The impact of capital market development on economic growth among MENA region countries

Middle East and North African (MENA) region is home to nearly 60% of the 1.4 trillion barrels of proven crude oil reserves and 46% of the 192 trillion standard cubic meters of natural gas reserves (OPEC, 2010). Although the capital market plays an important role in economic development in many countries, (MENA) region the role is not so clear. The region has participated less in the globalization and integration of international capital markets than have Asian and Latin American countries. Capital flows into the MENA region have been small. Countries in the region have had almost no direct access to the capital markets of industrial countries. The region has made only limited use of market-based income-hedging devices (such as product insurance and forward markets) despite its vulnerability to international price developments. Accordingly capital markets in the region are assumed to have not effectively utilized to generate economic growth due to structural and cultural factors albeit their potential prospects. Hence this study analyses and measures the historical impact of capital market development on the economic growth of four leading countries in the MENA region; Egypt and Tunisia (as non-oil driven economies); and Saudi Arabia and Kuwait (as oil exporter economies). In order to achieve the research aim, a quantitative method approach is adopted. Using 13 time periods (years) from 2002 to 2014 as the annual time-series data of the four countries, this study focuses on indicators that reflect the state of development of the capital market. This study used four variables as a General Index proxy for capital market development; (1) market capitalization ratio to GDP, (2) value of shares traded, (3) Number of shares traded, and (4) number of transactions, while gross domestic product (GDP) was used as a proxy for economic growth. In addition, the study used six macroeconomic variables as control variables, including (GDP/capita), saving rate ratio to GDP, investment rate ratio to GDP, interest rates, inflation, and exchange rates. The data of this study were analysed using Ordinary Least Squares (OLS) regression to examine the capital market development and economic growth relationship for the four countries. Pooled OLS regression analysis was adopted to examine the effects of development of the capital market on the economic growth of the countries as a group. The results of OLS regression indicate that the Egyptian capital market development had significant effects on economic growth, although there were mixed results when different proxies of capital market development indicators were used. In Tunisia, Saudi Arabia, and Kuwait, the level of capital market development had little influence on economic growth, and most of the results were insignificant when different proxies of capital market development indicators were used. However, using the OLS regression analysis model for the four countries combined showed that the development of the capital market had a significant impact on the economic growth of these countries. This study concluded that economic policy options consistent with maximizing economic performance and aiming at elevating economic growth should be developed through the integration of capital markets of the region. Therefore, policy makers should provide incentives to integrate the capital markets and unify economic structures where possible, by diverting funds to investment to further stimulate the growth of their economies. Keywords; Capital market development, Economic growth, Egypt, Tunisia, Saudi Arabia, Kuwait, MENA regio

The Quality Of The Learning Environment In Government Preschools In Jordan

Kajian ini dijalankan bagi menilai status kualiti persekitaran pembelajaran di prasekolah kerajaan di Jordan berdasarkan dimensi Ruang dan Peralatan/Perkakasan, Rutin Penjagaan Peribadi; Bahasa-Penaakulan; Aktiviti; Interaksi; Struktur Program dan; Ibu Bapa dan Staf. This study is undertaken to assess the status of the quality of the learning environment in government preschools in Jordan in terms of Space and Furnishing; Personal Care Routines; Language-Reasoning; Activities; Interaction; Program Structure; and Parents and Staff

Direct and Indirect Effects of IQ, Parental Help, Effort, and Mathematics Self-Concept on Mathematics Achievement

This study examined the structural relationships among cognitive constructs (intelligence and achievement) and affective constructs (perceived parental help, effort and self-concept). It was proposed that the relationships are not invariant across gender. The sample consisted of 219 boys and 133 girls from elementary and preparatory public schools in Al Ain in the United Arab Emirates. Intelligence (IQ) was measured by the Test of Non-verbal Intelligence (TONI) and parental help was measured by 4-Likert-type items. Effort was measured by 4-Likert-type items. Self-concept (SC) was measured by 8-Likert-type items taken from the SDQ I (Abu-Hilal, 2000). Mathematic Achievement was the scores of students in mathematics from school records. The structural model assumed that IQ would have an effect on parental help, effort, SC and achievement. Parental help would have an effect on effort, SC and achievement. Also, effort would have an effect on SC and achievement. Finally, SC would have an effect on achievement. The structural model was tested for invariance across gender. The measurement model proved to be invariant across gender and so was the structural model. The non-constrained model indicated that the structural relationships among the variables do vary according to gender. For example, boys benefited from parental help by exerting more effort while girls did not. Boys with high IQ exerted more effort than boys with low IQ; but girls with high IQ exerted the same amount of effort as girls with low IQ. The model explained 45% and 39% of the variance in math scores for boys and girls, respectively

Extractability of drug traces and metabolites from water media by polyurethane foam and block copolymer membranes

Intisar A. F. El-SharaaPaderborn, Univ., Diss., 201

Effect of improving management system on camel milk production

Twenty four lactating she-Camels of the Sudanese Kabbabish breed and were divided into two equal groups of 12 animals. The first group was managed in a semi intensive system by offering concentrates and roughage, free access to water and veterinary care. The other group was served as a control and managed traditionally within the experimental site. The milk sampling started at 10 days postpartum and continued for 12 successive months in biweekly intervals. The she-camels were hand milked twice a day, approximately 12-hours interval. To control suckling Sorar technique was used. Daily milk yield was determined using graded cylinders. The data were analyzed using SIGMA- STAT. computer software as described by Analysis of Variance (ANOVA). The results indicated that the average daily milk yield for the two management systems were 6.85 ± 1.32 lit/day and 3.14 ± 0.66 lit/day for semi-intensive and traditional system, respectively with highly significant (P<0.001) differences. This differences could be attributed to the management system and to the times of lactation from post-partum (P<0.05). The increase in average daily milk yield amounted to 53% under semi-intensive system compared to those under traditional system. The maximum average daily milk yield 8.7 ± 0.94 lit/day and 4.30 ± 0.59 lit/day was recorded in the 3rd month post-partum in semi-intensive and traditional system, respectively. A sharp decrease in daily milk yield in the traditional system after reaching the peak was observed in contrast to the semi-intensive system with a good persistency or stability after reaching to the peak of production. The results indicated that the trend of daily milk yield seemed to increase significantly (P<0.05) from the first day post-partum till reaching the peak in the 3rd month and then declining gradually through the lactation period. The minimum average daily yield was 5.1 ± 0.76 and 2.32 ± 0.39 lit/day in semi-intensive and traditional system, respectively with a range of 3.7 lit/day and 10 lit/day in semi-intensive system and 1.8 lit/day under and 5.2 lit/day for lowest and highest milk production of the groups. (Résumé d'auteur

Effect of farming system on camels calving interval in western Sudan

Eighteen (18) lactating she-camels and two mature male for mating were used to determine the effect of Management System on camel calving interval, The camels were maintained under semi-intensive (N = 9) and Traditional management system (N = 9) in North Kordofan State, western Sudan. The experimental females in each group kept together with the bull during 18 months. Blood samples were collected from jugular vein since 4-months post-partum and continue 14 successive months at monthly interval. The serum samples were separated and stored at -20°C and were analysed for progesterone concentration using progesterone specific radio immuno assay (RIA) kits. The results indicated that under semi-intensive system 77.8% of females had been pregnant in 5th - 8th month post-partum and the calving interval varying between 17 to 20 months. Under traditional system and during the experimental period 44.5% of females were pregnant in the 11th – 16th month and the calving interval varying between 23 to 28 months. The ratios of pregnant vs non-pregnant during experimental period in semi-intensive and traditional were 88.9% vs 11.1% and 44.5% vs 55.5%, respectively. Beside the behavioural signs progesterone level consider a good indicator for pregnancy in camels. In pregnant females Progesterone concentration increased significantly during early months. The range of Progesterone concentration varied between 1.10 – 5.76 ng/ml and 0.67 – 2.53 ng/ml in semi-intensive and traditional system, respectively. Our results allow quantifying this impact. With a supplemented diet including 2 Kg of concentrates and 5 kg of roughages per day, the fertility rate will be improved of 67%. It would be possible to expect more than two fold young camels in a year by supplemented 5 kg of concentrates. We conclude that under semi-intensive management dietary supplement during post-partum and early lactation period improves reproductive parameters for instance shortened calving interval. (Résumé d'auteur

Effect of dehydration on the pharmacokinetics of oxytetracycline hydrochloride administered intravenously in goats (Capra hircus)

1. The effects of various levels of dehydration induced by water deprivation were studied in six Nubian goats on the pharmacokinetics of oxytetracycline after intravenous administration (5 mg/kg). 2. In goats that had lost an average of 7.6% body weight after 2 days of water deprivation, the elimination rate constant of the drug was significantly decreased (P<0.01) and the total body clearance was significantly slower (P<0.001). No statistically significant changes were observed in the pharmacokinetic parameters describing the distribution of the drug at this dehydration level. 3. Water deprivation for 3 or 4 days resulted in a level of dehydration at which the goats lost an average of 10.3% or 12.7% of their body weight, respectively; significant changes were observed in the pharmacokinetic distribution and elimination parameters of oxytetracycline. The volume of distribution at steady state was significantly decreased (P<0.01). Significantly slower total body clearance (P<0.001) and subsequent prolongation of the elimination half-life were found at these dehydration levels. 4. The alterations caused by dehydration on the disposition kinetics of the drug should be considered for better definition of dosage regimens for sick, dehydrated animals

A dynamic model of transmission and elimination of peste des petits ruminants in Ethiopia

Peste des petits ruminants (PPR), a devastating viral disease of sheep and goats, has been targeted by the global community for eradication within the next 15 years. Although an efficacious attenuated live vaccine is available, the lack of knowledge about the transmission potential of PPR virus (PPRV) may compromise eradication efforts. By fitting a metapopulation model simulating PPRV spread to the results of a nationwide serological survey in Ethiopia, we estimated the level of viral transmission in an endemic setting and the vaccination coverage required for elimination. Results suggest that the pastoral production system as a whole acts as a viral reservoir, from which PPRV spills over into the sedentary production system, where viral persistence is uncertain. Estimated levels of PPRV transmission indicate that viral spread could be prevented if the proportion of immune small ruminants is kept permanently above 37% in at least 71% of pastoral village populations. However, due to the high turnover of these populations, maintaining the fraction of immune animals above this threshold would require high vaccine coverage within villages, and vaccination campaigns to be conducted annually. Adapting vaccination strategies to the specific characteristics of the local epidemiological context and small ruminant population dynamics would result in optimized allocation of limited resources and increase the likelihood of PPR eradication

A STUDY OF HEART DISEASE DIAGNOSIS USING MACHINE LEARNING AND DATA MINING

Heart disease is the leading cause of death for people around the world today. Diagnosis for various forms of heart disease can be detected with numerous medical tests, however, predicting heart disease without such tests is very difficult. Machine learning can help process medical big data and provide hidden knowledge which otherwise would not be possible with the naked eye. The aim of this project is to explore how machine learning algorithms can be used in predicting heart disease by building an optimized model. The research questions are; 1) What Machine learning algorithms are used in the diagnosis of heart disease? 2) How can Machine Learning techniques be used to minimize misdiagnosis (additional tests, and wrong treatment all resulting in greater monetary impact to the patient), 3) How can Machine Learning be used to detect early abnormalities, thus benefiting both patients and the healthcare system? We collected our dataset from the UCI repository and used Random Forest Classification algorithm for predicting heart disease. Then, we modified one of the hyperparameters called ‘N_Estimator’ to improve the model further. The findings and conclusion for each question are; 1) Machine learning algorithms used in predicting heart disease are Naïve Bayes, Decision Trees, Support Vector Machine, Bagging and Boosting, and RandomForest, concluding that these algorithms can achieve high accuracy in predicting heart disease. 2) Machine learning algorithms can analyze a large amount of data to assist medical professionals in making more informed decisions cost-effectively. 3) Machine Learning algorithms allowed us to analyze clinical data, draw relationships between diagnostic variables, design the predictive model, and tests it against the new case. The predictive model achieved an accuracy of 89.4 percent using RandomForest Classifier’s default setting to predict heart diseases. Furthermore, emerging areas for future research that emerged from this study include the opportunity for training and testing using our model with a larger dataset and modifying different hyperparameters for further improvement