L'écriture est une compétence fondamentale au cours de la scolarité primaire, permettant non seulement de communiquer des idées, mais aussi de développer la créativité des élèves. Cet article examine l’importance de l’enseignement de l’écriture aux élèves du primaire dans un contexte plurilingue. Nous proposerons des outils et des méthodes qui favorisent une écriture authentique et confiante.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

The global wind energy industry achieved a significant milestone by reaching a total capacity of one terawatt (TW) by the end of 2023, underscoring the increasing importance of wind energy as a sustainable energy source (Global Wind Energy Outlook, 2022). This study focuses on the simulation and dynamic analysis of an H-Darrieus wind turbine rotor using 3D Finite Element Analysis (FEA). Key structural parameters, including natural frequencies, associated vibration modes, and mass participation rates, were determined to optimize the rotor performance. A novel blade design is proposed in this work, offering a lighter and more robust alternative to traditional rotor blades manufactured from composites, like fiberglass-polyester, fiberglass-epoxy, or combinations with wood and carbon. The lighter design enhances the startup performance at low wind speeds, while the improved strength and fixing mechanisms ensure resilience against the increasingly severe sandstorms reported in recent years. The vibration dynamics of the rotor under critical wind loads were analyzed using the SolidWorks Simulation software, yielding highly satisfactory results. The stability and reliability of the rotor were validated, as the dynamic performance indices, and the quality criteria meet the requirements for optimal operation.

Let (X,d,μ) be a space of homogeneous type and L be a nonnegative self-adjoint operator on L2(X) whose heat kernels satisfy Gaussian upper bounds. In this article, we introduce the weighted variable Besov space associated with the operator L and demonstrate that Peetre maximal functions can be used to characterize this space. Furthermore, we provide a detailed study of its atomic decompositions.

The targeted resorption of socio-economic deficits at the local (municipal) level requires the use of a cartography of development in the multidimensional sense of the term, combined with that of the causes structural factors of its possible delay. This article provides for this purpose a municipal cartography of the development of M’sila province, and its sources assimilated to education, standard of living, economic activity, housing and social services. To ensure a certain objectivity in our research, we have chosen an automatic technique following the essential steps (choice of variables, normalization, weighting and aggregation) in the hierarchical classification of municipalities. This mapping of multidimensional local development clearly shows the development deficits at the municipal level, due to the delays accumulated for years, despite the generalization of basic education and attempt the local authorities to improve the economic and social conditions of the population. The results obtained show that on the whole, the urban municipalities have a local development index higher than 0.7 and rank in the first places, this index goes from a minimum (0.310) observed in the rural municipalities Tamsa, Zerzour, Sidi M’hammed and the ones located south of the province where the climate is dry and hot, the maximum index (0.860) recorded in two urban municipalities at namely M’sila and Bousaada, The high variability of development deficits shows that any development strategy would benefit from being rethought in order to provide more effective to the different situations experienced by rural municipalities in particular.

In an increasingly interconnected world, the intricacies of cross-cultural communication and language acquisition are of a crucial importance. Contrastive Rhetoric theory offers valuable insights into how rhetorical patterns are transferred from L1 to L2. While previous studies acknowledge the influence of culture on rhetoric, they often overlook the reasoning mechanisms shaping rhetorical choices. The present research addresses this gap within the context of Arabic discourse, focusing on Algerian academic corpus, by shifting the focus from surface cultural manifestations to the fundamental reasoning embedded within Arab culture. This study contributes to a deeper understanding of how major Arabic rhetorical patterns, including paraphrase, lexical couplets, and parallelism, are transferred to English compositions by Algerian students.

Recent climatic shifts and the growth of agricultural land have escalated the incidence of wheat field fires, presenting severe risks to both food security and local economies. This study aims to develop advanced predictive models to effectively forecast significant wheat fires in Barika, Algeria. We utilized a comprehensive dataset spanning from 2015 to 2023, which includes information on fire incidents and meteorological factors like temperature, humidity, precipitation, and wind speed. A sophisticated ensemble machine learning model was crafted, combining Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Random Forest (RF) in a stacked configuration to predict wheat fire events. Our analysis indicates that the ensemble model significantly outperforms traditional single-model approaches in terms of both accuracy and reliability. Employing these cutting-edge predictive techniques significantly bolsters firefighting measures, enhances resource management, and reduces the adverse effects of fires in agricultural zones. The employment of ensemble learning highlights its utility as a formidable tool in environmental management and crisis response. With more precise forecasts, this model facilitates improved emergency preparedness and strategic intervention plans, aiming to safeguard essential agricultural assets and support rural communities against the backdrop of mounting environmental pressures.

The article focuses on the generalization of Fuglede's theorem and the solvability of operator equations. Topics include extending Fuglede's theorem to non-normal operators, deriving criteria for solving operator equations such as AX − XB = C, and using inner inverses to establish necessary and sufficient conditions for operator equation solutions.