This article examines the influence of phonological awareness on vocabulary acquisition among dyslexic children in a multilingual Moroccan context. The study compares the performance of 20 children, both dyslexic and non-dyslexic, aged 6 to 16 years, assessed using tests for phoneme segmentation, deletion, and blending to measure phonological awareness, as well as receptive and expressive vocabulary tests. The results reveal that dyslexic children score significantly lower in phonological awareness, confirming the presence of phonological deficits within this group. However, correlation and regression analyses indicate weak and non-significant relationships between phonological awareness and vocabulary, contrary to existing literature. This divergence may be attributed to the small sample size, age dispersion, the use of non-adaptive standardized tests, and the complexity of the Moroccan linguistic context. To address these limitations, the development of a supervised classification model using a neural network is proposed. This model, based on local data, could refine diagnostics and identify diverse phonological and lexical profiles, thereby facilitating the development of tailored educational interventions. This study highlights the need for contextualized assessment tools and innovative approaches, integrating artificial intelligence, to better understand and support vocabulary acquisition in dyslexic children within multicultural environments.

Object tracking in real-time applications often relies on the tracking-by-detection paradigm, where object localization and temporal association are decoupled. While methods such as DeepSORT integrate a detector like YOLO with additional CNN-based ReID module for appearance matching, the ReID network imposes significant computational and memory burdens, especially on edge or CPU-only systems. This work explores the use of zero-training appearance descriptors extracted directly from YOLOv8's internal multi-scale feature maps (P3, P4, P5), used individually as lightweight alternatives to conventional ReID embeddings. Our method selects a feature scale based on object size and retrieves a fixed-length feature vector via a simple center-cell lookup. On the MOT20 train benchmark, our fastest configuration, (i.e. based on the P3 feature map) achieves more than 90% of the HOTA score of the standard DeepSORT with ReID, while reducing tracking time per frame from 0.279 s to 0.035 s on a CPU. These results demonstrate that YOLOv8's native features hold sufficient discriminative power for training-free, real-time object association, eliminating the need for costly ReID networks in resource-constrained deployments.

Many earlier efforts addressed the aircraft sequencing issue on the ground prior to departure. Different algorithms and methods were developed to sequence the departure aircraft depending on a variety of variables, including departure time, trajectory after takeoff, and performance. Even when this se-quence is established, we can easily identify a situation with a significant performance mismatch in a departure sequence for a variety of reasons, such as weather, work in progress, or non-routine events. This paper focuses on the performance impact on an aircraft departure sequence that follows the same Standard Instrument Departure SID after takeoff. It also presents various case study situations for the problem and suggests a solution.

E-learning environments are part of the self-learning ecosystem of which self-regulation is one of the main pillars. One of the key factors contributing to dropping out in an e-learning environment is the lack of self-regulatory skills. This skill is often absent in many learners, leading them to abandon their training. In this paper we propose a methodology, chatbot architectural design, allowing to help the learner to self-regulate in his/her learning by supporting him/her through a chatbot within the Moodle platform, which constitutes a metacognitive virtual assistant, to provide metacognitive messages. The aim is to improve the quality of learning in the e-learning environment.

Air traffic congestion issue in departure is currently considered to be a huge concern for air navigation service providers (ANSPs). To improve the air traffic flows, and ensure a fluent and minimum possible delay, ANSPs are using technologies and tools such as the departure manager DMAN. Using a variety of constraints and preferences, the DMAN program determines the target take-off times and the target startup approval times. The purpose of this paper is to enhance the efficiency of the DMAN program using a genetic algorithm.

Real-time vehicle detection and tracking is a major research problem in traffic surveillance systems as it aims to improve road safety, optimize traffic flow, and increase transportation efficiency. Traffic surveillance systems typically collect and analyze live traffic footages from multiple cameras, to provide valuable and rich information compared to other sensor-based approaches. However, real-time analysis of traffic footage can be challenging due to the complexity of detecting and tracking objects in the fields. In this paper, we propose an efficient and robust framework for vehicle detection and tracking that is suitable for low-resource and edge devices without compromising precision and accuracy. Our approach combines the Background Subtraction method (BS), an efficient moving object detector, with a deep CNN-based detector in a hybrid detection approach.

Handwriting is one of the basic skills and an important means of communicating with others and expressing oneself, as it is important in all aspects of life. Acquiring handwriting is also a complex skill that takes years of training to be mastered. Advances in technology have allowed robots to accomplish a diversity of human activities, provoking interest from educators, researchers, and practitioners in discovering the potential advantages of employing robots as an intervention for children with dysgraphia who have difficulty automating their handwriting. Dysgraphia can be treated very well thanks to a writing rehabilitation adapted to the needs and the importance of the child’s problem. The purpose of this article is to explore the potential benefits of integrating a social humanoid robot in interventions for the treatment of dysgraphia in children. In this context, our research focuses on introducing a social humanoid robot assistant in an educative context to assist dysgraphic children to acquire handwriting. This work is done by applying a new approach based on a deep learning classification algorithm using convolutional neural networks (CNN) to determine the presence of dysgraphia from the handwriting of elementary school children. In this study, we realized a humanoid robot that will assist a teacher by equipping the NAO robot: (1) to moderate a session of learning to handwrite an alphabet character, (2) to detect whether a child is dysgraphic or not using a convolutional neural network, and (3) to assist and monitor dysgraphic children by performing tasks and suggesting rehabilitation sessions. The results indicate that it can distinguish dysgraphic children from non-dysgraphic children with an accuracy of 75%, a specificity of 75%, and a precision of 60%. The results reveal that the robot was able to classify learners so that the human tutor could assign the students to the appropriate rehabilitation program based on their specific needs.

In recent years, the development of recommendation systems has aroused growing interest in many areas, especially in e-learning. However, the lack of support and personalization in this context leads the learners to lose their motivation to continue in the learning process. To overcome this problem, we focus on adapting learning resources to the needs of learners using a recommender system. Thus, most of the existing studies in this area don’t take into account the differences in the characteristics of learners. This problem can be mitigated by incorporating additional learner information into the recommendation process. Additionally, many recommender techniques experience cold start and rating sparsity issues. This article presents our proposed recommendation system in order to provide learners with appropriate learning resources to follow the learning process and maintain their motivation. We propose hybrid recommender system that combines recommendation techniques to solve the problem of retrieving relevant learning resources for learners and that can alleviate both the cold-start and data sparsity problems. Using this hybridization between different techniques is useful in the personalization of the learner’s profile. We consider different learner characteristics such as their preferences, their goal, and their level of knowledge, hence resulting in the generation of more accurate recommendations.

Since the beginning of the COVID-19 pandemic, many countries have adopted online education as an alternative to face-to-face courses. This has increased awareness of the importance of analyzing learning data left by students to improve and evaluate the learning process. This article presents a new tool, named TaBAT, created to work with different LMSs in the form of dashboards accessible online and allowing teachers to monitor the progress of their learners and at the same time and allow teachers to track the progress of their learners, while allowing learners to develop self-regulation skills and visualize their learning process. The results of a study conducted show that TaBAT helped learners to increase their progress and spend more time in the online course.

Since learning in higher education is increasingly taking place online, the multiplication of web-based educational content, learning management systems (LMSs) and collaborative communication platforms have generated a large volume of data on learners and their learning activities. In recent years, interest has been growing in analyzing this data to support real-time decision making and improve the learning experience. This paper presents the results of a study conducted in higher education in Morocco, which evaluates a learning analysis dashboards (LADs) for both teachers and learners. The study shows that the dashboard, called TABAT, allowed a synthetic visualization of learning progress in courses and led to improved student engagement and success rates.

Nowadays, the high automation of ships has resulted in an ever -increasing need for real time data exchange to monitor ships, cargo, and ensure safety and security on board. This evolution in ship technology and management, must be supported by reliable, and cost-effective communication carriers. The satellite communication services can deliver vital real time capability to keep the ship continuously connected to the maritime players while it is trading worldwide. At high seas, the only communication service that can meet the ship need in term of data exchange is the mobile satellite communication. The reliability of these communication services will contribute significantly in the success of the future implementation of the autonomous ship in the maritime industry. This paper consists of the presentation of an overview of the current satellite services and its comparison in term of reliability, cost-effectiveness, and its capability to meet the autonomous ship communication need for its safe operation. This study results in a proposal of the suitable satellite communication services that can support the implementation of autonomous ship.

E-learning constitutes an alternative to face to face traditional educational system. Unfortunately, many learners stop their online learning at its beginning. This paper aims system at exploring and identifying the critical success factors (CSFs) to predict learners' satisfaction, continuance and success in on line platform learning. We also proposed a hybrid model that classify success factors of online learning in four dimensions include the learner characteristics, the instructor characteristics, the system specificities and the social influence

In the next decades, hundreds of autonomous ships (AS) and Conventional ships (CS) will cross the Detroit of Gibraltar. For their autonomous operation, and remote control, these ships need to communicate with each other, with the vessel traffic system (VTS), with the port state control (PSC) and with the shore control centre (SCC). For a sustainable maritime industry, the biggest challenge will be the implementation of reliable and cost effective communication carrier to assure a continuous ships connectivity. Our contribution consists of quantifying and classifying the data to be transferred, the identification of ship mobility environment challenges, and the proposal of mobile WiMAX as a reliable, secure and cost effective maritime communication carrier. This communication carrier must be suitable for the dense maritime traffic, in the Detroit of Gibraltar, where conventional ships and autonomous ships will co-exist.