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اثر الترقية المهنية على الاداء التنظيمي لدى الاساتذة في التعليم الثانوي:دراسة ميدانية لثانويات من مدينة عين تموشنت
(UNIVERSITY OF AIN TEMOUCHENT, 2026) امال،مولسهول; فاطمة،حاج عمر
This study investigates the impact of professional promotion on organizational performance among secondary school teachers in Ain Temouchent, Algeria, with a focus on mediating variables such as organizational justice and job satisfaction. Using a descriptive-analytical and quantitative approach, a questionnaire was administered to a purposive sample of 150 teachers, and data were analyzed via SPSS. The results revealed no statistically significant direct effect of professional promotion on organizational performance (R=0.141, Sig=0.086). However, organizational justice showed a positive and significant relationship (R=0.297, Sig=0.000), as did job satisfaction (R=0.279, Sig=0.001). The study concludes that the effectiveness of professional promotion lies not in the promotion itself, but in the degree of fairness and job satisfaction it generates. It recommends enhancing transparency, linking promotions to competence and actual performance, and improving working conditions to strengthen organizational performance in educational institutions.
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Détection d’activité spectrale à l’aide de RTL-SDR et de l’IA
(2026) Belhocine Chaimaa; Benbachir Nada Chaimaa; YAGOUB Reda
Modern information processing systems have undergone significant evolution driven by digital transformation and the integration of intelligent technologies into data analysis. Physical signals are now converted into digital data to be processed using advanced mathematical algorithms, enabling the extraction of relevant information. Artificial intelligence plays a central role in improving classification, analysis, and automated decision-making performance. These technologies are widely applied across diverse fields such as telecommunications, surveillance, and complex data analysis, offering a deeper understanding of dynamic environments and enhanced system performance optimization. In this work, we developed an experimental approach based on the use of the RTL-SDR receiver for the acquisition of real radio frequency signals, followed by preprocessing and modeling using supervised learning algorithms including Random Forest, K-Nearest Neighbors (KNN), and XGBoost. The experimental results demonstrated a robust spectral activity detection capability, validated through confusion matrices and a comparative performance analysis of the models. Furthermore, a web application was designed to make spectral activity detection accessible and interactive. It incorporates a real-time radio signal capture interface, a CSV file import and analysis feature, and a dashboard for visualizing analysis results. This platform represents a practical contribution, providing a flexible tool for spectral analysis and detection in complex radio environments.
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Existence de solutions d’un problème aux limites avec impulsions
(2026) BENAISSA RAHAL Hanene; BELLATAR ZOKHA
In this thesis, we analyzed boundary value problems related to second-order impulsive differential equations and impulsive fractional differential equations. By applying the Banach contraction principle as well as the two fixed point theorems of Leray-Schauder, we established existence and uniqueness results for solutions under suitable hypotheses. This work confirms the importance of fixed point methods in the study of impulsive differential systems.
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Évaluation de l'activité antimicrobienne synergique (in vitro) des huiles essentielles extraites de trois plantes médicinales (Rosmarinus officinalis L, salvia officinalis et Laurus nobilis) issues de la région d'Aïn Témouchent
(2026) MESABIH Mouna; BENYOUCEF Fatima; RAHMANI Khaled
This work aims to study the antimicrobial activity of individual essential oils as well as cross- mixtures of three aromatic medicinal plants: rosemary (Rosmarinus officinalis L.), common sage (Salvia officinalis), and bay laurel (Laurus nobilis), whose essential oils were extracted by hydrodistillation. The antimicrobial activity revealed variable results between individual essential oils and cross- mixtures, with marked differences in efficacy depending on the type of target microbe and the nature of the composition used. Bay laurel oil proved to be the most effective against Gram-negative bacteria, particularly against Escherichia coli, with good activity against Gram-positive bacteria. Rosemary oil showed overall moderate activity with relative distinction against yeasts. In contrast, sage oil exhibited generally limited efficacy, with moderate activity only against Staphylococcus aureus. Binary and ternary compositions showed complex interactions ranging from strong synergy to marked antagonism. The binary mixture (rosemary + bay laurel) achieved the strongest synergy against most of the studied microbes, significantly surpassing the individual activity of each oil. The binary mixture (rosemary + sage) also recorded a strong and unexpected synergy against Klebsiella pneumoniae, while showing pronounced antagonism against other microbes. As for the ternary mixture, it yielded varied positive results with moderate to strong synergy against the majority of microbes. These results show that bay laurel oil is distinguished by high efficacy against Gram-negative bacteria, while rosemary oil stands out for its activity against yeasts. The study also confirms that cross-mixtures, particularly the binary mixture (rosemary + bay laurel), can generate strong synergy exceeding the individual activity of the oils, thus opening promising perspectives for the development of natural antimicrobial formulations. However, the appearance of antagonistic interactions in certain compositions calls for caution in the selection of mixtures and invites a deeper study of the chemical and biochemical mechanisms responsible for these interactions.
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