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We report an experiment conducted to gain insight into factors that may affect revenues in English auctions and lotteries, two commonly used charity fund-raising formats. In particular, we examine how changes in the marginal per capita return (MPCR) from the public component of bidding, and how changes in the distribution of values affect the revenue properties of each format. Although we observe some predicted comparative static effects, the dominant result is that lottery revenues uniformly exceed English auction revenues. The similarity of lottery and English auction bids across sales formats appears to drive the excess lottery revenues.

It is proposed that social capital, a societal‐level construct, can be measured at the individual level. This ‘personal social capital’ is a psychological construct defined as a logically linked sequence of social behaviours: community participation, social support and trust in others. Individuals who have more personal social capital will participate in their communities more and have more social support, greater trust in others and less psychological distress than those with less. It was also predicted that social values would influence levels of personal social capital, indirectly influencing distress. Structural equations modelling revealed that, within the construct of personal social capital, the strongest predictor of distress was community trust. Harmony values also directly predicted distress, while security values had an indirect effect via reduced community participation, social support and community trust.

Bench scale and flame manikin tests are two typical methods to evaluate thermal protective performance (TPP) of fire protective clothing. However, flame manikin test is limited to be widely used for its complication and high cost. The purpose of this paper is to develop a method to evaluate the thermal performance of protective clothing from the bench scale test results and garment parameters, which predicts the body burn injuries without conducting flame manikin tests.

Bench scale and flame manikin tests’ data were collected from the previous research literature and then statistical analysis was performed to quantitatively investigate the correlations between the two test methods. Equations were established to predict the TPP values accounting for the effects of entrapped air gap and thermal shrinkage. Fitting analysis was conducted to analyze the relationship between the predicted TPP values and total burn injury. Finally, a method to predict total burn injury from the TPP values was proposed and validated.

The results showed that when the TPP value was predicted with the effects of air gap and thermal shrinkage considered, there was an approximate linear relationship between the predicted TPP values and total burn injury from the manikin test. Therefore, the prediction model of burn injury was developed based on the correlation analysis and verified with a generally good accuracy.

This paper presented a new prediction method to evaluate the thermal performance of protective clothing, which saved significant time and cost compared to the conventional methods. It can provide useful information for burn injury prediction of protective clothing.

The purpose of this paper is to present a new particle swarm optimization (PSO) algorithm called HelixPSO for finding ribonucleic acid (RNA) secondary structures that have a low energy and are similar to the native structure.

Two variants of HelixPSO are described and compared to the recent algorithms Rna‐Predict, SARNA‐Predict, SetPSO and RNAfold. Furthermore, a parallel version of the HelixPSO is proposed.

For a set of standard RNA test sequences it is shown experimentally that HelixPSO obtains a better average sensitivity than SARNA‐Predict and SetPSO and is as good as RNA‐Predict and RNAfold. When best values for different measures (e.g. number of correctly predicted base pairs, false positives and sensitivity) over several runs are compared, HelixPSO performs better than RNAfold, similar to RNA‐Predict, and is outperformed by SARNA‐Predict. It is shown that HelixPSO complements RNA‐Predict and SARNA‐Predict well since the algorithms show often very different behavior on the same sequence. For the parallel version of HelixPSO it is shown that good speedup values can be obtained for small to medium size PC clusters.

The new PSO algorithm HelixPSO for finding RNA secondary structures uses different algorithmic ideas than the other existing PSO algorithm SetPSO. HelixPSO uses thermodynamic information as well as the centroid as a reference structure and is based on a multiple swarm approach.

This paper aims to compare the comprehensive rotorcraft analyses using the two different blade section property data sets for the blade natural frequencies, airloads, elastic deformations, the trimmed rotor pitch control angles and the blade structural loads of a small-scale model rotor in a blade vortex interaction (BVI) phenomenon.

The two different blade section property data sets for the first Higher-harmonic control Aeroacoustic Rotor Test (HART-I) are considered for the present rotor aeromechanics analyses. One is the blade property data set using the predicted values which is one of the estimated data sets used for the previous validation works. The other data set uses the measured values for an uninstrumented blade. A comprehensive rotorcraft analysis code, CAMRAD II (comprehensive analytical model of rotorcraft aerodynamics and dynamics II), is used to predict the rotor aeromechanics such as the blade natural frequencies, airloads, elastic deformations, the trimmed rotor pitch control angles and the blade structural loads for the three test cases with and without higher-harmonic control pitch inputs. In CAMRAD II modelling with the two different blade property data sets, the blade is represented as a geometrically nonlinear elastic beam, and the multiple-trailer wake with consolidation model is used to consider more elaborately the BVI effect in low-speed descending flight. The aeromechanics analysis result sets using the two different blade section property data sets are compared with each other as well as are correlated with the wind-tunnel test data.

The predicted blade natural frequencies using the two different blade section property data sets at non-rotating condition are quite similar to each other except for the natural frequency in the fourth flap mode. However, the natural frequencies using the predicted blade properties at nominal rotating condition are lower than those with the measured blade properties except for the second lead-lag frequency. The trimmed collective pitch control angle with the predicted blade properties is higher than both the wind-tunnel test data and the result using the measured blade properties in all the three test cases. The two different blade property data sets both give reasonable predictions on the blade section normal forces with BVI in the three test cases, and the two analysis results are reasonably similar to each other. The blade elastic deformations at the tip using the measured blade properties are correlated more closely with the wind-tunnel test data than those using the predicted blade properties in most correlation examples. In addition, the predictions of blade structural loads can be slightly or moderately improved by using the measured blade properties particularly for the oscillatory flap bending moments. Finally, the movement of the sectional centre of gravity location of the uninstrumented blade has a moderate influence on the blade elastic twist at the tip in the baseline case and the oscillatory flap bending moment in the minimum noise case.

The present comparison study on rotor aeromechanics analyses using the two different blade property data sets will show the influence of blade section properties on rotor aeromechanics analysis.

This paper is the first attempt to compare the aeromechanics analysis results using the two different blade section property data sets for all three test cases (baseline, minimum noise and minimum vibration) of HART-I in low-speed descending flight.

As the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become challenging tasks for consumers. Hence, knowing the closing price of a given auction would be an advantage, since this information will ensure a win in a given auction. However, predicting a closing price for an auction is not easy, since it is dependent on many factors. The purpose of this paper is to report on a predictor agent that utilises grey system theory to predict the closing price for a given auction.

The focus of the research is on grey system agent. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price in order to maximise the bidder's profit. The performance of this predictor agent is compared with two well‐known techniques, the Simple Exponential Function and the Time Series, in a simulated auction environment and in the eBay auction.

The grey theory agent gives a better result when less input data are made, while the Time Series Agent can be used with the availability of a lot of information. Although the Simple Exponential Function Agent is able to predict well with less input data, it is not an appropriate method to be applied in the prediction model since its formula is not realistic and applicable in predicting the online auction closing price. The experimental results also showed that using moving historical data produces a higher accuracy rate than using fixed historical data for all three agents.

Grey system theory prediction model, GM(1, 1) has not been applied in online auction prediction. In this paper the authors have applied grey theory into an agent to predict the closing price of an online auction, in order to increase the profit of bidders in the bidding stage. The experimental results show that the accuracy of the grey prediction model is more then 90 per cent, with less then eight historical data inputs.

The purpose of this paper is to theoretically compute and predict the needle penetration force (NPF) in woven denim fabrics with twill 3/1 weave pattern on the basis of geometrical, physical, and mechanical properties of yarns and fabric, and characteristics of sewing needle.

To predict the NPF by mathematical relations, the proposed models by Stylios and Xu (1995) and Lomov (1998) are extended for a twill woven structure. The NPF is calculated based on resistance forces due to yarn tensile elongation, yarn resistance to bending in the near of the sewing needle while the needle penetrates into the fabric, friction between weft and warp yarns, needle profile shape, and friction between sewing needle and yarns. In order to evaluate the obtained results, nine different denim fabric samples are produced, and five sewing needles with different sizes are used. The NPF is measured on the Instron tensile tester to simulate sewing process.

The results show that there is a good relationship between the predicted and experimental values of the NPF (R2=0.831, MSE=0.079, and MAPE=9.51 percent). Moreover, it is found that the performance of developed model to predict the NPF for needle sizes of 80, 90, 100, and 110 (Nm) is better than that of needle size of 120 (Nm). Generally, the developed theoretical model can predict the NPF in fabrics with twill 3/1 weave pattern.

The fabrics with twill weave pattern have a complicated structure than plain pattern. So, in this research work, the NPF of denim fabric with twill 3/1 weave pattern was theoretically predicted on the basis of yarn elongation, changing of yarn bent shape in the near of the sewing needle, and friction between warp and weft yarns. The NPF was measured in the successive cycle loading conditions similar to sewing machine process by using a designed and constructed instrument, which is mounted on the Instron tensile tester.

In order to accurately predict the uncertain and nonlinear characteristics of China's three clean energy generation, this paper presents a novel time-varying grey Riccati model (TGRM(1,1)) based on interval grey number sequences.

By combining grey Verhulst model and a special kind of Riccati equation and introducing a time-varying parameter and random disturbance term the authors advance a TGRM(1,1) based on interval grey number sequences. Additionally, interval grey number sequences are converted into middle value sequences and trapezoid area sequences by using geometric characteristics. Then the predicted formula is obtained by using differential equation principle. Finally, the proposed model's predictive effect is evaluated by three numerical examples of China's clean energy generation.

Based on the interval grey number sequences, the TGRM(1,1) is applied to predict the development trend of China's wind power generation, China's hydropower generation and China's nuclear power generation, respectively, to verify the effectiveness of the novel model. The results show that the proposed model has better simulated and predicted performance than compared models.

Due to the uncertain information and continuous changing of clean energy generation in the past decade, interval grey number sequences are introduced to characterize full information of the annual clean energy generation data. And the novel TGRM(1,1) is applied to predict upper and lower bound values of China's clean energy generation, which is significant to give directions for energy policy improvements and modifications.

The main contribution of this paper is to propose a novel TGRM(1,1) based on interval grey number sequences, which considers the changes of parameters over time by introducing a time-varying parameter and random disturbance term. In addition, the model introduces the Riccati equation into classic Verhulst, which has higher practicability and prediction accuracy.

The purpose of the research is to analyse the ability of nonfinancial factors to predict value creation in Finnish technology firms. Nonfinancial factors are defined in terms of a large set of variables on organizational characteristics, strategy, competitive stance, consistency of performance measurement, management control systems (MCSs), and quality of MCSs. Financial ratios are used as a benchmark. The hypotheses are that, firstly, nonfinancial factors include important information for prediction and, secondly, that they provide incremental information over financial ratios. The nonfinancial variables are drawn from a postal survey carried out in 1999. Financial variables for 1998–2001 are obtained for 40 private firms of the 110 firms responding to the survey. Shareholder value is estimated on the basis of the four‐year financial data for 2001. This value divided by the shareholder book value (estimated‐to‐book value ratio, EBV) as well as its drivers are predicted by past non‐financial and financial data. Partial Least Squares (PLS) method is used to analyse the importance of information in prediction. The results give support to the hypotheses. Moreover, the results show that nonfinancial factors yield important incremental information over financial ratios when predicting value drivers, that is, growth, profitability, and risk. Especially, financial ratios are weak in predicting growth.

The purpose of this paper is to investigate an alternative approach that can predict non‐linear relations.

An engineered approach to fabric development is described in which a radial basis function network is trained with worsted fabric constructional parameters to predict functional and aesthetic properties of fabrics. An objective method of fabric appearance evaluation with the help of digital image processing is introduced. The prediction of fabric properties by the network with changing basic fibre characteristics and fabric constructional parameters is found to have good correlation with the experimental values of fabric functional and aesthetic properties.

The radial basis function network can successfully predict the fabric functional and aesthetic properties from basic fibre characteristics and fabric constructional parameters with considerable accuracy. The network prediction is in good correlation with the actual experimental data. There is some error in predicting the fabric properties from the constructional parameters. The variation in the actual values and predicted values is because of small sample size. Moreover, the properties of worsted fabrics are greatly influenced by the finishing parameters which are not taken into consideration in the training of the network. Prediction performance can be further improved by including these parameters as input, during the training phase. In few cases, the network has predicted contradictory trends, which are found difficult to be explained.

The paper describes a radial basis function neural network model that can be used for the prediction of the fabric appearance values and comfort properties using fabric constructional parameters and some primary fibre mechanical properties as input parameters of the network.