Search results for: double log regression analysis

Authors: Kuan Lun Pan, Moo Been Chang

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Volatile organic compounds (VOCs) are one of major air contaminants, and they can react with nitrogen oxides (NOx) in atmosphere to form ozone (O3) and peroxyacetyl nitrate (PAN) with solar irradiation, leading to environmental hazards. In addition, some VOCs are toxic at low concentration levels and cause adverse effects on human health. How to effectively reduce VOCs emission has become an important issue. Thermal catalysis is regarded as an effective way for VOCs removal because it provides oxidation route to successfully convert VOCs into carbon dioxide (CO2) and water (H2O(g)). Single perovskite-type catalysts are promising for VOC removal, and they are of good potential to replace noble metals due to good activity and high thermal stability. Single perovskites can be generally described as ABO3 or A2BO4, where A-site is often a rare earth element or an alkaline. Typically, the B-site is transition metal cation (Fe, Cu, Ni, Co, or Mn). Catalytic properties of perovskites mainly rely on nature, oxidation states and arrangement of B-site cation. Interestingly, single perovskites could be further synthesized to form double perovskite-type catalysts which can simply be represented by A2B’B”O6. Likewise, A-site stands for an alkaline metal or rare earth element, and the B′ and B′′ are transition metals. Double perovskites possess unique surface properties. In structure, three-dimensional of B-site with ordered arrangement of B’O6 and B”O6 is presented alternately, and they corner-share octahedral along three directions of the crystal lattice, while cations of A-site position between the void of octahedral. It has attracted considerable attention due to specific arrangement of alternating B-site structure. Therefore, double perovskites may have more variations than single perovskites, and this greater variation may promote catalytic performance. It is expected that activity of double perovskites is higher than that of single perovskites toward VOC removal. In this study, double perovskite-type catalyst (La2CoMnO6) is prepared and evaluated for VOC removal. Also, single perovskites including LaCoO3 and LaMnO3 are tested for the comparison purpose. Toluene (C7H8) is one of the important VOCs which are commonly applied in chemical processes. In addition to its wide application, C7H8 has high toxicity at a low concentration. Therefore, C7H8 is selected as the target compound in this study. Experimental results indicate that double perovskite (La2CoMnO6) has better activity if compared with single perovskites. Especially, C7H8 can be completely oxidized to CO2 at 300oC as La2CoMnO6 is applied. Characterization of catalysts indicates that double perovskite has unique surface properties and is of higher amounts of lattice oxygen, leading to higher activity. For durability test, La2CoMnO6 maintains high C7H8 removal efficiency of 100% at 300oC and 30,000 h-1, and it also shows good resistance to CO2 (5%) and H2O(g) (5%) of gas streams tested. For various VOCs including isopropyl alcohol (C3H8O), ethanal (C2H4O), and ethylene (C2H4) tested, as high as 100% efficiency could be achieved with double perovskite-type catalyst operated at 300℃, indicating that double perovskites are promising catalysts for VOCs removal, and possible mechanisms will be elucidated in this paper.

Keywords: volatile organic compounds, Toluene (C7H8), double perovskite-type catalyst, catalysis

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Authors: Ebrahim Behrouzian Nejad, Rezvan Alipoor Sabzevari

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Cloud computing systems are large-scale distributed systems, so that they focus more on large scale resource sharing, cooperation of several organizations and their use in new applications. One of the main challenges in this realm is resource allocation. There are many different ways to resource allocation in cloud computing. One of the common methods to resource allocation are economic methods. Among these methods, the auction-based method has greater prominence compared with Fixed-Price method. The double combinatorial auction is one of the proper ways of resource allocation in cloud computing. This method includes two phases: winner determination and resource allocation. In this paper a new method has been presented to determine winner in double combinatorial auction-based resource allocation using Imperialist Competitive Algorithm (ICA). The experimental results show that in our new proposed the number of winner users is higher than genetic algorithm. On other hand, in proposed algorithm, the number of winner providers is higher in genetic algorithm.

Keywords: cloud computing, resource allocation, double auction, winner determination

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Authors: Wanatchapong Kongkaew

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This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Nermin Sökmen

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An effort estimation model is needed for software-intensive projects that consist of hardware, embedded software or some combination of the two, as well as high level software solutions. This paper first focuses on functional decomposition techniques to measure functional complexity of a computer system and investigates its impact on system development effort. Later, it examines effects of technical difficulty and design team capability factors in order to construct the best effort estimation model. With using traditional regression analysis technique, the study develops a system development effort estimation model which takes functional complexity, technical difficulty and design team capability factors as input parameters. Finally, the assumptions of the model are tested.

Keywords: functional complexity, functional decomposition, development effort, technical difficulty, design team capability, regression analysis

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Authors: G. I. Onwuka, A. Danbaba, S. U. Gulumbe

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This study investigated of Infant Mortality Rate as observed at a general hospital in Kaduna-South, Kaduna State, North West Nigeria. The causes of infant Mortality were examined. The data used for this analysis were collected at the statistics unit of the Hospital. The analysis was carried out on the data using Multiple Linear regression Technique and this showed that there is linear relationship between the dependent variable (death) and the independent variables (malaria, measles, anaemia, and coronary heart disease). The resultant model also revealed that a unit increment in each of these diseases would result to a unit increment in death recorded, 98.7% of the total variation in mortality is explained by the given model. The highest number of mortality was recorded in July, 2005 and the lowest mortality recorded in October, 2009.Recommendations were however made based on the results of the study.

Keywords: infant mortality rate, multiple linear regression, diseases, serial correlation

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Authors: Rita Ghosh, Joykrishna Dey

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PEG-based amphiphiles are of tremendous importance for its widespread applications in pharmaceutics, household purposes, and drug delivery. Previously, a number of single PEG-tailed amphiphiles having significant applications have been reported from our group. Therefore, it was of immense interest to explore the properties and application potential of PEG-based double tailed amphiphiles. Herein, for the first time, two novel double PEG-tailed amphiphiles having different PEG chain lengths have been developed. The self-assembly behavior of the newly developed amphiphiles in aqueous buffer (pH 7.0) was thoroughly investigated at 25 oC by a number of techniques including, 1H-NMR, and steady-state and time-dependent fluorescence spectroscopy, dynamic light scattering, transmission electron microscopy, atomic force microscopy, and isothermal titration calorimetry. Despite having two polar PEG chains both molecules were found to have strong tendency to self-assemble in aqueous buffered solution above a very low concentration. Surprisingly, the amphiphiles were shown to form stable vesicles spontaneously at room temperature without any external stimuli. The results of calorimetric measurements showed that the vesicle formation is driven by the hydrophobic effect (positive entropy change) of the system, which is associated with the helix-to-random coil transition of the PEG chain. The spectroscopic data confirmed that the bilayer membrane of the vesicles is constituted by the PEG chains of the amphiphilic molecule. Interestingly, the vesicles were also found to exhibit structural transitions upon addition of salts in solution. These properties of the vesicles enable them as potential candidate for drug delivery.

Keywords: double-tailed amphiphiles, fluorescence, microscopy, PEG, vesicles

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Authors: Ashwani Kumar

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Abstract- This study presents an analytical modeling approach for analyzing the drain current behavior in Tunnel Field-Effect Transistor (TFET) biosensors used for the detection of DNA biomolecules. The proposed model focuses on elucidating the relationship between the drain current and the presence of DNA biomolecules, taking into account the impact of various device parameters and biomolecule characteristics. Through comprehensive analysis, the model offers insights into the underlying mechanisms governing the sensing performance of TFET biosensors, aiding in the optimization of device design and operation. A non-local tunneling model is incorporated with other essential models to accurately trace the simulation and modeled data. An experimental validation of the model is provided, demonstrating its efficacy in accurately predicting the drain current response to DNA biomolecule detection. The sensitivity attained from the analytical model is compared and contrasted with the ongoing research work in this area.

Keywords: biosensor, double-gate TFET, DNA detection, drain current modeling, sensitivity

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Authors: Christos Lampropoulos, Maria Konsta, Ifigenia Apostolou, Vicky Dradaki, Tamta Sirbilatze, Irini Dri, Christina Kordali, Vaggelis Lambas, Kostas Argyros, Georgios Mavras

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Objectives: Mediterranean diet has been associated with lower incidence of cardiovascular disease and cancer. The purpose of our study was to examine the hypothesis that Mediterranean diet may protect against mortality and reduce admission duration in elderly, hospitalized patients. Methods: Sample population included 150 patients (78 men, 72 women, mean age 80±8.2). The following data were taken into account in analysis: anthropometric and laboratory data, dietary habits (MedDiet score), patients’ nutritional status [Mini Nutritional Assessment (MNA) score], physical activity (International Physical Activity Questionnaires, IPAQ), smoking status, cause and duration of current admission, medical history (co-morbidities, previous admissions). Primary endpoints were mortality (from admission until 6 months afterwards) and duration of admission, compared to national guidelines for closed consolidated medical expenses. Logistic regression and linear regression analysis were performed in order to identify independent predictors for mortality and admission duration difference respectively. Results: According to MNA, nutrition was normal in 54/150 (36%) of patients, 46/150 (30.7%) of them were at risk of malnutrition and the rest 50/150 (33.3%) were malnourished. After performing multivariate logistic regression analysis we found that the odds of death decreased 30% per each unit increase of MedDiet score (OR=0.7, 95% CI:0.6-0.8, p < 0.0001). Patients with cancer-related admission were 37.7 times more likely to die, compared to those with infection (OR=37.7, 95% CI:4.4-325, p=0.001). According to multivariate linear regression analysis, admission duration was inversely related to Mediterranean diet, since it is decreased 0.18 days on average for each unit increase of MedDiet score (b:-0.18, 95% CI:-0.33 - -0.035, p=0.02). Additionally, the duration of current admission increased on average 0.83 days for each previous hospital admission (b:0.83, 95% CI:0.5-1.16, p<0.0001). The admission duration of patients with cancer was on average 4.5 days higher than the patients who admitted due to infection (b:4.5, 95% CI:0.9-8, p=0.015). Conclusion: Mediterranean diet adequately protects elderly, hospitalized patients against mortality and reduces the duration of hospitalization.

Keywords: Mediterranean diet, malnutrition, nutritional status, prognostic factors for mortality

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Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

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Authors: Mohamed Youssef

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The proliferation of energy consumption in the built environment has made energy efficiency and savings strategies a priority objective for energy policies in most countries. Qatar is a clear example, where it has initiated several programs and institutions to mitigate the overuse of electricity consumption and control the energy load of the building by following global standards and spreading awareness campaigns. A Case study on the Office of Research and Graduate Studies at Qatar University has been investigated in this paper. The paper studied the rating load of existing buildings before and after retrofitting by using Carrier’s Hourly Analysis Program (HAP). The performance of the building has increased especially after using the LED light system instead of fluorescent light with a low payback period. GINAN paint and green roof have shown a considerable contribution to the reduction of electrical load in the building. In comparison, the double HR window had the least effect on the reduction of electricity consumption.

Keywords: energy conservation in Qatar, HAP, LED light, GINAN paint, green roof, double HR window

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Authors: Svetlana Gusarova

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The essential role in the recent development of world economy has led to the developing countries, notably to BRICS countries (Brazil, Russia, India, China, South Africa). Over the next 50 years the BRICS countries are expected to be the engines of global trade and economic growth. Trade cooperation of BRICS countries can enhance their economic development. BRICS countries were among Top 10 world exporters of office and telecom equipment, of textiles, of clothing, of iron and steel, of chemicals, of agricultural products, of automotive products, of fuel and mining products. China was one of the main trading partners of all BRICS countries, maintaining close relationship with all BRICS countries in the development of trade. Author analyzed trade complementarity of BRICS countries and revealed the high level of complementarity of their trade flows in connection with availability of specialization in different types of goods. The correlation and regression analysis of communication of Intra-BRICS merchandise turnover and their GDP (PPP) revealed very strong impact on the development of their economies.

Keywords: BRICS countries, trade cooperation, complementarity, regression analysis

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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: Illyas Md Isa, Sharifah Norain Mohd Sharif, Norhayati Hashima

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A mercury(II) sensor was developed by using multi-walled carbon nanotubes (MWCNTs) paste electrode modified with Zn/Al layered double hydroxide-3(4-methoxyphenyl)propionate nanocomposite (Zn/Al-HMPP). The optimum conditions by cyclic voltammetry were observed at electrode composition 2.5% (w/w) of Zn/Al-HMPP/MWCNTs, 0.4 M potassium chloride, pH 4.0, and scan rate of 100 mVs-1. The sensor exhibited wide linear range from 1x10-3 M to 1x10-7 M Hg2+ and 1x10-7 M to 1x10-9 M Hg2+, with a detection limit of 1x10-10 M Hg2+. The high sensitivity of the proposed electrode towards Hg(II) was confirmed by double potential-step chronocoulometry which indicated these values; diffusion coefficient 1.5445 x 10-9 cm2 s-1, surface charge 524.5 µC s-½ and surface coverage 4.41 x 10-2 mol cm-2. The presence of 25-fold concentration of most metal ions had no influence on the anodic peak current. With characteristics such as high sensitivity, selectivity and repeatability the electrode was then proposed as the appropriate alternative for the determination of mercury(II).

Keywords: cyclic voltammetry, mercury(II), modified carbon paste electrode, nanocomposite

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Authors: Adil Kaval, Digdem Muge Siyez

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With the negative effects of internet use in a person's life, the use of the Internet has become an issue. This subject was mostly considered as internet addiction, and it was investigated. In literature, it is noteworthy that some theoretical models have been proposed to explain the reasons for internet addiction. In addition to these theoretical models, it may be thought that the coping style for stressing events can be a predictor of internet addiction. It was aimed to test with logistic regression the effect of high school students' coping styles on internet addiction levels. Sample of the study consisted of 770 Turkish adolescents (471 girls, 299 boys) selected from high schools in the 2017-2018 academic year in İzmir province. Internet Addiction Test, Coping Scale for Child and Adolescents and a demographic information form were used in this study. The results of the logistic regression analysis indicated that the model of coping styles predicted internet addiction provides a statistically significant prediction of internet addiction. Gender does not predict whether or not to be addicted to the internet. The active coping style is not effective on internet addiction levels, while the avoiding and negative coping style are effective on internet addiction levels. With this model, % 79.1 of internet addiction in high school is estimated. The Negelkerke pseudo R2 indicated that the model accounted for %35 of the total variance. The results of this study on Turkish adolescents are similar to the results of other studies in the literature. It can be argued that avoiding and negative coping styles are important risk factors in the development of internet addiction.

Keywords: adolescents, coping, internet addiction, regression analysis

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: K. K. Lee, H. W. Han, H. L. Kang, T. A. Kim, S. H. Han

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For the robust optimization of the manufacturing product design, there are design objectives that must be achieved, such as a minimization of the mean and standard deviation in objective functions within the required sensitivity constraints. The authors utilized the sensitivity of objective functions and constraints with respect to the effective design variables to reduce the computational burden associated with the evaluation of the probabilities. The individual mean and sensitivity values could be estimated easily by using the 9 level orthogonal array based response surface models optimized by the stepwise regression. The present study evaluates a proposed procedure from the robust optimization of rubber domes that are commonly used for keyboard switching, by using the 9 level orthogonal array and stepwise regression along with a desirability function. In addition, a new robust optimization process, i.e., the I2GEO (Identify, Integrate, Generate, Explore and Optimize), was proposed on the basis of the robust optimization in rubber domes. The optimized results from the response surface models and the estimated results by using the finite element analysis were consistent within a small margin of error. The standard deviation of objective function is decreasing 54.17% with suggested sensitivity based robust optimization. (Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2017, S2455569)

Keywords: objective function, orthogonal array, response surface model, robust optimization, stepwise regression

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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: Valerio D’Andrea

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The GERmanium Detector Array (GERDA) experiment, located at the Laboratori Nazionali del Gran Sasso (LNGS) of INFN, searches for 0νββ of 76Ge. Germanium diodes enriched to ∼ 86 % in the double beta emitter 76Ge(enrGe) are exposed being both source and detectors of 0νββ decay. Neutrinoless double beta decay is considered a powerful probe to address still open issues in the neutrino sector of the (beyond) Standard Model of particle Physics. Since 2013, just after the completion of the first part of its experimental program (Phase I), the GERDA setup has been upgraded to perform its next step in the 0νββ searches (Phase II). Phase II aims to reach a sensitivity to the 0νββ decay half-life larger than 1026 yr in about 3 years of physics data taking. This exposing a detector mass of about 35 kg of enrGe and with a background index of about 10^−3 cts/(keV·kg·yr). One of the main new implementations is the liquid argon scintillation light read-out, to veto those events that only partially deposit their energy both in Ge and in the surrounding LAr. In this paper, the GERDA Phase II expected goals, the upgrade work and few selected features from the 2015 commissioning and 2016 calibration runs will be presented. The main Phase I achievements will be also reviewed.

Keywords: gerda, double beta decay, LNGS, germanium

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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: Illyas Md Isa, Sharifah Norain Mohd Sharif, Norhayati Hashim

Abstract:

A mercury(II) sensor was developed by using multi-walled carbon nano tubes (MWCNTs) paste electrode modified with Zn/Al layered double hydroxide-3(4-methoxyphenyl) propionate nano composite (Zn/Al-HMPP). The optimum conditions by cyclic voltammetry were observed at electrode composition 2.5% (w/w) of Zn/Al-HMPP/MWCNTs, 0.4 M potassium chloride, pH 4.0, and scan rate of 100 mVs-1. The sensor exhibited wide linear range from 1x10-3 M to 1x10-7 M Hg2+ and 1x10-7 M to 1x10-9 M Hg2+, with a detection limit of 1 x 10-10 M Hg2+. The high sensitivity of the proposed electrode towards Hg(II) was confirmed by double potential-step chronocoulometry which indicated these values; diffusion coefficient 1.5445 x 10-9 cm2 s-1, surface charge 524.5 µC s-½ and surface coverage 4.41 x 10-2 mol cm-2. The presence of 25-fold concentration of most metal ions had no influence on the anodic peak current. With characteristics such as high sensitivity, selectivity and repeatability the electrode was then proposed as the appropriate alternative for the determination of mercury.

Keywords: Cyclic voltammetry, Mercury(II), Modified carbon paste electrode, Nanocomposite

Procedia PDF Downloads 407

Authors: Oussema Tabbabi, Mondher Laabidi, Fethi Choubani, J. David

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This study presents a miniaturized antenna based on chiral metamaterials slotted ground plane. To decrease resonant frequency while keeping the antennas physical dimensions the same, we propose a two novel patch antennas with double Z and cross slots on the ground plane. The length of the each type of slot are also altered to investigate the effect on miniaturization performance. Resonance frequency reduction has been achieved nearly to 30% and 23% as well as size reduction of almost 28% and 22% for the double Z and the cross shape respectively.

Keywords: chiral metamaterials, miniaturized antenna, miniaturization, resonance frequency

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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: Kiurski S. Jelena, Aksentijević M. Snežana, Kecić S. Vesna, Oros B. Ivana

Abstract:

The prosperity of electronic equipment in photocopying environment not only has improved work efficiency, but also has changed indoor air quality. Considering the number of photocopying employed, indoor air quality might be worse than in general office environments. Determining the contribution from any type of equipment to indoor air pollution is a complex matter. Non-methane hydrocarbons are known to have an important role of air quality due to their high reactivity. The presence of hazardous pollutants in indoor air has been detected in one photocopying shop in Novi Sad, Serbia. Air samples were collected and analyzed for five days, during 8-hr working time in three-time intervals, whereas three different sampling points were determined. Using multiple linear regression model and software package STATISTICA 10 the concentrations of occupational hazards and micro-climates parameters were mutually correlated. Based on the obtained multiple coefficients of determination (0.3751, 0.2389, and 0.1975), a weak positive correlation between the observed variables was determined. Small values of parameter F indicated that there was no statistically significant difference between the concentration levels of non-methane hydrocarbons and micro-climates parameters. The results showed that variable could be presented by the general regression model: y = b0 + b1xi1+ b2xi2. Obtained regression equations allow to measure the quantitative agreement between the variation of variables and thus obtain more accurate knowledge of their mutual relations.

Keywords: non-methane hydrocarbons, photocopying process, multiple regression analysis, indoor air quality, pollutant emission

Procedia PDF Downloads 356

Authors: Fatemeh Roostaee, Zahra Nikmanesh

Abstract:

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

Procedia PDF Downloads 564

Authors: Lluís Bermúdez, Isabel Morillo

Abstract:

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

Procedia PDF Downloads 105

Authors: Abolfazl Hosseinkhani, Sepehr Sanaye

Abstract:

Different approaches have been used to predict the performance of the vertical axis wind turbines (VAWT), such as experimental, computational fluid dynamics (CFD), and analytical methods. Analytical methods, such as momentum models that use streamtubes, have low computational cost and sufficient accuracy. The double multiple streamtube (DMST) is one of the most commonly used of momentum models, which divide the rotor plane of VAWT into upwind and downwind. In fact, results from the DMST method have shown some discrepancy compared with experiment results; that is because the Darrieus turbine is a complex and aerodynamically unsteady configuration. In this study, analytical-experimental-based corrections, including dynamic stall, streamtube expansion, and finite blade length correction are used to improve the DMST method. Results indicated that using these corrections for a SANDIA 17-m VAWT will lead to improving the results of DMST.

Keywords: vertical axis wind turbine, analytical, double multiple streamtube, streamtube expansion model, dynamic stall model, finite blade length correction

Procedia PDF Downloads 116

Authors: Kazeem Adesina Dauda, Waheed Babatunde Yahya

Abstract:

To design a tree that maximizes within-node homogeneity, there is a need for a homogeneity measure that is appropriate for event history data with multiple risks. We consider the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in Classification Regression Tree (CART) and compare our results with the results of Fiona (2008) in which homogeneity measures were based on Martingale Residual. Data structure approach was used to validate the performance of our proposed techniques via simulation and real life data. The results of univariate competing risk revealed that: using Deviance and Cox-Snell residuals as a response in within node homogeneity classification tree perform better than using other residuals irrespective of performance techniques. Bone marrow transplant data and double-blinded randomized clinical trial, conducted in other to compare two treatments for patients with prostate cancer were used to demonstrate the efficiency of our proposed method vis-à-vis the existing ones. Results from empirical studies of the bone marrow transplant data showed that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822) in both event of interest and competing risks. Additionally, results from prostate cancer also reveal the performance of proposed model over the existing one in both causes, interestingly, Cox-Snell residual (MSE=0.01783563) outfit both the Martingale residual (MSE=0.1853148) and Deviance residual (MSE=0.8043366). Moreover, these results validate those obtained from the Monte-Carlo studies.

Keywords: within-node homogeneity, Martingale residual, modified Cox-Snell residual, classification and regression tree

Procedia PDF Downloads 250

Authors: Saleem Z. Ramadan

Abstract:

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

Procedia PDF Downloads 541