An effective DeepWINet CNN model for off-line text-independent writer identification

Pattern Analysis and Applications - Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has...     

Containerization technologies: taxonomies,applications and challenges

Modern scientific research challenges require new technologies, integrated tools, reusable and complex experiments in distributed computing infrastructures. But above all, computing power for efficient data processing and analyzing. Containers technologies have emerged as a new paradigm to address such intensive scientific applications problems. Their easy deployment in a reasonable amount of time and the few required computational resource make them more suitable. Containers are considered light virtualization solutions. They enable performance isolation and flexible deployment of complex, parallel, and high-performance systems. Moreover, they gained popularity to modernize and migrate scientific applications in computing infrastructure management. Additionally, they reduce computational time processing. In this paper, we first give an overview of virtualization and containerization technologies. We discuss the taxonomies of containerization technologies of the literature, and then we provide a new one that covers and completes those proposed in the literature. We identify the most important application domains of containerization and their technological progress. Furthermore, we discuss the performance metrics used in most containerization techniques. Finally, we point out research gaps in the related aspects of containerization technology that require more research.

     

Optimization of nutrient-induced germination of Bacillus sporothermodurans spores using response surface methodology

Spores of Bacillus sporothermodurans are known to be contaminant of dairy products and to be extremely heat-resistant. The induction of endospore germination before a heat treatment could be an efficient method to inactivate these bacteria and ensure milk stability. In this study, the nutrient-induced germination of B. sporothermodurans LTIS27 spores was studied. Testing the effect of 23 nutrient elements to trigger an important germination rate of B. sporothermodurans spores, only d-glucose, l-alanine, and inosine were considered as strong independent germinants. Both inosine and l-alanine play major roles as co-germinants with several other amino acids. A central composite experimental design with three factors (l-alanine, d-glucose, and temperature) using response surface methodology was used to optimize the nutrient-induced germination. The optimal rate of nutrient-induced germination (100%) of B. sporothermodurans spores was obtained after incubation of spore for 60 min at 35 °C in presence of 9 and 60 mM of d-glucose and l-alanine, respectively. The results in this study can help to predict the effect of environmental factors and nutrients on spore germination, which will be beneficial for screening of B. sporothermodurans in milk after induction their germination. Moreover, the chosen method of optimization of the nutrient-induced germination was efficient in finding the optimum values of three factors.     

Application of iterative dynamic programming to optimal control of a distiltation column

Iterative Dynamic Programming (IDP) is proven to be a useful technique for solving constrained dynamic optimisation problems. A high purity binary distillation column model has been chosen to investigate some of the IDP properties as well as its applicability. The investigated problems cover transitions from one steady state to another with the minimization of a quadratic cost function with associated terminal constraints.     

The creep of an elastoviscoplastic beam under a bending loading

Thermoplastics start to manifest a nonlinear mechanical behavior from relatively low loading levels. Under a bending solicitation, which generates a nonuniform stress field, the material behavior becomes more challenging. Indeed, a flexed specimen may have different behaviors from one point to another according to the local stress state. In the present work, a six-parameter rheological model is used to simulate the nonlinear behavior of an elastoviscoplastic beam, subjected to a three-point bending load. In the framework of Euler–Bernoulli theory, the mathematical formulation of a bent beam behavior involves the bending curvature function. This function allows the determination of the strain and stress fields along and through the beam. However, when the beam reaches the viscoplastic stage, the differential equation providing the bending curvature of the beam requires a numerical integration, which has been accomplished in this work. This theoretical modeling approach is supported by experimental creep tests carried out on polyamide specimens (PA6). The testing results are qualitatively consistent with the predictions of the proposed rheological model.     

Comparative study of conductometric glucose biosensor based on gold and on magnetic nanoparticles

The aim of this study was to show the feasibility and the performances of nanoparticle biosensing. A glucose conductometric biosensor was developed using two types of nanoparticles (gold and magnetic), glucose oxidase (GOD) being adsorbed on PAH (poly(allylamine hydrochloride)) modified nanoparticles, deposited on a planar interdigitated electrode (IDEs). The best sensitivities for glucose detection were obtained with magnetic nanoparticles (70 μM/mM and 3 μM of detection limit) compared to 45 μM/mM and 9 μM with gold nanoparticles and 30 μM/mM and 50 μM with GOD directly cross-linked on IDEs. When stored in phosphate buffer (20 mM, pH 7.3) at 4 °C, the biosensor showed good stability for more than 12 days.     

Modified insulator semiconductor electrode with functionalized nanoparticles for Proteus mirabilis bacteria biosensor development

The development of enzymatic sensors for biological purposes such as biomedicine, pharmacy, food industry, and environmental toxicity requires the purification step of the enzyme. To prevent the loss of the enzyme activity, a new strategy is held in order to immobilize the bacteria. It will constitute the biological sensing element leading to a high operational stability and multiple adaptations to various conditions such as temperature, pH and ionic strength changes. In this work we describe the development of a urea biosensor by immobilizing Proteus mirabilis bacteria onto an insulator–semiconductor electrode on functionalized Fe₃O₄ nanoparticles (NPs), using cationic, Poly (allylamine hydrochloride) then anionic, Poly (sodium 4-styrenesulfonate) polyelectrolytes, BSA (serum bovin albumin), and glutaraldehyde as a cross-linking agent. The response of P. mirabilis to urea addition is evaluated in homogeneous and heterogeneous phases. Before the immobilization step, the activity of urease produced from the P. mirabilis bacteria was attempted using the ion ammonium selective electrodes (ISEs). Adhesion of the bacteria cells on IS electrodes have been studied using contact angle measurements.After immobilization of the bacteria, on the (Si/SiO₂/Si₃N₄) and (Si/SiO₂) substrates, the relationship between the evolution of the flat band potential ?V_FB and the urea concentration is found to be linear for values ranging from 10^? 2 M to 10^? 5 M.     

State-Model Based Time-Domain Diagnosis of Internal Faults for Permanent Magnet Synchronous Machine Wind Application

Upon occurrence of an internal fault on the PMSM （permanent magnet synchronous machine）, the topology of the stator is amended causing structural imbalances due to the change of the connection within the windings. In this work, a state model of internal faults of the PMSM is developed. This model is in the （abc） reference frame. The modeling approach is based on the assumption that each stator phase is replaced by two major and minor sub-windings. This model is used subsequently in the residual generation for diagnosis. The fault indicators are obtained by the projection in parity space and estimated using the Luenberger observer. A scenario of fault inter-turn by the short-circuit occurring between phase （a and b） is validated by simulation.     

Multi-dimensional Capon spectral estimation using discrete Zhang neural networks

The minimum variance spectral estimator, also known as the Capon spectral estimator, is a high resolution spectral estimator used extensively in practice. In this paper, we derive a novel implementation of a very computationally demanding matched filter-bank based a spectral estimator, namely the multi-dimensional Capon spectral estimator. To avoid the direct computation of the inverse covariance matrix used to estimate the Capon spectrum which can be computationally very expensive, particularly when the dimension of the matrix is large, we propose to use the discrete Zhang neural network for the online covariance matrix inversion. The computational complexity of the proposed algorithm for one-dimensional (1-D), as well as for two-dimensional (2-D) and three-dimensional (3-D) data sequences is lower when a parallel implementation is used.     

A comparative study between three sensorless control strategies for PMSG in wind energy conversion system

This paper presents a comparative study of sliding mode, artificial neural network and model reference adaptive speed observers for a speed sensorless permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). Wind velocity and position sensorless operating methods for wind generation system using observer are proposed only by measuring phase voltages and currents. Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed mode. In addition the three speed observers are compared to verify the robustness against parameter variations.