Evaluation of Frasnian Shale reservoir, case studywell DAK-1, Ahnet Basin, southern Algeria

The evaluation of unconventional reservoir in term of future exploration plan where the geochemical data are not unavailable making us different results from logging and Gas Data However this paper aim to define Potential zone throught the estimation of total organic carbon(TOC) using Δ log R Method and thermal maturity by mean of gas ration technique combined with gamma-ray data of Frasnian shale formation encountered in DAK-1 well drilled in Ahnet Basin from 1552m to 1728m. The results suggest that the frasnian shale have fair to good potential genration with TOC ranging from 2% to 4%, with mature organic matter who producing wet gas,The potential zone positioned in the lower frasnian over a thikness of 10m.Keywords: Unconventional Reservoir; Evaluation;Total organic carbon (TOC), Thermal maturity, Gas Ratio; DAK-1 well; Ahnet Basin

An overview of hospital acquired infections and the role of the microbiology laboratory

Every year, many lives are lost because of the spread of infections in hospitals. These nosocomial infections, also called hospital acquired infections (HAI) are infections that patients acquire during the course of receiving healthcare treatment for other conditions. HAIs are a cause of significant morbidity and mortality in patients receiving healthcare, and the costs direct and indirect of these infections deplete the already limited financial resources allocated to healthcare delivery

Aerobic bacteriology of chronic suppurative otitis media: a hospital based study

Background: Chronic suppurative otitis media (CSOM) remains one of the most common childhood chronic infectious diseases worldwide, affecting diverse racial and cultural groups both in developing and industrialized countries. It involves considerable morbidity and can cause extra- and intra-cranial complications. The aim of this study was to determine the microbial diversity and the antibiogram of aerobic bacterial isolates among patients suffering from CSOM who attended the ENT Department of SMHS hospital, a tertiary care centre located in the heart of the Kashmir valley.Methods: A total of 154 patients clinically diagnosed with CSOM were enrolled in the study and the samples were obtained from each patient using sterile cotton swabs and cultured for microbial flora. Drug susceptibility testing for aerobic isolates was conducted using Kirby-Bauer disc diffusion method.Results: Out of total 154 ear swabs processed, microbial growth was seen in 138 (89.61%) while 16 (10.38%) samples showed no growth. In 102 (66.23%) samples mono-microbial growth was seen whereas 26 (16.88%) samples showed poly-microbial growth. The most frequent organism isolated was Pseudomonas aeroginsa followed by Staphylococcus aureus and Proteus sp. The most effective antibiotic against Pseudomonas aeroginsa was amikacin followed by imipenem and piperacillin plus tazobactam, while as Staphylococcus aureus showed maximum sensitivity to vancomycin.Conclusion: Otitis media linked with high levels of multiple antibiotic resistant bacteria is a major health concern in all age groups of the study population. An appropriate knowledge of the etiology and antibacterial susceptibility of microorganisms would contribute to a rational antibiotic use and the success of treatment for chronic supportive otitis media.

Efficient method for transformer models implementation in distribution load flow matrix

Introduction. Most distribution networks are unbalanced and therefore require a specific solution for load flow. There are many works on the subject in the literature, but they mainly focus on simple network configurations. Among the methods dedicated to this problem, one can refer to the load flow method based on the bus injection to branch current and branch current to bus voltage matrices. Problem. Although this method is regarded as simple and complete, its drawback is the difficulty in supporting the transformer model as well as its winding connection types. Nevertheless, the method requires the system per unit to derive the load flow solution. Goal. In the present paper, our concern is the implementation of distribution transformers in the modeling and calculation of load flow in unbalanced networks. Methodology. Unlike previous method, distribution transformer model is introduced in the topology matrices without simplifying assumptions. Particularly, topology matrices were modified to take into account all winding types of both primary and secondary sides of transformer that conserve the equivalent scheme of an ideal transformer in series with an impedance. In addition, the adopted transformer models overcome the singularity problem that can be encountered when switching from the primary to the secondary side of transformer and inversely. Practical value. The proposed approach was applied to various distribution networks such as IEEE 4-nodes, IEEE 13-nodes and IEEE 37-nodes. The obtained results validate the method and show its effectiveness.Вступ. Більшість розподільчих мереж незбалансовані і тому потребують спеціального рішення для потоку навантаження. У літературі є багато робіт на цю тему, але переважно вони присвячені простим мережевим конфігураціям. Серед методів, присвячених цій проблемі, можна назвати метод потоку навантаження, заснований на введенні шини в матрицю струму відгалуження і відгалуження струму в матрицю напруги шини. Проблема. Хоча цей метод вважається простим та повним, його недоліком є складність підтримки моделі трансформатора, а також типів з’єднання його обмоток. Проте метод вимагає системи на одиницю для отримання рішення про потік навантаження. Мета. У цій статті нас цікавить застосування розподільних трансформаторів для моделювання та розрахунку потоку навантаження у незбалансованих мережах. Методологія. На відміну від попереднього методу, модель розподільного трансформатора вводиться в матриці топології без спрощення припущень. Зокрема, матриці топології були змінені, щоб врахувати всі типи обмоток як первинної, так і вторинної сторін трансформатора, які зберігають еквівалентну схему послідовно ідеально включеного трансформатора з імпедансом. Крім того, прийняті моделі трансформаторів долають проблему сингулярності, з якою можна зіткнутися при перемиканні з первинної на вторинну обмотку трансформатора і навпаки. Практична цінність. Пропонований підхід був застосований до різних розподільних мереж, таких як IEEE з 4 вузлами, IEEE з 13 вузлами та IEEE з 37 вузлами. Отримані результати підтверджують метод та показують його ефективність

Use of multivariate analysis in mineral accumulation of rocket (Eruca sativa) accessions

The leafy vegetables contain high amount of mineral elements and health promoting compound. To solve nutritional problems in diet and reduced malnutrition among human population selection of specific cultivar among species would be help increasing elemental delivery in the human diet. While rocket plant observes several nutritional compounds no significant efforts have been made for genetic diversity for mineral composition of rocket plant accessions using multivariate analyses technique. The objective of this work was to evaluate variability for mineral accumulation of rocket accessions revealed by multivariate analysis to use further breeding program for achieve improving cultivar in targeting high nutrient concentration. A total twelve mineral element and twenty-three E. sativa accessions were investigated and considerable variation were observed in the most of concentration the principal component analysis explained that 77.67% of total variation accounted for four PC axis. Rocket accessions were classifies into three groups and present outcomes of experiments revealed that the first three principal components were highly valid to classify the examined accessions and separating mineral accumulations. Significant differences exhibited in mineral concentration among examined rocket accessions and the result could allow selecting those genotypes with higher elements

Pathologie des bétons fibrés après incendie

International audienceDe nombreux travaux ont permis d'analyser et comparer le comportement mécanique d'un béton classique et le comportement d'un béton similaire mais renforcé de fibres métalliques. Toutefois il existe peu d'études sur l'effet des fibres métalliques sur le comportement des bétons fibrés ayant subi un incendie. Le but de notre étude est essentiellement expérimental, il a pour objet d'analyser le comportement de ces 2 type de matériaux après incendie. Afin de mieux simuler les effets d'un incendie, un test à la flamme de gaz propane est mis au point. Les différents matériaux sont soumis à cet échauffement. L'évolution du gradient de température à l'intérieur des échantillons est suivie, et, en fin d'exposition, le comportement des mortiers est comparé à l'aide d'un essai de poinçonnement. L'évolution microstructurale du mortier est étudiée par diffraction X et observations au MEB. Les observations des fibres aux différentes températures permettent d'expliquer le comportement du mortier fibré. Les résultats d'essais effectués dans un four à moufle indiquent que :-Entre 400 et 500°C, la perte moyenne de la résistance est de l'ordre de 30 %. L'endommagement thermique du mortier renforcé ne cause pas de désordre gênant.-Au-delà de 500°C, la perte de résistance mécanique devient plus importante et crée une instabilité de la structure qui peut conduire à la ruine.-Entre 400 et 700°C, l'ajout fibres d'acier permet une meilleure résistance à la déformation et une rupture graduelle. L'énergie dissipée est très nettement augmentée. Les observations au MEB et les analyses à la microsonde montrent une oxydation des fibres d'acier aux hautes températures. Cette oxydation provoque une perte de résistance et de ductilité importante au-delà de 800°C, et peut limiter l'intérêt vis à vis du risque incendie

Un couplage Boussinesq - équation intégrale appliquée à l'interaction de la houle avec des obstacles bidimensionnels

6ppNational audienceOn présente ici une méthode de couplage entre les équations de Boussinesq "étendues", suivant la formulation potentielle de Jamois, et la méthode d'équation intégrale. La mise en œuvre est réalisée dans le cas d'un bassin à houle bidimensionnel. Des validations sont présentées

Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring.

The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenges due to computational intensity and vulnerability to adversarial samples. To address these issues, this paper introduces an optimization method that combines regularization and simulated micro-batching. This approach enables the training of DNNs in a robust, efficient, and resource-friendly manner for IoT security monitoring. Experimental results demonstrate that the proposed DNN model, including its performance in Federated Learning (FL) settings, exhibits improved attack detection and resistance to adversarial perturbations compared to benchmark baseline models and conventional Machine Learning (ML) methods typically employed in IoT security monitoring. Notably, the proposed method achieves significant reductions of 79.54% and 21.91% in memory and time usage, respectively, when compared to the benchmark baseline in simulated virtual worker environments. Moreover, in realistic testbed scenarios, the proposed method reduces memory footprint by 6.05% and execution time by 15.84%, while maintaining accuracy levels that are superior or comparable to state-of-the-art methods. These findings validate the feasibility and effectiveness of the proposed optimization method for enhancing the efficiency and robustness of DNN-based IoT security monitoring

Memory efficient federated deep learning for intrusion detection in IoT networks.

Deep Neural Networks (DNNs) methods are widely proposed for cyber security monitoring. However, training DNNs requires a lot of computational resources. This restricts direct deployment of DNNs to resource-constrained environments like the Internet of Things (IoT), especially in federated learning settings that train an algorithm across multiple decentralized edge devices. Therefore, this paper proposes a memory efficient method of training a Fully Connected Neural Network (FCNN) for IoT security monitoring in federated learning settings. The model‘s performance was evaluated against eleven realistic IoT benchmark datasets. Experimental results show that the proposed method can reduce memory requirement by up to 99.46 percentage points when compared to its benchmark counterpart, while maintaining the state-of-the-art accuracy and F1 score

An energy-efficient full-duplex MAC protocol for distributed wireless networks.

In this letter, we present an energy-efficient medium access control (MAC) protocol for distributed full-duplex (FD) wireless network, termed as energy-FDM. The key aspects of the energy-FDM include energy-efficiency, coexistence of distinct types of FD links, throughput improvement, and backward comparability with conventional half-duplex (HD) nodes. Performance evaluation demonstrates the effectiveness of proposed protocol as a viable solution for full-duplex wireless networks