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Although prior research has documented substantive knowledge of the benefits of high-performance work systems (HPWS), results regarding both sides of HPWS are inconsistent. To reconcile these inconsistencies, the authors identified two specific HR attributions—employee well-being HR attribution and performance HR attribution, and examined their roles in the relationship between team-level HPWS and employees' thriving at work and emotional exhaustion.

The authors collected multi-source data from 36 team leaders and 181 individuals. Given the nested nature of the data, the authors used Mplus 7.4 to conduct multilevel structural equation modeling for hypothesis testing.

The results showed that team-level HPWS and employee well-being HR attribution interact to affect psychological availability, which subsequently promotes thriving at work. However, team-level HPWS and employee performance HR attribution do not interact to influence role overload/psychological availability; team-level HPWS and employee well-being HR attribution do not interact to affect role overload.

Current literature has overlooked identifying key contingencies for both sides of HPWS effects on employee outcomes. Therefore, this study developed a mediated moderation model and incorporated HR attributions to explore two distinct pathways by which HPWS affects employees' thriving at work and emotional exhaustion. The present study helps to reconcile the inconsistent findings regarding the HPWS double-edged sword nature. In addition, the authors focused on HPWS at the team level, which is also underexplored in the existing HPWS research.

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Originating from a theoretical stance that situates knowledge organization in its social context, the study applies a multifaceted framework pertaining to five categories of textual data: the Seven Epitomes; biographical information about the classificationist Liu Xin; and the relevant intellectual, political, and technological history.

The study discovers seven principles contributing to the epistemic foundation of the catalogue's classification: the Han imperial library collection imposed as the literary warrant; government functions considered for structuring texts; classicist morality determining the main classificatory structure; knowledge perceived and organized as a unity; objects, rather than subjects, of concern affecting categories at the main class level; correlative thinking connecting all text categories to a supreme knowledge embodied by the Six Classics; and classicist moral values resulting in both vertical and horizontal hierarchies among categories as well as texts.

A major limitation of the study is its focus on the main classes, with limited attention to subclasses. Future research can extend the analysis to examine subclasses of the same scheme. Findings from these studies may lead to a comparison between the epistemic approach in the target classification and the analytic one common in today's bibliographic classification.

The study is the first to examine in depth the epistemic foundation of traditional Chinese bibliographic classification, anchoring the classification in its appropriate social and historical context.

The purpose of the paper is to propose a homogenization-based topology optimization method to optimize the design of variable-density cellular structure, in order to achieve lightweight design and overcome some of the manufacturability issues in additive manufacturing.

First, homogenization is performed to capture the effective mechanical properties of cellular structures through the scaling law as a function their relative density. Second, the scaling law is used directly in the topology optimization algorithm to compute the optimal density distribution for the part being optimized. Third, a new technique is presented to reconstruct the computer-aided design (CAD) model of the optimal variable-density cellular structure. The proposed method is validated by comparing the results obtained through homogenized model, full-scale simulation and experimentally testing the optimized parts after being additive manufactured.

The test examples demonstrate that the homogenization-based method is efficient, accurate and is able to produce manufacturable designs.

The optimized designs in our examples also show significant increase in stiffness and strength when compared to the original designs with identical overall weight.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.