Implementation of Safe Surgery Saves Lives initiative in Ahmed-Gasim’s Cardiac Center

Aim: This paper reports on the implementation of a Safe Surgery Saves Lives, in Ahmed-Gasim‟s Cardiac Center in 2011 using a change management framework. Background: Medical errors and incidence of traumatic injuries in surgical care services were recognized as a proportion of the total global burden of disease. Surgical care and procedures can potentially affect the lives of millions of people worldwide. Studies done by WHO found that wrong person, wrong procedure, and wrong site surgery is a preventable adverse event, and defined a core set of minimum standards that can be applied universally across borders and settings, and developed a Surgical Safety Checklist as a tool to ensure safety culture, teamwork, communications, information handoff, patient involvement, and systematic check of processes. Methods: A Users\u27 Guide to Managing Change in the Health Service Executive, HSE change model with major four phases; initiation, planning, implementation, and mainstreaming, was used to guide the implementation of the Safe Surgery Saves Live Initiative through using the WHO Surgical Safety Checklist in Ahmed-Gasim‟ Cardiac Center (AGCC). Results: Implementation of a surgery checklist improved safety culture, memory recall, communication, team work, systematic check process, and decrease medical errors, such as wrong patient, wrong site, and wrong procedure. Implementation of a surgery checklist did not delay cases or increase load of work

Seismic Evaluation and Optimization of Reinforced Concrete Multistory Building

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Ahmed Abdallah in April of 2020

Uptake of divalent manganese from aqueous solution using cation exchange resins

The present work deals with the removal of highly toxic manganese ion from aqueous solution using cation exchange resins namely, Amberjet 1500H, Amberjet 1300H and Amberlite IRC86. The study was carried out in media of various ionic strengths (1.98-9.98 mmol/L), different resin dose (0.25-8.0 gm) and a wide solution acidity range (0.001-1.0 M), in addition to at three temperatures (293-318 K).The aim of this study was to understand the mechanisms that govern manganese removal and find a suitable equilibrium isotherm and kinetic model for the manganese removal in a batch reactor. The experimental isotherm data were analyzed using the Langmuir, Freundlich, Temkin and Dubinin–Radushkevich (D–R) equations. The experimental data were analyzed using four adsorption kinetic models – the pseudo first- and second-order, intraparticle diffusion and the Elovich equations – to determine the best fit equation for the adsorption of manganese ions onto the resins. The rate constants, equilibrium capacities and related correlation coefficients for each kinetic model were calculated and discussed. Also, predicted qt values from the kinetic equations were compared with the experimental data. Thermodynamic parameters, involving ΔH, ΔS and ΔG were also calculated from graphical interpretation of the experimental dat

Identification of a new strain of Actinomadura isolated from Saharan soil and partial characterization of its antifungal compounds

One promising strain Actinomadura sp. AC170, isolated from Algerian Saharan soil, with strong antifungal activity against pathogenic and toxinogenic fungi, was selected for further studies. The 16S rRNA results showed a distinct phylogenetic lineage from the other species within the Actinomadura genus. The production of antibiotic substances was investigated using GYEA solid medium. The butanolic extract contained four bioactive spots detected on thin layer chromatography plates. Among these antibiotics, a complex called 170A, which showed the more interesting antifungal activity, was selected and purified by reverse-phase HPLC. This complex is composed of four compounds. Ultraviolet-visible, infrared, mass and H nuclear magnetic resonance spectroscopy studies showed that these molecules contain an aromatic ring substituted by aliphatic chains. These compounds differ from the known antibiotics produced by Actinomadura species

Efficient and Privacy-Preserving Ride Sharing Organization for Transferable and Non-Transferable Services

Ride-sharing allows multiple persons to share their trips together in one vehicle instead of using multiple vehicles. This can reduce the number of vehicles in the street, which consequently can reduce air pollution, traffic congestion and transportation cost. However, a ride-sharing organization requires passengers to report sensitive location information about their trips to a trip organizing server (TOS) which creates a serious privacy issue. In addition, existing ride-sharing schemes are non-flexible, i.e., they require a driver and a rider to have exactly the same trip to share a ride. Moreover, they are non-scalable, i.e., inefficient if applied to large geographic areas. In this paper, we propose two efficient privacy-preserving ride-sharing organization schemes for Non-transferable Ride-sharing Services (NRS) and Transferable Ride-sharing Services (TRS). In the NRS scheme, a rider can share a ride from its source to destination with only one driver whereas, in TRS scheme, a rider can transfer between multiple drivers while en route until he reaches his destination. In both schemes, the ride-sharing area is divided into a number of small geographic areas, called cells, and each cell has a unique identifier. Each driver/rider should encrypt his trip's data and send an encrypted ride-sharing offer/request to the TOS. In NRS scheme, Bloom filters are used to compactly represent the trip information before encryption. Then, the TOS can measure the similarity between the encrypted trips data to organize shared rides without revealing either the users' identities or the location information. In TRS scheme, drivers report their encrypted routes, an then the TOS builds an encrypted directed graph that is passed to a modified version of Dijkstra's shortest path algorithm to search for an optimal path of rides that can achieve a set of preferences defined by the riders

Impact of emotional exhaustions on turnover intentions : a mediating role of organizational commitment in higher education institutes of Saudi Arabia

Environment, organizational culture, development/promotion policies, facilities, compensation and political issues are some of the causes of reduction in the organizational commitment and increase in the level of emotional exhaustion and development of turnover intention among faculty of universities. The occupational psychology of faculty is influenced by diverse elements and specifically, there are various factors that play crucial role in overall organizational commitment emotional exhaustion and turnover intention. The faculty plays a vital role in ensuring the students and society for the quality education. For this study, the faculty members of various departments of selected universities in Riaz, KSA were the target population. The population for this study comprised faculty members of higher education institutions employing approximately 100 or more than 100 faculty members. The results show that all the study variables are significantly related with each others. The mediation results show that organizational commitment partially mediate between the emotional exhaustion and turnover intentions among the faculty of higher education institutions in Saudi Arabia.peer-reviewe

Development And Evaluation Of Terbutaline Sulphate Loaded-Biodegradable Microspheres For Pulmonary Delivery

Mikrosfera terbutalin sulfat (TBS) pelepasan tertahan dibangun menggunakan polimer PLA R 203H dan PLGA RG 504H. Sustained-release terbutaline sulphate (TBS) microspheres were developed using PLA R 203H and PLGA RG 504H polymers

ANALYSIS OF PRESSURE DROP AND HEAT TRANSFER OF ANNULAR DEPOSITION TEST UNIT

Deposition of wax on the internal wall of pipelines is often regarded as a problem since the tube diameter is reduced. Consequently, more power is needed to force the same amount of oil through the system. In order to design efficient sub-sea petroleum production facilities to achieve optimum production returns, it is necessary to understand the phenomena of the wax deposition and provide prediction for the nature of deposits. PETRONAS High Temperature/High Pressure Model Pipeline and Wax Deposition Facility, HT/HPMPWDF, is designed and installed, to investigate and model the process of the wax deposition. In this system, pressure drop and heat transfer are proposed as the key parameters to model the process of the wax deposition. Experiments were carried out to investigate and characterize the hydrothermal performance of the test section of the system. Pressure drop and temperature variation data throughout the test section of the deposition apparatus with varying flow condition were measured and processed analytically. Also, pressure drop and heat transfer data were predicted based on the available correlations. Comparison was made between the two models. For each parameter involved in the pressure drop calculations, a parametric analysis was performed to study its effect on the pressure drop estimation. The discrepancies between the measured and calculated pressure drop results were justified and a realistic pressure drop correlation was developed based on the equivalent length technique. The heat transfer was investigated in terms of the steady state energy balance. Also, several heat transfer correlations were used to predict the heat transfer. Comparison between the theoretical and experimental results reveals that Sandal et. a!. correlation for the convective heat transfer is produced the best agreement with the experimental results. The system is proved to provide an experimental data within an accuracy of 7 % AAPE for the pressure drop and 5% AAPE for the turbulent convective heat transfer. A steady state thermal energy balance of 6 % AAPE is achieved. It could be concluded that the proposed correlations have brought the system to the capability of modeling and predicting the wax deposition formation

A generic framework for context-dependent fusion with application to landmine detection.

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods