In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex OPTIMIZATION problem that guarantees the global optimal solution. The convex OPTIMIZATION problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

Let R be a commutative ring and I an ideal of R. The zero-divisor graph of R with respect to I, denoted by GI (R), is the simple graph whose vertex set is {xÎR\I | xyÎI, for some yÎR\I}, with two distinct vertices x and y are adjacent if and only if xyÎI. In this paper, we state a relation between zero-divisor graph of R with respect to an ideal and almost prime ideals of R. We then use this result to give a GRAPHICAL characterization for SPAP-rings.

Introduction GRAPHICAL mixture models provide a powerful tool to visually depict the conditional independence relationships between high-dimensional heterogeneous data. In the study of GRAPHICAL mixture models, the distribution of the mixture components is mostly considered multivariate normal with different covariance matrices. The resulting model is the Gaussian GRAPHICAL mixture model (GGMM). The nonparanormal GRAPHICAL mixture model (NGMM) has been introduced by replacing the normal assumption with a semiparametric Gaussian copula, which extends the nonparanormal GRAPHICAL model and mixture models. This study proposes clustering based on NGMM under two forms of ℓ, 1 penalty functions. Its performance is compared with clustering based on GGMM, in terms of cluster reconstruction and parameters estimation. Material and Methods The clustering based on NGMM is performed via a penalized EM algorithm under conventional and unconventional forms of ℓ, 1 penalty functions (denoted by NGMM0 and NGMM1, respectively) and its performance over Gaussian and non-Gaussian simulated data sets are compared with the Gaussian ones (represented by GGMM0 and GGMM1, respectively). Along with the conventional ℓ, 1 penalty, an alternative, unconventional penalty term is considered, which depends on the mixture proportions. Thus, the choice of mixture model distribution (Gaussian or nonparanormal) along with the choice of penalty function has emerged as the primary key of comparison. To better compare the studied methods in terms of robustness against outliers, we considered deterministic and random contamination mechanisms. The proposed methodology is applied to Wisconsin diagnostic breast cancer data set to diagnose malignant or benign cancer patients. Results and Discussion The results of the simulation study on normal and nonparanormal datasets in ideal and noisy settings, as well as the application of breast cancer data set, showed that clustering approaches based on NGMM (NGMM0 and NGMM1) are more efficient and robust in the recovery of true cluster assignments than the clustering based on GGMM (GGMM0 and GGMM1), whereas, the unconventional PMLEs (GGMM1 and NGMM1) are more efficient in estimating the elements of precision matrices than the conventional PMLEs (GGMM0 and NGMM0). Conclusion The performance of clustering methods depends on the choice of penalty function and model selection, such that the combination of the nonparanormal GRAPHICAL mixture model and the penalty term depending on the mixing proportions (NGMM1) is more accurate than Gaussian ones in terms of cluster reconstruction and parameters estimation.

The relationship between the manufacturer and supplier has changed from one of competition into that of greater cooperation during recent years. The selection and evaluation of suppliers are primary and important issues when an enterprise implements Supplier Relationship Management (SRM) and Total Quality Management (TQM). Much research indicates that most enterprises tend to evaluate their suppliers based largely on quality, delivery, and price performance. However, an analysis tool which can simultaneously integrate numerical decision criteria is rare, especially those suitable for evaluating various operational aspects, such as industry categories, purchase types, or production strategies. Based on this reason, an integrated model using multiple performance indices to construct a supplier performance evaluation chart (SPEC) is presented in this study. It is a graphic portrayal that can simultaneously display each supplier’s performance of quality, delivery, and price through the use of a categorical symbol and its location on the SPEC. Based on the expected levels for each evaluation criteria, the manager can mark a target region on the SPEC so that an existing or new supplier’s ability to achieve the preset level is effectively and conveniently indicated. Finally, an illustration is provided as a practical application.

GRAPHICAL passwords are the ways in which user click on the image or user can select the image to authenticate themselves instead of giving passwords. This technique is more secure that textual password techniques. In this article, the shoulder surfing preventive mechanism of GRAPHICAL password authentication is given. Finally the login password system is proposed to deal with such type of problems. First time, we are introducing a modified approach is given to resolve the shoulder surfing based on recall and recognition based concepts. Usually it is seen that the most common vulnerability of GRAPHICAL password is shoulder surfing attack. This research aims to analyze the usability feature of recognition based and recall based GRAPHICAL password methods and present a technique to apply an image based password that is safe from the shoulder surfing attack. In the similar context, the purpose of this paper is to present an alternative way to apply the recall and recognition based technique that will be protective for guess through shoulder surfing. And this GRAPHICAL technique will be easy to memories the authentication password and process of authentication.