This study examines the relationship between foreign direct investment and its determinants in Syria, Algeria, Morocco and Jordan during the period (1990-2010), using the Auto Regressive Distributed Lag ARDL.
The results of the study indicate that
the PMG model is the appropriate model, as the model concluded that there is a significant long-term relationship between the independent variables (except for the exchange rate) and foreign direct investment in the study countries, and therefore it is necessary to focus on the importance of determinants and take steps to develop policies that Encourages foreign direct investment. These measures can include developing market size and making laws more attractive to international trade. In addition, steps can be taken to keep inflation rates under control.
In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction.The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a
given sentence context, a continuous value indicating the difficulty in understanding a respective utterance. Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word- and character bigram frequencies and inclusion in wordlists to create a model able to assign a word or multiword expression a context-dependent complexity score. We can show that especially contextualised string embeddings can help with predicting lexical complexity.
This Study Seeks To Test The Combined Effect Of Fiscal Policy Tools (Public Spending And Taxes) On Private Investment In Syria. Time Series Data For These Variables Were Collected For The Period (1990-2010), And It Was Subjected To A Statistical Fin
ancial And Economic Study That Began By Analyzing The Growth Rates, And Components Of These Variables.This Was Followed By A Study Of The Stability Of Time Series. Finally, The Long-Term Co-Integration Equation For Private Investment In Syria Was Estimated Using The Autoregressive Distributed Lag Model (ARDL). The Results Of The Study Showed The Existence Of A Long-Term Relationship Between Private Investment As A Dependent Variable. Finally, Recommendations Were Made To Increase The Effectiveness Of Public Spending And Taxes In Positively Affecting Private Investment.
This research deals with the modeling of a Multi-Layers Feed Forward Artificial Neural
Networks (MLFFNN), trained using Gradient Descent algorithm with Momentum factor &
adaptive learning rate, to estimate the output of the neural network correspon
ding to the
optimal Duty Cycle of DC-DC Boost Converter to track the Maximum Power Point of
Photovoltaic Energy Systems. Thus, the DMPPT-ANN “Developed MPPT-ANN”
controller proposed in this research, independent in his work on the use of electrical
measurements output of PV system to determine the duty cycle, and without the need to
use a Proportional-Integrative Controller to control the cycle of the work of the of DC-DC
Boost Converter, and this improves the dynamic performance of the proposed controller to
determine the optimal Duty Cycle accurately and quickly. In this context, this research
discusses the optimal selection of the proposed MLFFNN structure in the research in terms
of determining the optimum number of hidden layers and the optimal number of neurons in
them, evaluating the values of the Mean square error and the resulting Correlation
Coefficient after each training of the neural network. The final network model with the
optimal structure is then adopted to form the DMPPT-ANN Controller to track the MPP
point of the PV system. The simulation results performed in the Matlab / Simulink
environment demonstrated the best performance of the proposed DMPPT-ANN controller
based on the MLFFNN neural network model, by accurately estimating the Duty Cycle and
improving the response speed of the PV system output to MPP access, , as well as finally
eliminating the resulting oscillations in the steady state of the Power response curve of PV
system compared with the use of a number of reference controls: an advanced tracking
controller MPPT-ANN-PI based on ANN network to estimate MPP point voltage with
conventional PI controller, a MPPT-FLC and a conventional MPPT-INC uses the
Incremental Conductance technique INC
Linear regression methods impose strong constraints on regression models, especially on
the error terms where it assumes that it is independent and follows normal distribution, and
this may not be satisfied in many studies, leading to bias that can
not be ignored from the
actual model, which affects the credibility of the study.
We present in this paper the problem of estimating the regression function using the
Nadarya Watson kernel and k- nearest neighbor estimators as alternatives to the parametric
linear regression estimators through a simulation study on an imposed model, where we
conducted a comparative study between these methods using the statistical programming
language R in order to know the best of these estimations. Where the mean squares errors
(MSE) was used to determine the best estimate.
The results of the simulation study also indicate the effectiveness and efficiency of the
nonparametric in the representation of the regression function as compared to linear
regression estimators, and indicate the convergence of the performance of these two
estimates.
These papers aim to study the estimation of the simple linear regression equation
coefficients using the least square method at different sample sizes and different sampling
methods. And so on, the main goal of this research is to try to determine
the optimum size
and the best sampling method for these coefficients. We used experimental data for a
population consist of 2000 students from different schools all over the country. We had
changed the sample size each time and calculate the coefficients and then compare these
coefficients for different sample sizes with their coefficients of the real population; and the
results have been shown that the estimation of the linear regression equation coefficients
are close from the real values of the coefficients of the regression line equation for the
population when the sample size closes the value (325). As it turns out that the Stratified
random sampling with proportional distribution with class sizes gives the best and most
accurate results to estimate linear regression equation with least square method.
في الآونة الأخيرة حدث تضخم للمعلومات على شكل أخبار ومقالات مختلفة وجزء كبير من هذه البيانات يكون بشكل غير منظم
In this research, we developed an algorithm to measure
the quality of processed digital video, with no
information about the video before processing, to know
how much the video was distorted as a result of
processing
The research aimed at studying the impact of the most
important economic and social factors affecting the adoption of new
irrigation techniques، namely water collective management in ALGhab
basin in Syria .The research accomplished by taking a
si
mple random sample of 264 farmers .Because of the nature of
dependent variable which is dichotomous ،(1= adoption of water
collective management،0=otherwise)،The binary logistic regression
was used.
This paper aims to test weak form efficiency in
Damascus , Amman , Muscat securities market .It examines daily
stock return index during ( 1 - 4- 2010 ) , ( 31 - 12 - 2016 ) using
normal distribution test , runs test , autocorrelation test , unit
root
test , variance ratio test , auto regressive integrated moving average
test
الصيغة الضعيفة للكفاءة
اختبار التوزيع الطبيعي
اختبار التكرارات
اختبار الارتباط التسلسلي
اختبار نسبة التباين
اختبار الانحدار الذاتي و المتوسط المتحرك المتكامل
weak form efficiency
normal distribution test
Runs test
Autocorrelation test
Variance ratio test
auto regressive integrated moving average test
المزيد..
