Search results for: mixed effect logistic regression model

Authors: Yenni Anggrayni

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The lack of awareness of the significant prevalence of school absenteeism in Indonesia, which ultimately results in high rates of school dropouts, is an unresolved issue. Therefore, this study aims to investigate the root causes of chronic absenteeism qualitatively and quantitatively using the bioecological systems paradigm in secondary schools for any reason. This study used an open-ended questionnaire to collect data from 1,148 students in six West Java Province districts/cities. Univariate and stepwise multiple logistic regression analyses produced a prediction model for the components. Analysis results show that poor teacher-student relationships, bullying by peers or teachers, negative perception of education, and lack of parental involvement in learning activities are the leading causes of chronic absenteeism. Another finding is to promote home-school partnerships to improve school climate and parental involvement in learning to address chronic absenteeism.

Keywords: bullying, chronic absenteeism, dropout of school, home-school partnerships, parental involvement

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Authors: Khadeeja Munawar, Shamsul Haque

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The claim that traumatic events influence the recalled memories and mental health has received mixed empirical support. This study examines the memories of a sample drawn from Pakistan, a country that has witnessed many life-changing socio-political events, wars, and natural disasters in 72 years of its history. A sample of 210 senior citizens (Mage = 64.35, SD = 6.33) was recruited from Pakistan. The aim was to investigate if participants retrieved more memories related to past traumatic events using a word-cueing technique. Each participant reported ten memories to ten neutral cue words. The results revealed that past traumatic events were not adversely affecting the memories and mental health of participants. When memories were plotted with respect to the ages at which the events happened, a pronounced bump at 11-20 years of age was seen. Memories within as well as outside of the bump were mostly positive. The multilevel logistic regression modelling showed that the memories recalled were personally important and played a role in enhancing resilience. The findings revealed that despite facing an array of ethnic, religious, political, economic, and social conflicts, the participants were resilient, recalled predominantly positive memories, and had intact mental health. The findings have clinical implications in Cognitive Behavioral Therapy (CBT). The patients can be made aware of their negative emotions, troublesome/traumatic memories, and the distorted thinking patterns and their memories can be restructured. The findings can also be used to teach Memory Specificity Training (MEST) by psycho-educating the patients around changes in memory functioning and enhancing the recall of memories, which are more specific, vivid, and filled with sensory details.

Keywords: cognitive behavioral therapy, memories, mental health, resilience, trauma

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Authors: Chuan Yang, Jing Bie, Panagiotis Psimoulis, Zhong Wang

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Modelling and understanding vehicle volume distribution over the urban network are essential for urban design and transport planning. The space syntax approach was widely applied as the main conceptual and methodological framework for contemporary vehicle volume models with the help of the statistical method of multiple regression analysis (MRA). However, the MRA model with space syntax variables shows a limitation in vehicle volume predicting in accounting for the crossed effect of the urban configurational characters and socio-economic factors. The aim of this paper is to construct models by interacting with the combined impact of the street network structure and socio-economic factors. In this paper, we present a multilevel linear (ML) and an agent-based (AB) vehicle volume model at an urban scale interacting with space syntax theoretical framework. The ML model allowed random effects of urban configurational characteristics in different urban contexts. And the AB model was developed with the incorporation of transformed space syntax components of the MRA models into the agents’ spatial behaviour. Three models were implemented in the same urban environment. The ML model exhibit superiority over the original MRA model in identifying the relative impacts of the configurational characters and macro-scale socio-economic factors that shape vehicle movement distribution over the city. Compared with the ML model, the suggested AB model represented the ability to estimate vehicle volume in the urban network considering the combined effects of configurational characters and land-use patterns at the street segment level.

Keywords: space syntax, vehicle volume modeling, multilevel model, agent-based model

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Authors: Tauha Hussain Ali, Shabir Hussain Khahro, Nafees Ahmed Memon

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Accurate prediction of project time for planning and bid preparation stage should contain realistic dates. Constructors use their experience to estimate the project duration for the new projects, which is based on intuitions. It has been a constant concern to both researchers and constructors to analyze the accurate prediction of project duration for bid preparation stage. In Pakistan, such study for time cost relationship has been lacked to predict duration performance for the construction projects. This study is an attempt to explore the time cost relationship that would conclude with a mathematical model to predict the time for the drainage rehabilitation projects in the province of Sindh, Pakistan. The data has been collected from National Engineering Services (NESPAK), Pakistan and regression analysis has been carried out for the analysis of results. Significant relationship has been found between time and cost of the construction projects in Sindh and the generated mathematical model can be used by the constructors to predict the project duration for the upcoming projects of same nature. This study also provides the professionals with a requisite knowledge to make decisions regarding project duration, which is significantly important to win the projects at the bid stage.

Keywords: BTC Model, project time, relationship of time cost, regression

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Authors: Kshanaprava Dhalsamant, Punyadarshini P. Tripathy, Shanker L. Shrivastava

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Potato is chosen as the food product for carrying out the natural convection mixed-mode solar drying experiments since they are easily available and globally consumed. The convective heat and mass transfer coefficients along with effective diffusivity were calculated considering both shrinkage and without shrinkage for the potato cylinders of different geometry (8, 10 and 13 mm diameters and a constant length of 50 mm). The convective heat transfer coefficient (hc) without considering shrinkage effect were 24.28, 18.69, 15.89 W/m2˚C and hc considering shrinkage effect were 37.81, 29.21, 25.72 W/m2˚C for 8, 10 and 13 mm diameter samples respectively. Similarly, the effective diffusivity (Deff) without considering shrinkage effect were 3.20×10-9, 4.82×10-9, 2.48×10-8 m2/s and Deff considering shrinkage effect were 1.68×10-9, 2.56×10-9, 1.34×10-8 m2/s for 8, 10 and 13 mm diameter samples respectively and the mass transfer coefficient (hm) without considering the shrinkage effect were 5.16×10-7, 2.93×10-7, 2.59×10-7 m/s and hm considering shrinkage effect were 3.71×10-7, 2.04×10-7, 1.80×10-7 m/s for 8, 10 and 13 mm diameter samples respectively. Increased values of hc were obtained by considering shrinkage effect in all diameter samples because shrinkage results in decreasing diameter with time achieving in enhanced rate of water loss. The average values of Deff determined without considering the shrinkage effect were found to be almost double that with shrinkage effect. The reduction in hm values is due to the fact that with increasing sample diameter, the exposed surface area per unit mass decreases, resulting in a slower moisture removal. It is worth noting that considering shrinkage effect led to overestimation of hc values in the range of 55.72-61.86% and neglecting the shrinkage effect in the mass transfer analysis, the values of Deff and hm are overestimated in the range of 85.02-90.27% and 39.11-45.11%, respectively, for the range of sample diameter investigated in the present study.

Keywords: shrinkage, convective heat transfer coefficient, effectivive diffusivity, convective mass transfer coefficient

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Authors: João Paulo Pascon

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In this work, a mixed 3D finite element formulation is proposed in order to analyze thermoviscoplastic materials under large strain levels and ductile damage. To this end, a tetrahedral element of linear order is employed, considering a thermoviscoplastic constitutive law together with the neo-Hookean hyperelastic relationship and a nonlocal Gurson`s porous plasticity theory The material model is capable of reproducing finite deformations, elastoplastic behavior, void growth, nucleation and coalescence, thermal effects such as plastic work heating and conductivity, strain hardening and strain-rate dependence. The nonlocal character is introduced by means of a nonlocal parameter applied to the Laplacian of the porosity field. The element degrees of freedom are the nodal values of the deformed position, the temperature and the nonlocal porosity field. The internal variables are updated at the Gauss points according to the yield criterion and the evolution laws, including the yield stress of matrix, the equivalent plastic strain, the local porosity and the plastic components of the Cauchy-Green stretch tensor. Two problems involving 3D specimens and ductile damage are numerically analyzed with the developed computational code: the necking problem and a notched sample. The effect of the nonlocal parameter and the mesh refinement is investigated in detail. Results indicate the need of a proper nonlocal parameter. In addition, the numerical formulation can predict ductile fracture, based on the evolution of the fully damaged zone.

Keywords: mixed finite element, large strains, ductile damage, thermoviscoplasticity

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Unchalee Inkampa, Tuanjai Somboonwiwat

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Painting process of automobiles and automobile parts, which is a continuous process based on EDP (Electrode position paint, EDP). Through EDP, all work pieces will be continuously sent to the painting process. Work process can be divided into 2 groups based on the running time: Painting Room 1 and Painting Room 2. This leads to continuous operation. The problem that arises is waiting for workloads onto Painting Room. The grading process EDP to Painting Room is a major problem. Therefore, this paper aim to develop production sequencing method by applying EDP to painting process. It also applied fixed rate launching for painting room and earliest due date (EDD) for EDP process and swap pairwise interchange for waiting time to a minimum of machine. The result found that the developed method could improve painting reduced waiting time, on time delivery, meeting customers wants and improved productivity of painting unit.

Keywords: sequencing, mixed model lines, painting process, electrode position paint

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Authors: Fujio Akagi, Hiroaki Ito, Shin-Ichi Inage

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The aim of this study is to develop a combustion model that can be applied uniformly to laminar and turbulent premixed flames while considering the effect of the Lewis number (Le). The model considers the effect of Le on the transport equations of the reaction progress, which varies with the chemical species and temperature. The distribution of the reaction progress variable is approximated by a hyperbolic tangent function, while the other distribution of the reaction progress variable is estimated using the approximated distribution and transport equation of the reaction progress variable considering the Le. The validity of the model was evaluated under the conditions of propane with Le > 1 and methane with Le = 1 (equivalence ratios of 0.5 and 1). The estimated results were found to be in good agreement with those of previous studies under all conditions. A method of introducing a turbulence model into this model is also described. It was confirmed that conventional turbulence models can be expressed as an approximate theory of this model in a unified manner.

Keywords: combustion model, laminar flame, Lewis number, turbulent flame

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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Authors: Ing-Jen Hung, Tzu-Chien Wang

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Nowadays, customers can interact with firms through miscellaneous online ads on different channels easily. In other words, customer now has innumerable options and limitless time to accomplish their commercial activities with firms, individualizing their own online customer journey. This kind of convenience emphasizes the importance of online advertisement allocation on different channels. Therefore, profound understanding of customer behavior can make considerable benefit from optimizing fund allocation on diverse ad channels. To achieve this objective, multiple firms utilize numerical methodology to create data-driven advertisement policy. In our research, we aim to exploit online customer click data to discover the correlations between each channel and their sequential relations. We use LSTM to deal with sequential property of our data and compare its accuracy with other non-sequential methods, such as CART decision tree, logistic regression, etc. Besides, we also classify our customers into several groups by their behavioral characteristics to perceive the differences between all groups as customer portrait. As a result, we discover distinct customer journey under each customer portrait. Our article provides some insights into marketing research and can help firm to formulate online advertising criteria.

Keywords: LSTM, customer journey, marketing, channel ads

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Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

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The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

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Authors: Danielle M. Barna, Kolbjørn Engeland, Thordis Thorarinsdottir, Chong-Yu Xu

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Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Often design flood values are needed at locations with insufficient data. Additionally, in hydrologic applications where flood retention is important (e.g., floodplain management and reservoir design), design flood values are required at different flood durations. A statistical approach to this problem is a development of a regression model for extremes where some of the parameters are dependent on flood duration in addition to being covariate-dependent. In hydrology, this is called a regional flood-duration-frequency (regional-QDF) model. Typically, the underlying statistical distribution is chosen to be the Generalized Extreme Value (GEV) distribution. However, as the support of the GEV distribution depends on both its parameters and the range of the data, special care must be taken with the development of the regional model. In particular, we find that the GEV is problematic when developing a GAMLSS-type analysis due to the difficulty of proposing a link function that is independent of the unknown parameters and the observed data. We discuss these challenges in the context of developing a regional QDF model for Norway.

Keywords: design flood values, bayesian statistics, regression modeling of extremes, extreme value analysis, GEV

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Authors: Esther Burkitt, Dawn Watling

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Background: Four types of patterns of simultaneous mixed emotions have been identified in middle childhood, adolescence and adulthood. The present study applied an analogue emotion scale which permits measuring of intensity of opposite valence emotions over time rather than bipolar ratings and used an exhaustive coding scheme to investigate whether children in early childhood experience previously identified and additional types of mixed emotional experiences. Methods: To explore the presence of simultaneous mixed emotion experiences in early childhood, 112 children (59 girls) aged 5 years 1 month - 7 years 2 months (X=6 years 1 month; SD = 10 months) were recruited across the UK. They were allocated on the basis of alternation by gender on class lists to one of two conditions hearing vignettes describing mixed emotion events in an age and gender matched protagonist or themselves (other, n = 57 and self, n = 55). Findings: New types of flexuous, vertical and other experiences were identified alongside sequential, prevalent, highly parallel and inverse types of experiences identified in older populations. Conclusions: The analogue emotion scale uncovered a broader range of simultaneous mixed emotional experiences than previously identified. The value of exploring the utility of the findings in emotion assessments is discussed along with suggestions to explore impacts of educational and cultural influences on children’s mixed emotional experiences.

Keywords: childhood, emotion, graphing, self

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Authors: Yangyang Zhou

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Rapid urbanization has caused the urban heat island effect, which will threaten the physical and mental health of urban dwellers, and blue-green spaces can mitigate the thermal environment effectively. In this study, we calculated the average LST from 2013 to 2022, Northeastmonsoon and Southwestmonsoon of Singapore, and compared the cooling effect differences of the four blue-green spaces. Then, spatial correlation and spatial autoregression model were conducted between cooling distance intensity (CDI) and 11 independent variables. The results reveal that (1) the highest mean land surface temperature (LST) in all years, Northeast monsoon and Southwest monsoon can reach 42.8 ℃, 41.6 ℃, and 42.9 ℃, respectively. (2) the temperature-changing tendency in the three time periods is similar to each other, while the overall LST changing trends of the Southwest monsoon are lower than all year and Northeast monsoon. (3) the cooling distance of the sea can reach 1200 m, and CEI is highly positively correlated with NDBI and BuildD and highly negatively correlated with SVF, NDVI and TreeH. LISA maps showed that the zones that passed the significance test between CDI, NDBI and BuildD were nearly the same locations; the same phenomenon also happened between CDI and SVF, NDVI and TreeH. (4) SLM had better regression results than SEM in all the regions; only 3 independent variables passed the significance test in region 1, and most independent variables can pass the significance test in other regions. Variables DIST and NDBI were significantly affecting the CDI in all the regions. In the whole region, all the variables passed the significance test, and NDBI (1.61), SVF (0.95) and NDVI (0.5) had the strongest influence on CDI.

Keywords: cooling effect, land surface temperature, thermal environment mitigation, spatial autoregression model

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Authors: Dana El-Mughayyar, Neil Manson, Erin Bigney, Eden Richardson, Dean Tripp, Edward Abraham

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Use of narcotics to treat pain has increased over the past two decades and is a contributing factor to the current public health crisis. Understanding the pre-operative risks of chronic narcotic use may be aided through investigation of psychological measures. The objective of the reported study is to determine predictors of narcotic use two years post-surgery in a thoracolumbar spine surgery population, including an array of psychological factors. A prospective observational study of 191 consecutively enrolled adult patients having undergone thoracolumbar spine surgery is presented. Baseline measures of interest included the Pain Catastrophizing Scale (PCS), Tampa Scale for Kinesiophobia, Multidimensional Scale for Perceived Social Support (MSPSS), Chronic Pain Acceptance Questionnaire (CPAQ-8), Oswestry Disability Index (ODI), Numeric Rating Scales for back and leg pain (NRS-B/L), SF-12’s Mental Component Summary (MCS), narcotic use and demographic variables. The post-operative measure of interest is narcotic use at 2-year follow-up. Narcotic use is collapsed into binary categories of use and no use. Descriptive statistics are run. Chi Square analysis is used for categorical variables and an ANOVA for continuous variables. Significant variables are built into a hierarchical logistic regression to determine predictors of post-operative narcotic use. Significance is set at α < 0.05. Results: A total of 27.23% of the sample were using narcotics two years after surgery. The regression model included ODI, NRS-Leg, time with condition, chief complaint, pre-operative drug use, gender, MCS, PCS subscale helplessness, and CPAQ subscale pain willingness and was significant χ² (13, N=191)= 54.99; p = .000. The model accounted for 39.6% of the variance in narcotic use and correctly predicted in 79.7% of cases. Psychological variables accounted for 9.6% of the variance over and above the other predictors. Conclusions: Managing chronic narcotic usage is central to the patient’s overall health and quality of life. Psychological factors in the preoperative period are significant predictors of narcotic use 2 years post-operatively. The psychological variables are malleable, potentially allowing surgeons to direct their patients to preventative resources prior to surgery.

Keywords: narcotics, psychological factors, quality of life, spine surgery

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Authors: Angelina Anani, Ignacio Ortiz Flores, Haitao Li

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The use of underground mining methods have increased significantly over the past decades. This increase has also been spared on by several mines transitioning from surface to underground mining. However, determining the transition depth can be a challenging task, especially when coupled with production schedule optimization. Several researchers have simplified the problem by excluding operational features relevant to production schedule optimization. Our research objective is to investigate the extent to which operational features of transition mines accounted for affect the optimal production schedule. We also provide a framework for factors to consider in production schedule optimization for transition mines. An integrated mixed-integer linear programming (MILP) model is developed that maximizes the NPV as a function of production schedule and transition depth. A case study is performed to validate the model, with a comparative sensitivity analysis to obtain operational insights.

Keywords: underground mining, transition mines, mixed-integer linear programming, production schedule

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load.

Keywords: cross-organization process mining, process behaviour, path of execution, polynomial regression model

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Authors: Wajid Ali Khan

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Extensive experimentation and numerical investigation are performed to predict the machining-induced residual stresses in the end milling of AISI 1045 steel, and an optimization code has been developed using the particle swarm optimization technique. Experiments were conducted using a single factor at a time and design of experiments approach. Regression analysis was done, and a mathematical model of the cutting process was developed, thus predicting the machining-induced residual stress with reasonable accuracy. The mathematical model served as the objective function to be optimized using particle swarm optimization. The relationship between the different cutting parameters and the output variables, force, and residual stresses has been studied. The combined effect of the process parameters, speed, feed, and depth of cut was examined, and it is understood that 85% of the variation of these variables can be attributed to these machining parameters under research. A 3D finite element model is developed to predict the cutting forces and the machining-induced residual stresses in end milling operation. The results were validated experimentally and against the Johnson-cook model available in the literature.

Keywords: residual stresses, end milling, 1045 steel, optimization

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Authors: Mona Shawky, Khaled M. Elsheikh, Heba M. Darwish, Eman Abd El Elsamea

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Seams are the basic component in the structure of any apparel. The seam quality of the garment is a term that indicates both the aesthetic and functional performance of the garment. Seam slippage is one of the important properties that determine garment performance. Lightweight fabrics are preferred for their aesthetic properties. Since seam slippage is one of the most occurable faults for woven garments, in this study, a design of experiment of the following sewing parameters (three levels of needle size, three levels of stitch density, three levels of the seam allowance, two levels of sewing thread count, and two fabric types) was used to obtain the effect of the interaction between different sewing parameters on-seam slippage force. Two lightweight polyester woven fabrics with different constructions were used with lock stitch 301 to perform this study. Regression equations which can predict seam slippage force in both warp and weft directions were concluded. It was found that fabric type has a significant positive effect on seam slippage force in the warp direction, while it has a significant negative effect on seam slippage force on weft direction. Also, the interaction between needle size and stitch density has a significant positive effect on seam slippage force on warp direction, while the interaction between stitch density and seam allowance has a negative effect on seam slippage force in the weft direction.

Keywords: needle size, regression equation, seam allowance, seam slippage, stitch density

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Authors: Kajingulu Malandala, Ranganai Edmore

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The asymmetric Laplace distribution (ADL) is commonly used as the likelihood function of the Bayesian quantile regression, and it offers different families of likelihood method for quantile regression. Notwithstanding their popularity and practicality, ADL is not smooth and thus making it difficult to maximize its likelihood. Furthermore, Bayesian inference is time consuming and the selection of likelihood may mislead the inference, as the Bayes theorem does not automatically establish the posterior inference. Furthermore, ADL does not account for greater skewness and Kurtosis. This paper develops a new aspect of quantile regression approach for count data based on inverse of the cumulative density function of the Poisson, binomial and Delaporte distributions using the integrated nested Laplace Approximations. Our result validates the benefit of using the integrated nested Laplace Approximations and support the approach for count data.

Keywords: quantile regression, Delaporte distribution, count data, integrated nested Laplace approximation

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Authors: A. Ishag Mohamed, A. A. Rabah

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The objective of this research is to develop a generalized correlation for the prediction of thermal conductivity of n-Alkanes and Alkenes. There is a minority of research and lack of correlation for thermal conductivity of liquids in the open literature. The available experimental data are collected covering the groups of n-Alkanes and Alkenes.The data were assumed to correlate to temperature using Filippov correlation. Nonparametric regression of Grace Algorithm was used to develop the generalized correlation model. A spread sheet program based on Microsoft Excel was used to plot and calculate the value of the coefficients. The results obtained were compared with the data that found in Perry's Chemical Engineering Hand Book. The experimental data correlated to the temperature ranged "between" 273.15 to 673.15 K, with R2 = 0.99.The developed correlation reproduced experimental data that which were not included in regression with absolute average percent deviation (AAPD) of less than 7 %. Thus the spread sheet was quite accurate which produces reliable data.

Keywords: N-Alkanes, N-Alkenes, nonparametric, regression

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Authors: Peter Ugbedeojo Nelson

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Despite the tremendous advancements and knowledge acquisition around entrepreneurship orientation (EO) research, there may still be more to learn on how environmental dynamics would permute organizational processes and determine the extent to which success would be achieved. Using the contingency theory, we test a model that proposes a moderating influence of external relational capital and environmental hostility on the EO-performance effect of 423 managers/owners of small and medium scale enterprises. The hypotheses were tested using Hayes simultaneous regression, and the results showed that all EO dimensions (risk-taking, innovation, and performance) had a main effect on performance while the moderating variables interacted well with risk-taking (more than other EO dimensions) to improve performance. However, external relational capital, more than environmental hostility, influences the EO-performance relationship. Our findings highlight the differential ways that EO dimensions interact with environmental contingencies to influence performance. Further studies can examine how competitive aggressiveness and autonomy are moderated by external relational capital and environmental hostility.

Keywords: external relational capital, entrepreneurial orientation, risk-taking, innovation, proactiveness

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Authors: Anna Siuda-Bak

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This paper adopts a comparative approach to examining the determinants of international mobility of German, Italian and British researchers who completed their doctoral education in the UK. Structured sampling and data collection techniques have been developed in order to retrieve information on participants from publicly available sources. This systematically collected data was supplemented with an on-line survey which captures participants’ job trajectories, including movements between positions, institutions and countries. In total, data on 949 German, Italian and British PhDs was collected. Logistic regression was employed to identify factors associated with one’s probability of moving outside the UK after his or her graduation. The predictor variables included factors associated with one’s PhD (field of study, ranking of the university which awarded the PhD degree) and family factors (having a child, nationality of the partner). Then, 9 constrained models were estimated to test the effect each variable has on probability of going to a specific destination, being English-speaking country, non-English speaking country or returning to the home country. The results show that females, arts and humanities graduates, and respondents with a partner from the UK are less mobile than their counterparts. The effect of the ranking of the university differed in two groups. The UK graduates from higher ranked universities were more likely to move abroad than to stay in the UK after their graduation. In contrast, non-UK natives from the same universities were less likely to be internationally mobile than non-UK natives from lower ranked universities. The nationality of the partner was the most important predictor of the specific destination choices. Graduates with partner from the home county were more likely to return home and those with a partner from the third country least likely to return.

Keywords: doctoral graduates, international mobility, nationality, UK

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Authors: Rachid Belhachemi, Pierre Rostan, Alexandra Rostan

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This paper develops a discrete-time option pricing model for index options. The model consists of two key ingredients. First, daily stock return innovations are driven by a continuous hidden threshold mixed skew-normal (HTSN) distribution which generates conditional non-normality that is needed to fit daily index return. The most important feature of the HTSN is the inclusion of a latent state variable with a continuum of states, unlike the traditional mixture distributions where the state variable is discrete with little number of states. The HTSN distribution belongs to the class of univariate probability distributions where parameters of the distribution capture the dependence between the variable of interest and the continuous latent state variable (the regime). The distribution has an interpretation in terms of a mixture distribution with time-varying mixing probabilities. It has been shown empirically that this distribution outperforms its main competitor, the mixed normal (MN) distribution, in terms of capturing the stylized facts known for stock returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence. Second, heteroscedasticity in the model is captured by a threeexogenous-factor GARCH model (GARCHX), where the factors are taken from the principal components analysis of various world indices and presents an application to option pricing. The factors of the GARCHX model are extracted from a matrix of world indices applying principal component analysis (PCA). The empirically determined factors are uncorrelated and represent truly different common components driving the returns. Both factors and the eight parameters inherent to the HTSN distribution aim at capturing the impact of the state of the economy on price levels since distribution parameters have economic interpretations in terms of conditional volatilities and correlations of the returns with the hidden continuous state. The PCA identifies statistically independent factors affecting the random evolution of a given pool of assets -in our paper a pool of international stock indices- and sorting them by order of relative importance. The PCA computes a historical cross asset covariance matrix and identifies principal components representing independent factors. In our paper, factors are used to calibrate the HTSN-GARCHX model and are ultimately responsible for the nature of the distribution of random variables being generated. We benchmark our model to the MN-GARCHX model following the same PCA methodology and the standard Black-Scholes model. We show that our model outperforms the benchmark in terms of RMSE in dollar losses for put and call options, which in turn outperforms the analytical Black-Scholes by capturing the stylized facts known for index returns, namely, volatility clustering, leverage effect, skewness, kurtosis and regime dependence.

Keywords: continuous hidden threshold, factor models, GARCHX models, option pricing, risk-premium

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Authors: Xin Chen, Xiao Yang, Rongrong Han, Lu Chen, Lingling Gao

Abstract:

Background: Leisure time physical activity (LTPA) benefits both pregnant women and their fetuses. The guidelines recommended that pregnant women should do at least 150 minutes of moderate-intensity aerobic physical activity throughout the week. The aim of this study was to investigate the rate of LTPA participation among Chinese pregnant women and to identify its predictors based on the health belief model. Methods: A cross-sectional study was conducted from June 2019 to September 2019 in Changchun, China. A total of 225 pregnant women aged 18 years or older with no severe physical or mental disease were recruited in the obstetric clinic. Self-administered questionnaires were used to collect data. LTPA was assessed by a pregnant physical activity questionnaire (PPAQ). A revised pregnancy physical activity health belief scale and social-demographic and perinatal characteristics factors were collected and used to predict LTPA participation. Data were analyzed using descriptive statistics and multivariate logistic regression. Results: The participants had a high level of perceived susceptibility, perceived severity, perceived benefits, and action clues, with mean item scores above 3.5. The predictors of LTPA in Chinese pregnant women were pre-pregnancy exercise habits [OR 3.236 (95% CI:1.632, 6.416)], perceived susceptibility score [OR 2.083 (95% CI:1.002, 4.331)], and perceived barriers score [OR 3.113 (95%CI:1.462, 6.626)]. Conclusions: The results of this study will lead to better identification of pregnant women who may not participate in LTPA. Healthcare professionals should be cognizant of issues that may affect LTPA participation among pregnant women, including pre-pregnancy exercise habits, perceived susceptibility, and perceived barriers.

Keywords: pregnancy, health belief model., leisure time physical activity, factors

Procedia PDF Downloads 80

Authors: Mohamed Chabi, Mahmoud I. Syam, Sarah Aw

Abstract:

In Summer 2012, the Foundation Program Unit of Qatar University has started implementing new ways of teaching Math by introducing MML (MyMathLab) as an innovative interactive tool to support standard teaching. In this paper, we focused on the effect of proper use of the Study Plan component of MML on student’s performance. Authors investigated the results of students of pre-calculus course during Fall 2013 in Foundation Program at Qatar University. The results showed that there is a strong correlation between study plan results and final exam results, also a strong relation between homework results and final exam results. In addition, the attendance average affected on the student’s results in general. Multiple regression is determined between passing rate dependent variable and study plan, homework as independent variable.

Keywords: MyMathLab, study plan, assessment, homework, attendance, correlation, regression

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Authors: Nicolas Delcey, Philippe Baucour, Didier Chamagne, Geneviève Wimmer, Auditeau Gérard, Bausseron Thomas, Bouger Odile, Blanvillain Gérard

Abstract:

This paper proposes a thermal study of the catenary/pantograph interface for a train in motion. A 2.5D complex model of the pantograph strip has been defined and created by a coupling between a 1D and a 2D model. Experimental and simulation results are presented and with a comparison allow validating the 2.5D model. Some physical phenomena are described and presented with the help of the model such as the stagger motion thermal effect, particular heats and the effect of the material characteristics. Finally it is possible to predict the critical thermal configuration during a train trip.

Keywords: electro-thermal studies, mathematical optimizations, multi-physical approach, numerical model, pantograph strip wear

Procedia PDF Downloads 328

Authors: Nur Suraya Anis Ahmad Bakhtiar, Hazizan Md Akil

Abstract:

In the present work, the dielectric properties of Epoxy/MWCNTs-muscovite HYBRID and MIXED composites based on ratio 30:70 were studies. The multi-wall carbon nanotubes (MWCNTs) were prepared by two method; (a) muscovite-MWCNTs hybrids were synthesized by chemical vapor deposition (CVD) and (b) physically mixing of muscovite with MWCNTs. The effect of different preparations of the composites and filler loading was evaluated. It is revealed that the dielectric constants of HYBRID epoxy composites are slightly higher compared to MIXED epoxy composites. It is also indicated that the dielectric constant increased by increases the MWCNTs filler loading.

Keywords: muscovite, epoxy, dielectric properties, hybrid composite

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Authors: Mohammad Abu-Shaira, Weishi Shi

Abstract:

Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

Procedia PDF Downloads 17