Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN:a "Conscious-Lab" development

Tri-flo cyclone,as a dense-medium separation device,is one of the most typical environmentally friendly industrial techniques in the coal washery plants.Surpris...     

IsomiR-eQTL: A Cancer-Specific Expression Quantitative Trait Loci Database of miRNAs and Their Isoforms

The identification of expression quantitative trait loci (eQTL) is an important component in efforts to understand how genetic variants influence disease risk. MicroRNAs (miRNAs) are short noncoding RNA molecules capable of regulating the expression of several genes simultaneously. Recently, several novel isomers of miRNAs (isomiRs) that differ slightly in length and sequence composition compared to their canonical miRNAs have been reported. Here we present isomiR-eQTL, a user-friendly database designed to help researchers find single nucleotide polymorphisms (SNPs) that can impact miRNA (miR-eQTL) and isomiR expression (isomiR-eQTL) in 30 cancer types. The isomiR-eQTL includes a total of 152,671 miR-eQTLs and 2,390,805 isomiR-eQTLs at a false discovery rate (FDR) of 0.05. It also includes 65,733 miR-eQTLs overlapping known cancer-associated loci identified through genome-wide association studies (GWAS). To the best of our knowledge, this is the first study investigating the impact of SNPs on isomiR expression at the genome-wide level. This database may pave the way for researchers toward finding a model for personalised medicine in which miRNAs, isomiRs, and genotypes are utilised.     

Loofa immobilized Bacillus sp. DSM 2523 as a whole cell biocatalyst for production of cyclodextrin glucanotransferase in an airlift reactor with a net draft tube

Cyclodextrin glucanotransferase (CGTase) production was studied using loofa-immobilized Bacillus sp. DSM 2523 and starch substrate. The bacterial cells from 2- and 4-day old cultures were used in the flask studies. Loofa and chitosan were added for cell immobilization and cell flocculation, respectively, and different treatments were considered according to the timing of their addition. The cell responses were evaluated for their degree of cell immobilization and level of CGTase activity. With the use of the selected treatment, testing was carried out in an airlift reactor with a net draft tube having specific geometric properties. The cell capacity for reusability also was examined. The production of CGTase was higher in the second cycle in the reactor operated at 0.25 vvm where the enzyme activity reaching 0.20?U/ml within 2?h. In the flask experiments, CGTase activity reached 0.23?U/ml after 19?h in the second cycle.     

Multiview Classification and Dimensionality Reduction of Scalp and Intracranial EEG Data through Tensor Factorisation

Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time, and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy for each set. The relative influence on the classification accuracy of the respective spatial, temporal, or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number of components in each mode and also by rejecting components with insignificant contribution to the classification accuracy.     

A multi-objective optimization of sensitivity and bandwidth of a 3-D MEMS bionic vector hydrophone

Analog Integrated Circuits and Signal Processing - A hydrophone with maximum sensitivity and bandwidth is required for many underwater and medical applications. This paper presents a first time...     

Using Clustering via Soccer League Competition Algorithm for Optimizing Power Consumption in WSNs (Wireless Sensor Networks)

Wireless Personal Communications - WSNs consist of small sensor nodes which have limited battery power, limited processing capability and limited memory unit. Self-organization and the lack of...     

Nonlinear dynamics of flexible nanopositioning systems with geometrical imperfection

Microsystem Technologies - In this paper, the nonlinear dynamics of flexible beams with geometrical imperfection and concentrated end-mass is investigated. Based on the Euler–Bernoulli beam...     

Optimum design of preform geometry and forming pressure in tube hydroforming using the equi-potential lines method

The quality of a tube-hydroformed component as well as the tooling cost and processing time involved in the process are notably affected by the component’s preform. Hence, it is necessary to produce a proper preform shape for the successful hydroforming from the initial tube to final shape. In this paper, the notion of equi-potential lines (EPLs) is used to find an appropriate preform shape in the tube hydroforming process for the first time. The EPLs generated between two conductors of different voltages show minimum work paths between the initial and final shapes. Based on this similarity, the EPLs method is utilized for preform shape design. Next, the forming pressure of the preform is determined using finite element analysis. Finally, the computationally expensive procedure introduced above is significantly facilitated by employing a multi-layer perceptron neural network which is trained using results from application of the procedure to a set of uniformly distributed random input vectors. Real-world examples are presented to demonstrate the applicability and efficiency of the proposed approach.     

Multi Objective Optimization with a New Evolutionary Algorithm

Various objectives are mainly met through decision making in real world. Achieving desirable condition for all objectives simultaneously is a necessity for conflicting objectives. This concept is called multi objective optimization widely used nowadays. In this study, a new algorithm, comprehensive evolutionary algorithm (CEA), is developed based on general concepts of evolutionary algorithms that can be applied for single or multi objective problems with a fixed structure. CEA is validated through solving several mathematical multi objective problems and the obtained results are compared with the results of the non-dominated sorting genetic algorithm II (NSGA-II). Also, CEA is applied for solving a reservoir operation management problem. Comparisons show that CEA has a desirable performance in multi objective problems. The decision space is accurately assessed by CEA in considered problems and the obtained solutions’ set has a great extent in the objective space of each problem. Also, CEA obtains more number of solutions on the Pareto than NSGA-II for each considered problem. Although the total run time of CEA is longer than NSGA-II, solution set obtained by CEA is about 32, 4.4 and 1.6% closer to the optimum results in comparison with NSGA-II in the first, second and third mathematical problem, respectively. It shows the high reliability of CEA’s results in solving multi objective problems.