This research study aspires to examine the impact of corporate governance on corporate performance, and researchers are increasingly fascinated by how to govern effectively, which will contribute to the performance of corporate sectors to the consequences of scandals, corporate bankruptcies worldwide. To achieve their objectives, the researcher used various corporate governance independent variables such as a board of directors, audit committees, and the meeting frequency. While the other variables return on Asset, Return on Equity is used as a dependent variable to measure the performance of companies. Secondary data from top listed companies in Pakistan stock exchange is used as the sample for 2014-2018. The findings indicate that the indicators of corporate governance have a major effect on corporate success. The size of the board has a positive effect on the company's success. The number of meetings hurts the company's results. The audit committee had a positive effect on the results of the company.
The objective of this study is to determine the prevalence and antibiotic resistance of Aeromonas spp. in spring, well and distribution waters in Van city and its province and to evaluate the Aeromonas spp. in terms of public health risks. For this purpose samples (n = 120) were collected from 19 natural springs, 10 wells and 91 public drinking water supply distribution systems. The membrane filter method was used for detection and counting of the bacteria. Several strains of Aeromonas spp. were isolated and identified. The antibiotic resistance of each strain was determined. Contamination was found in about 30% of the total number of samples studied. Three isolates of Aeromonas hydrophila (2.5%), two of Aeromonas sobria (1.6%), and thirty-one from Aeromonas caviae (25.8%) were found in all samples. The A. hydrophila isolates showed resistance to ceftazidime (66.6 %), cefotaxime, ampicilin and amoxicillin clavulanic acid (33.3 %). A. caviae was resistant to ampicilin (58.1 %), amoxicillin clavulanic acid (80.6 %), and ceftazidime (32.2 %) and A. sobria was resistant to amoxicillin clavulanic acid and ceftazidime (100 %), cefotaxime, ofloxacin, piperacillin and tetracycline(50 %). As a result these waters are not fit for consumption in terms of the Aeromonas spp. they contain and may be poses a potential public health hazard in terms of public health.
In this study, the performance of an integrated desiccant air conditioning system (IDACS) driven by solar energy is experimentally tested and predicted by back propagation artificial neural network (BP-ANN). The IDACS is generally includes a liquid desiccant dehumidification cycle combined with a vapor compression refrigeration cycle. The integrated system performance is assessed utilizing the system coefficient of performance (COP), outlet dry air temperature (Tda-out), and specific moisture removal (SMR). The training of the BP-ANN is accomplished utilizing experimental results previously published. The results of the BP-ANN model revealed the high accuracy in predicting system performance parameters compared with experimental values. The BP-ANN model has shown relative errors in the trained mode for COP, Tda-out, and SMR within ±0.005%, ±0.006%, and ±0.05%, respectively. On the other side, the BP-ANN model is inspected in the predictive mode as well. The relative errors of the model for COP, Tda-out, and SMR in the predictive mode are within ±0.006%, ±0.006%, and ±0.004%, respectively. The influences of some selected parameters, namely regeneration temperature, desiccant solution temperature in the condenser and evaporator, and strong solution concentration on the system performance are examined and discussed as well.
In this paper, a fractional Ricatti expansion method is proposed to solve a nonlinear time fractional Fitzhugh-Nagumo equation in the frame of conformable derivatives, we also apply it to the nonlinear fractional Newell-Whitehead -Segel and Zeldovich equations which are an particular case of fractional Fitzhugh-Nagumo equation. In order to illustrate the accuracy and validity of this method, some numerical solution are given.
Clustering is most common and well-established data mining technique for discovering patterns in data. Besides other types of data, clustering is also widely used for the same purpose for data acquired from educational settings. Among its di erent variants, K-means algorithm is popular in EDM community for its simplicity and ease of use. However, K-means algorithm itself does not impose the number of clusters. The optimal number of clusters in dataset remains a debatable issue. Di erent methods exist which can be used to estimate the number of clusters present in the dataset. In this paper, we present a comparison of di erent methods used for determining the optimal value of K. We use ve datasets and seven methods to nd the optimal number of clusters in these datasets. Two of the datasets have been extracted from educational settings. The other three are open datasets. We compare results obtained from di erent methods using these ve datasets. EDM community is growing rapidly and the researchers are experimenting with more and more methods. The analysis presented in this paper will help EDM practitioners to choose appropriate technique based on objective evaluation measures to determine the optimal value of K.
In this paper we present a new method to determine the numerical value of the Boltzmann constant k and its uncertainty. We have used Nitrogen gas in different pressure values in the range 6 kPa – 100 kPa, for three different volumes. In this experiment we have used a simple idea called static expansion method, simple equipment and simple equations for calculations in order to determine the Boltzmann constant. This method is suitable for students of high-school level as well as introductory higher education.