Search results for: multiple regression model

Authors: Charlotte K. Lee, Henrique R. N. Aguiar, Ralph Smith, James Baldock, Sam Botchey

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Background: Femoral neck stress fractures (FNSF) are uncommon, making up 1 to 7.2% of stress fractures in healthy subjects. FNSFs are prevalent in young women, military recruits, endurance athletes, and individuals with energy deficiency syndrome or female athlete triad. Presentation is often non-specific and is often misdiagnosed following the initial examination. There is limited research addressing the return–to–activity time after FNSF. Previous studies have demonstrated prognostic time predictions based on various imaging techniques. Here, (1) OxSport clinic FNSF practice standards are retrospectively reviewed, (2) FNSF cohort demographics are examined, (3) Regression models were used to predict return–to–activity prognosis and consequently determine bone stress risk factors. Methods: Patients with a diagnosis of FNSF attending Oxsport clinic between 01/06/2020 and 01/01/2020 were selected from the Rheumatology Assessment Database Innovation in Oxford (RhADiOn) and OxSport Stress Fracture Database (n = 14). (1) Clinical practice was audited against five criteria based on local and National Institute for Health Care Excellence guidance, with a 100% standard. (2) Demographics of the FNSF cohort were examined with Student’s T-Test. (3) Lastly, linear regression and Random Forest regression models were used on this patient cohort to predict return–to–activity time. Consequently, an analysis of feature importance was conducted after fitting each model. Results: OxSport clinical practice met standard (100%) in 3/5 criteria. The criteria not met were patient waiting times and documentation of all bone stress risk factors. Importantly, analysis of patient demographics showed that of the population with complete bone stress risk factor assessments, 53% were positive for modifiable bone stress risk factors. Lastly, linear regression analysis was utilized to identify demographic factors that predicted return–to–activity time [R2 = 79.172%; average error 0.226]. This analysis identified four key variables that predicted return-to-activity time: vitamin D level, total hip DEXA T value, femoral neck DEXA T value, and history of an eating disorder/disordered eating. Furthermore, random forest regression models were employed for this task [R2 = 97.805%; average error 0.024]. Analysis of the importance of each feature again identified a set of 4 variables, 3 of which matched with the linear regression analysis (vitamin D level, total hip DEXA T value, and femoral neck DEXA T value) and the fourth: age. Conclusion: OxSport clinical practice could be improved by more comprehensively evaluating bone stress risk factors. The importance of this evaluation is demonstrated by the population found positive for these risk factors. Using this cohort, potential bone stress risk factors that significantly impacted return-to-activity prognosis were predicted using regression models.

Keywords: eating disorder, bone stress risk factor, femoral neck stress fracture, vitamin D

Procedia PDF Downloads 178

Authors: Olivia Rachel Hwang, Yu-Hsuan Joni Shao

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Androgen Deprivation Therapy (ADT) has been a primary treatment for patients with advanced prostate cancer. However, it is associated with numerous adverse effects related to Metabolic Syndrome (MetS), including hypertension, diabetes, hyperlipidaemia, heart diseases and ischemic strokes. However, complications associated with ADT for prostate cancer in Taiwan is not well documented. The purpose of this study is to utilize the data from NHIRD (National Health Insurance Research Database) to examine the trajectory changes of MetS-related complications in men receiving ADT. The risks of developing complications after the treatment were analyzed with multivariate Cox regression model. Covariates including in the model were the complications before the diagnosis of prostate cancer, the age, and the year at cancer diagnosis. A total number of 17268 patients from 1997-2013 were included in this study. The exclusion criteria were patients with any other types of cancer or with the existing MetS-related complications. Changes in MetS-related complications were observed among two treatment groups: 1) ADT (n=9042), and 2) non-ADT (n=8226). The ADT group appeared to have an increased risk in hypertension (hazard ratio 1.08, 95% confidence interval 1.03-1.13, P = 0.001) and hyperlipidemia (hazard ratio 1.09, 95% confidence interval 1.01-1.17, P = 0.02) when compared with non-ADT group in the multivariate Cox regression analyses. In the risk of diabetes, heart diseases, and ischemic strokes, ADT group appeared to have an increased but not significant hazard ratio. In conclusion, ADT was associated with an increased risk in hypertension and hyperlipidemia in prostate cancer patients in Taiwan. The risk of hypertension and hyperlipidemia should be considered while deciding on ADT, especially those with the known history of hypertension and hyperlipidemia.

Keywords: androgen deprivation therapy, ADT, complications, metabolic syndrome, MetS, prostate cancer

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Authors: Pawthawala Nancy Manish, Syed Alay Hashim

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A hybrid rocket is a rocket engine which uses propellants in two different states of matter- one is in solid and the other either gas or liquid. A hybrid rocket exhibit advantages over both liquid rockets and solid rockets especially in terms of simplicity, stop-start-restart capabilities, safety and cost. This paper deals the design and development of a hybrid rocket having paraffin wax as solid fuel and liquid oxygen as oxidizer. Due to variation of pressure in combustion chamber there is significantly change in mass flow rate, burning rate and uneven regression along the length of the grain. This project describes the working model of a hybrid propellant rocket motor. We have designed a hybrid rocket thrust chamber based on the predetermined combustion chamber pressure and the properties of hybrid propellant. This project is all ready in working condition with normal oxygen injector. Now we have planned to modify the injector design to improve the combustion property. We will use spray type injector for injecting the oxidizer. This idea will increase the performance followed by the regression rate of the solid fuel. By employing mass conservation law, oxygen mass flux, oxidizer/fuel ratio and regression rate the thrust coefficient can be obtained for our current design. CATIA V5 R20 is our design software for the complete setup. This project is fully based on experimental evaluation and the collection of combustion and flow parameters. The thrust chamber is made of stainless steel and the duration of test is around 15-20 seconds (Maximum). These experiments indicates that paraffin based fuel provides the opportunity to satisfy a broad range of mission requirements for the next generation of the hybrid rocket system.

Keywords: burning rate, liquid oxygen, mass flow rate, paraffin wax and sugar

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Authors: Hanifeh Imanian, Morteza Kolahdoozan

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The purpose of this study is to present a mathematical phrase for calculating the dispersion rate of spilled oil in water column under non-breaking waves. In this regard, a multiphase numerical model is applied for which waves and oil phase were computed concurrently, and accuracy of its hydraulic calculations have been proven. More than 200 various scenarios of oil spilling in wave waters were simulated using the multiphase numerical model and its outcome were collected in a database. The recorded results were investigated to identify the major parameters affected vertical oil dispersion and finally 6 parameters were identified as main independent factors. Furthermore, some statistical tests were conducted to identify any relationship between the dependent variable (dispersed oil mass in the water column) and independent variables (water wave specifications containing height, length and wave period and spilled oil characteristics including density, viscosity and spilled oil mass). Finally, a mathematical-statistical relationship is proposed to predict dispersed oil in marine waters. To verify the proposed relationship, a laboratory example available in the literature was selected. Oil mass rate penetrated in water body computed by statistical regression was in accordance with experimental data was predicted. On this occasion, it was necessary to verify the proposed mathematical phrase. In a selected laboratory case available in the literature, mass oil rate penetrated in water body computed by suggested regression. Results showed good agreement with experimental data. The validated mathematical-statistical phrase is a useful tool for oil dispersion prediction in oil spill events in marine areas.

Keywords: dispersion, marine environment, mathematical-statistical relationship, oil spill

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Authors: Dijiang Lyu, Shaoying Li, Zhangzhi Tan, Zhifeng Wu, Feng Gao

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Nowadays, China is experiencing rapidly urban rail transit expansions in the world. The purpose of this study is to finely model factors influencing transit ridership at multi-time dimensions within transit stations’ pedestrian catchment area (PCA) in Guangzhou, China. This study was based on multi-sources spatial data, including smart card data, high spatial resolution images, points of interest (POIs), real-estate online data and building height data. Eight multiple linear regression models using backward stepwise method and Geographic Information System (GIS) were created at station-level. According to Chinese code for classification of urban land use and planning standards of development land, residential land-use were divided into three categories: first-level (e.g. villa), second-level (e.g. community) and third-level (e.g. urban villages). Finally, it concluded that: (1) four factors (CBD dummy, number of feeder bus route, number of entrance or exit and the years of station operation) were proved to be positively correlated with transit ridership, but the area of green land-use and water land-use negative correlated instead. (2) The area of education land-use, the second-level and third-level residential land-use were found to be highly connected to the average value of morning peak boarding and evening peak alighting ridership. But the area of commercial land-use and the average height of buildings, were significantly positive associated with the average value of morning peak alighting and evening peak boarding ridership. (3) The area of the second-level residential land-use was rarely correlated with ridership in other regression models. Because private car ownership is still large in Guangzhou now, and some residents living in the community around the stations go to work by transit at peak time, but others are much more willing to drive their own car at non-peak time. The area of the third-level residential land-use, like urban villages, was highly positive correlated with ridership in all models, indicating that residents who live in the third-level residential land-use are the main passenger source of the Guangzhou Metro. (4) The diversity of land-use was found to have a significant impact on the passenger flow on the weekend, but was non-related to weekday. The findings can be useful for station planning, management and policymaking.

Keywords: fine-scale modeling, Guangzhou city, multi-time dimensions, multi-sources spatial data, transit ridership

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Authors: Duchduen Bhanthumnavin

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One of the most important characteristics to increase competitive ability in undergraduate students after post COVID-19 era is goal setting and self-control. This correlational study aimes at investigating the influence of psycho-social antecedents on goal setting and self-control in 550 Thai university students. Results from multiple regression analysis revealed that the important predictors of this characteristic were reasoning ability, psychological immunity, attitudes toward competition, core self-evaluation, and family nurture, which yielded 54.28 predictive percentage in the total sample. Moreover, the analysis identified three at-risk groups, namely, male students, low GPA students, and students with siblings. Discussion and implications in general and for specific purposes for the at-risk groups were offered.

Keywords: antecedents, plan and self-control, predictors, university students

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: Aluisius Pratono, Deddy Marciano, Suyanto Suyanto

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The emerging model of community development social enterprise is one of the social enterprise models. However, a precise view of the community development enterprise is still lacking. Hence, this study was aimed at deepening the concept of the community development social enterprise model at the place management and development context. Multiple case studies in Indonesia context were observed to explore the typical criteria of the community development enterprise model in place-making practices. The research paradigm used interpretative approach, which involves dialectic process between the researchers and research participants. This study highlights some principles in the community-development enterprise, which cover an entrepreneurial dimension, social goals, participatory governance, and co-management. The result makes a contribution to conceptual literature occurs at the criteria of social enterprises by highlighting the typology of community development enterprise.

Keywords: community development enterprise, social purposes, economic project, participatory governance

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Authors: Riccardo Loggia, Francesca Pizzimenti, Francesco Lelli, Luigi Martirano

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Recent air emission regulations point toward the complete electrification of road vehicles. An increasing number of users are beginning to prefer full electric or hybrid, plug-in vehicle solutions, incentivized by government subsidies and the lower cost of electricity compared to gasoline or diesel. However, it is necessary to optimize charging stations so that they can simultaneously satisfy as many users as possible. The purpose of this paper is to present optimization algorithms that enable simultaneous charging of multiple electric vehicles while ensuring maximum performance in relation to the type of charging station.

Keywords: electric vehicles, charging stations, sharing model, fast charging, car park, power profiles

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Mohammad Akbar Paiman, Yasmin Nadeem Parpio, Naureen Akbarali, Khwaja Mir Islam Saeed, Murad Moosa Khan

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Background: Gender-based violence (GBV) is widely considered a significant public health problem that is associated with acute morbidity and mortality. GBV is commonly understood as a physical, sexual, and mental assault from intimate partners, sexual violence by non-partners, sexual assault of girls, and acts like trafficking women for sex. Objective: This study aimed to determine the factors associated with injuries and trauma among victims of GBV in Afghanistan. Method: We conducted a record-based analysis of the data collected by the Gender Department of the Family Protection Centre nationally between November 2013 and October 2019. Cross-tabulation between different variables such as age, sex, marital status, and type of violence and associations between different types of violence, age, gender, and geographical location was determined using the logistic regression model. Results: During the study period, there were a total of 58,160 GBV in Afghanistan. Most of the victims were women 98% with over three-quarters being adults 78%. Most of the victims were married 76%, followed by single 14%, widowed 5%, and engaged 5%. Over three-quarters of the violence, 73% was observed in the victim’s house while nearly one-quarter of the violence 24 % occurred in the perpetrator’s house. Conclusions: GBV is a significant public health problem in Afghanistan that needs to be addressed at multiple levels including policy, state, and community as well as by raising public awareness and education and a strong code of conduct against GBV by all stakeholders.

Keywords: gender-based violence, physical and psychological violence, injuries, Afghanistan

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Authors: Milena Serzante, Viktoriia Stankevich, Yousre Badir

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Companies have a significant impact on the environment and society, and sustainability is important not only for ethical concerns but also for financial and economic reasons. The aim of the study is to analyze how the sustainable performance of the company impacts the economy and the business's economic performance. To achieve this goal, such methods as the Pearson correlation, Multiple Linear Regression, Cook's distance method, K-nearest neighbor and COPRAS technique were implemented. The results revealed that there is no significant correlation between different indicators of sustainable development of the company and both GDP and Return on Equity. It indicates that the methodology of evaluating sustainability causes the difference in ranking companies based on sustainable performance.

Keywords: economic impact, sustainability evaluation, sustainable companies, economic indicators, sustainability, GDP, return on equity

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Authors: Almontas Vilutis, Vytenis Jankauskas

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The paper presents the results of the investigation of the dry sliding friction of wood-plastic composites (WPCs) against WC-Co cemented carbide. The dependence of the dynamic coefficient of friction on the main influencing factors (vertical load, temperature, and sliding distance) was investigated by evaluating their mutual interaction. Multiple regression analysis showed a high polynomial dependence (adjusted R2 > 0.98). The resistance of the composite to thermo-mechanical effects determines how temperature and force factors affect the magnitude of the coefficient of friction. WPC-B composite has the lowest friction and highest resistance compared to WPC-A, while composite and cemented carbide materials wear the least. Energy dispersive spectroscopy (EDS), based on elemental composition, provided important insights into the friction process.

Keywords: friction, composite, carbide, factors

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Elucir Gir, Myllena Nilce de Freitas Surmano, Laelson Rochelle Milanês Sousa, Mayra Gonçalves Menegueti, Ana Cristina de Oliveira E Silva, Renata Karina Reis

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Objective: To analyze the factors associated with the worsening of the quality of life of health professionals in the Southeast region of Brazil during the COVID-19 pandemic and its associated factors. Method: Analytical cross-sectional study carried out with health professionals from the southeastern region of Brazil. Data collection took place through an online survey with a form stored on the Survey Monkey platform. Bivariate analysis was used, and the chi-square test was adopted, followed by the multiple binary logistic regression model based on the stepwise method. Results: 3,493 health professionals participated in the study. Factors associated with worsening quality of life were: Professional Category (Nursing assistant) [OR 1.851 (95%CI 1.035-3.311) p= 0.038]; types of people who provided care (people in general) [OR 1.445 (95%CI 1.072-1.945) p=0.015]; Supply of good quality PPE by the institution where he works (no) [OR 1.595 (CI 95% 1.144-2.223) p= 0.006] and Supply of good quality PPE by the institution where he works (in part) [OR 1.563 (CI 95% 1.257-1.943) p < 0.001]. Conclusion: The factors associated with the worsening of the quality of life of health professionals during the COVID-19 pandemic were: Professional Category (Nursing assistant); types of people who provided assistance (people in general); Supply of sufficient PPE by the institution where you work (no) and Supply of good quality PPE by the institution where you work (in part). Future studies should investigate to what extent QoL can be improved based on modifiable factors.

Keywords: COVID-19, quality of life, health professionals, respiratory infections

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Authors: Qinglu Wu, Anna Wai-Man Choi, Peilian Chi

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Gratitude is one individual strength that not only facilitates the mental health, but also fosters the relationship satisfaction in the romantic relationship. In terms of moral effect theory and stress-and-coping theory of forgiveness, present study not only investigated the association between grateful disposition and relationship satisfaction, but also explored the mechanism by comprehensively examining the potential mediating roles of three profiles of forgiveness (trait forgivingness, decisional forgiveness, emotional forgiveness), another character strength that highly related to the gratitude and relationship satisfaction. Structural equation modeling was used to conduct the multiple mediator model with a sample of 103 Chinese college students in dating relationship (39 male students and 64 female students, Mage = 19.41, SD = 1.34). Findings displayed that both gratitude and relationship satisfaction positively correlated with decisional forgiveness and emotional forgiveness. Emotional forgiveness was the only mediator, and it completely mediated the relationship between gratitude and relationship satisfaction. Gratitude was helpful in enhancing individuals’ perception of satisfaction in romantic relationship through replacing negative emotions toward partners with positive ones after transgression in daily life. It highlighted the function of emotional forgiveness in personal healing and peaceful state, which is important to the perception of satisfaction in relationship. Findings not only suggested gratitude could provide a stability for forgiveness, but also the mechanism of prosocial responses or positive psychological processes on relationship satisfaction. The significant roles of gratitude and emotional forgiveness could be emphasized in the intervention working on the romantic relationship development or reconciliation.

Keywords: decisional forgiveness, emotional forgiveness, gratitude, relationship satisfaction, trait forgivingness

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Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

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Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: coastal erosion, prognostic model, DSAS, environmental safety

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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Authors: Michael Dolgin, Oz Hamtzani, Talma Kushnir

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A literature review has shown that while parents of children with cancer experience increased levels of psychological distress associated with their child's medical condition, considerable variability in parental adjustment is evident. Of the factors that may account for this variability, little attention has been devoted to the simultaneous interaction of three coping constructs and their role in parental adjustment: (1) Coping techniques employed, (2) Repertoire of coping techniques, and (3) Flexibility in applying coping techniques. While these constructs have been studied individually in relation to adjustment in general, studies to date have not included them together within a single conceptual model and research design and evaluated them in a clinical population. The objective of the current study was to determine how these three coping technique constructs interact to impact parental adjustment to pediatric cancer. A cross-sectional sample of 145 parents of children in active cancer treatment completed standardized measures of coping techniques, repertoire, flexibility, and parental distress. A hierarchical multiple regression analysis demonstrated that 37% of the variance in parental distress was predicted by the use of avoidance-focused coping techniques [F(1,118)=69.843, p<.001], with an additional 3% predicted by coping repertoire [F(2,117)=7.63, p=.00] for a total of 40% variance explained. Coping flexibility was found to mediate the relationship between coping repertoire and parental distress. These findings suggest that coping techniques employed by parents (problem/emotion-focused vs. avoidance-focused), as well as coping repertoire, significantly impact parental adjustment. Flexibility in applying coping techniques within one’s coping repertoire further contributes to parental adjustment. Implications for further study and clinical intervention will be presented.

Keywords: coping techniques, repertoire, flexibility, adjustment

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Authors: Sukanya Sripho

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The purpose of this research was to study the role of local administration management to promote culture based on philosophy of sufficiency economy to many communities in Thailand. The philosophy was given to the Thai people by their King and become one of the important policies from the Thai government. A total of 375 local people in main district, Amnadcharoen province were selected by random sampling. A questionnaire was used as the tool for collecting data. Descriptive statistics in this research included percentage, mean, and multiple regression analysis. The findings revealed that the role of facilitator was utilized the most from the management in order to promote culture based on philosophy of sufficiency economy to many communities in Thailand.

Keywords: administration, management, philosophy of sufficiency economy, facilitator

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Authors: Farzaneh Rajabighamchi, Stan van Hoesel, Christof Defryn

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The generalized traveling salesman problem (GTSP) is an extension of the traveling salesman problem (TSP) where the set of nodes is partitioned into clusters, and the salesman must visit exactly one node per cluster. In this research, we apply the definition of the GTSP to an order picker routing problem with multiple locations per product. As such, each product represents a cluster and its corresponding nodes are the locations at which the product can be retrieved. To pick a certain product item from the warehouse, the picker needs to visit one of these locations during its pick tour. As all products are scattered throughout the warehouse, the product clusters not separated geographically. We propose an exact LP model as well as heuristic and meta-heuristic solution algorithms for the order picking problem with multiple product locations.

Keywords: warehouse optimization, order picking problem, generalised travelling salesman problem, heuristic algorithm

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Haruna Maama

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Environmental, social and governance (ESG) reporting is gaining considerable attention despite being voluntary. Meanwhile, it consumes resources to provide ESG reporting, raising a question of its value relevance. The study examined the impact of ESG reporting on the market value of listed firms in SSA. The annual and integrated reports of 276 listed sub-Sahara Africa (SSA) firms. The integrated reporting scores of the firm were analysed using a content analysis method. A multiple regression estimation technique using a GMM approach was employed for the analysis. The results revealed that ESG has a positive relationship with firms’ market value, suggesting that investors are interested in the ESG information disclosure of firms in SSA. This suggests that extensive ESG disclosures are attempts by firms to obtain the approval of powerful social, political and environmental stakeholders, especially institutional investors. Furthermore, the market value analysis evidence is consistent with signalling theory, which postulates that firms provide integrated reports as a signal to influence the behaviour of stakeholders. This finding reflects the value placed on investors' social, environmental and governance disclosures, which affirms the views that conventional investors would care about the social, environmental and governance issues of their potential or existing investee firms. Overall, the evidence is consistent with the prediction of signalling theory. In the context of this theory, integrated reporting is seen as part of firms' overall competitive strategy to influence investors' behaviour. The findings of this study make unique contributions to knowledge and practice in corporate reporting.

Keywords: environmental accounting, ESG accounting, signalling theory, sustainability reporting, sub-saharan Africa

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Authors: Krishna Kistan Govender, Alex Masianoga

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The aim of this explanatory study was to critically understand how ethical leadership impacts employee creative behaviour, as well as the creative behaviour dimensions, in a South African transport and logistics SOE. A quantitative study was conducted using a pre-developed questionnaire, and data for 160 middle and executive managers was analysed through structural equation modelling and multiple regression techniques conducted with the Smart PLS statistical software. All five hypothesized relationships were supported, and it was confirmed that ethical leadership has a significant positive influence on employee creative behaviour, as well as on each of the creative behaviour dimensions, namely: idea exploration, idea generation, idea championing, and idea implementation.

Keywords: ethical leaders, employee creative behaviour, state-owned enterprises, South Africa

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Authors: Harish Kumar Sharma, Garima Sengar

Abstract:

Rice bran oil is considered comparatively nutritionally superior than different fats/oils. Therefore, model blends prepared from pure rice bran oil (RBO) and sunflower oil (SFO) were explored for changes in the different physicochemical parameters. Repeated deep fat frying process was carried out by using dried potato in order to study the thermal behaviour of pure rice bran oil, sunflower oil and their model blends. Pure rice bran oil and sunflower oil had shown good thermal stability during the repeated deep fat frying cycles. Although, the model blends constituting 60% RBO + 40% SFO showed better suitability during repeated deep fat frying than the remaining blended oils. The quantification of pure rice bran oil in the blended oils, physically refined rice bran oil (PRBO): SnF (sunflower oil) was carried by different methods. The study revealed that regression equations based on the oryzanol content, palmitic acid composition and iodine value can be used for the quantification. The rice bran oil can easily be quantified in the blended oils based on the oryzanol content by HPLC even at 1% level. The palmitic acid content in blended oils can also be used as an indicator to quantify rice bran oil at or above 20% level in blended oils whereas the method based on ultrasonic velocity, acoustic impedance and relative association showed initial promise in the quantification.

Keywords: rice bran oil, sunflower oil, frying, quantification

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Authors: Nazli Uren, Ayse Okur

Abstract:

Tactile comfort of a textile product is an essential property and a major concern when it comes to customer perceptions and preferences. The subjective nature of comfort and the difficulties regarding the simulation of human hand sensory feelings make it hard to establish a well-accepted link between tactile comfort and objective evaluations. On the other hand, shear behavior of a fabric is a mechanical parameter which can be measured by various objective test methods. The principal aim of this study is to determine the tactile comfort of commercially available denim fabrics by subjective measurements, create a tactile score database for denim fabrics and investigate the relations between tactile comfort and shear behavior. In-plane shear behaviors of 17 different commercially available denim fabrics with a variety of raw material and weave structure were measured by a custom design shear frame and conventional bias extension method in two corresponding diagonal directions. Tactile comfort of denim fabrics was determined via subjective customer evaluations as well. Aforesaid relations were statistically investigated and introduced as regression equations. The analyses regarding the relations between tactile comfort and shear behavior showed that there are considerably high correlation coefficients. The suggested regression equations were likewise found out to be statistically significant. Accordingly, it was concluded that the tactile comfort of denim fabrics can be estimated with a high precision, based on the results of in-plane shear behavior measurements.

Keywords: denim fabrics, in-plane shear behavior, linear regression estimation, tactile comfort

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Authors: Lixin Tian, Wei Xue

Abstract:

Many jobs in society go underground, such as mine mining, tunnel construction and subways, which are vital to the development of society. Once accidents occur in these places, the interruption of traditional wired communication is not conducive to the development of rescue work. In order to realize the positioning, early warning and command functions of underground personnel and improve rescue efficiency, it is necessary to develop and design an emergency ground communication system. It is easy to be subjected to narrowband interference when performing conventional underground communication. Spreading communication can be used for this problem. However, general spread spectrum methods such as direct spread communication are inefficient, so it is proposed to use parallel combined spread spectrum (PCSS) communication to improve efficiency. The PCSS communication not only has the anti-interference ability and the good concealment of the traditional spread spectrum system, but also has a relatively high frequency band utilization rate and a strong information transmission capability. So, this technology has been widely used in practice. This paper presents a PCSS communication model-multiple detection parallel combined spread spectrum (MDPCSS) communication system. In this paper, the principle of MDPCSS communication system is described, that is, the sequence at the transmitting end is processed in blocks and cyclically shifted to facilitate multiple detection at the receiving end. The block diagrams of the transmitter and receiver of the MDPCSS communication system are introduced. At the same time, the calculation formula of the system bit error rate (BER) is introduced, and the simulation and analysis of the BER of the system are completed. By comparing with the common parallel PCSS communication, we can draw a conclusion that it is indeed possible to reduce the BER and improve the system performance. Furthermore, the influence of different pseudo-code lengths selected on the system BER is simulated and analyzed, and the conclusion is that the larger the pseudo-code length is, the smaller the system error rate is.

Keywords: cyclic shift, multiple detection, parallel combined spread spectrum, PN code

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Authors: Hussain A. Alkharusi, Said S. Aldhafri, Hilal Z. Alnabhani, Muna Alkalbani

Abstract:

Analysing classroom assessments is one of the responsibilities of the teacher. It aims improving teacher’s instruction and assessment as well as student learning. The present study investigated factors that might explain variation in teachers’ practices regarding analysis of classroom assessments. The factors considered in the investigation included gender, in-service assessment training, teaching load, teaching experience, knowledge in assessment, attitude towards quantitative aspects of assessment, and self-perceived competence in analysing assessments. Participants were 246 in-service teachers in Oman. Results of a stepwise multiple linear regression analysis revealed that self-perceived competence was the only significant factor explaining the variance in teachers’ analysis of assessments. Implications for research and practice are discussed.

Keywords: analysis of assessment, classroom assessment, in-service teachers, self-competence

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Authors: Sudipto Jana

Abstract:

Background and significance of the study: Banking industry performs as a catalyst for industrial growth and agricultural growth, however, as well involves the existence and welfare of the citizens. The banking system in India was described by unmatched growth and the recreation of bunch making in the pre-liberalization era. At the time of financial sector reforms Reserve Bank of India issued a regulatory norm concerning capital adequacy, income recognition, asset classification and provisioning that have increasingly precede meeting by means of the international paramount performs. Bank management ceaselessly manages the triumph, effectiveness, productivity and performance of the bank as good performance, high productivity and efficiency authorizes the triumph of the bank management targets as well as aims of bank. In a comparable move toward performance of any economy depends upon the expediency and effectiveness of its financial system of nation establishes its economic growth indicators. Profitability and productivity are the most important relevant parameters of any banking group. Keeping in view of this, this study examines the profitability and productivity performance of the selected public sector banks in India. Methodology: This study is based on secondary data obtained from Reserve Bank of India database for the periods between 2006 and 2015. This study purposively selects four types of commercial banks, namely, State Bank of India, United Bank of India, Punjab National Bank and Allahabad Bank. In order to analyze the performance with relation to profitability and productivity, productivity performance indicators in terms of capital adequacy ratio, burden ratio, business per employee, spread per employee and advances per employee and profitability performance indicators in terms of return on assets, return on equity, return on advances and return on branch have been considered. In the course of analysis, descriptive statistics, correlation statistics and multiple regression have been used. Major findings: Descriptive statistics indicate that productivity performance of State Bank of India is very satisfactory than other public sector banks in India. But management of productivity is unsatisfactory in case of all the public sector banks under study. Correlation statistics point out that profitability of the public sector banks are strongly positively related with productivity performance in case of all the public sector banks under study. Multiple regression test results show that when profitability increases profit per employee increases and net non-performing assets decreases. Concluding statements: Productivity and profitability performance of United Bank of India, Allahabad Bank and Punjab National Bank are unsatisfactory due to poor management of asset quality as well as management efficiency. It needs government’s interference so that profitability and productivity performance are increased in the near future.

Keywords: India, productivity, profitability, public sector banks

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