Semi-analytical solution for free transverse vibrations of Euler–Bernoulli nanobeams with manifold concentrated masses

In the present study, transverse vibrations of nanobeams with manifold concentrated masses, resting on Winkler elastic foundations, are investigated. The model is based on the theory of nonlocal elasticity in the presence of concentrated masses applied to Euler–Bernoulli beams. A closed-form expression for the transverse vibration modes of Euler–Bernoulli beams is presented. The proposed expressions are provided explicitly as the function of two integrated constants, which are determined by the standard boundary conditions. The utilization of the boundary conditions leads to definite terms of natural frequency equations. The natural frequencies and vibration modes of the concerned nanobeams with different numbers of concentrated masses in different positions under some typical boundary conditions (simply supported, cantilevered, and clamped–clamped) have been analyzed by means of the proposed closed–form expressions in order to show their efficiency. It is worth mentioning that the effect of various nonlocal length parameters and Winkler modulus on natural frequencies and vibration modes are also discussed. Finally, the results are compared with those corresponding to a classical local model.     

Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates

Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92% for predicting a 1000 days’ exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields.     

Secondary prevention of coronary heart disease

Despite the significant reduction in cardiovascular mortality during the past three decades, atherosclerotic coronary heart disease (CHD) remains the leading cause of death and disability in the United States. Randomized clinical trials in patients with CHD have provided convincing evidence that risk factor modification is beneficial in decreasing all-cause mortality and cardiovascular morbidity and mortality. Multifactorial coronary risk reduction provides the most substantial benefit. Coronary risk reduction is associated with a decrease in cardiovascular-related hospital admissions, a reduced need for myocardial revascularization procedures, and an improved quality of life for the patients so treated. Control of coronary risk factors is an integral component of the optimal care of the patient with CHD.     

Role of NADH/NADPH oxidase-derived H2O2 in angiotensin II-induced vascular hypertrophy

The Maillard reaction between sugar and protein has been postulated as the cause for the browning and arrestment of caries lesions. This reaction has been implicated as the cause for decreased degradability of collagen in vivo. The aim of the present study was to verify the occurrence of the reaction in vivo. Carious and sound dentin samples were taken from extracted human teeth and analyzed for the fluorescence characteristic of the Maillard reaction and oxidation and, by HPLC, for Maillard products. In addition, physiological cross-links were analyzed by HPLC. Oxidation- and Maillard reaction-related fluorescence increased in collagenase digests from carious dentin. Advanced Maillard products (carboxymethyllysine and pentosidine) increased, whereas furosine, a marker for the initial reaction, was not observed consistently. This implies no direct addition of sugars to protein, but rather the addi-tion of smaller metabolites and glycoxidation products. In addition, the physiological cross-links hydroxylysinonorleucine and dihydroxylysinonorleucine decreased in carious dentin. Also for hydroxylysylpyridinoline, a decrease was observed, but not consistently. In conclusion, the caries process modifies amino acids in dentin collagen, which can lead to increased resistance against proteolysis and ultimately to caries arrestment.     

Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads

A computationally efficient and accurate forecasting model for highly dynamic electric load patterns of UK electric power grid is proposed and implemented using recurrent neuro-evolutionary algorithms. Cartesian genetic programming is used to find the optimum recurrent structure and network parameters to accurately forecast highly fluctuating load patterns. Fifty different models are trained and tested in diverse set of scenarios to predict single as well as more future instances in advance. The testing results demonstrated that the models are highly accurate as they attained an accuracy of as high as 98.95 %. The models trained to predict single future instances are tested to predict more future instances in advance, obtaining an accuracy of 94 %, thus proving their robustness to predict any time series.     

Isolation and identification of new strains of crude oil degrading bacteria from Kharg Island,Iran

Petroleum-degrading bacteria were isolated from polluted sites in Kharg Island, which is one of the most important ports for oil export in Iran. Both biochemical tests and 16S rRNA were applied to identify the strains. Experiments were performed in a mineral salt medium containing 2% of crude oil as the sole carbon. The results illustrated that the strains were: Brevibacillus sp., Microbacterium oxydans, Staphylococcus arlettae, Staphylococcus warneri, Methylobacterium persicinum, and Achromobacter xylosoxidans. Growth rates of these strains by its optical densities at the wavelength of 600 illustrated that Pseudomonas sp. (accession no. KC771232) had the highest growth rate (OD₆₀₀ 0.8).