Detecting The Regime Shift Via Wavelet Transform.

Recently, regime shifts or structure breaks had acquired very high attention in analyzing financial time series data

A Study Of Structure Breaks In Amman Stocks Market By Using Wavelet Transform.

Regime shifts or structure breaks acquire very high attention in analyzing financial time series data

Fitting ARIMA model for volatility insurance time series data

The volatility of stock  market data have contributed an essential section in risk study and it is very serious problem especially in emerging markets. Previously it is measured by standard deviation of the return. Therefore, in this article the volatility data will be predicted based on Autoregressive Integrated Moving Average model  (ARIMA) using insurance stock market data from Amman Stock Exchange (ASE) from January 2019 to December 2019. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, These results will be helpful for the investments. Keywords: ARIMA model, forecasting, Insurance Sector DOI: 10.7176/EJBM/11-36-13 Publication date: December 31st 201

Short Term Predicting Volatility Service Jordanian Sector

Stock market volatility have added an important section in risk scholarship and it is actual  problem particularly in emerging markets. Earlier, it is measured by standard deviation of the  return. Consequently, in this research the volatility data will be predicted based on ARIMA model (Autoregressive Integrated Moving Average model) of the service sector  in Amman Stock Exchange (ASE) from January 2019 to December 2019. Consequently this article shows that the ARIMA model has important results in prediction. Therefore, These outcomes will be helpful for the investments. Keywords: ARIMA model, forecasting, Service sector. DOI: 10.7176/EJBM/11-36-14 Publication date: December 31st 201

Variation in gas chromatography (GC) analysis in setting up laboratory protocols for waste to energy novel fixed bed reactor setups

Gas Chromatography coupled with Mass Spectrometry (GC/MS) has been applied in various analytical chemistry works. However, to fine tune a system that can serve the purposes of pyrolysis oil identification has proven to be a laborious effort, especially when considering the fact that no standard protocol exists for such analysis. In addition, obtained products were yielded from a newly commissioned unit with a unique and novel design. In this study, a US patent office claimed reactor [SULTAN-1, Pyrolysis Reactor System for the Conversion and Analysis of Organic Solid Waste, Patent application number: 15,487,351] that degrades polyolefinc virgin and waste materials to obtain petroleum refinery and petrochemical feedstock, has been commissioned. The reactor produces three distinct physical states of matter products accumulated as testing specimens, i.e. solids, gaseous and oil. The samples analysed in this work were of the gas and oil produced by pyrolysis of end of life tyre (ELTs) shavings that required to have a special recipe to work with in the laboratory. Various MS cords were utilised and experimental setups to fine tune the process, and special emphasis was given on the gas samples variation in this communication. To reach the desired analysis results with high repeatability, a plethora of experiences of lab personnel and laboratory-based experimental work was accumulated. Laboratory protocols were also setup for this work. These will be detailed along the process execution which yielded a standard laboratory best practice analytical method as part of the State of Kuwait newly initiated Government Initiative project

Isolation and antimicrobial activity of rutin and its derivatives from Ruta chalepensis (Rutaceae) growing in Iraq

Rutin (1), rutin 3'-methyl ether (2), and a new flavonol glycoside, 6-hydroxy-rutin 3',7-dimethyl ether (3), were isolated from the methanol extract of the fruits of Ruta chalepensis, collected from Diyala, Iraq. Their structures were elucidated by spectroscopic analyses, including 1D-, 2D-NMR and HRESIMS. Antimicrobial activity of compounds 1-3 was tested against four Gram +ve and Gram –ve bacterial strains, and the only fungal strain, Candida albicans, using the 96-well based resazurin microtitre assay

Perceptions of Assistant Principals’ and Principals’ of Bahrain Government Schools about the Impact of the Bahrain Teachers College Educational Leadership Program on Their Performance

This article examines the perceptions of school assistant principals and principals who completed the Bahrain Teachers College higher diploma of education leadership program about its impact on their performance. The study sample consisted of 141 program graduates from 9- cohorts. A multilevel concept in measuring the impact of the educational leadership program on the graduates’ performance was employed. The framework consisted of 4 levels: self-learning, changing others, embedding changes in school practices and sustainability of change and scaling up the school performance. The study questionnaire was designed on the basis of this framework. Results show that the Educational Leadership program positively affected its graduates’ performance in the 4 levels. The majority of the program graduates agreed that the program positively affected their personal qualities, leadership styles and practices to support school development, school staff, students’ performance and school ranking. The majority of the program graduates also agreed that the program positively affected their skills in dealing with curriculum innovations, professional development, research, strategic planning, staff appraisal, communication with community, using ICT, improving students’ learning and applying educational ethics.     Keywords: Education Leadership, School Principals’ Performance, Leadership Styles, School Practices, Leadership Skill

10 years of experience in adopting, implementing and evaluating progress testing for Saudi medical students

Objectives: The progress test (PT) is a comprehensive examination that is designed to match the knowledge acquisition necessary at graduation and monitors progress during the entire period of an undergraduate program. Qassim College of Medicine (QCM) began using the multi-institutional PT in the Kingdom of Saudi Arabia (KSA). This study aimed to determine if the PT can be utilized to assess the progress of medical students at different Saudi medical colleges with different educational approaches, as well as whether this testing modality could be accepted by other colleges. Methods: Beside the establishment of a PT committee, comprehensive blueprinting was crafted to sample 200 A-type multiple choice questions (MCQs) from different disciplines. The PT is a paper-and-pencil model and is answered in a 4-h period. All PT items followed a uniform design. Results: In total, 13 rounds of the progress test have been conducted. The number of participating colleges increased from three (with 285 students) in the first test (May 2012) to more than 20 (with &gt;6000 students) in the ninth round (February 2017). The average % scores for first-year students ranged from 3.0% to 7.9% while the average scores for fifth-year students ranged from 34.0% to 43.0%. Conclusion: The conduction of this meticulously crafted test to evaluate knowledge achievement at medical graduation is a fruitful tool and helps to provide constructive feedback for test-takers and other stakeholders relating to their relative positions among other fellows at the national level.</p

Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model

Abstract Recently, wavelet transforms have gained very high attention in many fields and applications such as physics, engineering, signal processing, applied mathematics and statistics. In this paper, we present the advantage of wavelet transforms in forecasting financial time series data. Amman stock market (Jordan) was selected as a tool to show the ability of wavelet transform in forecasting financial time series, experimentally. This article suggests a novel technique for forecasting the financial time series data, based on Wavelet transforms and ARIMA model. Daily return data from 1993 until 2009 is used for this study. 316 S. Al Wadi et a