In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.
Pseudomonas aeruginosa gram-negative, bacilli and facultative aerobic, P. aeruginosa cause cystic fibrosis patients, wounds, burns, and immunodeficienct patients, that have many virulence factors such as pyocyanin , cytotoxic ,biofilm formation and motility, Eighty-eight isolates belonging to P. aeruginosa were collected including the 66 clinical isolates obtained from different hospitals in Baghdad and were from different sources and 22 environmental isolates from previous studies of soil near oil fields. Microscopical and cultural characteristics were studied and diagnosed using biochemical tests, VITEC device, their ability to adhere to non-living (Polystyrene), living cell line (A549) and cytotoxicity of bacterial filtrate
... Show MoreAbstract Inflammation of periodontal tissues is the consequence of interaction between periodontal pathogens and immune system. This is associated with increased expression of inflammatory cytokines, which may exert destructive effect to the periodontal tissues when released over long period. The aim of this study was to chronologically track the homeostasis of oral keratinocytes following removal of periodontal pathogens. This was done by investigating expression of selected inflammatory markers and integrity of epithelial monolayers in vitro. Rat oral keratinocytes were stimulated with heat-killed Fusobacterium nucleatum and Porphyromonas gingivalis over 7-days then bacteria were washed away and epithelial cells re-cultured for 3-
... Show MoreA microbial desalination cell (MDC) is a new approach to bioelectrochemical systems. It provides a more sustainable way to electrical power production, saltwater desalination, and wastewater treatment at the same time. This study examined three operation modes of the MDC: chemical cathode, air cathode, and biocathode MDC, to give clear sight of this system's performance. The experimental work results for these three modes were recorded as power densities generation, saltwater desalination rates, and COD removal percentages. For the chemical cathode MDC, the power density was 96.8 mW/m2, the desalination rate was 84.08 ppm/hr, and the COD removal percentage was 95.94%. The air cathode MDC results were different
... Show MoreThe proper operation, and control of wastewater treatment plants, is receiving an increasing attention, because of the rising concern about environmental issues. In this research a mathematical model was developed to predict biochemical oxygen demand in the waste water discharged from Abu-Ghraib diary factory in Baghdad using Artificial Neural Network (ANN).In this study the best selection of the input data were selected from the recorded parameters of the wastewater from the factory. The ANN model developed was built up with the following parameters: Chemical oxygen demand, Dissolved oxygen, pH, Total dissolved solids, Total suspended solids, Sulphate, Phosphate, Chloride and Influent flow rate. The results indicated that the constructed A
... Show MoreEmotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In
... Show MoreThe university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed a
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The prevention of bankruptcy not only prolongs the economic life of the company and increases its financial performance, but also helps to improve the general economic well-being of the country. Therefore, forecasting the financial shortfall can affect various factors and affect different aspects of the company, including dividends. In this regard, this study examines the prediction of the financial deficit of companies that use the logistic regression method and its impact on the earnings per share of companies listed on the Iraqi Stock Exchange. The time period of the research is from 2015 to 2020, where 33 companies that were accepted in the Iraqi Stock Exchange were selected as a sample, and the res
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This study aims to identify the degree to which the first cycle teachers use different feedback patterns in the E-learning system, to identify the differences in the degree of use according to specialization, teaching experience, and in-service training in the field of classroom assessment as well as the interaction between them. The study sample consisted of (350) female teachers of the first cycle in the governmental schools in Muscat Governorate for the academic year 2020/2021. The study used a questionnaire containing four different feedback patterns: reinforcement, informative, corrective, and interpretive feedback. The psychometric properties of the questionnaire were verified in terms of validity
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