The present study focuses on the investigation of the behaviour of a reduced model of a reinforced soil massif by a sand column tested in the laboratory. These tests involved the installation of sand columns in clay specimens (kaolin) by different methods. The specimens thus obtained are subjected to the same oedometric loading program. The first part of this work aims to study the effect of the intensity of the compaction stress of the sand columns on the surrounding soil and the effect on the behaviour of the soil-column massif. The sand columns were installed with the soil replacement method and with compacting of the columns (WR_WC). Three different compaction stresses were used to install the columns. In the second part, a sand column 20 mm in diameter was installed by two methods: one with replacement of the soil (WR) and without compaction and the other with displacement of the soil (WD). A comparison between the two methods has been established. By determining the equivalent characteristics for the soil-column massif, this study made it possible to characterize the effect of the installation method of the columns on the settlements, the void ratioe of kaolin, the equivalent void ratio e_eq of the massif soil-column and on the compressibility parameters of the massif (equivalent compression index C_ceq and swelling index C_seq), by comparing the results obtained with those of the unreinforced soils that constitute the reference case. The results obtained showed that the techniques used for the installation of columns have significant effects on the behaviour of reinforced massifs.

In this paper, the exact solutions to the AB nonlinear system are investigated. This system is reduced via two different transformations to a sine-Gordon equation and a quasilinear equation for a new dependent variable ϕ. Solutions to a sineGordon equation and a quasilinear equation are found. Hence, the original system can well be solved for such ϕ. Also, a similar approach is proposed to solve analytically an eventual extension system for the case of variable coefficients.

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.

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.

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.

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.

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.

In this  study, we have studied the length-weight relationship, condition factor (Kr), gonado-somatic index (GSI) and size at first maturity of two Cichlidae species (Tilapia zillii and Hemichromis bimaculatus) from two lakes (Megarine and Temacine) of the northern Sahara (Algeria). Some 159 specimens of H. bimaculatus and 52 T. zillii were sampled between September 2012 and July 2013. The length-weight relationship revealed an isometric value b (3.0) for H. bimaculatus from Lake Megarine, while there was no significant difference for T. zillii from Lake Temacine. We noted a negative allometric growth pattern (b<3) of males and females of H. bimaculatus. Both sexes of T. zillii grew isometrically. The Kr did not differ significantly between months for the two studied species. It was less than 1 only in November and July for H. bimaculatus. For T. zillii, the monthly mean of Kr was always greater than 1 for all the studied period. The first sexual maturity of 50% of males was observed at 52.75mm in H. bimaculatus and 61mm in T. zillii. The smallest mature female had a total length of 60.26mm and 66mm for H. bimaculatus and T. zillii respectively. The reproduction period of H. bimaculatus and T. zillii begins in March with an extended spawning period. The overall sex ratio was in favor of females (1: 0.7) in H. bimaculatus, and in favor of males in T. zillii (1: 1.33). For both species, the sex ratio varied according to months and size classes.

The health systems in many countries are still unable to control the evolution and persistence of the COVID-19 pandemic despite the large mobilisation of national resources. International attention has focussed on finding a cure, and preventive measures and national and international strategies to be adopted and implemented with regard to other future pandemics have been neglected despite their predictability and high probability of occurrence. This work aims to anticipate a reading on experience feedback in light of the current pandemic situation, and to identify the main spatial elements of vulnerability in Batna, Algeria, which seems to control the ability of an urban area to prevent the spread of the COVID-19 virus. We used a digital model based on a multi-criteria approach implemented in a geo-decisional GIS database to serve as a decision support tool for dealing with an epidemiological situation as a preventive or curative action. The results from the model seem to adequately reflect the reality of confirmed incidents in Batna. In addition, the results of the analysis of the spatiotemporal evolution of the virus clearly confirm that the urban sectors characterised by high vulnerability are those that have recorded an increasing number of confirmed COVID-19 incidents since the start of the epidemic until December 2020.

Let A, A_{1},  A_{2}, B, B_{1}, B_{2}, C_{1} and C_{2} be linear bounded operators on Hilbert spaces. In this paper, by using generalized inverses, we establish necessary and sufficient conditions for the existence of a common solution and give the form of the general common solution of the operator equations A_{1}XB_{1}=C_{1} and A_{2}XB_{2}=C_{2}, we apply this result to determine new necessary and sufficient conditions for the existence of Hermitian solutions  and give the form of the general Hermitian solution to the operator equation AXB=C. As a consequence, we give necessary and sufficient condition for the existence of Hermitian solution to the operator equation AXA^{*}+BYB^{*}=C.

In wireless sensor networks (WSNs), multi-channel communication represents an attractive field due to its advantage in improving throughput and delivery rate. However, the major challenge that faces WSNs is the energy constraint. To overcome the channel assignment problem in an energy-efficient way, reinforcement learning (RL) approach is used. Though, RL requires several iterations to obtain the best solution, creating a communication overhead and time-wasting. In this paper, a heuristically accelerated reinforcement learning approach for channel assignment (HARL CA) in WSNs is proposed to reduce the learning iterations. The proposal considers the selected channel by the neighboring sender nodes as external information, used to accelerate the learning process and to avoid collisions, while the bandwidth of the used channel is regarded as an important factor in the scheduling process to increase the delivery rate. The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance.

The design and the evaluation of communication protocols in WSNs is a crucial issue. Generally, researchers use simulation methods to evaluate them. However, formal modelling and analysis techniques are an efficient alternative to simulation methods. Indeed, these techniques allow performance evaluation and model verification. In this paper, a formal approach is proposed to modelling and to evaluating the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) MAC protocol with a star topology. Moreover, the proposed approach deals with some properties that are not stated in most existing works. The approach uses Hierarchical Timed Coloured Petri Nets (HTCPNs) formalism to model the protocol and exploits the CPN-Tools to analyse the generated models. HTCPNs provide timed aspect which facilitates the consideration of time constraints inherent to the CSMA/CA protocol.