Classification of Alzheimer disease among susceptible brain regions

Statistical and machine learning techniques are frequently employed in the study of neuroimaging data for finding Alzheimer disease (AD) in clinical studies and in additional inquiries about research settings. AD affects the whole brain and as a result the quality of life, where most affected regions are the hippocampus (HP), middle temporal gyrus (MTG), entorhinal cortex, and posterior cingulate cortex (PCC). We used well-known classification methods to diagnose the affected regions of the brain at different stages of age using biomarker modalities and functional magnetic resonance imaging (fMRI) at the resting state, and later marked the affected brain region on MRI. We have used well-known support vector machine (SVM), Fisher's linear discriminant analysis, artificial neural network, and logistic regression for the classification of AD. In the context of receiver operating characteristic (ROC) curves, an SVM provided the best classification among AD stages. Moreover, analysis showed development of AD.     

Dye degradation study by incorporating Cu-doped ZnO photocatalyst into polyacrylamide microgel

Journal of Materials Science: Materials in Electronics - In this study, Cu-doped ZnO NPs (xCZO) were formed by the coprecipitation route by changing the concentration of dopant Cu from 0 to 24%...     

Green synthesis of silver nanoparticles from grape and tomato juices and evaluation of biological activities

The biosynthesis of silver nanoparticles (AgNPs) is substantial for its application in lots of fields. Tomato and grape fruit juices were used as a reducing and capping agents for the biosynthesis of AgNPs. Ultraviolet spectroscopic analysis offered peaks in the range of 396‒420 nm that indicate the production of AgNPs. Fourier transform infrared spectroscopy analysis revealed attachment of different functional groups with Ag ion in both tomato and grape fruit extracts NPs. The X‒ray diffraction analysis confirmed that the synthesised AgNPs have a face centred cubic confirmation. Scanning electron microscopy confirms the size of NPs that varies from 10 to 30 nm. The DPPH free radical scavenging assay, total antioxidant capacity, reducing power assay, total flavonoid contents and total phenolic contents determination confirmed that synthesised AgNPs are potent antioxidant agents; can be used as an effective scavenger of free radicals. Biosynthesised AgNPs also showed good antibacterial activity against Pseudomonas septica, Staphylococcus aureus, Micrococcus luteus, Enterobacter aerogenes, Bacillus subtilis and Salmonella typhi. Protein kinase inhibition activity showed a clear zone which indicates anticancerous potential of biosynthesised AgNPs. The efficacious bioactivities indicate that the tomato and grape derived AgNPs can be used efficiently in pharmaceutical and medical industries.Inspec keywords: silver, nanoparticles, nanomedicine, biomedical materials, nanofabrication, Fourier transform infrared spectra, X‐ray diffraction, scanning electron microscopy, microorganisms, antibacterial activity, enzymes, cancer, ultraviolet spectraOther keywords: silver nanoparticle green synthesis, grape juice, tomato juice, biological activity evaluation, ultraviolet spectroscopic analysis, silver nanoparticle production, Fourier transform infrared spectroscopy analysis, silver ion, X‐ray diffraction analysis, scanning electron microscopy, DPPH free radical scavenging assay, antioxidant capacity, flavonoid content, phenolic content determination, antioxidant agent, antibacterial activity, Pseudomonas septica, Staphylococcus aureus, Micrococcus luteus, Enterobacter aerogenes, Bacillus subtilis, Salmonella typhi, protein kinase, size 10 nm to 30 nm     

Intelligent computing to solve fifth-order boundary value problem arising in induction motor models

In this study, biologically inspired intelligent computing approached based on artificial neural networks (ANN) models optimized with efficient local search methods like sequential quadratic programming (SQP), interior point technique (IPT) and active set technique (AST) is designed to solve the higher order nonlinear boundary value problems arise in studies of induction motor. The mathematical modeling of the problem is formulated in an unsupervised manner with ANNs by using transfer function based on log-sigmoid, and the learning of parameters of ANNs is carried out with SQP, IPT and ASTs. The solutions obtained by proposed methods are compared with the reference state-of-the-art numerical results. Simulation studies show that the proposed methods are useful and effective for solving higher order stiff problem with boundary conditions. The strong motivation of this research work is to find the reliable approximate solution of fifth-order differential equation problems which are validated through strong statistical analysis.

     

Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery

Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensity is important as it can help authorities in emergency planning. Several manual, semi and fully automated techniques based on different principles have been developed for hurricane intensity estimation. In this paper, a deep convolutional neural network architecture is proposed for fully automated hurricane intensity estimation from satellite infrared (IR) images. The proposed architecture is robust to errors in annotation of the storm center with a smaller root-mean-squared error (RMSE) (8.82 knots) in comparison to the previous state of the art methods. A web server implementation of Deep-PHURIE and its pre-trained neural network model are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#Deep-PHURIE.

     

Experimental and numerical study on the composite adhesive joint reinforcement using wavy edge

The joints are usually the weakest part of the engineering structures. In this study, the employment of wavy edges for increasing the adhesive joint load-bearing capacity is considered. The effects of geometric parameters of the wavy edges on the strength of the adhesive joints were investigated, experimentally. Two different adhesives, Araldite 2015 and Epoxy RL440/HY441 as ductile and brittle adhesives were used, respectively. The finite element model was also developed for more investigation. The joint stress distributions were used successfully to explain the experimental observations. For the appropriate wavy joint configuration, compressive peel stress on the both ends of the adhesive led to a considerable delay in damage initiation and consequently increased the joint strength. The effects of geometrical parameters of the wavy edge on the joint strength were also examined. For the optimum configuration, the joint with wavy edge offered 32% more strength than the flat single lap joint.     

A shrinkage method for causal network detection of brain regions

We present a computationally as well as statistically efficient method of inferring causal networks for the brain regions. It is based on James‐Stein‐type shrinkage estimation of covariance matrix, suggested by (Opgen‐Rhein and Strimmer, BMC Syst Biol 1 ( 2007 ), 37‐40), among different brain regions of interest of the functional magnetic resonance imaging (fMRI) experiment, that enhance the accuracy of vector autoregressive (VAR) model coefficient estimates. We have shown that this approach is well suited for the small number of samples in time and large number of brain regions encountered in real fMRI experiments of seventeen healthy individuals. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 140–146, 2013     

Biomass as Feedstock in the Chemical Industry – An Examination from an Exergetic Point of View

Crude oil is the most important raw material for the chemical industry. Due to the continually rising price of crude oil, alternative carbon sources are becoming increasingly important. Biomass is basically the only available renewable carbon source. Because the chemical composition of biomass differs from fossil raw materials, the raw‐material change also offers many chances for new product properties and applications. At the same time, the chemical processes have to be redesigned if the feedstock changes to biomass. Here, the effects of a raw‐material change are examined on a rather generic level. The study is based on exergy balances and indicates that it is exergetically advisable to reconsider the previously established system of platform and basic chemicals. In general, exergy losses can be minimized if the synthesis pathways leading to the final products are adapted to the chemical structure of biomass.     

J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification

Neural Computing and Applications - Appearance-based gender classification is one of the key areas in pedestrian analysis, and it has many useful applications such as visual surveillance, predict...     

Molecular Dynamics Study of Surface Anisotropy in $$\hbox {Ag}_{60}\; \hbox {Cu}_{40}$$ Alloy at Nanoscale

In the present study, molecular dynamics simulation has been performed to investigate the anisotropic behavior of free standing \(\hbox {Ag}_{60}\; \hbox {Cu}_{40}\) nanorods. We choose different orientations with various cross sections to study the dynamics of thermal behavior of \(\hbox {Ag}_{60}\; \hbox {Cu}_{40}\) nanorods. The system is modeled using embedded atom method potentials. The radial distribution functions are analyzed to reveal the dynamic evolution of the structural behavior of nanorods with different orientations and sample sizes. The total energy and mean square displacement is also calculated to characterize the melting phenomenon of various samples. The melting temperature of the nanorods is found to be significantly size and orientation dependent, and it increases with the increase in cross-sectional area. The nanorods with low-index crystallographic surfaces such as (110) exhibit lowest melting temperature as compared to compact surfaces (111).