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.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

: In this paper, we have studied a different fault tolerant control (FTC) strategies for a three-phase induction motor (3p-IM). Further we introduce Backstepping controller (BC) and Input-output linearization controller (IOLC). To provide a direct comparison between these FTCs approaches, the performances are evaluated using the control of 3p-IM under failures, variable speed, and variable parameters. A comparison between the two control strategies is proposed to prove the most robust one. The simulation results show the robustness and good performance of the fault tolerant control with Input-output linearization controller compared to one with Backstepping controller. The FTC with IOLC is more stable and robust against failures, load torque perturbation and speed reversion

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.

In the industry, automated inspection is important for ensuring the high quality and allows acceleration of procedures for quality control of parts or mechanical assemblies. Although significant progress has been made in precision machining of complex surfaces, precision inspection of such surfaces remains a difficult problem. Thus the problem of the conformity of the parts of complex geometry is felt more and more. Motivated by the need to increase quality and reduce costs, and supported by the progress made in the field of it as well as the automation of production which in recent years has seen a considerable evolution in all these stages: from design to control through manufacturing. Due to, we used a 3D computer aided inspection technique on a physical gear using a coordinate measuring machine equipped with a “PC-DMIS” measurement and inspection software. Our work consists in developing a procedure for inspection for reproduction of gear profile by reconstruction of a circle involute gear from a cloud point’s measurement. In order to obtain a reliable result. In this works, we design the CAD-model of the part as accurately as possible (using a mathematical model) and matched with the 3D points cloud that represents the measurement that obtained from scanner. we compare the measurement cloud points from coordinate measurement machine with the mathematical model of construction by ICP (Iterative Closest Point) methods in order to obtain a conformed result and to show the impact of the dimensional inspection and geometric.

Purpose

The assessment of patient safety culture (PSC) is a major priority for healthcare providers. It is often realized using quantitative approaches (questionnaires) separately from qualitative ones (patient safety culture maturity model (PSCMM)). These approaches suffer from certain major limits. Therefore, the aim of the present study is to overcome these limits and to propose a novel approach to PSC assessment.

Design/methodology/approach

The proposed approach consists of evaluating PSC in a set of healthcare establishments (HEs) using the HSOPSC questionnaire. After that, principal component analysis (PCA) and K-means algorithm were applied on PSC dimensional scores in order to aggregate them into macro dimensions. The latter were used to overcome the limits of PSC dimensional assessment and to propose a quantitative PSCMM.

Findings

PSC dimensions are grouped into three macro dimensions. Their capitalization permits their association with safety actors related to PSC promotion. Consequently, a quantitative PSC maturity matrix was proposed. Problematic PSC dimensions for the studied HEs are “Non-punitive response to error”, “Staffing”, “Communication openness”. Their PSC maturity level was found underdeveloped due to a managerial style that favors a “blame culture”.

Originality/value

A combined quali-quantitative assessment framework for PSC was proposed in the present study as recommended by a number of researchers but, to the best of our knowledge, few or no studies were devoted to it. The results can be projected for improvement and accreditation purposes, where different PSC stakeholders can be implicated as suggested by international standards.

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.

The workplace, and more particularly the healthcare sector, has recently experienced a staggering increase in violence. These aggressive behaviors are resulting in considerable consequences on healthcare workers, both in terms of mental and physical health. In light of this observation, this study aims to provide a quantitative analysis of the potential causes leading to violence in Algerian hospitals, which have become the place where tensions arise, especially during these uncertain times caused by the COVID-19 pandemic. Therefore, we started with conducting a field survey, in order to highlight the main causes behind this violence, as well as the strategy in terms of how it is managed as a risk. Then we used ISHIKAWA diagrams to classify predefined causes into several categories and anticipate the likelihood of such violent behaviors. Finally, the results of this study revealed that working conditions were the main cause of violence in Algerian hospitals. In order to remedy this gap, we recommend improving the healthcare staff well-being, as well as prioritizing proactive measures preventing violent behaviors. 

Requirements of users in developing countries differ from those of developed countries. This difference can be seen through wheelchair displacement in infrastructures that don't meet international standards. However, developing countries are obliged to purchase products from developed countries that don't necessarily meet all user's requirements. The modification of these requirements will generate disruption on all the supply chain. This paper proposes a model for optimising the cost of requirement modification on the supply chain and seeks to evaluate the introduction of a new requirement on an existing product/process. This model is adapted to the redesign and development of products, such as wheelchairs, satisfying specific Algerian end-user requirements.

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.

Diabetic retinopathy is a severe retinal disease that can blur or distort the vision of the patient. It is one of the leading causes of blindness. Early detection of diabetic retinopathy can significantly help in the treatment. The recent development in the field of AI and especially Deep learning provides ambitious solutions that can be exploited to predict, forecast and diagnose several diseases in their early phases. This work aims towards finding an automatic way to classify a given set of retina images in order to detect the diabetic retinopathy. Deep learning concepts have been used with a convolutional neural network (CNN) algorithm to build a multi-classification model that can detect and classify disease levels automatically. In this study, a CNN architecture has been applied with several parameters on a dataset of diabetic retinopathy with different structures. At the current stage of this work, obtained results are highly encouraging.

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.

This chapter focuses on double gate (DG) Tunneling Field Effect Transistor (TFET), having band engineering and high - k dielectrics. The basic structure of TFET device is derived and developed by p-i-n diode, containing two heavily doped degenerated semiconductor “p” and “n” regions and lightly doped intrinsic “i” region, respectively. The chapter explores the idea of high-k dielectric engineering as well as band engineering concept with DG -TFET. TFET is a type of field effect device in which current transport phenomena occur due to quantum tunneling between source and channel. The estimation of device characteristics and performance of TFET is time consuming and costly due to lack of rapid advancement in technology. TFET devices have become the most popular switching device among semiconductor players. The chapter summarizes the obtained results by popular device analysis technique, modeling and simulation of DG -TFET.

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.