In this study, an efficient full-wave method is developed for characterizing the resonant frequencies, bandwidths, and quality factors of an inverted circular superconducting patch antenna. Our technique is based on the Galerkin procedure in the Hankel transform domain (HTD) combined with the complex resistive boundary conditions. With the use of suitable Green’s functions in the HTD, the analysis is performed for the case where the superconducting circular patches is printed on an anisotropic substrate. The numerical results obtained using this approach are compared with the experimental results. These comparisons were very good, which proves the correctness and the validity of the method. It is found that the optical properties combined with optimally-chosen structural parameters of anisotropic materials can maintain control of the resonant frequency and exhibit wider bandwidth characteristics.

This paper presents an experimental study to evaluate the effect of local mineral additions (pozzolan, slag and limestone) on the rheological behaviour of based cement binder’s pastes. The binary, ternary and quaternary binder pastes were prepared with the partial clinker cement replacement limited up to 20%, according with type CEM II specifications. The cements were characterized by their geometric shapes, the reactivity and the chemical composition. An experimental design plan was used to modelling the rheological behaviour of pastes. The relatives yield stress and plastic viscosity of binder’s pastes, with normal consistency, were determined. The results showed that all the tested compositions with additions follow the same rheological behaviour law according to the Bingham model. The binder pastes rheological parameters (yield stress and viscosity) are affected by mineral additions. The highest values of the rheological parameters were measured in binary and ternary cements with limestone and pozzolan. On the other hand, the lower viscosity among the tested pastes was obtained with slag addition. The statistical approach allowed us to obtain a satisfactory modelling of viscosity and yield stress with a coefficient of determination R² = 0.91 and 0.92, respectively and a satisfactory correlation between the viscosity and the water/binder ratio (W/B) for a normal consistency with a coefficient of determination R² = 0.91.

The current study focuses on the chemical composition, and evaluation of antioxidant and antibacterial activity of the aerial parts of Centaurea resupinata subsp. dufourii. Using different chromatographic methods nine compounds 1–9 were isolated. The structural identification of isolated compounds was achieved using several spectroscopic methods NMR techniques (1H NMR, 13C NMR, COSY, HSQC, HMBC) and mass spectroscopy (ESI-MS) and by comparison with literature data. The structures of these compounds were identified as nicotiflorin (1), apigetrin (2), chrysoeriol (3), apigenin (4), chrysin (5), daucosterol (6), β-sitosterol (7), taraxastrerol (8) and lupeol (9). The antibacterial and antioxidant activities of ethyl acetate and n-butanol extracts have been evaluated. The antioxidant activity was assessed in vitro using DPPH radical scavenging method, which showed that ethyl acetate extract possessed an interesting antioxidant potential (IC50 = 36.263 ± 0.005 μg/mL).  

The phytochemical profile obtained from LC-ESI-MS/MS analysis of the n-butanol extract (BEHL) from the North African endemic plant Hyacinthoides lingulata (Poir.) Rothm. brought about the identification of ten glycosylated derivatives of apigenin and luteolin flavones. For the same plant extract, in vitro anti-inflammatory (hypotonic induced hemolysis and heat induced haemolysis assay) and antioxidant (DPPH and β-Carotene) activities were evaluated observing high inflammatory inhibition by protecting membrane stability of erythrocyte in both heat (84.70 ± 0.24%) and hypotonic induced hemolysis (79.45 ± 0.12%). A remarkable hemostatic effect was also established by measuring the coagulation time (15.95 ± 1.05 s at a dose of 1 mg/mL) of decalcified plasma related to its phytochemical content. It is the first report on combined chemical components and biological evaluation of this specific plant.

A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsies are the main screening techniques, which require either, expensive devices or personal qualified; but some countries still lack access due to economic, social, or cultural issues. As an alternative diagnosis methodology for breast cancer, this study presents a computer-aided diagnosis system based on convolutional neural networks (CNN) using thermal images. We demonstrate that CNNs are faster, reliable and robust when compared with different techniques. We study the influence of data pre-processing, data augmentation and database size on several CAD models. Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50, and Inception. This study exhibits that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database (up to 33%) but without data-augmentation. Finally, this study proposes a computer-aided system for breast cancer diagnosis but also, it stands as baseline research on the influence of data-augmentation and database size for breast cancer diagnosis from thermal images with CNNs

Cet article est consacré à l’étude des performances de la génératrice asynchrone à cage double étoile (GASDE) en site isolé. Le système de commande est composé d’une GASDE raccordé à un bus continu et une charge en sortie de deux redresseurs à commande MLI. Une étude comparative entre la technique de commande conventionnelle et la commande adaptée basée sur l’introduction de la SVM-PI-flou et un nouvel estimateur de flux (flux virtuel statorique) afin d’améliorer la qualité d’énergie et d’atténuer les harmoniques du courant.

Background: Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters – No seasonal for forecasting the COVID-19 new cases in Algeria. 

Methods: Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24^th February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters – No seasonal. 

Results: The predictive values were obtained from the 1^st August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19. 

Conclusion: To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

In this paper, we consider a time-fractional reaction-diffusion system with the same nonlinearities of the Newton-Leipnik chaotic system. Through analytical tools and numerical results, we derive sufficient conditions for the asymptotic stability of the proposed model and show the existence of chaos. We also propose a nonlinear synchronization controller for a pair of systems and establish the local and global asymptotic convergence of the trajectories by means of fractional stability theory and the Lyapunov method.

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Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.

In this paper, we consider a coupled system of two nonlinear hyperbolic equations with variable-exponents in the damping and source terms. Under suitable assumptions on the intial data and the variable exponents, we prove a global existence theorem, using the Stable-set method. Then, we establish a decay estimate of the solution energy, by Komornik’s integral approach.

• The research paper aims to create development region, as itis a means for reorganizing the potential for development, as the research work dealt with a systematic vision based on the merging of the results of statistical analysis with the principles adopted in regional divisions, we have identified three regions with different developmental characteristics; the north eastern developmental region, the Constantine development region and the eastern high plains region. The results of the work are shown in a map of development regions were the final outputs of the research paper are prepared.
• Copyright of Annals of the University of Oradea, Geography Series / Analele Universitatii din Oradea, Seria Geografie is the property of University of Oradea, Department of Geography, Tourism & Territorial Planning and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.

Wireless sensor networks are deployed without predefined infrastructure and are generally left unattended. Indeed, the vulnerability of the wireless sensor networks to attacks comes principally from their inherent characteristics. As the data are transmitted over the air, it is very easy for an adversary to spy on traffic. Also, to meet the strict budgetary requirements, the sensor nodes tend to not be tamperproof and thus offer no protection against security attacks. Alongside with these vulnerabilities, the human intervention is always not allowed to deal with adversaries who attempt to compromise the network. Therefore, security systems are mainly needed to secure the network and ensure the protection against security threats. Indeed, cryptographic based systems are generally used to ensure security. However, due to the lack of memory and power (low computing, limited energy reserves) of the sensor nodes, most of these approaches are not suitable. Therefore, providing security while respecting the specific constraints of the sensors, represents one of the most important research issue in wireless sensor networks. Indeed, several studies have been conducted these last decades to propose lightweight and efficient security protocols for wireless sensor networks. In this paper, we review the most leading protocols and classify them based the addressed security issue. Also, we outline the main security constraints and challenges and present the future research directions based on the emerged application fields.

This paper presents a decision-making support system for situation risk assessment associated with critical alarms conditions in a gas facility. The system provides a human operator with advice on the confirmation and classification of occurred alarm. The input of the system comprises uncertain and incomplete information. In the light of uncertain and incomplete information, different uncertainties laws have been associated with the probabilistic assessment of the system loops which combine data of several sources to reach the ultimate classification. The implemented model used Observe-Orient-Decide-Act loop (OODA) combined with Bayesian networks. Results show that the system can classify the alarms system.

In the last years, predictive maintenance has gained a central position in condition-based maintenance tasks planning. Machine learning approaches have been very successful in simplifying the construction of prognostic models for health assessment based on available historical labeled data issued from similar systems or specific physical models. However, if the collected samples suffer from lack of labels (small labeled dataset or not enough samples), the process of generalization of the learning model on the dataset as well as on the newly arrived samples (application) can be very difficult. In an attempt to overcome such drawbacks, a new deep supervised learning approach is introduced in this paper. The proposed approach aims at extracting and learning important patterns even from a small amount of data in order to produce more general health estimator. The algorithm is trained online based on local receptive field theories of extreme learning machines using data issued from a propulsion system simulator. Compared to extreme learning machine variants, the new algorithm shows a higher level of accuracy in terms of approximation and generalization under several training paradigms.

Designing and manufacturing lattice structures with Topology Optimization (TO) and Additive Manufacturing (AM) techniques is a novel method to create light-weight components with promising potential and high design flexibility. This paper proposes a new design of lightweight-graded lattice structures to replace the internal solid volume of the turbine blade to increase its endurance of high thermal stresses effects. The microstructure design of unit cells in a 3D framework is conducted by using the lattice structure topology optimization (LSTO) technique. The role of the LSTO is to find an optimal density distribution of lattice structures in the design space under specific stress constraints and fill the inner solid part of the blade with graded lattice structures. The derived implicit surfaces modelling is used from a triply periodic minimal surfaces (TPMS) to optimize the mechanical performances of lattice structures. Numerical results show the validity of the proposed method. The effectiveness and robustness of the constructed models are analysed by using finite element analysis. The simulation results show that the graded lattice structures in the improved designs have better efficiency in terms of lightweight (33.41–40.32%), stress (25.52–48.55%) and deformation (7.35–19.58%) compared to the initial design.

In this paper, we present a new technique for constructing a nonstationary wavelet. The key idea relies on the following: for each wavelet level, we solve the Bezout’s equation and we propose a positive solution over the interval [–1, 1]. Using the Bernstein’s polynomials we approximate this proposed positive solution with the intention to perform a spectral factorization.

Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.