Performance Evaluation of Different PV-Array Configurations under Weak Light Conditions and Partial Shadings

The paper analyses empirical performance data of five commercial PV-plants in Germany. The purpose was on one side to investigate the weak light performance of the different PV-modules used. On the other hand it was to quantify and compare the shading losses of different PV-array configurations. The importance of this study relies on the fact that even if the behavior under weak light conditions or the shading losses might seem to be a relatively small percentage of the total yearly output; in projects where a performance guarantee is given, these variation can make the difference between meeting or not the conditions.When analyzing the data, a high dispersion was found. To reduce the optical losses and spectral effects, a series of data filters were applied based on the angle of incidence and absolute Air Mass. To compensate for the temperature effects and translate the values to STC (25°C), five different methods were assessed. At the end, the Procedure 2 of IEC 60891 was selected due to its relative simplicity, usage of mostly standard parameters found in datasheets, good accuracy even with missing values, and its potential to improve the results when the complete set of inputs is available.After analyzing the data, the weak light performance of the modules did not show a clear superiority of a certain technology or technology group over the others. Moreover, the uncertainties in the measurements restrictive the conclusiveness of the results.In the partial shading analysis, the landscape mounting of mc-Si PV-modules in free-field showed a significantly better performance than the portrait one. The cross-table string using CIGS modules did not proved the benefits expected and performed actually poorer than a regular one-string-per-table layout. Parallel substrings with CdTe showed a proper functioning and relatively low losses. Among the two product generations of CdTe analyzed, none showed a significantly better performance under partial shadings.

The importance of body-mass exponent optimization for evaluation of performance capability in cross-country skiing

Introduction Performance in cross-country skiing is influenced by the skier’s ability to continuously produce propelling forces and force magnitude in relation to the net external forces. A surrogate indicator of the “power supply” in cross-country skiing would be a physiological variable that reflects an important performance-related capability, whereas the body mass itself is an indicator of the “power demand” experienced by the skier. To adequately evaluate an elite skier’s performance capability, it is essential to establish the optimal ratio between the physiological variable and body mass. The overall aim of this doctoral thesis was to investigate the importance of body-mass exponent optimization for the evaluation of performance capability in cross-country skiing. Methods In total, 83 elite cross-country skiers (56 men and 27 women) volunteered to participate in the four studies. The physiological variables of maximal oxygen uptake (V̇O2max) and oxygen uptake corresponding to a blood-lactate concentration of 4 mmol∙l-1 (V̇O2obla) were determined while treadmill roller skiing using the diagonal-stride technique; mean oxygen uptake (V̇O2dp) and upper-body power output (Ẇ) were determined during double-poling tests using a ski-ergometer. Competitive performance data for elite male skiers were collected from two 15-km classical-technique skiing competitions and a 1.25-km sprint prologue; additionally, a 2-km double-poling roller-skiing time trial using the double-poling technique was used as an indicator of upper-body performance capability among elite male and female junior skiers. Power-function modelling was used to explain the race and time-trial speeds based on the physiological variables and body mass. Results The optimal V̇O2max-to-mass ratios to explain 15-km race speed were V̇O2max divided by body mass raised to the 0.48 and 0.53 power, and these models explained 68% and 69% of the variance in mean skiing speed, respectively; moreover, the 95% confidence intervals (CI) for the body-mass exponents did not include either 0 or 1. For the modelling of race speed in the sprint prologue, body mass failed to contribute to the models based on V̇O2max, V̇O2obla, and V̇O2dp. The upper-body power output-to-body mass ratio that optimally explained time-trial speed was Ẇ ∙ m-0.57 and the model explained 63% of the variance in speed. Conclusions The results in this thesis suggest that V̇O2max divided by the square root of body mass should be used as an indicator of performance in 15-km classical-technique races among elite male skiers rather than the absolute or simple ratio-standard scaled expression. To optimally explain an elite male skier’s performance capability in sprint prologues, power-function models based on oxygen-uptake variables expressed absolutely are recommended. Moreover, to evaluate elite junior skiers’ performance capabilities in 2-km double-poling roller-skiing time trials, it is recommended that Ẇ divided by the square root of body mass should be used rather than absolute or simple ratio-standard scaled expression of power output.

Optimal V. O2max-to-mass ratio for predicting 15 km performance among elite male cross-country skiers

The aim of this study was 1) to validate the 0.5 body-mass exponent for maximal oxygen uptake (V. O2max) as the optimal predictor of performance in a 15 km classical-technique skiing competition among elite male cross-country skiers and 2) to evaluate the influence of distance covered on the body-mass exponent for V. O2max among elite male skiers. Twenty-four elite male skiers (age: 21.4±3.3 years [mean ± standard deviation]) completed an incremental treadmill roller-skiing test to determine their V. O2max. Performance data were collected from a 15 km classicaltechnique cross-country skiing competition performed on a 5 km course. Power-function modeling (ie, an allometric scaling approach) was used to establish the optimal body-mass exponent for V . O2max to predict the skiing performance. The optimal power-function models were found to be race speed = 8.83⋅(V . O2max m-0.53) 0.66 and lap speed = 5.89⋅(V . O2max m-(0.49+0.018lap)) 0.43e0.010age, which explained 69% and 81% of the variance in skiing speed, respectively. All the variables contributed to the models. Based on the validation results, it may be recommended that V. O2max divided by the square root of body mass (mL⋅min−1 ⋅kg−0.5) should be used when elite male skiers’ performance capability in 15 km classical-technique races is evaluated. Moreover, the body-mass exponent for V . O2max was demonstrated to be influenced by the distance covered, indicating that heavier skiers have a more pronounced positive pacing profile (ie, race speed gradually decreasing throughout the race) compared to that of lighter skiers.

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.

Response surface models to predict broiler performance and applications for economic analysis

A study was carried out to elaborate response surface models using broiler performance data recovered from literature in order to predict performance and elaborate economic analyses. Nineteen studies published between 1995 and 2005 were retrieved using the systematic literature review method. Weight gain and feed conversion data were collected from eight studies that fulfilled the pre-established inclusion criteria, and a response surface model was adjusted using crude protein, environmental temperature, and age as independent variables. The models produced for weight gain (r² = 0.93) and feed conversion (r² = 0.85) were accurate, precise, and not biased. Protein levels, environmental temperature and age showed linear and quadratic effects on weight gain and feed conversion. There was no interaction between protein level and environmental temperature. Age and crude protein showed interaction for weight gain and feed conversion, whereas interaction between age and temperature was detected only for weight gain. It was possible to perform economic analyses to determine maximum profit as a function of the variables that were included in the model. It was concluded that the response surface models are effective to predict the performance of broiler chickens and allow the elaboration of economic analyses to optimize profit.

The Role of CEO Statements of Aggressiveness and the Competitive Aggressiveness of Firms: What is the Impact on Performance?

This research examines the impact of a CEO’s statements of aggressiveness on his or her organization’s competitive moves and subsequent performance. Hypotheses were developed based on previous work in Upper Echelon Theory and competitive dynamics. Based on this prior literature, it was hypothesized aggressive statements by CEOs will be associated with more aggressive organizations. It was also hypothesized these more aggressive organizations would display better performance than less aggressive organizations. A content analysis of letters to shareholders and trade publications was performed. This data was analyzed using multiple regression in SPSS 17 to test the hypotheses that aggressive statements by CEOs are associated with aggressive organizations and higher performance. Aggression scores for the content analysis were generated using the software package DICTION. The sample for the study was the organizations with the most revenue in two industries, automobile manufacturing and retailing. Data collection covered a five-year time span from 2003-2007, with performance data lagged one year. Control variables employed included CEO tenure, CEO background, organization size, and organization age. The findings indicate that CEO statements of aggressiveness do not significantly impact the competitive aggressiveness or the performance of their organizations. The implications of these findings are discussed and potential avenues for future research in the area are outlined.