Monitoring external workloads and countermovement jump performance may be useful for coaches. PURPOSE: The purpose of this study was to determine the effects of external load on player performance as measured by a CMJ and specific blood biomarkers throughout the preseason. METHODS:10 female division 1 basketball athletes had PlayerLoadTM (PL) monitored for all mandatory basketball training during six weeks of the preseason and CMJs were performed weekly. Blood biomarkers were collected before preseason and at the end of preseason. Data were analyzed via the Catapult Sport software (Openfield, Catapult, Innovations, Melbourne, VIC, Australia) to quantify all participant movement. Data from CMJs were analyzed via Sparta Science technology (SpartaTrac; SPARTA Performance Science, v1.2.4). Cumulative effect of physical activity (CTPL) was estimated as a sum of total PL up to each jump testing session divided by the number of days. Linear mixed-effects models were used to model data related to the efficacy of PL and CTPL. Athletes (id) and their positions were examined as potential random effects. RESULTS: The best fit model suggested a high-order polynomial pattern between PL and the number of days since the first jump testing session with a random effect for the intercept (marginal R2 = 0.290; conditional R2 = 0.471). The fixed effect for the slope of the first order term was found to be positive. There was a significant negative effect of CTPL on JH (p = 0.0037). The boot strapped model showed a marginal R2 of 0.0183 (95% CI [0.000952, 0.0744]) and a conditional R2 of 0.884 (95% CI [0.762, 0.956]). For RSImod, a significant negative association between RSImod and CTPL (p = 0.0039, 95% CI [-0.0002214, -4.597081e-05]). CONCLUSION: Workloads increase during preseason. CMJ height and RSImod may have limited utility in displaying the effects cumulative workloads. Position played did not impact workload or the impact of that workload on the player. PRACTICAL APPLICATION: Cumulative effect of physical activity may be tracked using CTPL derived from PL. Practitioners may be encouraged to monitor alternative countermovement variables to better understand performance response to the cumulative effect of physical activity.

Previous research has highlighted key taxonomic factors that have been found to influence human performance on vigilance tasks. However, previous literature has focused on research conducted in laboratory settings but has not examined vigilance tasks in remote environments. The present dissertation addresses this gap in the literature by examining human performance on a remote vigilance task, as well as workload and stress associated with the task. Qualitative data were collected to further understand the environment and distractions that participants experienced. Across three experiments, 372 participants were asked to complete a vigilance task and answer surveys pertaining to stress, workload, and ambient distractions. Experiment one manipulated the taxonomic factors of event rate and signal discrimination. Next, experiment two manipulated the taxonomic factors of event rate and source complexity. Finally, experiment three manipulated the taxonomic factors of event rate and task type. Across all three experiments, results identified significant differences in performance for the low and high event rate conditions, which are consistent with previous findings. Furthermore, signal discrimination type, source complexity, and task type (i.e., sensory vs. cognitive) resulted in significant differences between groups on measures of performance. All three experiments reported changes in perceived stress and increases in perceived workload. Ambient distractions, when they occurred, did impact performance, but only for experiment two. Overall, this study provides further support for several facets of the vigilance taxonomy and attempts to understand the impact of remote environments and ambient distractions on vigilance performance. Thus, these findings are advancing our understanding of the vigilance taxonomy and how environmental effects may influence human performance.

The effort-reward imbalance model allows us to see disparity in effort and reward and how this can be a predictor for a variety of constructs. The present study seeks to gather data utilizing the ERI modal in the nursing profession. Previous research has utilized the ERI model but methods for gathering data were not quick and efficient. This study seeks to utilize a database called Glassdoor to rapidly and effectively gather data. The researchers are interested in seeing the likelihood of nurses to recommend their company to a friend based on perceived effort and rewards. The sample included a random selection of 40 reviews from 40 randomly selected hospitals. To collect these random samplings, we used an excel random generator formula. We selected the 40 hospitals based on the corresponding number of the excel random generator and utilized the same method to select the 40 reviews. Sample words were developed through reviewing previous research. The frequency of each type of word was summed to create a numerical variable for effort and reward. Not only was the actual content of the review assessed, but the overall rating the user gave on Glassdoor for each particular variable was also used as reference to maintain accuracy. Bivariate correlations were conducted on the data to determine the strength of the effort-likelihood to recommend relationship and the reward-likelihood to recommend relationship. Results indicated that nurses who reported putting more effort into their company, were significantly more likely to recommend their company to a friend. Results also indicated that nurses who reported more rewards such as raises, compensation, and benefits were significantly more likely to recommend their company to a friend.

The aim of the research was to develop the non-intrusive physiological measure of using human facial skin temperature change as an indicator of mental workload. The forehead and nose temperature were obtained via thermography from the participants who drove in a simulator driving environment and/or in instrumented car experiments. The NASA TLX and the Modified Cooper-Harper metrics were adopted to assess the subjective workload for the validation of the physiological measure. Three driving experiments were conducted in order to acquire the physiological response and the workload score for the performed tasks. Forehead temperature was very stable throughout the experiments. Nose temperature dropped significantly after the experimental drive for all conditions in simulator test. Experiment 1 (NASA TLX Group: N=10; MCH Group: N=14) used simulator driving with different terrains as loading tasks. Neither the significant difference of the subjective workload nor the temperature drop was detected between different terrain conditions. In experiment 2 (N=33), mental workload was increased in a controlled manner by the introduction of mental arithmetic tests to the primary simulated drive. The mental arithmetic test conditions provoked a significantly greater nose temperature drop and also a higher perceived workload than the conditions without the arithmetic test. A weak correlation between the nose temperature drop and the subjective workload metric was yielded from the experiments. In Experiment 3 (N=13), facial temperature response and subjective workload score were compared between the simulator test and on-road driving. Driving in the simulator resulted in higher subjective workload and greater nose temperature drop than in real-car driving. When participants perceived a higher workload for a task, their nose temperature exhibited a greater drop. A significant correlation between the nose temperature change and the subjective workload score was found. Actual or potential applications of this research include real-time and unobtrusive mental workload assessment for human-system interaction development.

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The complexity and broadening of competencies have led to a number of assessment frameworks that advocate for the use of rater judgment in direct observation of clinical performance. The degree to which these assessment processes produce scores that are valid, are therefore vitally dependent on a rater’s cognitive ability. A number of theories suggest that many of the cognitive structures needed to complete rating tasks are capacity limited and may therefore become a source of difficulty when rating demands exceed resources. This thesis explores the role of rating demands on the performance and behaviour of raters in the assessment of clinical competence and asks: in what way do rating demands associated with rating clinical performance affect rater performance and behaviour? I hypothesized that as rating demands increase, rating performance declines and raters engage in cognitive avoidance strategies in order to complete the task. I tested this hypothesis by manipulating intrinsic and extraneous sources of load for raters in the assessment of clinical performance. Results consistently demonstrated that intrinsic load, specifically broadening raters’ focus by increasing the number of dimensions to be considered simultaneously, negatively affected indicators of rating quality. However, extraneous demands failed to result in the same effect in 2 of 3 experiments. When we explored the cognitive strategies raters engage under high load conditions we learned of a number of strategies to reduce cognitive work, including idiosyncratically minimizing intrinsic demands (leading to poor inter-rater reliability) and active elimination of sources of extraneous load, explaining both findings. When we induced extraneous load in manner that could not be easily minimized by raters, we also found impairments in rater performance, specifically the provision of feedback. I conclude that rating demands, whether induced intrinsically or by extraneous sources, impair rater performance affecting both the utility of scores and the opportunity for learner development. Implications for health professions education and future directions are discussed. / Dissertation / Doctor of Philosophy (PhD)

Yes / Next-generation cellular systems like fth generation (5G) is are expected to experience tremendous tra c growth. To accommodate such tra c demand, there is a need to increase the network capacity that eventually requires the deployment of more base stations (BSs). Nevertheless, BSs are very expensive and consume a lot of energy. With growing complexity of signal processing, baseband units are now consuming a signi cant amount of energy. As a result, cloud radio access networks (C-RAN) have been proposed as anenergy e cient (EE) architecture that leverages cloud computing technology where baseband processing is performed in the cloud. This paper proposes an energy reduction technique based on baseband workload consolidation using virtualized general purpose processors (GPPs) in the cloud. The rationale for the cloud based workload consolidation technique model is to switch o idle baseband units (BBUs) to reduce the overall network energy consumption. The power consumption model for C-RAN is also formulated with considering radio side, fronthaul and BS cloud power consumption. Simulation results demonstrate that the proposed scheme achieves an enhanced energy performance compared to the existing distributed long term evolution (LTE) RAN system. The proposed scheme saves up to 80% of energy during low tra c periods and 12% during peak tra c periods compared to baseline LTE system. Moreover, the proposed scheme saves 38% of energy compared to the baseline system on a daily average.