Management options for Pilbara Demersal Line Fishing

Fisheries Western Australia licensing arrangements allow all vessels with an unrestircted fishing boat licence (\u27wetline licence\u27) to take scalefish throughout the State. These arrangements have led to a high level of excess capacity in the licensed \u27wetline\u27 fishing fleet to exploit the scalefish resources. The declaration of the Kimberley Interim Line Fishery and forthcoming declaration the Northern Demersal Scalefish Fishery have resolved this problem for the Kimberley waters. However, the high level of excss capacity in the \u27wetline\u27 fleet to exploit the waters of the Pilbara remains a problem Preview access and develop a management plan for the Pilbara demersal scalefish resource along with the major objective of management changes is to put the total of demersal scalefish in these waters into a managed fishery environment over the next two or three years

Therapeutic drug monitoring, clinical metabolomics and pharmacometabolomics via solid phase microextraction (SPME): The first step towards an alternative rapid diagnostic tool

Personalized medicine is a branch of medicine that focuses on how a prescribed therapeutic treatment affects a specific individual as opposed to its general effects for the broader population. The goal of personalized medicine is to improve patient care by enabling concentrations of a therapeutic drug to be monitored in various biological compartments, while also measuring their effects in relation to the administered dose via therapeutic drug monitoring (TDM). Metabolomics—the study of all small endogenous and exogenous molecules within a cell, tissue, or organism—has recently been proposed as a method for developing patient-based metabolic profiles, which could enable clinicians to more effectively predetermine suitable courses of treatment for a variety of patients. The probability of success or failure for a given treatment is determined in large part by metabolic phenotyping, which considers several patient-based influential factors, such as age, diet, environment, and medical history. This approach allows treatment to be tailored to the needs of each individual patient, thereby avoiding under- or over-dosing or wasting time with unnecessary treatment options, which often occurs as a result of the current “trial and error” approach to personalized therapy. In this thesis, solid phase microextraction (SPME) coupled with liquid chromatography-mass spectrometry (LC-MS) is proposed as an alternative sample preparation tool for use in the field of personalized medicine. To this end, the work in this thesis presents the development of various SPME-based methods for TDM, and it explores SPME-based clinical metabolomics and proof-of-concept pharmacometabolomics for a range of biological matrices typically encountered in clinical practice, such as whole blood, serum, plasma, urine, and lung tissue. Furthermore, SPME is proposed as a practical tool for rapid diagnostics, as it can be directly coupled to sensitive detection methods like MS. While a number of preliminary steps are required before important diagnostic markers can be monitored—including the validation of these potential respective candidate biomarkers, which is already a major inherent challenge in metabolomics—the use of SPME for real-time TDM and point-of-care analysis of important metabolic markers remains feasible. This thesis consists of a brief introduction and 6 experimental chapters, with each successive chapter exploring increasingly complex samples of interest and discussing the challenges and limitations associated with their analysis. Moreover, each subsequent chapter also addresses the difficulties associated with performing solely TDM or metabolomics separately and how, particularly in vivo SPME, can overcome these challenges and be used to achieve both goals (TDM and metabolomics) simultaneously under even more complicated and dynamic circumstances. Specifically, Chapter 2 focuses on the therapeutic drug monitoring of TXA in plasma and urine samples from patients with chronic renal dysfunction who are undergoing cardiac surgery, while Chapter 3 presents a metabolomics study entailing the profiling of serum samples from various psoriatic patients. Chapters 4, 5, and 6 illustrate how SPME can be used to enable simultaneous TDM and metabolomics under more complicated and dynamic circumstances by using in vivo SPME for specifically tissue analysis. Chapter 4 explores lung tissue and perfusate metabolomics using a pre-clinical porcine model undergoing normothermic ex vivo lung perfusion (NEVLP). In contrast, Chapters 5 and 6 assess the use of in vivo SPME for the TDM of chemotherapy drugs administered via in vivo lung perfusion (IVLP) in pre-clinical porcine model (Chapter 5) and clinical human trial settings (Chapter 6), followed by proof-of-concept pharmacometabolomics. Finally, the potential use of SPME as a rapid diagnostic tool is showcased in Chapter 7—which shows the rapid analysis of TXA from plasma—concluding the thesis by further demonstrating that the dual goals of TDM and point-of-care testing for metabolic markers can be achieved with rapid analysis via the direct coupling of SPME to MS

Heat Flux Model Validation Utilizing Convolutional Neural Networks and Sub-surface Thermocouples for NSTX-U

A proof of concept convolutional neural network (CNN) has been developed to assist in operating tokamaks outside of existing empirical scalings for the heat flux width, λq [lambda-q]. NSTX-U has designed new plasma facing components (PFCs) to withstand increased halo current forces as well as elevated heat fluxes driven by increased poloidal field and neutral beam power compared to NSTX. Larger graphite tiles are castellated to 2.5 cm [centimenter] x 2.5 cm [centimeter] to reduce bending stresses. Maintaining PFCs below engineering limits will be an important consideration for operation of NSTX-U. Sub-surface thermocouples will be utilized to demonstrate validation of the heat load model, using the castellated designs to quantify the shot-integrated energy deposited in the NSTX-U divertor. A Convolutional Neural Network (CNN) has been trained using ANSYS simulations of PFC response to a variety of time-varying heat flux profiles. The CNN accepts time evolving thermocouple data and various 0-D engineering parameters and outputs heat flux model parameters, such as the poloidal field scaling of the heat flux width, λq [lambda-q]. The CNN enables high accuracy validation of the heat flux model despite a limited number of simulated NSTX-U shots, noise, and systematic errors in the thermocouple data. This application of machine learning to nuclear fusion diagnostics provides an alternative method to traditional analytical solution inversion, and may be ported over to other diagnostics in the future

The Relationship Between Age, Sport, and Years Coaching on Knowledge, Confidence, Preparedness, and Intention to Intervene With Youth Athletes Experiencing Mental Health Challenges

Introduction: Mental disorders can affect all ages and populations, but there is an increasingly high prevalence in the pediatric population. It is estimated that 1 in 5 youth between the ages of 9-17 experiences some degree of impairment from a diagnosable mental health disorder (Merikangas et al., 2010). Youth are often not equipped to seek professional support; making parents, teachers, and coaches key to detection and referral to professional help (Ng et al., 2021). The environment youth sports creates gives coaches the opportunity to have a greater impact on youth mental health (Das et al., 2016). Purpose: Determine if age, sport type, and years of coaching have any effect on youth coaches’ confidence, preparedness, knowledge, and intention to intervene in youth mental health challenges. Methods: Youth coaches were recruited from recreation departments and through social media. Participants completed four surveys to determine their confidence, preparedness, knowledge, and intention to intervene. Data Analysis: Multiple Pearson correlations examined the relationship between knowledge, confidence, preparedness and intention. A MANOVA examined the effect of sport and age on knowledge, preparedness, intention to intervene, and confidence. Results: A total of 134 participants completed the surveys. The average scores of the four surveys were: Knowledge = 8.45, Confidence = 7.47, Preparedness = 3.41, and Intention = 5.13. The Pearson correlation revealed statistically significant results for knowledge (r = 0.450, p pp Discussion:Age, sport, and number of years coaching did not have any effect on coaches’ survey scores. Coaches are confident, but are not prepared to help youth athletes experiencing mental health challenges. Youth coaches who are more knowledgeable, are more likely to offer help. These results heightened the need for more mental health training. Future research should investigate this topic on a larger scale utilizing youth coaches across the nation

Factors that Differentiate Prescription Stimulant Misusers from those At-Risk for Misuse: Expectancies, Perceived Safety, and Diversion [post-print]

BACKGROUND: The nonmedical use of prescription stimulants (NMUPS) is one of the most prevalent illicit behaviors on college campuses. While numerous risk factors for NMUPS have been identified, it is unknown how nonusing students who meet several risk factors for NMUPS differ from those who have used, which may inform intervention efforts. We expected that users would evidence greater cognitive enhancement and anxiety/arousal expectancies and intentions to use, and lower guilt/dependence expectancies, perceptions of NMUPS-related harm, and academic self-efficacy. METHODS: Between 2014 and 2016, students (N = 121; 65% female) at two demographically dissimilar colleges in the Northeastern and Midwestern United States who reported lifetime NMUPS or endorsed two or more NMUPS risk factors (i.e., recent marijuana use, recent binge drinking, grade point average RESULTS: A MANCOVA showed that at-risk nonusers had lower cognitive expectancies, higher guilt/dependence expectancies, and higher anxiety/arousal expectancies compared to users. ANCOVAs and Chi-square tests showed that nonusers also perceived NMUPS to be more harmful and were less likely to divert their medication if prescribed. The groups did not differ on academic self-efficacy or total number of risk factors endorsed. However, recent marijuana use was more prevalent in users. CONCLUSIONS: Targeted preventive interventions for NMUPS should focus on students who are using marijuana and should aim to maintain lower positive and higher negative stimulant expectancies and reaffirm potential NMUPS-related harms

Review of the Effectiveness of Impulse Testing for the Evaluation of Cable Insulation Quality and Recommendations for Quality Testing

Abstract— This project investigates impulse breakdown testing as a means of determining the as constructed standard of MV power cable. A literature survey is undertaken to elucidate the place of this test in an overall cable test regime and to determine the factors that impact on the performance of the test method. Testing was undertaken on ESB Networks cables to establish if a merit order ranking was feasible based on this test and to determine if the test could detect defects in the inner semiconducting layer. Based on this, conclusions and recommendations are made regarding the overall applicability and usefulness of this test

Roadside sobriety tests and attitudes toward a regulated cannabis market

BACKGROUND: Many argue that prohibition creates more troubles than alternative policies, but fewer than half of American voters support a taxed and regulated market for cannabis. Some oppose a regulated market because of concerns about driving after smoking cannabis. Although a roadside sobriety test for impairment exists, few voters know about it. The widespread use of a roadside sobriety test that could detect recent cannabis use might lead some voters who currently oppose a regulated market to support it. In contrast, a question that primes respondents about the potential for driving after cannabis use might lead respondents to be less likely to support a regulated market. METHODS: Phone interviews with a national sample of 1002 registered voters asked about support for a regulated cannabis market and support for such a market if a reliable roadside sobriety test were widely available. RESULTS: In this sample of registered voters, 36% supported a regulated cannabis market. Exploratory chi-square tests revealed significantly higher support among men and Caucasians but no link to age or education. These demographic variables covaried significantly. Logistic regression revealed that gender, ethnicity, and political party were significant when all predictors were included. Support increased significantly with a reliable roadside sobriety test to 44%, but some respondents who had agreed to the regulated market no longer agreed when the sobriety test was mentioned. Logistic regression revealed that ethnicity and political affiliation were again significant predictors of support with a reliable sobriety test, but gender was no longer significant. None of these demographic variables could identify who would change their votes in response to the reliable roadside test. CONCLUSION: Increased awareness and use of roadside sobriety tests that detect recent cannabis use could increase support for a regulated cannabis market. Identifying concerns of voters who are not Caucasian or Democrats could help alter cannabis policy

Learn And Work: A Hybrid Educational Model For Engineering Education

Traditional models of education are undergoing significant change in recent times due to evolving graduate attributes, shaped in no small part by the changing demands of modern industrial practices. Technology is one of the key elements of the factory of the future. Advances in manufacturing and digital technologies facilitate automation and offer significant benefits in a variety of areas. Academic programmes that feature industrial work placement have long been a feature of engineering education in TU Dublin. The BSc in Process Instrumentation and Automation is a three-year programme that goes further in that it evenly balances on-campus instruction with work placement. The programme was specifically devised in response to industry feedback that had identified significant skills shortages in the areas of industrial instrumentation and automation. It is a hybrid between the apprenticeship model of education (www.apprenticeship.ie) and the traditional engineering degree model and directly addresses industry\u27s immediate need for experienced graduates. Participation in the programme is sponsored by Irish Medtech Skillnet, a learning network for companies in the medical technology and engineering sector that responds to the training needs of that sector. This is one step in the lifelong learning path of a modern graduate. This paper will provide a detailed critical review of the ‘learn and work’ model; strengths, challenges and opportunities offered by this mode of engineering education