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The purpose of this study is to address the mixed predictions about the relationship between general skills training and turnover intention of early career graduates by examining the mediating mechanisms of perceived organizational support (POS) and job satisfaction (JS) through which this relationship might be enacted. This study adopts organizational support theory as the guiding theory and examines the concept of POS as critical for predicting and explaining relationships in the conceptual framework.

A quantitative survey method was used on a sample of 147 Chinese early career graduate trainees. Analysis was conducted using partial least square-based structural equation modelling (PLS-SEM).

The main finding is that participation in general skills training (PGST) does not directly impact turnover intention, rather POS is a mechanism through which this negative relationship operates. This study also found significant evidence for serial mediation by POS on PGST and its relationship with turnover intention. Importantly, JS only has an effect on turnover intention when in the presence of serial mediation by POS.

Cross-sectional study of a small survey sample. Nonetheless, the findings have major implications for research theories on the relationship of general skills training with employee turnover.

PGST does not directly impact turnover intention, rather POS is a mechanism through which this negative relationship operates.

This research emphasizes the important role of POS in the relationship between early career graduate trainees’ PGST and their turnover intentions.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

To provide a selective bibliography in the emerging area of library content personalization for the benefit of library and information professionals.

A range of recently published works (in the period 1993–2004), which aim to provide pragmatic application of content personalization rather than theoretical works, are discussed and sorted into “classified” sections to help library professionals understand more about the various options for formulating content as per the specific needs of their clientele.

This paper provides information about each category of tool and technique of personalization, indicating what is achieved and how particular developments can help other libraries or professionals. It recognises that personalization of library resources is a viable way of helping users deal with the information explosion, conserving their time for more productive intellectual tasks. It identifies how computer and information technology has enabled document mapping to be more efficient, especially because of the ease with which a document can be indexed and represented with multiple terms, and confirms that this same functionality can be used to represent a user's interests, facilitating the easy linking of relevant sources to prospective users. Personalization of library resources is an effective way for maximizing user benefit.

This is not an exhaustive list of developments in personalization. Rather it identifies a mix of products and solutions that are of immediate use to librarians.

A very useful source of pragmatic applications of personalization so far, that can guide a practicing professional interested in creating similar solutions for more productive information support in his/her library.

This paper fulfils an identified need for a “review of technology” for LIS practitioners and offers practical help to any professional exploring solutions similar to those outlined in this paper.