Institutional repository of university M'Hamed Bougara Boumerdes
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A novel anti-candidiasis cream formulation based on Melissa officinalis and Lavandula stoechas essential oils synergism
No full textCandida albicans is the yeast strain that causes candidiasis, which can vary from minor skin and soft tissue infections to potentially fatal infections like candidemia, which can get worse due to drug resistance. Natural substances could be used as antifungal agents’ alternatives, especially against C. albicans. Thereby, the main objective of this research was to define the chemical content and evaluate the bioactivity of Melissa officinalis and Lavandula stoechas essential oils (EOs). Additionally, the synergistic effect of both oils was carried out against C. albicans, in order to formulate EOs combination-based creams. Their chemical compositions were analyzed using the combination of gas chromatography-flame ionization detector (GC-FID) and GC-mass spectrometry (GC-MS). The interaction with both EOs was also evaluated and analyzed by GC-FID and GC-MS, and the development of anti-candidiasis formulations was tested in vitro. Our results showed that the main compounds of M. officinalis EO (MOEO) were geranial (35.7%) and neral (24.1%), whereas, 1.8-cineole (61.9%) was dominant in Lavandula stoechas essential oil (LSEO) and the main compounds of the combination between both EOs were 1,8-cineole and geranial. Moreover, the MOEO exhibited substantial antifungal action against all tested microorganisms with MIC values ranging from 0.125 to 0.0019. This EO also showed synergistic effect when combined with that of L. stoechas. These findings, suggest the good bioactive qualities of the topical creams developed against C. albicans, revealing their potential to be possibly applied as an antifungal agent in industrial pharmaceutics
Oven temperature control using siemens S7-1200 PLC
No full text66 pThis report presents a comprehensive study on the design and implementation of a temperature control system for an oven using the Siemens S7-1200 Programmable Logic Controller (PLC) and a Human Machine Interface (HMI). It focuses on the PLC's programming environment, hardware configuration, and integration with the oven's sensors and actuators.
The Hardware setup comprises the S7-1200 PLC, HMI, RTD sensor, measurement and control interfacing circuits, and heaters, which collectively enable data acquisition, signal processing, and actuation within the system. After acquiring oven data, identification of the system was performed using two different approaches, which are the Smith and Nishikawa methods.
The PID control methodology employed in this study as extensive experiments and evaluations are conducted to analyze the performance of the developed control system.
The system's response to various setpoint changes, disturbances, and load variations is tested experimentally to assess its stability, robustness, and accuracy in temperature regulation.
The results obtained demonstrate the effectiveness of the proposed control system in maintaining the desired oven temperature within a tight range, even in the presence of external disturbances.
The findings and insights obtained from this research provide valuable guidelines for designing and implementing control systems in similar industrial ovens (such as in: Pharmaceutical industry, Oil and Gas, Dairy industry … etc.)
Analyse du stress de tuyauterie de puits «HNIA07»
No full text60 p. : ill. ; 30 cmCe Mémoire présente une étude approfondie sur l'analyse de stress des systèmes de tuyauterie, mettant en avant son importance dans divers secteurs industriels tels que le pétrole et le gaz, la pétrochimie et la production d'énergie. Divisé en plusieurs sections, ce travail commence par une présentation des entreprises impliquées, notamment Sonatrach et GCB, suivi d'un examen des fondements théoriques de l'analyse de stress, en se concentrant sur la théorie de la résistance des matériaux et la théorie des poutres
LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
No full textAnomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques’ applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor’s variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers
Analyse de la fiabilité structurelle et conception de réservoirs composites de stockage d'hydrogène
No full text98 p. : ill. ; 30 cmL’object if de cette thèse est l’analyse de la fiabilité et la conception de réservoirs
composites de stockage d’hydrogène à haute pression. Pour ce faire, deux approches ont été
adoptées : dans le premier cas, la méthode de Monte Carlo est utilisée pour analyser les
distributions des marges de sécurité et illustrer la variabilité de la réponse mécanique de la
structure en fonction de plusieurs paramètres de conception. Dans le deuxième cas, la méthode
de Subset Simulation est employée pour développer un cadre de calcul permettant la
quantification des très petites probabilités de défaillance des réservoirs composites de stockage
d’hydrogène à haute pression. L’étude a montré la faisabilité et la précision de ces deux
méthodes dans la prédict ion de la pression d'éclatement. En outre, l’étude a révélé que les
incertitudes associées à la pression interne et à l'épaisseur des couches circonférentielles
affectent de manière significative la fiabilité de la structure et peuvent conduire à une réduction
de la marge de sécurité et à la défaillance du réservoi
Mechanical characterization and impact resistance of a novel hybrid composite based on salvadora persica roots and glass fibers
No full textThe observation of fibers in salvadora persica roots inspired us to consider the idea of using them as reinforcement to create an innovative composite. The current work focuses on the volumetric mass density, extraction, molding, and mechanical testing of composites and hybrid composites made from salvadora persica roots and glass fibers reinforced with two types of polyester matrix, chosen due their characteristics suitable for use in different orientations. Various extraction and combination methods have been used to identify an optimal approach for obtaining fibers from salvadora persica roots, considering its chemical composition (hemicellulose, pectin, and lignin). In this investigation, the hand lay-up method was used to mold specimens with different geometries. The composite and hybrid composite were combined with a polyester matrix and subjected to various mechanical tests namely; tensile, impact resistance, and water absorption. The results indicate that reinforcing polyester resins with SP fibers, whether long or short, enhances the overall mechanical properties of the composite. Additionally, improved adhesion between salvadora persica roots fibers and resin was observed
Multivariable control of «Shell Heavy Oil» fractionator column
No full text51 p. : ill. ; 30 cmA Shell Heavy Oil Fractionator (SHOF) is a type of distillation column widely used in oil refineries. It is designed for the separation of heavy crude oil into various components based on differences in their boiling points. This project aims to control a Shell Heavy Oil Fractionator (SHOF), which is characterized by high nonlinear dynamic behavior and strong loop interactions. The Relative Gain Array (RGA) was used to determine the best loop pairing configuration for the system, and the Dynamic Relative Magnitude Array (DRMA) was used to determine the level of interaction. A multivariable control system is designed to control the fractionator using a Proportional-Integral (PI) controller, which is tuned using the Biggest Log Modulus Tuning (BLT) method
Investigating wave propagation in sigmoid-FGM imperfect plates with accurate Quasi-3D HSDTs
No full textIn this research paper, and for the first time, wave propagations in sigmoidal imperfect functionally graded material plates are investigated using a simplified quasi-three-dimensionally higher shear deformation theory (Quasi-3D HSDTs). By employing an indeterminate integral for the transverse displacement in the shear components, the number of unknowns and governing equations in the current theory is reduced, thereby simplifying its application. Consequently, the present theories exhibit five fewer unknown variables compared to other Quasi-3D theories documented in the literature, eliminating the need for any correction coefficients as seen in the first shear deformation theory. The material properties of the functionally graded plates smoothly vary across the cross-section according to a sigmoid power law. The plates are considered imperfect, indicating a pore distribution throughout their thickness. The distribution of porosities is categorized into two types: even or uneven, with linear (L)-Type, exponential (E)-Type, logarithmic (Log)-Type, and Sinus (S)-Type distributions. The current quasi-3D shear deformation theories are applied to formulate governing equations for determining wave frequencies, and phase velocities are derived using Hamilton's principle. Dispersion relations are assumed as an analytical solution, and they are applied to obtain wave frequencies and phase velocities. A comprehensive parametric study is conducted to elucidate the influences of wavenumber, volume fraction, thickness ratio, and types of porosity distributions on wave propagation and phase velocities of the S-FGM plate. The findings of this investigation hold potential utility for studying and designing techniques for ultrasonic inspection and structural health monitoring
التدقيق الجبائي ودوره في مكافحة التهرب الضريبي :دراسة حالة مديرية الضرائب لولاية بومرداس
No full text134p.:ill.;30cm
التنبؤ بأسعار الأسهم في سوق الأوراق المالية ودوره في اتخاذ القرارات الإستثمارية
No full text234 ص. ، 30 سمتهدف هذه الدراسة إلى إعطاء نبذة عن أسواق الأوراق المالية وكذا نمذجة السلسلة الأسبوعية لأسعار إغلاق أسهم شركة سامسونج باستخدام نماذج ARCH واختبار مدى قدرة النموذج المختار على التنبؤ بأسعار الإغلاق في المدى القصير، ولغرض تحقيق أهداف الدراسة تم استخدام عينة مكونة من بيانات أسبوعية لأسعار الإغلاق لأسهم شركة سامسونج المدرجة في بورصة فراكفورت الألمانية بواقع 492 مشاهدة بصفة أسبوعية مأخوذة من الفترة الممتدة من 02/01/2012 إلى 31/05/2021 مقيمة بعملة اليورو، كما اعتمدنا في الدراسة على مجموعة من الأدوات الإحصائية من بينها البرنامج الإحصائي Eviewe.12.
توصلت الدراسة إلى أن أسعار أسهم الإغلاق الأسبوعية لشركة سامسونج لا تتبع فرضية السير العشوائي وبأنها قادرة على التنبؤ على المدى القصير مما يدل على كفاءة بورصة فرانكفورت على المستوى الضعيف وكان أحسن نموذج لنمذجة سلسلة الأسعار هو نموذج ARCH(4)- (2,3)ARM