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Water distribution networks (WDNs) must deliver water to its customers 24/7. Disruption of this important service after a strong seismic event impedes post-disaster activities and poses health and sanitation problems. Hence, WDNs must be able to quickly restore services after the occurrence of a major seismic event. This ability to return the water service can be a metric for resilience. The purpose of this paper is to quantify the resilience by developing a framework that translates various restoration strategies into an improved resilience measure for a multisource WDN.

This paper used a quantitative risk assessment method in developing the framework for the resilience quantification of WDN. Prim’s algorithm, Horn’s algorithm and maximum slope method are used for the restoration analysis conducted in this study.

This paper provides resilience indices of the WDN for each repair scenarios. Then, the resilience indices are used to determine the most efficient and optimized repair scenario to restore the hypothetically damaged WDN owing to Level 1 and Level 2 seismic events.

The developed framework of this study only focuses on the robustness, rapidity and resourcefulness properties of resilience.

This study aims to help the water district in the maintenance, repair and evaluation of WDN against seismic events. The results from the study can be used in preparing the disaster management plan of the local water district to repair possible pipelines. This study also serves as a starting point to more complex and comprehensive research about the resilience quantification of WDNs with the consideration of optimal restoration sequence in the future.

The developed framework in the resilience quantification of WDN is original, as it uses optimal restoration strategies to represent the rapidity property of resilience.

Transport infrastructure resilience is of paramount importance for societies, therefore its quantification is urgently needed. A resilience assessment framework based on well-informed resilience indices is presented and applied for assessing the resilience of representative bridges exposed to earthquakes.

The framework quantifies the robustness of bridges against different seismic hazard scenarios, by using realistic fragility functions and the rapidity of the recovery and/or retrofitting after the occurrence of a certain degree of damage, based on realistic restoration functions.

Two different approaches for the modeling of the restoration tasks are examined. Both direct losses due to structural damage and indirect losses due to traffic disruption are estimated.

A new cost-based resilience index is introduced and alternate approaches for expressing the restoration strategies are examined and assessed. The results facilitate owners to enhance cost-based resilience management toward more resilient infrastructure.

For coastal bridges, the ability to recover traffic functions after the earthquake has crucial implications for post-disaster reconstruction, which makes resilience become a significant index to evaluate the seismic behavior. However, the deterioration of the material is particularly prominent in coastal bridge, which causes the degradation of the seismic behavior. As far, the research studies on resilience of coastal bridges considering multiple degradation factors and different disaster prevention capability are scarce. For further evaluating the seismic behavior of coastal bridge in the long-term context, the seismic resilience is conducted in this paper with considering multiple durability damage.

The fuzzy theory and time-varying fragility analysis are combined in this paper to obtain the life-cycle resilience of coastal bridges.

The results show that durability damage has a remarkable impact on the resilience. After 100 years of service, the seismic resilience of bridge with poor disaster-prevention capability has greatest reduction, about 18%. In addition, the improvement of the disaster prevention capability can stabilize the resilience of the bridge at a higher level.

In this paper, the time-varying fragility analysis of case bridge are evaluated with considering chloride ion erosion and concrete carbonization, firstly. Then, combining fuzzy theory and fragility analysis, the triangular fuzzy values of resilience parameters under different service period are obtained. Finally, the life-cycle resilience of bridge in different disaster prevention capability is analyzed.

The purpose of this study was to investigate how Lean Six Sigma (LSS) may help mitigate the impact of COVID-19 within health care environments. The goals of this study were to understand the current knowledge of LSS and COVID-19 through a systematic review of the current literature, identify the gap in the current knowledge of LSS in COVID-19 mitigation within health care environments and define the principles of LSS, within organizational resilience that support a health care organization’s ability to mitigate the impact of COVID-19.

A narrative literature review was conducted to identify relevant research. A total of 21 subject matter experts (SMEs) meeting the inclusion criteria were approached through a guided interview process. Content analysis was conducted to describe how LSS principles contribute to supporting health care organizations operating in the era of COVID-19.

Study results report that personal safety is the primary subject, followed by supporting dimensions of process redesign, and telemedicine. LSS topics that directly relate to COVID-19 are in four thematic areas: tools, applications, benefits and challenges. Particular areas of application, techniques, challenges and benefits are identified and discussed that could be applied proactively and reactively, to organizational and supply chain resilience to recover from COVID-19.

There were a number of limitations to the generalizability of this work. The sample size was small and purposeful, thus, external validity of the study results are not determined. The SMEs in this study have not implemented the practices noted in the results at the time of the study, and knowledge of results is limited to the study aims.

This study of LSS principles and COVID-19 has implications for practitioners and offers specific guidance for areas of health care adoption of LSS techniques and tools that benefit patient safety, challenges for the user to be mindful of and potential benefits in resilience of operations in the era of COVID-19.

The maritime industry is increasingly impacted by the Internet of things (IoT) through the automation of ships and port activities. This increased automation creates new security vulnerabilities for the maritime industry in cyberspace. Any obstruction in the global supply chain due to a cyberattack can cause catastrophic problems in the global economy. This paper aims to review automatic identification systems (AISs) aboard ships for cyber issues and weaknesses.

The authors do so by comparing the results of two receiver systems of the AIS in the Port of Houston; the JAMSS system aboard the Space Station and the “Harborlights” system for traffic control in the Port.

The authors find that inconsistent information is presented on the location of same ships at the same time in the Port. Upon further investigation with pilots, the authors find that these inconsistencies may be the result of the strength of power with which an AIS is transmitted. It appears the power may be reduced to the AIS in port but that it varies within port and varies by pilot operators. This practice may open the AIS system for tampering.

Further, this inconsistency may require further policy regulation to properly address cyber information in a port.

People with autism and their families struggle with travel because of its complexity. This study aims to promote travel participation by exploring the travel needs and patterns of people with autism and their families.

A total of 28 participants were interviewed. Data were analyzed via constructivist grounded theory.

People with autism and their families displayed five travel patterns: mutual support, relatives’ visitation, independent travel, expanded socialization and package tours. These patterns were adopted in a stepwise fashion as the autistic individuals’ abilities improved. The travel challenges and support needs of Chinese autistic people and their families were identified.

The findings can inform accessibility tourism, promote an understanding of autistic people’s tourism activities among the public and industry marketers and offer strategic guidance about family travel for this population.

This effort responds to a call to investigate disability-related issues. The study evaluated the travel behavior of people with autism and their families, from a developmental perspective, presenting a new angle in research on accessibility tourism.

This paper aims to deal with the thermal resistance of multilayer nonwovens. The effect of fibre denier, cross-sectional shape and positioning within the layers were analysed with respect to the thermal resistance. Moreover, effect of compression on thermal resistance of the multilayer nonwoven structure have also be studied.

The study involves multiple layering of thermal bonded nonwoven webs and the effect of fibre denier and positioning of different nonwovens from the hot plate. To avoid the increase in thermal resistance because of the air gaps between layers, the nonwovens were enclosed within an acrylic frame to compress them to a thickness of 12 mm. Compressional behaviour of the nonwovens were tested at a rate of 5 mm/min with peak compressive load of 50 N. Multilayer nonwoven assemblies were tested for thermal resistance with compressive pressure of 3.5 gf/cm² and compared with that tested at zero pressure.

In the study, three-layered nonwoven structure, provided better thermal resistance than their single component counterparts. The structural characteristic of the multilayer nonwovens affected the conductive, convective and the radiative heat transfer. In a multi-layer nonwoven, the top most layer should have the finest fibre as possible. Second preference may be given to the middle and followed by bottom layers in terms of fibre fineness. However, fine solid fibres performed poorly in terms of compression and recovery resulting in poor thermal resistance under compressive load.

The experimental approach of controlling thickness while evaluating the thermal resistance will help in nullify the effect of air gaps between the layer interface, thus focussing on the effect of fibre denier and the positioning of nonwovens. This paper also discusses the unique properties of fine solid fibre and hollow fibres and their role in providing better thermal insulation for extreme cold weather applications.

The experience of the Philadelphia College of Textiles and Science with the use of Kawabata Evaluation System for Fabrics (KES‐F) since 1984 and more recently, 1988, with the Fabric Assurance by Simple Testing (FAST) is described. Although previous projects have involved the effect of chemical and mechanical finishing and sponging on the hand and mechanical properties of fabrics, the major emphasis here correlates the processability of fabrics in tailored clothing manufacturing, especially in manually operated overfeed sewing, with the tensile‐elongation and shear properties of these fabrics. It is anticipated that programmable sewing machines will require instructions based on the mechanical properties of the fabrics being processed.

Prior studies have largely overlooked the potentially negative consequences of a buyer’s relational capital (RC) with a supplier for supply-side resilience, assuming a positive linear relationship between the constructs. Meanwhile, the focus of research has been at an organisational level without incorporating the role of boundary spanning individuals at the interface between buyer and supplier. Drawing on social capital and boundary spanning theory, the purpose of this paper is to: re-examine the relationship between RC and supply-side resilience, challenging the linear assumption; and investigate how both the strength and diversity of a boundary spanner’s ties moderate this relationship.

Survey data are collected from 248 firms and validated using a subset of 57 attentive secondary respondents and archival data. The latent moderated structural equation method is applied to analyse the data.

An inverted U-shaped relationship between RC and supply-side resilience is identified. Tie strength in particular has a positive moderating effect on the relationship. More specifically, the downward RC–supply-side resilience relationship flips into an upward curvilinear relationship when boundary spanning individuals develop stronger ties with supplier personnel.

A deeper insight into the RC–supply-side resilience relationship is provided. Findings are based on Chinese manufacturing firms and cross-sectional data meaning further research is needed to determine their generalisability.

In evaluating how to enhance supply-side resilience, buying firms must decide whether the associated collaborative benefits of developing RC outweigh the potential costs. Managers also need to be concerned with the impact of developing RC between organisations and enhancing the tie strength of individuals simultaneously.

The paper goes beyond the linear relationship between RC and supply-side resilience. Incorporating the moderating role of boundary spanners identifies a novel phenomenon whereby the RC–resilience relationship flips from an inverted to a U-shaped curve.

Cyber resilience in cyber supply chain (CSC) systems security has become inevitable as attacks, risks and vulnerabilities increase in real-time critical infrastructure systems with little time for system failures. Cyber resilience approaches ensure the ability of a supply chain system to prepare, absorb, recover and adapt to adverse effects in the complex CPS environment. However, threats within the CSC context can pose a severe disruption to the overall business continuity. The paper aims to use machine learning (ML) techniques to predict threats on cyber supply chain systems, improve cyber resilience that focuses on critical assets and reduce the attack surface.

The approach follows two main cyber resilience design principles that focus on common critical assets and reduce the attack surface for this purpose. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles. The critical assets include Cyber Digital, Cyber Physical and physical elements. We consider Logistic Regression, Decision Tree, Naïve Bayes and Random Forest classification algorithms in a Majority Voting to predicate the results. Finally, we mapped the threats with known attacks for inferences to improve resilience on the critical assets.

The paper contributes to CSC system resilience based on the understanding and prediction of the threats. The result shows a 70% performance accuracy for the threat prediction with cyber resilience design principles that focus on critical assets and controls and reduce the threat.

Therefore, there is a need to understand and predicate the threat so that appropriate control actions can ensure system resilience. However, due to the invincibility and dynamic nature of cyber attacks, there are limited controls and attributions. This poses serious implications for cyber supply chain systems and its cascading impacts.

ML techniques are used on a dataset to analyse and predict the threats based on the CSC resilience design principles.

There are no social implications rather it has serious implications for organizations and third-party vendors.

The originality of the paper lies in the fact that cyber resilience design principles that focus on common critical assets are used including Cyber Digital, Cyber Physical and physical elements to determine the attack surface. ML techniques are applied to various classification algorithms to learn a dataset for performance accuracies and threats predictions based on the CSC resilience design principles to reduce the attack surface for this purpose.