Thesis (MBA.) -- University of Limpopo, 2022 / The purpose of this study was to investigate the effects of training and development on employee performance at Mamelodi Clinics, Gauteng Province. This study was guided by the following objectives. To determine the perceived effect of training and development courses on skills development of professional nurses in the Mamelodi clinics, Gauteng Province. To explore the perceived effect of training and development courses on the performance of professional nurses in the Mamelodi clinics, Gauteng Province. To examine which of the attended short courses are more effective in improving the skills of professional nurses. To find out which short courses, according to the professional nurses in the study, were found to be ineffective and unnecessary. The study was of importance to future researchers and academicians as it added new insight into the existing information with regards to employee training and development. It also provided the department of health, both nationally and provincially with an understanding of successes and challenges inherent to training and development and their corresponding effect on employee performance. As a result, this had a contribution towards how training and development are carried out within the organisation. A qualitative research design was deployed in this study to allow investigation of the possible relationship between training and development as well as to establish a comparison between the two. The study population was 45 staff of Mamelodi Clinics which were approved for this study. The data was collected through a questionnaire. Percentages, means, cross-tabulation were used as means of data analysis. The findings were presented using tables and figures. In terms of training and development, the study was able to show that all Mamelodi Clinics under department of health Gauteng Province, has a range of training programmes for different staff of their clinics, and are of high quality standard and very effective. As a result, employee skills, overall performance of staff, and general competence of the employee has sharply increased due to training and development v methods and strategies put in place by department of health, both provincially and nationally. The study showed that in terms of employee performance and motivation, training and development programmes help in increasing employee motivation and thus performance. Employees are thus able to successfully be aligned with the goals, aims and missions of the clinics and the department of health, as well as the Batho Pele principles. The study concludes that training and development have positive effects on employees. The study was able to find that within the clinics, employees are given adequate chance to engage in training and development courses. The study concludes further that more training and development programmes should be undertaken. The study recommends that there should be regularly assessments on employees and their subsequent need for further training and development courses in order to increase employee satisfaction and performance.

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Remote desktop solutions have widespread adoption across the world, allowing people to connect to a computer remotely from anywhere in the world. One widely used solution is TigerVNC which uses the RFB protocol for communication between a client and server. TigerVNC supports several encoding types, which use different techniques to compress image data. Currently, there is a lack of a performance evaluation frameworks for VNC software that makes it possible to measure the performance of not only different encoders, but also the performance of the system that chooses which encoding to use for different parts of the image. This thesis presents a framework that was developed to evaluate the performance of TigerVNC server in real-world scenarios. The framework includes a tool that records an X session losslessly and a benchmarking suite that processes a recorded session, providing data regarding execution time and compression ratio. Benchmarks were run using several encoding settings with different recorded sessions representing real-world scenarios. Results show that TigerVNC server has a good tradeoff between compression ratio and execution time. The work done in this thesis lays a foundation on which future research can be done, leading to improvements in the TigerVNC project.

Cognitive radio networks (CRNs) are a promising solution to the channel (spectrum) congestion problem. This dissertation presents work on the two main issues in CRNs: spectrum management and cross-layer protocol design. The objective of spectrum management is to enable the efficient usage of spectrum resources in CRNs, which protects primary users' activities and ensures the effective spectrum sharing among nodes. We consider to improve the spectrum sensing efficiency and accuracy, so that the spectrum sensing cost is reduced. We consider the pre-phase of spectrum sensing and provide structures for sensing assistance. Besides the spectrum sensing phase, the sharing of spectrum, or the channel allocation, among nodes is also the main component in the spectrum management. We provide our approach to achieve a reliable and effective channel assignment. The channel availabilities for different nodes in CRNs are dynamic and inconsistent. This poses challenges on the MAC layer protocols for CRNs. Moreover, due to the lack of knowledge on primary users, they can suddenly become available during the secondary users' data transmission. Therefore, for a end-to-end data transmission in CRNs, the routing algorithm is different from the existing routing algorithms in traditional networks. We consider the cross-layer protocol design, and propose the solutions for efficient data transmission. We propose the novel routing protocol design considering the boundaries of PUs. Also, an effective structure for reliable end-to-end data transmission is presented, which makes use of the area routing protocol. We build a USRP/Gnuradio testbed for the performance evaluation of our protocols. / Computer and Information Science

With the rapid development of mobile devices technology, they are becoming very popular and a part of our everyday lives. These devices, which are equipped with wireless radios, such as cellular and WiFi radios, affect almost every aspect of our lives. People use smartphone and tablets to access the Internet, watch videos, chat with their friends, and etc. The wireless connections that these devices provide is more convenient than the wired connections. However, there are two main challenges in wireless networks: error-prone wireless links and network resources limitation. Network coding is widely used to provide reliable data transmission and to use the network resources efficiently. Network coding is a technique in which the original packets are mixed together using algebraic operations. In this dissertation, we study the applications of network coding in making the wireless transmissions robust against transmission errors and in efficient resource management. In many types of data, the importance of different parts of the data are different. For instance, in the case of numeric data, the importance of the data decreases from the most significant to the least significant bit. Also, in multi-layer videos, the importance of the packets in different layers of the videos are not the same. We propose novel data transmission methods in wireless networks that considers the unequal importance of the different parts of the data. In order to provide robust data transmissions and use the limited resources efficiently, we use random linear network coding technique, which is a type of network coding. In the first part of this dissertation, we study the application of network coding in resource management. In order to use the the limited storage of cache nodes efficiently, we propose to use triangular network coding for content distribution. We also design a scalable video-on-demand system, which uses helper nodes and network coding to provide users with their desired video quality. In the second part, we investigate the application of network coding in providing robust wireless transmissions. We propose symbol-level network coding, in which each packet is partitioned to symbols with different importance. We also propose a method that uses network coding to make multi-layer videos robust against transmission errors. / Computer and Information Science

Thesis advisor: Monica O'Reilly-Jacob / Background: Emergency department crowding (EDC) is a major issue affecting hospitals in the United States and has devastating consequences, including an increased risk of patient mortality. Solutions to address EDC are traditionally focused on adding resources, including increased nurse staffing ratios. However, these solutions largely ignore the value of the experience and expertise that each nurse possesses and how those attributes can impact patient outcomes. This dissertation uses Benner’s Novice to Expert theory of professional development to describe how individual emergency nurse expertise influences patient length of stay in the emergency department and how it can be part of the strategy in addressing EDC.Purpose: The purpose of this program of research was to identify, articulate, and demonstrate a new approach to emergency nurse performance evaluation that integrates patient outcome data and emergency nurse characteristics. Methods: First, in a scoping review, we explored the different approaches to measuring nurse performance using patient outcome data and identified common themes. Second, a concept analysis introduced Emergency Nurse Efficiency as a novel framework to understand how emergency nurses can be evaluated using patient outcome data. Finally, a retrospective correlational study established the association between nurse expertise and emergency patient length of stay. Results: In Chapter Two of this dissertation, the researchers conducted a scoping review of nurse performance metrics and identified twelve articles for inclusion. We identified three themes: the emerging nature of these metrics in the literature, variability in their applications, and performance implications. We further described an opportunity for future researchers to work with nurse leaders and staff nurses to optimize these metrics. In Chapter Three, we performed a concept analysis to introduce a novel metric, called Emergency Nurse Efficiency, that is a measurable attribute that changes as experience is gained and incorporates the positive impact of an individual nurse during a given time while subtracting the negative. Using this measurement to evaluate ED nurse performance could guide staff development, education, and performance improvement initiatives. In Chapter Four, we performed a retrospective correlational analysis and administered an online survey to describe the relationship between individual emergency nurses, and their respective level of expertise, and their patients’ ED LOS. We found that, when accounting for patient-level variables and the influence of the ED physicians, emergency nurses are a statistically significant predictor of their patients’ ED LOS. A higher level of clinical expertise among emergency likely produces a lower ED LOS for their patients, and nurse leaders should seek to better understand these metrics for professional development and quality improvement activities. Conclusions: This dissertation made substantial knowledge contributions to the literature regarding the evaluation of individual emergency nurses and the influence that they have on patient outcomes. It established, first, that the measurement of individual nurse performance is varied and inconsistent; second, that considering emergency nursing as a team activity similar to professional sports results in a conceptual framework that can evaluate individual performance within a group context; and, third, that there is a relationship between the individual emergency nurse and their patients’ ED LOS, and that relationship can be further understood within Benner’s Novice to Expert theoretical model. We recommend that nurse leaders use these data as part of their strategy to decrease EDC. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Connell School of Nursing. / Discipline: Nursing.

No / Grid computing is a computational concept based on an infrastructure that integrates and collaborates the use of high end computers, networks, databases and scientific instruments owned and managed by several organisations. It involves large amounts of data and computing which require secure and reliable resource sharing across organisational domains. Despite its high computing performance orientation, communication delays between grid computing nodes is a big hurdle due to geographical separation in a realistic grid computing environment. Communication schemes such as broadcasting, multicasting and routing should, therefore, take communication delay into consideration. Such communication schemes in a grid computing environment pose a great challenge due to the arbitrary nature of its topology. In this context, a heuristic algorithm for multicast communication is proposed for grid computing networks with finite capacity and bursty background traffic. The scheme facilitates inter-node communication for grid computing networks and it is applicable to a single-port mode of message passing communication. The scheme utilises a queue-by-queue decomposition algorithm for arbitrary open queueing network models, based on the principle of maximum entropy, in conjunction with an information theoretic decomposition criterion and graph theoretic concepts. Evidence based on empirical studies indicates the suitability of the scheme for achieving an optimal multicast communication cost, subject to system decomposition constraints.

With the thriving technology and the great increase in the usage of computer networks, the risk of having these network to be under attacks have been increased. Number of techniques have been created and designed to help in detecting and/or preventing such attacks. One common technique is the use of Network Intrusion Detection / Prevention Systems NIDS. Today, number of open sources and commercial Intrusion Detection Systems are available to match enterprises requirements but the performance of these Intrusion Detection Systems is still the main concern. In this paper, we have tested and analyzed the performance of the well know IDS system Snort and the new coming IDS system Suricata. Both Snort and Suricata were implemented on three different platforms (ESXi virtual server, Linux 2.6 and FreeBSD) to simulate a real environment. Finally, in our results and analysis a comparison of the performance of the two IDS systems is provided along with some recommendations as to what and when will be the ideal environment for Snort and Suricata.

This thesis presents a novel solution for CPU architecture simulation with a primary focus on cache miss prediction using machine learning techniques. The solution consists of two main components: a configurable application designed to generate detailed execution traces via DynamoRIO and a machine learning model, specifically a Long Short-Term Memory (LSTM) network, developed to predict cache behaviors based on these traces. The LSTM model was trained and validated using a comprehensive dataset derived from detailed trace analysis, which included various parameters like instruction sequences and memory access patterns. The model was tested against unseen datasets to evaluate its predictive accuracy and robustness. These tests were critical in demonstrating the model’s effectiveness in real-world scenarios, showing it could reliably predict cache misses with significant accuracy. This validation underscores the viability of machine learning-based methods in enhancing the fidelity of CPU architecture simulations. However, performance tests comparing the LSTM model and DynamoRIO revealed that while the LSTM achieves satisfactory accuracy, it does so at the cost of increased processing time. Specifically, the LSTM model processed 25 million instructions in 45 seconds, compared to DynamoRIO’s 41 seconds, with additional overheads for loading and executing the inference process. This highlights a critical trade-off between accuracy and simulation speed, suggesting areas for further optimization and efficiency improvements in future work.