Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Mapping low-Reynolds-number microcavity flows using microfluidic screening devices

Low-Reynolds-number flows in cavities, characterized by separating and recirculating flows are increasingly used in microfluidic applications such as mixing and sorting of fluids, cells, or particles. However, there is still a lack of guidelines available for selecting the appropriate or optimized microcavity configuration according to the specific task at hand. In an effort to provide accurate design guidelines, we investigate quantitatively low-Reynolds-number cavity flow phenomena using a microfluidic screening platform featuring rectangular channels lined with cylindrical cavities. Using particle image velocimetry (PIV), supported by computational fluid dynamics (CFD) simulations, we map the entire spectrum of flows that exist in microcavities over a wide range of low-Reynolds numbers (Re = 0.1, 1, and 10) and dimensionless geometric parameters. Comprehensive phase diagrams of the corresponding microcavity flow regimes are summarized, capturing the gradual transition from attached flow to a single vortex and crossing through two- and three-vortex recirculating systems featuring saddle-points. Finally, we provide design insights into maximizing the rotational frequencies of recirculating single-vortex microcavity systems. Overall, our results provide a complete and quantitative framework for selecting cavities in microfluidic-based microcentrifuges and vortex mixers.     

Synthesis,Thermal and Morphological Properties of Polyurethanes Containing Azomethine Linkage

This paper presents synthesis, photophysical, electrochemical, thermal and morphological properties of Schiff bases containing various side-group substitutions and polyurethanes (PUs) containing azomethine linkage. Morphological properties of PUs containing azomethine bonding were investigated by scanning electron microscopy (SEM). SEM images showed that PU containing azomethine consist of semi-crystalline particles. Thermal transitions in PUs containing azomethine units were studied using DSC. The obtained DSC curves showed that PUs containing azomethine are semi-crystalline materials due to they contain both crystallization and melting peaks. Electrochemical properties also investigated by using cyclic voltammetry (CV). According to the cyclic voltommagrams and CV data, PUs containing azomethine have below 2.0 eV electrochemical band gap.     

Numerical treatment of a quasilinear initial value problem with boundary layer

The paper deals with the singularly perturbed quasilinear initial value problem exhibiting initial layer. First the nature of solution of differential problem before presenting method for its numerical solution is discussed. The numerical solution of the problem is performed with the use of a finite-fitted difference scheme on an appropriate piecewise uniform mesh (Shishkin-type mesh). An error analysis shows that the method is first-order convergent except for a logarithmic factor, in the discrete maximum norm, independently of the perturbation parameter. Finally, numerical results supporting the theory are presented.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

A SIMPLIFIED AES ALGORITHM AND ITS LINEAR AND DIFFERENTIAL CRYPTANALYSES

In this paper, we describe a simplified version of the Advanced Encryption Standard algorithm. This version can be used in the classroom for explaining the Advanced Encryption Standard. After presentation of the simplified version, it is easier for students to understand the real version. This simplified version has the advantage that examples can be worked by hand. We also describe attacks on this version using both linear and differential cryptanalysis. These too can be used in the classroom as a way of explaining those kinds of attacks.     

IVA using complex multivariate GGD: application to fMRI analysis

Multidimensional Systems and Signal Processing - Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective...     

Nanomaterials for Antiangiogenic Therapies for Cancer: A Promising Tool for Personalized Medicine

Angiogenesis is one of the hallmarks of cancer. Several studies have shown that vascular endothelium growth factor (VEGF) plays a leading role in angiogenesis progression. Antiangiogenic medication has gained substantial recognition and is commonly administered in many forms of human cancer, leading to a rising interest in cancer therapy. However, this treatment method can lead to a deteriorating outcome of resistance, invasion, distant metastasis, and overall survival relative to its cytotoxicity. Furthermore, there are significant obstacles in tracking the efficacy of antiangiogenic treatments by incorporating positive biomarkers into clinical settings. These shortcomings underline the essential need to identify additional angiogenic inhibitors that target numerous angiogenic factors or to develop a new method for drug delivery of current inhibitors. The great benefits of nanoparticles are their potential, based on their specific properties, to be effective mechanisms that concentrate on the biological system and control various important functions. Among various therapeutic approaches, nanotechnology has emerged as a new strategy for treating different cancer types. This article attempts to demonstrate the huge potential for targeted nanoparticles and their molecular imaging applications. Notably, several nanoparticles have been developed and engineered to demonstrate antiangiogenic features. This nanomedicine could effectively treat a number of cancers using antiangiogenic therapies as an alternative approach. We also discuss the latest antiangiogenic and nanotherapeutic strategies and highlight tumor vessels and their microenvironments.