Shape optimization for tumor location

In non-invasive thermal diagnostics, accurate correlations between the thermal image at skin surface and interior human physiology are desired. In this work, an estimation methodology to determine unknown geometrical parameters of an embedded tumor is proposed. We define a functional that represents the mismatch between a measured experimental temperature profile, which may be obtained by infrared thermography on the skin surface, and the solution of an appropriate boundary problem. This functional is related to the geometrical parameters through the solution of the boundary problem, in such a way that finding the minimum of this functional form also means finding the unknown geometrical parameters of the embedded tumor. Sensitivity analysis techniques coupled with the adjoint method were considered to compute the shape derivative of the functional. Then, a nonmonotone spectral projected gradient method was implemented to solve the optimization problem of finding the optimal geometric parameters.     

Effect of aspect ratio on convection in a porous enclosure with partially active thermal walls

The aim of the present numerical investigation is to understand the effect of aspect ratio and partially thermally active zones on convective flow and heat transfer in a rectangular porous enclosure. Five different heating and cooling zones are considered along the vertical walls while the remaining portions of the sidewalls and top and bottom of the enclosure are adiabatic. The Brinkman-Forchheimer extended Darcy model is used in the study. The governing equations are solved by the finite volume method with the SIMPLE algorithm. The computations are carried out for a wide range of parameters and the results are presented graphically. The results reveal that the location of heating and cooling zones has a significant influence on the flow pattern and the corresponding heat transfer in the enclosure. The rate of heat transfer approaches to a constant value for very low values of the Darcy number. The heat transfer rate is decreased on increasing the aspect ratio.     

Application of the Galerkin and Least-Squares Finite Element Methods in the solution of 3D Poisson and Helmholtz equations

This paper presents the numerical solution, by the Galerkin and Least Squares Finite Element Methods, of the three-dimensional Poisson and Helmholtz equations, representing heat diffusion in solids. For the two applications proposed, the analytical solutions found in the literature review were used to compare with the numerical solutions. The analysis of results was made from the L₂ norm (average error throughout the domain) and L_∞ norm (maximum error in the entire domain). The results of the two applications (Poisson and Helmholtz equations) are presented and discussed for testing of the efficiency of the methods.     

Two augmentation preconditioners for nonsymmetric and indefinite saddle point linear systems with singular (1, 1) blocks

In this paper, two preconditioners based on augmentation are introduced for the solution of large saddle point-type systems with singular (1, 1) blocks. We study the spectral characteristics of the preconditioners, show that all eigenvalues of preconditioned matrices are strongly clustered. Finally, numerical experiments are also reported for illustrating the efficiency of the presented preconditioners.     

Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method

A hybrid forecasting method is proposed which leverages from statistical and neural network techniques to perform multi-step ahead forecasting. The proposed method is based on the disaggregation of time series components, the prediction of each component individually and the reassembling of the extrapolations to obtain an estimation for the global data. The STL decomposition procedure from the literature [5] is implemented to obtain the seasonal, trend and irregular components of the time series, whilst Generalized Regression Neural Networks (GRNN) [12] are used to perform out-of sample extrapolations of the seasonal and residual components. The univariate Theta model is employed for the estimation of the directional component. The application of the GRNN is based on the dynamic calibration of the training process for each of the seasonal and irregular components individually. The proposed hybrid forecasting method is applied to 60 time series from the NN3 competition and 227 time series from the M1 Competition dataset, to obtain 18 out-of sample predictions. The results from the application demonstrate that the proposed method can outperform standard statistical techniques in the literature. One of the main contributions of the current research lies in the investigation of the strengths and weaknesses of the GRNN in extrapolating structural components of time series.     

Mean square exponential stability in high-order stochastic impulsive BAM neural networks with time-varying delays

In this paper, a class of stochastic impulsive high-order BAM neural networks with time-varying delays is considered. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order BAM neural networks.     

Hydrothermal synthesis of hollow ZnSnO3 microspheres and sensing properties toward butane

Hollow ZnSnO₃ microspheres were successfully prepared by hydrothermal method at 160 °C for 12 h. The prepared material was characterized by field emission scanning electron microscope (FESEM), transmission electron microscope (TEM) and X-ray diffraction measurements (XRD). The average diameter of the hollow ZnSnO₃ microspheres was in the range of 400-600 nm. Compared with solid ZnSnO₃ microspheres structure, the hollow ZnSnO₃ microspheres showed better response (S) to butane. To 500 ppm butane, the sensor response (S) of the hollow ZnSnO₃ microspheres was 5.79 at the optimum operating temperature of 380 °C, and the response and recovery time were 0.3 s and 0.65 s, respectively. The sensitivities of sensors based on this material were linear with the concentration of butane in the range of 100-1000 ppm.     

Study on design and application of fully automatic miniature surface plasmon resonance concentration analyzer

A fully automatic miniature surface plasmon resonance (SPR) concentration analyzer having high performance and low cost and developed using a Spreeta™ sensor was designed for field applications and concentration analysis. As in the case of Biacore™ instruments, the automatic sampling system of this device can introduce air segments between the sample/regeneration solution and buffer solution in the pipeline, which effectively prevents mixing of the solutions. A temperature sensor (AD 590) and temperature compensation method are used, which make the device insensitive to temperature fluctuations. A real-time data-smoothing algorithm for the SPR detection data is adopted; this can reduce the noise level to 5 × 10⁻⁷ RIU (refractive index units). The noise level of the sensorgram is 3.5% of the original level. Two types of self-prepared sensing chips—SMX-BSA (bovine serum albumin coated with sulfamethoxazole) and SMX-CM5 (carboxymethyl dextran coated with sulfamethoxazole)—are used to analyze the concentrations of sulfamethoxazole (SMX) standard solutions. Each chip's SMX calibration curve is established within the measurement range of 0-2000 ng/ml, and both limits of detection (LOD) are 2 ng/ml. One cycle of assay time is less than 15 min.     

Formal model for assigning human resources to teams in software projects

Context

Human resources play a critical role in software project success. However, people are still the least formalized factor in today’s process models. Generally, people are assigned to roles and project teams are formed on the basis of project leaders’ experience of people, constraints (e.g. availability) and skill requirements. Yet this process has to take multiple factors into account. Few works in the literature model this process. Most of these are informal proposals focusing on the individual assignment of people to project tasks and do not consider other aspects like team formation as a whole.

Objective

In this paper we formulate a formal model for assigning human resources to software project teams. Additionally, we describe the key results of the knowledge management process enacted to output the elements of the model.

Method

The model elements were identified using the Delphi expert consultation method and applying psychological tests. The proposed model was implemented in a software tool and validated on two software development organization assignment scenarios.

Results

We built a formal model for the process of assigning human resources to software project teams. This model takes into account as many factors as possible and aids the assignment of individuals to project roles, as well as the formation of the team as a whole.We found that the rules that were identified to form software development project teams are useful. From the tests we found that model implementation was feasible (all the executions of the implemented problem-solving algorithms output feasible solutions in response times that can be considered as acceptable).

Conclusion

Using the Delphi method we were able to propose software project roles and competences. Psychological tests and data mining tools identified useful rules for forming software project teams. These were used to build a formal model. This model was built into a tool that returns role assignments in acceptable response times. This decision support tool helps managers assign people to roles and to form teams. Using the tool, project leaders can flexibly evaluate different team make-ups, taking into account several factors, as well as different constraints and objectives.     

改进的二值图像复原方法研究

为了解决传统基于GBB方法的正则化二值图像复原算法放大背景噪声的问题,提出了一种基于GBB方法的改进的正则化二值图像复原算法。根据提出的算法进行实验,定性的对两种算法的图像复原结果进行了比较。同时,利用ISNR选取最佳参数,且在不同程度噪声的影响下定量的评价了两种复原方法的结果。实验结果表明,该方法对于模糊二值图像的复原效果有显著提升,有效降低了噪声对图像复原的影响。