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The purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.

In this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.

The numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.

The paper provides a useful reference for further theoretical research and engineering application.

The purpose of this paper is to develop robust metamodels, which allow propagating parametric uncertainties, in the presence of localized nonlinearities, with reduced cost and without significant loss of accuracy.

The proposed metamodels combine the generalized polynomial chaos expansion (gPCE) for the uncertainty propagation and reduced order models (ROMs). Based on the computation of deterministic responses, the gPCE requires prohibitive computational time for large-size finite element models, large number of uncertain parameters and presence of nonlinearities. To overcome this issue, a first metamodel is created by combining the gPCE and a ROM based on the enrichment of the truncated Ritz basis using static residuals taking into account the stochastic and nonlinear effects. The extension to the Craig–Bampton approach leads to a second metamodel.

Implementing the metamodels to approximate the time responses of a frame and a coupled micro-beams structure containing localized nonlinearities and stochastic parameters permits to significantly reduce computation cost with acceptable loss of accuracy, with respect to the reference Latin Hypercube Sampling method.

The proposed combination of the gPCE and the ROMs leads to a computationally efficient and accurate tool for robust design in the presence of parametric uncertainties and localized nonlinearities.

Global sensitivity can measure the influence of input variables on model responses and is of positive significance for the improvement design of structural systems. This work aims to study the global sensitivity of structural models by combining the active subspace theory and neural network.

This study aims to improve the efficiency of global sensitivity analysis for high-dimensional structural systems, a novel method based on active subspace and surrogate model is proposed. Active subspace can reduce the dimension of input variables, and an adaptive scaling strategy is proposed to improve the accuracy in finding the active subspace. The uncertainty propagation of active variables and model response is performed through the artificial neural network. Then the global sensitivity analysis is carried out.

Several examples are studied by using the Monte Carlo simulation method and the proposed method. Comparison of the results shows that the proposed method has preferable accuracy and low computational cost.

The proposed method provides a practicable tool for the variance-based sensitivity analysis of structural systems. Apart from sensitivity analysis, the method can be also extended for use in other fields relating to uncertainty propagation.

The purpose of this study is to improve the computational efficiency and accuracy of fatigue reliability analysis.

By absorbing the advantages of Markov chain and active Kriging model into the hierarchical collaborative strategy, an enhanced active Kriging-based hierarchical collaborative model (DCEAK) is proposed.

The analysis results show that the proposed DCEAK method holds high accuracy and efficiency in dealing with fatigue reliability analysis with high nonlinearity and small failure probability.

The effectiveness of the presented method in more complex reliability analysis problems (i.e. noisy problems, high-dimensional issues etc.) should be further validated.

The current efforts can provide a feasible way to analyze the reliability performance and identify the sensitive variables in aeroengine mechanisms.

To improve the computational efficiency and accuracy of fatigue reliability analysis, an enhanced active DCEAK is proposed and the corresponding fatigue reliability framework is established for the first time.

The purpose of this paper is to use α‐dense curves for solving Boolean equations, 0‐1 integer programming problems such as the shortest path problem or the knapsack problem.

The paper's aim is to present the applications in Boolean algebra and 0‐1 integer programming of a new method based on α‐dense curves first developed at the beginning of the 1980s by Yves Cherruault and Arthur Guillez. The α‐dense curves generalize the space filling curves (Peanocurves,…) and fractal curves. The main idea consists in expressing n variables by means of a single one.

Apply the method to Boolean algebra and 0‐1 integer programming.

The paper presents a new method based on α‐dense curves for solving Boolean equations and 0‐1 integer programming problems.

The primary purpose of this paper is the development of a fire–structure interface (FSI) model, which is referred in this study as a simplified “dual-layer” model. It is oriented for design purposes, in the cases where fire-compartments exceed the “regular” dimensions, as they are defined by the guidelines of the codes (EN 1991-1-2).

The model can be used at the post-processing stage of computational fluid dynamics (CFD) analysis and it is based on the gas-temperature field (spatial and temporal) of the fire-compartment. To use the “dual-layer” model, first the gas-temperature (discrete) function along the height of the fire-compartment, at discrete plan–view points should be determined through the output of the CFD analysis. The model “compresses” the point data to (spatial) virtual zones, which are divided into two layers (with respect to the height of the fire-compartment) of uniform temperature: the upper (hot) layer and the lower (cold) layer.

The model calculates the temporal evolution of the gas-temperature in the fire compartment in every virtual zone which is divided in two layers (hot and cold layer).

The main advantage of this methodology is that actually only three different variables (height of interface upper-layer temperature and lower-layer temperature) are exported during the post-processing stage of the CFD analysis, for every virtual zone. Next, the gas-temperature can be used for the determination of the temperature profile of structural members using simple models that are proposed in EN 1993-1-2.

This study aims to explore how status differences relate to strained working relationships with co-workers and clients. Two statuses, gender and occupation, are examined using data from veterinarians and animal health technologists (AHTs). Competing perspectives regarding exposure to stressful relationships and access and effectiveness of work-related resources are considered.

An explanatory sequential mixed-methods design is used that combines quantitative survey data with open-ended qualitative data. The survey data are used to examine how interpersonal strain and access to work-related resources vary by status. The qualitative data are used to illustrate how strain is experienced by these workers and aids in interpreting the quantitative findings.

Status is linked to interpersonal client strain and access to resources. Challenging work is widely available to all three groups, but is more beneficial in reducing higher status veterinarians’ client strain. Autonomy is a scarce resource for the lowest status group (female AHTs), yet appears effective in reducing co-worker strain for everyone. Unexpectedly, work overload and market concerns appear to aggravate work-related strain and greater numbers of the lowest status group exacerbates interpersonal tensions with clients.

This paper contributes by examining stressful interactions experienced by two occupations who work side-by-side in the same employment settings, but who vary significantly by gender representation and occupational status. The authors argue that in addition to gender and occupational status, the organizational health of employing clinics and the feminization of veterinary practice may offer insights into how status differences are related to interpersonal conflict experienced in these work places.

In the domain of healthcare, both process efficiency and the quality of care can be improved through the use of dedicated pervasive technologies. Among these applications are so-called real-time location systems (RTLS). Such systems are designed to determine and monitor the location of assets and people in real time through the use of wireless sensor networks. Numerous commercially available RTLS are used in hospital settings. The nursing home is a relatively unexplored context for the application of RTLS and offers opportunities and challenges for future applications. The paper aims to discuss these issues.

This paper sets out to provide an overview of general applications and technologies of RTLS. Thereafter, it describes the specific healthcare applications of RTLS, including asset tracking, patient tracking and personnel tracking. These overviews are followed by a forecast of the implementation of RTLS in nursing homes in terms of opportunities and challenges.

By comparing the nursing home to the hospital, the RTLS applications for the nursing home context that are most promising are asset tracking of expensive goods owned by the nursing home in order to facilitate workflow and maximise financial resources, and asset tracking of personal belongings that may get lost due to dementia.

This paper is the first to provide an overview of potential application of RTLS technologies for nursing homes. The paper described a number of potential problem areas that can be addressed by RTLS.

The usefulness of risk disclosures (RDs) to support equity investors’ investment decisions is highly discussed. As prior research criticizes the extensive aggregation of risk information in existing empirical research, this paper aims to provide an attempt to identify disaggregated risk information associated with cumulative abnormal stock returns (CARs).

The sample consists of 2,558 RDs of companies listed in the S&P 500 index. The RDs were filed within 10 K filings between 2011 and 2017. First, this study automatically extracted 35,685 key phrases that occurred in a maximum of 1.5% of the RDs. Second, this study performed stepwise regressions of these key phrases and identified 67 (78) key phrases that show positive (negative) associations with CARs.

The paper finds that investors seem to value most the more common key phrases just below the 1.5% rarest key phrase threshold and business-related key phrases from RDs. Furthermore, investors seem to perceive key phrases that contain words indicating uncertainty (impacts) as a negative (positive) rather than a positive (negative) signal.

The research approach faces limitations mainly due to the selection of the included key phrases, the focus on CARs and the methodological choice of the stepwise regression analysis.

The study reveals the potential for companies to increase the information value of their RDs for equity investors by providing tailored information within RDs instead of universal phrases. In addition, the research indicates that the tailored RDs encouraged by the SEC contain relevant information for investors. Furthermore, the results may guide the attention of equity investors to relevant text passages whose deeper analysis might be useful with regard to investors’ capital market decisions.

The purpose of our study is to examine the direct and mediating effects of attention-deficit hyperactivity disorder (ADHD) symptoms and urgency, lack of premeditation, lack of perseverance and sensation seeking (UPPS) impulsivity traits on entrepreneurial self-efficacy and entrepreneurial intention as well as to test the moderation impact of ADHD symptoms in the link between entrepreneurial self-efficacy and start-up intention.

The stratified random sampling was approached to recruit the data from 2,566 university students in Vietnam. Cronbach’s alpha and confirmatory factor analysis were used to test the reliability and validity of scales. Then, Pearson correlation analysis was utilized to test direct effects, while PROCESS macro was approached to test moderation and mediation impacts.

The study found evidence that ADHD symptoms, sensation seeking, lack of premeditation and lack of perseverance are significantly and directly conducive to the formation of entrepreneurial intention. Yet, ADHD symptoms might weaken the link between entrepreneurial self-efficacy and entrepreneurial intention. Entrepreneurial self-efficacy was also found to partially mediate the link between sensation seeking, lack of premeditation, and lack of perseverance and intention to become an entrepreneur.

The findings provide constructive recommendations for policymakers and educators to nurture and foster university students’ entrepreneurial activities as well as to restrain the negative effects of ADHD symptoms on youths.

Understanding the impacts of psychiatric symptoms, such as ADHD and UPPS impulsivity, on entrepreneurial activities provide useful insights to individuals with ADHD symptoms, the community and the society to restrain the detrimental impacts of psychological disorder symptoms and consider entrepreneurship as a career choice.

The study is expected to have a significant contribution to psychological entrepreneurship literature by broadening our horizons of the links between psychiatric symptoms and entrepreneurial intentions. Especially, this study reveals that ADHD symptoms and UPPS impulsive traits are significantly correlated with intention to become entrepreneurs and the link between entrepreneurial self-efficacy and entrepreneurial intention become weaker when the degree of ADHD symptoms is high.