Correct Prediction of the Vibration Behavior of a High Power Ultrasonic Transducer by FEM Simulation

This paper presents a study on three types of finite element analyses of high power ultrasonic transducer by using the finite element commercial software called ANSYS. The transducer geometry was treated as a 2D axi- symmetric model, 3D quart and full 3D model. For all of the simulations the modeled transducer was used in modal analysis and harmonic solutions to understand its mechanical behavior and its natural frequency. A comparison was made between each type of modeling and experimental results. This comparison allows the parameters of FEM models to be iteratively adjusted and optimized and also leads to selection of the best modeling type. The achieved FEM results exhibited a remarkably high predictive potential of ANSYS in modeling and simulation and allowed control on the design and on the vibration behavior of the high power ultrasonic transducer.     

Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization–simulation–optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization–simulation–optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.     

pH Responsive Abelmoschus esculentus Mucilage and Administration of Methotrexate: In-Vitro Antitumor and In-Vivo Toxicity Evaluation

The rapid progression in biomaterial nanotechnology apprehends the potential of non-toxic and potent polysaccharide delivery modules to overcome oral chemotherapeutic challenges. The present study is aimed to design, fabricate and characterize polysaccharide nanoparticles for methotrexate (MTX) delivery. The nanoparticles (NPs) were prepared by Abelmoschus esculentus mucilage (AEM) and chitosan (CS) by the modified coacervation method, followed by ultra-sonification. The NPs showed much better pharmaceutical properties with a spherical shape and smooth surface of 213.4–254.2 nm with PDI ranging between 0.279–0.485 size with entrapment efficiency varying from 42.08 ± 1.2 to 72.23 ± 2.0. The results revealed NPs to possess positive zeta potential and a low polydispersity index (PDI). The in-vitro drug release showed a sustained release of the drug up to 32 h with pH-dependence. Blank AEM -CS NPs showed no in-vivo toxicity for a time duration of 14 days, accompanied by high cytotoxic effects of optimized MTX loaded NPs against MCF-7 and MD-MBA231 cells by MTT assay. In conclusion, the findings advocated the therapeutic potential of AEM/CS NPs as an efficacious tool, offering a new perspective for pH-responsive routing of anticancer drugs with tumor cells as a target.     

Tepary Bean Starch Part III: In vitro Digestibility

In vitro digestibility of starch from tepary bean, Phaseolus acutifolius var. latifolius, was determined in comparison with tepary bean flour and maize starch. The extent of sample hydrolysis by α-amylase was measured as mg reducing sugar (maltose) released per 100 mg substrate. After 2 h incubation at 37°C, values obtained for tepary starch, tepary flour and maize starch treated in various ways were as follows: raw 8.0, 8.6 and 25.6; freeze dried 2.6, 3.2 and 17.8; autoclaved 7.4, 5.7 and 27.7; cooked (15 min) 81.7, 23.1 and 87.8; resp. Raw tepary starch was more resistant to hydrolysis than maize starch due, perhaps, to differences in granule structure and amylose content. Freeze-drying and autoclaving slightly decreased digestibility of both tepary starch and flour. Cooking greatly increased susceptibility to hydrolysis for each substrate. The rate of increase was reduced after 15 min exposure to enzymes, and no appreciable difference was found between cooked tepary and maize starches.     

A robust unified approach to analyzing methylation and gene expression data

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines.     

PiSHi: click the images and I tell if you are a human

This paper introduces pictorial intelligent system for human identification (PiSHi), an image-based captcha which uses three human cognitive abilities to distinguish humans from machines. The first is the human ability to easily recognise the image’s upright orientation. The second is the human brain’s ability in recognising a picture’s content when it is only partially visible. And the third is the human ability in unconscious decision making when encountering pictorial challenges. This work models such complicated human patterns in problem solving for the first time. In order to extract these behavioural patterns and save them in a pattern database, we have implemented our own captcha and performed a series of experiments. PiSHi’s interface presents the user with a set of distorted pictures and asks her to click on the upright orientation of all the pictures in any preferred order. Next, it captures the user’s interaction patterns, compares them with the ones saved in the pattern database, and grants her a corresponding credit. Based on this credit, the user either passes or fails the test, and participates in updating the picture database. Our experiments indicate that human users can solve our proposed captcha effectively—with an accuracy of 99.44 %. Besides, our proposed system is secure against several types of attacks including random guessing and reverse image search engines. The results offer the possibility of utilising the identified human behavioural models in practical captchas.     

Phase Separation and Spatial Morphology in Sodium Silicate Glasses by AFM,Light Scattering and NMR

The morphological and structural properties of sodium silicate (Na₂O–SiO₂) glasses were analyzed using atomic force microscopy (AFM) and light scattering following thermal treatments. AFM observations indicated that the glass surface microstructure evolves during the phase separation mechanisms from continuous interpenetrating phases in the spinodal decomposition process to separated droplets embedded in a continuous matrix for the nucleation/growth one. Raman mapping gave evidence of a phase separation through the nucleation/growth process with formation of silica‐rich clusters characterized by higher polymerization degree as separate droplets. The variations in inhomogeneities versus temperature investigated by Brillouin are exponential for spinodal decomposition and linear in the case of nucleation/growth mechanism. Nuclear magnetic resonance spectroscopy was used to investigate the spatial distribution of the various Q_n species present in thermally treated glasses and allows determining fractal dimension between two and three.     

Accurate analysis of spectral regrowth of nonlinear power amplifier driven by cyclostationary modulated signals

In this paper we present an exact analytical expression to calculate the spectral regrowth at the output of a nonlinear power amplifier (PA) using the higher order cumulants and Poisson summation formula. This PA is driven by the filtered digitally modulated signals. To improve the accuracy of the calculations, the cyclosationarity of the input signal is considered. Moreover, closed-form expressions for the 1-dB compression and saturation points are extracted as a function of the PA model parameters, higher order statistics of the input signal, and the transfer function of the pulse shaping filter. In addition, an analytical expression for the adjacent channel power (ACP) and a closed-form expression of the ACP ratio are derived. This is followed by investigation of the effect of the PA nonlinearity on the performance of receiver. Simulation studies are carried out to verify the accuracy of the derived expressions. Excellent agreement between the analytical and simulation results is achieved.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Natural Vibrations of Open-Variable Thickness Circular Cylindrical Shells in High Temperature Field

The feasibility of using the transfer matrix method (TMM) to analyze open-variable thickness circular cylindrical shells exposed to a high-temperature field is explored theoretically. In the approach to the problem, the thermal degradation (TG) of thermoelastic characteristics of the material is considered. Natural frequencies and mode shapes for the cylindrical shells are investigated in detail by combining the vibration theory with the TMM. The governing equations of vibration for this system are expressed by the matrix differential equations, and the coefficient matrices are derived. After the relationship between the transfer matrix and the coefficient matrix is established, the fourth-order Runge-Kutta method is used numerically to solve the matrix equation. Once the transfer matrix of single component has been obtained, the product of each component matrix can compose the matrix of the entire structure. The frequency equations and mode shape are formulated in terms of the elements of the structural matrices. Finite-element numerical simulation has validated the present formulas of natural frequencies. Numerical illustrations, supplying pertinent information on the implications of the TG, are presented for various curvatures, aspect ratios, boundary conditions, and thickness ratios, and the pertinent conclusions are outlined.