The purpose of this dissertation is to research aspects of the historical development of Nursing Education and Nursing Management at the Carinus Nursing College from 1949 to 1987; to determine and explain how the College has adapted and coped with historical change and to determine whether proposals for the future can be made. Research has been done by applying the general systems theory and by using the standard methods of historical analysis. Data has been collected by means of oral history, literature search and documentation. The variables isolated are the College as a system; the government or influential super systems; resources such as financial, personnel and students and material inputs; and throughput or processing the work in the output, which leads to the professional nurse. The models used are Bucheles' organizational system, Sharma's flow chart pattern, Mintzberg's parts of organizational systems, and power flows and as shown in Emery, Feibleman and Friends relations and rules of interaction in systems thinking. Parsons' "imperatives of maintenance of a system" as well as Alvin Toffler's "second and third wave phenomena as responses to change" were two further models that were used. By using Robert Buchele's model, the work is divided into four parts: - i) the College as a system ii) the super systems iii) the resources iv) the throughput or processing. A further design that emerged was that two eras could be distinguished, within which three historical phases: - Early, Middle and Late are developed.

In this thesis, different Machine learning (ML) algorithms were used in the detection of anomalies using a dataset from a Gas pipeline SCADA system which was generated by Mississippi State University’s SCADA laboratory. This work was divided into two folds: Binary Classification and Categorized classification. In the binary classification, two attack types namely: Command injection and Response injection attacks were considered. Eight Machine Learning Classifiers were used and the results were compared. The Light GBM and Decision tree classifiers performed better than the other algorithms. In the categorical classification task, Seven (7) attack types in the dataset were analyzed using six different ML classifiers. The light gradient-boosting machine (LGBM) outperformed all the other classifiers in the detection of all the attack types. One other aspect of the categorized classification was the use of an autoencoder in improving the performance of all the classifiers used. The last part of this thesis was using SHAP plots to explain the features that accounted for each attack type in the dataset.

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Robots and other automated systems have potential use in many different fields. As the scope of robot applications that robots are used for increases, there is a growing desire to have human operators manage multiple robots. Typical methods of enabling operators to multi-task in this way involve some combination of user interfaces that support human cognition and advanced robot autonomy. Our research explores a complementary method of managing multiple robots by utilizing operator teams. The evidence suggests that for appropriate task scenarios, two cooperating operators can be more than twice as effective as one operator working alone.

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In the operation of unmanned vehicles, safety is a primary concern. This thesis focuses on the use of computer vision in the development of a situational awareness system that allows for safe deployment and operation of a ground robot from an unmanned aerial vehicle (UAV). A method for detecting utility cables in 3D range images is presented. This technique finds areas of an image that represent edges in 3D space, and uses the Hough transform to find those edges that take the shape of lines, indicating potential utility cables. A mission plan for stereo image capture is laid out as well for overcoming some weaknesses of the stereo vision system; this helps ensure that all utility cables in a scene are detected. In addition, the system partitions the point cloud into best-fit planes and uses these planes to locate areas of the scene that are traversable by a ground robot. Each plane's slope is tested against an acceptable value for negotiation by the robot, and the drop-off between the plane and its neighbors is examined as well. With the results of this analysis, the system locates the largest traversable region of the terrain using concepts from graph theory. The system displays this region to the human operator with the drop-offs between planes clearly indicated. The position of the robot is also simulated in this system, and real-time feedback regarding dangerous moves is issued to the operator. After a ground robot is deployed to the chosen site, the system must be capable of tracking it in real time as well. To this end, a software routine that uses ARToolkit's marker tracking capabilities is developed. This application computes the distance to the robot, as well as the horizontal distance from camera to the robot; this allows the flight controller to issue the proper commands to keep the robot centered underneath the UAV. / Master of Science

In order to operate UAVs under human supervisory control in more complex arenas such as urban environments, an obstacle detection system is a requirement to achieve safe navigation. The development of a system capable of meeting these requirements is presented. The first stage of development was sensor selection and initial testing. After this, the sensor was combined with a servomotor to allow it to rotate and provide obstacle detection coverage in front, below, and to both sides of the UAV. Utilizing a PC-104 single board computer running LabView Real-time for on-board control of the sensor and servomotor, a stand alone obstacle detection system was developed meeting the requirements of light weight, low power, and small size. The detection performance of the system for several parameters has been fully characterized. A human subjects study was conducted to assess the any advantages resulting from the addition of the obstacle detection system compared to that of a normal nadir camera. The study demonstrated that users with access to the three-dimensional display were able to navigate an obstacle course with greater success than those with only a camera. Additional development into more advanced visualization of the environment has potential to increase effectiveness of this obstacle detection system. / Master of Science

Clinical supervision is integral to promoting the professional development of counselors-in-training and gatekeeping the counseling services provided by counselor trainees (Bernard & Goodyear, 2014). Despite the value of studying participants’ retrospective perceptions about or reflections upon supervision, the supervisory process in which supervision transpires is infrequently quantified and measured (Holloway, 1982; Holloway, 1987). As described by most developmental supervision models, clinical supervision is “a process with sequential and qualitatively distinct stages through which supervisors and trainees progress” (Littrell, Lee-Borden, & Lorenz, 1976, p. 134). In order to capture these stages and phenomena with observable and measurable units, the author used six states of interest to measure the supervision process, which exhibit the progressively complex nature of clinical supervision. The six states include: (a) social interfacing (non-skills phase), (b) reflecting on foundational competencies, (c) deepening case conceptualization, (d) processing the relational management, (e) overcoming personal and multicultural barriers, and (f) furthering professional development. These states underpin the codebook of this study and are used to conceptualize the supervision process. Although the interactions between the supervisor and supervisee are transient and difficult to grasp, supervisory interactions move from one state to another. Indeed, state-transitional dynamics of clinical supervision are subject to a constellation of factors that supervisors and supervisees initially bring in and constantly reinforce, such as supervisory styles, supervisee developmental levels, supervisory working alliance, and supervisee satisfaction with clinical supervision. By using Markov chain analysis, this study detects the overall transitional dynamics of supervisory dyads and investigates how transitional dynamics vary based on the aforementioned variables that manifest themselves as supervision dynamics unfold and closely interface with other supervision variables. Results of this study provide implications for clinical supervisors, counselor educators, and counselors-in-training.