A comparison of methods to calculate the optimal load for maximal power output in the power clean

The aim of this study was to compare three calculation methods to determine the load that maximises power output in the power clean. Five male athletes (height=179.8 10.5cms, weight 91 .8 8.8kg, power dean 1RM = 117.0 20.5kg) performed two per cleans at 10% increments from 50% to 100% of 1RM. Bar displacement data was collected using a Ballistic Measurement System (BMS) and vertical ground reaction force (VGRF) data was measured by a Kistler 9287B Force Plate. Power output was calculated for BMS (system mass), BMS (bar mass) and VGRF/BMS system mass. Optimal load was determined to be 70% for the BMS (system mass) and VGRF BMS (system mass) methods and 90% for the BMS (bar mass) method. Sports scientists should be aware of the technical issues underlying these findings due to the practical ramifications for athlete testing and training.

The suboptimality of optimal punishments in Counot supergames

We investigate, within a Cournot supergame, whether previous termoptimalnext term punishments may become suboptimal when economies of scale are not negligible. We show that, for sufficiently strong increasing returns to scale, the critical discount factor associated to grim trigger strategies is lower than the one associated to previous termoptimalnext term penal codes.

Optimal punishments with detection lags

In this note we characterize previous termoptimalnext term two-phase previous termpunishmentsnext term in the presence of an n period previous termdetection lag,next term i.e. when cheating on an implicit agreement can be kept secret for a certain time period. Two simple applications are also provided.

Optimal linear data fusion for systems with missing measurements

In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.

Optimal redistribution when educational background matters

Higher education plays an important role in determining individuals lifetime
earnings. In turn, the decision to become educated depends to a large extent on innate ability and on family characteristics, including both family
wealth and educational background. In this paper, we abstract from family
income differences to concentrate on the effects of fiscal policies on the
decision to undertake higher education when the educational background
matters. In a dynamic framework, where successive generations are linked by educational background, we consider a government that uses both linear income taxation and a lump-sum subsidy to education. Conditions for
optimality of each policy are derived. The factors that determine the sign
and magnitude of the tax rate and the subsidy are identified and include
concerns for redistribution, efficiency and the educational externality on
future generations

Optimal models for the provision of postgraduate programmes in TESOL for tertiary teachers of English in a developing country

Investigates the major theoretical and practical issues related to distance education and English language teacher retraining. Suggests a number of models for the provision of Australian postgraduate programmes in TESOL for the upgrading of the teaching qualifications of Vietnamese tertiary teachers of English.

The holy grail : the search for the optimal gaar

The sustainability of a taxation system relies in part in reducing effective tax avoidance as far as is possible. Tax avoidance impacts on the economy both because it results in revenue loss and because it diverts people into devising, marketing and implementing avoidance arrangements, rather than engaging in economically productive activities. Revenue losses in turn weaken the government's ability to formulate and implement fiscal policies. Moreover, the very integrity of a tax system, necessary for its long-term sustainability, is attacked by tax avoidance. The goal of equity is undermined by tax avoidance as typically these schemes are accessed by the wealthy. Thus, tax avoidance results in an unfair shift in the tax burden to the less wealthy in society. As one commentator recently noted, combating 'tax avoidance ... will serve to lessen the overall burden on all taxpayers'. Further, legislative responses to tax avoidance increase the complexity of a taxation scheme. Tax avoidance schemes also undermine the neutrality of a taxation system by distorting trade and investment decisions. Thus, tax avoidance attacks every aspect of a fair, and hence, sustainable, taxation system.

Integrating simulated annealing and delta technique for constructing optimal prediction intervals

This paper aims at developing a new criterion for quantitative assessment of prediction intervals. The proposed criterion is developed based on both key measures related to quality of prediction intervals: length and coverage probability. This criterion is applied as a cost function for optimizing prediction intervals constructed using delta technique for neural network model. Optimization seeks out to minimize length of prediction intervals without compromising their coverage probability. Simulated Annealing method is employed for readjusting neural network parameters for minimization of the new cost function. To further ameliorate search efficiency of the optimization method, parameters of the network trained using weight decay method are considered as the initial set in Simulated Annealing algorithm. Implementation of the proposed method for a real world case study shows length and coverage probability of constructed prediction intervals are better than those constructed using traditional techniques.

Diffusion layer parameters influencing optimal fuel cell performance

The performance of polymer electrolyte fuel cells (PEFCs) is substantially influenced by the morphology of the gas diffusion layer. Cells utilising sintered gas diffusion layers made with a low pore volume Acetylene Black carbon, at an optimised thickness, showed better performance compared with cells containing Vulcan XC-72R carbon. The cells were optimised using both oxygen and air as oxidants showing that different conditions were required in each case to achieve optimum cell performance. A model, in which the hydrophobicity and porosity of the diffusion layer affect water impregnation and gas diffusion through the gas diffusion layer, is presented to explain the influence of the diffusion layer morphology on cell performance.

Construction of optimal prediction intervals for load forecasting problems

Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions.

Designing optimal fault tolerant Jacobian for robotic manipulators

Fault tolerance of robotic manipulators is determined based on the fault tolerance measures. In this study a Jacobian of a 7DOF optimal fault tolerant manipulator is designed based on optimality of worse case relative manipulability and worse case dexterity from geometric perspective instead of numerical solution of constrained optimisation problem or construction of optimal Jacobean through a desired null space. The proposed Jacobean matrix is optimal and equally fault tolerant for a single joint failure within any joint of the manipulators.

Optimal actuator fault tolerance for static nonlinear systems based on minimum output velocity jump

Fault tolerance for a class of non linear systems is addressed based on the velocity of their output variables. This paper presents a mapping to minimize the possible jump of the velocity of the output, due to the actuator failure. The failure of the actuator is assumed as actuator lock. The mapping is derived and it provides the proper input commands for the healthy actuators of the system to tolerate the effect of the faulty actuator on the output of the system. The introduced mapping works as an optimal input reconfiguration for fault recovery, which provides a minimum velocity jump suitable for static nonlinear systems. The proposed mapping is validated through different case studies and a complementary simulation. In the case studies and the simulation, the mapping provides the commands to compensate the effect of different faults within the joints of a robotic manipulator. The new commands and the compare between the velocity of the output variables for the health and faulty system are presented.