The Impact of Government Expenditure on the Palestinian Economy: A CGE Analysis

An important question in growth theory is whether government expenditure promotes economic growth. However the empirical evidence is inconclusive. This paper investigates the impact of increasing government expenditure by 25% from the base line on the aggregate Palestinian economy variables. A simulation of increasing government expenditure is carried out using a 2015 Palestinian Social Accounting Matrix (SAM) and Computable General Equilibrium (CGE). We simulate   the impact of a 25% increase in government expenditure, which could come about due to the Palestinian reconciliation agreement that has ended a decade old political divide between the West Bank and Gaza Strip.  The simulation results illustrate that real GDP increases by 4.73%.  The real private consumption declined by 2.60%.  Import and export are increased by 3.09% and 10.53% in real term respectively.  Net taxes increase by 1.23%, as a percentage of GDP the trade deficit declines by 2.00 percentages. Real exchange rate appreciated by 12.9 % from the base line.  In addition absorption increases by 3.12 % in real terms. Keywords: government expenditure, Social Accounting Matrix, Computable General Equilibrium, Palestine. JEL Classification: E62, H50, C23 C68, D58, E62, F15, H62, I3

Monitoring Physiological Variables with Membrane Probes

This project has demonstrated the possibility of using membrane probes in rodents to monitor physiological variables for extended periods of time. The utility of these probes in physiological studies of microgravity has been demonstrated. The feasibility of developing on-line sensors has also been demonstrated and allows for the possibility of developing real-time automated monitoring systems which can be used in ground-base physiological research as well as in research and medical monitoring in space. In addition to space applications these techniques can be extended to medical monitoring in critical care situations on earth as well as facilitating research in many human and animal diseases

Додавання та множення в кодах золотої пропорції в порозрядному режимі, починаючи зі старших розрядів

Автором пропонується нестандартна мінімізована форма (MN-форма) представлення чисел у коді класичної золотої пропорції. У цій формі може бути представлене будь-яке дійсне число, причому в коді будуть відсутні послідовності цифр "111". Використання MN-форми дозволяє здійснювати та контролювати порозрядні обчислення, виконуючи арифметичні операції в MSDF-режимі (починаючи зі старшого розряду). Розроблено алгоритми виконання деяких арифметичних операцій, зокрема, алгебраїчного додавання та множення, для чисел, представлених у MN-формі.Author proposes to write the golden ratio base numbers in the nonstandard minimized form (MN-form). Any real number can be presented in this form avoiding the digit sequence "111". The MN-form use allows to carry out and control on-line computations, executing arithmetic operations in the Most Significant Digit First (MSDF)-mode. The algorithms of execution of some arithmetic operations, including the addition, subtraction and multiplication, are developed for MN-form numbers

Real-time Global Illumination Decomposition of Videos

We propose the first approach for the decomposition of a monocular color video into direct and indirect illumination components in real time. We retrieve, in separate layers, the contribution made to the scene appearance by the scene reflectance, the light sources and the reflections from various coherent scene regions to one another. Existing techniques that invert global light transport require image capture under multiplexed controlled lighting, or only enable the decomposition of a single image at slow off-line frame rates. In contrast, our approach works for regular videos and produces temporally coherent decomposition layers at real-time frame rates. At the core of our approach are several sparsity priors that enable the estimation of the per-pixel direct and indirect illumination layers based on a small set of jointly estimated base reflectance colors. The resulting variational decomposition problem uses a new formulation based on sparse and dense sets of non-linear equations that we solve efficiently using a novel alternating data-parallel optimization strategy. We evaluate our approach qualitatively and quantitatively, and show improvements over the state of the art in this field, in both quality and runtime. In addition, we demonstrate various real-time appearance editing applications for videos with consistent illumination

Supporting effective monitoring and knowledge building in online collaborative learning systems

This paper aims to report on an experience of using an innovative groupware tool to support real, collaborative learning. We base the success of on-line collaborative learning on extracting relevant knowledge from interaction data analysis in order to provide learners and instructors with efficient awareness, feedback, and monitoring as regards individual and group performance and collaboration. Monitoring is especially important for online instructors since they can use this valuable provision of information as a meta cognitive tool for regulating the collaborative learning process more conveniently and provide adequate support when needed. In addition, learning and knowledge building may be greatly enhanced by presenting selected knowledge to learners as for their particular skills exhibited during interaction, such as the impact and effectiveness of their contributions. Indeed, by letting learners be aware of both their own and others’ progress in the process of knowledge building may promote learners’ participation and boost group performance. The ultimate goal of this paper is to provide a model to achieve a more effective support and assessment of the collaborative process while enhancing and improving the learning experience. To validate this study, a real online learning environment is employed to support asynchronous collaborative activities.Peer ReviewedPostprint (author's final draft

Digital Receiver-based Electronic Intelligence System Configuration for the Detection and Identification of Intrapulse Modulated Radar Signals

An optimum electronic intelligence system configuration incorporating the state of the art technologies and achieving the highest parameter accuracies while processing the complex intrapulse modulated radar signals is presented in this paper. The system is based on the quad digital receiver, a state of the art single board solution for the detection and analysis of modern radar signals. The system consists of base line interferometry  configuration for high accuracy direction finding measurement with sector selection based on amplitude direction finding technique. Advanced signal processing algorithms with time frequency analysis are implemented in real time in field programmable gate array to extract all the basic as well as advanced parameters of frequency and phase modulations such as chirp, barker, and poly-phase (Frank, P1-P4) codes in addition to the pulse and continuous wave signals. The intercepted intrapulse modulated signal parameters have been extracted with very high accuracy and sensitivity.Defence Science Journal, 2014, 64(2), pp. 152-158. DOI: http://dx.doi.org/10.14429/dsj.64.509

Hybrid regression model for near real-time urban water demand forecasting

[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.This work has been partially supported by CAPES Foundation of Brazil’s Ministry of Education. The data were provided by SABESP, São Paulo state water management company.Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics. 309:532-541. doi:10.1016/j.cam.2016.02.009S53254130

Reflections on the Construction Path Based on the Online Practice Base of Human Resource Management in HEI

The development of the integration of Industry-University-Research (later abbreviated as “IUR”) is an important part of the reform of higher education institutions（later abbreviated as“HEI”）. Through continuous practice, exploration, debugging and updating, it will, on the one hand, help to improve the degree of adaptation of theoretical teaching to the real world, and on the other hand, it can better help students to make the transition from family to society and complete the metamorphosis of the fully socialized process.In addition to a solid foundation in macro theory, the teaching of management courses, as compared to other categories, requires a very different approach to management in different industries, regions, corporate cultures and leadership styles. Therefore, use methods in line with local circumstances is a very important step in developing good HR managers.Due to the reality that students are unable to travel across the country for practice, the online experience becomes a great tool to aid practice.The study is based on the teaching of human resource management to students in higher education,and supplemented by the technical support of the platform provided by emerging enterprises. After observing and reflecting on the feedback from students after using the platform, the idea of a pathway for the construction of an online practice base is proposed to lay the foundation for subsequent research. Keywords: IUR, human resource management, online platform, practice base DOI: 10.7176/JEP/14-15-01 Publication date:May 31st 202

BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies

We introduce BAYES-LOSVD, a novel implementation of the non-parametric extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We employ bayesian inference to obtain robust LOSVDs and associated uncertainties. Our method relies on principal component analysis to reduce the dimensionality of the base of templates required for the extraction and thus increase the performance of the code. In addition, we implement several options to regularise the output solutions. Our tests, conducted on mock spectra, confirm the ability of our approach to model a wide range of LOSVD shapes, overcoming limitations of the most widely used parametric methods (e.g. Gauss-Hermite expansion). We present examples of LOSVD extractions for real galaxies with known peculiar LOSVD shapes, i.e. NGC4371, IC0719 and NGC4550, using MUSE and SAURON integral-field unit (IFU) data. Our implementation can also handle data from other popular IFU surveys (e.g. ATLAS3D, CALIFA, MaNGA, SAMI). Details of the code and relevant documentation are freely available to the community in the dedicated repositories.Comment: 13 pages, 7 figures. Accepted for publication in Astronomy & Astrophysics. Public repository with the code can be found at: https://github.com/jfalconbarroso/BAYES-LOSV