Anthropometry, physical performance, and ultrasound patellar tendon abnormality in elite junior basketball players: a cross-sectional study

Objective: Patellar tendinopathy has been reported to be associated with many intrinsic risk factors. Few have been fully investigated. This cross-sectional study examined the anthropometric and physical performance results of elite junior basketball players with normal or abnormal patellar tendons to see if any measures were associated with changes in tendon morphology.

Methods: Agility, leg strength, endurance, and flexibility were measured in 71 male and 64 female players. A blinded radiologist ultrasonographically examined their patellar tendons and athletes were grouped as having normal or abnormal tendons. One-way ANOVA was used to test for differences in anthropometric and physical performance data for athletes whose tendons were normal or abnormal (unilateral or bilateral tendinopathy) on ultrasound.

Results: Results show that females with abnormalities in their tendons had a significantly better vertical jump (50.9±6.8 cm) than those with normal tendons (46.1±5.4 cm) (p = 0.02). This was not found in males. In males, the mean sit and reach in those with normal tendons (13.2±6.7 cm) was greater (p<0.03) than in unilateral tendinopathy (10.3±6.2 cm) or in bilateral tendinopathy (7.8±8.3 cm). In females, those with normal tendons (13.3±4.8 cm) and bilateral tendinopathy (15.8±6.2 cm) were distinctly different from those with unilateral tendinopathy (7.9±6.6 cm).

Conclusion: Flexibility and vertical jump ability are associated with patellar tendinopathy and the findings warrant consideration when managing young, jumping athletes.

The influence of strategic HRM and sector on perceived performance in health services organizations

In this study, we examine a variety of management characteristics of for-profit and not-for-profit organizations in the health services (HS) industry. Data collected from Australian senior executives are used to test the relationships between managerial constructs such as employee commitment, customer demandingness, strategic HRM orientation and the adoption of human capital-enhancing human resource (HR) practices and perceived overall performance. Data analysis conducted using the Partial Least Square Modeling show a statistically significant path from commitment to employees, customer demandingness and strategic HRM orientation to the adoption of human capital-enhancing HR practices (such as selective staffing, comprehensive training, and performance appraisal) to perceived organizational performance. The results also show that private sector health service organizations have a higher level of perceived performance.

Performance and anthropometric characteristics of prospective elite junior Australian footballers : a case study in one junior team

The aim of the study was to compare anthropometric and physical performance data of players who were selected for a Victorian elite junior U18 Australian rules football squad. Prior to the selection of the final training squad, 54 players were assessed using a battery of standard anthropometric and physical performance tests. Multivariate analysis (MANOVA) showed significant (p < 0.05) differences between selected and non-selected players when height, mass, 20-m sprint, agility and vertical jump height were considered collectively. Univariate analysis revealed that the vertical jump was the only significant (p < 0.05) individual test and a near significant trend (p = 0.07) for height differentiating between selected and non-selected players with medium effect sizes for all other tests except endurance. In this elite junior football squad, physical characteristics can be observed that discriminate between players selected and non-selected, and demonstrates the value of physical fitness testing within the talent identification process of junior (16–18 years) players for squad and/or team selection. Based on MANOVA results, the findings from this study suggest team selection appeared to be related to a generally higher performance across the range of tests. Further, age was not a confounding variable as players selected tended to be younger than those non-selected. These findings reflect the general consensus that, in state-based junior competition, there is evidence of promoting overall player development, selecting those who are generally able to fulfil a range of positions and selecting players on their potential.

On self-propagating methodological flaws in performance normalization for strength and power sports

Performance in strength and power sports is greatly affected by a variety of anthropometric factors. The goal of performance normalization is to factor out the effects of confounding factors and compute a canonical (normalized) performance measure from the observed absolute performance. Performance normalization is applied in the ranking of elite athletes, as well as in the early stages of youth talent selection. Consequently, it is crucial that the process is principled and fair. The corpus of previous work on this topic, which is significant, is uniform in the methodology adopted. Performance normalization is universally reduced to a regression task: the collected performance data are used to fit a regression function that is then used to scale future performances. The present article demonstrates that this approach is fundamentally flawed. It inherently creates a bias that unfairly penalizes athletes with certain allometric characteristics, and, by virtue of its adoption in the ranking and selection of elite athletes, propagates and strengthens this bias over time. The main flaws are shown to originate in the criteria for selecting the data used for regression, as well as in the manner in which the regression model is applied in normalization. This analysis brings into light the aforesaid methodological flaws and motivates further work on the development of principled methods, the foundations of which are also laid out in this work.

The effects of training on performance and performance-related states in individual elite athletes : a dynamic approach

There are difficulties undertaking controlled training studies with elite athletes. Thus, data from non-elite performers are often presented in scientific journals and subsequently used to guide general training principles. This information may not be transferable or specific enough to inform training practices in an individual elite athlete. However, the nature of athletic participation at elite levels provides the opportunity to collect training data, performance-related variables, and performance data of elite athletes over long periods. In this paper, we describe how dynamic linear models provide an opportunity to use these data to inform training. Data from an elite female triathlete collected over a 111-day training period were used to model the relationship between training and self-reported fatigue. The dynamic linear model analysis showed the independent effects of the three modes of triathlon training on fatigue, how these can change across time, and the possible influence of other unmeasured variables. This paper shows the potential for the use of dynamic linear models as an aid to planning training in elite athletes.

Board committee meetings and firm financial performance : an investigation of Australian companies

This article examines how the frequency of board committee meetings impacts on Australian firms’ financial performance. Data were collected from 118 Australian listed companies – including 26 financial firms and 92 nonfinancial firms – for the period 1999–2007. Analysis of that data shows that the frequencies of audit committee meetings and remuneration committee meetings are positively and significantly associated with return on equity and return on assets. The frequencies of risk committee meetings do not show any significant effects on the financial performance of Australian firms. Estimated results are found to be robust after controlling for internal as well as external governance mechanisms that might affect Australian firm performance.

Analyses and comparison of counter-movement jump performance and self-rated recovery in state under-18s Australian Rules Football players during a national championship

Recovery of team sport athletes during multiple competitive games is an important area for strength and conditioning coaches to monitor as it facilitates for athletes to be ready to perform (11,13). Utilising athletic performance data in conjunction with self-rated reporting measures can help determine if in fact a player or team has recovered sufficiently or shown a trend towards recovery prior to a competitive match (11). Positive improvement in recovery variables can provide confidence in the effectiveness of recovery methods used and assist in determining the training schedule in order to positively manipulate the fitness-fatigue relationship (3).

Various methods of analysing the recovery of athletes have been reported in the literature and are available to the strength and conditioning coach. These include subjective, self-rated scales and perceived level of recovery questionnaires (11,12,13). Athletic performance measures during exercises such as the counter movement jump (CMJ) have also been analysed, predominantly utilising force plates to obtain kinetic data. (5,13,14). However, such equipment can be difficult to transport, requires continual calibration and is costly to purchase. A linear transducer can provide important information on CMJ variables in the assessment of athletic movements and due to its size and portability could serve as a valuable tool to assist strength and conditioning coaches, (8,10), and potentially enable the monitoring of recovery.

Previous studies have investigated the fatigue effects of competitive games in various sports (11,13,14) including Australian Rules Football (AFL) at the senior elite league level (5, 6). To the authors’ knowledge, however, there is yet to be a study investigating the recovery response in AFL players, specifically in players 18 years and under competing in the National Under 18s Championships. Australian Rules football is an extremely physically demanding and fatiguing sport where players participate in games time exceeding 120 minutes duration, covering large distances (~12-18km, position dependent) with many high intensity efforts performed at random times throughout the game (2,6,16). Hence, it would seem pertinent to analyse the fatigue effects of competitive matches in an Australian Rules Under-18’s National Championship and the subsequent recovery from these games.

The aim of this study was to analyse and compare two self-rated subjective measures of recovery; they being muscle soreness (MS) of the lower body, overall perceived total recovery (TR), and the performance measure of peak velocity (PV) obtained from a CMJ analysed with a linear transducer. Data collection occurred between rounds four and five of the Australian Football League Under-18’s National Championship, representing a four-day recovery analysis period between matches.

Nonparametric discovery of learning patterns and autism subgroups from therapeutic data

Autism Spectrum Disorder (ASD) is growing at a staggering rate, but, little is known about the cause of this condition. Inferring learning patterns from therapeutic performance data, and subsequently clustering ASD children into subgroups, is important to understand this domain, and more importantly to inform evidence-based intervention. However, this data-driven task was difficult in the past due to insufficiency of data to perform reliable analysis. For the first time, using data from a recent application for early intervention in autism (TOBY Play pad), whose download count is now exceeding 4500, we present in this paper the automatic discovery of learning patterns across 32 skills in sensory, imitation and language. We use unsupervised learning methods for this task, but a notorious problem with existing methods is the correct specification of number of patterns in advance, which in our case is even more difficult due to complexity of the data. To this end, we appeal to recent Bayesian nonparametric methods, in particular the use of Bayesian Nonparametric Factor Analysis. This model uses Indian Buffet Process (IBP) as prior on a binary matrix of infinite columns to allocate groups of intervention skills to children. The optimal number of learning patterns as well as subgroup assignments are inferred automatically from data. Our experimental results follow an exploratory approach, present different newly discovered learning patterns. To provide quantitative results, we also report the clustering evaluation against K-means and Nonnegative matrix factorization (NMF). In addition to the novelty of this new problem, we were able to demonstrate the suitability of Bayesian nonparametric models over parametric rivals.

It’s not all about language ability: motivational cultural intelligence matters in call center performance

his study focuses on the role of motivational cultural intelligence (CQ) in call center performance. Call centers mainly rely on verbal communication with language ability playing a significant role in delivery of tasks. This study argues that motivational CQ, or the interest and efficacy when interacting with individuals from culturally diverse backgrounds, plays a significant role in call center performance. This study was conducted in the Philippines, one of the top destinations for offshore services like call centers. Studies were conducted at two time points to determine the relationship between language ability, motivational CQ, and task performance. At Time 1, the language ability of 125 call center agent applicants was determined and assessed. At Time 2 which was conducted six months later, performance data were obtained and the level of the motivational CQ of the respondents measured. Results show that language ability is positively and significantly related to task performance. However, when motivational CQ was included, the relationship between language ability and task performance became non-significant, which conveys the full mediating role of motivational CQ in that relationship.

Automated analysis of performance and energy consumption for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

Integrating internet-of-things with the power of cloud computing and the intelligence of big Data analytics- a three layered approach

This paper is written through the vision on integrating Internet-of-Things (IoT) with the power of Cloud Computing and the intelligence of Big Data analytics. But integration of all these three cutting edge technologies is complex to understand. In this research we first provide a security centric view of three layered approach for understanding the technology, gaps and security issues. Then with a series of lab experiments on different hardware, we have collected performance data from all these three layers, combined these data together and finally applied modern machine learning algorithms to distinguish 18 different activities and cyber-attacks. From our experiments we find classification algorithm RandomForest can identify 93.9% attacks and activities in this complex environment. From the existing literature, no one has ever attempted similar experiment for cyber-attack detection for IoT neither with performance data nor with a three layered approach.

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning.

Accountability, completeness, credibility and the audit expectations gap

This article deals with two concerns in achieving greater accountability in social reports: the lack of completeness of reporting, and the lack of credibility of reports. The article focuses, in particular, on the role of social audits in improving the completeness and credibility of reporting, thereby reducing the audit expectations gap. We suggest that this gap arises due to an over-emphasis on the validity of performance data at the expense of addressing completeness and credibility, both of which, we argue, require stakeholder involvement. The article reviews recent guidelines aimed at ensuring that companies produce reports that are complete in all material respects including those produced by the Global Reporting Initiative and the Federation des Experts Comptables Europeens, focusing particularly on AccountAbility's AA1000 Standard and AA1000S Assurance Standard. Finally, the article considers the development of a practical approach to social audit following principles increasingly being incorporated into developing assurance guidelines aimed at reducing the audit expectations gap.

Are workers with a disability less productive or less understood? An empirical investigation from an entrepreneurial business planning perspective

This study investigated selected work-performance data of a large call centre using the entrepreneurial business planning paradigm as a theoretical framework and tested the hypothesis that levels of productivity would be different for each group between workers with a disability and workers without a disability. On five measures of productivity, no significant differences were discernible but on a sixth measure, length of employment, it was found that disability workers remained in employment significantly longer. These results strongly refute the ‘intuitive wisdom’ that workers with a disability are less productive. The results support a growing body of corporate experience and descriptive research indicating that workers with a disability perform as well as or better than their non-disability colleagues. Yet workers with a disability remain disproportionately under-employed. The key to translating the growing evidence of this research into higher levels of employment of workers with disabilities will depend upon employers adopting an entrepreneurial approach to the planning of human resource management.