In recent years the development of health science to improve people’s lives and reduce the death rate from cardiovascular disease, researchers have invested in the solution of stents to treat cardiovascular disease. Usually a permanent implant (metal stent) is used to treat a temporary disease, effective on elastic recoil and negative remodeling, but promoting intimate proliferation. This is combated by an active stent, which nevertheless induces chronic inflammation and delayed healing (because of active drugs), with the risk of late thrombosis. The idea of resolution leads to the study of the behavior of temporary stent biodegradable and bioresorbable, once the healing process is completed. The purpose of this study is to reduce the disadvantages of metal stents, to do this; a biodegradable material (polylactic acid) is used. The fatigue behavior of a stent after its placement using geometric parameters selected from clinical cases (diastole and systole). A finite element numerical study in the field of biomaterial fatigue is proposed in order to investigate and understand the biodegradable behavior of the stent. The results of the numerical study show the predicted lifetime of the biodegradable fragrance.

The integration of a fault tolerance mechanism in critical real-time embedded systems is an important and required property to ensure the continuity of delivering the expected service even in the presence of faults to avoid catastrophic consequences that can be generated in the event of failure of these systems. In this research paper we present a solution to tolerate permanent faults of one processor in heterogeneous distributed real-time embedded systems by using software redundancy solutions based on active and passive replication of dependent tasks in the point-to-point connection. The methodology proposed consists to generate a distribution/scheduling of tasks on hardware architecture and also to tolerate permanent faults of a single processor by executing simultaneously two replicas of a task, the first which ends its execution blocks the second is running. this principle saves very considerable time in distribution/scheduling length with and without errors.

Contrairement à l’abstention-surveillance, la surveillance active est une modalité de prise en charge curative. Elle vise à retarder le traitement d’une tumeur peu agressive jusqu’au moment où elle le deviendra tout en restant dans la fenêtre de curabilité de la maladie. À travers une lecture de littératures, nous allons essayer de maitre la lumière sur la place et les modalités de la surveillance active, dans les groupes favorables des cancers de la prostate et de répondre aux questions suivantes : Pourquoi la surveillance active ? Pour qui ? Comment l’instauré ? et quand doit-on l’arrêter ? La majorité des essais, cliniques publiés s’accordent à dire, que la surveillance active est une attitude parfaitement adaptée aux patients du groupe favorable d’AMICO, voire même une partie du groupe intermédiaire bas risque. Les résultats en matière de survie globale et d’évènement métastatiques, sont similaires à ceux des patients traités d’emblée par chirurgie et ou radiothérapie, avec en plus moins de toxicité. La surveillance est basée essentiellement sur le dosage périodique du PSA, rebiopsie selon des protocoles propres à chaque équipe. La décision du passage aux traitements invasifs, sera conditionnée par la progression du score Gleason, selon des algorithmes dont certains, sont déjà validés à l’international. La surveillance active, doit faire partie intégrante des décisions de prise en charge des adénocarcinomes prostatiques localisés favorables. Cette attitude nous permet, d’éviter de surtraiter un grand nombre, de petites lésions non évolutives, tout en ayant la possibilité et les moyens, de rattraper les lésions qui progressent.

Image classification has been one of the most widely topic in artificial intelligence, deep models need larger datasets and powerful hardware to improve the highperformance classification. ImageNet Challenge was started in 2010 to classify 100,000 test images into 1000 different classes. Tiny ImageNet challenge is similar to ImageNet challenge, where images are taken from the standard ImageNet and resized to be 64x64. In this paper a fine-tuned Xception to classify images into the 200 classes is presented using the standard Tiny ImageNet dataset, the down-sampling (64x64) of images and the low similarity inter-class makes feature extraction and classification difficult and more challenging. We used a transfer learning algorithm to fine-tune the Xception architecture using the Extreme version of the Inception module to achieve a high validation accuracy of 65.14%.

The novel coronavirus outbreak (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents the actual greatest global public health crisis. The lack of efficacious drugs and vaccines against this viral infection created a challenge for scientific researchers in order to find effective solutions. One of the promising therapeutic approaches is the search for bioactive molecules with few side effects that display antiviral properties in natural sources like medicinal plants and vegetables. Several computational and experimental studies indicated that flavonoids especially flavonols and their derivatives constitute effective viral enzyme inhibitors and possess interesting antiviral activities. In this context, the present study reviews the efficacy of many dietary flavonols as potential antiviral drugs targeting the SARS-CoV-2 enzymes and proteins including Chymotrypsin-Like Protease (3CL^pro), Papain Like protease (PL^pro), Spike protein (S protein) and RNA-dependent RNA polymerase (RdRp), and also their ability to interact with the angiotensin-converting enzyme II (ACE2) receptor. The relationship between flavonol structures and their SARS-CoV-2 antiviral effects were discussed. On the other hand, the immunomodulatory, the anti-inflammatory and the antiviral effects of secondary metabolites from this class of flavonoids were reported. Also, their bioavailability limitations and toxicity were predicted.

Floods become major concerns in most gobe regions due to socio-economic and environmental consequences caused by these phenomena in recent decades. Most Algerian cities are exposed to flood risks and suffered from its consequences. The purpose of this paper is the spatialization of flood hazard in the city of Chemora (Algeria) by hydraulic modelling in a GIS environment whose objective is prevention, which requires a set of hydrological and hydraulic informations in order to achieve a comprehensive and effective management.

As renewable energy elucidation, the solar refrigeration of fruits such as date palm is a storage alternate to preserve food in healthy parameters of conditioning. This statistical and numeric study investigates the energy gain cost case around the diverse dimensions’ models of positive cold stores (02, 04, and 06 cold rooms), concerning energetic disparity and numerous financial fluctuations of the applied systems. The results of computation and analysis regarding panels of construction, equipment, consumption, and maintenance for classic, absorption, and adsorption refrigeration systems that conserve dates palm into these three cold stores. In the end, the comparison of technical and economic elements in tables and figures by enumerating their advantages and inconveniences. Classic Bitzer, Absorption WFC SC 5, and Adsorption AG ACS 15 and 08 are models in which their evaluation is relating to their costs. In Biskra, these results mean that adsorption chiller termed AG ACS (15 plus 08) is illustrious by its parameters of simplicity, lifespan, safety, and security, valued to 1147.5 €/m² and median cost up ten years of using is 92972 €

Today’s, and near future, communication networks rely heavily on capacity expansion to keep pace with the massive number of mobile devices and ever-increasing mobile traffic. This expansion can be achieved through three major ingredients, namely, adding more wireless-spectrum, efficient usage of this spectrum, and adequate networks’ architectures. In this paper, a proposition for integrating these three ingredients in a cognitive-radio-inspired framework is presented. The focus is on the integration of the idle spectrum resources of different wireless networks into a single mobile heterogeneous wireless network. This framework is based on a conceptual network-architecture articulated with a generic and cooperative spectrum-harvesting scheme. The former brings the necessary agility for such heterogeneous environments, the latter keeps the network supplied with the vital spectrum resources. In our proposal, we make use of cross-correlated sequences (CCSs) for context-aware events’ signaling purposes. This choice is motivated by the particularly interesting characteristics of CCSs, namely, duration shortness, robustness to bad radio conditions, detection rather than decoding, and low probability of collision. As an illustration, we propose a reporting and detection scheme, in the context of OFDMA systems, and provide performance results from simulations to validate our proposal.

The direct power control of the doubly fed induction generator using conventional controllers is characterized by unsatisfactory performance: high ripples of stator powers and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the higher law sliding mode developed on the super twisting algorithm associated with the fuzzy logic control in order to realize optimal command performance, Finally, the efficiency of the envisaged control scheme, is investigated against. The proposed regulation scheme is efficient in reducing the powers ripples; successfully suppress chatter and the effects of parametric variations that do not affect the performance of the regulation.

This paper proposes the fundamental aspects of hybrid nonlinear control which is composed of the super twisting algorithm (STA) based second order sliding mode control applying fuzzy logic method (FSOSMC), with pertinent simulation results for a doubly fed induction machine (DFIM) drive. To minimize chattering effect phenomenon due to Signum function employed in sliding mode algorithm, a new method is proposed. This technique consists in replacing the signum function by fuzzy switching function in the SOSMC to minimize flux and torque ripples. This FSOSMC is associated to the double direct torque control DDTC of the doubly fed induction machine (DFIM) by combining the advantages of fuzzy logic (FL) and the advantages of super-twisting sliding mode. The FSOSMC-DDTC strategy is compared with a PI-DDTC and SOSMC-DDTC. Simulation results demonstrate good efficiency and excellent robustness of the hybrid nonlinear controller.

This article investigates a non-equilibrium chaotic system in view of commensurate and incommensurate fractional orders and with only one signum function. By varying some values of the fractional-order derivative together with some parameter values of the proposed system, different dynamical behaviors of the system are explored and discussed via several numerical simulations. This system displays complex hidden dynamics such as inversion property, chaotic bursting oscillation, multistabilty, and coexisting attractors. Besides, by means of adapting certain controlled constants, it is shown that this system possesses a three-variable offset boosting system. In conformity with the performed simulations, it also turns out that the resultant hidden attractors can be distributively ordered in a grid of three dimensions, a lattice of two dimensions, a line of one dimension, and even arbitrariness in the phase space. Through considering the Caputo fractional-order operator in all performed simulations, phase portraits in two- and three-dimensional projections, Lyapunov exponents, and the bifurcation diagrams are numerically reported in this work as beneficial exit results.

In this work, we propose a new algorithm to improve existing techniques used in the field of spectroscopic data regression analysis. In particular, it combines the power of nonlinear kernel regressors (kernel ridge regression [KRR], kernel principal component regression [KPCR], and Gaussian process regression [GPR]) with an optimization based on nondominated sorting multi-objective genetic algorithm (NSGAII) to filter the residual outliers in the prediction space and leverage points in the features space. The proposed algorithm, contrary to most existing robust algorithms, simultaneously optimizes many complementary objectives for an automatic adaptation and thus a better outliers detection. It is well known that the elimination of outliers greatly improves the regression model. It is thus the aim of this work to develop a new robust regression algorithm. It has been applied on five different datasets, and the results are compared to both classical nonlinear regression methods and the commonly used robust regression methods robust continuum regression (RCR), partial robust M-regression (PRM), robust principal component regression (RPCR), robust PLSR (RSIMPLS), and locally weighted regression (LWR). They show that the proposed algorithm outperforms the classical nonlinear regression methods and is a promising competitor to the robust methods outperforming most of them. Even though the results obtained are only from five datasets, this algorithm can be considered an interesting contribution for improving data analysis in the field of chemometrics.

In this paper, the role of introducing Germanium (Ge)/IGZO heterostructure in enhancing the Infrared (IR) photodetection properties of thin-film phototransistor (Photo- TFT) is presented. Numerical models for the investigated device are developed using ATLAS device simulator. The influence of Ge photosensitive layer thickness on the sensor IR photoresponse is carried out. It is revealed that the optimized IR Photo-TFT based on p-Ge/IGZO heterojunction can offer improved IR responsivity of 4.1×10(exp2) A/W, and over 10(exp6) of sensitivity. These improvements are attributed to the role of the introduced p-Ge/IGZO heterostructure in promoting IR photodetection ability and improved separation and transfer mechanisms of photo-exited electron/hole pairs. The photosensor is then implemented in an optical inverter gate circuit in order to assess its switching capabilities. It is found that the proposed phototransistor shows an improved optical gain thus indicating its excellent performance. Therefore, providing high IR responsivity and low dark noise effects, the optimized Ge/IGZO IR Photo-TFT can be a potential alternative photosensor for designing optoelectronic systems with high-performance and ultralow power consumption.

Purpose

The objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO₂ generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO₂) emitted by an industrial fire in a plant located in an industrial region site in Algeria.

Design/methodology/approach

The SA was defined for the following input parameters: wind speed, NO₂ emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.

Findings

The study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO₂ initial concentration has the most influence on the predicted NO₂ levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO₂ concentration estimation.

Originality/value

The study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

Mining itemsets for association rule generation is a fundamental data mining task originally stemming from the traditional market basket analysis problem. However, enumerating all frequent itemsets, especially in a dense dataset, or with low support thresholds, remains costly. In this paper, a novel theorem builds the relationship between frequent closed itemsets (FCIs) and frequent generator itemsets (FGIs) and proves that the process of mining FCIs is equivalent to mining FGIs, unified with their full-support and extension items. On the basis of this theorem, a generator-based algorithm for mining FCIs, called GrAFCI⁺, is proposed and explained in details including its correctness. The comparative effectiveness of the algorithm in terms of scalability is first investigated, along with the compression rate—a measure of the interestingness of a given FIs representation. Extensive experiments are further undertaken on eight datasets and four state-of-the-art algorithms, namely DCI_CLOSED*, DCI_PLUS, FPClose, and NAFCP. The results show that the proposed algorithm is more efficient regarding the execution time in most cases as compared to these algorithms. Because GrAFCI⁺ main goal is to address the runtime issue, it paid a memory cost, especially when the support is too small. However, this cost is not high since GrAFCI⁺ is seconded by only one competitor out of four in memory utilization and for large support values. As an overall assessment, GrAFCI⁺ gives better results than most of its competitors.