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The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy.

The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN.

CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%.

This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.

The existing practice in automatic indexing is reviewed, and it is shown that the standard theories for the specification of term values (or weights) are not adequate. New techniques are introduced for the assignment of weights to index terms, based on the characteristics of individual document collections. The effectiveness of some of the proposed methods is evaluated.

Nicolas Dupont, the owner of Chateau de Montana, a struggling (and old) boutique hotel in Crans-Montana Ski Resort, Switzerland, wished to renovate and reposition his family-owned hotel to target higher room rates. Dupont commissioned Olga Mitireva and Yulia Belopilskaya as consultants to assess the proposition. The consultants had to extract cues for the room rate of the repositioned hotel from comparable hotels. However, the room rates varied significantly across similar hotels due to their differing characteristics and locations. It was a cognitive challenge to read the patterns from a few comparable hotels. They collected the data of 200 hotels from similar locations and simulated room prices using hedonic regression models.

Les Championnats du monde de ski alpin 1987 de Crans‐Montana (CM 87) figurent sans équivoque au rang des grands événements faisant l'objet des réflexions du 37ème Congrès de l'A.I.E.S.T. à Calgary (Canada).

Devotes the entire journal issue to managing human behaviour in US industries, with examples drawn from the airline industry, trading industry, publishing industry, metal products industry, motor vehicle and parts industry, information technology industry, food industry, the airline industry in a turbulent environment, the automotive sales industry, and specialist retailing industry. Outlines the main features of each industry and the environment in which it is operating. Provides examples, insights and quotes from Chief Executive Officers, managers and employees on their organization’s recipe for success. Mentions the effect technology has had in some industries. Talks about skilled and semi‐skilled workers, worker empowerment and the formation of teams. Addresses also the issue of change and the training that is required to deal with it in different industry sectors. Discusses remuneration packages and incentives offered to motivate employees. Notes the importance of customers in the face of increased competition. Extracts from each industry sector the various human resource practices that companies employ to manage their employees effectively ‐ revealing that there is a wide diversity in approach and what is right for one industry sector would not work in another. Offers some advice for managers, but, overall, fails to summarize what constitutes effective means of managing human behaviour.