Search results for: statistical data analysis

Authors: Khajeerat Phumpurk

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This research aims to study the knowledge, attitude toward household accounting of people in Bangkeaw Samutsongkhram. The sample use in this study was the people in tambol bangkeao Mueang Samut songkhram province. The Sample size for 100 household by using the accidental sampling and data collection was a questionnaire. Statistical analysis for frequency, percentage, mean, minimum, maximum value and standard deviation. It was found that the most of the respondent are farmers for 63.4. Most of them are male, with average of age 49.54 years. The education is vocational. The average household income is 60,873.74 per year. The respondent has the debt with the bank for 64.4 percent. The greatest influence to do the household accounting of farmers is the bank expertise. All the advice about doing household accounts get it from the staff of the bank for agriculture and agricultural cooperatives. The farmers for 57.3 do household accounting during the night time.

Keywords: Bangkeaw Samutsongkhram, household accounting, knowledge, sufficiency economy

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Authors: Niloofar Fathizaviyehkord

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Just as two hands cannot make a good boxer, knowing two or more languages cannot make a skillful interpreter. Interpreting, either consecutive or simultaneous, is a cognitively demanding task requiring not only linguistic and discourse knowledge but also strategic competence. Moreover, experience level can play a very crucial role in the strategies interpreters may employ since translation and interpretation quality is a matter of experience, besides other factors, as well. With regard to the significance of strategic competence, this study investigated what strategies are mainly employed by interpreters, what strategies are employed more frequently, and whether experience level can affect the choice of strategies by interpreters or not. To collect the necessary data, the first retrospective interviews were held with 20 interpreters experienced more or less in simultaneous and consecutive interpretation to see what strategies other than those classified in the literature are employed by interpreters. Then, several classifications of strategies in literature were merged with those emerging from the retrospective interviews to come up with a comprehensive questionnaire on interpreting strategies. After seeking five experts’ opinions regarding the wording/content of the questionnaire, it was given to 60 interpreters. The statistical analysis of the questionnaire data and experience level through ANOVA showed experience level could affect the choice of strategies. This study closes with the theoretical/practical implications of the findings for interpreter training.

Keywords: experience level, consecutive and simultaneous, interpretation strategies, translation

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Authors: Bin Mu, Site Li, Shijin Yuan

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Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.

Keywords: AQI forecast, principal component analysis, genetic algorithm, back propagation neural network model

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Authors: Agnes Anyango Andollo, Jane Achieng Achola

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The use of Information and Communication Technologies (ICTs) has become one of the driving forces in facilitation of learning in most colleges. The ability to effectively harness the technology varies from college to college. The study objective was to determine the lecturers’, institutional attributes and policies that influence the utilization of ICT by the lecturers’. A cross sectional survey design was employed in order to empirically investigate the extent to which lecturers’ personal, institutional attributes and policies influence the utilization of ICT to facilitate learning. The target population of the study was 295 lecturers who facilitate learning at KMTC-Nairobi. Structured self-administered questionnaire was given to the lecturers. Quantitative data was scrutinized for completeness, accuracy and uniformity then coded. Data were analyzed in frequencies and percentages using Statistical Package for Social Sciences (SPSS) version 19, this was a reliable tool for quantitative data analysis. A total of 155 completed questionnaires administered were obtained from the respondents for the study that were subjected to analysis. The study found out that 93 (60%) of the respondents were male while 62 (40%) of the respondents were female. Individual’s educational level, age, gender and educational experience had the greatest impact on use of ICT. Lecturers’ own beliefs, values, ideas and thinking had moderate impact on use of ICT. And that institutional support by provision of resources for ICT related training such as internet, computers, laptops and projectors had moderate impact (p = 0.049) at 5% significant level on use of ICT. The study concluded that institutional attributes and ICT policy were keys to utilization of ICT by lecturers at KMTC Nairobi also mandatory policy on use of ICT by lecturers to facilitate learning was key. It recommended that policies should be put in place for Technical support to lecturers when in problem during utilization of ICT and also a mechanism should be put in place to make the use of ICT in teaching and learning mandatory.

Keywords: policy, computers education, medical training institutions, ICTs

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Authors: Mohamed Ali Abdennadher

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Digital rock physics, an emerging field leveraging advanced imaging and numerical techniques, offers a promising approach to investigating the mechanical properties of granular materials without extensive physical experiments. This study focuses on using X-Ray Computed Tomography (CT) to capture the three-dimensional (3D) structure of coarse-grained soil at the particle level, combined with finite element analysis (FEA) to simulate the soil's behavior under compression. The primary goal is to establish a reliable virtual testing framework that can replicate laboratory results and offer deeper insights into soil mechanics. The methodology involves acquiring high-resolution CT scans of coarse-grained soil samples to visualize internal particle morphology. These CT images undergo processing through noise reduction, thresholding, and watershed segmentation techniques to isolate individual particles, preparing the data for subsequent analysis. A custom Python script is employed to extract particle shapes and conduct a statistical analysis of particle size distribution. The processed particle data then serves as the basis for creating a finite element model comprising approximately 500 particles subjected to one-dimensional compression. The FEA simulations explore the effects of mesh refinement and friction coefficient on stress distribution at grain contacts. A multi-layer meshing strategy is applied, featuring finer meshes at inter-particle contacts to accurately capture mechanical interactions and coarser meshes within particle interiors to optimize computational efficiency. Despite the known challenges in parallelizing FEA to high core counts, this study demonstrates that an appropriate domain-level parallelization strategy can achieve significant scalability, allowing simulations to extend to very high core counts. The results show a strong correlation between the finite element simulations and laboratory compression test data, validating the effectiveness of the virtual experiment approach. Detailed stress distribution patterns reveal that soil compression behavior is significantly influenced by frictional interactions, with frictional sliding, rotation, and rolling at inter-particle contacts being the primary deformation modes under low to intermediate confining pressures. These findings highlight that CT data analysis combined with numerical simulations offers a robust method for approximating soil behavior, potentially reducing the need for physical laboratory experiments.

Keywords: X-Ray computed tomography, finite element analysis, soil compression behavior, particle morphology

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Authors: Fikru Fentaw Abera

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Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. Such hydrologic changes will affect almost every aspect of human well-being. The goal of this paper is to assess the impact of climate change on the hydrology of Upper Guder catchment located in northwest of Ethiopia. The GCM derived scenarios (HadCM3 A2a & B2a SRES emission scenarios) experiments were used for the climate projection. The statistical downscaling model (SDSM) was used to generate future possible local meteorological variables in the study area. The down-scaled data were then used as input to the soil and water assessment tool (SWAT) model to simulate the corresponding future stream flow regime in Upper Guder catchment of the Abay River Basin. A semi distributed hydrological model, SWAT was developed and Generalized Likelihood Uncertainty Estimation (GLUE) was utilized for uncertainty analysis. GLUE is linked with SWAT in the Calibration and Uncertainty Program known as SWAT-CUP. Three benchmark periods simulated for this study were 2020s, 2050s and 2080s. The time series generated by GCM of HadCM3 A2a and B2a and Statistical Downscaling Model (SDSM) indicate a significant increasing trend in maximum and minimum temperature values and a slight increasing trend in precipitation for both A2a and B2a emission scenarios in both Gedo and Tikur Inch stations for all three bench mark periods. The hydrologic impact analysis made with the downscaled temperature and precipitation time series as input to the hydrological model SWAT suggested for both A2a and B2a emission scenarios. The model output shows that there may be an annual increase in flow volume up to 35% for both emission scenarios in three benchmark periods in the future. All seasons show an increase in flow volume for both A2a and B2a emission scenarios for all time horizons. Potential evapotranspiration in the catchment also will increase annually on average 3-15% for the 2020s and 7-25% for the 2050s and 2080s for both A2a and B2a emissions scenarios.

Keywords: climate change, Guder sub-basin, GCM, SDSM, SWAT, SWAT-CUP, GLUE

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Authors: Alireza Monzavi Chaleshtari, Mahnaz Aliakbari Dehkordi, Mina Gholampour, Majid Saffarinia, Tayebeh Mohtashami, Amin Asadi Hieh

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The study aimed to assess the impact of positive psychology education on the social well-being of high school girls experiencing premenstrual syndrome (PMS). The statistical population comprised high school girls with PMS, with 30 randomly selected participants divided into two groups: 15 in the experimental group and 15 in the control group. The research employed a pre-test and post-test design using a standard questionnaire to evaluate premenstrual syndrome symptoms over a 7-day period before menstruation to a maximum of 2 days after menstruation, along with the Social Keys welfare questionnaire. During the study, the experimental group underwent an 8-session positive psychology group program. Data analysis was conducted using analysis of covariance. The results indicated a significant positive effect of positive psychology training on enhancing the social well-being of girls (p < 0.05). In conclusion, the findings suggest that positive psychology interventions can effectively increase social well-being among high school girls experiencing premenstrual syndrome.

Keywords: positive psychology, premenstrual syndrome, social welfare, girls

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Authors: Afia Naheed, Manmohan Singh, David Lucy

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This work is based on a mathematical as well as statistical study of an SEIJTR deterministic model for the interpretation of transmission of severe acute respiratory syndrome (SARS). Based on the SARS epidemic in 2003, the parameters are estimated using Runge-Kutta (Dormand-Prince pairs) and least squares methods. Possible graphical and numerical techniques are used to validate the estimates. Then effect of the model parameters on the dynamics of the disease is examined using sensitivity and uncertainty analysis. Sensitivity and uncertainty analytical techniques are used in order to analyze the affect of the uncertainty in the obtained parameter estimates and to determine which parameters have the largest impact on controlling the disease dynamics.

Keywords: infectious disease, severe acute respiratory syndrome (SARS), parameter estimation, sensitivity analysis, uncertainty analysis, Runge-Kutta methods, Levenberg-Marquardt method

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Seval Kizildag, Ozge Can Aran

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The effective intervention of counselors’ in conflict between spouses may be effective in increasing the quality of marital relationship. At this point, it is necessary for counselors to consider their own value systems at first and then reflect this correctly to the counseling process. For this reason, it is primarily important to determine the needs of counselors. Starting from this point of view, in this study, it is aimed to reveal the perspective of counselor candidates about the universal values in marriage relation. The study group of the survey was formed by sampling, which is one of the prospective sampling methods. As a criterion being a candidate for counseling area and having knowledge of the concepts of the Marriage and Family Counseling course is based, because, that candidate students have a comprehensive knowledge of the field and that students have mastered the concepts of marriage and family counseling will strengthen the findings of this study. For this reason, 61 counselor candidates, 32 (52%) female and 29 (48%) male counselor candidates, who were about to graduate from a university in south-east Turkey and who took a Marriage and Family Counseling course, voluntarily participated in the study. The average age of counselor candidates’ is 23. At the same time, 70 % of the parents of these candidates brought about their marriage through arranged marriage, 13% through flirting, 8% by relative marriage, 7% through friend circles and 2% by custom. The data were collected through Demographic Information Form and a form titled ‘Universal Values Form in Marriage’ which consists of six questions prepared by researchers. After the data were transferred to the computer, necessary statistical evaluations were made on the data. The qualitative data analysis was used on the data which was obtained in the study. The universal values which include six basic values covering trustworthiness, respect, responsibility, fairness, caring, citizenship, determined under the name as ‘six pillar of character’ are used as base and frequency values of the data were calculated trough content analysis. According to the findings of the study, while the value which most students find the most important value in marriage relation is being reliable, the value which they find the least important is to have citizenship consciousness. Also in this study, it is found out that counselor candidates associate the value of being trustworthiness ‘loyalty’ with (33%) as the highest in terms of frequency, the value of being respect ‘No violence’ with (23%), the value of responsibility ‘in the context of gender roles and spouses doing their owns’ with (35%) the value of being fairness ‘impartiality’ with (25%), the value of being caring ‘ being helpful’ with (25%) and finally as to the value of citizenship ‘love of country’ with (14%) and’ respect for the laws ‘ with (14%). It is believed that these results of the study will contribute to the arrangements for the development of counseling skills for counselor candidates regarding value in marriage and family counseling curricula.

Keywords: caring, citizenship, counselor candidate, fairness, marriage relationship, respect, responsibility, trustworthiness, value system

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Authors: Zikra Faiz, Nisar Abid, Muhammad Waqas

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This study aim to examine the challenges faced by teachers during teaching to those students who are intellectually disable, suffering from autism spectrum disorder, learning disability, and ADHD at the primary level. The descriptive research design of quantitative approach was adopted to conduct this study; a cross-sectional survey method was used to collect data. The sample was comprised of 258 (43 male and 215 female) teachers who teach at special education institutes of Lahore district selected through proportionate stratified random sampling technique. Self-developed questionnaire was used which was comprised of 22 closed-ended items. Collected data were analyzed through descriptive and inferential statistical techniques by using Statistical Package for Social Sciences (SPSS) version 21. Results show that teachers faced problems during group activities, to handle bad behavior and different disabilities of students. It is concluded that there was a significant difference between male and female teachers perceptions about challenges faced during teaching with developmental disable students. Furthermore, there was a significant difference exist in the perceptions of teachers regarding challenges faced during teaching to students with developmental disabilities in term of teachers’ age and area of specialization. It is recommended that developmentally disable student require extra attention so that, teacher should trained through pre-service and in-service training to teach developmentally disabled students.

Keywords: intellectual disability, autism spectrum disorder, ADHD, learning disability

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Authors: Pouriya Darabiyan, Hadis Nazari, Kourosh Zarea, Saeed Ghanbari, Zeinab Raiesifar, Morteza Khafaie, Hanna Tuvesson

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Introduction Since the beginning of mobile phone addiction, alexithymia, depression, anxiety and stress have been stated as risk factors for Internet addiction, so this study was conducted with the aim of investigating the relationship between Alexithymia and Mobile phone addiction along with the mediating role of anxiety, stress and depression. Materials and methods In this descriptive-analytical and cross-sectional study in 2022, 412 students School of Nursing & Midwifery of Ahvaz Jundishapur University of Medical Sciences were included in the study using available sampling method. Data collection tools were: Demographic Information Questionnaire, Toronto Alexithymia Scale (TAS-20), Depression, Anxiety, Stress Scale (DASS-21) and Mobile Phone Addiction Index (MPAI). Frequency, Pearson correlation coefficient test and linear regression were used to describe and analyze the data. Also, structural equation models and path analysis method were used to investigate the direct and indirect effects as well as the total effect of each dimension of Alexithymia on Mobile phone addiction with the mediating role of stress, depression and anxiety. Statistical analysis was done by SPSS version 22 and Amos version 16 software. Results Alexithymia was a predictive factor for mobile phone addiction. Also, Alexithymia had a positive and significant effect on depression, anxiety and stress. Depression, anxiety and stress had a positive and significant effect on mobile phone addiction. Depression, anxiety and stress variables played the role of a relative mediating variable between Alexithymia and mobile phone addiction. Alexithymia through depression, anxiety and stress also has an indirect effect on Internet addiction. Conclusion Alexithymia is a predictive factor for mobile phone addiction; And the variables of depression, anxiety and stress play the role of a relative mediating variable between Alexithymia and mobile phone addiction.

Keywords: alexithymia, mobile phone, depression, anxiety, stress

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Authors: U. Datta

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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.

Keywords: co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection

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Authors: Wale Agbaje

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The aim of this research study is to assess the impact of financial statement fraud on profitability of some selected Nigerian manufacturing firms covering (2002-2016). The specific objectives focused on to ascertain the effect of incorrect asset valuation on return on assets (ROA) and to ascertain the relationship between improper expense recognition and return on assets (ROA). To achieve these objectives, descriptive research design was used for the study while secondary data were collected from the financial reports of the selected firms and website of security and exchange commission. The analysis of covariance (ANCOVA) was used and STATA II econometric method was used in the analysis of the data. Altman model and operating expenses ratio was adopted in the analysis of the financial reports to create a dummy variable for the selected firms from 2002-2016 and validation of the parameters were ascertained using various statistical techniques such as t-test, co-efficient of determination (R2), F-statistics and Wald chi-square. Two hypotheses were formulated and tested using the t-statistics at 5% level of significance. The findings of the analysis revealed that there is a significant relationship between financial statement fraud and profitability in Nigerian manufacturing industry. It was revealed that incorrect assets valuation has a significant positive relationship and so also is the improper expense recognition on return on assets (ROA) which serves as a proxy for profitability. The implication of this is that distortion of asset valuation and expense recognition leads to decreasing profit in the long run in the manufacturing industry. The study therefore recommended that pragmatic policy options need to be taken in the manufacturing industry to effectively manage incorrect asset valuation and improper expense recognition in order to enhance manufacturing industry performance in the country and also stemming of financial statement fraud should be adequately inculcated into the internal control system of manufacturing firms for the effective running of the manufacturing industry in Nigeria.

Keywords: Althman's Model, improper expense recognition, incorrect asset valuation, return on assets

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Authors: Helena Bolini, Erick Esmerino, Adriano Cruz, Juliana Paixao

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The currently concern for health has increased demand for low-calorie ingredients and functional foods as probiotics. Understand the reasons that infer on food choice, besides a challenging task, it is important step for development and/or reformulation of existing food products. The use of appropriate multivariate statistical techniques, such as External Preference Map (PrefMap), associated with regression by Partial Least Squares (PLS) can help in determining those factors. Thus, this study aimed to determine, through PLS regression analysis, the sensory attributes considered drivers of liking in probiotic petit suisse cheeses, strawberry flavor, sweetened with different sweeteners. Five samples in same equivalent sweetness: PROB1 (Sucralose 0.0243%), PROB2 (Stevia 0.1520%), PROB3 (Aspartame 0.0877%), PROB4 (Neotame 0.0025%) and PROB5 (Sucrose 15.2%) determined by just-about-right and magnitude estimation methods, and three commercial samples COM1, COM2 and COM3, were studied. Analysis was done over data coming from QDA, performed by 12 expert (highly trained assessors) on 20 descriptor terms, correlated with data from assessment of overall liking in acceptance test, carried out by 125 consumers, on all samples. Sequentially, results were submitted to PLS regression using XLSTAT software from Byossistemes. As shown in results, it was possible determine, that three sensory descriptor terms might be considered drivers of liking of probiotic petit suisse cheese samples added with sweeteners (p<0.05). The milk flavor was noticed as a sensory characteristic with positive impact on acceptance, while descriptors bitter taste and sweet aftertaste were perceived as descriptor terms with negative impact on acceptance of petit suisse probiotic cheeses. It was possible conclude that PLS regression analysis is a practical and useful tool in determining drivers of liking of probiotic petit suisse cheeses sweetened with artificial and natural sweeteners, allowing food industry to understand and improve their formulations maximizing the acceptability of their products.

Keywords: acceptance, consumer, quantitative descriptive analysis, sweetener

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Authors: Marie Bieber, Fabrice Cosson, Olivier Schmitt

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Europe's Global Navigation Satellite System, Galileo, provides worldwide positioning and navigation services. The satellites in space are only one part of the Galileo system. An extensive ground infrastructure is essential to oversee the satellites and ensure accurate navigation signals. High reliability and availability of the entire Galileo system are crucial to continuously provide positioning information of high quality to users. Outages are tracked, and operational availability is regularly assessed. A highly flexible and adaptive tool has been developed to automate the Galileo system availability analysis. Not only does it enable a quick availability consolidation, but it also provides first steps towards improving the data quality of maintenance tickets used for the analysis. This includes data import and data preparation, with a focus on processing strings used for classification and identifying faulty data. Furthermore, the tool allows to handle a low amount of data, which is a major constraint when the aim is to provide accurate statistics.

Keywords: availability, data quality, system performance, Galileo, aerospace

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Authors: Odón R. Sánchez-Ccoyllo, Elizabeth Ayma-Choque, Alan Llacza

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Sulphur dioxide (SO₂) and surface-ozone (O₃) concentrations are associated with diseases. The objective of this research is to evaluate the effectiveness of the air-quality-WRF-Chem model with a horizontal resolution of 5 km x 5 km. For this purpose, the measurements of the hourly SO₂ and O₃ concentrations available in three air quality monitoring stations in Lima, Peru were used for the purpose of validating the simulations of the SO₂ and O₃ concentrations obtained with the WRF-Chem model in February 2018. For the quantitative evaluation of the simulations of these gases, statistical techniques were implemented, such as the average of the simulations; the average of the measurements; the Mean Bias (MeB); the Mean Error (MeE); and the Root Mean Square Error (RMSE). The results of these statistical metrics indicated that the simulated SO₂ and O₃ values over-predicted the SO₂ and O₃ measurements. For the SO₂ concentration, the MeB values varied from 0.58 to 26.35 µg/m³; the MeE values varied from 8.75 to 26.5 µg/m³; the RMSE values varied from 13.3 to 31.79 µg/m³; while for O₃ concentrations the statistical values of the MeB varied from 37.52 to 56.29 µg/m³; the MeE values varied from 37.54 to 56.70 µg/m³; the RMSE values varied from 43.05 to 69.56 µg/m³.

Keywords: ground-ozone, lima, sulphur dioxide, WRF-chem

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

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System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

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Authors: Fatai Shola Afolabi

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The study identified and evaluates the causes of building failure and examined the effects of building failure with respect to cost in Lagos State, Nigeria. The method employed in the collection of data includes the administration of questionnaire to professionals in the construction industry and case studies for the sites. A purposive sampling technique was used for selecting the sites visited, and selecting the construction professionals. Descriptive statistical techniques such as frequency distribution and percentages and mean response analysis were used to analyze data. The study revealed that the major causes of building failures were bad design, faulty construction, over loading, non-possession of approved drawings, Possession of approved drawings but non-compliance, and the use of quarks. In the two case studies considered, the total direct loss to the building owners was thirty eight million three hundred and eight five thousand, seven hundred and twenty one naira (38,385,721) which is about One hundred and ninety four thousand, eighty hundred and fifty one dollars ($194,851) at one hundred and ninety seven naira to one US dollars, central bank Nigeria of exchange rate as at 14th March, 2015.

Keywords: building structures, building failure, building collapse, structural failure, cost, direct loss

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Shilpa Avarebeel

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Background: India is one of the seven identified countries in South-East Asia region, regularly reporting dengue infection and may soon transform into a major niche for dengue epidemics. Objective: To study the clinical profile of dengue in our setting with special reference to respiratory complication. Study design: Descriptive and exploratory study, for one year in 2014. All patients confirmed as dengue infection were followed and their clinical profile, along with outcome was determined. Study proforma was designed based on the objective of the study and it was pretested and used after modification. Data was analyzed using statistical software SPSS-Version 16. Data were expressed as mean ±S .D for parametric variables and actual frequencies or percentage for non-parametric data. Comparison between groups was done using students’ t-test for independent groups, Chie square test, one-way ANOVA test, Karl Pearson’s correlation test. Statistical significance is taken at P < 0.05. Results: Study included 134 dengue positive cases. 81% had dengue fever, 18% had dengue hemorrhagic fever, and one had dengue shock syndrome. Most of the cases reported were during the month of June. Maximum number of cases was in the age group of 26-35 years. Average duration of hospital stay was less than seven days. Fever and myalgia was present in all the 134 patients, 16 had bleeding manifestation. 38 had respiratory symptoms, 24 had breathlessness, and 14 had breathlessness and dry cough. On clinical examination of patients with respiratory symptoms, all twenty-eight had hypoxia features, twenty-four had signs of pleural effusion, and four had ARDS features. Chest x-ray confirmed the same. Among the patients with respiratory symptoms, the mean platelet count was 26,537 c/cmm. There was no statistical significant difference in the platelet count in those with ARDS and other dengue complications. Average four units of platelets were transfused to all those who had ARDS in view of bleeding tendency. Mechanical ventilator support was provided for ARDS patients. Those with pleural effusion and pulmonary oedema were given NIV (non-invasive ventilation) support along with supportive care. However, steroids were given to patients with ARDS and 10 patients with signs of respiratory distress. 100%. Mortality was seen in patients with ARDS. Conclusion: Dengue has to be checked for those presenting with fever and breathlessness. Supportive treatments remain the cornerstone of treatment. Platelet transfusion has to be given only by clinical judgment. Steroids have no role except in early ARDS, which is controversial. Early NIV support helps in speedy recovery of dengue patients with respiratory distress.

Keywords: adult respiratory distress syndrome, dengue fever, non-invasive ventilation, pulmonary complication

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Authors: Navid Ebrahmi Madiseh, Sina Esfandiarpour-Broujeni, Rahil Razeghi

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Introduction: Quantitative analysis of game-related statistical parameters is widely used to evaluate basketball performance at both individual and team levels. Non-free throw shooting plays a crucial role as the primary scoring method, holding significant importance in the game's technical aspect. It has been explored the predictive value of game-related statistics in relation to various contextual and situational variables. Many similarities and differences also have been found between different age groups and levels of competition. For instance, in the World Basketball Championships after the 2010 rule change, 2-point field goals distinguished winners from losers in women's games but not in men's games, and the impact of successful 3-point field goals on women's games was minimal. The study aimed to identify and compare discriminant shooting-related statistics between winning and losing teams in men’s and women’s FIBA-U19-Basketball-World-Cup-2023 tournaments. Method: Data from 112 observations (2 per game) of 16 teams (for each gender) in the FIBA-U19-Basketball-World-Cup-2023 were selected as samples. The data were obtained from the official FIBA website using Python. Specific information was extracted, organized into a DataFrame, and consisted of twelve variables, including shooting percentages, attempts, and scoring ratio for 3-pointers, mid-range shots, paint shots, and free throws. Made% = scoring type successful attempts/scoring type total attempts¬ (1)Free-throw-pts% (free throw score ratio) = (free throw score/total score) ×100 (2)Mid-pts% (mid-range score ratio) = (mid-range score/total score) ×100 (3) Paint-pts% (paint score ratio) = (Paint score/total score) ×100 (4) 3p_pts% (three-point score ratio) = (three-point score/total score) ×100 (5) Independent t-tests were used to examine significant differences in shooting-related statistical parameters between winning and losing teams for both genders. Statistical significance was p < 0.05. All statistical analyses were completed with SPSS, Version 18. Results: The results showed that 3p-made%, mid-pts%, paint-made%, paint-pts%, mid-attempts, and paint-attempts were significantly different between winners and losers in men (t=-3.465, P<0.05; t=3.681, P<0.05; t=-5.884, P<0.05; t=-3.007, P<0.05; t=2.549, p<0.05; t=-3.921, P<0.05). For women, significant differences between winners and losers were found for 3p-made%, 3p-pts%, paint-made%, and paint-attempt (t=-6.429, P<0.05; t=-1.993, P<0.05; t=-1.993, P<0.05; t=-4.115, P<0.05; t=02.451, P<0.05). Discussion: The research aimed to compare shooting-related statistics between winners and losers in men's and women's teams at the FIBA-U19-Basketball-World-Cup-2023. Results indicated that men's winners excelled in 3p-made%, paint-made%, paint-pts%, paint-attempts, and mid-attempt, consistent with previous studies. This study found that losers in men’s teams had higher mid-pts% than winners, which was inconsistent with previous findings. It has been indicated that winners tend to prioritize statistically efficient shots while forcing the opponent to take mid-range shots. In women's games, significant differences in 3p-made%, 3p-pts%, paint-made%, and paint-attempts were observed, indicating that winners relied on riskier outside scoring strategies. Overall, winners exhibited higher accuracy in paint and 3P shooting than losers, but they also relied more on outside offensive strategies. Additionally, winners acquired a higher ratio of their points from 3P shots, which demonstrates their confidence in their skills and willingness to take risks at this competitive level.

Keywords: gender, losers, shoot-statistic, U19, winners

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Authors: Nasrin Badrkhani

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Teaching is an interaction between the teacher, student, and the concept being taught, especially within the classroom setting. As society increasingly values thoughtful and creative individuals, there is a growing need to adopt modern, active teaching methods. These methods should engage students in activities that foster problem-solving, creativity, cooperation, and scientific thinking skills. Modern teaching methods emphasize student involvement, gradual and continuous learning (process-centered approaches), and holistic evaluation of students' abilities and talents. A shift from teacher-centered to student-centered teaching is crucial. Among these modern methods are group work, role-playing, group discussions, and activities that engage students in evaluating societal values. This research employs a survey and a 38-question Likert scale questionnaire to explore teachers' perspectives on the impact of modern teaching methods on the quality of education. The study also examines the relationship between these perspectives and variables such as gender, major, and teaching experience. The statistical population consists of high school teachers in Shiraz, Iran, with sampling done using the Morgan table. Discriminant analysis was used for the initial analysis of the questions, and Cronbach's Alpha test was employed for the final examination. SPSS Software was used for statistical analysis, including T-tests and one-way ANOVA. The results indicate that teachers in this city generally have positive attitudes towards the use of modern teaching methods, except when it comes to engaging in judgments concerning societal values. There is no significant difference in viewpoints based on gender or educational background. The findings are consistent with similar studies conducted both within Iran and internationally.

Keywords: learning, modern methods, student, teacher, teaching

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Authors: Agustinus Rudatyo Himamunanto, Anastasia Rita Widiarti

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Transliteration using Javanese manuscripts is one of methods to preserve and legate the wealth of literature in the past for the present generation in Indonesia. The transliteration manual process commonly requires philologists and takes a relatively long time. The automatic transliteration process is expected to shorten the time so as to help the works of philologists. The preprocessing and segmentation stage firstly done is used to manage the document images, thus obtaining image script units that will compile input document images free from noise and have the similarity in properties in the thickness, size, and slope. The next stage of characteristic extraction is used to find unique characteristics that will distinguish each Javanese script image. One of characteristics that is used in this research is the number of black pixels in each image units. Each image of Java scripts contained in the data training will undergo the same process similar to the input characters. The system testing was performed with the data of the book Hamong Tani. The book Hamong Tani was selected due to its content, age and number of pages. Those were considered sufficient as a model experimental input. Based on the results of random page automatic transliteration process testing, it was determined that the maximum percentage correctness obtained was 81.53%. The percentage of success was obtained in 32x32 pixel input image size with the 5x5 image window. With regard to the results, it can be concluded that the automatic transliteration model offered is relatively good.

Keywords: Javanese script, character recognition, statistical, automatic transliteration

Procedia PDF Downloads 337

Authors: Ali Razmi, Saeed Golian

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Climate change is known to have the potential to impact adversely hydrologic patterns for variables such as rainfall, maximum and minimum temperature and sea level rise. Long-term average of these climate variables could possibly change over time due to climate change impacts. In this study, trend analysis was performed on rainfall, maximum and minimum temperature and water level data of a coastal area in Manhattan, New York City, Central Park and Battery Park stations to investigate if there is a significant change in the data mean. Partial Man-Kendall test was used for trend analysis. Frequency analysis was then performed on data using common probability distribution functions such as Generalized Extreme Value (GEV), normal, log-normal and log-Pearson. Goodness of fit tests such as Kolmogorov-Smirnov are used to determine the most appropriate distributions. In flood frequency analysis, rainfall and water level data are often separately investigated. However, in determining flood zones, simultaneous consideration of rainfall and water level in frequency analysis could have considerable effect on floodplain delineation (flood extent and depth). The present study aims to perform flood frequency analysis considering joint probability distribution for rainfall and storm surge. First, correlation between the considered variables was investigated. Joint probability distribution of extreme water level and temperature was also investigated to examine how global warming could affect sea level flooding impacts. Copula functions were fitted to data and joint probability of water level with rainfall and temperature for different recurrence intervals of 2, 5, 25, 50, 100, 200, 500, 600 and 1000 was determined and compared with the severity of individual events. Results for trend analysis showed increase in long-term average of data that could be attributed to climate change impacts. GEV distribution was found as the most appropriate function to be fitted to the extreme climate variables. The results for joint probability distribution analysis confirmed the necessity for incorporation of both rainfall and water level data in flood frequency analysis.

Keywords: climate change, climate variables, copula, joint probability

Procedia PDF Downloads 358

Authors: Alireza Nikbakht Shahbazi

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There are various factors that affect climate changes; drought is one of those factors. Investigation of efficient methods for estimating climate change impacts on drought should be assumed. The aim of this paper is to investigate climate change impacts on drought in Karoon3 watershed located south-western Iran in the future periods. The atmospheric general circulation models (GCM) data under Intergovernmental Panel on Climate Change (IPCC) scenarios should be used for this purpose. In this study, watershed drought under climate change impacts will be simulated in future periods (2011 to 2099). Standard precipitation index (SPI) as a drought index was selected and calculated using mean monthly precipitation data in Karoon3 watershed. SPI was calculated in 6, 12 and 24 months periods. Statistical analysis on daily precipitation and minimum and maximum daily temperature was performed. LRAS-WG5 was used to determine the feasibility of future period's meteorological data production. Model calibration and verification was performed for the base year (1980-2007). Meteorological data simulation for future periods under General Circulation Models and climate change IPCC scenarios was performed and then the drought status using SPI under climate change effects analyzed. Results showed that differences between monthly maximum and minimum temperature will decrease under climate change and spring precipitation shall increase while summer and autumn rainfall shall decrease. The precipitation occurs mainly between January and May in future periods and summer or autumn precipitation decline and lead up to short term drought in the study region. Normal and wet SPI category is more frequent in B1 and A2 emissions scenarios than A1B.

Keywords: climate change impact, drought severity, drought frequency, Karoon3 watershed

Procedia PDF Downloads 239

Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

Procedia PDF Downloads 206

Authors: Ali Akbar Heydari

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Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values.

Keywords: control chart, markov chain approach, statistical design, synthetic, variable parameter

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

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Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

Procedia PDF Downloads 199