Training Programme Impact in Improving the Working Memory of Students with Learning Disabilities in Reading Arabic

The study aimed to investigate the effect of a training program on improving working memory for students with learning difficulties in reading Arabic. The study sample consisted of (10) students with learning disabilities from Basic Education students from the fifth and sixth grades, and those between the ages (10-11) years as a pilot group and (10) students from the same stage and of the same age as a control group. The researcher used a working memory battery, Raven Test, and a training program he prepared to achieve the study goal. The results showed that there are statistically significant differences in the fields of working memory scale between the members of the experimental and control groups in the dimensional measurement, and in favor of the experimental group to which the training program was applied, by applying the measure of working memory in its three fields. The results also showed that there are statistically significant differences between the pre and post measurements of the areas of the working memory scale in favor of the post application, and for the benefit of the experimental group,This confirms the effectiveness of the training program used. Some recommendations were suggested

Mechanical and physical properties of fly ash foamed concrete

Foamed concrete has become most commercial material in construction industry. Fly ash is receiving more attention now since their uses generally improve the properties of blended cement concrete, cost saving and reduction of negative environmental affects. The physical and mechanical properties of foamed concrete differ according to a different type of mixture and its composition. Therefore, this research investigates physical and mechanical properties of fly ash foamed concrete. Fly ash was used as fine aggregate. Six series of fly ash foamed concrete for target densities (1000, 1100,1200,1300,1400 and 1500 kg/m ) with constant cement to fly ash ratio (1:1.5) and cement to water ratio (1:0.65) by weight were prepared and tested. Tests were conducted to study physical properties (work ability, water absorption, drying shrinkage and carbonation) and mechanical strengths properties (compressive, splitting tensile and flexural strengths). Three types of specimens (cube, cylinder and prism) were used in different quantity and different purposes. The specimens of drying shrinkage test were opened after one day but, others specimens were demoulded after three days and subjected to air curing under room temperature. As result, the findings from this project are very encouraging towards the use of fly ash foamed concrete density of 1100 and 1200 kg/m 3 in block application due to its compressive strength (3.7 – 6.7 MPa) whereas density of 1300, 1400 and 1500 kg/m 3 3 in structural application due to its high compressive strength (10 – 18.8 MPa) and moderate water absorption that was below 10%. It was also found that the physical properties of fly ash foamed concrete are high drying shrinkage between -666 to 1022 micro strain, high water absorption for density less than 1300 kg/m , higher workability (115 -180 mm diameter) and high carbonation depth that make it a good breathable material that removes carbon dioxide from our environment. Lastly comparative analyses were done to determine the relationships between the various mechanical properties parameters of the fly ash foamed concrete, namely the compressive strength, flexural strength, splitting tensile strength and mathematical equations were derived

Economic Growth, Inflation, and Monetary Policy in Pakistan: Preliminary Empirical Estimates

There is a growing debate in the emerging market on the choice of an appropriate monetary or exchange rate policy that could lead to a sustainable economic growth. Inflation targeting has become one of these policy alternatives and has recently been implemented in some of the emerging markets in Asia and Latin America. Given the recent remarkable economic performance of the Pakistan, this issue has also been discussed at various policy forums in Pakistan. An important pre-condition for the success of inflation-targeting is to identify the leading indicators of inflation and develop a model to reasonably forecast inflation. This is the main objective of this paper. Besides an overview of the experience, the main focus of the paper is to provide some preliminary empirical estimates for inflation equation and its causal relationship with other macroeconomic variables.

Noisy Relativistic Quantum Games in Noninertial Frames

The influence of noise and of Unruh effect on quantum Prisoners' dilemma is investigated both for entangled and unentangled initial states. The noise is incorporated through amplitude damping channel. For unentangled initial state, the decoherence compensates for the adverse effect of acceleration of the frame and the effect of acceleration becomes irrelevant provided the game is fully decohered. It is shown that the inertial player always out scores the noninertial player by choosing defection. For maximally entangled initially state, we show that for fully decohered case every strategy profile results in either of the two possible equilibrium outcomes. Two of the four possible strategy profiles become Pareto Optimal and Nash equilibrium and no dilemma is leftover. It is shown that other equilibrium points emerge for different region of values of decoherence parameter that are either Pareto optimal or Pareto inefficient in the quantum strategic spaces. It is shown that the Eisert et al miracle move is a special move that leads always to distinguishable results compare to other moves. We show that the dilemma like situation is resolved in favor of one player or the other.Comment: 14 pages and 6 figure

A perspective of the Malaysian highway energy consumption and future power supply

In this short communication, we discuss the energy consumption trends in the Malaysian road transport sector, with a special emphasis on the energy losses due to vehicle aerodynamic drag on highways. The recent trends of energy consumption in the Malaysian road transport sector are reviewed. It is evidently shown that the aerodynamic losses represented exceed 1.2. MTOE annually since 2002. A novel concept of vertical-axis wind turbine (VAWT) farms for harvesting aerodynamic energy losses on Malaysian highways is preliminarily proposed. The novel concept aims at providing a sustainable and green energy source for the lighting of the highway network in the country

Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while ?Ant-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman’s statistical test

Learning Multi-Tree Classification Models with Ant Colony Optimization

Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain