University of Biskra

This edition contains papers dedicated on research results that were presented at the 12th Annual International Conference on Material Science and Engineering (ICMSE 2024, 26-28 July 2024, Hangzhou, Zhejiang, China) that emphasis a broad spectrum of issues and solutions related to mechanical engineering - design machines, tools and equipment; reliability and durability of engineering structures, big data and applications of artificial intelligence, engineering management and ecological safety. The publication will be helpful for many specialists in machinery, information technologies, production organisation and environmental engineering.

Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

The thermal performance of a single pass solar air heater with five fins attached was investigated experimentally. Longitudinal fins were used inferior the absorber plate to increase the heat exchange and render the flow fluid in the channel uniform. The effect of mass flow rate of air on the outlet temperature, the heat transfer in the thickness of the solar collector, and the thermal efficiency were studied. Experiments were performed for two air mass flow rates of 0.012 and 0.016 kg s(-1). Moreover, the maximum efficiency values obtained for the 0.012 and 0.016 kg s(-1) with and without fins were 40.02%, 51.50% and 34.92%, 43.94%, respectively. A comparison of the results of the mass flow rates by solar collector with and without fins shows a substantial enhancement in the thermal efficiency.

Light is necessary for vision; it enables us to sense and perceive our surroundings and in many direct and indirect ways, via eye and skin, affects our physiological and psychological health. The use of light in built environments has comfort, behavioural, economic and environmental consequences. Daylight has many particular benefits including excellent visual performance, permitting good eyesight, effective entrainment of the circadian system as well as a number of acute non-image forming effects and the important role of vitamin D production. Some human responses to daylight seem to be well defined whilst others require more research to be adequately understood. This paper presents an overview of current knowledge on how the characteristics of daylight play a role in fulfilling these and other functions often better than electric lighting as conventionally delivered.

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

Chenopodium quinoa Willd., a high quality grain crop, is resistant to abiotic stresses (drought, cold, and salt) and offers an optimal source of protein. Quinoa represents a symbol of crop genetic diversity across the Andean region. In recent years, this crop has undergone a major expansion outside its countries of origin. The activities carried out within the framework of the International Year of Quinoa provided a great contribution to raise awareness on the multiple benefits of quinoa as well as to its wider cultivation at the global level. FAO is actively involved in promoting and evaluating the cultivation of quinoa in 26 countries outside the Andean region with the aim to strengthen food and nutrition security. The main goal of this research is to evaluate the adaptability of selected quinoa genotypes under different environments outside the Andean region. This paper presents the preliminary results from nine countries. Field evaluations were conducted during 2013/2014 and 2014/2015 in Asia (Kyrgyzstan and Tajikistan), and the Near East and North African countries (Algeria, Egypt, Iraq, Iran, Lebanon, Mauritania, and Yemen). In each country, the trials were carried out in different locations that globally represent the diversity of 19 agrarian systems under different agro-ecological conditions. Twenty-one genotypes of quinoa were tested using the same experimental protocol in all locations consisting in a randomized complete block design (RCBD) with three replicates. Some genotypes showed higher yields and the Q18 and Q12 landraces displayed greater adaptation than others to new environmental conditions. The Q21 and Q26 landraces were evaluated with stable and satisfactory levels of yield (>1 t.ha(-1)) in each of the different trial sites. This production stability is of considerable importance especially under climate change uncertainty. While these results suggest that this Andean crop is able to grow in many different environments, social, and cultural considerations remain crucial regarding its possible introduction as a staple food in new cropping systems around the world.

This paper presents the use of a recent developed algorithm inspired by the hunting mechanism of antlions in nature, called ant lion optimizer (ALO) algorithm for solving optimal reactive power dispatch (ORPD) problem considering a large-scale power system. The ORPD is formulated as a complex combinatorial optimization problem with nonlinear characteristic. The ALO algorithm is inspired from the hunting mechanism of antlions. One of the most interesting things in antlions is that they have a unique hunting behaviour and exhibit high capability of escaping the local optima stagnation. The ALO is used to find the set of optimal control variables of ORPD problem, such as generators terminal voltage, position of tap changers of transformers, and number of switchable capacitor banks. The performance and feasibility of the proposed algorithm are demonstrated through several simulation cases on IEEE 30-bus, IEEE 118-bus power systems and large-scale power system IEEE 300-bus power system. Comparison of obtained results with those reported in the literature shows clearly the superiority of ALO algorithm over other recently published algorithms in regards to real power losses and computational time, and hence confirmation of the efficiency of ALO algorithm in providing near-optimal solution.

The present study addresses the issue of outdoor thermal comfort in a hot and dry climate in relation to urban geometry. This experimental work, conducted in an old desert city, aims to provide some quantitative knowledge on the effectiveness of traditional design forms in ensuring a comfortable thermal environment outdoors under extreme summer conditions. The study focused on the role of the geometry of urban canyons. Air temperature, air humidity and wind speed were measured during summer 2003 in various urban streets in the old Saharan city of Beni-Isguen, Algeria (32.40N). The short-wave and long-wave radiation fluxes received by a human body from the 3D surroundings were also measured in order to allow an accurate calculation of the heat gained by a pedestrian. Bio-meteorological methodology was used and thermal comfort was expressed by means of the physiologically equivalent temperature (PET) index. The results show that the heat stress in a hot-dry climate is very high in unobstructed locations in contrast to sheltered urban sites. The vertical street profile is of prime importance in the resulting thermal sensation. Building materials were also found to play a decisive role. Deep streets together with high thermal capacity materials mitigate the heat stress in the daytime. The high and heavy walls provide more shading and more heat storage, leading to lower surfaces temperatures. Hence, a human body absorbs less short-wave radiation owing to reduced direct exposure, and also less radiant heat from the surrounding environment is absorbed by the body. In contrast, air temperature and air humidity show little dependence on the urban geometry. Therefore, these factors are less relevant indicators for outdoor thermal comfort in the summertime.

Vehicular Ad hoc NETworks (VANETs) play a key role in the design and development of Intelligent Transportation Systems (ITS) that aim to improve road safety and transportation productivity. VANETs cover vehicle-to-vehicle and vehicle-to-roadside communications. One of the most important challenges of this type of network is the timely and reliable dissemination of messages among vehicular nodes that enable drivers to take appropriate decisions to improve road safety. In the past decade, many routing protocols for VANETs that can support reliability and safety requirements have been proposed. These protocols suffer from several limitations, including complexity, lack of scalability to large scale networks, routing overheads, etc. To address these limitations, various bio-inspired approaches have been proposed to route packets among vehicular nodes in an optimized manner. We survey recent proposed bio-inspired routing algorithms for the VANET environment. In particular, we identify the key features, strengths, and weaknesses of these algorithms and compare them by using various criteria. Moreover, we propose a unified formal model of the bio-inspired multimodular approaches applied to VANET routing. We highlight main future research directions in this area.

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

In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.

This study presents experimental results about the effect of incorporating waste rubber aggregates in combination with waste glass powder or silica sand powder obtained from dune natural sand, on the performances of cementitious mixtures. Rubber aggregates (RW) were used to replace crushed sand in concrete mixes with ratios of 10%, 20%, 40% and 60%, while glass powder (GP) and natural sand powder (SP) were used to replace 15% of the cement weight. Nine different forms of concrete with the separate wastes and with the combination of them were designed and prepared. The mixtures were characterized in the fresh and hardened states by means of workability, fresh density, compressive and tensile strengths , propagation of ultrasonic waves and deformability tests. The water/binder ratio and superplasticizer percentage of all mixtures were maintained constant. The results showed that the strength increased with the incorporation of glass powder and rubber aggregates, especially with 10% and 20% RW contents. In addition, the developed rubberized concrete with the incorporation of glass powder presented higher fresh density and deformability, compared to the cementitious rubberized mixtures without GP. Furthermore, the simultaneous incorporation of rubber waste and glass powder enhanced the concretes workability due to the low GP and RW water absorptions. • The effect of combined rubber and glass wasteson the properties of concrete in its fresh and hardened state. • Glass powder increased the rubberized concrete workability. • The composed mixture (RW-GP)showed better compressive and tensile strengths behavior at later age. • The rubberized concrete becomes more ductile with the addition of waste glass powder.

This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.

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.

Cellular signaling pathways involved in the maintenance of the equilibrium between cell proliferation and apoptosis have emerged as rational targets that can be exploited in the prevention and treatment of cancer. Epigallocatechin-3-gallate (EGCG) is the most abundant phenolic compound found in green tea. It has been shown to regulate multiple crucial cellular signaling pathways, including those mediated by EGFR, JAK-STAT, MAPKs, NF-κB, PI3K-AKT-mTOR, and others. Deregulation of the abovementioned pathways is involved in the pathophysiology of cancer. It has been demonstrated that EGCG may exert anti-proliferative, anti-inflammatory, and apoptosis-inducing effects or induce epigenetic changes. Furthermore, preclinical and clinical studies suggest that EGCG may be used in the treatment of numerous disorders, including cancer. This review aims to summarize the existing knowledge regarding the biological properties of EGCG, especially in the context of cancer treatment and prophylaxis.

In recent years, the network technology known as Internet of Vehicles (IoV) has been developed to improve road safety and vehicle security, with the goal of servicing the digital demands of car drivers and passengers. However, the highly dynamical network topology that characterizes these networks, and which often leads to discontinuous transmissions, is one of the most significant challenges of IoV. To address this issue, IoV infrastructure-based components known as roadside units (RSU) are designed to play a critical role by providing continuous transmission coverage and permanent connectivity. However, the main challenges that arise when deploying RSUs are balancing IoVs' performances and total cost so that optimal vehicle service coverage is provided with respect to some target Quality of Service (QoS) such as: service coverage, throughput, low latency, or energy consumption. This paper provides an in-depth survey of RSU deployment in IoV networks, discussing recent research trends in this field, and summarizing of a number of previous papers on the subject. Furthermore, we highlight that two classes of RSU deployment can be found in the literature-static and dynamic-the latter being based on vehicle mobility. A comparison between the existing RSU deployment schemes proposed in existing literature, as well as the various networking metrics, are presented and discussed. Our comparative study confirms that the performance of the different RSU placement solutions heavily depends on several factors such as road shape, particularity of road segments (like accident-prone ones), wireless access methods, mobility model, and vehicles' distribution over time and space. Besides that, we review the most important RSU placement approaches, highlighting their strengths and limitations. Finally, this survey concludes by presenting some future research directions in this domain.

The integration of generative artificial intelligence (AI) into adaptive and personalized learning represents a transformative shift in the educational landscape.This research paper investigates the impact of incorporating generative AI into adaptive and personalized learning environments, with a focus on tracing the evolution from conventional artificial intelligence methods to generative AI and identifying its diverse applications in education.The study begins with a comprehensive review of the evolution of generative AI models and frameworks.A framework of selection criteria is established to curate case studies showcasing the applications of generative AI in education.These case studies are analysed to elucidate the benefits and challenges associated with integrating generative AI into adaptive learning frameworks.Through an in-depth analysis of selected case studies, the study reveals tangible benefits derived from generative AI integration, including increased student engagement, improved test scores and accelerated skill development.Ethical, technical and pedagogical challenges related to generative AI integration are identified, emphasizing the need for careful consideration and collaborative efforts between educators and technologists.The findings underscore the transformative potential of generative AI in revolutionizing education.By addressing ethical concerns, navigating technical challenges and embracing human-centric approaches, educators and technologists can collaboratively harness the power of generative AI to create innovative and inclusive learning environments.Additionally, the study highlights the transition from Education 4.0 to Education 5.0, emphasizing the importance of social-emotional learning and human connection alongside personalization in shaping the future of education.

This article proposes direct power control (DPC) associated with space vector modulation (SVM) for a shunt active power filter (SAPF). This control is suggested in order to overcome the drawbacks of the conventional DPC, where the SVM is able to decrease the high active and reactive power ripples maintaining a fixed switching frequency. In addition, the performance of the proposed scheme is improved by replacing PI controllers with supertwisting second-order sliding mode controllers (ST-SMCs) in the active and reactive power control loops. This technique provides high robustness and dynamics for external perturbations. The proposed control DPC-SVM based on ST-SMC is investigated by simulation and practical implementation using MATLAB/Simulink with a real-time interface based on a dSPACE 1104 board.