National Engineering School of Tunis

We describe Curves+, a new nucleic acid conformational analysis program which is applicable to a wide range of nucleic acid structures, including those with up to four strands and with either canonical or modified bases and backbones. The program is algorithmically simpler and computationally much faster than the earlier Curves approach, although it still provides both helical and backbone parameters, including a curvilinear axis and parameters relating the position of the bases to this axis. It additionally provides a full analysis of groove widths and depths. Curves+ can also be used to analyse molecular dynamics trajectories. With the help of the accompanying program Canal, it is possible to produce a variety of graphical output including parameter variations along a given structure and time series or histograms of parameter variations during dynamics.

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

Poly(vinyl alcohol) is a promising class of synthetic polymer biodegradable under a two-step metabolism consisting of an oxidation and hydrolysis.

The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

Lipopeptides are microbial surface active compounds produced by a wide variety of bacteria, fungi, and yeast. They are characterized by high structural diversity and have the ability to decrease the surface and interfacial tension at the surface and interface, respectively. Surfactin, iturin, and fengycin of Bacillus subtilis are among the most popular lipopeptides. Lipopepetides can be applied in diverse domains as food and cosmetic industries for their emulsification/de-emulsification capacity, dispersing, foaming, moisturizing, and dispersing properties. Also, they are qualified as viscosity reducers, hydrocarbon solubilizing and mobilizing agents, and metal sequestering candidates for application in environment and bioremediation. Moreover, their ability to form pores and destabilize biological membrane permits their use as antimicrobial, hemolytic, antiviral, antitumor, and insecticide agents. Furthermore, lipopeptides can act at the surface and can modulate enzymes activity permitting the enhancement of the activity of certain enzymes ameliorating microbial process or the inhibition of certain other enzymes permitting their use as antifungal agents. This article will present a detailed classification of lipopeptides biosurfactant along with their producing strain and biological activities and will discuss their functional properties and related applications.

This paper describes a learning approach based on training convolutional neural networks (CNN) for a traffic sign classification system. In addition, it presents the preliminary classification results of applying this CNN to learn features and classify RGB-D images task. To determine the appropriate architecture, we explore the transfer learning technique called “fine tuning technique”, of reusing layers trained on the ImageNet dataset in order to provide a solution for a four-class classification task of a new set of data.

Permanent magnet synchronous motors (PMSMs) drives using three-phase voltage-source inverters (VSIs) are currently used in many industrial applications. The reliability of VSIs is one of the most important factors to improve the reliability and availability levels of the drive. Accordingly, this paper presents a robust fault diagnostic method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in three-phase PMSM drives. The proposed observer-based algorithm relies on an adaptive threshold for fault diagnosis. Current sensor and open-circuit faults can be distinguished and the faulty sensors and/or power semiconductors are effectively isolated. The proposed technique is robust to machine parameters and load variations. Several simulation and experimental results using a vector-controlled PMSM drive are presented, showing the diagnostic algorithm robustness against false alarms and its effectiveness in both IGBTs and current sensors fault diagnosis.

In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications.

The aim of this paper is to present the interest of implementing digital controllers using field-programmable gate array (FPGA) components. To this purpose, a variety of current control techniques, which is applied to alternating current machine drives, is designed and implemented. They consist of on-off current controllers, proportional-integral current controller, and predictive current controller. The quality of the regulated current is significantly improved. It is mainly due to a very important reduction of the execution time delay. Indeed, in all described techniques, the execution time of the designed hardware architectures is only a few microseconds. This time reduction derives directly from the possibility offered by FPGAs to design very powerful dedicated architectures. Numerous experimental results are given in order to illustrate the efficiency of FPGA-based solutions to achieve high-performance control of electrical systems.

Delay Tolerant Networks are wireless networks where disconnections may occur frequently due to propagation phenomena, node mobility, and power outages. Propagation delays may also be long due to the operational environment (e.g. deep space, underwater). In order to achieve data delivery in such challenging networking environments, researchers have proposed the use of store-carry-and-forward protocols: there, a node may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. Additionally, multiple message replicas are often propagated to increase delivery probability. This combination of long-term storage and replication imposes a high storage overhead on untethered nodes (e.g. handhelds). Thus, efficient buffer management policies are necessary to decide which messages should be discarded, when node buffers are operated close to their capacity. In this paper, we propose efficient buffer management policies for delay tolerant networks. We show that traditional buffer management policies like drop-tail or drop-front fail to consider all relevant information in this context and are, thus, sub-optimal. Using the theory of encounter-based message dissemination, we propose an optimal buffer management policy based on global knowledge about the network. Our policy can be tuned either to minimize the average delivery delay or to maximize the average delivery rate. Finally, we introduce a distributed algorithm that uses statistical learning to approximate the global knowledge required by the the optimal algorithm, in practice. Using simulations based on a synthetic mobility model and real mobility traces, we show that our buffer management policy based on statistical learning successfully approximates the performance of the optimal policy in all considered scenarios. At the same time, our policy outperforms existing ones in terms of both average delivery rate and delivery delay.

Information systems are frequently exposed to various types of threats which can cause different types of damages that might lead to significant financial losses. Information security damages can range from small losses to entire information system destruction. The effects of various threats vary considerably: some affect the confidentiality or integrity of data while others affect the availability of a system. Currently, organizations are struggling to understand what the threats to their information assets are and how to obtain the necessary means to combat them which continues to pose a challenge. To improve our understanding of security threats, we propose a security threat classification model which allows us to study the threats class impact instead of a threat impact as a threat varies over time. This paper addresses different criteria of information system security risks classification and gives a review of most threats classification models. We define a hybrid model for information system security threat classification in order to propose a classification architecture that supports all threat classification principles and helps organizations implement their information security strategies.

The closest tensors of higher symmetry classes are derived in explicit form for a given elasticity tensor of arbitrary symmetry. The mathematical problem is to minimize the elastic length or distance between the given tensor and the closest elasticity tensor of the specified symmetry. Solutions are presented for three distance functions, with particular attention to the Riemannian and log-Euclidean distances. These yield solutions that are invariant under inversion, i.e., the same whether elastic stiffness or compliance are considered. The Frobenius distance function, which corresponds to common notions of Euclidean length, is not invariant although it is simple to apply using projection operators. A complete description of the Euclidean projection method is presented. The three metrics are considered at a level of detail far greater than heretofore, as we develop the general framework to best fit a given set of moduli onto higher elastic symmetries. The procedures for finding the closest elasticity tensor are illustrated by application to a set of 21 moduli with no underlying symmetry.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants.

In order to increase the reliability and availability of wind turbines, condition monitoring and fault diagnosis are considered crucial means to achieve these goals. In this context, direct drives wind turbines based on permanent-magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. However, several statistical studies point out that power converters are a significant contributor to the overall failure rate of modern wind turbines. Accordingly, this paper presents a new algorithm for multiple open-circuit faults diagnosis in full-scale back-to-back converters, applied in PMSG drives used for wind turbine systems. The proposed method is based on a Luenberger observer and on an adaptive threshold, which can guarantee a reliable diagnosis independently of the drive operating conditions. Several simulation and experimental results using a PMSG drive with a full-scale converter are presented, showing the diagnostic algorithm effectiveness and robustness against false alarms for both generator- and grid-side converters.

Achieving a power envelope of few milliwatts combined with tight performance constraints is emerging as one of the key challenges for battery-powered and low cost Internet-of-things (IoT) end-nodes. IoT devices have to cope with highly time-varying workloads, characterized by intermittent “race-to-sleep” bursts of compute-intensive operations mingled with long periods of low activity. Architectural heterogeneity provides a possible solution to harmonize these competing constraints; the availability of diverse cores optimized for diverse tasks, but able to run the same code is advantageous for IoT devices. In this paper, we introduce Zero-riscy and Micro-riscy, two novel RISC-V cores targeting mixed arithmetic/control applications and control-oriented tasks respectively. We compare them with the DSP-enhanced open-source Riscy core [1]. Zero-riscy is 2.2× smaller than Riscy and provides a 2× energy boost for mixed control/arithmetic code with limited DSP. Micro-riscy is 1.6× smaller than Zero-riscy (∼11.6 kgates in UMC 65nm), has a power envelope of just 100μW at 160MHz and it is 1.4× more energy efficient than Zero-riscy on pure control code.

At the onset of 21st century, the pollution of surface and groundwater by toxic metals continues to represent a challenge for the authorities responsible for environmental protection. The uncontrolled rejection of metals in aquatic ecosystems such as Ag, As, Be, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Tl and Zn, constitute a serious threat to human and animal health. Several methods of treatment of waters polluted by metals have been proposed during the last several decades. However, the technique of precipitation of metals remains the most favorable option on an industrial scale due to reasons of cost-effectiveness, performance, and simplicity. The present review presents current knowledge on various technical alternatives for precipitation of metals. The discussion relates to the individual characteristics of the metal contaminants, as well as their behavior compared to various techniques of precipitation.

BACKGROUND: Diabetes has become a serious health problem and a major risk factor associated with troublesome health complications, such as metabolism disorders and liver-kidney dysfunctions. The inadequacies associated with conventional medicines have led to a determined search for alternative natural therapeutic agents. The present study aimed to investigate and compare the hypoglycemic and antilipidemic effects of kombucha and black tea, two natural drinks commonly consumed around the world, in surviving diabetic rats. METHODS: Alloxan diabetic rats were orally supplied with kombucha and black tea at a dose of 5 mL/kg body weight per day for 30 days, fasted overnight, and sacrificed on the 31st day of the experiment. Their bloods were collected and submitted to various biochemical measurements, including blood glucose, cholesterol, triglcerides, urea, creatinine, transaminases, transpeptidase, lipase, and amylase activities. Their pancreases were isolated and processed to measure lipase and α-amylase activities and to perform histological analysis. RESULTS: The findings revealed that, compared to black tea, kombucha tea was a better inhibitor of α-amylase and lipase activities in the plasma and pancreas and a better suppressor of increased blood glucose levels. Interestingly, kombucha was noted to induce a marked delay in the absorption of LDL-cholesterol and triglycerides and a significant increase in HDL-cholesterol. Histological analyses also showed that it exerted an ameliorative action on the pancreases and efficiently protected the liver-kidney functions of diabetic rats, evidenced by significant decreases in aspartate transaminase, alanine transaminase, and gamma-glytamyl transpeptidase activities in the plasma, as well as in the creatinine and urea contents. CONCLUSIONS: The findings revealed that kombucha tea administration induced attractive curative effects on diabetic rats, particularly in terms of liver-kidney functions. Kombucha tea can, therefore, be considered as a potential strong candidate for future application as a functional supplement for the treatment and prevention of diabetes.

This paper focuses on sensor fault detection and isolation (FDI) for electrical systems. A new, easy and fast FDI algorithm is proposed, keeping system performances unchanged under certain faulty sensor conditions when reconfigurations are available. The proposed FDI algorithm is derived from a parity space approach and is based on temporal redundancies. It is insensitive to parameter variations since no model knowledge is required. Also, it is available for a large class of electrical systems such as single- or three-phase power converters, dc or ac electrical drives, etc. Moreover, the residual threshold used for FDI is accurately defined and is suitable for the whole operating range. Simulations results are presented to illustrate the good functionality of theoretical developments. Numerous experimental results are also shown to validate the effectiveness of the proposed FDI algorithm and to highlight its advantages for the control of electrical systems.

This paper presents an easy and a robust sensor fault detection and isolation (FDI) and fault tolerant control (FTC) of a single phase PWM rectifier for electrical railway traction application. Catenary current sensor and dc link voltage sensor failures are considered. The FDI method is based on observers and residual generation. The different FDI algorithm steps allow a good detection and isolation of the sensor fault and identify the faulty sensor. The reconfiguration strategy consists of two steps with open loop control and closed loop control working. Simulation results are presented to illustrate the good performance of the FTC procedure. Experimental results are also presented to show the effectiveness of the proposed FDI and FTC algorithms and good performances of the rectifier after the reconfiguration.

This paper presents a neural approach to detect and locate automatically an interturn short-circuit fault in the stator windings of the induction machine. The fault detection and location are achieved by a feedforward multilayer-perceptron neural network (NN) trained by back propagation. The location process is based on monitoring the three-phase shifts between the line current and the phase voltage of the machine. The required data for training and testing the NN are experimentally generated from a three-phase induction motor with different interturn short-circuit faults. Simulation, as well as experimental, results are presented in this paper to demonstrate the effectiveness of the used method.