Knowledge Management Technologies

The knowledge process is part of organizational life. This paper is focused on the achievements that make Jordan companies ready to implementation KM process. It aims to help in the process of using, sharing, applying the new knowledge. A survey was designed and sent to the all levels of pharmaceutical firms to investigate the factors that effect the KM implementation in order to improve products, services, The result lead recommends that There is a lack of empirical support for the effectiveness and importance of practical knowledge management strategies. The knowledge systems should build with the realization of human knowledge and ideas. Knowledge management requires more attention to IT systems and to the people who sharing knowledge. Firms should use all the options available to motivate employees to put the knowledge in work. Keywords: Organization strategy. Individual Experiences. Mechanism &Technology. Implantation KM process

The Data Traffic and Data Warehouses Store Managing and Controlling

The new technology provides a number of problems, such as protection and security, better switch between towers and networks, the accelerating rate of technology improvements. The research aimed to fully addressed the data traffic managing and controlling; in the service companies. The paper answer the question, how networks communication firms manage and control data warehouses, scalable data warehouse, and processing data traffic, also the majority of the data traffic online, which requires massive bandwidth. The researcher recommend that corporate networks should have control standards and characteristics stand out in data warehouses, and the corporate network should fully care of the efficient traffic data management in order to reduce the data traffics problem and security issues. Keywords: Data traffic, data warehouses, data management, privacy, security issues

Empirical Mode Decomposition Combined with Local Linear Quantile Regression for Automatic Boundary Correction

Empirical mode decomposition (EMD) is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a newtwo-stagemethod to automatically decrease the boundary effects present inEMD.At the first stage, local polynomial quantile regression (LLQ) is applied to provide an efficient description of the corrupted and noisy data.The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD

Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

This paper mainly forecasts the daily closing price of stockmarkets.We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ).We use the proposed technique, EMDLLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposedmethod, in which EMD-LPQ, EMD, andHolt-Winter methods are compared.The proposed EMD-LPQ model is determined to be superior to the EMDandHolt- Winter methods in predicting the stock closing prices

Effect of Ramadan Fasting on Anthropometric Measures and some Biochemical Parameters among Type2 Diabetic Patients in Gaza Governorate, Gaza Strip

Objectives: The aim of this study is to assess the effect of Ramadan fasting on anthropometric measures and some biochemical parameters among type 2 diabetes (T2D) patients in Gaza Governorate, Gaza Strip. Materials and methods: The design of the study is a case control (2:1). The study was carried out during the last Ramadan (late of July to August, 2011) in Gaza Governorate. A total of 80 patients suffering from the diabetes type 2, aged 40 to 65 years, have no history of diabetic complications or other diseases and treated with the same of oral hypoglycemic drugs (OHD), and compared with 40 healthy individuals as control. Anthropometric and biochemical analysis were carried out one week before Ramadan and one week before its end. Data (obtained through questionnaire interview) were analyzed using SPSS version 14. Results: The mean (± SD) of age of the participants was 53.21 (± 7.459) years for diabetic patients and 54.84 (± 6.798) years for controls (p>0.05). When the results were summed up and compared statistically, there was a statistically significant reduction in the mean of body weight (p=0.038 and p=0.000 respectively) and body mass index (BMI) (p=0.001 and p=0.000 respectively) at the end of Ramadan month in both groups as compared to pre-Ramadan. This study also found a statistically reduction in the mean (± SD) of serum fasting blood glucose (FBG) during Ramadan as compared to values before Ramadan in both groups (p=0.000 and p=0.000, respectively). A statistically significant increase in the mean (± SD) of serum triglycerides (TG) levels was also observed at the end of fasting among diabetic group (p-value=0.000). Among diabetic group, the mean (± SD) of HDL-C levels also showed significant reduction (P=0.000), while significant elevation in control group was observed (P=0.000) during Ramadan as compared to values before Ramadan. There was also statistically significant elevation in the mean (± SD) of serum total cholesterol (TC) (p-value=0.000 in both groups) and low density lipoprotein cholesterol (LDL-C) (p-value=0.000 in both groups) during the period of fasting as compared to the period before fasting in both groups. In addition, during the two periods, there were no statistical differences in the mean (± SD) of serum creatinine (p=0.0.193 and p=0.147 respectively) and urea levels (p=0.560 and p=0.143 respectively) in both groups. Concerning the glycated hemoglobin (HbA1c), the results also showed no statistical differences in the mean (± SD) of HbA1c levels (p=0.133 and p=0.905 respectively) in both groups. Conclusion: Ramadan fast is relatively safe among type 2 diabetic patients provided that they should be properly educated about drug regimen adjustment, diet control, daily activities and possible sudden complications. Keywords: Ramadan fasting, Biochemical parameters, Type 2 diabetes, Anthropometric measurement, Gaza Strip, Palestine

Recurrent right sublingual ranula, concomitant with ipsilateral submandibular salivary gland aplasia

AbstractINTRODUCTIONOral ranula is a retention cyst that arises from the salivary gland with recurrence rate of up to 25% after complete excision of ranula and up to 2% in case of complete excision of ranula and sublingual gland.Major salivary gland aplasia is a rare finding that is usually associated with other developmental anomalies.PRESENTATION OF CASEWe report a 15-year-old female patient presented with recurrent intraoral cystic swelling that was documented to be sublingual ranula. CT scan revealed also the absence of right submandibular salivary gland with persistence of its Whartons duct. This combination has never been reported previously.DISCUSSIONThe combination of recurrent sublingual ranula associated with aplasia of ipsilateral submandibular salivary gland and persistence of Whartons duct has never been reported before in the literature, a finding that may provide the base for future research.CONCLUSIONFurther research may prove similar associations between oral ranula and salivary gland aplasia, which may have clinical implications on diagnostic and management plan decisions

Robust Wavelet Estimation to Eliminate Simultaneously the Effects of Boundary Problems, Outliers, and Correlated Noise

Classical wavelet thresholding methods suffer from boundary problems caused by the application of the wavelet transformations to a finite signal. As a result, large bias at the edges and artificial wiggles occur when the classical boundary assumptions are not satisfied. Although polynomial wavelet regression and local polynomial wavelet regression effectively reduce the risk of this problem, the estimates from these two methods can be easily affected by the presence of correlated noise and outliers, giving inaccurate estimates. This paper introduces two robust methods in which the effects of boundary problems, outliers, and correlated noise are simultaneously taken into account. The proposed methods combine thresholding estimator with either a local polynomial model or a polynomial model using the generalized least squares method instead of the ordinary one. A primary step that involves removing the outlying observations through a statistical function is considered as well. The practical performance of the proposed methods has been evaluated through simulation experiments and real data examples. The results are strong evidence that the proposed method is extremely effective in terms of correcting the boundary bias and eliminating the effects of outliers and correlated noise

Cortico-hippocampal computational modeling using quantum-inspired neural networks

Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model

Particulate emissions from a 350 kW wood pellet heater

The particulate mass and size distribution was investigated for a biomass wood-pellet air heater with a direct comparison with an equivalent oil-fired burner using the same cross-flow air heater system. Five wood pellet fuels were investigated from different sources and the influence on particle mass and size distribution was determined. The influence of burner excess air on gaseous and particulate emissions was determined. The optimum excess air for minimum emissions and maximum thermal efficiency for the pellet burner was higher at 42% than for the oil burner at 23%. The thermal efficiency of the pellet heater was determined to be slightly less than that of the oil heater. The main reason for this was operation of the heater at higher excess air levels with pellets. The hydrocarbon and particulate carbon fraction emissions were lower for the pellet burner but the CO and NOx emissions were higher. Composition differences between different pellet manufacturers, due to the use of different wood sources, were significant and this produced significant variation in the stoichiometric A/F, which without oxygen feedback control, resulted in different excess air levels for the same pellet feed rate. This resulted in a significant influence of pellet composition on emissions due to excess air variations. Particulate mass and number emissions were low for the biomass pellet burner and similar to the oil burner, provided both burners were at their optimum excess air operational condition. Particulate emissions increased dramatically if the excess air was reduced to 23%