Search results for: Logistic regression analysis

Authors: Rachel C. F. Sun

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This study examined the predictive effects of moral competence, prosocial norms and positive behavior recognition on school misbehavior among Chinese junior secondary school students. Results of multiple regression analysis showed that students were more likely to misbehave in school when they had lower levels of moral competence and prosocial norms, and when they perceived their positive behavior being less likely recognized. Practical implications were discussed on how to guide students to make the right choices to behave appropriately in school. Implications for future research were also discussed.

Keywords: Moral competence, positive behavior recognition, prosocial norms, school misbehavior.

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Authors: N.Peatciyammal, B.Balachandar, M.Dinesh Kumar, K.Tamilarasan, C.Muthukumaran

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Enzymatic hydrolysis of starch from natural sources finds potential application in commercial production of alcoholic beverage and bioethanol. In this study the effect of starch concentration, temperature, time and enzyme concentration were studied and optimized for hydrolysis of Potato starch powder (of mesh 80/120) into glucose syrup by immobilized (using Sodium arginate) α-amylase using central composite design. The experimental result on enzymatic hydrolysis of Potato starch was subjected to multiple linear regression analysis using MINITAB 14 software. Positive linear effect of starch concentration, enzyme concentration and time was observed on hydrolysis of Potato starch by α-amylase. The statistical significance of the model was validated by F-test for analysis of variance (p ≤ 0.01). The optimum value of starch concentration, enzyme concentration, temperature, time and were found to be 6% (w/v), 2% (w/v), 40°C and 80min respectively. The maximum glucose yield at optimum condition was 2.34 mg/mL.

Keywords: Alcoholic beverage, Central Composite Design, Enzymatic hydrolysis, Glucose yield, Potato Starch.

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Authors: Anjaneyulu M.V.L.R., Harikrishna M., Chenchuobulu S.

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Rapid urbanization, industrialization and population growth have led to an increase in number of automobiles that cause air pollution. It is estimated that road traffic contributes 60% of air pollution in urban areas. A case by case assessment is required to predict the air quality in urban situations, so as to evolve certain traffic management measures to maintain the air quality levels with in the tolerable limits. Calicut city in the state of Kerala, India has been chosen as the study area. Carbon Monoxide (CO) concentration was monitored at 15 links in Calicut city and air quality performance was evaluated over each link. The CO pollutant concentration values were compared with the National Ambient Air Quality Standards (NAAQS), and the CO values were predicted by using CALINE4 and IITLS and Linear regression models. The study has revealed that linear regression model performs better than the CALINE4 and IITLS models. The possible association between CO pollutant concentration and traffic parameters like traffic flow, type of vehicle, and traffic stream speed was also evaluated.

Keywords: CO pollution, Modelling, Traffic stream parameters.

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: Local nonlinear estimation, LWPR algorithm, Online training method.

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Authors: Pisit Potjanajaruwit

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The objectives of the study were to determine the marketing mix factors that influencing tourist’s destination decision making for cultural tourism in the Chonburi province. Both quantitative and qualitative data were applied in this study. The samples of 400 cases for quantitative analysis were tourists (both Thai and foreign) who were interested in cultural tourism in the Chonburi province, and traveled to cultural sites in Chonburi and 14 representatives from provincial tourism committee of Chonburi and local tourism experts. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. The study found that Thai and foreign tourists are influenced by different important marketing mix factors. The important factors for Thai respondents were physical evidence, price, people, and place at high importance level. For foreign respondents, physical evidence, price, people, and process were high importance level, whereas, product, place and promotion were moderate importance level.

Keywords: Chonburi Province, Decision Making for cultural tourism, Marketing Mixed.

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Authors: K. Knüppel, G. Meyer, P. Nyhuis

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The increasing interconnectedness and complexity of  production processes raise the susceptibility of production systems to  failure. Therefore, the ability to respond quickly to failures is  increasingly becoming a competitive factor. The research project  "Sustainable failure management in manufacturing SMEs" is  developing a methodology to identify failures in the production and  select preventive and reactive measures in order to correct failures  and to establish sustainable failure management systems.

 

Keywords: Failure categorization, failure management, logistic performance, production optimization.

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Authors: Bundit Pungnirund

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The objective of this research was to study the influence of marketing mix on customers purchasing behavior. A total of 397 respondents were collected from customers who were the patronages of the Chatuchak Plaza market. A questionnaire was utilized as a tool to collect data. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. Data were analyzed by using Statistical Package for the Social Sciences. The findings revealed that the majority of respondents were male with the age between 25-34 years old, hold undergraduate degree, married and stay together. The average income of respondents was between 10,001-20,000 baht. In terms of occupation, the majority worked for private companies. The research analysis disclosed that there were three variables of marketing mix which included price (X2), place (X3), and product (X1) which had an influence on the frequency of customer purchasing. These three variables can predict a purchase about 30 percent of the time by using the equation; Y1 = 6.851 + .921(X2) + .949(X3) + .591(X1). It also found that in terms of marketing mixed, there were two variables had an influence on the amount of customer purchasing which were physical characteristic (X6), and the process (X7). These two variables are 17 percent predictive of a purchasing by using the equation: Y2 = 2276.88 + 2980.97(X6) + 2188.09(X7).

Keywords: Influences, Marketing Mixed, Purchasing Behavior.

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Authors: R. Sadeghzadegan, S. A. Naeini, A. Mirzaii

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In this article, the results of a series of carefully conducted laboratory test program were represented to determine the small strain shear modulus of sand mixed with a range of kaolinite including zero to 30%. This was experimentally achieved using a triaxial cell equipped with bender element. Results indicate that small shear modulus tends to increase, while clay content decreases and effective confining pressure increases. The exponent of stress in the power model regression analysis was not sensitive to the amount of clay content for all sand clay mixtures, while coefficient A was directly affected by change in clay content.

Keywords: Small shear modulus, bender element test, plastic fines, sand.

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Authors: Leszek Chybowski, Artur Bejger, Katarzyna Gawdzińska

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Subversion analysis is a tool used in the TRIZ (Theory of Inventive Problem Solving) methodology. This article introduces the history and describes the process of subversion analysis, as well as function analysis and analysis of the resources, used at the design stage when generating possible undesirable situations. The article charts the course of subversion analysis when applied to a fuel injection nozzle of a marine engine. The work describes the fuel injector nozzle as a technological system and presents principles of analysis for the causes of a cracked tip of the nozzle body. The system is modelled with functional analysis. A search for potential causes of the damage is undertaken and a cause-and-effect analysis for various hypotheses concerning the damage is drawn up. The importance of particular hypotheses is evaluated and the most likely causes of damage identified.

Keywords: Complex technical system, fuel injector, function analysis, importance analysis, resource analysis, sabotage analysis, subversion analysis, TRIZ.

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Authors: Debasis Sarkar, Jatan Talati

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This paper is an attempt to develop an Integrated Mass Rapid Transit System (MRTS) for a smart city project in Western India. Integrated transportation is one of the enablers of smart transportation for providing a seamless intercity as well as regional level transportation experience. The success of a smart city project at the city level for transportation is providing proper integration to different mass rapid transit modes by way of integrating information, physical, network of routes fares, etc. The methodology adopted for this study was primary data research through questionnaire survey. The respondents of the questionnaire survey have responded on the issues about their perceptions on the ways and means to improve public transport services in urban cities. The respondents were also required to identify the factors and attributes which might motivate more people to shift towards the public mode. Also, the respondents were questioned about the factors which they feel might restrain the integration of various modes of MRTS. Furthermore, this study also focuses on developing a utility equation for respondents with the help of multiple linear regression analysis and its probability to shift to public transport for certain factors listed in the questionnaire. It has been observed that for shifting to public transport, the most important factors that need to be considered were travel time saving and comfort rating. Also, an Integrated MRTS can be obtained by combining metro rail with BRTS, metro rail with monorail, monorail with BRTS and metro rail with Indian railways. Providing a common smart card to transport users for accessing all the different available modes would be a pragmatic solution towards integration of the available modes of MRTS.

Keywords: Mass rapid transit systems, smart city, metro rail, bus rapid transit system, multiple linear regression, smart card, automated fare collection system.

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Authors: Manisha Rathi, Thierry Chaussalet

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Hospital staff and managers are under pressure and concerned for effective use and management of scarce resources. The hospital admissions require many decisions that have complex and uncertain consequences for hospital resource utilization and patient flow. It is challenging to predict risk of admissions and length of stay of a patient due to their vague nature. There is no method to capture the vague definition of admission of a patient. Also, current methods and tools used to predict patients at risk of admission fail to deal with uncertainty in unplanned admission, LOS, patients- characteristics. The main objective of this paper is to deal with uncertainty in health system variables, and handles uncertain relationship among variables. An introduction of machine learning techniques along with statistical methods like Regression methods can be a proposed solution approach to handle uncertainty in health system variables. A model that adapts fuzzy methods to handle uncertain data and uncertain relationships can be an efficient solution to capture the vague definition of admission of a patient.

Keywords: Admission, Fuzzy, Regression, Uncertainty

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Authors: Hyoup-Sang Yoon

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The quality of video transmitted by mobile ad hoc networks (MANETs) can be influenced by several factors, including protocol layers; parameter settings of each protocol. In this paper, we are concerned with understanding the functional relationship between these influential factors and objective video quality in MANETs. We illustrate a systematic statistical design of experiments (DOE) strategy can be used to analyze MANET parameters and performance. Using a 2^k factorial design, we quantify the main and interactive effects of 7 factors on a response metric (i.e., mean opinion score (MOS) calculated by PSNR with Evalvid package) we then develop a first-order linear regression model between the influential factors and the performance metric.

Keywords: Evalvid, full factorial design, mobile ad hoc networks, ns-2.

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Authors: H. Silvestre

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The aim of this paper is to know the sociodemographic and operational-financial determinants of the services quality perceived by users of the national health services. Through the use of an inquiry conducted by the Ministry of Health, comprehending 16.936 interviews in 2006, we intend to find out if there is any characteristic that determines the 2006 inquiry results. With the revision of the literature we also want to know if the operational-financial results have implications in hospitals users- perception on the quality of the received services. In order to achieve our main goals we will make use of the regression analysis to find out the possible dimensions that determine those results.

Keywords: Management by Results, Quality Approach, Tableau de Bord, Total Quality Management, Services quality.

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Authors: Pupong Pongcharoen, Aphirak Khadwilard, Anothai Klakankhai

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The importance of supply chain and logistics management has been widely recognised. Effective management of the supply chain can reduce costs and lead times and improve responsiveness to changing customer demands. This paper proposes a multi-matrix real-coded Generic Algorithm (MRGA) based optimisation tool that minimises total costs associated within supply chain logistics. According to finite capacity constraints of all parties within the chain, Genetic Algorithm (GA) often produces infeasible chromosomes during initialisation and evolution processes. In the proposed algorithm, chromosome initialisation procedure, crossover and mutation operations that always guarantee feasible solutions were embedded. The proposed algorithm was tested using three sizes of benchmarking dataset of logistic chain network, which are typical of those faced by most global manufacturing companies. A half fractional factorial design was carried out to investigate the influence of alternative crossover and mutation operators by varying GA parameters. The analysis of experimental results suggested that the quality of solutions obtained is sensitive to the ways in which the genetic parameters and operators are set.

Keywords: Genetic Algorithm, Logistics, Optimisation, Supply Chain.

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Authors: Zheng-Yan Ruan, Chien-Hao Wang, Hong-Jen Lin, Chien-Peng Huang, Ying-Hao Chen, En-Cheng Yang, Chwan-Lu Tseng, Joe-Air Jiang

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Bees play a vital role in pollination. This paper focuses on the weighing process of honey. Honey is usually stored at the comb in a hive. Bee farmers brush bees away from the comb and then collect honey, and the collected honey is weighed afterward. However, such a process brings strong negative influences on bees and even leads to the death of bees. This paper therefore presents an Internet of Things-based weight monitoring system which uses weight sensors to measure the weight of honey and simplifies the whole weighing procedure. To verify the system, the weight measured by the system is compared to the weight of standard weights used for calibration by employing a linear regression model. The R² of the regression model is 0.9788, which suggests that the weighing system is highly reliable and is able to be applied to obtain actual weight of honey. In the future, the weight data of honey can be used to find the relationship between honey production and different ecological parameters, such as bees’ foraging behavior and weather conditions. It is expected that the findings can serve as critical information for honey production improvement.

Keywords: Internet of Things, weight, honey, bee.

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Authors: M. Siosemarde, M. Byzedi

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Saturated hydraulic conductivity of Soil is an important property in processes involving water and solute flow in soils. Saturated hydraulic conductivity of soil is difficult to measure and can be highly variable, requiring a large number of replicate samples. In this study, 60 sets of soil samples were collected at Saqhez region of Kurdistan province-IRAN. The statistics such as Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Bias Error (MBE) and Mean Absolute Error (MAE) were used to evaluation the multiple linear regression models varied with number of dataset. In this study the multiple linear regression models were evaluated when only percentage of sand, silt, and clay content (SSC) were used as inputs, and when SSC and bulk density, Bd, (SSC+Bd) were used as inputs. The R, RMSE, MBE and MAE values of the 50 dataset for method (SSC), were calculated 0.925, 15.29, -1.03 and 12.51 and for method (SSC+Bd), were calculated 0.927, 15.28,-1.11 and 12.92, respectively, for relationship obtained from multiple linear regressions on data. Also the R, RMSE, MBE and MAE values of the 10 dataset for method (SSC), were calculated 0.725, 19.62, - 9.87 and 18.91 and for method (SSC+Bd), were calculated 0.618, 24.69, -17.37 and 22.16, respectively, which shows when number of dataset increase, precision of estimated saturated hydraulic conductivity, increases.

Keywords: dataset, precision, saturated hydraulic conductivity, soil and statistics.

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Authors: Fulya Zaralı, Harun Resit Yazgan

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Logistics centers represent areas that all national and international logistics and activities related to logistics can be implemented by the various businesses. Logistics centers have a key importance in joining the transport stream and the transport system operations. Therefore, it is important where these centers are positioned to be effective and efficient and to show the expected performance of the centers. In this study, the location selection problem to position the logistics center is discussed. Alternative centers are evaluated according certain criteria. The most appropriate center is identified using the axiomatic design method.

Keywords: Logistic center, axiomatic design, facility location, information systems.

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Authors: Chalermsak Leetrakool, Komson Jirapattarasilp

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This research was to study effect of rotational speed and eccentric factors, which were affected on looseness of bearing. The experiment was conducted on three rotational speeds and five eccentric distances with 5 replications. The results showed that influenced factor affected to looseness of bearing was rotational speed and eccentric distance which showed statistical significant. Higher rotational speed would cause on high looseness. Moreover, more eccentric distance, more looseness of bearing. Using bearing at high rotational with high eccentric of shaft would be affected bearing fault more than lower rotational speed. The prediction equation of looseness was generated by regression analysis. The prediction has an effected to the looseness of bearing at 91.5%.

Keywords: Bearing, Looseness, Rotational speed, Eccentric

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Authors: Chiung-En Huang, Cheng-Yu Tsai, Shane-Chung Lee

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This study aim at the influence of college students’ exercise and leisure motivations on the leisure benefits while using the leisure involvement as a moderator. Whereby, the research tools used in this study included the application of leisure motivation scale, leisure involvement scale and leisure benefits scale, and a hierarchical regression analysis was performed by using a questionnaire-based survey, in which, a total of 1,500 copies of questionnaires were administered and 917 valid questionnaires were obtained, achieving a response rate of 61.13%. Research findings explore that leisure involvement has a moderating effect on the relationship between the leisure motivation and leisure benefits.

Keywords: Leisure motivation, leisure involvement, leisure benefits, moderator.

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-ofsample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: Financial algorithm, GPU, S&P 500, stock market prediction.

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Authors: S. Aberkane, N. Djabali, S. Fafi, A. Baghezza

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Chronic illnesses affect many Algerians. It is possible to investigate the impact of illness representations and coping on quality of life and whether illness representations are indirectly associated with quality of life through their influence on coping. This study aims at investigating the relationship between illness perception, coping strategies and quality of life with chronic illness. Illness perceptions are indirectly associated with the quality of life through their influence on coping mediation. A sample of 316 participants with chronic illness living in the region of Batna, Algeria, has been adopted in this study. A correlation statistical analysis is used to determine the relationship between illness perception, coping strategies, and quality of life. Multiple regression analysis was employed to highlight the predictive ability of the dimensions of illness perception and coping strategies on the dependent variables of quality of life, where mediation analysis is considered in the exploration of the indirect effect significance of the mediator. This study provides insights about the relationship between illness perception, coping strategies and quality of life in the considered sample (r = 0.39, p < 0.01). Therefore, it proves that there is an effect of illness identity perception, external and medical attributions related to emotional role, physical functioning, and mental health perceived, and these were fully mediated by the asking for assistance (c’= 0.04, p < 0.05), the guarding (c’= 0.00, p < 0.05), and the task persistence strategy (c’= 0.05, p < 0.05). The findings imply partial support for the common-sense model of illness representations in a chronic illness population. Directions for future research are highlighted, as well as implications for psychotherapeutic interventions which target unhelpful beliefs and maladaptive coping strategies (e.g., cognitive behavioral therapy).

Keywords: Chronic illness, coping, illness perception, quality of life, self-regulation model.

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Authors: Sheetal Sharma

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Urban planning is associated with land transformation from natural areas to modified and developed ones which leads to modification of natural environment. The basic knowledge of relationship between both should be ascertained before proceeding for the development of natural areas. Changes on land surface due to build up pavements, roads and similar land cover, affect surface water flow. There is a gap between urban planning and basic knowledge of hydrological processes which should be known to the planners. The paper aims to identify these variations in surface flow due to urbanization for a temporal scale of 40 years using Storm Water Management Mode (SWMM) and again correlating these findings with the urban planning guidelines in study area along with geological background to find out the suitable combinations of land cover, soil and guidelines. For the purpose of identifying the changes in surface flows, 19 catchments were identified with different geology and growth in 40 years facing different ground water levels fluctuations. The increasing built up, varying surface runoff are studied using Arc GIS and SWMM modeling, regression analysis for runoff. Resulting runoff for various land covers and soil groups with varying built up conditions were observed. The modeling procedures also included observations for varying precipitation and constant built up in all catchments. All these observations were combined for individual catchment and single regression curve was obtained for runoff. Thus, it was observed that alluvial with suitable land cover was better for infiltration and least generation of runoff but excess built up could not be sustained on alluvial soil. Similarly, basalt had least recharge and most runoff demanding maximum vegetation over it. Sandstone resulted in good recharging if planned with more open spaces and natural soils with intermittent vegetation. Hence, these observations made a keystone base for planners while planning various land uses on different soils. This paper contributes and provides a solution to basic knowledge gap, which urban planners face during development of natural surfaces.

Keywords: Runoff, built up, roughness, recharge, temporal changes.

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Authors: Asim Tanvir, Numera Rafaqat

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This study discusses the stumbling blocks stifling the adoption of GPS technology in the public sector of Pakistan. This study has been carried out in order to describe the value of GPS technology and its adoption at various public sector organisations in Pakistan. Sample size for the research conducted was 200; personnel working in public sector having age above 29 years were surveyed. Data collected for this research has been quantitatively analysed with the help of SPSS. Regression analysis, correlation and cross tabulation were the techniques used to determine the strength of relationship between key variables. Findings of this research indicate that main hurdles in GPS adoption in the public sector of Pakistan are lack of awareness about GPS among masses in general and the stakeholders in particular, lack of initiative on part of government in promoting new technologies, unavailability of GPS infrastructure in Pakistan and prohibitions on map availability because of security reasons.

Keywords: Adoptability issues, Growth of GPS, GPSinfrastructure, Public sectors in Pakistan

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

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

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

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Authors: Daniel Puciato, Piotr Oleśniewicz, Julita Markiewicz-Patkowska, Krzysztof Widawski, Michał Rozpara, Władysław Mynarski, Agnieszka Gawlik, Małgorzata Dębska, Soňa Jandová

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Physical activity as a part of people’s everyday life reduces the risk of many diseases, including those induced by lifestyle, e.g. obesity, type 2 diabetes, osteoporosis, coronary heart disease, degenerative arthritis, and certain types of cancer. That refers particularly to professionally active people, including the early senior group working on non-manual positions. The aim of the study is to evaluate the relationship between physical activity and the socio-economic status of non-manual workers from Wroclaw—one of the biggest cities in Poland, a model setting for such investigations in this part of Europe. The crucial problem in the research is to find out the percentage of respondents who meet the health-related recommendations of the World Health Organization (WHO) concerning the volume, frequency, and intensity of physical activity, as well as to establish if the most important socio-economic factors, such as gender, age, education, marital status, per capita income, savings and debt, determine the compliance with the WHO physical activity recommendations. During the research, conducted in 2013, 1,170 people (611 women and 559 men) aged 21–60 years were examined. A diagnostic poll method was applied to collect the data. Physical activity was measured with the use of the short form of the International Physical Activity Questionnaire with extended socio-demographic questions, i.e. concerning gender, age, education, marital status, income, savings or debts. To evaluate the relationship between physical activity and selected socio-economic factors, logistic regression was used (odds ratio statistics). Statistical inference was conducted on the adopted ex ante probability level of p<0.05. The majority of respondents met the volume of physical effort recommended for health benefits. It was particularly noticeable in the case of the examined men. The probability of compliance with the WHO physical activity recommendations was highest for workers aged 21–30 years with secondary or higher education who were single, received highest incomes and had savings. The results indicate the relations between physical activity and socio-economic status in the examined women and men. People with lower socio-economic status (e.g. manual workers) are physically active primarily at work, whereas those better educated and wealthier implement physical effort primarily in their leisure time. Among the investigated subjects, the youngest group of non-manual workers have the best chances to meet the WHO standards of physical activity. The study also confirms that secondary education has a positive effect on the public awareness on the role of physical activity in human life. In general, the analysis of the research indicates that there is a relationship between physical activity and some socio-economic factors of the respondents, such as gender, age, education, marital status, income per capita, and the possession of savings. Although the obtained results cannot be applied for the general population, they show some important trends that will be verified in subsequent studies conducted by the authors of the paper.

Keywords: International physical activity questionnaire, non-manual workers, physical activity, socio-economic factors, WHO.

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: Injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer.

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Authors: A. P. Singh

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The results reported in this paper are the part of an extensive laboratory investigation undertaken to study the effects of fibre parameters on the permeability and strength characteristics of steel fibre reinforced concrete (SFRC). The effect of varying fibre content and curing age on the water permeability, compressive and split tensile strengths of SFRC was investigated using straight steel fibres having an aspect ratio of 65. Samples containing three different weight fractions of 1.0%, 2.0% and 4.0% were cast and tested for permeability and strength after 7, 14, 28 and 60 days of curing. Plain concrete samples were also cast and tested for reference purposes.

Permeability was observed to decrease significantly with the addition of steel fibres and continued to decrease with increasing fibre content and increasing curing age. An exponential relationship was observed between permeability and compressive and split tensile strengths for SFRC as well as PCC. To evaluate the effect of fibre content on the permeability and strength characteristics, the Analysis of Variance (ANOVA) statistical method was used. An a level (probability of error) of 0.05 was used for ANOVA test. Regression analysis was carried out to develop relationship between permeability, compressive strength and curing age.

Keywords: Permeability, grade of concrete, fibre shape, fibre content, curing age, steady state, Darcy’s law, method of penetration.

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Authors: Sarah Beaver, Renee Bryce

Abstract:

Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: Dini Endah Setyo Rahaju, Dian Retno Sari Dewi

Abstract:

This paper presents an application of the improved QFD method for determining the specifications of kitchen utensils rack. By using the improved method, the subjective nature in original QFD was reduced; particularly in defining the relationship between customer requirement and engineering characteristics. The regression analysis that was used for obtaining the relationship functions between customer requirement and engineering characteristics also accommodated the inaccurateness of the competitive assessment results. The improved method which is represented in the form of a mathematical model had become a formal guidance to allocate the resource for improving the specifications of kitchen utensils rack. The specifications obtained had led to the achievement of the highest feasible customer satisfaction.

Keywords: Customer satisfaction, kitchen utensils rack design, QFD, specifications.

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Authors: F. A. M. Elfaki, Amar Abobakar, M. Azram, M. Usman

Abstract:

This paper discusses regression analysis of partly interval-censored failure time data, which is occur in many fields including demographical, epidemiological, financial, medical and sociological studies. For the problem, we focus on the situation where the survival time of interest can be described by the additive hazards model in the present of partly interval-censored. A major advantage of the approach is its simplicity and it can be easily implemented by using R software. Simulation studies are conducted which indicate that the approach performs well for practical situations and comparable to the existing methods. The methodology is applied to a set of partly interval-censored failure time data arising from anti D in Rhesus D negative studies.

Keywords: Anti D in Rhesus D negative, Cox’s model, EM algorithm.

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