University of Batna 2

The objective of this work is to evaluate the physico-chemical quality of the groundwater of the Merdja plain and to determine the sources of mineralization. This quality is influenced by several environmental and anthropogenic factors such as geological context, climate, precipitation and interaction between groundwater and aquifers and human activities. A Principal Component Analysis (PCA) on samples taken from several wells spread over the entire Tebessa plain (Merdja) allowed us to detect two axes that explain 73.4% of the information. The first axis describes the variables related to mineralisation and the second one describes those related to agricultural activity. Multidimensional Positioning (MDS) confirmed the interaction of physico-chemical parameters between them and their influence on groundwater quality by highlighting three groups of wells according to their physico-chemical characteristics, particularly those containing high concentrations of nitrates. This contamination is mainly the result of spreading the fertilisers and wastes that are dumped into the plain without treatment. Salinization is the result of long-term interactions between groundwater and geological formations.

Examines the market for wireless sensor networks in the era and expansion of the Internet of Things. Over the past decade, the fast expansion of the Internet of Things (IoT) paradigm and wireless communication technologies has raised many scientific and engineering challenges that call for ingenious research efforts from both academia and industry. The IoT paradigm now covers several technologies beyond RFID and wireless sensor networks (WSNs). In fact, the number of potential application fields has already exceeded expectations. According to Cisco IBSG, more than 50 billion devices are expected to be connected to the Internet by 2020, with around 20 percent from the industry sector. Therefore, integrating the IoT concept and industrial WSNs (IWSNs) is an attractive choice for industrial processes, which may optimize operational efficiency, automation, maintenance, and rationalization. Moreover, IoT ensures large-scale interconnection between machines, computers, and people, enabling intelligent industrial operations. This emergent technological evolution has led to what has become the Industrial IoT (IIoT). IIoT will bring promising opportunities, along with new challenges.

Juvenile Idiopathic Arthritis (JIA) is a group of chronic heterogenous disorders that manifests as joint inflammation in patients aged <16 years. Globally, approximately 3 million children and young adults are suffering from JIA with prevalence rates consistently higher in girls. The region of Africa and Middle East constitute a diverse group of ethnicities, socioeconomic conditions, and climates which influence the prevalence of JIA. There are only a few studies published on epidemiology of JIA in the region. There is an evident paucity of adequate and latest data from the region. This review summarizes the available data on the prevalence of JIA and its subtypes in Africa and Middle East and discusses unmet needs for patients in this region. A total of 8 journal publications were identified concerning epidemiology and 42 articles describing JIA subtypes from Africa and Middle East were included. The prevalence of JIA in Africa and Middle East was observed to be towards the lower range of the global estimate. We observed that the most prevalent subtype in the region was oligoarticular arthritis. The incidence of uveitis and anti-nuclear antibody (ANA) positivity were found to be lower as compared to the incidence from other regions. There is a huge unmet medical need in the region for reliable epidemiological data, disease awareness, having regional and local treatment guidelines and timely diagnosis. Paucity of the pediatric rheumatologists and economic disparities also contribute to the challenges regarding the management of JIA.

BACKGROUND: Pediatric Rheumatology is an orphan specialty in Africa which is gradually gaining importance across the continent. MAIN BODY: This commentary discusses the current state of affairs in the sphere of Pediatric Rheumatology across Africa and offers practical strategies to navigate the challenges encountered in research, models of care, education and training. We outline the establishment, opportunities of growth and achievements of the Pediatric Society of the African League Against Rheumatism (PAFLAR). CONCLUSION: This commentary lays the foundation for establishment of a formidable framework and development of partnerships for the prosperity of Pediatric Rheumatology in Africa and beyond.

The problem of soil water erosion is one of the primary causes of agro-pedological heritage degradation. The combined effect of natural factors and inappropriate human actions has weakened the soil, which seriously threatens the region’s fertile lands and soils, which can ultimately lead to an irreversible situation of desertification. This study focuses on analysis and mapping of the vulnerability to erosion in Oued el-Hai watershed, Algeria, based on a technical methodology that combines the universal soil loss equation (USLE) with the geographic information system (GIS) tools. The results are organized into three main classes of different rate values, from one area to another, depending on the influence of different factors that control the erosion process. The highest loss rate value is greater than 30 t·ha−1·yr−1 and covers 23.2% of the total area, mainly located in the mountainous areas with steep slopes. However, the minimum potential erosion rate value is mainly located on the plain, with an average of 10 t·ha−1·yr−1 covering 45.2% of the total area of the watershed. The estimate of potential water erosion has given alarming results. The total area of the watershed could lose a rate of 16.69 t·ha−1·yr−1 (on average) each year. The method and results described in this article are valuable for understanding the soil erosion risk and are useful for managing and planning land use that will avoid land degradation. Hence, the results of this study are considered an important document which constitutes a decision support tool in terms of the management and protection of natural resources.

Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019-2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics.

OBJECTIVES: To identify the changes in rheumatology service delivery across the five regions of Africa from the impact of the COVID-19 pandemic. METHODS: The COVID-19 African Rheumatology Study Group created an online survey consisting of 40 questions relating to the current practices and experiences of rheumatologists across Africa. The CHERRIES checklist for reporting results of internet e-surveys was adhered to. RESULTS: A total of 554 completed responses were received from 20 countries, which include six in Northern Africa, six in West Africa, four in Southern Africa, three in East Africa and one in Central Africa. Consultant grade rheumatologists constituted 436 (78.7%) of respondents with a mean of 14.5 ± 10.3 years of experience. A total of 77 (13.9%) rheumatologists avoided starting a new biologic. Face-to-face clinics with the use of some personal protective equipment continued to be held in only 293 (52.9%) rheumatologists' practices. Teleconsultation modalities found usage as follows: telephone in 335 (60.5%), WhatsApp in 241 (43.5%), emails in 90 (16.3%) and video calls in 53 (9.6%). Physical examinations were mostly reduced in 295 (53.3%) or done with personal protective equipment in 128 (23.1%) practices. Only 316 (57.0%) reported that the national rheumatology society in their country had produced any recommendation around COVID-19 while only 73 (13.2%) confirmed the availability of a national rheumatology COVID-19 registry in their country. CONCLUSION: COVID-19 has shifted daily rheumatology practices across Africa to more virtual consultations and regional disparities are more apparent in the availability of local protocols and registries.

Sentinel lymph node (SLN) sampling is important for evaluating the nodal stage of breast cancer when the axillary nodes are clinically free of metastasis. The intraoperative frozen section (IFS) of SLN is used for lymph node assessment. This meta-analysis aims to provide evidence about the diagnostic accuracy and the applicability of IFS of SLN in breast cancer patients. Data were collected by searching PubMed, Cochrane, Scopus, and Web of Science electronic databases for trials matching our eligibility criteria. The statistical analysis included the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and pooled studies' diagnostic odds ratio outcomes. The analyses were conducted using the Open Meta-analyst software. This meta-analysis pooled the results of 110 studies. The overall sensitivity of IFS for SLN metastasis was 74.7%; 95% CI [72.0, 77.2], P < 0.001. It was 31.4% 95% CI [25.2, 38.3], P < 0.001 for the micro-metastasis, and 90.2%; 95% CI [86.5, 93.0], P < 0.001 for the macro-metastasis. The overall specificity was 99.4%; 95% CI [99.2, 99.6], P < 0.001. The overall positive likelihood ratio was 121.4; 95% CI [87.9, 167.6], P < 0.001, and the overall negative likelihood ratio was 0.226; 95% CI [0.186, 0.274], P < 0.001. The overall diagnostic odds ratio of IFS for diagnosing SLN metastasis was 569.5; 95% CI [404.2, 802.4], P < 0.001. The intraoperative frozen section of SLN has good sensitivity for diagnosing breast cancer macro-metastasis. However, the sensitivity is low for micro-metastasis. The specificity is very satisfactory.

The coronavirus (COVID-19) pandemic poses an unprecedented global challenge, impacting profoundly on health and wellbeing, daily life, and the economy around the world. The COVID-19 pandemic has also changed education forever. The COVID-19 has resulted in schools shut all across the world. Globally, all children at schools or students at universities are out of the classroom. As a result, education has changed dramatically, with the notable rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Batna 2 University -situated in East of Algeria- is one of the universities suggested after the spread of COVID-19 in March, that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. All institutes and departments, including the Industrial Engineering department, are started using the e-learning Moodle platform to publish courses for all degrees of study and establish online sessions, especially for Ph.D. students.

Abstract An efficient green synthesis of 1‐amidoalkyl‐2‐naphthol derivatives 4 a‐s has been developed, employing phenylboronic acid, by a three component one‐pot condensation reaction of 2‐naphthol, a wide range of functionalized aromatic aldehydes and acetamide, or acrylamide under solvent‐free conditions. This new protocol has the rewards of ecologically benign, easy workup along with good to excellent yields. Moreover, selected compounds were screened towards inhibition of cholinesterase. In vitro studies showed that some synthesized compounds have significant both AChE and BChE inhibitory activities compared to Galanthamine (reference drug). Compounds 4 p and 4 s were the most effective showing high potential AChE and BChE inhibitory activities with respective IC 50 values of 13.81±0.54 μM and 31.70±1.29 μM. Subsequent evaluation of the α‐glucosidase inhibitory activity of the target molecules revealed that compound 4 s with an IC 50 value of 2.05±0.05 μM exhibited highest activity. These preliminary results provide promising sources of multifunctional agents and sheds light on their potential usage in medicine and in the pharmaceutical industries. Using molecular docking approach, we tried to get insights into binding interactions of our synthesized compounds to act as AChE and BChE inhibitors and understand the facts that underlie the relationships between structural modifications on these ligands and their efficacy.

Anti-lock Braking System (ABS) is used in automobiles to prevent slipping and locking of wheels after the brakes are applied. Its control is a rather complicated problem due to its strongly nonlinear and uncertain characteristics. The aim of this paper is to investigate the wheel slip control of the ground vehicle, comprising two new strategies. The first strategy is the Sliding Mode Controller (SMC) and the second one is the Fuzzy Sliding Mode Controller (FSMC), which is a combination of fuzzy logic and sliding mode, to ensure the stability of the closed-loop system and remove the chattering phenomenon introduced by classical sliding mode control. The obtained simulation results reveal the efficiency of the proposed technique for various initial road conditions.

In this paper, we propose a novel, secure, and intelligent IoT approach based on agent, we have implemented in the health care domain, where we developed an intelligent patient monitoring system for monitoring the patients heart rate automatically. Our system is more intelligent that can anticipate the critical condition before it even happens, send a message to the patient family, doctors, nurses, as well as hospital in-charge personal, and launch an alarm to be assisted by the nearest people in place. Also, our architecture ensures the authentication, authorization, and data sensing confidentiality. Hospitals and medical clinics can utilize our system to monitor their outpatients who are in danger of unpredictable health conditions. The approach presented in the paper can also be applied to other IoT domains.

Abstract A series of bis(4‐amino‐5‐cyano‐pyrimidines) was synthesized and evaluated as dual inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). To further explore the multifunctional properties of the new derivatives, their antioxidant and antibacterial activities were also tested. The results showed that most of these compounds could effectively inhibit AChE and BChE. Particularly, compound 7c exhibited the best AChE inhibitory activity (IC 50 = 5.72 ± 1.53 μM), whereas compound 7h was identified as the most potent BChE inhibitor (IC 50 = 12.19 ± 0.57 μM). Molecular modeling study revealed that compounds 7c, 7f , and 7b showed a higher inhibitory activity than that of galantamine against both AChE and BChE. Anticholinesterase activity of compounds 7h, 7b , and 7c was significant in vitro and in silico for both enzymes, since these compounds have hydrophobic rings (Br‐phenyl, dimethyl, and methoxyphenyl), which bind very well in both sites. In addition to cholinesterase inhibitory activities, these compounds showed different levels of antioxidant activities. Indeed, in the superoxide–dimethyl sulfoxide alkaline assay, compound 7j showed very high inhibition (IC 50 = 0.37 ± 0.28 μM). Also, compound 7l exhibited strong and good antibacterial activity against Staphylococcus epidermidis and Staphylococcus aureus , respectively. Taking into account the results of biological evaluation, further modifications will be designed to increase potency on different targets. In this study, the obtained results can be a new starting point for further development of multifunctional agents for the treatment of Alzheimer's disease.

Anemia diagnosis is crucial for pediatric patients due to its impact on growth and development. Traditional methods, like blood tests, are effective but pose challenges, such as discomfort, infection risk, and frequent monitoring difficulties, underscoring the need for non-intrusive diagnostic methods. In light of this, this study proposes a novel method that combines image processing with learning-driven data representation and model behavior for non-intrusive anemia diagnosis in pediatric patients. The contributions of this study are threefold. First, it uses an image-processing pipeline to extract 181 features from 13 categories, with a feature-selection process identifying the most crucial data for learning. Second, a deep multilayered network based on long short-term memory (LSTM) is utilized to train a model for classifying images into anemic and non-anemic cases, where hyperparameters are optimized using Bayesian approaches. Third, the trained LSTM model is integrated as a layer into a learning model developed based on recurrent expansion rules, forming a part of a new deep network called a recurrent expansion network (RexNet). RexNet is designed to learn data representations akin to traditional deep-learning methods while also understanding the interaction between dependent and independent variables. The proposed approach is applied to three public datasets, namely conjunctival eye images, palmar images, and fingernail images of children aged up to 6 years. RexNet achieves an overall evaluation of 99.83 ± 0.02% across all classification metrics, demonstrating significant improvements in diagnostic results and generalization compared to LSTM networks and existing methods. This highlights RexNet's potential as a promising alternative to traditional blood-based methods for non-intrusive anemia diagnosis.

Active suspension systems aim to increase ride comfort by reducing body acceleration due to road disturbances. This research proposes a new fuzzy logic controller (FLC) for such systems which includes acceleration error as third input. The objective of the proposed 3-inputs FLC is to improve vehicle comfort and ensure passenger safety. To assess the performances of the newly introduced controller, a theorical study is conducted on a quarter-car system with a four-wheel independent suspension design. Simulation results demonstrate high performances of the proposed 3-inputs FLC compared with conventional PID and 2-inputs FLC. The acceleration error as an additional FLC input was, to the best of our knowledge, not explored yet. Hence, this contribution offers a new method for the design of more effective active suspension systems.

The concept of the Internet of Things (IoT) aims at connecting physical objects to the internet and allows them to provide different services and to communicate among various objects. Within the IoT scope, the discovery of IoT services remains a challenge mainly because of the vast amount, mobility, heterogeneity, and wide distribution of services deployed in constrained devices. In fact, the context has a significant role to enable provision of adequate services to the users based on their surrounding environments. In addition, the context can help IoT services to adapt with the dynamic environment changes. In this paper, we aim to address how the context can be described for the IoT environments, and how it affects to the discovery of IoT services in the internet of things. For this reason, we propose an ontological based model for the IoT context, aiming at describing the different contexts of the main entities constituting the IoT environment. The proposed model is extensible, independent of domain and taking into account the constraints of the IoT like availability.

The Internet of Things (IoT) has gained a significant attention in the last years. It covers multiple domains and applications such as smart home, smart healthcare, IT transportation...etc. The highly dynamic nature of the IoT environment brings to the service discovery new challenges and requirements. As a result, discovering the desirable services has become very challenging. In this paper, we aim to address the IoT service discovery problem and investigate the existing solutions to tackle this problem in many aspects, therefore we present a new and full comparative study of the most representative (or outstanding) service discovery approaches in the literature over four perspectives: (1) the IoT service description model, (2) the mechanism of IoT service discovery, (3) the adopted architecture and (4) the context awareness. After each of these perspectives, we discuss the existing solutions as well as the main challenges that face the service discovery issue in the IoT domain. Besides, we adopt a classification of the IoT service discovery approaches based on their mechanism of discovery, and we propose a set of requirements that need to be considered when defining an IoT service in the IoT domain.

The Internet of things (IoT) is the integration of information space and physical space, becoming more and more popular in several places. In this paper, we will present QoS service composition approach based on multi-population genetic algorithm based on Fog-IoT computing, IoT-cloud architecture problems led us to use the 5-layared architecture implemented on a Fog computing system especially the transport layer. Our work was focus on this transport layer where we divided it into four sub-layers (security, storage, pre-processing & monitoring), it allows us to have promising advantages. Secondly, we implemented a multi-population genetic algorithm (MPGA) based on a QoS model, we considered seven QoS dimensions, i.e. Cost, response time, reliability, reputation, location, security and availability. Experimental results show the excellent results of MPGA in terms of fitness value and execution time to handle our ambulance emergency study case.

Artificial Intelligence (AI) knows a high exploitation in medical computing to enhance patient care by accelerating processes and increasing accuracy, thus providing improvements healthcare in general. Temperature is an important health factor that has to be regularly monitored and even early detected in some situations. Thus, this paper aims to invest in the advances in Internet of Things (IoT) and in Machine Learning (ML) techniques to develop a monitoring system that is able to forecast body temperature. The proposed solution consists in: i) designing and implementing a wearable device using a temperature sensor and a micro-controller, to monitor body temperature permanently, then ii) those monitored measurements are collected and stored as a time-series dataset in a cloud storage server accessible by doctors, and iii) finally the time-series dataset is used by machine learning forecasting techniques to get early body temperature values for the next hours.

This paper exposes a novel optimization technique named Water Cycle Algorithm (WCA) for solving Economic Dispatch (ED) problem considering practical generator constraints as Prohibited Operating Zones (POZ) and Ramp Rate Limits (RRL) for smooth and non-smooth fuel cost curve of objective function without and with valve point effect, respectively. The proposed technique is applied on three test systems including 6-units, 15-units and 40-units in order to investigate its ability for solving constrained and complex ED problems. The simulation results using the suggested method are compared with other metaheuristic optimization techniques in literature to assess the effectiveness of WCA for the same test systems.