Layer-based Composite Reputation Bootstrapping

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

Predictive Value of Technical Throwing Skills on Nomination Status in Youth and Long-Term Career Attainment in Handball

Background Research on talent in sports aims to identify predictors of future performance. This study retrospectively investigated 1) relationships between young handball field players’ technical throwing skills and (a) their potential nomination to youth national teams and (b) their long-term career attainment 10 years later, and 2) associations between nomination status and career attainment. Results Results from retrospectively predicting nomination status and career attainment using logistic regression analyses show that technical throwing skills were partly able to explain players’ nomination status (Nagelkerke R2: females 9.2%, males 13.1%) and career attainment (Nagelkerke R2: 9.8% for female players). Here, variables throwing velocity and time on exercise showed statistically significant effects. In addition, nomination status and career attainment were shown to be associated using chi-square tests (w of .37 and .23 for female and male players, respectively) and nomination status as a predictor increased the prediction of career attainment remarkably (Nagelkerke R2: females 20.3%, males 12.7%). Conclusions Given these results, basic technical throwing skills may serve rather as a prerequisite in this age group on national level, emphasizing its importance already on lower levels and in younger age groups. Furthermore, advantages from entering the national TID system early especially for females are discussed.

Examining the validity of chiropractic grade point averages for predicting National Board of Chiropractic Examiners Part I exam scores

ABSTRACT Objective The main objective of this study was to evaluate the validity of grade point average (GPA) for predicting the National Board of Chiropractic Examiners (NBCE) Part I exam scores using chiropractic GPA. Methods Data were collected during the January 2019 computer-based testing administration of the NBCE's Part I exam. The sample size was n = 2278 of test takers from 18 domestic and 4 international chiropractic educational institutions. Six regression models were developed and tested to predict the Part I domain scores from chiropractic GPA while controlling for self-reported demographic variables. Residuals from the models were disaggregated by pre–chiropractic GPA. Results Chiropractic GPA revealed a positive, statistically significant correlation with sex. The chiropractic GPA was found to be a significant predictor of the Part I domain scores. A different perspective was obtained when residuals (observed minus predicted) were collected and split by the pre–chiropractic GPA. Very good students tended to be underpredicted, while other students were overpredicted. Conclusion This study builds on the cascading evidence from educational literature by providing additional results suggesting that undergraduate (prechiropractic) GPA as well as the GPA obtained in doctor of chiropractic programs are related to the future performance on the NBCE Part I exam. The results provide a first glance at the connection between the standardized test scores, which are often used for instructors' and institutional evaluation and the GPA obtained in a doctor of chiropractic program.

Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

Credit is one of the most significant elements in banks and financial institutions. It can also be described as unpredicted events, which mainly occur in the form of either assets or liabilities. The risk occurrence is that the facility recipients have no willingness and ability to repay their debt to the bank, which is a default that is synonymous with credit risk. Credit ratings are a way to decrease and measure credit risk and, therefore, manage it appropriately. Credit rating is an approach for estimating the features and recipients of facilities’ performance based on quantitative criteria, including the company’s financial information. The anticipated future performance allows the applicants to obtain facilities with the exact specifications. In this study, due to the need and significance of calculating the credit risk concept, a novel method based on the hybrid method of artificial neural networks and an improved version of Owl search algorithm (IOSA) and forecasting of C5 risk of decision tree credit is done. This algorithm has two major parts. The decision tree runs based on an IOSA to provide the best weighting of the neural network. The weights created along with the problem data are then given as the input to the main network, and the data are classified. The algorithm has the highest level of accuracy, 96% that is much higher than other algorithms. The results also show a precision of 0.885 and a recall of 0.83 for 618 true positive samples. The proposed method has the highest accuracy and reliability toward the other comparative methods. The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.

Workforce Diversity and Organizational Performance during COVID-19: Evidence from Pharmaceutical Companies

The aim of this study is to identify the impact of workforce diversity on organizational performance. For achieving research objectives and testing hypothesis the primary data collected with help of adopted questionnaire. The respondents were considered from Pharmaceutical companies which are operating in Sindh, Pakistan. Sample was consisting of 300. Findings of this study confirmed all proposed hypothesis are found to have significant impact of Gender diversity during covid-19, Age diversity during covid-19, Education diversity during covid-19 and Experience diversity during covid-19 on Organizational performance during covid-19 in pharmaceutical companies. However, new insights of this study revealed that gender diversity during covid-19 has more positive and significant impact with respect to other workforce diversity variables due to higher beta value. For better future performance this study results suggest to top management of Pharmaceutical companies should manage the workforce diversity.

Employee Turnover and Firm Performance: Large-Sample Archival Evidence

Employee turnover is a significant cost for businesses and a key human capital metric, but firms do not disclose this measure. We examine whether turnover is informative about future firm performance using a large panel of turnover data extracted from employees’ online profiles. We find that turnover is negatively associated with future financial performance (one-quarter ahead return on assets and sales growth). The negative association between turnover and future performance is stronger for small firms, for young firms, for firms with low labor intensity, when the local labor market is tight, and when the firm is trying to replace departing employees. The negative association disappears when turnover is very low, suggesting that a certain amount of turnover can be beneficial. Consistent with the concern that turnover increases operational uncertainty, we find a positive association between turnover and the uncertainty of future financial performance. Finally, we find a significant association between turnover and future stock returns, suggesting that investors do not fully incorporate turnover information. Our findings answer the call from the Securities and Exchange Commission to determine the importance of turnover disclosure. This paper was accepted by Brian Bushee, accounting.

Forecasting Commodity Prices Using the Term Structure

The aim of this study is to test the ability of the yield curve on US government bonds to forecast the future evolution in the prices of commodities often used in as raw materials. We consider the monthly prices of nine commodities for more than 30 years. Our findings, confirmed by several parametric and non-parametric tests, are robust and indicate that the ability to forecast future performance changes over time. Specifically, between 1986 and the early 2000s the yield curve was quite successful in forecasting monthly changes in commodity prices, but that success diminished in the period following. One possible explanation for this outcome is the increased flow of capital into the commodity market resulting in stronger correlations with the equity markets and a breakdown of the obvious relationship between commodities and business cycle. Our findings are important for asset pricing, commodity traders and policy makers.

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.

CORRELATES OF MINDANAO STATE UNIVERSITY ENGLISH PERFORMANCE IN THE SYSTEM ADMISSION AND SCHOLARSHIP EXAMINATION (SASE) IN MSU COMMUNITY COLLEGES

The performance of students in the English concept is a crucial determinant of scholarship and success in the tertiary level of education. This study aimed at finding out the correlates of MSU English performance in the System Admission and Scholarship Examination (SASE) in MSU Community Colleges. Specifically, it has sought to describe the profile of teachers, secondary students, community colleges, and its correlation with the English performance of students. A descriptive-correlational design has been applied in the study and conducted within the 11 community colleges of the university. The secondary students as respondents were selected based on stratified random sampling and assisted to answer the researcher-made instrument. The frequency and percentage distribution, mean, standard deviation, and the correlational tool has been applied in the analysis of the data. Findings revealed that familys income, parents occupation, and the school facilities have been found to have a significant relationship with the SASE performance in English. Thus, these factors are crucial to consider in the analysis of the future performance of the secondary students to be admitted into the college. It is recommended that parents should assess their roles as active partners of the school in educating their children. They should be encouraged by the school to give priority in their activities to support and get involved in the school activities and in following up their childrens accomplishments. A speech laboratory shall promote various programs aimed at improving the performance of students in English. The English Elective Program as the existing program shall be optimized by all colleges of the university.

The Perception of Risk Disclosure Characteristics on the Credit Default Swap Market - An Automated Analysis

This paper evaluates the associations between credit default swap (CDS) spreads and risk disclosure characteristics, especially the expected qualitative and the expected quantitative impacts of risks on companies' future performance and information on risk management. We find that CDS investors can benefit from information on expected risk impacts and from information on risk management, which is important for the current discussion of the Securities and Exchange Commission (SEC) on risk disclosure regulation. However, for companies, the disclosure of such information can be either beneficial or costly, depending on the initial risk perception of CDS investors prior to the publication of risk disclosures and on the disclosed risk factors. Furthermore, we expand the literature by automatically measuring the mentioned risk disclosure characteristics using dictionary-based approaches.