A Novel Hybrid Particle Swarm Optimization and Sine Cosine Algorithm for Seismic Optimization of Retaining Structures

This study introduces an effective hybrid optimization algorithm, namely Particle Swarm Sine Cosine Algorithm (PSSCA) for numerical function optimization and automating optimum design of retaining structures under seismic loads. The new algorithm employs the dynamic behavior of sine and cosine functions in the velocity updating operation of particle swarm optimization (PSO) to achieve faster convergence and better accuracy of final solution without getting trapped in local minima. The proposed algorithm is tested over a set of 16 benchmark functions and the results are compared with other well-known algorithms in the field of optimization. For seismic optimization of retaining structure, Mononobe-Okabe method is employed for dynamic loading condition and total construction cost of the structure is considered as the objective function. Finally, optimization of two retaining structures under static and seismic loading are considered from the literature. As results demonstrate, the PSSCA is superior and it could generate better optimal solutions compared with other competitive algorithms

Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241 Am and 133 Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations

Application of Artificial Intelligence for Determining the Volume Percentages of a Stratified Regime’s Three-Phase Flow, Independent of the Oil Pipeline’s Scale Thickness

As time passes, scale builds up inside the pipelines that deliver the oil or gas product from the source to processing plants or storage tanks, reducing the inside diameter and ultimately wasting energy and reducing efficiency. A non-invasive system based on gamma-ray attenuation is one of the most accurate diagnostic methods to detect volumetric percentages in different conditions. A system including two NaI detectors and dual-energy gamma sources ( 241 Am and 133 Ba radioisotopes) is the recommended requirement for modeling a volume-percentage detection system using Monte Carlo N particle (MCNP) simulations. Oil, water, and gas form a three-phase flow in a stratified-flow regime in different volume percentages, which flows inside a scaled pipe with different thicknesses. Gamma rays are emitted from one side, and photons are absorbed from the other side of the pipe by two scintillator detectors, and finally, three features with the names of the count under Photopeaks 241 Am and 133 Ba of the first detector and the total count of the second detector were obtained. By designing two MLP neural networks with said inputs, the volumetric percentages can be predicted with an RMSE of less than 1.48 independent of scale thickness. This low error value guarantees the effectiveness of the intended method and the usefulness of using this approach in the petroleum and petrochemical industries

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Optimizing the Gamma Ray-Based Detection System to Measure the Scale Thickness in Three-Phase Flow through Oil and Petrochemical Pipelines in View of Stratified Regime

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source ( 241 Am and 133 Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating the gamma-ray to the test pipe and receiving the intensity of the photons by the detector, time characteristics with the names of sample SSR, sample mean, sample skewness, and sample kurtosis were extracted from the received signal, and they were introduced as the inputs of a GMDH neural network. The neural network was able to predict the scale thickness value with an RMSE of less than 0.2, which is a very low error compared to previous research. In addition, the feature extraction technique made it possible to predict the scale value with high accuracy using only one detector

Phenotype and genetic determination of resistance to common disinfectants among bioflm-producing and non-producing Pseudomonas aeruginosa strains from clinical specimens in Iran

Background: Pseudomonas aeruginosa is a common pathogen in Hospitalized patients, and its various resistance mechanisms contribute to patient morbidity and mortality. The main aims of the present study were to assess the susceptibility of bioflm-producing and non-producing P. aeruginosa isolates to the fve commonly used Hospital disinfectants, to evaluate the synergistic efect of selected disinfectants and Ethylene-diamine-tetra acetic acid (EDTA), and the efect of exposure to sub-inhibitory concentrations of Sodium hypochlorite on antimicrobial susceptibility test. Results: The results showed that sodium hypochlorite 5% and Ethanol 70% were the most and least efective disinfectants against P. aeruginosa, respectively. The addition of EDTA signifcantly increased the efectiveness of the selected disinfectants. The changes in the antibiotic-resistance profles after exposure to sub-inhibitory concentrations of disinfectants were observed for diferent classes of antibiotics (Carbapenems, Aminoglycosides, Cephalosporins, Fluoroquinolones). As well as near the all isolates harbored efux pump genes and 117 (97.5%) of isolates produced bioflm. Conclusion: In the current study, the mixture of disinfectant and EDTA were the most suitable selection to disinfect Hospital surfaces and instruments. Also, it was clear that exposure to sub-inhibitory concentrations of Sodium hypochlorite results in resistance to some antibiotics in P. aeruginosa species. Strong and intermediate bioflm formers belonged to MDR/XDR strains. Future studies should include more complex microbial communities residing in the Hospitals, and more disinfectants use in Hospitals. Keywords: Nosocomial infection, Disinfectant-resistance, Bioflm, Hospital disinfectants, Pseudomonas aeruginosa, Clinical isolate

Monitoring of fisheries resources in artificial reefs in east of Kish Island waters

Artificial reefs are manmade materials deployed under water in order to improve environment and increase the exploitation of fishing area. Usage of artificial technic has developed due to increase of world population and need to supply of protein, aim to restoring of natural specially rehabilitation of demersal fishes. It has effected to increase the production in order sustainable exploitation. Coasts and Islands have destruction due to over harvesting from ecosystems and other activities by humans and natural, these caused many aquatic as demersal fishes has endangered in Persian Gulf and Oman Sea. The artificial reef is one way or method that can improve the environment and restore the aquatic. Iranian fisheries has established an artificial reefs area in west of Kish Island. This area has studied during one year, Data of assembled fishes and physical sampling were collected in seasonal during spring, summer, autumn and wintered. There were tow treatment for sampling as artificial reefs site and a control site. Collected data has analyzed and evaluated by SPSS and Exel. The results showed that there was significant difference between the sites and assembled fishes in artificial reefs were more than control site. Consequently the artificial reefs can be a tool and technic to improve the marine environment and increase the production of fishes, especially the demersal fishes

Neonatal, infant, and under-5 mortality and morbidity burden in the Eastern Mediterranean region: findings from the Global Burden of Disease 2015 study

Objectives Although substantial reductions in under-5 mortality have been observed during the past 35 years, progress in the Eastern Mediterranean Region (EMR) has been uneven. This paper provides an overview of child mortality and morbidity in the EMR based on the Global Burden of Disease (GBD) study. Methods We used GBD 2015 study results to explore under-5 mortality and morbidity in EMR countries. Results In 2015, 755,844 (95% uncertainty interval (UI) 712,064–801,565) children under 5 died in the EMR. In the early neonatal category, deaths in the EMR decreased by 22.4%, compared to 42.4% globally. The rate of years of life lost per 100,000 population under 5 decreased 54.38% from 177,537 (173,812–181,463) in 1990 to 80,985 (76,308–85,876) in 2015; the rate of years lived with disability decreased by 0.57% in the EMR compared to 9.97% globally. Conclusions Our findings call for accelerated action to decrease child morbidity and mortality in the EMR. Governments and organizations should coordinate efforts to address this burden. Political commitment is needed to ensure that child health receives the resources needed to end preventable deaths