Dans notre système éducatif, l’accès à la formation pédagogique pour l’enseignement primaire est accessible aux personnes dotées du certificat de l’école primaire. Or, le système éducatif les plus performants organisent un dis- positif de pré-sélection afin de former les meilleurs pour assurer un enseigne- ment de qualité. Le recours à la théorie des intelligences multiples, est salutaire pour le recrutement des futurs enseignants dans notre système éducatif. A partir de cette théorie, la formation initiale des enseignants et leurs recrutements sont spécifiquement bordés de trois axes à savoir : interpersonnel, linguistique et lo- gique/mathématique, pourquoi mettre de côté les autres formes d’intelligences ? Or l’enseignant du primaire doit bénéficier aujourd’hui d’une formation totale.

Assanvo

On January 2021, cases affected by coronavirus epidemic are constantly increasing, Libyan Ministry of Health provides the vaccine to the people those who are most at risk. The purpose of this study was to assess and verify the adverse effects of the first dose of the AstraZeneca COVID-19 vaccine. The study conducted at the Aljmail city, west region of Libya. The study was cross-sectional study during the period of August 31st and November 5th, 2021. The method involved 133 adult Libyan participants of both gender ageing more than 18 years old. The preliminary data were 54.0% who developed post-vaccination symptoms. The participant's aged 60 years and more with chronic diseases were more likely to have adverse effects after receiving the first dose of vaccine. In conclusion, AstraZeneca vaccine was good and effective but this study indicates a need for a large and long period study to confirm the safety of the vaccine use in the adult people.

Mediterranean journal of pharmacy and pharmaceutical sciences

This study aims to investigate the relationship between green marketing elements and customer purchase intention. To conceptualize green marketing, the researcher has researched the literature and identified the green marketing elements which include Green products, Green price, Green place, Green promotion, and Green Distribution. By using the snowball sampling technique, questionnaires from the respondents were collected as part of the study's survey methodology. According to the study's findings, there is a clear, substantial correlation between customer purchase intention and Green marketing.

Santhosh sandy

Freeze desalination (FD) is a method in which saline water is cooled below its freezing point and freshwater is separated from the brine in the form of ice crystals. FD is relatively insensitive to the salinity of the feed solution, making it suitable for desalination of high concentration brines such as the brine rejected from the seawater desalination plants. The design of the FD system and the thermochemical behavior of the brine upon freezing are critical factors in the energy performance of this method. To date, thermochemical properties of the concentrated seawater during cooling, such as the threshold of formation of ice and salt-hydrates and their corresponding cooling load of formation, are not well known. Likewise, the optimal configuration of the FD system to achieve the maximum energy efficiency has not been investigated. This work provides comprehensive data about the cooling load of freezing of concentrated brine rejected from seawater desalination plants along with the threshold of formation of ice and salt-hydrates backed-up by validation. Furthermore, the optimal configuration of the FD system is identified and the effects of the compressor isentropic efficiency and effectiveness of the system’s heat exchangers on the work consumption of the FD system were investigated.

Aly elhefny

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%).

Mehdhar s. a. m. al-gaashani

In the last decade, we have observed the usage of artificial intelligence algorithms and machine learning models in industry, education, healthcare, entertainment, and several other areas. In this paper, we focus on using machine learning algorithms in the loan approval process of financial institutions. First, we briefly review some prior research papers that dealt with loan approval predictions using machine learning models. Next, we analyze the loan approval prediction dataset we downloaded from Kaggle, which was used in this paper to compare several machine learning classification models. During this analysis, we observed that credit scores and loan terms are the attributes that probably most affect the result. Next, we divided the dataset into a training set (80%) and a test set (20%). We trained 27 various machine learning models in MATLAB. Three models were optimized with Bayesian optimization to find the best hyperparameters with minimum error. We used 5-fold cross-validation for the validations to prevent overfitting during the training. In the following step, we used the test set on trained models to measure the models’ accuracy on unseen data. The result showed that the best accuracy both on validation and test data, more than 98%, was reached with neural networks and ensemble classification models.

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