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Study Background and Objectives:Excessive perioperative opioid prescribing has been detrimental to public health, contributing to the elevated prevalence of opioid use disorder. Since 2016, rigorous regulation of opioid prescribing has reduced over-prescription, but has also led to opioid-phobia. The 2022 CDC guideline promotes person-centered decisions on pain management by relaxing restrictions on opioid prescription. The determination of opioid requirements for surgical pain management is influenced by various factors and stakeholders. Despite extensive research, the mechanisms underlying perioperative pain management and the persistence of opioid use after surgery remain unclear. Clinicians currently lack tools to guide opioid prescription in clinical settings, and patients often face a dearth of information regarding expected pain levels, proper opioid use, and options for surgical pain management. The main objective of my doctoral project is to disentangle the intricate relationships among patients, healthcare providers, and policy changes in perioperative opioid prescription for pain management and to identify key intervention points to balance the beneficial effects of proper opioids use against the risks of addition. Another objective is to develop a risk score algorithm for perioperative opioid requirements to help with decision-making in clinical practice. Materials and Methods:In chapter 1, I undertook a systematic review and meta-analysis, and investigated the percentage of adult patients scheduled for general surgeries who received opioid analgesia for perioperative pain management, the quantities of opioids prescribed to patients, the actual quantities consumed, the percentage of patients without prior opioid exposure experiencing prolonged opioid use, and the evolution of perioperative opioid prescription patterns since the policy changes. A causal loop diagram was used to visualize the complex conceptual framework of perioperative pain management and post-surgical prolonged use of opioids based on insights derived from the systematic review and meta-analysis. In chapter 2 and 3, data from patients aged 18-64 years undergoing one of 12 commonly performed procedures (e.g., laparoscopic cholecystectomy) from 2015 to 2018 at a single institution were analyzed. Perioperative opioid requirements (none/low, medium, high) were determined based on patients’ self-reported pain scores and opioid prescription/administration from 30 days before to 2 weeks after surgery. Patients’ clinical and procedure-related factors were collected as potential predictors. Random forest, the Least Absolute Shrinkage and Selection Operator (LASSO), and multinomial logistic regression were used to develop prediction models. Models’ performance, including discrimination, calibration, classification measures were evaluated. A nomogram based on multinomial logistic regression was generated as a score tool, and decision curve analysis was used to examine the clinical utility of the final prediction model dichotomizing the opioid prescription as none/sparing versus medium/high requirements. Results: My systematic review and meta-analysis revealed that around 85% of surgical patients received opioids perioperatively. The pooled mean total amount of opioids dispensed was 210 MME per patient per surgical procedure. Notably, only approximately 44% of the prescribed opioids were consumed. Among opioid-naïve patients who initiated opioid use perioperatively, 7.1% persisted in opioid use beyond the conventional three-month postoperative recovery timeframe. Intervention programs (such as setting up maximum limits of opioids prescription, providing trainings to health providers, monitoring opioids prescription behaviors, providing health education to patients, etcetera) reduced perioperative opioid prescription by 38% and opioid consumption by 63.2%. The causal loop diagram illustrates a balancing feedback loop between policy and over-prescription, highlighting the pivotal role of a decision tool in reducing the over-prescription of perioperative opioids while ensuring the fulfillment of opioid needs for effective perioperative pain management. To develop a decision-aid tool based on prediction models, I included 2733 patients in the training dataset and 1081 in the testing dataset, all of whom underwent general surgeries. All prediction models demonstrated moderate discrimination in the testing dataset. The null hypothesis of perfect calibration intercepts and calibration slopes was rejected. In analyses restricted to patients undergoing laparoscopic cholecystectomy, model discrimination remained similar while model calibration improved. The revised LASSO model had an accuracy of around 65% in the testing dataset, classifying future cases correctly into opioid requirements groups in laparoscopic cholecystectomy cohort. Features in the final laparoscopic cholecystectomy model included the use of opioid/NSAID/anti-depressant before surgery, emergency surgery, anesthesia type, and surgical indication for cholelithiasis/cholecystitis. A nomogram was created to guide perioperative opioids use among laparoscopic cholecystectomy patients, and the decision curve analysis demonstrated the clinical utility of the prediction model; it generated higher net benefits than the strategy of prescribing no opioids or opioid sparing to surgical patients and the strategy of prescribing medium or high opioids doses to all patients, with a broad threshold probability from 18% to 92%. Conclusions:In summary, this dissertation described the historically high levels of perioperative opioid prescriptions and highlighted their adverse impacts: persistent opioid use and community diversion. Although the implementation of guidance and policies has significantly reduced nationwide over-prescriptions of opioids, it is essential to recognize the potential benefits of appropriate opioid use in perioperative pain management. The incorporation of a machine-learning approach with subject-matter knowledge may achieve more accurate predictions of opioid requirements than employing machine-learning techniques alone and increase the interpretability of the prediction model. Notably, the surgery-specific model demonstrated superior performance than the model for general surgeries. Future studies should further validate the conceptual model of perioperative opioid prescription and misuse in real-world scenarios, enhance model discrimination, extend external validation efforts, and develop electronic applications tailored to contemporary medical practices.

The power industry is currently in the process of re-inventing itself. The unbundling of the traditional monopolistic structure that gave birth to a deregulated electricity market, the mass tendency towards a greener use of energy, the new emphasis on distributed generation and alternative renewable resources, and new emerging technologies have revolutionized the century old industry. Recent blackouts offer testimonies of the crucial role played by protection relays in a reliable power system. It is argued that embracing the paradigm shift of adaptive protection is a fundamental step towards a reliable power grid. The adaptive philosophy of protection systems acknowledges that relays may change their characteristics in order to tailor their operation to prevailing system conditions. The purpose of this dissertation is to present methodology to implement a security/dependability adaptive protection scheme. It is argued that the likelihood of hidden failures and potential cascading events can be significantly reduced by adjusting the security/dependability balance of protection systems to better suit prevailing system conditions. The proposed methodology is based on Wide Area Measurements (WAMs) obtained with the aid of Phasor Measurement Units (PMUs). A Data Mining algorithm known as Decision Trees is used to classify the power system state and to predict the optimal security/dependability bias of a critical protection scheme. / Ph. D.

Traditional methods for predicting transient stability of power systems such as the direct method, the time domain approach, and the energy function methods do not work well for online transient stability predictions problems. With the advent of Phasor Measurement Units (PMUs) in power systems, it is now possible to monitor the behavior of the system in real time and provide important information for transient stability assessment and enhancement. Techniques such as the rotor oscillation prediction method based on time series have made the prediction of system stability possible for real-time applications. However, methods of this type require more than 300 milliseconds after the start of a transient event to make reliable predictions. The dissertation provides an alternate prediction method for transient stability by taking advantage of the available PMUs data. It predicts transient stability using apparent impedance trajectories obtained from PMUs, decision trees, and FLDSD method. This method enables to find out the strategic locations for PMUs installation in the power system to rapidly predict transient stability. From the simulations performed, it is realized that system stability can be predicted in approximately 200 milliseconds (12 cycles). The main advantage of this method is its simplicity as the PMUs can record the apparent impedance trajectories in real-time without any previous calculations. Moreover, using decision trees built in CART, transient stability prediction becomes straightforward and computationally very fast. The optimum locations for PMUs placement can also be determined using this technique. After the transient instability prediction by the apparent impedance trajectories, a slow- coherency based intelligent controlled islanding scheme is also developed to restore the stability of system. It enables the generators in the same island to stay in synchronism and the imbalance between the generators and load demand is minimized. / Ph. D.

According to the proposed definition and classification of power system stability addressed by IEEE and CIGRE Task Force, voltage stability refers to the stability of maintaining the steady voltage magnitudes at all buses in a power system when the system is subjected to a disturbance from a given operating condition (OC). Cascading outage due to voltage collapse is a probable consequence during insecure voltage situations. In this regard, fast responding and reliable voltage security assessment (VSA) is effective and indispensable for system to survive in conceivable contingencies. This paper aims at establishing an online systematic framework for voltage security assessment with high-speed data streams from synchrophasors and phasor data concentrators (PDCs). Periodically updated decision trees (DTs) have been applied in different subjects of security assessments in power systems. However, with a training data set of operating conditions that grows rapidly, re-training and restructuring a decision tree becomes a time-consuming process. Hoeffding-tree-based method constructs a learner that is capable of memory management to process streaming data without retaining the complete data set for training purposes in real-time and guarantees the accuracy of learner. The proposed approach of voltage security assessment based on Very Fast Decision Tree (VFDT) system is tested and evaluated by the IEEE 118-bus standard system. / Master of Science / Voltage security is one of the most critical issues in the power systems operation. Given an operating condition (OC), Voltage Security Assessment (VSA) provides a tool to access whether the system is capable to withstand disturbances if there is one or more than one elements is not functioning appropriately on the power grid. Traditional methods of VSA require the knowledge of network topologies and the computational contingency analysis of various circumstances. With trained models, decision-tree-based VSA is able to assess the voltage security status by collectible measurements among the system in a real-time manner. The system topology may alter over and over by system operators in order to meet the needs of heavy load demand and power quality requirements. The proposed approach based on Very Fast Decision Tree (VFDT) system is capable of updating trained decision-tree models regarding to changes of system topology. Therefore, the updated decision-tree models is able to handle different system topology and to provide accurate security assessment of current OC again.

Today's electric power system is operated under increasingly stressed conditions. As electrical demand increases, the existing grid is operated closer to its stable operating limits while maintaining high reliability of electric power delivery to its customers. Protective schemes are designed to account for pressures towards unstable operation, but there is always a tradeoff between security and dependability of this protection. Adaptive relaying schemes that can change or modify their operation based on prevailing system conditions are an example of a protective scheme increasing reliability of the power system. The purpose of this thesis is to validate and analyze implementation of the Security-Dependability Adaptive Voting Scheme. It is demonstrated that this scheme can be implemented with a select few Phasor Measurement Units (PMUs) reporting positive sequence currents to a Phasor Data Concentrator (PDC). At the PDC, the state of the power system is defined as Stressed or Safe and a set of relays either vote or perform normal operation, respectively. The Adaptive Voting Scheme was implemented using two configurations: hardware- and software-based PDC solutions. Each was shown to be functional, effective, and practical for implementation. Practicality was based on the latency of Wide Area Measurement (WAM) devices and the added latency of relay voting operation during Stressed conditions. Phasor Measurement Units (PMUs), Phasor Data Concentrators (PDCs), and relay operation delays were quantified to determine the benefits and limitations of WAMS protection and implementation of the voting scheme. It is proposed that the delays injected into the existing protection schemes would have minimal effect on the voting scheme but must be accounted for when implementing power system controls due to the real-time requirements of the data. / Master of Science

Although ‘immersive experiences' and ‘immersion' are popular keywords in the themed entertainment industry today, they lack clear definition and criteria in their design and application. Nevertheless, there has been an increase in guest demand for these types of experiences and for these spaces to develop innovative methods that increase the degree to which audiences are able to engage with them. This thesis iterates cohesive design criteria for immersive experiences as they continue their trend toward increased engagement, interactivity, and guest agency in physical manifestations of imaginary worlds. This trend is explored through contextual references from the industry and an assessment of their implementation of the criteria towards increasingly immersive spaces. However, the current trajectory of these experiences does not demonstrate holistic consideration of the various elements required to progress towards true “advanced immersion” for the future of themed design. A new design practice is put forward for practitioners: “Immersive Exploration Experiences.” This framework progresses towards advanced immersion by considering varied guest roles and offering personalized, branching narrative structures as a key tenet. The Immersive Exploration Experiences concept is further illustrated with example design implementations accompanied by exploration tree diagrams that show the combination of dynamic, interactive, and engaging elements. The intent of this thesis is to provide a template for creating next generation immersive experiences which are built upon dynamic guest roles, transformative spaces, repeatable narratives, and other advanced elements that give every guest an opportunity to create their own path through an imaginary world.

Osteological sex estimation is a key component of the biological profile in forensic anthropological casework. However, there are still limitations with current methodologies for the skull as well as inadequate classification accuracies. Therefore, the purpose of this research is to improve osteological sex classification accuracies for the skull by combining morphological and metric variables into multiple models using decision trees and random forest (RF) modeling. The sample was derived from four U.S.-based skeletal collections and consisted of 403 individuals of European American and African American population affinities. Twenty-one morphological traits and 21 metric variables of the skull were selected for analysis, and intraobserver error was assessed to determine which variables should be incorporated into the models. Additionally, two-way ANOVAs and aligned rank transformation were utilized to examine the effects of sex, age, population affinity, and secular change on the variables. To generate the trees and RF models, 80% of the sample was used for model training and 20% of the sample was used for holdout validation testing. Multiple decision trees and RF models were generated that incorporated morphological, metric, and combined variables. Models were generated for the African American and European American samples, as well as for the pooled populations. The predictive accuracy of the models was assessed utilizing the holdout validation sample and the out-of-bag error. Overall, the majority of the combined data decision trees and RF models achieved higher classification accuracies compared to the separate morphological and metric models. Additionally, the pooled and European American models frequently achieved higher accuracies compared to the African American models. The combined data models also resulted in higher accuracies compared to popular osteological sex estimation methods for the skull. Therefore, the combined data models have great potential for use by forensic anthropologists and bioarchaeologists for estimating osteological sex from the skull.