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Type d'Item :Item, Valeur predictive de l’étude de la vitesse de l’onde de pouls dans la détèction de la maladie rénale diabétique(Université Sétif 1 - Ferhat ABBAS , Faculté de Médecine, 2026) KHELOUFI , Hichem; SAADA , Ferielle DirecteurDiabetic kidney disease (DKD) is a major microangiopathic complication of type 2 diabetes (T2D) and is associated with substantial cardio-renal morbidity and mortality. The identification of non-invasive vascular biomarkers for the early detection of renal involvement remains a key clinical challenge. Pulse wave velocity (PWV), a surrogate of arterial stiffness, may represent a relevant marker of cardio-renal dysfunction. The aim of this study was to assess the predictive value of PWV for the detection of early DKD in patients with T2D. Methods: We conducted a retrospective, cross-sectional, multicenter study including 210 T2D patients divided into two groups: without DKD and with early DKD. Clinical, biological, metabolic, renal and cardiovascular parameters were collected, including carotid-femoral PWV measured by applanation tonometry. Statistical analyses included between-group comparisons, correlation analyses, multivariable logistic regression and ROC curve analysis. Results: Early DKD was identified in 51.4% of participants. Arterial stiffness was increased, with a mean PWV of 13.4 ± 3.61 m/s, and severe stiffness (>12 m/s) observed in 59% of subjects. Early DKD was significantly associated with diabetes duration, hypertension, history, sedentary lifestyle, inflammation, metabolic parameters and diabetic retinopathy. PWV was significantly higher in the DKD group and correlated with renal parameters (ACR, eGFR) as well as cardiometabolic factors. ROC curve analysis demonstrated the discriminative performance of PWV for DKD screening. Conclusion: PWV appears to be a non-invasive vascular biomarker associated with early DKD in patients with T2D. Its integration with classical renal markers such as ACR and eGFR could enhance early screening and cardio-renal risk stratification.Type d'Item :Item, Contribution à l’étude de l’effet de l’interaction sol-structure sur le comportement des éoliennes sous chargement sismique(Université Sétif 1 - Ferhat ABBAS , Faculté de Technologie, 2026) MELLAS , Nesrine; HEBBACHE , Kamel Encadrant; MABROUKI , Abdelhak Co-EncadrantIn a context of energy transition where renewable energies are essential for preserving the environment ,This study evaluates the seismic performance of offshore and onshore wind turbine support structures by examining two complementary approaches. The first part analyses the effects of pile diameter, soil type and soil-structure interaction (ISS) using SAP2000 finite element software. The structural and geotechnical models are validated by vibrating table tests and analytical solutions, respectively. The results demonstrate that larger pile diameters improve seismic energy absorption, while soil characteristics (loose or dense sands) significantly influence energy dissipation and stability. The second part proposes an innovative approach using lead rubber bearings (LRBs) at the base of wind turbines. An 8 MW wind turbine model on stiff clay soil is developed with SSI integration via the Winkler model. The results indicate that LRB isolators significantly reduce accelerations and stresses at the base, improve seismic performance and reduce fatigue damage, thereby extending the service life of structures. The study also highlights the importance of optimizing the damping ratio for different seismic conditions. This research highlights the crucial importance of accurate SSI modelling and presents seismic isolation as a promising solution for enhancing the resilience of wind turbines in high seismic risk areas, thereby contributing to the sustainable development of renewable energy.Type d'Item :Item, Fabrication du Camembert avec différents laits : évaluation comparative des caractéristiques selon l'origine laitière(Sétif 1 Université Ferhat Abbas. Faculté des Sciences de la Nature et de la Vie, 2025) BERBACHI , Roumaissa; KHALOUA , Bouthaina Alaa Alrahman; MOUFFOK , Abdenacer EncadrentThis comparative study evaluated the influence of dairy origin (cow, goat and sheep) on the technological, microbiological, and sensory characteristics of Camembert-type cheese. Three batches were produced from pasteurized milks according to a standardized protocol, including inoculation with specific starter cultures and a six-week ripening period. The results reveal that sheep's milk, characterized by a higher dry matter and protein content, presented the most favorable cheese yield (21.5%), significantly surpassing goat's milk (15.73%) and cow's milk (12.95%). The pH values measured at the end of ripening were within acceptable ranges for all batches. From a microbiological perspective, only the sheep's milk cheese showed contamination by enterobacteria, potentially linked to a higher initial microbial load. Cheeses made from cow's and goat's milk fully complied with current sanitary standards. Sensory evaluation demonstrated a marked preference for the sheep's milk Camembert, praised for its creamy and melting texture as well as its balanced aromatic profile. This work confirms the determining impact of the dairy source on Camembert quality. It provides essential data for optimizing manufacturing processes and diversifying cheese offerings, while highlighting the importance of microbiological control for sheep's milkType d'Item :Item, Impact de l'origine laitière sur les propriétés sensorielles et technologiques de fromage Feta : vache, chèvre et brebis(Sétif 1 Université Ferhat Abbas. Faculté des Sciences de la Nature et de la Vie, 2025) LALIOUI , Ikram; LALMI , Meriem; MOUFFOK , Abdenacer EncadrentThis study compared the microbiological, physicochemical, and sensory qualities of feta cheeses made from three different types of milk (cow, goat, sheep) to evaluate the impact of raw material on the final product. After collection and analysis, the milks were pasteurized and processed following the same protocol. The analyses revealed significant differences in the initial composition of the milks (fat, protein, and lactose content). Sheep milk stood out for its nutritional richness and higher cheese yield, while cow milk produced a softer cheese. Microbiologically, all cheeses complied with health standards (Absence of Enterobacteria, staphylococci and salmonellae). Sensory evaluation ranked the sheep milk Feta highest for its creamy texture and rich aroma. This study confirms the crucial role of milk type in feta quality and provides insights to improve artisanal productionType d'Item :Item, Structured Emotion Analysis from Arabic Text(Setif 1 University - Ferhat ABBAS , Faculty of Sciences, 2026) SENATOR , Ferial; Abdelaziz , LAKHFIF Supervisor; Chahrazed , MEDIANI Co-SupervisorIn the field of Natural Language Processing (NLP), emotion analysis aims to map textual content with a predefined set of human emotions, typically including joy, anger, fear, surprise, disgust, and sadness. Current state-of-the-art research mainly focuses on identifying emotions in text using categories inspired by psychological theories, such as Ekman’s (1992) basic emotions. Despite the importance of emotion detection, most analyses are shallow and insufficient for tasks that require a deeper understanding of emotional meaning in context. Such applications necessitate addressing key questions, including identifying the cause that triggered the emotion (Cause), determining who experienced it (Experiencer), and more generally addressing structural questions such as who did what (Cue), to whom (Target), why (Cause), and how (Manner). This doctoral thesis aims to propose original and effective solutions to address the lack of resources and models dedicated to the structural analysis of emotions in Arabic text. To achieve this, we introduce a novel approach for analyzing the argument structure of emotions in Arabic, leveraging recent advances in Transformer-based architectures and, in particular, the capabilities of large language models (LLMs) for Arabic. The main contributions of this thesis are multifold. The first contribution consists of the construction and annotation of the first Arabic corpus dedicated to structured emotion analysis, named ‘AraERL’. The thesis also provides an in-depth examination of the impact of each semantic argument on the performance of emotion identification. In addition, it explores the use of ChatGPT for annotating Arabic texts with seman- tic roles and emotions through an interlingual annotation projection approach. The work further evaluates ChatGPT’s ability to accurately translate English semantic and emotional annotation into Arabic. Finally, it offers a comprehensive comparison of the performance of open large language models for these tasks.