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.      

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.     

Structural and electrical properties of donor-doped NBT system synthesized by microwave-assisted solid-state reaction route

Journal of Materials Science: Materials in Electronics - A-site donor-doped NBT systems (Na0.5Bi0.5)1-xMxTi(1-x/4)O3 (M?=?La and Sm) were synthesized by microwave-assisted solid-state...     

Multi‐bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli ) is one of the most investigated biological systems. In this study, the authors developed a multi‐bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high‐level functional behaviour. Using this approach, they simulated the transient and steady‐state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi‐bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio‐inspired systems such as nano‐bots is discussed.Inspec keywords: cell motility, microorganisms, Boolean functionsOther keywords: multibit Boolean approach, conventional BNs, high‐level functional behaviour, steady‐state responses, chemoreceptor sensory module, drift velocity, chemotactic drifting, multibit Boolean methodology, complex biological networks, bio‐inspired systems, multibit Boolean model, chemotactic drift, dynamic biological systems, equivalent modular structure, Boolean networks, simple construction, chemotaxis network, bacterium Escherichia coli, biological systems     

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.     

Synthesis of mesoporous geopolymeric powder from LD slag as superior adsorbent for Zinc (II) removal

Linz-Donawitz(LD) slag Geopolymer(LDSGP), a porous aluminosillicate geopolymeric adsorbent, has been synthesized from steel plant LD slag for efficient removal of Zinc(II) ions from wastewater, thus presenting a solution for converting industrial waste to adsorbent for wastewater treatment. The colloid paste of raw LD slag and the alkaline activator (10?M NaOH?+?sodium silicate (1:1 w/w)) has been cured for 3?days at low temperature to geopolymerize the calcium oxide rich LD slag. The BET surface area of LDSGP adsorbent (30.84?m²/g) has improved considerably compared to raw LDS (4.85?m²/g) and the FESEM and HRTEM images reveal the presence of micropetal and cauliflower like structures at outer surface of the adsorbent particles. The mesoporous nature of LDSGP adsorbent can be understood by analyzing N₂ adsorption-desorption and pore size distribution plot. The PXRD pattern of LDSGP adsorbent powder confirms the presence of Ca₂SiO₄ and Ca₃SiO₅ in the geopolymeric matrix. Langmuir isotherm model correlates the batch adsorption data of Zn²⁺ ions onto LDSGP particles at 298?K, 308?K and 318?K. The maximum Zn²⁺ ions adsorption capacity of LDSGP is 86?mg/g at 318?K. The adsorption kinetic data is correlated with pseudo-second-order model indicating chemisorption of Zn²⁺ ions onto LD slag geopolymeric powder adsorbent.