriptive cross-sectional study all licensed physicians (n= 206) and a representative sample of paramedical staff (n= 372) exercising at UHC Hached Sousse (Tunisia) during two months (January and February 2014). Data were collected using a self-administered and pre-tested questionnaire, which composed by 21 questions. In order to determinate factors associated with vaccination against hepatitis B among HWs, this questionnaire was based on the Health Belief Model, one of the most classical behavior theories. Logistic regression with the stepwise method of Hosmer and Lemeshow was used to identify the determinants of the use of vaccination against HBV. Results: The response rates were 79.8%. Fifty two percent believe that HBV is frequent in our healthcare units and 60.6% consider it a severe infection. The prevalence of HWs vaccination was 39%, 95% CI [34.49%; 43.5%]. In multivariate analysis, determinants of the use of vaccination against HBV among HWs were young age (p=10-4), male gender (p = 0. 006), high or very high importance accorded to health (p = 0.035), perception membership in a risk group for HBV infection (p = 0.038) and very favorable or favorable opinion about vaccination against HVB (p=10-4). Conclusion: The results of our study should be considered in any strategy for preventing VHB infection in HWs. In the mean time, coverage with standard vaccines should be improved also by supplying complete information on the risks of VHB infection and on the safety and efficacy of vaccination.

Keywords: Hepatitis B virus, healthcare workers, prevalence, vaccination

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Authors: Iman Fakhrian, Ali Salehi Manzari

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Consistent Profitability is one of the most important priorities in manufacturing companies. One of the fundamental factors for increasing the companies profitability is cost management. Isfahan's mobarakeh steel company is one of the largest producers of the slab product grades in the middle east. Raw material cost constitutes about 70% of the company's expenditures. The costs of the ferro alloys have a remarkable contribution of the raw material costs. This research aims to determine the ferro alloys which have significant effect on the variability of the standard cost of the slab product grades. Used data in this study were collected from standard costing system of isfahan's mobarakeh steel company in 2022. The results of conducting the regression analysis model show that expense items: 03020, 03045, 03125, 03130 and 03150 have dominant role in variability of the standard cost of the slab product grades. In other words, the mentioned ferro alloys have noticeable and significant role in variability of the standard cost of the slab product grades.

Keywords: consistent profitability, ferro alloys, slab product grades, regression analysis

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Authors: Karwan Khudhir

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A cross-sectional study was carried out to determine the prevalence of musculoskeletal disorders (MSDs) and psychosocial factors associated with it, among Kurdistan nursing professionals. Simple random sampling was used to select 220 nurses and data were collected by self-administrative questionnaire. Results of the study showed that the overall prevalence of MSDs among Kurdistan nurses was 74% in different body regions and, by body regions, neck pain was reported to be the highest complaint of twelve-month MSDs (48.4%) compared to other body parts. Logistic regression analysis indicated 6 variables that are significantly associated with musculoskeletal disorders: smoking (OR=19.472, 95% CI: 5.396, 70.273), BMI (OR= 5.106, 95% CI: 1.735, 15.025), physical activity (OR=8.639, 95% CI: 3.075, 24.271), psychological demand (OR=6.685, 95% CI: 3.318, 13.468), social support (OR=3.143, 95% CI: 1.202, 4.814) and job satisfaction (OR=2.44, 95% CI: 1.04, 5.63). Prevention strategies and health education which emphasizes on psychosocial risk factors and how to improve working conditions should be introduced.

Keywords: Kurdistan Region, Iraq, musculoskeletal disorders, nurses, psycho-social factors

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Authors: Bowen Li, Dan Shu, Shiguang Pang, Li Wang, Qian Liu

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Background: Every year, approximately 429,000 adolescents aged 0-19 are diagnosed with cancer worldwide. The diagnosis brings about substantial psychological pressure and caregiving responsibilities for family members and impacts the families significantly. Family resilience has been found to reduce caregiver distress and can also foster post-traumatic growth in cancer survivors. However, current research on family resilience in childhood cancer mainly focuses on individual caregiver resilience and child adaptation, with less attention given to categorizing family resilience among caregivers of children with cancer. Method: A total of 292 caregivers of children with cancer were recruited from four tertiary hospitals in central China from July 2022 to March 2024. This study was approved by the ethics committee, and participants provided informed consent, with the option to withdraw at any time. The Family Resilience Assessment Scale was used to measure family resilience among caregivers of children with cancer. The Quality of Life scale-family, The Perceived Social Support Scale, and The Connor-Davidson Resilience Scale were used to measure potential influencing factors. This study used latent profile analysis (LPA) to identify latent categories of family resilience among caregivers of children with cancer. Binary logistic regression was used to analyze the influencing factors of family resilience. Results: The results reveal two distinct categories: "high family resilience" and "low family resilience." "Low family resilience" group accounts for 85.96% of the total while "high family resilience" group is 14.04%. "High family resilience" scores higher across all dimensions compared to "low family resilience". Within-group comparisons reveals that "family communication and problem-solving" and "empowering the meaning of adversity" received the highest scores, while "utilizing social and economic resources" scores the lowest. "Maintaining a positive attitude" scores similarly high to "family communication and problem-solving" in the high family resilience group, whereas it scores similarly low to "utilizing social and economic resources" in the low family resilience group. In single-factor analysis, residence, number of siblings, caregiver's education level, resilience, social support, quality of life, physical well-being and psychological well-being showed significant difference between two categories. In binary logistic regression analysis, households with only one child are more likely to exhibit low family resilience, whereas high personal resilience is associated with a high level of family resilience. Conclusion: Most families with children suffering from cancer require strengthened family resilience. Support for utilizing socio-economic resources is important for both high and low family resilience families. Single-child families and caregivers with lower resilience require more attention. These findings imply the development of targeted interventions to enhance family resilience among families with children of cancer. Future studies could involve children and other family members for a comprehensive understanding of family resilience. Longitudinal studies are necessary to explore the dynamic changes in family resilience throughout the cancer journey.

Keywords: cancer children, caregivers, family resilience, latent profile analysis

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Fumiya Sugino, Naohiro Nakamura, Yuji Miyazu

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In earthquake resistant design of buildings, evaluation of vibration characteristics is important. In recent years, due to the increment of super high-rise buildings, the evaluation of response is important for not only the first mode but also higher modes. The knowledge of vibration characteristics in buildings is mostly limited to the first mode and the knowledge of higher modes is still insufficient. In this paper, using earthquake observation records of a SRC building by applying frequency filter to ARX model, characteristics of first and second modes were studied. First, we studied the change of the eigen frequency and the damping ratio during the 3.11 earthquake. The eigen frequency gradually decreases from the time of earthquake occurrence, and it is almost stable after about 150 seconds have passed. At this time, the decreasing rates of the 1st and 2nd eigen frequencies are both about 0.7. Although the damping ratio has more large error than the eigen frequency, both the 1st and 2nd damping ratio are 3 to 5%. Also, there is a strong correlation between the 1st and 2nd eigen frequency, and the regression line is y=3.17x. In the damping ratio, the regression line is y=0.90x. Therefore 1st and 2nd damping ratios are approximately the same degree. Next, we study the eigen frequency and damping ratio from 1998 after 3.11 earthquakes, the final year is 2014. In all the considered earthquakes, they are connected in order of occurrence respectively. The eigen frequency slowly declined from immediately after completion, and tend to stabilize after several years. Although it has declined greatly after the 3.11 earthquake. Both the decresing rate of the 1st and 2nd eigen frequencies until about 7 years later are about 0.8. For the damping ratio, both the 1st and 2nd are about 1 to 6%. After the 3.11 earthquake, the 1st increases by about 1% and the 2nd increases by less than 1%. For the eigen frequency, there is a strong correlation between the 1st and 2nd, and the regression line is y=3.17x. For the damping ratio, the regression line is y=1.01x. Therefore, it can be said that the 1st and 2nd damping ratio is approximately the same degree. Based on the above results, changes in eigen frequency and damping ratio are summarized as follows. In the long-term study of the eigen frequency, both the 1st and 2nd gradually declined from immediately after completion, and tended to stabilize after a few years. Further it declined after the 3.11 earthquake. In addition, there is a strong correlation between the 1st and 2nd, and the declining time and the decreasing rate are the same degree. In the long-term study of the damping ratio, both the 1st and 2nd are about 1 to 6%. After the 3.11 earthquake, the 1st increases by about 1%, the 2nd increases by less than 1%. Also, the 1st and 2nd are approximately the same degree.

Keywords: eigenfrequency, damping ratio, ARX model, earthquake observation records

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Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

Procedia PDF Downloads 437

Authors: Omar Dib, Rita Yaacoub, Luc Eveleigh, Nathalie Locquet, Hussein Dib, Ali Bassal, Christophe B. Y. Cordella

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The potential of front-face fluorescence coupled with chemometric techniques, namely parallel factor analysis (PARAFAC) and multiple linear regression (MLR) as a rapid analysis tool to characterize Lebanese virgin olive oils was investigated. Fluorescence fingerprints were acquired directly on 102 Lebanese virgin olive oil samples in the range of 280-540 nm in excitation and 280-700 nm in emission. A PARAFAC model with seven components was considered optimal with a residual of 99.64% and core consistency value of 78.65. The model revealed seven main fluorescence profiles in olive oil and was mainly associated with tocopherols, polyphenols, chlorophyllic compounds and oxidation/hydrolysis products. 23 MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for 12 predicted variables), thus satisfactory prediction performances. Acid values, peroxide values, and Delta K had the models with the highest predictions, with R values of 0.89, 0.84 and 0.81 respectively. Among fatty acids, linoleic and oleic acids were also highly predicted with R values of 0.8 and 0.76, respectively. Factors contributing to the model's construction were related to common fluorophores found in olive oil, mainly chlorophyll, polyphenols, and oxidation products. This study demonstrates the interest of front-face fluorescence as a promising tool for quality control of Lebanese virgin olive oils.

Keywords: front-face fluorescence, Lebanese virgin olive oils, multiple Linear regressions, PARAFAC analysis

Procedia PDF Downloads 437

Authors: Bersufekad Wubie

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Introduction: Near visual impairment is presenting near visual acuity of the eye worse than N6 at a 40 cm distance. Teachers' regular duties, such as reading books, writing on the blackboard, and recognizing students' faces, need good near vision. If a teacher has near-visual impairment, the work output is unsatisfactory. Objective: The study was aimed to assess the prevalence and associated factors near vision impairment among school teachers at Gondar city Northwest Ethiopia, August 2022. Methods: To select 567 teachers in Gondar city schools, an institutional-based cross-sectional study design with a multistage sampling technique were used. The study was conducted in selected schools from May 1 to May 30, 2022. Trained data collectors used well-structured Amharic and English language questionnaires and ophthalmic instruments for examination. The collected data were checked for completeness and entered into Epi data version 4.6, then exported to SPSS version 26 for further analysis. A binary and multivariate logistic regression model was fitted. And associated factors of the outcome variable. Result: The prevalence of near visual impairment was 64.6%, with a confidence interval of 60.3%–68.4%. Near visual impairment was significantly associated with age >= 35 years (AOR: 4.90 at 95% CI: 3.15, 7.65), having prolonged years of teaching experience (AOR: 3.29 at 95% CI: 1.70, 4.62), having a history of ocular surgery (AOR: 1.96 at 95% CI: 1.10, 4.62), smokers (AOR: 2.21 at 95% CI: 1.22, 4.07), history of ocular trauma (AOR : 1.80 at 95%CI:1.11,3.18 and uncorrected refractive error (AOR:2.01 at 95%CI:1.13,4.03). Conclusion and recommendations: This study showed the prevalence of near vision impairment among school teachers was high, and it is not a problem of the presbyopia age group alone; it also happens at a young age. So teachers' ocular health should be well accommodated in the school's eye health.

Keywords: Gondar, near visual impairment, school, teachers

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Authors: Maurice Bilioniere, Katie Lanneau

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Suddenly appeared in 2011, invasions of sargasso seaweeds Fluitans and Natans are a climatic hazard which causes many problems in the Caribbean. Faced with the growth and frequency of the phenomenon of massive sargasso stranding on their coasts, the French West Indies are moving towards the path of industrial recovery. In this context of innovative projects, we will analyze the necessary requirements for the management and performance of the supply chain, taking into account the observed volatility of the sargasso input. Our prospective approach will consist in studying the theoretical framework of modeling a hybrid supply chain by coupling the discreet event simulation (DES) with a valuation of the process costs according to the "activity-based costing" method (ABC). The PREVA approach (PRocess EVAluation) chosen for our modeling has the advantage of evaluating the financial flows of the logistic process using an analytical model chained with an action model for the evaluation or optimization of physical flows.

Keywords: sargasso, PREVA modeling, supply chain, ABC method, discreet event simulation (DES)

Procedia PDF Downloads 161

Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Atefeh Esmailnejad, Mohammad Abbaszadeh Hasiri

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Coxiella burnetii is a gram-negative obligatory intracellular bacterium that causes Q fever, a significant zoonotic disease. Sheep, cattle, and goats are the most commonly reported reservoirs for the bacteria, but infected cats and dogs have also been implicated in the transmission of the disease to human. The aim of present study was to investigate the presence of antibodies against Coxiella burnetii among companion dogs in Fars province, South of Iran. A total of 181 blood samples were collected from asymptomatic dogs, mostly referred to Veterinary Hospital of Shiraz University for regular vaccination. The IgG antibody detection against Coxiella burnetii was made by indirect Enzyme-linked Immunosorbent Assay (ELISA), employing phase I and II Coxiella burnetii antigens. A logistic regression model was developed to analyze multiple risk factors associated with seropositivity. An overall seropositivity of 7.7% (n=14) was observed. Prevalence was significantly higher in adult dogs above five years (18.18 %) compared with dogs between 1 and five years (7.86 %) and less than one year (6.17%) (P=0.043). Prevalence was also higher in male dogs (11.21 %) than in female (2.7 %) (P=0.035). There were no significant differences in the prevalence of positive cases and breed, type of housing, type of food and exposure to other farm animals (P>0.05). The results of this study showed the presence of Coxiella burnetii infection among the companion dogs population in Fars province. To our knowledge, this is the first study regarding Q fever in dogs carried out in Iran. In areas like Iran, where human cases of Q fever are not common or remain unreported, the public health implications of Q fever seroprevalence in dogs are quite significant.

Keywords: Coxiella burnetii, dog, Iran, Q fever

Procedia PDF Downloads 290