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In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

This study investigated the mediating role of job satisfaction and the moderating role of career growth opportunity in the relationship between public service motivation (PSM) and public employees' turnover intention.

The authors recruited 587 public employees from Yunnan Province, China to test moderation and mediation hypotheses. The authors conducted confirmatory factor analysis to determine the discriminant and convergent validity of the measures of PSM, turnover intention, job satisfaction and career growth opportunity. Finally, the authors carried out bootstrapping to ascertain direct, indirect and conditional indirect effects.

PSM had a negative effect on public employees' turnover intention, but this relationship was partially mediated by job satisfaction. Career growth opportunity moderated the association between job satisfaction and turnover intention. In particular, the indirect effect of PSM on turnover intention through job satisfaction weakened under high career growth opportunities.

The results highlighted the significance of PSM and career growth opportunity in shaping public employees' work-related attitudes and behaviors. Public organizations should consider PSM a key criterion in recruitment and selection and pay more attention to the significance of intervening in career growth to satisfy public employees' psychological needs related to individual career development.

This study contributes to the literature on the disputed link between PSM and turnover intention and uncovered the underlying mechanism through which PSM affects public employees' turnover intention by proposing job satisfaction and career growth opportunity as a mediator and moderator, respectively.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Neurotechnologies such as brain-computer interfaces (BCIs) are rapidly moving out of laboratories and onto frontline employees' (FLEs) heads. BCIs offer thought-controlled device operation and real-time adjustment of work tasks based on employees’ mental states, balancing the potential for optimal well-being with the risk of exploitative employee treatment. Despite its profound implications, a considerable gap exists in understanding how BCIs affect FLEs. This article’s purpose is to investigate BCIs’ impact on FLEs’ well-being.

This article uses a conceptual approach to synthesize interdisciplinary research from service marketing, neurotechnology and well-being.

This article highlights the expected impact from BCIs on the work environment and conceptualizes what BCIs entail for the service sector and the different BCI types that may be discerned. Second, a conceptual framework is introduced to explicate BCIs’ impact on FLEs’ well-being, identifying two mediating factors (i.e. BCI as a stressor versus BCI as a resource) and three categories of moderating factors that influence this relationship. Third, this article identifies areas for future research on this important topic.

Service firms can benefit from integrating BCIs to enhance efficiency and foster a healthy work environment. This article provides managers with an overview of BCI technology and key implementation considerations.

This article pioneers a systematic examination of BCIs as workplace technology, investigating their influence on FLEs’ well-being.