Postcholecystectomy Syndrome : After Two Years And Beyond 127 Successful Laparoscopic Cholecystectomies In The Hospital Universiti Sains Malaysia

Cholecystectomy is considered the gold standard treatment for symptomatic gallstones disease especially with the advent of laparoscopic approach, which is known to have well documented advantages. Nevertheless, 6% to 39% of the patients continue to have either persistent symptoms or emergence of new symptoms suggestive of gallstone disease after the laparoscopic cholecystectomy. All the previous studies were looking at the postcholecystectomy symptoms or syndrome one year after the surgery. Furthermore, there is no local data regarding the postcholecystectomy syndrome as yet

Étude du comportement de colonnes en béton à haute résistance armé d’armature longitudinale et latérale en matériaux composites de PRFV et PRFB soumises à des charges axiales et de flexion

Fiber-reinforced-polymer (FRP) bars are considered as alternative to steel bars to avoid corrosion problems and ensure that structures lave long service lives. Using high-strength concrete (HSC) with glass-fiber-reinforced polymer (GFRP) and basalt-fiber-reinforced polymer (BFRP) as internal reinforcement can allow designers to reduce member size and increase the structure’s life span. Given HSC’s brittle nature, its use-especially in columns-should be investigated to prevent undesirable brittle failure. This research takes charge of providing experimental database as well as comparing the results with normal strength concrete (NSC) columns with similar dimensions tested in the literature. The main objective of this research is to investigate the structural performance of HSC columns reinforced with FRP under eccentric loading. Full-scale columns were tested under monotonic loading with different levels of eccentricity. Test variables included eccentricity to depth ratio; reinforcement type (GFRP and Basalt FRP vs. steel); concrete strength (HSC vs NSC) and longitudinal reinforcement ratio. All specimens measured 400 × 400 mm square cross section and 2000 mm height. The experimental results are reported in terms of axial load-deflection behavior, mode of failure, maximum tensile and compressive strains developed in rebars and moment-curvature. The test results showed that HSC can be effectively integrated with both GFRP and BFRP reinforcement with HSC and the specimens reached their peak strength with no damage to GFRP or BFRP rebar on either side of the tested specimens. Using HSC allowed the columns to reach higher peak load and develop higher tensile strain in the rebars compared to columns made with NSC. Columns reinforced with GFRP or BFRP behaved in a similar manner at all the tested levels of eccentricity. The failure of FRPRC columns were classified into three different zones depending on the curvature achieved at the peak load. Moreover, an analytical model has been developed by using a layer-by-layer approach to predict the axial-flexural interaction diagram and the moment-curvature relationship for square FRP-RC columns using different concrete strengths. The model predictions were in a good agreement with the experimental results.Les barres d’armature en polymère renforcé de fibres (PRF) sont considérées comme une alternative aux barres d’armature en acier face aux problèmes de corrosion, afin de garantir aux structures une longue durée de vie utile. L’utilisation de béton à haute résistance (BHR) avec des barres d’armature en polymère renforcé de fibres de verre (PRFV) et en polymère renforcé de fibres de basalte (PRFB) comme armatures internes peut permettre aux concepteurs de réduire la taille des éléments et d’augmenter la durée de vie des structures. Étant donné la fragilité du BHR, son utilisation en particulier dans les poteaux doit être étudiée afin d’éviter une rupture fragile indésirable. Ce projet de recherche a pour objectif de fournir une base de données expérimentales et de comparer les résultats expérimentaux aux résultats de poteaux en béton de résistance normale (BRN), de dimensions similaires rapportés dans la littérature. L’objectif principal de ce projet de recherche est d’étudier les performances structurales de poteaux en BHR armé d’armatures en PRF et soumis à un chargement excentrique. Des poteaux pleine grandeur ont été testés sous chargement monotone avec différentes excentricités. Les paramètres d’essais comprenaient le rapport excentricité/côté, le type d’armature (PRFV, PRFB, acier), la résistance en compression du béton (BHR, BRN) et le taux d’armature longitudinal. Tous les spécimens avaient une section transversale carrée de 400 x 400 mm une hauteur de 2000 mm. Les résultats expérimentaux sont présentés selon le comportement charge axiale – flèche, les modes de rupture, les déformations maximales en traction et en compression développées dans les barres d’armature et la réponse moment-courbure. Les résultats des essais ont montré que le BHR peut être utilisé efficacement avec les armatures en PRFV et en PRFB et que les spécimens ont atteint leur résistance maximale sans endommagement des armatures en PRFV ou en PRFB des deux côtés des spécimens testés. L’utilisation du BHR a permis aux poteaux d’atteindre une charge maximale plus élevée et de développer une déformation en traction plus élevée dans les barres d’armature par rapport aux poteaux fabriqués avec du BRN. Les poteaux avec armatures en PRFV et en PRFB ont eu des comportements similaires pour les mêmes niveaux d’excentricités. La rupture des poteaux en béton armé de PRF a été classée en trois zones différentes en fonction de la courbure correspondante à la charge maximale. De plus, un modèle analytique a été développé en utilisant une approche couche par couche pour prédire le diagramme d’interaction et la réponse moment-courbure des poteaux carrés en béton de différentes résistances en compression armé de PRF. Les prévisions du modèle concordent bien avec les résultats expérimentaux

The relationship between knowledge management and innovation: empirical study on AUC and Mansoura University

The purpose of this research is to examine the relationship between knowledge Management (KM) and innovation capability in two universities. They are the American University in Cairo and Mansoura University. Given the scarcity of studies that investigated these variables within the higher education context, we borrowed gold et al model that links KM to performance effectiveness in business sector and adapted it to the higher education context. According to this model, KM is seen as KM infrastructure (Culture, structure, and technology), and KM processes (k-acquisition, k-conversion, k-application, and k-protection). The findings show that AUC supersedes Mansoura University in terms of KM infrastructure, KM processes, and innovation. Also, results show that there is a significant and positive relationship between KM infrastructure, KM processes, and Innovatio

Analysis Of Initial Stresses, Long Term Deformation And Rock Lining Interaction In Tunnels

For the design of underground structures in rocks, the initial stresses in the rock mass are a pre-requisite for any analysis. The hydraulic fracturing technique is the only practical method for determining these initial stresses at great depth. For vertical fractures, existing solutions for calculation of stresses are satisfactory. For horizontal or mixed-mode fractures, appropriate solutions are required.;Closed-form solutions for horizontal and mixed-mode fractures including strength anisotropy are developed and applied to several case histories. The reinterpreted horizontal stresses agreed with results derived from convergence measurements and they are also consistent with field observations of excavation performance, indicating that the stresses are correct and readily applicable to practice. Stress values obtained using the conventional method in these cases are too low and may lead to unsafe design.;With the initial stresses correctly determined, the stability of tunnels immediately after excavation may be evaluated. For this purpose, Closed-form solutions for the stresses and displacements around unlined circular tunnels in cross-anisotropic rocks such as shales are derived. For convenience of application, design charts are prepared for the determination of stresses and displacements for given values of initial stresses and the elastic parameters.;After the stability conditions during construction are satisfied, the long-term deformation and consequent stress built-up in the lining are important design considerations, so as to ensure that the structural integrity of the lining is not affected. An experimental study is carried out to investigate the characteristics of the time-dependent deformation of Queenston Shale. The study has shown that Queenston Shale exhibits long-term time-dependent deformation upon stress relief and that the deformation is non-linearly stress dependent. This deformation is represented by a model consisting of three Kelvin units connected in series. The predicted swelling deformations using this model are in good agreement with the measured values in laboratory tests.;Using the theory of viscoelasticity, closed-form solutions for the time-dependent stresses and displacements in the rock mass and the lining of tunnels driven in swelling rocks are derived. A semi-analytical approach is then developed to account for the increase of the values of the moduli of rock as the pressure built-up behind the lining increases with time. It is shown that this solution taking into account stress-dependency of swelling reduces significantly the final stresses and displacements in the lining and the final pressure built-up behind the lining. Therefore, use of the analytical method developed will lead to a more economical design

Bio-equivalence study of two tilmicosin phosphate formulations (Micotil 300® and Cozina 300®) in broiler chickens

Background: The present study was designed to assess the comparative bio-equivalence of Micotil 300® and Cozina 300® in healthy broiler chickens after oral administration of both products in a dose of 15 mg tilmicosin base/kg body wt.Methods: Twenty four broiler chickens were divided equally into two groups (12 chickens for each group). The first group was designed to study the pharmacokinetics of Micotil 300®, while the 2nd group was designed to study the pharmacokinetics of Cozina 300®. Each broiler chicken in both groups was orally administered with 15 mg tilmicosin/kg body wt. Blood samples were obtained from the wing vein and collected immediately before and at 0.08, 0.16, 0.25, 0.5, 1, 2, 4, 6, 8, 12 and 24 hours after a single oral administration.Results: The disposition kinetics of Micotil 300® and Cozina 300® following oral administration of 15 mg tilmicosin/kg body wt revealed that the maximum blood concentration [Cmax] were 1.73 and 1.67 μg/ml and attained at [tmax] of 2.01 and 2.04 hours, respectively.Conclusions: Cozina 300® is bioequivalent to Micotil 300® since the ratios of Cmax, AUC0-24 andAUC0-∞ (T/R) were 0.96, 0.93 and 0.91 respectively. These are within the bio-equivalence acceptance range. Micotil 300® and Cozina 300® are therefore bioequivalent and interchangeable

Big Data Analytics for Complex Systems

The evolution of technology in all fields led to the generation of vast amounts of data by modern systems. Using data to extract information, make predictions, and make decisions is the current trend in artificial intelligence. The advancement of big data analytics tools made accessing and storing data easier and faster than ever, and machine learning algorithms help to identify patterns in and extract information from data. The current tools and machines in health, computer technologies, and manufacturing can generate massive raw data about their products or samples. The author of this work proposes a modern integrative system that can utilize big data analytics, machine learning, super-computer resources, and industrial health machines’ measurements to build a smart system that can mimic the human intelligence skills of observations, detection, prediction, and decision-making. The applications of the proposed smart systems are included as case studies to highlight the contributions of each system. The first contribution is the ability to utilize big data revolutionary and deep learning technologies on production lines to diagnose incidents and take proper action. In the current digital transformational industrial era, Industry 4.0 has been receiving researcher attention because it can be used to automate production-line decisions. Reconfigurable manufacturing systems (RMS) have been widely used to reduce the setup cost of restructuring production lines. However, the current RMS modules are not linked to the cloud for online decision-making to take the proper decision; these modules must connect to an online server (super-computer) that has big data analytics and machine learning capabilities. The online means that data is centralized on cloud (supercomputer) and accessible in real-time. In this study, deep neural networks are utilized to detect the decisive features of a product and build a prediction model in which the iFactory will make the necessary decision for the defective products. The Spark ecosystem is used to manage the access, processing, and storing of the big data streaming. This contribution is implemented as a closed cycle, which for the best of our knowledge, no one in the literature has introduced big data analysis using deep learning on real-time applications in the manufacturing system. The code shows a high accuracy of 97% for classifying the normal versus defective items. The second contribution, which is in Bioinformatics, is the ability to build supervised machine learning approaches based on the gene expression of patients to predict proper treatment for breast cancer. In the trial, to personalize treatment, the machine learns the genes that are active in the patient cohort with a five-year survival period. The initial condition here is that each group must only undergo one specific treatment. After learning about each group (or class), the machine can personalize the treatment of a new patient by diagnosing the patients’ gene expression. The proposed model will help in the diagnosis and treatment of the patient. The future work in this area involves building a protein-protein interaction network with the selected genes for each treatment to first analyze the motives of the genes and target them with the proper drug molecules. In the learning phase, a couple of feature-selection techniques and supervised standard classifiers are used to build the prediction model. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges around 100%. The third contribution is the ability to build semi-supervised learning for the breast cancer survival treatment that advances the second contribution. By understanding the relations between the classes, we can design the machine learning phase based on the similarities between classes. In the proposed research, the researcher used the Euclidean matrix distance among each survival treatment class to build the hierarchical learning model. The distance information that is learned through a non-supervised approach can help the prediction model to select the classes that are away from each other to maximize the distance between classes and gain wider class groups. The performance measurement of this approach shows a slight improvement from the second model. However, this model reduced the number of discriminative genes from 47 to 37. The model in the second contribution studies each class individually while this model focuses on the relationships between the classes and uses this information in the learning phase. Hierarchical clustering is completed to draw the borders between groups of classes before building the classification models. Several distance measurements are tested to identify the best linkages between classes. Most of the nodes show a high-performance measurement where accuracy, sensitivity, specificity, and F-measure ranges from 90% to 100%. All the case study models showed high-performance measurements in the prediction phase. These modern models can be replicated for different problems within different domains. The comprehensive models of the newer technologies are reconfigurable and modular; any newer learning phase can be plugged-in at both ends of the learning phase. Therefore, the output of the system can be an input for another learning system, and a newer feature can be added to the input to be considered for the learning phase

PARAMETRIC SENSITIVITY ANALYSIS FOR THE PERFORMANCE OF STEAM JET EJECTOR

The paper aims for parametric sensitivity analysis for the performance of the steam jet ejector in a steam power plant. This paper contains a one-dimensional mathematical model for the steam jet ejector major components; the chest chamber, the mixing chamber and the diffuser. It presents the entrainment ratio as a function of operating parameters. The developed equations were solved iteratively for parametric evaluation and latter, for studying the effect of the operating parameters on the efficiency of the steam jet ejector. The efficiency of the ejector will reach its maximum when the compression ratio and the driving pressure are kept at minimum. In case of a positive incremental increase of the driving pressure, a larger size of ejector is required. More over, an optimum temperature ratio of a slight fraction over one needed to be maintained for best working conditions. With these deductions, this research paper will provide a temporary optimization for the efficiency of a setup ejector in case of off-design parameters