Physiological and Performance Correlates of Squash Physical Performance

The physiological and performance attributes of elite squash players were investigated. Thirty-one players (21 males, world ranking [WR] 42-594; 10 females, WR 7-182) completed a battery of fitness tests which included an aerobic squash-specific physical performance test (SPPT), repeated-sprint ability (RSA), change-of-direction speed (COD), acceleration (5-m sprint), body composition and force development (countermovement jump) assessments. The SPPT provided a finishing lap score, V̇O2max, average movement economy and the lap corresponding to a blood lactate concentration of 4 mM.L-1. Players were ranked and assigned to HIGH or LOW performance tiers. Two-way ANOVA (performance level*sex) revealed higher ranked players performed better (p < 0.05) for SPPT final lap (d = 0.35), 4 mM.L-1 lap (d = 0.52) and COD (d = 0.60). SPPT displayed a ‘very-large’ correlation with 4 mM.L-1 lap (r = 0.86), ‘large’ correlations with COD (r = 0.79), RSA (r = 0.79), sum-of-7 skinfolds (r = 0.71) and V̇O2max (r = 0.69), and a ‘trivial’ correlation with average movement economy (r = 0.02). Assessments of cardiovascular fitness (i.e. 4 mM.L-1 lap), RSA, COD and body composition appear highly pertinent for performance profiling of squash players. Regular, submaximal assessment of the 4 mM.L-1 lap during the SPPT may offer a practical athlete monitoring approach for elite squash players.

Performance profiling of the unit trust funds in Malaysia with data mining techniques

Background: Millennials are exposed to many investment opportunities, and they have shown their interest in gaining more income via investments. One popular investment avenue is unit trusts. However, analysing unit trusts’ financial data and gaining valuable insights may not be as simple because not everyone has the required financial knowledge and adequate time to perform in-depth analytics on the numerous financial data. Furthermore, it is not easy to compile the performance of each unit trust available in Malaysia. The primary objective of this research is to identify unit trust funds that provide higher returns than their average peers via performance profiling. Methods: This research proposed a performance profiling on Malaysia unit trust funds using the two data mining techniques, i.e., Expectation Maximisation (EM) and Apriori, to assist amateur retail investors to choose the right unit trust based on their risk tolerance. EM clustered the unit trust funds in Malaysia into several groups based on their annual financial performances. This was then followed by finding the rules associated with each cluster by applying Apriori. The resulted rules shall serve the purpose of profiling the clustered unit trust funds. Retail investors can then select their preferred unit trust funds based on the performance profile of the clusters. Results: The yearly average total return of the financial year 2018 and 2019 was used to evaluate unit trust funds’ performance in the clusters. The evaluation results indicated that the profiling could provide valuable and insightful information to retail investors with varying risk appetites. Conclusions: This research has demonstrated that the financial performance profiling of unit trust funds could be acquired via data mining approaches. This valuable information is crucial to unit trust investors for selecting suitable funds in investment.

Probability Analysis to Improve the Confidence in Profiling Accuracy

Performance profiling for the system is necessary and has already been widely supported by hardware performance counters (HPC). HPC is based on the registers to count the number of events in a time interval and uses system interruption to read the number from registers to a recording file. The profiled result approximates the actual running states and is not accurate since the profiling technique uses sampling to capture the states. We do not know the actual running states before, which makes the validation on profiling results complex. Jianwei YinSome experiments-based analysis compared the running results of benchmarks running on different systems to improve the confidence of the profiling technique. But they have not explained why the sampling technique can represent the actual running states. We use the probability theory to prove that the expectation value of events profiled is an unbiased estimation of the actual states, and its variance is small enough. For knowing the actual running states, we design a simulation to generate the running states and get the profiled results. We refer to the applications running on production data centers to choose the parameters for our simulation settings. Comparing the actual running states and the profiled results shows they are similar, which proves our probability analysis is correct and improves our confidence in profiling accuracy.

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.

Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations

Force plate assessments, such as countermovement jumps and isometric mid-thigh pulls, examine performances (e.g., jump height, force, power) and movement strategies (e.g., asymmetries, durations), and are best suited to characterize and monitor physical capabilities, not predict injuries. To begin applying force plate technologies, users must first; (1) develop a data management plan to visualize and capture data over time; (2) select appropriate force plates for their scenario; (3) design appropriate testing protocols to ensure valid and reliable data. Force plate assessments may be added to existing testing, serve as separate testing batteries for annual profile testing to compare individuals and understand initial physical capabilities, or for more frequent testing (i.e., monthly or weekly) to monitor training-related adaptations or neuromuscular fatigue. Although these assessments inform evidence-based program designs, human performance practitioners must understand the considerations for conducting appropriate force plate testing, as well as proper visualizations and management of force plate data. Thus, the aim of this review is to provide evidence-based practices for utilizing force plates in tactical populations (e.g., military, firefighters, police). This includes best practices to implement testing for performance profiling, training adaptations, and monitoring neuromuscular fatigue and force asymmetries. Of note, due to the large amount of force-time metrics to choose from, this article provides general examples of important metrics to monitor and training recommendations based on changes to these force-time metrics, followed by specific examples in three case studies.

Evaluation of a Clinical Pharmacist Specialist Transition of Care Pathway to Manage Heart Failure Readmissions During a Provider Shortage

Background: Evidence supports scheduling early follow-up after heart failure (HF) hospitalization with a provider capable of managing hypervolemia. Often this service is provided by cardiologists or specialty nurse practitioners. Continuity or “familiar” providers may be better positioned to identify decompensating HF in patients who have advanced HF and/or multiple complicating medical problems. The objective of this study was to evaluate whether a clinical pharmacy specialist (CPS) service, covering the role of a “familiar” provider in an advanced HF specialty clinic (AHFC) during a staffing shortage, may prevent readmission metrics from worsening. Methods: We evaluated the entire, eligible concurrent cohorts, representing 175 AHFC-CPS and 273 control patient-admissions, respectively. Study- and disease-specific predictors for readmission were assessed. A matched cohort of 202 patient-admissions (101 AHFC-CPS:101 NO-CPS) were evaluated. Results: Subjects were predominantly white, elderly males. While overall “clinic [performance] profiling” outcomes for readmissions (p = 0.43) and mortality (p = 0.66) did not statistically differ between the AHFC-CPS and NO-CPS groups, an imbalance in severity of illness persisted. A survival curve and analysis were constructed, and the hazard ratio for all-cause mortality was 0.69 (p = 0.033). Conclusions: This retrospective project supports the premise that AHFC-CPS intervention may be a suitable alternative to maintain the volume status for AHFC patients during a staffing short-fall. More work needs to be done to determine intervention effect size, predictors for readmission, specifically in advanced cardiovascular disease, and to evaluate CPS opportunities in the provision of independent HF care, particularly for patients with advanced HF.