The Calendar Impact And Trading Behavior: An Empirical Evidence From Around The Globe

This paper is aimed to determine a change in the stock market’s returns or its volatility from the globally selected Islamic mutual funds during the month of Ramadan, as all Muslims around the world eagerly and enthusiastically follow the rituals of the holy month of Ramadan. The paper uses monthly data of equity funds for those which are domiciled and those which operate globally within the period of January 2004 until December 2009. It is interesting to note that the empirical results provide no supporting evidence for the effect of the Ramadan month on the Islamic equity fund performance when examined using a dummy variable for the Ramadan month. Nevertheless, the evidence reveals that the volatility of stock returns remarkably decreases during this month. The reason for the decrease in volatility may be the result of the speed of economic activities during that month. Although, there is a decline in stock return volatility in the month of Ramadan, the return indicates no significant change

Histogram of oriented gradients front end processing: an FPGA based processor approach

The Field Programmable Gate Array (FPGA) implementation of the commonly used Histogram of Oriented Gradients (HOG) algorithm is explored. The HOG algorithm is employed to extract features for object detection. A key focus has been to explore the use of a new FPGA-based processor which has been targeted at image processing. The paper gives details of the mapping and scheduling factors that influence the performance and the stages that were undertaken to allow the algorithm to be deployed on FPGA hardware, whilst taking into account the specific IPPro architecture features. We show that multi-core IPPro performance can exceed that of against state-of-the-art FPGA designs by up to 3.2 times with reduced design and implementation effort and increased flexibility all on a low cost, Zynq programmable system

IPPro: FPGA based image processing processor

n/

Enhancing the Mechanical Properties of Concrete and Self-Healing Phenomena by adding Bacteria, Silica fume and Fibres

Concrete which is the most useable material in the world after the water has flaws, it is susceptible to cracking over time. These cracks occur in the form of shear cracks, flexural cracks, tension cracks, shrinkage cracks etc. With these cracks, some hair-like cracks also occur in concrete which are not visible during the visual inspection. The propagation of these cracks in concrete allows the water and many other chemicals to seep inside the concrete and leads to a decrease in its properties. Such properties include decreasing durability, erosion of rebars, and progressive failure in the concrete strength. Therefore, the repair of hair-like cracks is also essential for the long-term safety of structures. In the present study the Silica fume, and Polypropylene fibres are added to a rich concrete along with the bacteria named Bacillus Subtilis and Calcium Lactate for enhancement of its mechanical properties and self-healing phenomena. The effect of bacteria in the healing phenomenon and other properties is compared to normal concrete by casting the cylinders and beams. The slump, compressive strength, tensile strength, and self-healing phenomena are tested and found the increase in mechanical properties of concrete. The self-healing phenomena of cracks is observed by the Scanning Electron Microscope (SEM)

A comparative analysis of machine learning approaches for plant disease identification

Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages.Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG) have been used.Results:  382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves.Conclusion:  From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants

A Comparison Between Schlumberger and Wenner Configurations in Delineating Subsurface Water Bearing Zones: A Case Study of Rawalakot Azad Jammu and Kashmir, Pakistan

The Schlumberger and Wenner Electrical Resistivity Survey techniques have been used in comparison for the determination of groundwater potential in District Rawalakot, Azad Jammu and Kashmir. The terameter SAS4000 with accessories was used for data acquisition. The data were processed by employing IPI2WIN software to determine the depth, thickness and true resistivity of the subsurface layers. The present study indicated the subsurface depth coverage of Schlumberger configuration is greater than Wenner configuration. The apparent resistivity maps using both Wenner and Schlumberger techniques at the same locations have been prepared at 3m, 4m, 9m, 10m, 27m, 30m, 50m, 51m, 100m, and 150m depths respectively for groundwater assessment. The differences in resistivity contour closures, in both types of maps, arise due to lateral variations of subsurface lithology. Longitudinal conductance, transverse resistance and anisotropic maps were also prepared. The different contour closures in the Wenner map were due to mixed lithology of alluvium with variable water contents. The subsurface geology i.e. clay, sandstone boulder clay, and dry sandy soil were interpreted which are in close agreement with the surface geology of the area. The aquifers of the project area are designated as confined and unconfined good water potential indicated by low values of resistivity. The water-bearing strata consist of sand, gravel, boulder clay and sandy clay

Surface Water Regulation in Texas: Problems and Solutions

According to the 2017 Texas State Water Plan, Texas will experience an 8.9 million acre foot water shortage by 2070. The question is what role surface and groundwater will play in alleviating this shortfall. The 2016 Capstone project to Comptroller Hegar assessed the potential for ground water to meet these predicted water needs (the Brady et al. report). This report is a follow on report focused on surface water. In several ways, surface water poses a more complex task because one cannot point to a single regulatory institution with simple fixes. Indeed, in many respects, surface water institutions in Texas are relatively sophisticated. From the extensive WAM modeling used by the Texas Commission on Environment Quality (TCEQ) to the comprehensive 50-year water plans produced by the Texas Water Development Board (TWBD), Texas is significantly ahead of other states in their water planning and management. However, our analysis has identified three major problem areas, the solutions to which are the focus of this report