Search results for: multivariate regression analysis

Authors: S. Deswal, M. Pal

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The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90O; whereas, multiple inclined plunging jets have jet impact angle of θ = 600. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (KLa) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modelling mass transfer by multiple plunging jets.

Keywords: mass transfer, multiple plunging jets, multi-linear regression, earth sciences

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Authors: Osman Zorbaz, Selen Demirtas-Zorbaz, Ozlem Ulas

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The factors that cause adolescents to drop out school were several. One of the frameworks about school dropout focuses on the contextual factors around the adolescents whereas the other one focuses on individual factors. It can be said that both factors are important equally. In this study, both adolescent’s individual factors (anti-social behaviors, academic success) and contextual factors (parent academic involvement, parent academic support, number of siblings, living with parent) were examined in the term of school dropout. The study sample consisted of 346 high school students in the public schools in Ankara who continued their education in 2015-2016 academic year. One hundred eighty-five the students (53.5%) were girls and 161 (46.5%) were boys. In addition to this 118 of them were in ninth grade, 122 of them in tenth grade and 106 of them were in eleventh grade. Multiple regression and one-way ANOVA statistical methods were used. First, it was examined if the data meet the assumptions and conditions that are required for regression analysis. After controlling the assumptions, regression analysis was conducted. Parent academic involvement, parent academic support, number of siblings, anti-social behaviors, academic success variables were taken into the regression model and it was seen that parent academic involvement (t=-3.023, p < .01), anti-social behaviors (t=7.038, p < .001), and academic success (t=-3.718, p < .001) predicted school dropout whereas parent academic support (t=-1.403, p > .05) and number of siblings (t=-1.908, p > .05) didn’t. The model explained 30% of the variance (R=.557, R2=.300, F5,345=30.626, p < .001). In addition to this the variance, results showed there was no significant difference on high school students school dropout levels according to living with parents or not (F2;345=1.183, p > .05). Results discussed in the light of the literature and suggestion were made. As a result, academic involvement, academic success and anti-social behaviors will be considered as an important factors for preventing school drop-out.

Keywords: adolescents, anti-social behavior, parent academic involvement, parent academic support, school dropout

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Authors: Nzereogu Stella Kosi, Igwe Ogbonnaya, Emeh Chukwuebuka Odinaka

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A comparative analysis of the expansion rate and soil erodibility of some gullies in Nnewi and Nnobi both of Nanka Formation were studied. The study involved an integration of field observations, geotechnical analysis, slope stability analysis, multivariate statistical analysis, gully expansion rate analysis, and determination of the soil erodibility factor (K) from Revised Universal Soil Loss Equation (RUSLE). Fifteen representative gullies were studied extensively, and results reveal that the geotechnical properties of the soil, topography, vegetation cover, rainfall intensity, and the anthropogenic activities in the study area were major factors propagating and influencing the erodibility of the soils. The specific gravity of the soils ranged from 2.45-2.66 and 2.54-2.78 for Nnewi and Nnobi, respectively. Grain size distribution analysis revealed that the soils are composed of gravel (5.77-17.67%), sand (79.90-91.01%), and fines (2.36-4.05%) for Nnewi and gravel (7.01-13.65%), sand (82.47-88.67%), and fines (3.78-5.02%) for Nnobi. The soils are moderately permeable with values ranging from 2.92 x 10-5 - 6.80 x 10-4 m/sec and 2.35 x 10-6 - 3.84 x 10⁻⁴m/sec for Nnewi and Nnobi respectively. All have low cohesion values ranging from 1–5kPa and 2-5kPa and internal friction angle ranging from 29-38° and 30-34° for Nnewi and Nnobi, respectively, which suggests that the soils have low shear strength and are susceptible to shear failure. Furthermore, the compaction test revealed that the soils were loose and easily erodible with values of maximum dry density (MDD) and optimum moisture content (OMC) ranging from 1.82-2.11g/cm³ and 8.20-17.81% for Nnewi and 1.98-2.13g/cm³ and 6.00-17.80% respectively. The plasticity index (PI) of the fines showed that they are nonplastic to low plastic soils and highly liquefiable with values ranging from 0-10% and 0-9% for Nnewi and Nnobi, respectively. Multivariate statistical analyses were used to establish relationship among the determined parameters. Slope stability analysis gave factor of safety (FoS) values in the range of 0.50-0.76 and 0.82-0.95 for saturated condition and 0.73-0.98 and 0.87-1.04 for unsaturated condition for both Nnewi and Nnobi, respectively indicating that the slopes are generally unstable to critically stable. The erosion expansion rate analysis for a fifteen-year period (2005-2020) revealed an average longitudinal expansion rate of 36.05m/yr, 10.76m/yr, and 183m/yr for Nnewi, Nnobi, and Nanka type gullies, respectively. The soil erodibility factor (K) are 8.57x10⁻² and 1.62x10-4 for Nnewi and Nnobi, respectively, indicating that the soils in Nnewi have higher erodibility potentials than those of Nnobi. From the study, both the Nnewi and Nnobi areas are highly prone to erosion. However, based on the relatively lower fine content of the soil, relatively lower topography, steeper slope angle, and sparsely vegetated terrain in Nnewi, soil erodibility and gully intensity are more profound in Nnewi than Nnobi.

Keywords: soil erodibility, gully expansion, nnewi-nnobi, slope stability, factor of safety

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Authors: Liu Yao, B. T. Wan Maseri, Wan Mohd, B. T. Nurul Izzah, Mohd Shah, Wei Wei

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Effectively managing knowledge has become a vital weapon for businesses to survive or to succeed in the increasingly competitive market. But do they perform environmental analysis when managing knowledge? If yes, how is the level and significance? This paper established a conceptual framework covering the basic knowledge management activities (KMA) to examine their contribution towards organizational performance (OP). Environmental analysis (EA) was then investigated from both internal and external aspects, to identify its effects on that contribution. Data was collected from 400 Chinese SMEs by questionnaires. Cronbach's α and factor analysis were conducted. Regression results show that the external analysis presents higher level than internal analysis. However, the internal analysis mediates the effects of external analysis on the KMA-OP relation and plays more significant role in the relation comparing with the external analysis. Thus, firms shall improve environmental analysis especially the internal analysis to enhance their KM practices.

Keywords: knowledge management, environmental analysis, performance, mediating, small sized enterprises, medium sized enterprises

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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

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

Keywords: adolescents, anemia, correlates, obesity

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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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: Valeria Abreu Minero, Edward Barker, Hannah Dickson, Francois Husson, Sandra Flynn, Jennifer Shaw

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Purpose – The purpose of this paper is to identify offender typologies based on aspects of the offenders’ psychopathology and their associations with crime scene behaviours using data derived from the National Confidential Enquiry into Suicide and Safety in Mental Health concerning homicides in England and Wales committed by offenders in contact with mental health services in the year preceding the offence (n=759). Design/methodology/approach – The authors used multiple correspondence analysis to investigate the interrelationships between the variables and hierarchical agglomerative clustering to identify offender typologies. Variables describing: the offender’s mental health history; the offenders’ mental state at the time of offence; characteristics useful for police investigations; and patterns of crime scene behaviours were included. Findings – Results showed differences in the offender’s histories in relation to their crime scene behaviours. Further, analyses revealed three homicide typologies: externalising, psychosis and depression. Analyses revealed three homicide typologies: externalising, psychotic and depressive. Practical implications – These typologies may assist the police during homicide investigations by: furthering their understanding of the crime or likely suspect; offering insights into crime patterns; provide advice as to what an offender’s offence behaviour might signify about his/her mental health background; findings suggest information concerning offender psychopathology may be useful for offender profiling purposes in cases of homicide offenders with schizophrenia, depression and comorbid diagnosis of personality disorder and alcohol/drug dependence. Originality/value – Empirical studies with an emphasis on offender profiling have almost exclusively focussed on the inference of offender demographic characteristics. This study provides a first step in the exploration of offender psychopathology and its integration to the multivariate analysis of offence information for the purposes of investigative profiling of homicide by identifying the dominant patterns of mental illness within homicidal behaviour.

Keywords: offender profiling, mental illness, psychopathology, multivariate analysis, homicide, crime scene analysis, crime scene behviours, investigative advice

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Authors: Moses Balina, Archbald Bahizi

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Introduction: Early onset neonatal sepsis is a systemic infection in a newborn baby during the first week after birth and contributes to 50% of neonatal deaths each year. Risk factors for early onset neonatal sepsis, which can be maternal, health care provider, or health care facility associated, can be prevented with access to quality antenatal care. Objective: The objective of the study was to assess early onset neonatal sepsis and antenatal factors associated with Fort Portal Regional Referral Hospital. Methodology: A cross sectional study design was used. The study involved 60 respondents who were mothers of breastfeeding neonates being treated for early onset neonatal sepsis at Fort Portal Regional Referral Hospital neonatal intensive care unit. Simple random sampling was used to select study participants. Data were collected using questionnaires, entered in Stata 16, and analysed using logistic regression. Results: The prevalence of early onset neonatal sepsis at Fort Portal Regional Referral Hospital was 25%. Multivariate analysis revealed that institutional factors were the only antenatal factors found to be significantly associated with early onset neonatal sepsis at Fort Portal Regional Referral Hospital (p < 0.01). Bivariate analysis revealed that attending antenatal care at a health centre III or IV instead of a hospital (p = 0.011) and attending antenatal care in health care facilities with no laboratory investigations (p = 0.048) were risk factors for early onset neonatal sepsis in the newborn at Fort Portal Regional Referral Hospital. Conclusion: Antenatal factors were associated with early onset neonatal sepsis, and health care facility factors like lower level health centre and unavailability of quality laboratory investigations to pregnant women contributed to early onset neonatal sepsis in the newborn. Mentorships, equipping/stocking laboratories, and improving staffing levels were necessary to reduce early onset neonatal sepsis.

Keywords: antenatal factors, early onset neonatal sepsis, neonates 0-7 days, fort portal regional referral hospital

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Authors: Mensah-Akoto Julius, Kenichi Matsui

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This paper examines the impact of COVID-19 on waste management workers in Ghana. A questionnaire survey was conducted among 60 waste management workers in Accra metropolis, the capital region of Ghana, to understand the impact of the COVID-19 pandemic on waste generation, workers’ safety in collecting solid waste, and service delivery. To find out correlations between the pandemic and safety of waste management workers, a regression analysis was used. Regarding waste generation, the results show the pandemic led to the highest annual per capita solid waste generation, or 3,390 tons, in 2020. Regarding the safety of workers, the regression analysis shows a significant and inverse association between COVID-19 and waste management services. This means that contaminated wastes may infect field workers with COVID-19 due to their direct exposure. A rise in new infection cases would have a negative impact on the safety and service delivery of the workers. The result also shows that an increase in economic activities negatively impacts waste management workers. The analysis, however, finds no statistical relationship between workers’ service deliveries and employees’ salaries. The study then discusses how municipal waste management authorities can ensure safe and effective waste collection during the pandemic.

Keywords: Covid-19, waste management worker, waste collection, Ghana

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Authors: Joonas Pääkkönen

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In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.

Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling

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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: Tahani Salman Alangari

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Student success can be impacted by internal factors such as their emotional well-being and external factors such as organizational support and instructional support in the classroom. This study is to identify at least one factor that forecasts student engagement. It is a cross-sectional, conducted on 6206 teachers and encompassed three years of data collection and observations of math instruction in approximately 50 schools and 300 classrooms. A multiple linear regression revealed that a model predicting student engagement from emotional support, classroom organization, and instructional support was significant. Four linear regression models were tested using hierarchical regression to examine the effects of independent variables: emotional support was the highest predictor of student engagement while instructional support was the lowest.

Keywords: student engagement, emotional support, organizational support, instructional support, well-being

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Authors: Fidelis E. Uwumiro, Chimaobi O. Nwevo, Favour O. Osemwota, Victory O. Okpujie, Emeka S. Obi, Omamuyovbi F. Nwoagbe, Ejiroghene Tejere, Joycelyn Adjei-Mensah, Christopher N. Ekeh, Charles T. Ogbodo

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Several risk factors associated with cardiovascular events have been identified as specific to Chronic Kidney Disease (CKD). This study endeavors to validate these CKD-specific risk factors using up-to-date national-level data, thereby highlighting the crucial significance of confirming the validity and generalizability of findings obtained from previous studies conducted on smaller patient populations. The study utilized the nationwide inpatient sample database to identify adult hospitalizations for CKD from 2016 to 2020, employing validated ICD-10-CM/PCS codes. A comprehensive literature review was conducted to identify both traditional and CKD-specific risk factors associated with cardiovascular events. Risk factors and cardiovascular events were defined using a combination of ICD-10-CM/PCS codes and statistical commands. Only risk factors with specific ICD-10 codes and hospitalizations with complete data were included in the study. Cardiovascular events of interest included cardiac arrhythmias, sudden cardiac death, acute heart failure, and acute coronary syndromes. Univariate and multivariate regression models were employed to evaluate the association between chronic kidney disease-specific risk factors and cardiovascular events while adjusting for the impact of traditional CV risk factors such as old age, hypertension, diabetes, hypercholesterolemia, inactivity, and smoking. A total of 690,375 hospitalizations for CKD were included in the analysis. The study population was predominantly male (375,564, 54.4%) and primarily received care at urban teaching hospitals (512,258, 74.2%). The mean age of the study population was 61 years (SD 0.1), and 86.7% (598,555) had a CCI of 3 or more. At least one traditional risk factor for CV events was present in 84.1% of all hospitalizations (580,605), while 65.4% (451,505) included at least one CKD-specific risk factor for CV events. The incidence of CV events in the study was as follows: acute coronary syndromes (41,422; 6%), sudden cardiac death (13,807; 2%), heart failure (404,560; 58.6%), and cardiac arrhythmias (124,267; 18%). 91.7% (113,912) of all cardiac arrhythmias were atrial fibrillations. Significant odds of cardiovascular events on multivariate analyses included: malnutrition (aOR: 1.09; 95% CI: 1.06–1.13; p<0.001), post-dialytic hypotension (aOR: 1.34; 95% CI: 1.26–1.42; p<0.001), thrombophilia (aOR: 1.46; 95% CI: 1.29–1.65; p<0.001), sleep disorder (aOR: 1.17; 95% CI: 1.09–1.25; p<0.001), and post-renal transplant immunosuppressive therapy (aOR: 1.39; 95% CI: 1.26–1.53; p<0.001). The study validated malnutrition, post-dialytic hypotension, thrombophilia, sleep disorders, and post-renal transplant immunosuppressive therapy, highlighting their association with increased risk for cardiovascular events in CKD patients. No significant association was observed between uremic syndrome, hyperhomocysteinemia, hyperuricemia, hypertriglyceridemia, leptin levels, carnitine deficiency, anemia, and the odds of experiencing cardiovascular events.

Keywords: cardiovascular events, cardiovascular risk factors in CKD, chronic kidney disease, nationwide inpatient sample

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Authors: Jacinta Musyoka, Wesley Too

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Cervical cancer is the second leading cause of cancer-related deaths in Kenya and the second most common cancer among women, yet preventable following prevention strategies put in place, which includes vaccination with Human Papilloma Virus Vaccine (HPPV) among the young adolescent girls. Kenya has the highest burden of cervical cancer and the leading cause of death among women of reproductive age and is a known frequent type of cancer amongst women. This is expected to double by 2025 if the necessary steps are not taken, which include vaccinating girls between the ages of 9 and 14 and screening women. Parental decision is critical in ensuring that their daughters receive this vaccine. Hence this study sought to establish parental willingness and factors associate with the acceptability to vaccine adolescent daughters against the human papilloma virus for cervical cancer prevention in Machakos County, Eastern Region of Kenya. Method: Cross-sectional study design utilizing a mixed methods approach was used to collect data from Nguluni Health Centre in Machakos County; Matungulu sub-county, Kenya. This study targeted all parents of adolescent girls seeking health care services in the Matungulu sub-county area who were aged 18 years and above. A total of 220 parents with adolescent girls aged 10-14 years were enrolled into the study after informed consent were sought. All ethical considerations were observed. Quantitative data were analyzed using Multivariate regression analysis, and thematic analysis was used for qualitative data related to perceptions of parents on HPVV. Results, conclusions, and recommendations- ongoing. We expect to report findings and articulate contributions based on the study findings in due course before October 2022

Keywords: adolescents, human papilloma virus, kenya, parents

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Authors: Mohammed-Hareef Asunramu, Sana Hashemi, Raja Ohri, Luc Worthington, Nadia Zaman, Junkai Zhu

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Background: The 2012 Health and Social Care Act committed to a ‘parity of esteem between mental and physical health services. Although this investment, aimed to both increase the quality of services and ensure the retention of mental health staff, questions remained regarding its ability to prevent mental health problems. One possible solution is a focus on the social determinants of health which have been shown to impact mental health. Aim: To examine the relationship between the index of multiple deprivations (IMD) and the prevalence of common mental health disorders (CMD) for CCGs in NHS England between 2019 and 2020. Design and setting: Cross-sectional analysis of 189 CCGs in NHS England. Methods: A multivariate linear regression model was utilized with CMD as outcome variable and IMD, age and ethnicity as explanatory variables. Datasets were obtained from Public Health England and the latest UK Census. Results: CCG IMD was found to have a significantly positive relationship with CMD. For every 1-point increase in IMD, CMD increases by 0.25%. Ethnicity had a significantly positive relationship with CMD. For every 1% increase in the population that identifies as BME, there is a 0.03% increase in CMD. Age had a significantly negative relationship with CMD. For every 1% increase in the population aged 60+, there is a 0.11% decrease in CMD. Conclusion: This study demonstrates that addressing mental health issues may require a multi-pronged approach. Beyond budget increases, it is essential to prioritize health equity, with careful considerations towards ethnic minorities and different age brackets.

Keywords: deprivation, health inequality, mental health, social determinants

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Authors: Altayeb Abdalla Ahmed

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Skeletal phenotype is a product of a balanced interaction between genetics and environmental factors throughout different life stages. Therefore, interlimb proportions are variable between populations. Although interlimb proportion indices have been used in anthropology in assessing the influence of various environmental factors on limbs, an extensive literature review revealed that there is a paucity of published research assessing interlimb part correlations and possibility of reconstruction. Hence, this study aims to assess the relationships between upper and lower limb parts and develop regression formulae to reconstruct the parts from one another. The left upper arm length, ulnar length, wrist breadth, hand length, hand breadth, tibial length, bimalleolar breadth, foot length, and foot breadth of 376 right-handed subjects, comprising 187 males and 189 females (aged 25-35 years), were measured. Initially, the data were analyzed using basic univariate analysis and independent t-tests; then sex-specific simple and multiple linear regression models were used to estimate upper limb parts from lower limb parts and vice-versa. The results of this study indicated significant sexual dimorphism for all variables. The results indicated a significant correlation between the upper and lower limbs parts (p < 0.01). Linear and multiple (stepwise) regression equations were developed to reconstruct the limb parts in the presence of a single or multiple dimension(s) from the other limb. Multiple stepwise regression equations generated better reconstructions than simple equations. These results are significant in forensics as it can aid in identification of multiple isolated limb parts particularly during mass disasters and criminal dismemberment. Although a DNA analysis is the most reliable tool for identification, its usage has multiple limitations in undeveloped countries, e.g., cost, facility availability, and trained personnel. Furthermore, it has important implication in plastic and orthopedic reconstructive surgeries. This study is the only reported study assessing the correlation and prediction capabilities between many of the upper and lower dimensions. The present study demonstrates a significant correlation between the interlimb parts in both sexes, which indicates a possibility to reconstruction using regression equations.

Keywords: anthropometry, correlation, limb, Sudanese

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Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Lluís Bermúdez, Isabel Morillo

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Road safety involves carrying out a determined and explicit policy to reduce accidents. In the city of Barcelona, through the Local Road Safety Plan 2013-2018, in line with the framework that has been established at the European and state level, a series of preventive, corrective and technical measures are specified, with the priority objective of reducing the number of serious injuries and fatalities. In this work, based on the data from the accidents managed by the local police during the period 2010-2019, an analysis is carried out to verify whether the measures established in the Plan to reduce the accident rate have had an effect or not and to what extent. The analysis focuses on the type of accident and the type of vehicles involved. Different count regression models have been fitted, from which it can be deduced that the number of serious and fatal victims of the accidents that have occurred in the city of Barcelona has been reduced as the measures approved by the authorities.

Keywords: accident reduction, count regression models, road safety, urban traffic

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

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

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

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Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the spark plug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria in terms of its GDP.

Keywords: maritime transport, economy, GDP, regression, port

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Authors: Phan Thi Song Thuong

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This paper uses data from a survey on the green consumption behavior of Hanoi residents in October 2022. Data was gathered from a survey conducted in ten districts in the center of Hanoi, with 393 respondents. The hypothesis focuses on understanding the factors that may affect green consumption behavior, such as demographic characteristics, concerns about the environment and health, people living around, self-efficiency, and mass media. A number of methods, such as the T-test, exploratory factor analysis, and a linear regression model, are used to prove the hypotheses. Accordingly, the results show that gender, age, and education level have separate effects on the green consumption behavior of respondents.

Keywords: green consumption, urban residents, environment, sustainable, linear regression

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Authors: Fatemeh Roostaee, Zahra Nikmanesh

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This study was done to investigate the role of religious coping components on occupational stress and the quality of working life of midwives. The method of study was descriptive-correlation. The sample was comprised of all midwives in maternity hospitals in Zahedan during 1393. Participants were selected through applying census method. The instruments of data collection were three questionnaires: the quality of working life, occupational stress, and religious opposition. For statistical analysis, Pearson correlation and step by step regression analysis methods were used. The results showed that there is a significant negative relationship between the component of religious activities (r=-0/454) and occupational stress, and regression analysis was also shown that the variable of religious activities has been explained 45% of occupational stress variable changes. The Pearson correlation test showed that there isn't any significant relationship between religious opposition components and the quality of life. Therefore, it is necessary to present essential trainings on (the field of) strengthening compatibility strategies and religious activities to reduce occupational stress.

Keywords: the quality of working life, occupational stress, religious, midwife

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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: Madhat Abu Al-Naeem, Ismail Yusoff, Ng Tham Fatt, Yatimah Alias

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Groundwater in Gaza strip is increasingly being exposed to anthropic and natural factors that seriously impacted the groundwater quality. Physiochemical data of groundwater can offer important information on changes in groundwater quality that can be useful in improving water management tactics. An integrative hydrochemical and statistical techniques (Hierarchical cluster analysis (HCA) and factor analysis (FA)) have been applied on the existence ten physiochemical data of 84 samples collected in (2000/2001) using STATA, AquaChem, and Surfer softwares to: 1) Provide valuable insight into the salinization sources and the hydrochemical processes controlling the chemistry of groundwater. 2) Differentiate the influence of natural processes and man-made activities. The recorded large diversity in water facies with dominance Na-Cl type that reveals a highly saline aquifer impacted by multiple complex hydrochemical processes. Based on WHO standards, only (15.5%) of the wells were suitable for drinking. HCA yielded three clusters. Cluster 1 is the highest in salinity, mainly due to the impact of Eocene saline water invasion mixed with human inputs. Cluster 2 is the lowest in salinity also due to Eocene saline water invasion but mixed with recent rainfall recharge and limited carbonate dissolution and nitrate pollution. Cluster 3 is similar in salinity to Cluster 2, but with a high diversity of facies due to the impact of many sources of salinity as sea water invasion, carbonate dissolution and human inputs. Factor analysis yielded two factors accounting for 88% of the total variance. Factor 1 (59%) is a salinization factor demonstrating the mixing contribution of natural saline water with human inputs. Factor 2 measure the hardness and pollution which explained 29% of the total variance. The negative relationship between the NO3- and pH may reveal a denitrification process in a heavy polluted aquifer recharged by a limited oxygenated rainfall. Multivariate statistical analysis combined with hydrochemical analysis indicate that the main factors controlling groundwater chemistry were Eocene saline invasion, seawater invasion, sewage invasion and rainfall recharge and the main hydrochemical processes were base ion and reverse ion exchange processes with clay minerals (water rock interactions), nitrification, carbonate dissolution and a limited denitrification process.

Keywords: dendrogram and cluster analysis, water facies, Eocene saline invasion and sea water invasion, nitrification and denitrification

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Authors: A. Bouzabata, A. Boukhari

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Curcuma longa L. (Zingiberaceae), commonly known as turmeric, has a long history of traditional uses for culinary purposes as a spice and a food colorant. The present study aimed to document the ethnobotanical knowledge about Curcuma longa and to assess the variation in the herbalists’ experience in Northeastern Algeria. Data were collected by semi-structured questionnaires and direct interviews with 30 herbalists. Ethnobotanical indices, including the fidelity level (FL%), the relative frequency citation (RFC) and use value (UV) were determined by quantitative methods. Diversity in the knowledge was analyzed using univariate, non-parametric and multivariate statistical methods. Three main categories of uses were recorded for C. longa: for food, for medicine and for cosmetic purposes. As a medicine, turmeric was used for the treatment of gastrointestinal, dermatological and hepatic diseases. Medicinal and food uses were correlated with both forms of use (rhizome and powder). The age group did not influence the use. Multivariate analyses showed a significant variation in traditional knowledge, associated with the use value, origin, quality and efficacy of the drug. These findings suggested that the geographical origin of C. longa affected the use in Algeria.

Keywords: curcuma, indices, knowledge, variation

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Authors: Encarnación Álvarez, Rosa M. García-Fernández, Juan F. Muñoz, Francisco J. Blanco-Encomienda

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The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index.

Keywords: poverty line, poor, risk of poverty, Monte Carlo simulations, sample

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Authors: Saima Shafiq, Najma Iqbal Malik

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This study was aimed to explore the relationship of perceived stigma, perception of burden and psychological distress among parents of intellectually disabled children. The study also aimed to explore the moderating role of perceived social support on all the variables of the study. The sample of the study comprised of (N = 250) parents of intellectually disabled children. The present study utilized the co-relational research design. It consists of two phases. Phase-I consisted of two steps which contained the translation of two scales that were used in the present study and tried out on the sample of parents (N = 70). The Affiliated Stigma Scale and Care Giver Burden Inventory were translated into Urdu for the present study. Phase-1 revealed that translated scaled entailed satisfactory psychometric properties. Phase -II of the study was carried out in order to test the hypothesis. Correlation, linear regression analysis, and t-test were computed for hypothesis testing. Hierarchical regression analysis was applied to study the moderating effect of perceived social support. Findings revealed that there was a positive relationship between perceived stigma and psychological distress, perception of burden and psychological distress. Linear regression analysis showed that perceived stigma and perception of burden were positive predictors of psychological distress. The study did not show the moderating role of perceived social support among variables of the present study. The major limitation of the study is the sample size and the major implication is awareness regarding problems of parents of intellectually disabled children.

Keywords: perceived stigma, perception of burden, psychological distress, perceived social support

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

Procedia PDF Downloads 52