University of Batna 1

In this article, we assess the structural equivalence of the Zimbardo Time Perspective Inventory (ZTPI) across 26 samples from 24 countries ( N = 12,200). The ZTPI is proven to be a valid and reliable index of individual differences in time perspective across five temporal categories: Past Negative, Past Positive, Present Fatalistic, Present Hedonistic, and Future. We obtained evidence for invariance of 36 items (out of 56) and also the five-factor structure of ZTPI across 23 countries. The short ZTPI scales are reliable for country-level analysis, whereas we recommend the use of the full scales for individual-level analysis. The short version of ZTPI will further promote integration of research in the time perspective domain in relation to many different psycho-social processes.

and allied fusarioid genera (www.fusarium.org).

This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach.

This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.

The aim of this review is to present and discuss the research work reported in the literature on the use of glutamic acid and its derivatives as corrosion inhibitors for metals in different aggressive solutions. Mass loss and electrochemical techniques were among the most often used techniques to evaluate the corrosion inhibition efficiency of the used inhibitor. Glutamic acid can act as an efficient corrosion inhibitor, but it can in other cases show an opposite effect, which accelerates the corrosion process; all depend on the experimental conditions. Highest values of inhibition efficiency were obtained in the presence of ions as Zn 2+ and ions halides. Glutamic acid derivatives have shown a good ability to use it as an effective corrosion inhibitor for metal in an acidic solution. The development of computational modeling helps to design new glutamic acid derivatives and to understand the inhibition mechanism of those compounds.

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

This paper describes a fault-tolerant control system for a high-performance induction motor drive that propels an electrical vehicle (EV) or hybrid electric vehicle (HEV). In the proposed control scheme, the developed system takes into account the controller transition smoothness in the event of sensor failure. Moreover, due to the EV or HEV requirements for sensorless operations, a practical sensorless control scheme is developed and used within the proposed fault-tolerant control system. This requires the presence of an adaptive flux observer. The speed estimator is based on the approximation of the magnetic characteristic slope of the induction motor to the mutual inductance value. Simulation results, in terms of speed and torque responses, show the effectiveness of the proposed approach.

In this paper, a novel rotor speed estimation method using model reference adaptive system (MRAS) is proposed to improve the performance of a sensorless vector control in the very low and zero speed regions. In the classical MRAS method, the rotor flux of the adaptive model is compared with that of the reference model. The rotor speed is estimated from the fluxes difference of the two models using adequate adaptive mechanism. However, the performance of this technique at low speed remains uncertain and the MRAS loses its efficiency, but in the new MRAS method, two differences are used at the same time. The first is between rotor fluxes and the second between electromagnetic torques. The adaptive mechanism used in this new structure contains two parallel loops having Proportional-integral controller and low-pass filter. The first and the second loops are used to adjust the rotor flux and electromagnetic torque. To ensure good performance, a robust vector control using sliding mode control is proposed. The controllers are designed using the Lyapunov approach. Simulation and experimental results show the effectiveness of the proposed speed estimation method at low and zero speed regions, and good robustness with respect to parameter variations, measurement errors, and noise is obtained.

This paper deals with the problem of detection and diagnosis of induction motor faults. Using the fuzzy logic strategy, a better understanding of heuristics underlying the motor faults detection and diagnosis process can be achieved. The proposed fuzzy approach is based on the stator current Concordia patterns. Induction motor stator currents are measured, recorded, and used for Concordia patterns computation under different operating conditions, particularly for different load levels. Experimental results are presented in terms of accuracy in the detection of motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis if the input data are processed in an advantageous way, which is the case of the Concordia patterns.

This paper proposes a strategy to minimize the losses of an induction motor propelling an electric vehicle (EV). The proposed control strategy, which is based on a direct flux and torque control scheme, utilizes the stator flux as a control variable, and the flux level is selected in accordance with the torque demand of the EV to achieve the efficiency-optimized drive performance. Moreover, among EV's motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach

The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.

Bacterial endophytes constitute an essential part of the plant microbiome and are described to promote plant health by different mechanisms. The close interaction with the host leads to important changes in the physiology of the plant. Although beneficial bacteria use the same entrance strategies as bacterial pathogens to colonize and enter the inner plant tissues, the host develops strategies to select and allow the entrance to specific genera of bacteria. In addition, endophytes may modify their own genome to adapt or avoid the defense machinery of the host. The present review gives an overview about bacterial endophytes inhabiting the phytosphere, their diversity, and the interaction with the host. Direct and indirect defenses promoted by the plant-endophyte symbiont exert an important role in controlling plant defenses against different stresses, and here, more specifically, is discussed the role against biotic stress. Defenses that should be considered are the emission of volatiles or antibiotic compounds, but also the induction of basal defenses and boosting plant immunity by priming defenses. The primed defenses may encompass pathogenesis-related protein genes (PR family), antioxidant enzymes, or changes in the secondary metabolism.

In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ > Mg2+ > Na+ > K+, and Cl− > SO42− > HCO3− > NO3−, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.

In this paper, new sensors based on a double-gate (DG) graphene nanoribbon field-effect transistor (GNRFET), for high-performance DNA and gas detection, are proposed through a simulation-based study. The proposed sensors are simulated by solving the Schrödinger equation using the mode space non-equilibrium Green's function formalism coupled self-consistently with a 2D Poisson equation under the ballistic limits. The dielectric and work function modulation techniques are used for the electrical detection of DNA and gas molecules, respectively. The behaviors of both the sensors have been investigated, and the impacts of variation in geometrical and electrical parameters on the sensitivity of sensors have also been studied. In comparison to other FET-based sensors, the proposed sensors provide not only higher sensitivity but also better electrical and scaling performances. The obtained results make the proposed DG-GNRFET-based sensors as promising candidates for ultra-sensitive, small-size, low-power and reliable CMOS-based DNA, and gas sensors.

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.

A new method for ECG compression is presented. After the pyramidal wavelet decomposition, the resultant coefficients are subjected to an iterative threshold until a fixed percentage target of wavelet coefficients to be zeroed is reached. Next, the lossless Huffman's coding is used to increase the compression ratio. Quality preservation for good compression ratios is reported.

In this paper, we address the problem of confidentiality of keyframes, which are extracted from diagnostic hysteroscopy data using video summarization. We propose an image color coding method aimed at increasing the security of keyframes extracted from diagnostic hysteroscopy videos. In this regard, we use a 2-D logistic map to generate the cryptographic keys sequences, which relies on mixing and cascading the orbits of the chaotic map in order to generate the stream keys for the encryption algorithm. The encrypted images produced by our proposed algorithm exhibit randomness behavior, providing a high-level of security for the keyframes against various attacks. The experimental results and security analysis from different perspectives verify the superior security and high efficiency of our proposed encryption scheme compared to other state-of-the-art image encryption algorithms. Furthermore, the proposed method can be combined with mobile-cloud environments and can be generalized to ensure the security of cloud contents as well as important data during transmission.

The construction industry is a major sector in the economy of developing countries. During the last two decades in Algeria, many large-scale construction projects have been launched to develop the basic infrastructure facilities of the country. However, most of these projects experience extensive delays. The objective of this paper is to identify the causes of delay in the Algerian construction industry and assess their importance according to the main project stakeholders, which are the owner, the contractor and the consultant. Data were collected through a questionnaire and direct interviews of a sample of construction experts including 16 owners, 16 contractors and 20 consultants. Fifty-nine causes of delay were identified in this research. The results indicate that the five most important causes are slow change orders, unrealistic contract duration, slow variation orders in extra quantities, delays in payment of performed work and ineffective planning and scheduling by contractors. The study revealed that owner-related causes are the most important sources of delay. The findings of this research can be used to guide the improvements of the construction industry in Algeria.

This study examined the role of different psychological coping mechanisms in mental and physical health during the initial phases of the COVID-19 crisis with an emphasis on meaning-centered coping. A total of 11,227 people from 30 countries across all continents participated in the study and completed measures of psychological distress (depression, stress, and anxiety), loneliness, well-being, and physical health, together with measures of problem-focused and emotion-focused coping, and a measure called the Meaning-centered Coping Scale (MCCS) that was developed in the present study. Validation analyses of the MCCS were performed in all countries, and data were assessed by multilevel modeling (MLM). The MCCS showed a robust one-factor structure in 30 countries with good test-retest, concurrent and divergent validity results. MLM analyses showed mixed results regarding emotion and problem-focused coping strategies. However, the MCCS was the strongest positive predictor of physical and mental health among all coping strategies, independently of demographic characteristics and country-level variables. The findings suggest that the MCCS is a valid measure to assess meaning-centered coping. The results also call for policies promoting effective coping to mitigate collective suffering during the pandemic. Este estudio examinó el papel de diferentes estrategias de afrontamiento psicológico en la salud mental y física durante las fases iniciales de la crisis de COVID-19. 11,227 personas de 30 países representando todos los continentes participaron en el estudio y completaron medidas de malestar psicológico (depresión, estrés y ansiedad), soledad, bienestar, salud física, medidas de afrontamiento centrado en el problema y en la emoción, y una medida denominada Escala del Afrontamiento Centrado en el Sentido (MCCS) que fue desarrollada en este estudio. El análisis de validación de la MCCS se realizó en todos los países, y los datos se evaluaron mediante un modelo multinivel. La MCCS mostró una estructura unifactorial en 30 países con buenos resultados de validez test-retest, concurrente y divergente. Los análisis mostraron resultados mixtos en cuanto a las estrategias de afrontamiento centradas en la emoción y en el problema. La MCCS fue el predictor positivo más fuerte de salud física y mental, independientemente de las características demográficas y las variables a nivel de país. Los resultados sugieren que la MCCS es un insrumento fiable para medir afrontamiento centrado en el sentido. Estos resultados pueden servir para dirigir políticas que promuevan un afrontamiento eficaz con el fin de mitigar el sufrimiento colectivo durante la pandemia.

The field of computer-assisted language learning has recently brought about a notable change in English as a Foreign Language (EFL) writing. Starting from October 2022, students across different academic fields have increasingly depended on ChatGPT-4 as a helpful resource for addressing particular challenges in EFL writing. This study aimed to investigate the use and acceptance of ChatGPT-4 in students’ EFL writing. To this end, an experiment was conducted with 76 undergraduate students from a private school in Algeria. The participants were randomly allocated into two groups: experimental group (n = 37) and control group (n = 39). Additionally, a questionnaire was administered. The results showed that the experimental group (EG) outperformed the control group (CG). Besides, the findings revealed that students in the EG in post-test outperformed their pre-test scores. The findings also revealed substantial improvements in the EG’s views of perceived usefulness, perceived ease of use, attitudes, and behavioral intention. According to the results, ChatGPT-4 helped boost students' EFL writing skills, which ultimately led to their acceptance. Students appear particularly interested in ChatGPT-4 because of its potential usefulness in putting what they learn about EFL writing into practice. Some suggestions and recommendations were provided.