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The purpose of this paper is to attempt to realize the optimization of cascading failure process of urban transit network based on Load‐Capacity model, for better evaluating and improving the operation of transit network.

Robustness is an essential index of stability performance for urban transit systems. In this paper, firstly, the static robustness of transit networks is analyzed based on the complex networks theory. Aiming at random and intentional attack, a concrete algorithm process is proposed on the basis of Dijstra algorithm. Then, the dynamic robustness of the networks, namely cascading failure, is analyzed, and the algorithm process is presented based on the Load‐Capacity model. Finally, the space‐of‐stations is adopted to build the network topology of Foshan transit network, and then the simulation analyses of static and dynamic robustness are realized.

Results show that transit network is robust to random attack when considering static robustness, but somewhat vulnerable to intentional attack. For dynamic robustness analysis, a large‐scale cascade of transit network may be triggered when the tolerance parameter α is less than a value, so that the robustness of transit network can be improved through some reasonable measures.

The results of this study provide useful information for urban transit network robustness optimization.

An effective method for analyzing the static and dynamic robustness of transit network is provided in this paper.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

To improve robustness of a dairy supply chain (SC) against corruption, the purpose of this paper is to propose a systemic model of a corruption impacted dairy SC, exposing relationships among SC operations, risks and the impact of corruption.

Cases from the dairy industry in New Zealand (NZ) are used for thematic analysis of interview data collected from participants at senior levels of NZ dairy firms. Based on these and other inputs from literature, a systemic model is built subsequently.

Mitigating certain risks can significantly alleviate the impact of corruption, an external factor, on supply chain performance (SCP). The causal loop diagram (CLD) developed here brings out the modifying effect of corruption on dairy risks and SCP.

The illustration of the CLD helps business managers better understand the interactions among risk variables and explains the systemic reasons for SC vulnerability.

This is the first paper to construct a holistic system to comprehensively reveal the interactions of supply chain risks (SCRs) and the impact of corruption. Also, by utilizing SCR interactions, this study indicates a pathway to mitigate the negative effects of corruption through improving dairy SC robustness.

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

The purpose of this paper is to investigate the distinction and relationships between the significant strategic capabilities for managing risks in supply chains. This intersectional review exposes a substantial conceptual contradiction between the perspectives reported by various researchers. Further, the current paper classifies the literature into four categories according to the broad objectives investigated by the research papers.

Initially, a bibliometric analysis aligned with the concepts of a systematic literature review is conducted followed by a descriptive review focusing on models and methods. The software called BibExcel is utilized to extract and analyze the bibliographic information in a textual form from the research articles associated with strategic capabilities of the logistics sector. The results are exported to the software known as Gephi to visualize keyword co-occurrence analysis as networks. A well-structured descriptive review is also conducted to identify avenues for future research.

Despite conventional supply chain capabilities like efficiency and effectiveness, eight significant strategic capabilities of supply chains for managing risks are identified from the literature. These capabilities with positive connotations include flexibility, reliability, resilience, robustness, agility, adaptability, alignment and responsiveness. Considering the vast literature on flexibility/reliability along with its numerous dimensions and scope, the authors found that resilience, robustness, agility, adaptability, alignment and effectiveness are achievable through flexibility/reliability. Accordingly, it is appropriate to state reliability and flexibility as supply chain capabilities to achieve the other six supply chain competencies. Furthermore, the entire literature in this domain can be classified into four genres according to the addressed objectives, namely, concept development/validation, capability assessment, network design and performance evaluation.

The information revealed from the keyword co-occurrence analysis along with the research implications provided in the penultimate section will assist budding researchers in framing novel and promising research objectives. Supply chain administrators and policymakers can utilize the literature classification and the notable references provided in this review for locating potential methods for assessing supply chain strategic capabilities, designing the supply chain and evaluating the performance of the supply chain.

An integrated bibliometric and descriptive literature review procedure is utilized in this paper. Furthermore, this critical review is the first work on comprehensively mapping the research relationships among various strategic capabilities required for mitigating supply chain risks.

This study aims mainly to test the effect of audit committee independence and expertise attributes on corporate social responsibility (CSR) reporting, assurance and global reporting initiative (GRI) framework adoption and to investigate how CSR committee existence moderates this main relationship.

The study uses a large global sample that includes all (59,172) firm-year observations having CSR-related data in the Thomson Reuters Eikon database for a period between 2002 and 2019. The empirical analyses are based on random-effects logistic panel regression and Hayes methodology for the moderation analysis.

The study finds that audit committee independence and expertise are significantly associated with CSR reporting, CSR report assurance and GRI framework adoption. Moderation analysis largely supports the existence of a substitution role between audit and CSR committees and implies that audit committees are significant predictors of CSR reporting, assurance and GRI framework adoption mostly in the absence of the CSR committee.

The findings propose audit committee members be extra-vigilant in CSR reporting and assurance practices arising from undertaking substitution roles with the CSR committee. Hence, firms may configure their corporate structure in line with the results such as augmenting the audit committee with independent and expert members if they do not constitute a CSR committee. If firms establish a CSR committee, audit committee members may allocate less time to CSR reporting and assurance and more time to financial reporting quality.

This is the first study, to the best of the authors’ knowledge, to investigate the direct and indirect effect of audit committees’ attributes not only on CSR disclosure but also on GRI implementation and CSR reporting external assurance, considering the CSR committee’s possible substitutability or complementarity moderating role. This research develops a deeper understanding of audit committees’ non-financial role.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

This research investigates Airbnb’s financial implications in emerging economies and their potential to influence stock market profitability.

Employing a multifaceted approach, the study combines parametric and nonparametric tests, robustness checks, and regression analysis to assess the impact of Airbnb’s announcements on emerging economy stock markets.

Airbnb’s announcements affect emerging economies' stock markets with a distinct pattern of cumulative abnormal returns (CAR): negative before the announcement and positive afterward. Informed investors strategically leverage this opportunity through short selling before the announcement and acquiring positions following it. Regression analysis validates these trends, revealing that stock index returns and inbound tourism affect CAR before announcements, while GDP growth influences CAR afterward. Announcements pertaining to emerging economies exert a more pronounced impact on stock indices compared to city-specific announcements, with COVID-19 period announcements demonstrating greater significance in abnormal returns than non-COVID-19 period announcements.

This study advances existing literature through a comprehensive range of statistical tests, differentiation between emerging countries and cities, introduction of five macroeconomic variables, and reliance on credible primary Airbnb data. It highlights the potential for investors to leverage Airbnb announcements in emerging markets for stock market profits, emphasizing the need for adaptive investment strategies considering broader macroeconomic factors.

This paper aims to address the issue of designing aerodynamically robust empennage. Aircraft design optimization often narrowed to analysis of cruise conditions does not take into account other flight phases (manoeuvres). These, especially in unmanned air vehicle sector, can be significant part of the whole flight. Empennage is a part of the aircraft, with crucial function for manoeuvres. It is important to consider robustness for highest performance.

Methodology for robust wing design is presented. Surrogate modelling using kriging is used to reduce the optimization cost for high-fidelity aerodynamic calculations. Analysis of varying flight conditions, angle of attack, is made to assess robustness of design for particular mission. Two cases are compared: global optimization of 11 parameters and optimization divided into two consecutive sub-optimizations.

Surrogate modelling proves its usefulness for cutting computational time. Optimum design found by splitting problem into sub-optimizations finds better design at lower computational cost.

It is demonstrated, how surrogate modelling can be used for analysis of robustness, and why it is important to consider it. Intuitive split of wing design into airfoil and planform sub-optimizations brings promising savings in the optimization cost.

Methodology presented in this paper can be used in various optimization problems, especially those involving expensive computations and requiring top quality design.