Search results for: statistical arbitrage

Authors: Iva Buchberger, Vesna Kovač

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Global education policy trends and recommendations underline the importance of (distributive) school leadership as a school effectiveness key factor. In this context, the broader aim of this research (supported by the Croatian Science Foundation) is to identify school leadership characteristics in Croatian schools and to examine the correlation between school leadership and school effectiveness. The aim of the proposed conference paper is to focus on the school leadership characteristics which are additionally explained with school leadership facilitators that contribute to (distributive) school leadership development. The aforementioned school leadership characteristics include the following dimensions: (a) participation in the process of making different types of decisions, (b) influence in the decision making process, (c) social interactions between different stakeholders in the decision making process in schools. Further, the school leadership facilitators are categorized as follows: (a) principal’s activities (such as providing support to different stakeholders and developing mutual trust among them), (b) stakeholders’ characteristics (such as developed stakeholders’ interest and competence to participate in decision-making process), (c) organizational and material resources (such as school material conditions, the necessary information and time as resources for making decisions). The data were collected by a constructed and validated questionnaire for examining the school leadership characteristics and facilitators from teachers’ perspective. The main population in this study consists of all primary schools in Croatia while the sample is comprised of 100 primary schools, selected by random sampling. Furthermore, the sample of teachers was selected by an additional procedure taking into consideration the independent variables of sex, work experience, etc. Data processing was performed by standard statistical methods of descriptive and inferential statistics. Statistical program IBM SPSS 20.0 was used for data processing. The results of this study show that there is a (positive) correlation between school leadership characteristics and school leadership facilitators. Specifically, it is noteworthy to mention that all the dimensions of school leadership characteristics are in positive correlation with the categories of school leadership facilitators. These results are indicative for the education policy creators who should ensure positive and supportive environment for the school leadership development including the development of school leadership characteristics and school leadership facilitators.

Keywords: distributive school leadership, school effectiveness , school leadership characteristics, school leadership facilitators

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Authors: Majda Ibrahim Aljaroudi, Jwaher Alburake

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The study aimed to identify the active role of the teacher in the management of the effective classroom environment for high school students from the viewpoint of the teachers, and to identify whether there were statistically significant differences between the averages of the respondents regarding the active role of the high school teachers in managing effective classroom environment in Riyadh, and also the total score depending on the variables of the study (qualifications, years of experience, training and development programs). This study used the descriptive survey approach where a questionnaire has been built and consisted of (35) items about five areas as a tool to measure the teacher's role in the management of effective classroom environment for high school students. The study population consisted of (1313) high school teachers in the government schools in south of Riyadh. It consisted of (70) teachers who were selected randomly. It used the appropriate statistical methods to analyze data by using statistical packages (SPSS). The study found the following results: • Most of the study sample members agreed on their role in the effective classroom environment management for high school students in government schools in Riyadh with an average (3.91 out of 5), which falls in the fifth category of Quintet scale (from 3.41 to 4.20) that refers to the option "often". • There are statistically significant differences between the mean responses of the study sample about the active role of the teacher in the effective classroom environment management for high school students regarding the concept of order in the classroom depending on the variable of years of experience for the benefit of teachers who have over 10 years of experience. There are statistically significant differences between the mean responses of the study sample about the teacher's active role in the effective classroom environment management for high school students regarding the educational process for maintaining the order in the classroom depending on the variable of training and development programs for the benefit of the teachers who have more than (5) courses. Due to the results of the study the researcher recommended a number of recommendations to improve the teacher's role in the effective classroom environment management for high school students.

Keywords: effective management, active learning, educational sciences, pedagogical sciences

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

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Authors: Magda Milleyde de Sousa Lima, Letícia Lima Aguiar, Marina Guerra Martins, Erika Veríssimo Dias Sousa, Lizandra Sampaio de Oliveira, Lívia Moreira Barros, Joselany Áfio Caetano

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Patients with chronic kidney disease are vulnerable to episodes which make the safety of their health vulnerable, mainly due to the treatment process that exposes them to high rates of interventions during hemodialysis sessions. Some factors associated with health care contribute to the risk of death and complications. However, there are a small number of scientific studies evaluating the level of safety of hemodialysis clinics, and the sociodemographic characteristics of patients and professionals associated with this safety. Therefore, the present study aims to examine the level of patient safety in hemodialysis clinics in the Brazilian capital, to identify the sociodemographic and clinical factors of patients and nursing staff associated with the level of safety. This is an observational, descriptive and quantitative research conducted in three hemodialysis clinics placed in the city of Fortaleza-CE, Brazil, from September to November 2019. The sample was formed after a sample calculation for finite inhabitants of correlation with 200 chronic renal patients, 30 nursing technicians and seven nurses. Conventional sampling was used based on the inclusion criteria: being present at the hemodialysis session on the day the researcher performed the data collection and being 18 years of age or older. Participants who presented communication difficulties to listen to and/or answer the sociodemographic and clinical questionnaire were excluded. Two instruments were applied: sociodemographic and clinical characterization form and Chronic Renal Patient Safety Assessment Scale on Hemodialysis (EASPRCH). The data were analyzed using the Kruskal Walls Test for categorical variables and Spearman correlation coefficient for non-categorical variables, using the Statistical Package SPSS version 20.0. The present study respected the ethical and legal principles determined by resolution 466/2012 of the National Health Council, under the approval of the Ethics and Research Committee with an opinion number: 3,255,635. The results showed that a hemodialysis clinic presented unsafe care practices of 32 points in the EASPRCH (p=0.001). A statistical association was identified between the level of safety and the variables of the patients: level of education (p=0.018), family income (p=0.049), type of employment (p=0.012), venous access site (p=0.009), use of medication during the session (p=0.008) and time of hemodialysis (p=0.002). When evaluating the profile of nurses, a statistical association was evidenced between the level of safety with the variables: marital status (p=0.000), race (p=0.017), schooling (p= 0.000), income (p=0.013), age (p=0.000), clinic workload (p=0.000), time working with hemodialysis (p=0.000), time working in the clinic (p= 0.007) and clinic sizing (p=0.000). In order, the sociodemographic factors of nursing technicians associated with the level of patient safety were: race (p= 0.001) and weekly workload at (p=0.010). Therefore, it is concluded that there is a non-conformity in the level of patient safety in one of the clinics studied and, that sociodemographic and clinical factors of patients and health professionals corroborate the level of safety of the health unit.

Keywords: hemodialysis, nursing, patient safety, quality improvement

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Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees

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The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.

Keywords: solar energy, solar radiation, ERA-5, potential energy

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Authors: Chaudhary Itisha, Shankar Manu

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Patient satisfaction represents a crucial aspect in the evaluation of health care services. Preoperative teaching provides the patient with pertinent information concerning the surgical process and the intended surgical procedure as well as anticipated patient behavior (anxiety, fear), expected sensation, and the probable outcomes. Although patient education is part of Accreditation protocols, it is not uniform at most places. The aim of this study was to try to assess the benefit of preoperative patient education on selected post-operative outcome parameters; mainly, post-operative pain scores, requirement of additional analgesia, return to activity of daily living and overall patient satisfaction, and try to standardize few education protocols. Dependent variables were measured before and after the treatment on a study population of 302 volunteers. Educational intervention was provided by the Investigator in the preoperative period to the study group through personal counseling. An information booklet contained detailed information was also provided. Statistical Analysis was done using Chi square test, Mann Whitney u test and Fischer Exact Test on a total of 302 subjects. P value <0.05 was considered as level of statistical significance and p<0.01 was considered as highly significant. This study suggested that patients who are given a structured, individualized and elaborate preoperative education and counseling have a better ability to cope up with postoperative pain in the immediate post-operative period. However, there was not much difference when the patients have had almost complete recovery. There was no difference in the requirement of additional analgesia among the two groups. There is a positive effect of preoperative counseling on expected return to the activities of daily living and normal work schedule. However, no effect was observed on the activities in the immediate post-operative period. There is no difference in the overall satisfaction score among the two groups of patients. Thus this study concludes that there is a positive benefit as suggested by the results for pre-operative patient education. Although the difference in various parameters studied might not be significant over a long term basis, they definitely point towards the benefits of preoperative patient education. 

Keywords: patient education, post-operative pain, postoperative outcomes, patient satisfaction

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Authors: Marlon P. Perez

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The study entitled “The Effect of Leader Motivating Language on Work Performance and Job Satisfaction as Perceived by the Employees of SoroSoro Ibaba Development Cooperative (SIDC)” primarily aims to evaluate the effect of leader’s use of motivating language in terms of the three types of speech acts namely, direction-giving language, empathetic language and meaning-making language with regard to the work performance and job satisfaction of the employees. The study made use of the descriptive method of this research that it followed certain processes in gathering the necessary and accurate information. Furthermore, survey questionnaires were used in order to congregate the respondents’ outlooks, opinions, and insight in the study. These survey questionnaires were distributed to one hundred fifty (150) employees from the five (5) outlets of SoroSoro Ibaba Development Cooperative (SIDC) in Batangas City who were chosen as the respondents of the study. However, only hundred twenty (120) out of one hundred fifty (150) or eighty (80) percent of the questionnaires were retrieved. Moreover, to accomplish the objectives of the study, different statistical treatments were used for the interpretation and analysis of the gathered data. These were the relative frequency, weighted mean, one-way analysis of variance and Pearson r. Based on those statistical treatments, researchers came up with the following results: first, most of the respondents were below 35 years old, males, college graduates and in regular status; second, direction-giving language, empathetic language, and meaning-making language affect the work performance and job satisfaction of the employees to a great extent; third, there was a non-significant difference with regards to the effect of leader motivating language on the work performance and job satisfaction of the employee; and, last, there was a significant relationship on the assessment of the effect of leader motivating language on work performance and job satisfaction when grouped according to respondents’ profile. Based on these results, various recommendations were conceptualized such as the designing of proposed activities like communication workshop and team-building to augment the communication between the leader and an employee. These activities could help for the development and attainment of an excellent communication within the different organizations and companies that are very important to any business success.

Keywords: leader motivating language, work performance, job satisfaction, employees

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Authors: Somruay Apichatibutarapong

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The purpose of this study was to address and comparison of the attitudes towards the statistics course for undergraduate students. Data were collected from 120 students in Faculty of Sciences and Technology, Suan Sunandha Rajabhat University who enrolled in the statistics course. The quantitative approach was used to investigate the assessment and comparison of attitudes towards statistics course. It was revealed that the overall attitudes somewhat agree both in pre-test and post-test. In addition, the comparison of students’ attitudes towards the statistic course (Form A) has no difference in the overall attitudes. However, there is statistical significance in all dimensions and overall attitudes towards the statistics course (Form B).

Keywords: statistics attitude, student’s attitude, statistics, attitude test

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Authors: J. K. Karapetyan

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In this paper seismological-statistical analysis of actual instrumental data on the main tremor of the Great Japan earthquake 11.03.2011 is implemented for finding out the dependence between maximal values of peak ground accelerations (PGA) and epicentric distances. A number of peculiarities of manifestation of accelerations' maximum values at the interval of long epicentric distances are revealed which do not correspond with current scales of seismic intensity.

Keywords: earthquakes, instrumental records, seismic hazard, Japan

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Authors: M. Jalali Azizpour, H.Mohammadi Majd

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In this paper, the effect of WC-12Co particle Temperature in HVOF thermal spraying process on the coating thickness has been studied. The statistical results show that the spray distance and oxygen-to-fuel ratio are more effective factors on particle characterization and thickness of HVOF thermal spraying coatings. Spray Watch diagnostic system, scanning electron microscopy (SEM), X-ray diffraction and thickness measuring system were used for this purpose.

Keywords: HVOF, temperature, thickness, velocity, WC-12Co

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Authors: Mohamed Khalifa Zayet, Salma Belal Eiid, Mushira Mohamed Dahaba

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Introduction: Malignant tumors may not be easily detected by traditional radiographic techniques especially in an anatomically complex area like maxillofacial region. At the same time, the advent of biological functional MRI was a significant footstep in the diagnostic imaging field. Objective: The purpose of this study was to define the malignant metabolic profile of maxillofacial lesions using diffusion MRI and magnetic resonance spectroscopy, as adjunctive aids for diagnosing of such lesions. Subjects and Methods: Twenty-one patients with twenty-two lesions were enrolled in this study. Both morphological and functional MRI scans were performed, where T1, T2 weighted images, diffusion-weighted MRI with four apparent diffusion coefficient (ADC) maps were constructed for analysis, and magnetic resonance spectroscopy with qualitative and semi-quantitative analyses of choline and lactate peaks were applied. Then, all patients underwent incisional or excisional biopsies within two weeks from MR scans. Results: Statistical analysis revealed that not all the parameters had the same diagnostic performance, where lactate had the highest areas under the curve (AUC) of 0.9 and choline was the lowest with insignificant diagnostic value. The best cut-off value suggested for lactate was 0.125, where any lesion above this value is supposed to be malignant with 90 % sensitivity and 83.3 % specificity. Despite that ADC maps had comparable AUCs still, the statistical measure that had the final say was the interpretation of likelihood ratio. As expected, lactate again showed the best combination of positive and negative likelihood ratios, whereas for the maps, ADC map with 500 and 1000 b-values showed the best realistic combination of likelihood ratios, however, with lower sensitivity and specificity than lactate. Conclusion: Diffusion weighted imaging and magnetic resonance spectroscopy are state-of-art in the diagnostic arena and they manifested themselves as key players in the differentiation process of orofacial tumors. The complete biological profile of malignancy can be decoded as low ADC values, high choline and/or high lactate, whereas that of benign entities can be translated as high ADC values, low choline and no lactate.

Keywords: diffusion magnetic resonance imaging, magnetic resonance spectroscopy, malignant tumors, maxillofacial

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Authors: C. Bejrananda, Y. Lee, T. Khamkaew

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With the rise of the importance of air transportation in the 21st century, the role of economics in airport planning and decision-making has become more important to the urban structure and land value around it. Therefore, this research aims to examine the relationship between an airport and its impacts on the distribution of urban land uses and land values by applying the Alonso’s bid rent model. The New Bangkok International Airport (Suvarnabhumi International Airport) was taken as a case study. The analysis was made over three different time periods of airport development (after the airport site was proposed, during airport construction, and after the opening of the airport). The statistical results confirm that Alonso’s model can be used to explain the impacts of the new airport only for the northeast quadrant of the airport, while proximity to the airport showed the inverse relationship with the land value of all six types of land use activities through three periods of time. It indicates that the land value for commercial land use is the most sensitive to the location of the airport or has the strongest requirement for accessibility to the airport compared to the residential and manufacturing land use. Also, the bid-rent gradients of the six types of land use activities have declined dramatically through the three time periods because of the Asian Financial Crisis in 1997. Therefore, the lesson learned from this research concerns about the reliability of the data used. The major concern involves the use of different areal units for assessing land value for different time periods between zone block (1995) and grid block (2002, 2009). As a result, this affect the investigation of the overall trends of land value assessment, which are not readily apparent. In addition, the next concern is the availability of the historical data. With the lack of collecting historical data for land value assessment by the government, some of data of land values and aerial photos are not available to cover the entire study area. Finally, the different formats of using aerial photos between hard-copy (1995) and digital photo (2002, 2009) made difficult for measuring distances. Therefore, these problems also affect the accuracy of the results of the statistical analyses.

Keywords: airport development area, economic rents, spatial pattern, suvarnabhumi international airport

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Authors: Aizzat Md. Nasurdin, Noor Hazlina Ahmad, Cheng Ling Tan

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This study aims to examine the role of career advancement and job security as predictors of employee commitment to their organization. Data was collected from 580 frontline employees attached to two departments of 29 luxury hotels in Peninsular Malaysia. Statistical results using Partial Least Squares technique provided support for the proposed hypotheses. In view of the findings, theoretical and practical implications are discussed.

Keywords: organizational commitment, career advancement, job security, frontline employees, luxury hotels, Malaysia

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Authors: Zeinu Ahmed Rabba, Derek D Stretch

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Remote sensing contributes valuable information to streamflow estimates. Usually, stream flow is directly measured through ground-based hydrological monitoring station. However, in many developing countries like Ethiopia, ground-based hydrological monitoring networks are either sparse or nonexistent, which limits the manage water resources and hampers early flood-warning systems. In such cases, satellite remote sensing is an alternative means to acquire such information. This paper discusses the application of remotely sensed rainfall data for streamflow modeling in Gilgel Ghibe basin in Ethiopia. Ten years (2001-2010) of two satellite-based precipitation products (SBPP), TRMM and WaterBase, were used. These products were combined with the PyTOPKAPI hydrological model to generate daily stream flows. The results were compared with streamflow observations at Gilgel Ghibe Nr, Assendabo gauging station using four statistical tools (Bias, R², NS and RMSE). The statistical analysis indicates that the bias-adjusted SBPPs agree well with gauged rainfall compared to bias-unadjusted ones. The SBPPs with no bias-adjustment tend to overestimate (high Bias and high RMSE) the extreme precipitation events and the corresponding simulated streamflow outputs, particularly during wet months (June-September) and underestimate the streamflow prediction over few dry months (January and February). This shows that bias-adjustment can be important for improving the performance of the SBPPs in streamflow forecasting. We further conclude that the general streamflow patterns were well captured at daily time scales when using SBPPs after bias adjustment. However, the overall results demonstrate that the simulated streamflow using the gauged rainfall is superior to those obtained from remotely sensed rainfall products including bias-adjusted ones.

Keywords: Ethiopia, PyTOPKAPI model, remote sensing, streamflow, Tropical Rainfall Measuring Mission (TRMM), waterBase

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Authors: Wanvipa Thongborisute, Punyaphat Sirithanabadeekul, Pichit Suvanprakorn, Anan Jiraviroon

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Background: Acne vulgaris is one of the most common dermatologic problems, and can have a significant psychological and physical effect on patients. Propionibacterium acnes' roles in acne vulgaris involve the activation of toll-like receptor 4 (TLR4), and toll-like receptor 2 (TLR2) pathways. By activating these pathways, inflammatory events of acne lesions, comedogenesis and sebaceous lipogenesis can occur. Currently, there are several topical agents commonly use in treating acne vulgaris that are known to have an effect on TLRs, such as retinoic acid and adapalene, but these drugs still have some irritating effects. At present, there is an alarming increase in rate of bacterial resistance due to irrational used of antibiotics both orally and topically. For this reason, acne treatments should contain bioactive molecules targeting at the site of action for the most effective therapeutic effect with the least side effects. Sparganium stoloniferumis a Chinese aquatic herb containing a compound called Sparstolonin B (SsnB), which has been reported to selectively blocks Toll-like receptor 2 (TLR2) and Toll-like receptor 4 (TLR4)-mediated inflammatory signals. Therefore, this topical TLR2 and TLR4 antagonist, in a form of Sparganium stoloniferum-derived compound containing SsnB, should give a benefit in reducing inflammation of acne vulgaris lesions and providing an alternative treatments for patients with this condition. Materials and Methods: The objectives of this randomized double blinded split faced placebo controlled trial is to study the safety and efficacy of the Sparganium stoloniferum-derived compound. 32 volunteered patients with mild to moderate degree of acne vulgaris according to global acne grading system were included in the study. After being informed and consented the subjects were given 2 topical treatments for acne vulgaris, one being topical 2.40% Sparganium stoloniferum extraction (containing Sparstolonin B) and the other, placebo. The subjects were asked to apply each treatment to either half of the face daily morning and night by randomization for 8 weeks, and come in for a weekly follow up. For each visit, the patients went through a procedure of lesion counting, including comedones, papules, nodules, pustules, and cystic lesions. Results: During 8 weeks of experimentation, the result shows a reduction in total lesions number between the placebo and the treatment side show statistical significance starting at week 4, where the 95% confidence interval begin to no longer overlap, and shows a trend of continuing to be further apart. The decrease in the amount of total lesions between week 0 and week 8 of the placebo side shows no statistical significant at P value >0.05. While the decrease in the amount of total lesions of acne vulgaris of the treatment side comparing between week 0 and week 8 shows statistical significant at P value <0.001. Conclusion: The data demonstrates that 2.40% Sparganium stoloniferum extraction (containing Sparstolonin B) is more effective in treating acne vulgaris comparing to topical placebo in treating acne vulgaris, by showing significant reduction in the total numbers of acne lesions. Therefore, this topical Sparganium stoloniferum extraction could become a potential alternative treatment for acne vulgaris.

Keywords: acne vulgaris, sparganium stoloniferum, sparstolonin B, toll-like receptor 2, toll-like receptor 4

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Authors: Maryam Sayadi, Rana Sedighpour, Hossein Rezaie

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In this study, in order to evaluate water quality of Gamasiab and Gharehsoo rivers located in Kermanshah province, the information of a 5-year statistical period during the years 2014-2018 was used. To evaluate the hydrochemistry of water, first the type and hydrogeochemical facies of river water were determined using Stiff and Piper diagrams. Then, based on Gibbs diagram and combination diagrams, the factors controlling the chemical parameters of the two rivers were identified. Saturation indices were used to predict the possibility of dissolution and deposition of some minerals. Then, in order to classify water in different sections, fourteen water quality indicators for different uses along with WHO standard were used. Finally, factor analysis was used to determine the processes affecting the hydrochemistry of the two rivers. The results of this study showed that in both rivers, the predominant type and facies are bicarbonate of calcite. Also, the main factor in changing the chemical quality of water in both Gamasiab and Gharehsoo rivers is the water-rock reaction. According to the results of factor analysis in both rivers, two factors have the greatest impact on water quality in the region. Among the parameters of Gamasiab river in the first factor, HCO3-, Na+ and Cl-, respectively, had the highest factor loads, and in the second factor, SO42- and Mg2+ were selected as the main parameters. The parameters Ca2+, Cl- and Na have the highest factor loads in the first factor and in the second factor Mg2+ and SO42- have the highest factor loads in Gharehsoo river. The dissolution of carbonate formations due to their abundance and expansion in the two basins has a more significant effect on changing water chemistry. It has saturated the water of rivers with aragonite, calcite and dolomite. Due to the low contribution of the second factor in changing the chemical parameters, the water of both rivers is saturated with respect to evaporative minerals such as gypsum, halite and anhydrite in all stations. Based on Schoeller diagrams, Wilcox and other quality indicators in these two sections, the amount of main physicochemical parameters are in the desired range for drinking and agriculture. The results of Langelier, Ryznar, Larson-Skold and Puckorius indices showed that water is corrosive in industry.

Keywords: factor analysis, hydrochemical, saturation index, surface water quality

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: M. Jalali Azizpour

Abstract:

In this paper, the effect of WC-12Co particle Temperature in HVOF thermal spraying process on the coating thickness has been studied. The statistical results show that the spray distance and oxygen-to-fuel ratio are more effective factors on particle characterization and thickness of HVOF thermal spraying coatings. Spray Watch diagnostic system, scanning electron microscopy (SEM), X-ray diffraction and thickness measuring system were used for this purpose.

Keywords: HVOF, temperature thickness, velocity, WC-12Co

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Authors: Bronwen Wade

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Over the last half-century, child welfare systems have increasingly relied on quantitative risk assessment tools, such as actuarial or predictive risk tools. These tools are developed by performing statistical analysis of how attributes captured in administrative data are related to future child maltreatment. Some scholars argue that attributes in administrative data can serve as proxies for race and that quantitative risk assessment tools reify racial bias in decision-making. Others argue that these tools provide more “objective” and “scientific” guides for decision-making instead of subjective social worker judgment. This study performs a systematic review of the literature on the impact of quantitative risk assessment tools on racial disproportionality; it examines methodological biases in work on this topic, summarizes key findings, and provides suggestions for further work. A search of CINAHL, PsychInfo, Proquest Social Science Premium Collection, and the ProQuest Dissertations and Theses Collection was performed. Academic and grey literature were included. The review includes studies that use quasi-experimental methods and development, validation, or re-validation studies of quantitative risk assessment tools. PROBAST (Prediction model Risk of Bias Assessment Tool) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) were used to assess the risk of bias and guide data extraction for risk development, validation, or re-validation studies. ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions) was used to assess for bias and guide data extraction for the quasi-experimental studies identified. Due to heterogeneity among papers, a meta-analysis was not feasible, and a narrative synthesis was conducted. 11 papers met the eligibility criteria, and each has an overall high risk of bias based on the PROBAST and ROBINS-I assessments. This is deeply concerning, as major policy decisions have been made based on a limited number of studies with a high risk of bias. The findings on racial disproportionality have been mixed and depend on the tool and approach used. Authors use various definitions for racial equity, fairness, or disproportionality. These concepts of statistical fairness are connected to theories about the reason for racial disproportionality in child welfare or social definitions of fairness that are usually not stated explicitly. Most findings from these studies are unreliable, given the high degree of bias. However, some of the less biased measures within studies suggest that quantitative risk assessment tools may worsen racial disproportionality, depending on how disproportionality is mathematically defined. Authors vary widely in their approach to defining and addressing racial disproportionality within studies, making it difficult to generalize findings or approaches across studies. This review demonstrates the power of authors to shape policy or discourse around racial justice based on their choice of statistical methods; it also demonstrates the need for improved rigor and transparency in studies of quantitative risk assessment tools. Finally, this review raises concerns about the impact that these tools have on child welfare systems and racial disproportionality.

Keywords: actuarial risk, child welfare, predictive risk, racial disproportionality

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Authors: Rami Hashish, Manon Limousis-Gayda, Caitlin H. McCleery

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Introduction: A leading cause of emergency room visits among youth (in the United States), is sports-related traumatic brain injuries. Mild traumatic brain injuries (mTBIs), also called concussions, are caused by linear and/or angular acceleration experienced at the head and represent an increasing societal burden. Due to the developing nature of the brain in youth, there is a great risk for long-term neuropsychological deficiencies following a concussion. Accordingly, the purpose of this paper is to investigate incidence rates of concussion across gender for the five most common youth sports in the United States. These include basketball, track and field, soccer, baseball (boys), softball (girls), football (boys), and volleyball (girls). Methods: A PubMed search was performed for four search themes combined. The first theme identified the outcomes (concussion, brain injuries, mild traumatic brain injury, etc.). The second theme identified the sport (American football, soccer, basketball, softball, volleyball, track, and field, etc.). The third theme identified the population (adolescence, children, youth, boys, girls). The last theme identified the study design (prevalence, frequency, incidence, prospective). Ultimately, 473 studies were surveyed, with 15 fulfilling the criteria: prospective study presenting original data and incidence of concussion in the relevant youth sport. The following data were extracted from the selected studies: population age, total study population, total athletic exposures (AE) and incidence rate per 1000 athletic exposures (IR/1000). Two One-Way ANOVA and a Tukey’s post hoc test were conducted using SPSS. Results: From the 15 selected studies, statistical analysis revealed the incidence of concussion per 1000 AEs across the considered sports ranged from 0.014 (girl’s track and field) to 0.780 (boy’s football). Average IR/1000 across all sports was 0.483 and 0.268 for boys and girls, respectively; this difference in IR was found to be statistically significant (p=0.013). Tukey’s post hoc test showed that football had significantly higher IR/1000 than boys’ basketball (p=0.022), soccer (p=0.033) and track and field (p=0.026). No statistical difference was found for concussion incidence between girls’ sports. Removal of football was found to lower the IR/1000 for boys without a statistical difference (p=0.101) compared to girls. Discussion: Football was the only sport showing a statistically significant difference in concussion incidence rate relative to other sports (within gender). Males were overall more likely to be concussed than females when football was included (1.8x), whereas concussion was more likely for females when football was excluded. While the significantly higher rate of concussion in football is not surprising because of the nature and rules of the sport, it is concerning that research has shown higher incidence of concussion in practices than games. Interestingly, findings indicate that girls’ sports are more concussive overall when football is removed. This appears to counter the common notion that boys’ sports are more physically taxing and dangerous. Future research should focus on understanding the concussive mechanisms of injury in each sport to enable effective rule changes.

Keywords: gender, football, soccer, traumatic brain injury

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Authors: Larissa Bolliger, Junoš Lukan, Mitja Lustrek, Dirk De Bacquer, Els Clays

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Introduction and Aim: Chronic workplace stress and its health-related consequences like mental and cardiovascular diseases have been widely investigated. This project focuses on the sources and context of psychosocial daily workplace stress in a real-world setting. The main objective is to analyze and model real-time relationships between (1) psychosocial stress experiences within the natural work environment, (2) micro-level work activities and events, and (3) physiological signals and behaviors in office workers. Methods: An Ecological Momentary Assessment (EMA) protocol has been developed, partly building on machine learning techniques. Empatica® wristbands will be used for real-life detection of stress from physiological signals; micro-level activities and events at work will be based on smartphone registrations, further processed according to an automated computer algorithm. A field study including 100 office-based workers with high-level problem-solving tasks like managers and researchers will be implemented in Slovenia and Belgium (50 in each country). Data mining and state-of-the-art statistical methods – mainly multilevel statistical modelling for repeated data – will be used. Expected Results and Impact: The project findings will provide novel contributions to the field of occupational health research. While traditional assessments provide information about global perceived state of chronic stress exposure, the EMA approach is expected to bring new insights about daily fluctuating work stress experiences, especially micro-level events and activities at work that induce acute physiological stress responses. The project is therefore likely to generate further evidence on relevant stressors in a real-time working environment and hence make it possible to advise on workplace procedures and policies for reducing stress.

Keywords: ecological momentary assessment, real-time, stress, work

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: Seher Arslankaya, Bahar Uludağ

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Meeting the needs and demands of customers and pleasing the customers are important requirements for companies in food sectors where the growth of competition is significantly unpredictable. Customer satisfaction is also one of the key concepts which is mainly driven by wide range of customer preference and expectation upon products and services introduced and delivered to them. In order to meet the customer demands, the companies that engage in food sectors are expected to have a well-managed set of Total Quality Management (TQM), which sets out to improve quality of products and services; to reduce costs and to increase customer satisfaction by restructuring traditional management practices. It aims to increase customer satisfaction by meeting (their) customer expectations and requirements. The achievement would be determined with the help of customer satisfaction surveys, which is done to obtain immediate feedback and to provide quick responses. In addition, the surveys would also assist the making of strategic planning which helps to anticipate customer future needs and expectations. Meanwhile, periodic measurement of customer satisfaction would be a must because with the better understanding of customers perceptions from the surveys (done by questioners), the companies would have a clear idea to identify their own strengths and weaknesses that help the companies keep their loyal customers; to stand in comparison toward their competitors and map out their future progress and improvement. In this study, we propose a survey based on customer satisfaction measurement method and its statistical analysis for sales and logistic activities of food firms. Customer satisfaction would be discussed in details. Furthermore, after analysing the data derived from the questionnaire that applied to customers by using the SPSS software, various results obtained from the application would be presented. By also applying ANOVA test, the study would analysis the existence of meaningful differences between customer demographic proportion and their perceptions. The purpose of this study is also to find out requirements which help to remove the effects that decrease customer satisfaction and produce loyal customers in food industry. For this purpose, the customer complaints are collected. Additionally, comments and suggestions are done according to the obtained results of surveys, which would be useful for the making-process of strategic planning in food industry.

Keywords: customer satisfaction measurement and analysis, food industry, SPSS, TQM

Procedia PDF Downloads 250

Authors: E. Gouya Bozorg

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The issue of ranking of sports teams, in particular soccer teams is of primary importance in the professional sports. However, it is still based on classical statistics and models outside of area of mathematics. Rigorous mathematics and then statistics despite the expectation held of them have not been able to effectively engage in the issue of ranking. It is something that requires serious pathology. The purpose of this study is to change the approach to get closer to mathematics proper for using in the ranking. We recommend using theoretical mathematics as a good option because it can hermeneutically obtain the theoretical concepts and criteria needful for the ranking from everyday language of a League. We have proposed a framework that puts the issue of ranking into a new space that we have applied in soccer as a case study. This is an experimental and theoretical study on the issue of ranking in a professional soccer league based on theoretical mathematics, followed by theoretical statistics. First, we showed the theoretical definition of constant number Є = 1.33 or ‘golden number’ of a soccer league. Then, we have defined the ‘efficiency of a team’ by this number and formula of μ = (Pts / (k.Є)) – 1, in which Pts is a point obtained by a team in k number of games played. Moreover, K.Є index has been used to show the theoretical median line in the league table and to compare top teams and bottom teams. Theoretical coefficient of σ= 1 / (1+ (Ptx / Ptxn)) has also been defined that in every match between the teams x, xn, with respect to the ability of a team and the points of both of them Ptx, Ptxn, and it gives a performance point resulting in a special ranking for the League. And it has been useful particularly in evaluating the performance of weaker teams. The current theory has been examined for the statistical data of 4 major European Leagues during the period of 1998-2014. Results of this study showed that the issue of ranking is dependent on appropriate theoretical indicators of a League. These indicators allowed us to find different forms of ranking of teams in a league including the ‘special table’ of a league. Furthermore, on this basis the issue of a record of team has been revised and amended. In addition, the theory of ranking can be used to compare and classify the different leagues and tournaments. Experimental results obtained from archival statistics of major professional leagues in the world in the past two decades have confirmed the theory. This topic introduces a new theory for ranking of a soccer league. Moreover, this theory can be used to compare different leagues and tournaments.

Keywords: efficiency of a team, ranking, special table, theoretical mathematic

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Authors: T. K. Dutta, K. K. Das, N. Dutta

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The salient feature of this paper is primarily concerned with Ricker’s population model: f(x)=x e^(r(1-x/k)), where r is the control parameter and k is the carrying capacity, and some fruitful results are obtained with the following objectives: 1) Determination of bifurcation values leading to a chaotic region, 2) Development of Statistical Methods and Analysis required for the measure of Fractal dimensions, 3) Calculation of various fractal dimensions. These results also help that the invariant probability distribution on the attractor, when it exists, provides detailed information about the long-term behavior of a dynamical system. At the end, some open problems are posed for further research.

Keywords: Feigenbaum universality, chaos, Lyapunov exponent, fractal dimensions

Procedia PDF Downloads 302

Authors: H. Starzyczna, M. Stoklasa, K. Matusinska

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This article deals with the benefits of regional brands for companies in the Czech Republic. Research was focused on finding out the expected and actual benefits of regional brands for companies. The data were obtained by questionnaire survey and analysed by IBM SPSS. Representative sample of 204 companies was created. The research analysis disclosed the expected benefits that the regional brand should bring to companies. But the actual benefits are much worse. The statistical testing of hypotheses revealed that the benefits depend on the region of origin, which surprised both us and the regional coordinators.

Keywords: Brand, regional brands, product protective branding programs, brand benefits

Procedia PDF Downloads 346

Authors: Dhouibi Mohamed, Stirbu Bogdan, Chabotier André, Pirlot Marc

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Effects of erosion and wear on the performance of small caliber guns have been analyzed throughout numerical and experimental studies. Mainly, qualitative observations were performed. Correlations between the volume change of the chamber and the maximum pressure are limited. This paper focuses on the development of a numerical model to predict the maximum pressure evolution when the interior shape of the chamber changes in the different weapon’s life phases. To fulfill this goal, an experimental campaign, followed by a numerical simulation study, is carried out. Two test barrels, « 5.56x45mm NATO » and « 7.62x51mm NATO,» are considered. First, a Coordinate Measuring Machine (CMM) with a contact scanning probe is used to measure the interior profile of the barrels after each 300-shots cycle until their worn out. Simultaneously, the EPVAT (Electronic Pressure Velocity and Action Time) method with a special WEIBEL radar are used to measure: (i) the chamber pressure, (ii) the action time, (iii) and the bullet velocity in each barrel. Second, a numerical simulation study is carried out. Thus, a coupled interior ballistic model is developed using the dynamic finite element program LS-DYNA. In this work, two different models are elaborated: (i) coupled Eularien Lagrangian method using fluid-structure interaction (FSI) techniques and a coupled thermo-mechanical finite element using a lumped parameter model (LPM) as a subroutine. Those numerical models are validated and checked through three experimental results, such as (i) the muzzle velocity, (ii) the chamber pressure, and (iii) the surface morphology of fired projectiles. Results show a good agreement between experiments and numerical simulations. Next, a comparison between the two models is conducted. The projectile motions, the dynamic engraving resistances and the maximum pressures are compared and analyzed. Finally, using this obtained database, a statistical correlation between the muzzle velocity, the maximum pressure and the chamber volume is established.

Keywords: engraving process, finite element analysis, gun barrel erosion, interior ballistics, statistical correlation

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Authors: E. Happ, A. Albert Tóth

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The aim of the research is to investigate the forms of the demands of leisure tourism in a West-Hungarian industrial city, Győr. Today, Győr is still a traditional industrial city, its industry is mainly based on vehicle industry, but the role of tourism is increasing in the life of the city as well. Because of the industrial nature and the strong economy of the city, the ratio of business tourists is high. It can be stated that MICE tourism is dominating in Győr. Developments of the last decade can help the city with new tourism products to increase the leisure tourism. The new types of tourism – besides business tourism – can help the providers to increase the occupancy rates and the demand at the weekends. The research demonstrates the theoretical background of the topic, and it shows the present situation of the tourism in Győr with secondary data. The secondary research contains statistical data from the Hungarian Statistical Office and the city council, and it is based on the providers’ data. The next part of the paper shows the potential types of leisure tourism with the help of primary research. The primary research contains the results of an online questionnaire with a sample of 1000 potential customers. It is completed with 10 in-depth interviews with tourism experts, who explained their opinions about the opportunities of leisure tourism in Győr from the providers’ side. The online questionnaire was filled out in spring 2017 by customers, who have already stayed in Győr or plan to visit the city. At the same time in-depth interviews were made with hotel managers, head of touristic institutions and employees at the council. Based on the research it can be stated that the touristic supply of Győr allows the increase of the leisure tourism ratio in the city. Primarily, the cultural and health tourism show potential development, but the supply side of touristic services can be developed in order to increase the number of guest nights. The tourism marketing needs to be strengthened in the city, and a distinctive marketing activity - from other cities - is needed as well. To conclude, although Győr is an industrial city, it has a transforming industrial part, and tourism is also strongly present in its economy. Besides the leading role of business tourism, different types of leisure tourism have the opportunity to take place in the city.

Keywords: business tourism, Győr, industrial city, leisure tourism, touristic demand

Procedia PDF Downloads 279

Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 358