Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people.     

Investigation of Heat‐Induced Degradation of Virgin Olive Oil Using Front Face Fluorescence Spectroscopy and Chemometric Analysis

A simple setup using a 365‐nm light‐emitting diode coupled to a USB spectrometer through an optical fiber, in a front‐face fluorescence configuration, was used to investigate the heat‐induced deterioration of virgin olive oil at different heating temperatures and times. The samples were heated for 30, 60, 120 and 180 min for every temperature setting of 140, 160 and 180 °C, respectively. Two important results are reported in this article. First, a neo‐formed compound around 665 nm due to the degradation of chlorophyll was observed. This new peak was attributed to pyropheophytins. The second result showed an important rise of the peak around 489 nm, which corresponded to the oxidation products. The correlation obtained between the peroxide value and the 489 nm peak using principal component analysis revealed the mechanism of the oxidation process. It further showed that the peak around 489 nm is a direct consequence of the degradation of hydroperoxide.     

The gray level aura matrices for textured image segmentation

Inspired by an intuitive analogy that exists between the gray level textures and the miscibility in the multiphase fluids, the aura concept was developed from set theory tools in order to modeling the texture image. The gray level aura matrix (GLAM) has been then proposed to generalize the gray level cooccurrence matrix (GLCM) which remains very popular in the texture analysis. The GLAM indicates how much each gray level is present in the neighborhood of each other gray level. The neighborhood is defined by a structuring element as one used in mathematical morphology. The GLAM is mainly used and studied in synthesis and classification of textures framework but very few works are devoted to the segmentation. The aim of this paper is to exploit the GLAM for the segmentation of textured images. Experiments results over synthetic and real images show the efficiency of the GLAM. The influence of the shape and the size of the structuring element on the segmentation results are also studied.     

Schema Evolution in Data Warehouses

In this paper, we address the issues related to the evolution and maintenance of data warehousing systems, when underlying data sources change their schema capabilities. These changes can invalidate views at the data warehousing system. We present an approach for dynamically adapting views according to schema changes arising on source relations. This type of maintenance concerns both the schema and the data of the data warehouse. The main issue is to avoid the view recomputation from scratch especially when views are defined from multiple sources. The data of the data warehouse is used primarily in organizational decision-making and may be strategic. Therefore, the schema of the data warehouse can evolve for modeling new requirements resulting from analysis or data-mining processing. Our approach provides means to support schema evolution of the data warehouse independently of the data sources. Received 20 March 2000 / Revised 5 January 2001 / Accepted in revised form 20 April 2001     

Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

In semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation’s dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated.     

Sol gel transitions during acid gelation of milk containing modified waxy maize starch. Differences between chemical and bacterial acidification measured using rheological and spectroscopic techniques

Modified waxy maize starch (1%, w/v) was added to skim milk and the mixtures were heated and homogenized. Acidification was conducted at 40 °C, using either glucono-δ-lactone (GDL) or a commercial starter culture. The physico-chemical changes occurring during acidification were monitored using small oscillatory rheology, diffusing wave spectroscopy and ultrasound spectroscopy. A similar gelling behaviour was noted for GDL and bacterial-induced gels; however, a difference was noted in the values of storage modulus (G′). The presence of starch did not seem to affect the development of the gel structure, nor the mobility and positional correlations of the casein micelles during acidification. On the other hand, starch increased the final storage modulus, G′ of the acid milk gels. These results indicate the absence of direct interactions between micelles and the modified starch granules.     

Evaluating of air flow movements and thermal comfort in a model room with Euler equation: Two dimensional study

This paper presents the results of a study that consists of estimating the temperature distribution and air flow movement in a model room with a numerical model based on the Euler equations. Numerical results obtained for two scenarios of ventilation and heating are compared with the predictions of a Navier–Stokes model, as well as with experimental results. A comparison of the local thermal comfort indices PMV and PPD obtained experimentally and numerically is also presented. Results show that the Euler model is capable of properly estimating the temperature distribution, the air movement and the comfort indices in the room. Furthermore, the use of Euler equations allows a reduction of computational time in the order of 30% compared to the Navier–Stokes modeling.     

Magnetohydrodynamic bio‐nanoconvective Naiver slip flow of micropolar fluid in a stretchable horizontal channel

The purpose of this paper is to formulate and analyze a nano‐bio transport model for magnetohydrodynamic convective flow, heat, and mass diffusion of micropolar fluid containing gyrotactic microorganisms through a horizontal channel. Both the walls are considered to be stretched, and the Navier slip boundary condition is taken into account. The governing bio‐nano transport partial differential equations are rendered to ordinary differential equations using similarity variables. The resulting normalized self‐similar boundary value problem is solved computationally with the Matlab bvp4c function. The effect of the controlling parameters on the nondimensional velocity, temperature, nanoparticle concentration, and motile microorganism density functions, and their gradients at the wall are visualized graphically and in a tabular form and expounded at length. Validation with a previous simpler model is included. All physical quantities, except the local Nusselt number, increases with an increase in the velocity slip and magnetic parameters. The present problem finds applications in industries related to pharmaceutical, nanofluidic devices, microbial enhanced oil recovery, modeling oil, and gas‐bearing sedimentary basins.     

Influence of moisture content on the contact angle and surface tension measured on birch wood surfaces

European Journal of Wood and Wood Products - Surface tension of solid wood surfaces affects the wettability and thus the adhesion of various adhesives and wood coatings. By measuring the contact...