Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

A stabilized volume‐averaging finite element method for flow in porous media and binary alloy solidification processes

A stabilized equal‐order velocity–pressure finite element algorithm is presented for the analysis of flow in porous media and in the solidification of binary alloys. The adopted governing macroscopic conservation equations of momentum, energy and species transport are derived from their microscopic counterparts using the volume‐averaging method. The analysis is performed in a single domain with a fixed numerical grid. The fluid flow scheme developed includes SUPG (streamline‐upwind/Petrov–Galerkin), PSPG (pressure stabilizing/Petrov–Galerkin) and DSPG (Darcy stabilizing/Petrov–Galerkin) stabilization terms in a variable porosity medium. For the energy and species equations a classical SUPG‐based finite element method is employed. The developed algorithms were tested extensively with bilinear elements and were shown to perform stably and with nearly quadratic convergence in high Rayleigh number flows in varying porosity media. Examples are shown in natural and double diffusive convection in porous media and in the directional solidification of a binary‐alloy. Copyright © 2004 John Wiley & Sons, Ltd.     

Pricing decisions for three-echelon supply chain with advertising and quality effort-dependent fuzzy demand

Simultaneous influence of market power structure, advertising and quality efforts on the optimal pricing decisions and performance of a multi-echelon supply chain under uncertainty have received scant attention in the literature. We focus on this gap by examining a serial decentralised three-echelon supply chain consisting of a supplier, a manufacturer and a retailer. The uncertainty associated with customer demand, marginal production costs and effort costs is expressed as linguistic or fuzzy variables. We analyse centralised supply chain to obtain all benchmark parameters. We investigate four decentralised supply chain cases through fuzzy game theoretic approach. We compare among three Stackelberg games with various supply chain leaderships and a vertical Nash. Our key findings are as follows. First, the manufacturer puts least quality effort while retailer puts least advertising effort when they act as Stackelberg leader in a three-echelon supply chain. Second, supplier's per unit price is governed by the channel leadership in three-echelon supply chain while retail prices are less affected by the leadership. We investigate the impact of fuzzy degree of quality and advertising sensitivity on optimal profit distribution among agents. We present numerical analysis to illustrate the importance of the derived theoretical results and discuss additional managerial insights.     

Observation of ZBCP Above T C at Au/Y1Ba2Cu3O7−δ Heterostructure with a Nano Hole: A Possible Evidence for the Cooper Pairs above T C

Fabrication of a nanojunction by making a nano- hole on PMMA layer deposited in between Au and Y₁Ba₂Cu₃O_7?δ (YBCO) films is reported. We have measured the current-voltage characteristics across Au/YBCO nanojunction at different temperatures and studied Andreev Reflection (AR) phenomena. The presence of Zero Bias Conductance Peak (ZBCP) due to AR, which is considered to be the well-known feature of d-wave superconductors, has been noticed at temperatures below and above the transition temperature (T _C ～87 K) up to 130 K. The result obtained indicates the possible- existence of the phase-incoherent Cooper pairs above T _C for Au/YBCO.     

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.     

A perspective on functionalizing colloidal quantum dots with DNA

This perspective provides an overview of the techniques that have been developed for the conjugation of DNA to colloidal quantum dots (QDs), or semiconductor nanocrystals. Methods described include: ligand exchange at the QD surface, covalent conjugation of DNA to the QD surface ligands, and one-step DNA functionalization on core QDs or during core/shell QD synthesis in aqueous solution, with an emphasis on the most recent progress in our lab. We will also discuss emerging trends in DNA-functionalized QDs for potential applications.      

Variable-coupling-induced optical trapping in optical waveguides via dressed continuum

We have studied a photonic structure, based on a semi-classical approach, considering simultaneous interaction of two side-coupled bent waveguides having variable coupling interactions (Gaussian and exponential-type) with a dressed continuum. We have examined the condition of optical trapping in spatial domain during propagation of a light field through the waveguide. The variation in parametric conditions leading to a typical trapping (quasi-trapping) phenomenon for both the cases of exponential and Gaussian coupling interactions is discussed. Simulation of such phenomenon of trapping in our macroscopic photonic structure is possible for all even relative positions of the side waveguides coupled to the dressed continuum. As far as the onset of quasi-trapping is concerned, the coupling width in the Gaussian coupling case is smaller than that in the exponential case. In this context, the Gaussian profile is found more tolerant than the exponential one so far as the separation between the side-coupled waveguides is concerned. Perfect trapping is observed to sustain by setting proper conditions while launching initial light in the side waveguides.     

Overview of the scientific production in the Pharmacy area in Brazil: profile and productivity of researchers granted with fellowships by the National Council for Scientific and Technological Development

In Brazil, the National Council for Scientific and Technological Development (CNPq) distributes productivity fellowships in research (RS) as a recognition to individuals with outstanding productivity levels in their areas. The aim of this study is to evaluate the scientific production of the Brazilian Pharmacy area, one division of the Health Sciences Great Area of CNPq, considering the profile and productivity levels of RS fellows. The results showed that most of the 156 active RS fellows in 2015 were female, with doctorate completed in the Southeast region (mainly in University of São Paulo) and with research activities developed in the South and Southeast regions. Most of their work was published in journals classified as B1 and B2 Qualis in Pharmacy by the Coordination for the Improvement of Higher Education Personnel (CAPES), with high prevalence of publications in local journals and/or specialized on medicinal plants. Besides, they featured much dependence on advising and productivity indexes related to the category and level of RS fellowship. The evolution of such data must be continually evaluated to determine the influence of CNPq productivity fellowships on performance and stratification of researchers in the Pharmacy area in Brazil.