In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies

The measurements and tests of the samples conducted in the laboratories of the College of Agriculture included isolating bio-fertilizers and testing the efficiency of isolates that fix atmospheric nitrogen and solubilize phosphorous compounds. Bacteria were isolated and identified from the rhizosphere soils of different plants collected from various agricultural areas. A total of 74 bacterial isolates were obtained based on the phenotypic characteristics of the developing colonies, as well as biochemical and microscopic traits. The results of isolation and identification showed that among the 74 bacterial isolates, there were 15 isolates of A. chroococcum, 13 of Az. lipoferum, 13 of B. megaterium, 10 of P. putida, 10 of Actinomycetes, and n

A single-crystalline semi-polar gallium nitride (11-22) was grown on m-plane (10-10) sapphire substrate by metal organic chemical vapor deposition. Three-step approach was introduced to investigate the grain size evolution for semi-polar (11-22) GaN. Such approach was achieved due to the optimized gallium to ammonia ratio and temperature variations, which led to high quality (11-22) oriented gallium nitride epilayers. The full width at half maximum values along (-1-123) and (1-100) planes for the overgrowth temperature of 1080°C were found to be as low as 0.37° and 0.49°, respectively. This was an indication of the enhanced coalescence and reduction in root mean square roughness as seen by atomic force microscopy. Surface analysi

The aims of this study are to measure the defect rate and analyze the problems of production of ready concrete mixture plant by using Six Sigma methodology which is a business strategy for operations improvement depending basically on the application of its sub-methodology DMAIC improvement cycle and the basic statistical tools where the process sigma level of concrete production in the case study was 2.41 σ.

The influence of dye laser Rhodamine 6G (R6G) on the molecular structure of silica aerogel prepared by normal drying method is reported. The study also tests the effect of dye concentration on morphological and physical properties. Fourier Transform Infrared Spectroscopy (FTIR) was used to examine this effect, in addition to Field Emission Scanning Electron Microscopy (FESEM), contact angle, and surface area measurement. It was found from FTIR data that the dye laser stays with the inner structure of samples and, at high concentration, it gives a good influence by reducing (OH) band and increasing (CH) band, leading to changing the contact angle from (123^á´¼) to (145^á´¼). Whereas particle size varied from 22 n

    This paper deals with finding an approximate solution to the index-2 time-varying linear differential algebraic control system based on the theory of variational formulation. The solution of index-2 time-varying differential algebraic equations (DAEs) is the critical point of the equivalent variational formulation. In addition, the variational problem is transformed from the indirect into direct method by using a generalized Ritz bases approach. The approximate solution is found by solving an explicit linear algebraic equation, which makes the proposed technique reliable and efficient for many physical problems. From the numerical results, it can be implied that very good efficiency, accuracy, and simplicity of the pre

The present study was designed to investigate the possibility of exploiting the interspecies interaction of microbial cells in order to enhance the production of prodigiosin by local isolate S. marcescens S23. Prodigiosin is a promising drug owing to its characteristics of antibacterial, antifungal, immunosuppressive and anticancer activities. S. marcescens S23 was isolated from soil sample and already recognized via morphological, biochemical and molecular identification process. The first step was to detect the optimal conditions for maximum prodigiosin production using chemically defined liquid medium. The results revealed that the optimal conditions for prodigiosin production were sucrose as carbon source; peptone as nitrogen source;