In this study, the solid and liquid wastes from the olive oil processing industry were evaluated as substrates for Yarrowia lipolytica growth with the aim of lipase production. Olive mill wastewater and olive oil cake seemed to provide the necessary nutrients and physical support for yeast growth and enzyme production. The highest lipolytic activity of 850 IU dm(3) was achieved after 4 days of submerged cultivation in supplemented olive mill wastewater. In addition, olive oil cake appeared to be a convenient substrate for lipase production under a solid-state fermentation mode. Lipase production was further improved by media supplementation and/or change in the physical settings of the experiment. However, the most significant improvement of lipase production under solid-state fermentation was achieved by an alkaline treatment of the substrate (more than 10-fold), when the amount of produced lipase reached up to approximate to 40 IU g(-1) of substrate.

Abstract: For electrical drives good dynamic performance is mandatory so as to respond to the changes in command speed and torques, so various speed control techniques are being used for real time applications. The speed of a DC motor can be controlled using various controllers like PID Controller, Fuzzy Logic Controller, Particle Swarm Optimization (PSO) and Hybrid Fuzzy-PSO Controller. Fuzzy-PSO Controller is recently getting increasing emphasis in process control applications. The paper describes application of Hybrid Fuzzy-PSO Controller in an enhancement of stability and accuracy of the SEDC Motor under the effect of the external disturbances and noise that uses the Fuzzy-PSO Controller for enhancement of stability and accuracy of the SEDC Motor under the effect of the external disturbances and noise is implemented in MATLAB/SIMULINK.  The simulation study indicates the superiority Hybrid FuzzyPSO Controller over the Particle Swarm Optimization (PSO) and fuzzy logic controller separately. This control seems to have a lot of promise in the applications of power electronics. The speed of the SEDC motor can be adjusted to a great extent so as to provide easy control and high performance. There are several conventional and numeric types of controllers intended for controlling the SEDC motor speed and executing various tasks: PID Controller, Fuzzy Logic Controller; or the combination between them: FuzzySwarm, Fuzzy-Neural Networks, Fuzzy-Genetic Algorithm, Fuzzy-Ants Colony, Fuzzy-Particle Swarm Optimization. We describe in this paper the use of Hybrid Fuzzy-PSO Controller for enhancement of stability and accuracy of the SEDC Motor under the effect of the external disturbances and noise We describe in this paper the use of Particle Swarm Optimization (PSO) for designing an optimal fuzzy logic controller of a SEDC Motor. In this case, our approach will optimize the membership functions of a fuzzy logic controller (FLC) using PSO and the obtained results were simulated on Matlab environment.

Keywords: Separately Excited DC Motor (SEDC), Fuzzy Logic Controller (FLC), Particle Swarm Optimization(PSO).

Prevalence of bacterial infection among hospital traumatic patients in relation to ABO blood group

Background

There are many studies demonstrated a correlation between blood group antigens and susceptibility to infectious diseases such as bacteria, parasites and viruses.

Objectives

To assess the prevalence of bacterial infection among patients in the trauma hospital, and to assess the susceptibility of ABO blood groups to the isolated bacteria.

Methods and Findings

166 samples included, wound swabs, sputum and midstream urine were received for routine culture diagnostic procedures from the in-patients at Abosleem Traumatic Hospital and ABO group was obtained from Blood bank documented system for each patient. A correlation between isolated organisms and ABO system was determined. 51% patients were infected during their stay in the hospital by one of the following isolates: Pseudomonas (22 …

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.

Abstract

Painful diabetic neuropathy is poorly controlled by analgesics and requires high doses of opioids, triggering side effects and reducing patient quality of life. This study investigated whether enhanced Rab7-mediated lysosomal targeting of peripheral sensory neuron μ-opioid receptors (MORs) is responsible for diminished opioid responsiveness in rats with streptozotocin-induced diabetes. In diabetic animals, significantly impaired peripheral opioid analgesia was associated with a loss in sensory neuron MOR and a reduction in functional MOR G-protein-coupling. In control animals, MORs were retained mainly on the neuronal cell membrane. In contrast, in diabetic rats, they were colocalized with upregulated Rab7 in LampI-positive perinuclear lysosome compartments. Silencing endogenous Rab7 with intrathecal Rab7-siRNA or, indirectly, by reversing nerve growth factor deprivation in peripheral sensory neurons not only prevented MOR targeting to lysosomes, restoring their plasma membrane density, but also rescued opioid responsiveness toward better pain relief. These findings elucidate in vivo the mechanisms by which enhanced Rab7 lysosomal targeting of MORs leads to a loss in opioid antinociception in diabetic neuropathic pain. This is in contrast to peripheral sensory neuron MOR upregulation and antinociception in inflammatory pain, and provides intriguing evidence that regulation of opioid responsiveness varies as a function of pain pathogenesis.

This paper considers a common problem that all industries contest with in practice ie the breaking down of machines that influence production and cost directly. In a majority of industrial applications, acquiring optimum utilisation of all available resources for existing and future predicted demand is a major function throughout all the levels of management. This paper uses a number of analytical tools and software that support one another ie Witness Simulation, Bayesian Network Modelling and Hugin Software. The use of expert experience and knowledge has been incorporated throughout the study as it is vital to model building and greater understanding of machine breakdown. This study uses discrete event simulation and Bayesian network modelling collectively to understand machine breakdowns to increase efficiency within a cement manufacturing plant ie the Crusher Machine. The Bayesian network modelling implemented by the Hugin Software is used to generate probabilities which are transferred into a discrete event simulation model using Witness Software based on the historical data, expert knowledge and opinions. The model simulates the three parameters of the machine life based on consumption of each parameter. This is translated into a probability failure rate that changes as the model is running. The model demonstrates decisions based on the probability of failure from the Bayesian model and based on life consumption of the different variables in the simulation model.