An efficient microcalcifications detection based on dual spatial/spectral processing

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparison with their surrounding tissue. Spatial non linear enhancement can be applied for microcalcification detection. However, efficiency of a such approach depends on breast density: in case of extreme breast density, the contrast between microcalcification’s details and their surrounding tissue is attenuated leading to a limitation of spatially based approaches. In that case, frequency analysis such as wavelet based analysis can be more relevant for dissociating microcalcifications. The main goal of Computer Aided Detection systems (CAD) is to detect breast cancer at an early stage for all breast density classes by using entropies to enhance and then detect microcalcification details. Accordingly, we combine our approach a spatial Automatic Non Linear Stretching (ANLS) and Shannon Entropy based Wavelet Coefficient Thresholding (SE_WCT). Validation of the proposed approach is done on the Mammographic Image Analysis Society (MIAS) database. The evaluation of the contrast is based on the Second-Derivative-Like measure of enhancement(SDME). Accordingly, it yields to a mean SDME of 78.8dB on the whole database. The performance metric for evaluating our proposed CAD is the Receiver Operating Characteristic(ROC) curve and the free-response ROC (FROC). An area under the ROC curve A _z = 0.92 is obtained as well as 97.14 % of True Positives (TP) with 0,48 False positives per image (FP).     

Covariance-guided One-Class Support Vector Machine

In one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. Boundary-based methods like One-Class Support Vector Machine (OSVM) preferentially separates the data from outliers along the large variance directions. On the other hand, retaining only the low variance directions can result in sacrificing some initial properties of the original data and is not desirable, specially in case of limited training samples. This paper introduces a Covariance-guided One-Class Support Vector Machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM improves upon the OSVM method by controlling the direction of the separating hyperplane through incorporation of the estimated covariance matrix from the training data. Our proposed method is a convex optimization problem resulting in one global optimum solution which can be solved efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM libraries. Comparative experimental results with contemporary one-class classifiers on numerous artificial and benchmark datasets demonstrate that our method results in significantly better classification performance.     

Dual modularity optimization for detecting overlapping communities in bipartite networks

Many algorithms have been designed to discover community structure in networks. These algorithms are mostly dedicated to detecting disjoint communities. Very few of them are intended to discover overlapping communities, particularly the bipartite networks have hardly been explored for the detection of such communities. In this paper, we describe a new approach which consists in forming overlapping mixed communities in a bipartite network based on dual optimization of modularity. To this end, we propose two algorithms. The first one is an evolutionary algorithm dedicated for global optimization of the Newman’s modularity on the line graph. This algorithm has been tested on well-known real benchmark networks and compared with several other existing methods of community detection in networks. The second one is an algorithm that locally optimizes the graph Mancoridis modularity, and we have adapted to a bipartite graph. Specifically, this second algorithm is applied to the decomposition of vertices, resulting from the evolutionary process, and also characterizes the overlapping communities taking into account their semantic aspect. Our approach requires a priori no knowledge on the number of communities searched in the network. We show its interest on two datasets, namely, a group of synthetic networks and real-world network whose structure is also difficult to understand.     

Sliding wear transition for the CW614 brass alloy

Dry sliding wear tests were performed on a CW614 brass alloy using a pin-on-ring configuration. Wear kinetics were measured within a load range of 20–80 N and sliding velocity ranging from 1 to 7 m/s. Chemical compositions, morphologies and microstructures of worn surfaces and wear debris were characterised by scanning electron microscope (SEM) and energy dispersive X-ray spectrometer (EDS). Two main wear regimes have been observed: severe wear and mild wear. The results of wear tests and metallographic investigations on worn surfaces have been summarised in a wear mechanism map. It was found that the wear transition is controlled by a critical temperature at the contact surface.     

Efficiency of optimization algorithms on the adjustment of process parameters for geometric accuracy enhancement of denture plate in single point incremental sheet forming

Neural Computing and Applications - Single point incremental forming is a sheet metal forming technique with great potential for use in prototyping and custom manufacture. Although this technology...     

Chemical composition and antimicrobial activity of the essential oils from four Ruta species growing in Algeria

Antimicrobial properties of plants essential oils have been investigated in order to suggest them as potential tools to overcome the microbial drug resistance and the increasing incidence of food borne diseases problems. The aim of this research is to study the antibacterial and antifungal effects of four traditional plants essential oils, Ruta angustifolia, Ruta chalepensis, Ruta graveolens and Ruta tuberculata, against standard bacterial and fungal strains. The chemical compounds of the oils were examined by GC/MS. Results revealed a powerful antifungal activity against filamentous fungi. Aspergillus fumigatus and Cladosporium herbarum are the most sensitive strains to these oils with MIC values less than 3.5 μg ml⁻¹ for certain oils, reaching 7.8 μg ml⁻¹ for other. GC/MS essay exhibited ketones as the most abundant constituent of these oils except for R. tuberculata essential oil which has a completely different composition, monoterpenes alcohols being the most abundant. These compositions explain their potential antifungal activity.     

Durability of steel/CFRP double strap joints exposed to sea water,cyclic temperature and humidity

This paper discusses the mechanical performance of steel/CFRP double strap joints subjected to harsh environments. Seventy-five joint specimens were exposed to various conditions, including (1) simulated sea-water at 20 °C and 50 °C up to 1 year in a temperature-controlled sea-water tank, (2) constant and (3) cyclic temperatures with a high level of relative humidity (RH) up to 1000 h in an environmental chamber. Tensile tests were undertaken to determine the mechanical properties of the unexposed and exposed specimens. Significant reductions in joint strength and stiffness were found for the specimens exposed to simulated sea-water after 1 year of exposure, while the specimens exposed to combinations of temperature and humidity in the environmental chamber showed little decrease in joint strength and stiffness after 1000 h of exposure. A model was developed to predict the strength and stiffness degradation of the joints in sea-water and the results compared well with the experimental results.     

Verification of duration systems using an approximation approach

We consider the verification problem of invariance properties for timed systems modeled by (extended) Timed Graphs with duration variables,This problem is in general case undecidable,Nevertheless we give in this paper a technique extending a given system into another one containing the initial computations as well as additional ones.Then we define a digitization technique allowing the translation from the continuous case to the discrete one.Using this digitization,we show that to each real computation in the initial system corresponds a discrete computation in the extended system.Then,we show that the extended system corresponds to a very close approximation of the initial one,allowing per consequent,a good analysis of invariance properties of the initial system.     

Experimental and finite element analysis of a double strap joint between steel plates and normal modulus CFRP

Strengthening of steel structures using externally-bonded carbon fibre reinforced polymers ‘CFRP’ is a rapidly developing technique. This paper describes the behaviour of axially loaded flat steel plates strengthened using carbon fibre reinforced polymer sheets. Two steel plates were joined together with adhesive and followed by the application of carbon fibre sheet double strap joint with different bond lengths. The behaviour of the specimens was further investigated by using nonlinear finite element analysis to predict the failure modes and load capacity. In this study, bond failure is the dominant failure mode for normal modulus (240 GPa) CFRP bonding which closely matched the results of finite elements. The predicted ultimate loads from the FE analysis are found to be in good agreement with experimental values.     

An experimental and numerical investigation on torsional strengthening of solid and box-section RC beams using CFRP laminates

The issue of maintenance and repair/upgrading of existing structures has become a major issue, particularly extending the service lifespan of bridges. Fibre reinforced polymer (FRP) has shown great promise as a state-of-the-art material in flexural and shear strengthening as external reinforcement. However, little attention has been paid to torsional strengthening in terms of both experimental and numerical research. This paper reports the experimental work in an overall investigation of torsional strengthening of solid and box-section reinforced concrete beams with externally-bonded CFRP. This was found to be a viable method of torsional strengthening. Numerical work was carried out using non-linear finite element (FE) modelling. Good agreement in terms of torque–twist behaviour, steel and CFRP reinforcement responses, and crack patterns was achieved. The unique failure modes of all the specimens were modelled correctly as well.