Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

Characterization of boride coatings on a ductile cast iron

In this work, the EN-GJS-400-15 cast iron was pack-borided in a powder mixture composed of 5% B₄C, 5% NaBF₄ and 90% SiC at the three temperatures: 900, 950 and 1000°C for 2, 4 and 6 h, respectively. The pack-borided EN-GJS-400-15 cast iron was characterized by the following experimental techniques: optical microscopy, XRD analysis and Microhardness Vickers tester. The growth kinetics of boride layers was also investigated. As a consequence, the boron activation energy was found to be 212.28 kJ mol^–1 for the EN-GJS-400-15 cast iron. Based on a regression model, a useful equation was derived to estimate the boride layer thickness as a function of the boriding parameters (time and temperature). A good agreement was then obtained between the predicted values of boride layers thicknesses and those measured experimentally. In addition, an iso-thickness diagram was proposed to be used as a simple tool to select the boride layers thicknesses according to the potential applications of EN-GJS-400-15 cast iron in industry.     

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

Abstract—This article presents an efficient multi-objective optimization approach based on the supervised big bang–big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The MathWorks, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.     

Hypocholesterolaemic effect of yoghurt containing Bifidobacterium pseudocatenulatum G4 or Bifidobacterium longum BB536

The effect of a yoghurt supplement containing Bifidobacterium pseudocatenulatum G4 or Bifidobacterium longum BB536 on plasma lipids, lipid peroxidation and the faecal excretion of bile acids was examined in rats fed a cholesterol-enriched diet. After 8 weeks, the rats in the positive control (PC) group who were fed the cholesterol-enriched diet showed significant increases in plasma total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, and malondialdehyde (MDA). However, groups fed a cholesterol-enriched diet supplemented with yoghurt containing B. pseudocatenulatum G4 or B. longum BB536 had significantly lower plasma TC, LDL-C, very-low-density lipoprotein (VLDL) cholesterol, and MDA than had the PC group after 8 weeks of treatment. In addition, faecal excretion of bile acids was markedly increased in the rats fed the yoghurt containing B. pseudocatenulatum G4 or B. longum BB536 as compared to the PC and NC groups.     

pH Responsive Abelmoschus esculentus Mucilage and Administration of Methotrexate: In-Vitro Antitumor and In-Vivo Toxicity Evaluation

The rapid progression in biomaterial nanotechnology apprehends the potential of non-toxic and potent polysaccharide delivery modules to overcome oral chemotherapeutic challenges. The present study is aimed to design, fabricate and characterize polysaccharide nanoparticles for methotrexate (MTX) delivery. The nanoparticles (NPs) were prepared by Abelmoschus esculentus mucilage (AEM) and chitosan (CS) by the modified coacervation method, followed by ultra-sonification. The NPs showed much better pharmaceutical properties with a spherical shape and smooth surface of 213.4–254.2 nm with PDI ranging between 0.279–0.485 size with entrapment efficiency varying from 42.08 ± 1.2 to 72.23 ± 2.0. The results revealed NPs to possess positive zeta potential and a low polydispersity index (PDI). The in-vitro drug release showed a sustained release of the drug up to 32 h with pH-dependence. Blank AEM -CS NPs showed no in-vivo toxicity for a time duration of 14 days, accompanied by high cytotoxic effects of optimized MTX loaded NPs against MCF-7 and MD-MBA231 cells by MTT assay. In conclusion, the findings advocated the therapeutic potential of AEM/CS NPs as an efficacious tool, offering a new perspective for pH-responsive routing of anticancer drugs with tumor cells as a target.     

Stabilization of controlled positive discrete‐time T‐S fuzzy systems by state feedback control

This paper deals with sufficient conditions of asymptotic stability and stabilization for nonlinear discrete‐time systems represented by a Takagi–Sugeno‐type fuzzy model whose state variables take only nonnegative values at all times t for any nonnegative initial state. This class of systems is called positive systems. The conditions of stabilizability are obtained with state feedback control. This work is based on multiple Lyapunov functions. The results are presented in linear matrix inequalities form. A real plant is studied to illustrate this technique. Copyright © 2010 John Wiley & Sons, Ltd.     

Study of microstructure, phases and microhardness of metallic coatings deposited by flame thermal spray

The recharging technique by thermal spraying offers the opportunity of renovating the worn surface parts of a machine element to give it again a new technical life despite its previous degradation in service. This process has consequently interesting economic impacts. In order to improve the adherence between 100Cr6 steel deposits and the substrate material (left worn crankshafts), company SNC ATRA of Béjaïa uses at present a composite formed by (100Cr6 steel/molybdenum bond coat of 0.2 mm thick/crankshaft substrate). As a matter of fact, it is shown in the present work that the molybdenum bond coat is not appropriate since, for the 0.2 mm thickness, lateral cracks are observed in the middle of the bond coat. On the other hand, our experiment is that a deposit of 100Cr6 steel projected directly on the substrate seems more promising since no gaps or cracks were detected at the “deposit/substrate” interface of this two-material composite. Lastly, phase analysis using X-ray diffraction confirmed that during spraying process, a stable -phase (bcc) of 100Cr6 wire was transformed to a new phase of γ-phase (fcc). The coatings exhibited the higher microhardness which would contribute to increase wear resistance.