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Real-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.

Sensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.

The proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.

This research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

The conventional statistical method of three-dimensional tolerance analysis requires numerous pseudo-random numbers and consumes enormous computations to increase the calculation accuracy, such as the Monte Carlo simulation. The purpose of this paper is to propose a novel method to overcome the problems.

With the combination of the quasi-Monte Carlo method and the unified Jacobian-torsor model, this paper proposes a three-dimensional tolerance analysis method based on edge sampling. By setting reasonable evaluation criteria, the sequence numbers representing relatively smaller deviations are excluded and the remaining numbers are selected and kept which represent deviations approximate to and still comply with the tolerance requirements.

The case study illustrates the effectiveness and superiority of the proposed method in that it can reduce the sample size, diminish the computations, predict wider tolerance ranges and improve the accuracy of three-dimensional tolerance of precision assembly simultaneously.

The proposed method may be applied only when the dimensional and geometric tolerances are interpreted in the three-dimensional tolerance representation model.

The proposed tolerance analysis method can evaluate the impact of manufacturing errors on the product structure quantitatively and provide a theoretical basis for structural design, process planning and manufacture inspection.

The paper is original in proposing edge sampling as a sampling strategy to generating deviation numbers in tolerance analysis.

The purpose of this paper is to propose a novel mathematical model to present the three-dimensional tolerance of a discrete surface and to carry out an approach to analyze the tolerance of an assembly with a discrete surface structure. A discrete surface is a special structure of a large surface base with several discrete elements mounted on it, one, which is widely used in complex electromechanical products.

The geometric features of discrete surfaces are separated and characterized by small displacement torsors according to the spatial relationship of discrete elements. The torsor cluster model is established to characterize the integral feature variation of a discrete surface by integrating the torsor model. The influence and accumulation of the assembly tolerance of a discrete surface are determined by statistical tolerance analysis based on the unified Jacobian-Torsor method.

The effectiveness and superiority of the proposed model in comprehensive tolerance characterization of discrete surfaces are successfully demonstrated by a case study of a phased array antenna. The tolerance is evidently and intuitively computed and expressed based on the torsor cluster model.

The tolerance analysis method proposed requires much time and high computing performance for the calculation of the statistical simulation.

The torsor cluster model achieves the three-dimensional tolerance representation of the discrete surface. The tolerance analysis method based on this model predicts the accumulation of the tolerance of components before their physical assembly.

This paper proposes the torsor cluster as a novel mathematical model to interpret the tolerance of a discrete surface.

The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution.

In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm.

The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods.

The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts.

A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms.

A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated.

The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited.

The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications.

The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

The purpose of this paper is to investigate the mechanism of transmitting economic policy uncertainty (EPU) shocks to capital structure.

The authors adopt a novel approach that bridges the asset pricing implications of EPU and the debt-financing decisions of Chinese firms by introducing a variable “policy-risk-induced equity return” (PRER). PRER is the product of the EPU beta and the EPU shock. Differentiating firms as per the signs of the EPU beta helps to shed light on the deep questions of whether their respective leverage targets and speeds of adjustment are different and how the targets and speeds are determined.

The empirical evidence shows that it is the equity market that channels EPU shocks to capital structure through PRER in China. Firms with positive (negative) EPU betas have PRER impact negatively (positively) the leverage target, conforming to the market-timing theory. EPU and non-policy uncertainty shocks cause the speed of adjustment to change over time. Overall, the intertemporal relation between EPU and leverage is negative. These results are robust to alternative leverage measures and after controlling for non-policy uncertainty shocks and conventional firm characteristics and have implications for academic research, policymaking, market stability, and corporate financing.

This study is the first to probe for, and provide insights into, the underlying reason why EPU impacts capital structure by connecting asset pricing to corporate financing for a large sample of Chinese publicly traded firms.

This review aims to summarize previous research on work–family relationships in the tourism and hospitality contexts. It then integrates the various approaches into a holistic model and identifies important areas for future research.

Over 150 research papers from the past 20 years were retrieved from Elsevier Science Direct, SAGE, Emerald, Taylor & Francis and EBSCOHost. In total, 77 papers reporting empirical research were analyzed in terms of concepts, theories, antecedents, consequences and methods.

The major findings on work and family issues in the tourism and hospitality contexts were synthesized. Critical topics for future research were identified. A holistic model of the factors that affect work and family was developed to improve the consistency of future research.

An overview of work–family studies will provide a solid research background to tourism and hospitality faculty members and graduate students who are considering research in this area. This paper is a general review of previous research, and the review focus is relatively global.

This paper is the first comprehensive summary and narrative review of work and family studies in tourism and hospitality.

Loneliness is known to adversely impact employee health, performance and affective commitment. This study involves a quantitative cross-sectional analysis of online survey data reported by adults employed in the United States (n = 5,927) to explore how loneliness and other related factors may influence avoidable absenteeism and turnover intention.

Worker loneliness was assessed using the UCLA Loneliness Scale (Version 3). Composite variables were constructed as proxy measures of worker job and personal resources. Structural equation modeling (SEM) was used to examine independent variable effects on dependent outcomes of (a) work days missed in the last month due to stress (stress-related absenteeism) and (b) likelihood to quit within the next year (turnover intention).

The job resources of social companionship, work-life balance and satisfaction with communication had significant negative relationships to loneliness in the SEM, as did the personal resources of resilience and less perceived alienation. Results further show lonely workers have significantly greater stress-related absenteeism (p = 0.000) and higher turnover intention ratings (p = 0.000) compared to workers who are not lonely. Respondent demographics (age, race and gender) and other occupational characteristics also produced significant outcomes.

Study findings underscore the importance of proactively addressing loneliness among workers and facilitating job and personal resource development as an employee engagement and retention strategy.

Loneliness substantially contributes to worker job withdrawal and has negative implications for organizational effectiveness and costs.

This paper aims to investigate the relationship between negatively perceived organizational politics (NPOP) on creative propensity (CP), while also studying their interplay with organizational commitment (OC) and job satisfaction (JS).

A survey technique was used in three different IT-related companies in an emerging market. Customers also included foreign companies for two of the three. In terms of age, these companies were roughly a decade old or more. Respondents were employees who were involved in IT-related jobs. Instrument was used five-point Likert scale. Data analysis involved partial least squares structural equation modeling (PLS-SEM).

JS appears to have a positive relationship with OC. Both factors have been shown to have a positive relationship with CP. NPOP appears to have a negative relationship with not only CP but also JS and OC.

This research aids in bridging a gap in research and contributes to literature with respect to the relationship among CP with JS, OC and NPOP. There is a need for greater research with respect to the relationship of CP with JS, OC and particularly with NPOP between these factors. This research would aid in bridging the gap by investigating relationships among these factors. Globalization and international business may increase the importance of creativity for IT-related as well as other companies. Businesses in emerging markets may require creativity for growth, survival and catching up to companies in developed markets or for competitiveness. With the prevalence of politics in organizations, it may be of strategic value for businesses in emerging markets to be able to calibrate these factors to increase their potential for creativity.