Search results for: statistical characteristics

Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: S. Zhang, L. Zhang, X. Chen

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Due to the large uncertainty on the mechanical properties of FRP (fibre reinforced plastic), the reliability evaluation of FRP structures are currently receiving much attention in industry. However, possible statistical correlation between ply mechanical properties has been so far overlooked, and they are mostly assumed to be independent random variables. In this study, the statistical correlation between ply mechanical properties of uni-directional and plain weave composite is firstly analyzed by a combination of Monte-Carlo simulation and finite element modeling of the FRP unit cell. Large linear correlation coefficients between the in-plane mechanical properties are observed, and the correlation coefficients are heavily dependent on the uncertainty of the fibre volume ratio. It is also observed that the correlation coefficients related to Poisson’s ratio are negative while others are positive. To experimentally achieve the statistical correlation coefficients between in-plane mechanical properties of FRP, all concerned in-plane mechanical properties of the same specimen needs to be known. In-plane shear modulus of FRP is experimentally derived by the approach suggested in the ASTM standard D5379M. Tensile tests are conducted using the same specimens used for the shear test, and due to non-uniform tensile deformation a modification factor is derived by a finite element modeling. Digital image correlation is adopted to characterize the specimen non-uniform deformation. The preliminary experimental results show a good agreement with the numerical analysis on the statistical correlation. Then, failure probability of laminate plates is calculated in cases considering and not considering the statistical correlation, using the Monte-Carlo and Markov Chain Monte-Carlo methods, respectively. The results highlight the importance of accounting for the statistical correlation between ply mechanical properties to achieve accurate failure probability of laminate plates. Furthermore, it is found that for the multi-layer laminate plate, the statistical correlation between the ply elastic properties significantly affects the laminate reliability while the effect of statistical correlation between the ply strength is minimal.

Keywords: failure probability, FRP, reliability, statistical correlation

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Authors: Huijie Wang, Bin Zhang

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In China, the middle reaches of the Yangtze River are well-developed, boasting a wealth of different types of watershed landscape. In this regard, landscape character assessment (LCA) can serve as a basis for protection, management and planning of trans-regional watershed landscape types. For this study, we chose the middle reaches of the Yangtze River in Wuhan metropolitan area as our study site, wherein the water system consists of rich variety in landscape types. We analyzed trans-regional data to cluster and identify types of landscape characteristics at two levels. 55 basins were analyzed as variables with topography, land cover and river system features in order to identify the watershed landscape character types. For watershed landscape, drainage density and degree of curvature were specified as special variables to directly reflect the regional differences of river system features. Then, we used the principal component analysis (PCA) method and hierarchical clustering algorithm based on the geographic information system (GIS) and statistical products and services solution (SPSS) to obtain results for clusters of watershed landscape which were divided into 8 characteristic groups. These groups highlighted watershed landscape characteristics of different river systems as well as key landscape characteristics that can serve as a basis for targeted protection of watershed landscape characteristics, thus helping to rationally develop multi-value landscape resources and promote coordinated development of trans-regions.

Keywords: GIS, hierarchical clustering, landscape character, landscape typology, principal component analysis, watershed

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Authors: Mohammad A. Alolayan, John S. Evans, James K. Hammitt

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The value of statistical life has been estimated for a middle eastern country with high economical subsidization system. In this study, in-person interviews were conducted on a stratified random sample to estimate the value of mortality risk. Double-bounded dichotomous choice questions followed by open-ended question were used in the interview to investigate the willingness to pay of the respondent for mortality risk reduction. High willingness to pay was found to be associated with high income and education. Also, females were found to have lower willingness to pay than males. The estimated value of statistical life is larger than the ones estimated for western countries where taxation system exists. This estimate provides a baseline for monetizing the health benefits for proposed policy or program to the decision makers in an eastern country. Also, the value of statistical life for a country in the region can be extrapolated from this this estimate by using the benefit transfer method.

Keywords: mortality, risk, VSL, willingness-to-pay

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Authors: Dinku Seyoum Zeleke, Rong Fung Huang, Ching Min Hsu

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Comparison of flow and mixing characteristics between non-oscillating jet and transversely oscillating jet was investigated experimentally. Flow evolution process was detected by using high-speed digital camera, and jet spread width was calculated using binary edge detection techniques by using the long-exposure images. The velocity characteristics of transversely oscillating jet induced by a V-shaped fluidic oscillator were measured using single component hot-wire anemometer. The jet spread width of non-oscillating jet was much smaller than the jet exit gap because of behaving natural jet behaviors. However, the transversely oscillating jet has a larger jet spread width, which was associated with the excitation of the flow by self-induced oscillation. As a result, the flow mixing characteristics desperately improved both near-field and far-field. Therefore, this transversely oscillating jet has a better turbulence intensity, entrainment, and spreading width so that it augments flow-mixing characteristics desperately.

Keywords: flow mixing, transversely oscillating, spreading width, velocity characteristics

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Authors: Kafeero Frank

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This study aimed at developing a frame work for open source software adoption in an institution of higher learning in Uganda, with the case of KIU as a study area. There were mainly four research questions based on; individual staff interaction with open source software forum, perceived FOSS characteristics, organizational characteristics and external characteristics as factors that affect open source software adoption. The researcher used causal-correlation research design to study effects of these variables on open source software adoption. A quantitative approach was used in this study with self-administered questionnaire on a purposively and randomly sampled sample of university ICT staff. Resultant data was analyzed using means, correlation coefficients and multivariate multiple regression analysis as statistical tools. The study reveals that individual staff interaction with open source software forum and perceived FOSS characteristics were the primary factors that significantly affect FOSS adoption while organizational and external factors were secondary with no significant effect but significant correlation to open source software adoption. It was concluded that for effective open source software adoption to occur there must be more effort on primary factors with subsequent reinforcement of secondary factors to fulfill the primary factors and adoption of open source software. Lastly recommendations were made in line with conclusions for coming up with Kyambogo University frame work for open source software adoption in institutions of higher learning. Areas of further research recommended include; Stakeholders’ analysis of open source software adoption in Uganda; Challenges and way forward. Evaluation of Kyambogo University frame work for open source software adoption in institutions of higher learning. Framework development for cloud computing adoption in Ugandan universities. Framework for FOSS development in Uganda IT industry

Keywords: open source software., organisational characteristics, external characteristics, cloud computing adoption

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Authors: Maxim A. Kadatskiy, Konstantin V. Khishchenko

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Iron alloys are widespread components in various types of structural materials which are exposed to intensive thermal and mechanical loads. Various quantum-statistical cell models with the approximation of self-consistent field can be used for the prediction of the behavior of these materials under extreme conditions. The application of these models is even more valid, the higher the temperature and the density of matter. Results of Hugoniot calculation for iron alloys in the framework of three quantum-statistical (the Thomas–Fermi, the Thomas–Fermi with quantum and exchange corrections and the Hartree–Fock–Slater) models are presented. Results of quantum-statistical calculations are compared with results from other reliable models and available experimental data. It is revealed a good agreement between results of calculation and experimental data for terra pascal pressures. Advantages and disadvantages of this approach are shown.

Keywords: alloy, Hugoniot, iron, terapascal pressure

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Authors: Hyeon-Kyo Lim, Sang-Hun Byun

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Risk assessment is carried out in most industrial plants for accident prevention, but there exists insufficient data for statistical decision making. It is commonly said that risk can be expressed as a product of consequence and likelihood of a corresponding hazard factor. Eventually, therefore, risk assessment involves human decision making which cannot be objective per se. This study was carried out to comprehend perceptive characteristics of human beings in industrial plants. Subjects were shown a set of illustrations describing scenes of industrial plants, and were asked to assess the risk of each scene with not only linguistic variables but also numeric scores in the aspect of consequence and likelihood. After that, their responses were formulated as fuzzy membership functions, and compared with those of university students who had no experience of industrial works. The results showed that risk level of industrial workers were lower than those of any other groups, which implied that the workers might generally have a tendency to neglect more hazard factors in their work fields.

Keywords: fuzzy, hazard, linguistic variable, risk assessment

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Authors: Ibtihaj Al-Siyabi

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The crisis of COVID-19 posed enormous challenges to the statistical providers. While official statistics were disrupted by the pandemic and related containment measures, there was a growing and pressing need for real-time data and statistics to inform decisions. This paper gives an account of the way the pandemic impacted the operations of the National Statistical Offices (NSOs) in general in terms of data collection and methods used and the main challenges encountered by them based on international surveys. It highlights the performance of the Statistical Centre for the Cooperation Council for the Arab Countries of the Gulf, GCC-STAT, and its responsiveness to the pandemic placing special emphasis on environmental statistics. The paper concludes by confirming the GCC-STAT’s resilience and success in facing the challenges.

Keywords: NSO, COVID-19, statistics, crisis, pandemic

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Authors: Suresh Ramachandra

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Accounting values of firms are determined by strategies that firms pursue which are influenced by board characteristics specific to firms. Using two broad constructs of board characteristics, namely, board capital and board control, in the Malaysian context, this research attempts to infer their conjoint relevance to accounting values. The results of this research indicate that firms are able to increase their accounting values by deliberately selecting board characteristics which include director reputation and political affiliations.

Keywords: accounting values, board characteristics, board capital, board control

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Authors: Hussaini Bala

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There are different opinions in the literature on the relationship between Audit Committee characteristics and earnings management. The mix of opinions makes the direction of their relationship ambiguous. This study investigated the relationship between Audit Committee characteristics and earnings management of listed food and beverages Firms in Nigeria. The study covered the period of six years from 2007 to 2012. Data for the study were extracted from the Firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences. The dependent variable was generated using two steps regression in order to determine the discretionary accrual of the sample Firms. Multiple regression was employed to run the data of the study using Random Model. The results from the analysis revealed a significant association between audit committee characteristics and earnings management of the Firms. While audit committee size and committees’ financial expertise showed an inverse relationship with earnings management, committee’s independence, and frequency of meetings are positively and significantly related to earnings management. In line with the findings, the study recommended among others that listed food and beverages Firms in Nigeria should strictly comply with the provision of Companies and Allied Matters Act (CAMA) and SEC Code of Corporate Governance on the issues regarding Audit Committees. Regulators such as SEC should increase the minimum number of Audit Committee members with financial expertise and also have a statutory position on the maximum number of Audit Committees meetings, which should not be greater than four meetings in a year as SEC code of corporate governance is silent on this.

Keywords: audit committee, earnings management, listed Food and beverages size, leverage, Nigeria

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Authors: Bhavna Malik

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The study is build on a theoretical framework that links characteristics of job, key attitudes and intention to leave, why faculty may be disengaging from institutional service. The literature indicates that job characteristics, key attitudes and intention to leave are very important for effective organizational functioning. In general, the literature showed that some job characteristics might be the antecedents of job satisfaction and the aggregate variable job scope was positively associated with organizational commitment, and these key attitudes predicted intention to leave negatively. The present study attempted to propose a new integrative model of the relationships among job characteristics, key attitudes, and intention to leave. The main purpose of the present study is to examine the effects of job characteristics on intention to leave. While examining the role of job characteristics, the mediating roles of key attitudes were taken into account in order to better understand how job characteristics affect the exhibition of intention to leave. The secondary purpose is to investigate the effects of job characteristics on key attitudes, and the effects of key attitudes on intention to leave. Job characteristics of remuneration, resource for professional activities, career opportunities were positively associated with the work attitude of job satisfaction. The aggregate job scope was positively associated with the work attitude of organizational commitment although no single job characteristic was significantly associated with organizational commitment. Commitment, however, did not significantly affect time spent on institutional service. Two job characteristics—time spent on research and time spent on teaching—were negatively associated with this behavior. In general, the literature showed that some job characteristics might be the antecedents of job satisfaction and the aggregate variable job scope was positively associated with organizational commitment, and these key attiudes predicted intention to leave negatively. In turn, job satisfaction and organizational commitment were negatively associated with the intention to leave. In addition to these, organizational commitment was negatively associated with the intention to leave. However, no significant direct association was found between job characteristics and intention to leave.

Keywords: Job Characteristics Model, job satisfaction, organizational commitment, intention to leave

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Authors: H. Nouri, I. E. Achouri, A. Grimes, H. Ait Said, M. Aissou, Y. Zebboudj

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This paper aims to analysis the behaviour of DC corona discharge in wire-to-plate electrostatic precipitators (ESP). Current-voltage curves are particularly analysed. Experimental results show that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method of least squares. Least squares problems that of into two categories: linear or ordinary least squares and non-linear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. A closed-form solution (or closed form expression) is any formula that can be evaluated in a finite number of standard operations. The non-linear problem has no closed-form solution and is usually solved by iterative.

Keywords: electrostatic precipitator, current-voltage characteristics, least squares method, electric field, magnetic field

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Authors: Ali Bakhtiari Gharibdosti, Mohammad Hosein Bijeh Keshavarzi, Samira Alijani

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To understand internal characteristics relationships and determine factors which explain under consideration characteristics in rapeseed varieties, 10 rapeseed genotypes were implemented in complete accidental plot with three-time repetitions under drought stress in 2009-2010 in research field of agriculture college, Islamic Azad University, Karaj branch. In this research, 11 characteristics include of characteristics related to growth, production and functions stages was considered. Variance analysis results showed that there is a significant difference among rapeseed varieties characteristics. By calculating simple correlation coefficient under both conditions, normal and drought stress indicate that seed function characteristics in plant and pod number have positive and significant correlation in 1% probable level with seed function and selection on the base of these characteristics was effective for improving this function. Under normal and drought stress, analyzing the main factors showed that numbers of factors which have more than one amount, had five factors under normal conditions which were 82.72% of total variance totally, but under drought stress four factors diagnosed which were 76.78% of total variance. By considering total results of this research and by assessing effective characteristics for factor analysis and selecting different components of these characteristics, they can be used for modifying works to select applicable and tolerant genotypes in drought stress conditions.

Keywords: correlation, drought stress, factor analysis, rapeseed

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: Mohammad Shokoohi-Yekta, Hamid Mirebrahim

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A body of research has been devoted to investigating the preferences of computer programmers. These researches used various questionnaires to find out what programming language is most popular among programmers. The problem with such research is that the programmers are usually familiar with only a few languages; therefore, disregarding a number of other languages which might have characteristics that match their preferences more closely. To overcome such a problem, we decided to investigate the preferences of programmers in regards to the characteristics of languages, which help us to discover the languages that include the most characteristics preferred by the users. We conducted a user study to measure the preferences of programmers on different characteristics of programming languages and then tried to compare existing languages in the areas of application, Web and system programming. Overall, the results of our study indicated that the Ruby programming language has the highest preference score in the two areas of application and Web, and C++ has the highest score in the system area. The results of our study can also help programming language designers know the characteristics they should consider when developing new programming languages in order to attract more programmers.

Keywords: object orientation, programming language design, programmers' preferences, characteristic

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Authors: Okolie Chukwulozie Paul, Iwenofu Chinwe Onyedika, Sinebe Jude Ebieladoh, M. C. Nwosu

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The research work is based on the statistical analysis of the processing data. The essence is to analyze the data statistically and to generate a design model for the production mix of soap manufacturing products in Bejoy manufacturing company Nkpologwu, Aguata Local Government Area, Anambra state, Nigeria. The statistical analysis shows the statistical analysis and the correlation of the data. T test, Partial correlation and bi-variate correlation were used to understand what the data portrays. The design model developed was used to model the data production yield and the correlation of the variables show that the R2 is 98.7%. However, the results confirm that the data is fit for further analysis and modeling. This was proved by the correlation and the R-squared.

Keywords: General Linear Model, correlation, variables, pearson, significance, T-test, soap, production mix and statistic

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Authors: Da-Ming Huang, Ya-Ting Tsai, Shyh-Hau Wang

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Ultrasound signals backscattered from the soft tissues are mainly depending on the size, density, distribution, and other elastic properties of scatterers in the interrogated sample volume. The quantitative analysis of ultrasonic backscattering is frequently implemented using the statistical approach due to that of backscattering signals tends to be with the nature of the random variable. Thus, the statistical analysis, such as Nakagami statistics, has been applied to characterize the density and distribution of scatterers of a sample. Yet, the accuracy of statistical analysis could be readily affected by the receiving signals associated with the nature of incident ultrasound wave and acoustical properties of samples. Thus, in the present study, efforts were made to explore such effects as the ultrasound operational modes and attenuation of biological tissue on the estimation of corresponding Nakagami statistical parameter (m parameter). In vitro measurements were performed from healthy and pathological fibrosis porcine livers using different single-element ultrasound transducers and duty cycles of incident tone burst ranging respectively from 3.5 to 7.5 MHz and 10 to 50%. Results demonstrated that the estimated m parameter tends to be sensitively affected by the use of ultrasound operational modes as well as the tissue attenuation. The healthy and pathological tissues may be characterized quantitatively by m parameter under fixed measurement conditions and proper calibration.

Keywords: ultrasound backscattering, statistical analysis, operational mode, attenuation

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making.

Keywords: data analytics, mathematical sciences, optimization, machine learning, interdisciplinary collaboration, practical applications

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Authors: Dongjoo Kim, Chang-Sik Son, Won-Seok Kang

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Adequate sleep affects various parts of one’s overall physical and mental life. As one of the methods in determining the appropriate amount of sleep, this research presents a heart rate based sleep quality index. In order to evaluate sleep quality using the heart rate, sleep data from 280 subjects taken over one month are used. Their sleep data are categorized by a three-part heart rate range. After categorizing, some features are extracted, and the statistical significances are verified for these features. The results show that some features of this sleep quality index model have statistical significance. Thus, this heart rate based sleep quality index may be a useful discriminator of sleep.

Keywords: sleep, sleep quality, heart rate, statistical analysis

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Authors: Abdeghani Qadem

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Climate change is now an undeniable reality with increasing impacts on water systems worldwide, especially leading to severe drought episodes. The Southern Mediterranean region is particularly affected by this drought, which can have devastating consequences on water resources. Morocco, due to its geographical location in North Africa and the Southern Mediterranean, is especially vulnerable to these effects of climate change, particularly drought. In this context, this article focuses on the study of climate variability and drought characteristics in the Saiss Plateau region and its adjacent areas with the Middle Atlas, using specific statistical indices. The study begins by analyzing the annual precipitation variation, with a particular emphasis on data homogenization and gap filling using a regional vector. Then, the analysis delves into drought episodes in the region, using the Standardized Precipitation Index (SPI) over a 12-month period. The central objective is to accurately assess significant drought changes between 1980 and 2015, based on data collected from nine meteorological stations located in the study area.

Keywords: climate variability, regional vector, drought, standardized precipitation index, Saiss Plateau, middle atlas

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Authors: K. B. Rajasekarababu, J. Karthik, G. Vinayagamurthy

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This paper is intended to explain the influence of major axis on aerodynamic characteristics of elliptical section. Many engineering applications such as off shore structures, bridge piers, civil structures and pipelines can be modelled as a circular cylinder but flow over complex bodies like, submarines, Elliptical wing, fuselage, missiles, and rotor blades, in which the parameters such as axis ratio can influence the flow characteristics of the wake and nature of separation. Influence of Major axis in Flow characteristics of elliptical sections are examined both experimentally and computationally in this study. For this research, four elliptical models with varying major axis [*AR=1, 4, 6, 10] are analysed. Experimental works have been conducted in a subsonic wind tunnel. Furthermore, flow characteristics on elliptical model are predicted from k-ε turbulence model using the commercial CFD packages by pressure based transient solver with Standard wall conditions.The analysis can be extended to estimation and comparison of Drag coefficient and Fatigue analysis of elliptical sections.

Keywords: elliptical section, major axis, aerodynamic characteristics, k-ε turbulence model

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Authors: Jungyeol Hong, Dongjoo Park

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The primary purpose of this study is to develop a methodological framework to predict daily subway ridership at a station-level and to examine the association between subway ridership and bus demand incorporating commercial business facility in the vicinity of each subway station. The socio-economic characteristics, land-use, and built environment as factors may have an impact on subway ridership. However, it should be considered not only the endogenous relationship between bus and subway demand but also the characteristics of commercial business within a subway station’s sphere of influence, and integrated transit network topology. Regarding a statistical approach to estimate subway ridership at a station level, therefore it should be considered endogeneity and heteroscedastic issues which might have in the subway ridership prediction model. This study focused on both discovering the impacts of bus demand, commercial business characteristics, and network topology on subway ridership and developing more precise subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers entire Seoul city in South Korea and includes 243 stations with the temporal scope set at twenty-four hours with one-hour interval time panels each. The data for subway and bus ridership was collected Seoul Smart Card data from 2015 and 2016. Three-Stage Least Square(3SLS) approach was applied to develop daily subway ridership model as capturing the endogeneity and heteroscedasticity between bus and subway demand. Independent variables incorporating in the modeling process were commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. As a result, it was found that bus ridership and subway ridership were endogenous each other and they had a significantly positive sign of coefficients which means one transit mode could increase another transportation mode’s ridership. In other words, two transit modes of subway and bus have a mutual relationship instead of the competitive relationship. The commercial business characteristics are the most critical dimension among the independent variables. The variables of commercial business facility rate in the paper containing six types; medical, educational, recreational, financial, food service, and shopping. From the model result, a higher rate in medical, financial buildings, shopping, and food service facility lead to increment of subway ridership at a station, while recreational and educational facility shows lower subway ridership. The complex network theory was applied for estimating integrated network topology measures that cover the entire Seoul transit network system, and a framework for seeking an impact on subway ridership. The centrality measures were found to be significant and showed a positive sign indicating higher centrality led to more subway ridership at a station level. The results of model accuracy tests by out of samples provided that 3SLS model has less mean square error rather than OLS and showed the methodological approach for the 3SLS model was plausible to estimate more accurate subway ridership. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017R1C1B2010175).

Keywords: subway ridership, bus ridership, commercial business characteristic, endogeneity, network topology

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: Nishi Bhuvandas, P. V. Timbadiya, P. L. Patel, P. D. Porey

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Recent perceived climate variability raises concerns with unprecedented hydrological phenomena and extremes. Distribution and circulation of the waters of the Earth become increasingly difficult to determine because of additional uncertainty related to anthropogenic emissions. According to the sixth Intergovernmental Panel on Climate Change (IPCC) Technical Paper on Climate Change and water, changes in the large-scale hydrological cycle have been related to an increase in the observed temperature over several decades. Although many previous research carried on effect of change in climate on hydrology provides a general picture of possible hydrological global change, new tools and frameworks for modelling hydrological series with nonstationary characteristics at finer scales, are required for assessing climate change impacts. Of the downscaling techniques, dynamic downscaling is usually based on the use of Regional Climate Models (RCMs), which generate finer resolution output based on atmospheric physics over a region using General Circulation Model (GCM) fields as boundary conditions. However, RCMs are not expected to capture the observed spatial precipitation extremes at a fine cell scale or at a basin scale. Statistical downscaling derives a statistical or empirical relationship between the variables simulated by the GCMs, called predictors, and station-scale hydrologic variables, called predictands. The main focus of the paper is on the need for using statistical downscaling techniques for projection of local hydrometeorological variables under climate change scenarios. The projections can be then served as a means of input source to various hydrologic models to obtain streamflow, evapotranspiration, soil moisture and other hydrological variables of interest.

Keywords: climate change, downscaling, GCM, RCM

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Authors: Fouad Berrabeh, Sabir Messalti

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The performances of the photovoltaic systems are very dependent on different conditions, such as solar irradiation, temperature, etc. Therefore, it is very important to provide detailed studies for different cases in order to provide continuously power, so the photovoltaic system must be properly sized. This paper presents the modelling and simulation of the photovoltaic cell using single diode model. I-V characteristics and P-V characteristics are presented and it verified at different conditions (irradiance effect, temperature effect, series resistance effect).

Keywords: photovoltaic cell, BP SX 150 BP solar photovoltaic module, irradiance effect, temperature effect, series resistance effect, I–V characteristics, P–V characteristics

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Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

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Authors: Shishen Xie, Yingda L. Xie

Abstract:

Tuberculosis (TB) is a potentially serious infectious disease that remains a health concern. The Interferon Gamma Release Assay (IGRA) is a blood test to find out if an individual is tuberculous positive or negative. This study applies statistical analysis to the clinical data of interferon-gamma levels of seventy-three subjects who diagnosed pulmonary TB in an anti-tuberculous treatment. Data analysis is performed to determine if there is a significant decline in interferon-gamma levels for the subjects during a period of six months, and to infer if the anti-tuberculous treatment is effective.

Keywords: data analysis, interferon gamma release assay, statistical methods, tuberculosis infection

Procedia PDF Downloads 281

Authors: Leo Carlo B. Rondina

Abstract:

Self-image is an important factor to improve the self-esteem of the personnel. The purpose of the study is to determine the self-image of the police. The respondents were the 503 policemen assigned in different Police Station in Davao City, and they were chosen with the used of random sampling. With the used of Exploratory Factor Analysis (EFA), latent construct variables of police image were identified as follows; professionalism, obedience, morality and justice and fairness. Further, ordinal regression indicates statistical characteristics on ages 21-40 which means the age of the respondent statistically improves self-image.

Keywords: police image, exploratory factor analysis, ordinal regression, Galatea effect

Procedia PDF Downloads 257

Authors: Sayyed Yaghoub Zolfegharifar, Khairul Anuar Kassim, Hossein Khoramrooz, Khodayar Farhadiasl, Sadegh Jahan

Abstract:

Iran is one of the world's seismically active countries so that it experiences many small to medium earthquakes annually and a large earthquake every ten years. Due to seism tectonic conditions and special geographical and climatic position, Iran has the potential to create numerous severe earthquakes. Therefore, seismicity studies and seismic zonation of seismic zones of the country are necessary. In this article, the effect of local site conditions on the characteristics of seismic design in Rahmormoz will be examined. After analyzing the seismic hazard for Rahmormoz through deterministic and statistical methods and preparing the necessary geotechnical models based on available data, the ground response will be analyzed for different parts of the city based on four inputs and acceleration level estimated for bedrock through the equivalent linear method and by means of Deep Soil program. Finally, through the analysis of the obtained results, the seismic profiles of the ground surface for different parts of the city will be presented.

Keywords: seismic microzonation, ground response, resonance spectrum, period, site conditions

Procedia PDF Downloads 320