Investigating online recognition for blood donor retention : an experiential donor value approach

Recognising that charitable behaviour can be motivated by public recognition and emotional satisfaction, not-for-profit organisations have developed strategies that leverage self-interest over altruism by facilitating individuals to donate conspicuously. Initially developed as novel marketing programs to increase donation income, such conspicuous tokens of recognition are being recognised as important value propositions to nurture donor relationships. Despite this, there is little empirical evidence that identifies when donations can be increased through conspicuous recognition. Furthermore, social media’s growing popularity for self-expression, as well as the increasing use of technology in donor relationship management strategies, makes an examination of virtual conspicuous tokens of recognition in relation to what value donors seek particularly insightful. Therefore, this research examined the impact of experiential donor value and virtual conspicuous tokens of recognition on blood donor intentions. Using online survey data from 186 Australian blood donors, results show that in fact emotional value is a stronger predictor of intentions to donate blood than altruistic value, while social value is the strongest predictor of intentions if provided with recognition. Clear linkages between dimensions of donor value (altruistic, emotional and social) and conspicuous donation behaviour (CDB) were identified. The findings provide valuable insights into the use of conspicuous donation tokens of recognition on social media, and contribute to our understanding into the under-researched areas of donor value and CDB.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

Variable selection by orthogonal descriptors and prediction of R-f value of phenol and aniline derivatives

Orthogonal descriptors is a viable method for variable selection, but this method strongly depend on the orthogonalisation ordering of the descriptors. In this paper, we compared the different methods used for order the descriptors. It showed that better results could be achieved with the use of backward elimination ordering. We predicted R-f value of phenol and aniline derivatives by this method, and compared it with classical algorithms such as forward selection, backward elimination, and stepwise procedure. Some interesting hints were obtained.

Prediction of soil adsorption coefficients from retention parameters on three reversed-phase liquid chromatographic columns

Reversed-phase high-performance liquid chromatographic (RP-HPLC) retention parameters, which are determined by the intermolecular interactions in retention process, can be considered as the chemical molecular descriptors in linear free energy relationships (LFERs). On the basis of the characterization and comparison of octadecyl-bonded silica gel (ODS), cyano-bonded silica gel (CN), and phenyl-bonded silica gel (Ph) columns with linear solvation energy relationships (LSERs), a new multiple linear regression model using RP-HPLC retention parameters on ODS and CN columns as variables for estimation of soil adsorption coefficients was developed. It was tested on a set of reference substances from various chemical classes. The results showed that the multicolumn method was more promising than a single-column method was for the estimation of soil adsorption coefficients. The accuracy of the suggested model is identical with that of LSERs.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Whole crop cereals 2. Prediction of apparent digestibility and energy value from in vitro digestion techniques and near infrared reflectance spectroscopy and of chemical composition by near infrared reflectance spectroscopy

Samples of whole crop wheat (WCW, n = 134) and whole crop barley (WCB, n = 16) were collected from commercial farms in the UK over a 2-year period (2003/2004 and 2004/2005). Near infrared reflectance spectroscopy (NIRS) was compared with laboratory and in vitro digestibility measures to predict digestible organic matter in the dry matter (DOMD) and metabolisable energy (ME) contents measured in vivo using sheep. Spectral models using the mean spectra of two scans were compared with those using individual spectra (duplicate spectra). Overall NIRS accurately predicted the concentration of chemical components in whole crop cereals apart from crude protein. ammonia-nitrogen, water-soluble carbohydrates, fermentation acids and solubility values. In addition. the spectral models had higher prediction power for in vivo DOMD and ME than chemical components or in vitro digestion methods. Overall there Was a benefit from the use of duplicate spectra rather than mean spectra and this was especially so for predicting in vivo DOMD and ME where the sample population size was smaller. The spectral models derived deal equally well with WCW and WCB and Would he of considerable practical value allowing rapid determination of nutritive value of these forages before their use in diets of productive animals. (C) 2008 Elsevier B.V. All rights reserved.

Peak modeling approach to accurate assignment of first-dimension retention times in comprehensive two-dimensional chromatography

Modeling of first-dimension retention of peaks based on modulation phase and period allows reliable prediction of the modulated peak distributions generated in the comprehensive two-dimensional chromatography experiment. By application of the inverse process, it is also possible to use the profile of the modulated peaks (their heights or areas) to predict the shape and parameters of the original input chromatographic band (retention time, standard deviation, area) for the primary column dimension. This allows an accurate derivation of the firstdimension retention time (RSD 0.02%) which is equal to that for the non-modulated experiment, rather than relying upon the retention time of the major modulated peak generated by the modulation process (RSD 0.16%). The latter metric can produce a retention time that differs by at least the modulation period employed in the experiment, which displays a discontinuity in the retention time vs modulation phase plot at the point of the 180° out-ofphase modulation. In contrast, the new procedure proposed here gives a result that is essentially independent of modulation phase and period. This permits an accurate value to be assigned to the first-dimension retention. The proposed metric accounts for the time on the seconddimension, the phase of the distribution, and the holdup time that the sampled solute is retained in the modulating interface. The approach may also be based on the largest three modulated peaks, rather than all modulated peaks. This simplifies the task of assigning the retention time with little loss of precision in band standard deviation or retention time, provided that these peaks are not all overloaded in the first or second dimension.

Looking beyond HRM practices in enhancing employee retention in BPOs: focus on employee–organisation value fit

This paper argues that the effectiveness of HRM practices in tackling employee retention can be enhanced by improving the compatibility between employee and organisational values. We test our hypothesis using structural equation modelling on a sample of 258 employees in business process outsourcing (BPO) firms in the Philippines. The results show that the fit between employee and organisation values positively and partially mediates the effects of HRM practices on employee retention. However, employee–organisation value clash in US-owned BPOs was found to have a negative effect on employee retention. Because employees are less likely to leave when they share similar values as their organisations, HRM practices can be used strategically to improve the employee–organisation value fit to improve retention. The implications of the findings for HR managers of BPOs in developing countries are fully discussed.

Prognostic value of the Geneva prediction rule in patients in whom pulmonary embolism is ruled out

The prognosis of patients in whom pulmonary embolism (PE) is suspected but ruled out is poorly understood. We evaluated whether the initial assessment of clinical probability of PE could help to predict the prognosis for these patients.