Search results for: statistical classifiers

Authors: Iliya Bitrus Abaje

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This study examines the evidence of climate change scenario in Kaduna State from the analysis of temperature and rainfall data (1976-2015) from three meteorological stations along a geographic transect from the southern part to the northern part of the State. Different statistical methods were used in determining the changes in both the temperature and rainfall series. The result of the linear trend lines revealed a mean increase in average temperature of 0.73oC for the 40 years period of study in the State. The plotted standard deviation for the temperature anomalies generally revealed that years of temperatures above the mean standard deviation (hotter than the normal conditions) in the last two decades (1996-2005 and 2006-2015) were more than those below (colder than the normal condition). The Cramer’s test and student’s t-test generally revealed an increasing temperature trend in the recent decades. The increased in temperature is an evidence that the earth’s atmosphere is getting warmer in recent years. The linear trend line equation of the annual rainfall for the period of study showed a mean increase of 316.25 mm for the State. Findings also revealed that the plotted standard deviation for the rainfall anomalies, and the 10-year non-overlapping and 30-year overlapping sub-periods analysis in all the three stations generally showed an increasing trend from the beginning of the data to the recent years. This is an evidence that the study area is now experiencing wetter conditions in recent years and hence climate change. The study recommends diversification of the economic base of the populace with emphasis on moving away from activities that are sensitive to temperature and rainfall extremes Also, appropriate strategies to ameliorate the scourge of climate change at all levels/sectors should always take into account the recent changes in temperature and rainfall amount in the area.

Keywords: anomalies, linear trend, rainfall, temperature

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Authors: Sawsen Jarray, Tahani Hallek, Mabrouk Montacer

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The tectonically damaged terrain in Tunisia's Northwest is seen in the country's numerous oil leaks. Finding a genetic link between these oil seeps and the area's putative source rocks is the goal of this investigation. Here, we use aliphatic biomarkers assessed by GC-MS to describe the organic geochemical data of 18 oil seeps samples and 4 source rocks (M'Cherga, Fahdene, Bahloul, and BouDabbous). In order to establish correlations between oil and oil and oil and source rock, terpanes, hopanes, and steranes biomarkers were identified. The source rocks under study were deposited in a marine environment and were suboxic, with minor signs of continental input for the M'Cherga Formation. There is no connection between the Fahdene and Bahloul source rocks and the udied oil seeps. According to the biomarkers C27 18-22,29,30trisnorneohopane (Ts) and C27 17-22,29,30-trisnorhopane (Tm), these source rocks are mature and have reached the oil window. Regarding oil seeps, geochemical data indicate that, with the exception of four samples that showed some continental markings, the bulk of samples were deposited in an open marine environment. These most recent samples from oil seeps have a unique lithology (marl) that distinguishes them from the others (carbonate). There are two classes of oil seeps, according to statistical analysis of relationships between oil and oil and oil and source rocks. The first comprised samples that showed a positive connection with carbonate-lithological and marine-derived BouDabbous black shales. The second is a result of M'Cherga source rock and is made up of oil seeps with remnants of the terrestrial environment and a lithology with a marl trend. The Fahdene and Bahloul source rocks have no connection to the observed oil seeps. There are two different types of hydrocarbon spills depending on their link to tectonic deformations (oil seeps) and outcropping mature source rocks (oil impregnations), in addition to the existence of two generations of hydrocarbon spills in Northwest Tunisia (Lower Cretaceous/Ypresian).

Keywords: petroleum seeps, source rocks, biomarkers, statistic, Northern Tunisia

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Authors: Ismail Aslantas

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Value-Added Models (VAMs), the statistical methods for evaluating the effectiveness of teachers and schools based on student achievement growth, has attracted decision-makers’ and researchers’ attention over the last decades. As a result of this attention, many studies have conducted in recent years to discuss these statistical models from different aspects. This research focused on the importance of conceptual variables in VAM estimations; therefor, this research was undertaken to examine the extent to which value-added effectiveness estimates for teachers can be affected by using context predictions. Using longitudinal data over three years from the international school context, value-added teacher effectiveness was estimated by ordinary least-square value-added models, and the effectiveness of the teachers was examined. The longitudinal dataset in this study consisted of three major sources: students’ attainment scores up to three years and their characteristics, teacher background information, and school characteristics. A total of 1,027 teachers and their 35,355 students who were in eighth grade were examined for understanding the impact of model specifications on the value-added teacher effectiveness evaluation. Models were created using selection methods that adding a predictor on each step, then removing it and adding another one on a subsequent step and evaluating changes in model fit was checked by reviewing changes in R² values. Cohen’s effect size statistics were also employed in order to find out the degree of the relationship between teacher characteristics and their effectiveness. Overall, the results indicated that prior attainment score is the most powerful predictor of the current attainment score. 47.1 percent of the variation in grade 8 math score can be explained by the prior attainment score in grade 7. The research findings raise issues to be considered in VAM implementations for teacher evaluations and make suggestions to researchers and practitioners.

Keywords: model specification, teacher effectiveness, teacher performance evaluation, value-added model

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Authors: Acumsiri Tantikarnpanit

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This paper aims to examine the relationship between urbanization and income inequality in Thailand during the period 2002–2020. Using a panel of data for 76 provinces collected from Thailand’s National Statistical Office (Labor Force Survey: LFS), as well as geospatial data from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite Day/Night band (VIIRS-DNB) satellite for nineteen selected years. This paper employs two different definitions to identify urban areas: 1) Urban areas defined by Thailand's National Statistical Office (Labor Force Survey: LFS), and 2) Urban areas estimated using nighttime light data from the DMSP and VIIRS-DNB satellite. The second method includes two sub-categories: 2.1) Determining urban areas by calculating nighttime light density with a population density of 300 people per square kilometer, and 2.2) Calculating urban areas based on nighttime light density corresponding to a population density of 1,500 people per square kilometer. The empirical analysis based on Ordinary Least Squares (OLS), fixed effects, and random effects models reveals a consistent U-shaped relationship between income inequality and urbanization. The findings from the econometric analysis demonstrate that urbanization or population density has a significant and negative impact on income inequality. Moreover, the square of urbanization shows a statistically significant positive impact on income inequality. Additionally, there is a negative association between logarithmically transformed income and income inequality. This paper also proposes the inclusion of satellite imagery, geospatial data, and spatial econometric techniques in future studies to conduct quantitative analysis of spatial relationships.

Keywords: income inequality, nighttime light, population density, Thailand, urbanization

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Olatundun Bukola Ezekiel, Adejumo Olusoji

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A feeding trial was conducted with 100 two-weeks old broiler chicken to evaluate the influence of inclusion in broiler diets at 0, 2.5, 5, 7.5 and 10% neem kernel (used to replace equal quantity of maize) on their performance, organ weight and gut morphometry. The birds were randomly allotted to five dietary treatments, each treatment having four replicates consisting of five broilers in a completely randomized design. The diets were formulated to be iso-nitrogenous (23% CP). Weekly feed intake and changes in body weight were calculated and feed efficiency determined. At the end of the 28-day feeding trial, four broilers per treatment were selected and sacrificed for carcass evaluation. Results were subjected to statistical analysis using the analysis of variance procedures of Statistical Analysis Software The treatment means were presented with group standard errors of means and where significant, were compared using the Duncan multiple range test of the same software. The results showed that broilers fed 2.5% neem kernel inclusion diets had growth performance statistically comparable to those fed the control diet. Birds on 5, 7.5 and 10% neem kernel diets showed significant (P<0.05) increase in relative weight of liver. The absolute weight of spleen also increased significantly (P<0.05) in birds on 10 % neem kernel diet. More than 5 % neem kernel diets gave significant (P<0.05) increase in the relative weight of the kidney. The length of the small intestine significantly increased in birds fed 7.5 and 10% neem kernel diets. Significant differences (P<0.05) did not occur in the length of the large intestine, right and left caeca. It is recommended that neem kernel can be included up to 2.5% in broiler chicken diet without any deleterious effects on the performance and physiological status of the birds.

Keywords: broiler chicken, growth performance, gut morphometry, neem kernel, organ weight

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Authors: M. T. Alodat

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In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions.

Keywords: normal distribution, moments, Fisher information, symmetric distributions

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Authors: Abdulaziz Alakeel, Roaa Alamri, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Ibrutinib is a selective, potent, and irreversible small-molecule inhibitor of Bruton's tyrosine kinase (BTK). It forms a covalent bond with a cysteine residue (CYS-481) at the active site of Btk, leading to inhibition of Btk enzymatic activity. The drug is indicated to treat certain type of cancers such as mantle cell lymphoma (MCL), chronic lymphocytic leukaemia and Waldenström's macroglobulinaemia (WM). Cardiac failure is a condition referred to inability of heart muscle to pump adequate blood to human body organs. There are multiple types of cardiac failure including left and right-sided heart failure, systolic and diastolic heart failures. The aim of this review is to evaluate the risk of cardiac failure associated with the use of ibrutinib and to suggest regulatory recommendations if required. Methodology: Signal Detection team at the National Pharmacovigilance Center (NPC) of Saudi Food and Drug Authority (SFDA) performed a comprehensive signal review using its national database as well as the World Health Organization (WHO) database (VigiBase), to retrieve related information for assessing the causality between cardiac failure and ibrutinib. We used the WHO- Uppsala Monitoring Centre (UMC) criteria as standard for assessing the causality of the reported cases. Results: Case Review: The number of resulted cases for the combined drug/adverse drug reaction are 212 global ICSRs as of July 2020. The reviewers have selected and assessed the causality for the well-documented ICSRs with completeness scores of 0.9 and above (35 ICSRs); the value 1.0 presents the highest score for best-written ICSRs. Among the reviewed cases, more than half of them provides supportive association (four probable and 15 possible cases). Data Mining: The disproportionality of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by WHO-UMC to measure the reporting ratio. Positive IC reflects higher statistical association while negative values indicates less statistical association, considering the null value equal to zero. The results of (IC=1.5) revealed a positive statistical association for the drug/ADR combination, which means “Ibrutinib” with “Cardiac Failure” have been observed more than expected when compared to other medications available in WHO database. Conclusion: Health regulators and health care professionals must be aware for the potential risk of cardiac failure associated with ibrutinib and the monitoring of any signs or symptoms in treated patients is essential. The weighted cumulative evidences identified from causality assessment of the reported cases and data mining are sufficient to support a causal association between ibrutinib and cardiac failure.

Keywords: cardiac failure, drug safety, ibrutinib, pharmacovigilance, signal detection

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Authors: Jiří Vágner, Dana Fialová

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The Covid-19 pandemic and its effects have triggered significant changes in travel behavior. On the contrary to a deep decline in international tourism, domestic tourism has recovered. It has not fully replaced the total volume of national tourism so far. However, from a regional point of view, and especially according to the type of destinations, regional targeting has changed significantly compared to the previous period. Urban destinations, which used to be the domain of foreign tourists, have been relatively orphaned, in contrast to destinations tied to natural attractions, which have seen seasonal increases. Even here, at a lower hierarchical geographic level, we can observe the differentiation resulting from the existing localization and infrastructure. The case study is focused on the three largest water reservoirs of the Vltava Cascade in Czechia– Lipno, Orlík, and Slapy. Based on a detailed field survey, in the periods before and during the pandemic, as well as available statistical data (Tourdata; Czech Statistical Office, Czech Cadaster and Ordnance Survey), different trends in the exploitation of these destinations with regard to existing or planned infrastructure are documented, analyzed and explained. This gives us the opportunity to discuss on concrete examples of generally known phenomena that are usually neglected in tourism: slum, brownfield, greenfield. Changes in travel behavior – especially the focus on spending leisure time individually in naturally attractive destinations – can affect the use of sites, which can be defined as a tourist or recreational slum, brownfield, but also as a tourist greenfield development. Sociocultural changes and perception of destinations by tourists and other actors represent, besides environmental changes, major trends in current tourism.

Keywords: Covid-19 pandemic, czechia, sociocultural and environmental impacts, tourist infrastructure, travel behavior, the Vltava Cascade water reservoirs

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Authors: A. L. I. Prasanna, E. Liyanage, S. A. Rajaratne, K. P. A. P. Kariyawasam, A. A. J. Rajaratne

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Maintaining or improving the muscle strength of the injured body part is essential to optimize performance among disabled athletes. General conditioning and strengthening exercises might be ineffective if not sufficiently intense enough or targeted for each participant’s specific impairment. Specific strengthening programme, targeted to the affected body part, are essential to improve the strength of impaired muscles and increase in strength will help reducing the impact of disability. Methods: The muscle strength of hip, knee and ankle joints was assessed in a group of randomly selected disabled athletes, using the Medical Research Council (MRC) grading. Those having muscle strength of grade 4 or less were selected for this study (24 in number) and were given and a custom made exercise program designed to strengthen their hip, knee or ankle joint musculature, according to the muscle or group of muscles affected. Effectiveness of the strengthening program was assessed after a period of 3 months. Results: Statistical analysis was done using the Minitab 16 statistical software. A Mann-Whitney U test was used to compare the strength of muscle group before and after exercise programme. A significant difference was observed after the three month strengthening program for knee flexors (Left and Right) (P =0.0889, 0.0312) hip flexors (left and right) (P=0.0312, 0.0466), hip extensors (Left and Right) (P=0.0478, 0.0513), ankle plantar flexors (Left and Right) (P=0.0466, 0.0423) and right ankle dorsiflexors (P= 0.0337). No significant difference of strength was observed after the strengthening program in the knee extensors (left and right), hip abductors (left and right) and left ankle dorsiflexors. Conclusion: Impairment specific exercise programme appear to be beneficial for disabled athletes to significantly improve the muscle strength of the affected joints.

Keywords: muscle strengthening programme, disabled athletes, physiotherapy, rehabilitation sciences

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Authors: Osvaldo N. Oliveira Jr.

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The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

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Authors: Sauradipta Ganguly, Akhato Sumi, Sanjeet Kumar Hom, Ajan T. Lotha

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Populus deltoides is a hardwood used predominantly for the manufacturing of plywood, matchsticks, and paper in India and hence has a higher economical significance. Wood-decaying fungi cause serious damage to Populus deltoides products, as the wood itself is perishable and vulnerable to decaying agents, decreasing their aesthetical value which in return results in significant monetary loss for the wood industries concerned. The aim of the study was to determine the antifungal activity of Cedrus deodara oil against three primary wood-decaying fungi namely white-rot fungi (Trametes versicolor), brown-rot fungi (Oligoporus placentus) and soft-rot fungi (Xylaria acuminata) on Populus deltoides samples under optimum laboratory conditions. The susceptibility of Populus deltoides samples on the fungal attack and the ability of deodar oil to control colonization of the wood rotting fungi on the samples were assessed. Three concentrations of deodar oil were considered for the study as treating solutions, i.e., 4%, 5%, and 6%. The Populus deltoides samples were treated with treating solutions, and the ability of the same to prevent a fungal attack on the samples were assessed using accelerated test in the laboratory at Biochemical Oxygen Demand incubator at temperature (25 ± 2°C) and relative humidity 70 ± 4%. Efficacy test and statistical analysis of deodar oil against Trametes versicolor, Oligoporus placentus, and Xylariaacuminataon P. deltoides samples exhibited light, minor and negligible mycelia growth at 4 %, 5% and 6% concentrations of deodar oil, respectively. Whereas, moderate to heavy attack was observed on the surface of the control samples. Statistical analysis further established that the treatments were statistically significant and had significantly inhibited fungal growth of all the three fungus spp by almost 3 to 5 times.

Keywords: populus deltoides, Trametes versicolor, Oligoporus placentus, Xylaria acuminata, Deodar oil, treatment

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Authors: Ali̇ Bayram, Semih Dalkilic, Remzi Yigiter

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N-methyl-D-aspartate (NMDA) receptor is a subtype of glutamate receptor and plays a pivotal role in learning, memory, neuronal plasticity, neurotoxicity and synaptic mechanisms. Animal experiments were suggested that glutamate-induced excitotoxic injuriy and NMDA receptor blockage lead to amnesia and other neurodegenerative diseases including Alzheimer’s disease (AD), Huntington’s disease, amyotrophic lateral sclerosis. Aim of this study is to investigate association between NMDA receptor coding gene GRIN2B expression level and Alzheimer disease. The study was approved by the local ethics committees, and it was conducted according to the principles of the Declaration of Helsinki and guidelines for the Good Clinical Practice. Peripheral blood was collected 50 patients who diagnosed AD and 49 healthy control individuals. Total RNA was isolated with RNeasy midi kit (Qiagen) according to manufacturer’s instructions. After checked RNA quality and quantity with spectrophotometer, GRIN2B expression levels were detected by quantitative real time PCR (QRT-PCR). Statistical analyses were performed, variance between two groups were compared with Mann Whitney U test in GraphpadInstat algorithm with 95 % confidence interval and p < 0.05. After statistical analyses, we have determined that GRIN2B expression levels were down regulated in AD patients group with respect to control group. But expression level of this gene in each group was showed high variability. İn this study, we have determined that NMDA receptor coding gene GRIN2B expression level was down regulated in AD patients when compared with healthy control individuals. According to our results, we have speculated that GRIN2B expression level was associated with AD. But it is necessary to validate these results with bigger sample size.

Keywords: Alzheimer’s disease, N-methyl-d-aspartate receptor, NR2B, GRIN2B, mRNA expression, RT-PCR

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Authors: Prashant Verma, Prafulla K. Swain, K. K. Singh, Mukti Khetan

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Objective: In India, around 20,000 women die every year due to abortion-related complications. In the modelling of count variables, there is sometimes a preponderance of zero counts. This article concerns the estimation of various count regression models to predict the average number of spontaneous abortion among women in the Punjab state of India. It also assesses the factors associated with the number of spontaneous abortions. Materials and methods: The study included 27,173 married women of Punjab obtained from the DLHS-4 survey (2012-13). Poisson regression (PR), Negative binomial (NB) regression, zero hurdle negative binomial (ZHNB), and zero-inflated negative binomial (ZINB) models were employed to predict the average number of spontaneous abortions and to identify the determinants affecting the number of spontaneous abortions. Results: Statistical comparisons among four estimation methods revealed that the ZINB model provides the best prediction for the number of spontaneous abortions. Antenatal care (ANC) place, place of residence, total children born to a woman, woman's education and economic status were found to be the most significant factors affecting the occurrence of spontaneous abortion. Conclusions: The study offers a practical demonstration of techniques designed to handle count variables. Statistical comparisons among four estimation models revealed that the ZINB model provided the best prediction for the number of spontaneous abortions and is recommended to be used to predict the number of spontaneous abortions. The study suggests that women receive institutional Antenatal care to attain limited parity. It also advocates promoting higher education among women in Punjab, India.

Keywords: count data, spontaneous abortion, Poisson model, negative binomial model, zero hurdle negative binomial, zero-inflated negative binomial, regression

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Authors: Mohammad Imani, JabraeilNasl Seraji, Abolfazl Zakerian

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Background: Reaction time is the amount of time it takes to respond to a stimulus. In fact The time that passes between the introduction of a stimulus and the reaction by the subject to that stimulus. The aim of this interventional study is evaluation of bright light effects on concentration and diffuse attention reaction time, tension, angry, fatigue and alertness among shift workers. There are several incentives that can reduce the reaction time or added. Bright light as one of the environmental factors can reduce reaction time. Material &Method: This cross-sectional descriptive study was conducted in 1391, in 88 subjects (44 Fixed morning worker and 44 shift worker ) In a 24 h time (13-16-19-22-1-4-7-10) in an ordinary light situation after a randomly selected sample size calculation, concentration and diffuse attention test (reaction time) has been done. After intervention and using of bright light (4500lux), again reaction time test was done. After analyzing by ElISA method obtained data were analyzed by statistical software SPSS 19 and using T-test and ANOVA statistical analysis. Results: Between average of reaction time tests in ordinary light exposed to fixed morning workers and bright light exposed to shift worker, with 95% CI, (P>%5) there was no significant relationship. After the intervention and the use of bright light (4500 lux),between average of concentration and diffused attention reaction time tests in ordinary light exposure on the fixed morning workers and bright light exposure shift workers with 95% CI, (P<5%) there was significant relationship. Conclusion: In sometimes of 24 h during ordinary light exposure concentration and diffused attention reaction time has changed in shift workers. After intervention, during bright light (4500lux) exposure as a light shower, focused and diffuse attention reaction time, tension ,angry and fatigue decreased.

Keywords: bright light, reaction time, tension, angry, fatigue, alertness

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Authors: Eric Wilson Santos Cabral, Marta Monteiro Da Costa Cruz, Rodrigo Pirola Pestana, Vivian Andréa Parreira

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In the transportation sector in Brazil, there is a great challenge that is the maintenance of profit in the face of the great variation in the price of diesel. This directly affects the variable cost of transport companies. Within the railways, part of the great challenges is to overcome the annual budget, cargo and ore transported, thus reducing costs compared to previous years, becoming more efficient each year. Within this scenario, the railway companies are looking for effective measures, aiming at reducing the ratio of liter of diesel consumed by KTKB (Kilometer Gross Ton multiplied by thousand). This ratio represents the indicator of energy efficiency of some railroads in Brazil and in other countries. In this study, we sought to analyze the behavior of the energy efficiency indicator on two parts: The first, with the application of technologies used in locomotives, such as the start-stop system of the diesel engine and the system of tracking and monitoring of fuel. The second, evaluation of the behavior of the variation of the type of cargo transported (loading mix). The study focused on locomotive technology will be carried out using statistical analysis, behavioral evaluation in different operating conditions, such as maneuvers for trains, service trains and freight trains. The analysis will also cover the evaluation of the loading mix made using statistical analysis of the existing railroad database, comparing the energy efficiency per loading mine and type of product. With the completion of this study, the railway undertakings should be able to better target decision-making in order to achieve substantial reductions in transport costs.

Keywords: railway transport, energy efficiency, railway technology, fuel consumption

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Authors: Nikolaos Papadimas, Joanna Bennett, Amir Sakhaei, Timothy Dodwell

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Composite modeling of consolidation processes is playing an important role in the process and part design by indicating the formation of possible unwanted prior to expensive experimental iterative trial and development programs. Composite materials in their uncured state display complex constitutive behavior, which has received much academic interest, and this with different models proposed. Errors from modeling and statistical which arise from this fitting will propagate through any simulation in which the material model is used. A general hyperelastic polynomial representation was proposed, which can be readily implemented in various nonlinear finite element packages. In our case, FEniCS was chosen. The coefficients are assumed uncertain, and therefore the distribution of parameters learned using Markov Chain Monte Carlo (MCMC) methods. In engineering, the approach often followed is to select a single set of model parameters, which on average, best fits a set of experiments. There are good statistical reasons why this is not a rigorous approach to take. To overcome these challenges, A hierarchical Bayesian framework was proposed in which population distribution of model parameters is inferred from an ensemble of experiments tests. The resulting sampled distribution of hyperparameters is approximated using Maximum Entropy methods so that the distribution of samples can be readily sampled when embedded within a stochastic finite element simulation. The methodology is validated and demonstrated on a set of consolidation experiments of AS4/8852 with various stacking sequences. The resulting distributions are then applied to stochastic finite element simulations of the consolidation of curved parts, leading to a distribution of possible model outputs. With this, the paper, as far as the authors are aware, represents the first stochastic finite element implementation in composite process modelling.

Keywords: data-driven , material consolidation, stochastic finite elements, surrogate models

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Renata Markiewicz-Żukowska, Patryk Nowakowski, Sylwia K. Naliwajko, Jakub M. Bołtryk, Katarzyna Socha, Anna Puścion-Jakubik, Jolanta Soroczyńska, Maria H. Borawska

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Selenium (Se) is an essential element for human health. As an integral part of glutathione peroxidase, it has antioxidant, anti-inflammatory and anticancer activities. Unfortunately, Se dietary intake is often insufficient, especially in regions where the soil is low in Se. Therefore, in search for good sources of Se, the content of this element in food products should be monitored. Food product can be considered as a source of Se when its standard portion covers above 15% of recommended daily allowance. In the case of nuts, 42g is recognized as the standard portion. The aim of this study was to determine the Se content in nuts and to answer the question of whether the studied nuts can be considered as a source of Se in the diet. The material for the study consisted of 10 types of nuts (12 samples of each one): almonds, Brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts. The nuts were mineralized using microwave technique (Berghof, Germany). The content of Se was determined by atomic absorption spectrometry method with electrothermal atomization in a graphite tube with Zeeman background correction (Hitachi, Japan). The accuracy of the method was verified on certified reference material: Simulated Diet D. The statistical analysis was performed using Statistica v. 13.0 software. Statistical significance was determined at p < 0.05 level. The highest content of Se was found in Brazil nuts (4566.21 ± 3393.9 µg/kg) and the lowest in almonds (36.07 ± 18.8 µg/kg). A standard portion (42g) of almonds, brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts covers the recommended daily allowance for Se respectively in: 2, 192, 28, 2, 16, 7, 4, 3, 12, 6%. Brazil nuts, cashews and macadamia nuts can be considered as a good source of Se in diet.

Keywords: atomic absorption spectrometry, diet, nuts, selenium

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Authors: M. Arun, A. Kannan

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Textile industries cater to varied customer preferences and contribute substantially to the economy. However, these textile industries also produce a considerable amount of effluents. Prominent among these are the azo dyes which impart considerable color and toxicity even at low concentrations. Azo dyes are also used as coloring agents in food and pharmaceutical industry. Despite their applications, azo dyes are also notorious pollutants and carcinogens. Popular techniques like photo-degradation, biodegradation and the use of oxidizing agents are not applicable for all kinds of dyes, as most of them are stable to these techniques. Chemical coagulation produces a large amount of toxic sludge which is undesirable and is also ineffective towards a number of dyes. Most of the azo dyes are stable to UV-visible light irradiation and may even resist aerobic degradation. Adsorption has been the most preferred technique owing to its less cost, high capacity and process efficiency and the possibility of regenerating and recycling the adsorbent. Adsorption is also most preferred because it may produce high quality of the treated effluent and it is able to remove different kinds of dyes. However, the adsorption process is influenced by many variables whose inter-dependence makes it difficult to identify optimum conditions. The variables include stirring speed, temperature, initial concentration and adsorbent dosage. Further, the internal diffusional resistance inside the adsorbent particle leads to slow uptake of the solute within the adsorbent. Hence, it is necessary to identify optimum conditions that lead to high capacity and uptake rate of these pollutants. In this work, commercially available activated carbon was chosen as the adsorbent owing to its high surface area. A typical azo dye found in textile effluent waters, viz. the monoazo Acid Orange 10 dye (CAS: 1936-15-8) has been chosen as the representative pollutant. Adsorption studies were mainly focused at obtaining equilibrium and kinetic data for the batch adsorption process at different process conditions. Studies were conducted at different stirring speed, temperature, adsorbent dosage and initial dye concentration settings. The Full Factorial Design was the chosen statistical design framework for carrying out the experiments and identifying the important factors and their interactions. The optimum conditions identified from the experimental model were validated with actual experiments at the recommended settings. The equilibrium and kinetic data obtained were fitted to different models and the model parameters were estimated. This gives more details about the nature of adsorption taking place. Critical data required to design batch adsorption systems for removal of Acid Orange 10 dye and identification of factors that critically influence the separation efficiency are the key outcomes from this research.

Keywords: acid orange 10, activated carbon, optimum adsorption conditions, statistical design

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Authors: Akshay Patil

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Objective: Mediation analysis identifies causal pathways by testing the relationships between the NCR, the OS, and an intermediate variable that mediates the relationship between the Nimotuzumab-cisplatin-radiation (NCR) and OS. Introduction: In randomized controlled trials, the primary interest is in the mechanisms by which an intervention exerts its effects on the outcomes. Clinicians are often interested in how the intervention works (or why it does not work) through hypothesized causal mechanisms. In this work, we highlight the value of understanding causal mechanisms in randomized trial by applying causal mediation analysis in a randomized trial in oncology. Methods: Data was obtained from a phase III randomized trial (Subgroup of HPVNOCP). NCR is reported to significantly improve the OS of patients locally advanced head and neck cancer patients undergoing definitive chemoradiation. Here, based on trial data, the mediating effect of NCR on patient overall survival was systematically quantified through progression-free survival(PFS), disease free survival (DFS), Loco-regional failure (LRF), and the disease control rate (DCR), Overall response rate (ORR). Effects of potential mediators on the HR for OS with NCR versus cisplatin-radiation (CR) were analyzed by Cox regression models. Statistical analyses were performed using R software Version 3.6.3 (The R Foundation for Statistical Computing) Results: Effects of potential mediator PFS was an association between NCR treatment and OS, with an indirect-effect (IE) 0.76(0.62 – 0.95), which mediated 60.69% of the treatment effect. Taking into account baseline confounders, the overall adjusted hazard ratio of death was 0.64 (95% CI: 0.43 – 0.96; P=0.03). The DFS was also a significant mediator and had an IE 0.77 (95% CI; 0.62-0.93), 58% mediated). Smaller mediation effects (maximum 27%) were observed for LRF with IE 0.88(0.74 – 1.06). Both DCR and ORR mediated 10% and 15%, respectively, of the effect of NCR vs. CR on the OS with IE 0.65 (95% CI; 0.81 – 1.08) and 0.94(95% CI; 0.79 – 1.04). Conclusion: Our findings suggest that PFS and DFS were the most important mediators of the OS with nimotuzumab to weekly cisplatin-radiation in HPVNOCP.

Keywords: mediation analysis, cancer data, survival, NCR, HPV negative oropharyngeal

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Authors: Arin Ghazarian, Cyril Rakovski

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Differential privacy has become the leading technique to protect the privacy of individuals in a database while allowing useful analysis to be done and the results to be shared. It puts a guarantee on the amount of privacy loss in the worst-case scenario. Differential privacy is not a toggle between full privacy and zero privacy. It controls the tradeoff between the accuracy of the results and the privacy loss using a single key parameter called

Keywords: arrhythmia, cardiology, differential privacy, ECG, epsilon, medi-cal data, privacy preserving analytics, statistical databases

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Authors: Elizabeth Adebomi Akinlotan, Olukemi Odukoya

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INTRODUCTION: Physical Activity is defined as activity that involves bodily movement which is done as a part of daily activity in the form of working, playing, active transportation such as walking and also as a form of recreational activity. Physical inactivity has been identified as the fourth leading risk factor for global mortality and morbidity causing an estimated 3.2 million deaths globally and 5.5% of total deaths and it remains a pressing public health issue. There is a shift in the major causes of death from communicable to non-communicable diseases in many developed countries and this is fast becoming the case in developing countries. Physical activity is an important determinant of health and has been associated with lower mortality rates as it reduces the risk of developing chronic diseases such as diabetes mellitus, hypertension, stroke, cancer and osteoporosis. It improves musculoskeletal health, controls weight and reduces symptoms of depression. AIM: The aim is to study the knowledge, attitude and practices of physical activity among adults in Alimosho local government area. METHODOLOGY: This was a descriptive cross sectional survey designed to study the knowledge, attitude and practice of physical activity among adults in Alimosho Local Government Area. The study population were 250 adults aged 18-65 who were residents of the area of more than 6 months duration and had no chronic disease condition or physical disability. A multistage sampling method was used to select the respondents and data was collected using interviewer administered questionnaires. The data was analyzed with the use of EPI-info 2007 statistical software. Chi Square was thereafter used to test the association between selected variables. The level of statistical significance was set at 5% (p<0.05). RESULTS: In general, majority (61.6%) of the respondents had a good knowledge of what physical activity entails, 34.0% had fair knowledge and 4.4% had poor knowledge. There was a favorable attitude towards physical activity among the respondents with 82.4% having an overall positive attitude. Below a third of the respondents (26.4%) reported having a high physical activity (METS > 3001) while 40.0% had moderate (601-3000 METS) levels of activity and 33.6% were inactive (<600METS). There is statistical significance between the gender of the respondent and the levels of physical activity (p=0.0007); 75.2% males reached the minimum recommendations while 24.8% were inactive and 55.0% females reached the minimum recommendations while 45.0% were inactive. Results also showed that of 95 respondents who were satisfied with their levels of physical activity, 33.7% were insufficiently active while 66.3% were either minimally active or highly active and of 110 who were unsatisfied with their levels of physical activity, 72.0% were above the minimum recommendations while 38.0% were insufficiently active. CONCLUSION: In contrast to the high level of knowledge and favorable attitude towards physical activity, there was a lower level of practice of high or moderate physical activities. It is recommended that more awareness should be created on the recommended levels of physical activity especially for the vigorous intensity and moderate intensity physical activity.

Keywords: METS, physical activity, physical inactivity, public health

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Nino Paresashvili, Nino Abesadze

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The modern world faces huge challenges. Globalization changed the socio-economic conditions of many countries. The current processes in the global environment have a different impact on countries with different cultures. However, an alleviation of poverty and improvement of living conditions is still the basic challenge for the majority of countries, because much of the population still lives under the official threshold of poverty. It is very important to stimulate youth employment. In order to prepare young people for the labour market, it is essential to provide them with the appropriate professional skills and knowledge. It is necessary to plan efficient activities for decreasing an unemployment rate and for developing the perfect mechanisms for regulation of a labour market. Such planning requires thorough study and analysis of existing reality, as well as development of corresponding mechanisms. Statistical analysis of unemployment is one of the main platforms for regulation of the labour market key mechanisms. The corresponding statistical methods should be used in the study process. Such methods are observation, gathering, grouping, and calculation of the generalized indicators. Unemployment is one of the most severe socioeconomic problems in Georgia. According to the past as well as the current statistics, unemployment rates always have been the most problematic issue to resolve for policy makers. Analytical works towards to the above-mentioned problem will be the basis for the next sustainable steps to solve the main problem. The results of the study showed that the choice of young people is not often due to their inclinations, their interests and the labour market demand. That is why the wrong professional orientation of young people in most cases leads to their unemployment. At the same time, it was shown that there are a number of professions in the labour market with a high demand because of the deficit the appropriate specialties. To achieve healthy competitiveness in youth employment, it is necessary to formulate regional employment programs with taking into account the regional infrastructure specifications.

Keywords: unemployment, analysis, methods, tendencies, regulation mechanisms

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Authors: Ozan Kahraman, Hao Feng

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

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

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Authors: Ph. Fauquet-Alekhine

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Decision-making occurs several times an hour when working in high risk industry and an erroneous choice might have undesirable outcomes for people and the environment surrounding the industrial plant. Industrial decisions are very often made in a context of acute stress. Time pressure is a crucial stressor leading decision makers sometimes to boost up the decision-making process and if it is not possible then shift to the simplest strategy. We thus found it interesting to update the characterization of the stress factors impairing decision-making at Chinon Nuclear Power Plant (France) in order to optimize decision making contexts and/or associated processes. The investigation was based on the analysis of reports addressing safety events over the last 3 years. Among 93 reports, those explicitly addressing decision-making issues were identified. Characterization of each event was undertaken in terms of three criteria: stressors, biases impairing decision making and weaknesses of the decision-making process. The statistical analysis showed that biases were distributed over 10 possibilities among which the hypothesis confirmation bias was clearly salient. No significant correlation was found between criteria. The analysis indicated that the main stressor was time pressure and highlights an unexpected form of stressor: the trust asymmetry principle of the expert. The analysis led to the conclusion that this stressor impaired decision-making from a psychological angle rather than from a physiological angle: it induces defensive bias of self-esteem, self-protection associated with a bias of confirmation. This leads to the hypothesis that this stressor can intervene in some cases without being detected, and to the hypothesis that other stressors of the same kind might occur without being detected too. Further investigations addressing these hypotheses are considered. The analysis also led to the conclusion that dealing with these issues implied i) decision-making methods being well known to the workers and automated and ii) the decision-making tools being well known and strictly applied. Training was thus adjusted.

Keywords: bias, expert, high risk industry, stress.

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Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

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Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

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Authors: Sibel Karacaoglu, Fatma Ç. Kayapinar

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The aim of the study is to investigate the effect of the core training program on physical fitness characteristics and body composition in male volleyball players. 26 male university volleyball team players aged between 19 to 24 years who had no health problems and injury participated in the study. Subjects were divided into training (TG) and control groups (CG) as randomly. Data from twenty-one players who completed all training sessions were used for statistical analysis (TG,n=11; CG,n=10). A core training program was applied to the training group three days a week for 10 weeks. On the other hand, the control group did not receive any training. Before and after the 10-week training program, pre- and post-testing comprised of body composition measurements (weight, BMI, bioelectrical impedance analysis) and physical fitness measurements including flexibility (sit and reach test), muscle strength (back, leg and grip strength by dynamometer), muscle endurance (sit-ups and push-ups tests), power (one-legged jump and vertical jump tests), speed (20m sprint, 30m sprint) and balance tests (one-legged standing test) were performed. Changes of pre- and post- test values of the groups were determined by using dependent t test. According to the statistical analysis of data, no significant difference was found in terms of body composition in the both groups for pre- and post- test values. In the training group, all physical fitness measurements improved significantly after core training program (p<0.05) except 30m speed and handgrip strength (p>0.05). On the hand, only 20m speed test values improved after post-test period (p<0.05), but the other physical fitness tests values did not differ (p>0.05) between pre- and post- test measurement in the control group. The results of the study suggest that the core training program has positive effect on physical fitness characteristics in male volleyball players.

Keywords: body composition, core training, physical fitness, volleyball

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Authors: Hai L. Tran

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When covering events and issues, the news media often employ both personal accounts as well as facts and figures. However, the process of using numbers and narratives in the newsroom is mostly operated through trial and error. There is a demonstrated need for the news industry to better understand the specific effects of storytelling and data-driven reporting on the audience as well as explanatory factors driving such effects. In the academic world, anecdotal evidence and statistical evidence have been studied in a mutually exclusive manner. Existing research tends to treat pertinent effects as though the use of one form precludes the other and as if a tradeoff is required. Meanwhile, narratives and statistical facts are often combined in various communication contexts, especially in news presentations. There is value in reconceptualizing and theorizing about both relative and collective impacts of numbers and narratives as well as the mechanism underlying such effects. The current undertaking seeks to link theory to practice by providing a complete picture of how and why people are influenced by information conveyed through quantitative and qualitative accounts. Specifically, the cognitive-experiential theory is invoked to argue that humans employ two distinct systems to process information. The rational system requires the processing of logical evidence effortful analytical cognitions, which are affect-free. Meanwhile, the experiential system is intuitive, rapid, automatic, and holistic, thereby demanding minimum cognitive resources and relating to the experience of affect. In certain situations, one system might dominate the other, but rational and experiential modes of processing operations in parallel and at the same time. As such, anecdotes and quantified facts impact audience response differently and a combination of data and narratives is more effective than either form of evidence. In addition, the present study identifies several media variables and human factors driving the effects of statistics and anecdotes. An integrative model is proposed to explain how message characteristics (modality, vividness, salience, congruency, position) and individual differences (involvement, numeracy skills, cognitive resources, cultural orientation) impact selective exposure, which in turn activates pertinent modes of processing, and thereby induces corresponding responses. The present study represents a step toward bridging theoretical frameworks from various disciplines to better understand the specific effects and the conditions under which the use of anecdotal evidence and/or statistical evidence enhances or undermines information processing. In addition to theoretical contributions, this research helps inform news professionals about the benefits and pitfalls of incorporating quantitative and qualitative accounts in reporting. It proposes a typology of possible scenarios and appropriate strategies for journalists to use when presenting news with anecdotes and numbers.

Keywords: data, narrative, number, anecdote, storytelling, news

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