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The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability.

This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements.

This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality.

Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs.

As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.

Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude.

Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation.

The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references.

When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks.

The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations.

Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.

In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features.

This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network.

Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs.

The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy.

The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

As a response to the growing operational and disruptive threats to water distribution networks (WDNs), researchers have developed a vast array of methods for the reliability analysis of WDNs. In order to follow this growing number of methods, this paper reviews and documents in one place the historical developments in the reliability analysis of WDN.

A systematic literature review (SLR) is carried out to summarize the state-of-the-art research on reliability analysis of WDNs. In conducting this systemic literature review, the authors adopted an iterative approach to define appropriate keywords, analyze and synthesize data and finalizing the classification results.

First, the hydraulic approach to reliability analysis is currently pervasive, and relatively little academic research has addressed the topological reliability analysis of WDNs. Second, in order to provide a comprehensive picture of the network reliability, a different approach that integrates topological and hydraulic attributes seems a more effective method. Third, the conventional reliability analysis methods are only effective for demonstrating a snapshot of these networks at a given point in time. The availability of methods that enable researchers to evaluate the reliability in response to changes in its variables is still a major challenge.

The present paper facilitates future research in the reliability analysis of WDNs by providing a source of references for researchers and water utilities. Further, this article makes a contribution to the literature by offering a roadmap for future reliability analysis of WDNs by reviewing the evolution of the current reliability analysis methods throughout history.

This study aims to comprehend the role of leagility and resilience in developing sustainable global supply chains, mitigating short-term disruptions and long-term economic impacts from various disasters, in the context of Australian civil infrastructure projects.

The study employs systematic review approach to establish that addressing project success dimensions (i.e. leagility, resilience and sustainability) requires an integrated and extended supply chain approach, encompassing traditional supply chain strategic model elements (i.e. cost/capital, quality and service goals) and supply chain eco-system (i.e. organisations, societies, economies and nature).

The study underscores the need to enhance supply chain leagility and resilience to achieve sustainability. This can be achieved by developing skills needed to plan across project phases and time frames, aligning with short and long-term organisational goals, assuming smart risks in the face of uncertainty.

This study extends the traditional supply chain strategic model by introducing new priorities to minimise the consequences of disruptions and to effectively respond to them. The integration of leanness, agility and resilience ensures a sustainable supply chain even in the times of uncertainty, disruption and volatility.

This research provides an opportunity for practitioners and policymakers to rethink and redesign the conventional supply chain model of cost, capital, quality and service objectives. It introduces pioneering concepts by acknowledging and incorporating emerging priorities, especially in Australian civil infrastructure projects. The study integrates leagility and resilience into the existing strategic framework, adding crucial dimensions for sustainable supply chains in infrastructure companies.

The purpose of this paper is to rank critical risks and determine major categories of risks to be considered by public–private partnerships (PPPs) investors when investing in “smart” transportation infrastructure. Such investment is sorely needed in many mega cities around the world currently suffering from serious impacts of traffic congestion, pollution and lack of usability of transport systems.

The study used literature review focused upon smart transportation infrastructure projects financed by PPP arrangements to create a questionnaire which was refined by subject matter experts and then completed by 126 experienced respondents. Exploratory factor analysis was used to create major categories emerging from the collected data. Interviews with ten experts were used to validate the findings.

The most highly major ranked risks shared by these participants were lack of expertise in complex project implementation, political interference, lack of PPP project data and lack of a collaboration mechanism between government and private sectors. Factor analysis showed that in terms of risk likelihood, stakeholder engagement, implementation process issues, the natural environment, data-sharing and technology complexity emerged. In terms of risk impact, major factors were stakeholder engagement, trust versus resistance issues, the natural environment and factors concerning uncertainty.

This paper addresses a somewhat unexplored area, the risks involved in investing in PPP smart transportation infrastructure. Such infrastructure projects are embedded in their environments, and approaches using a complexity lens can emerge overriding risk concerns for investors when undertaking such projects.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

The purpose of this paper is to review the different interpretations of four key performance indicators of water distribution networks (WDNs): reliability, resilience, redundancy and robustness. It then addresses a range of metrics which have been developed to assess the performance of critical infrastructures, in particular WDNs.

The paper provides a comprehensive review and categorization of performance indicators of WDNs. The main focus is on papers addressing performance indicators of water distribution systems, additionally papers on application of complex system approach to critical infrastructures are also included.

Due to this complexity, a wide range of interpretation of WDNs performance indicators exists in the literature. This represents a significant impediment toward universally accepted interpretation of these indicators Accurate assessment of WDNs’ performance depends on clear definition of system performance indicators as well as accurate quantifying of these indicators. The application of 18 metrics as a basis for assessing the system performance have been reviewed in this paper and none are particularly significant as standalone values. Combination of these indicators are required to accurately indicate the performance of WDNs.

The authors believe that this paper can be a valuable source of information for academic researchers and practitioners and suggests a roadmap for future works.

International studies have emphasised the importance of effective teaching for student learning. However, few studies have investigated teaching practices in Bangladesh. The purpose of the study set out to explore the mathematics teaching behaviour of secondary school level of Bangladesh.

A convenient sampling strategy was used to collect 502 grade nine students’ perceptions from 15 mathematics classes within Dhaka Metropolitan City (DMC). A valid and reliable student questionnaire was used where the students rated their mathematics teachers' teaching behaviours identified in the dynamic model of educational effectiveness (DMEE).

The findings from the quantitative analysis of the student evaluations revealed that the mathematics teachers demonstrated structuring, modelling, questioning, teacher–student interactions and teacher dealing with misbehaviours in their daily classroom teaching.

A small sample of mathematics classes were chosen in the study; hence, the findings may not be totally representative nor generalisable to the population of secondary mathematics teachers within Dhaka, Bangladesh.

The study conducted in Bangladesh contributes to the international dimension to educational effectiveness research. The findings from the study provide partial support for teaching factors and dimensions in DMEE (Creemers and Kyriakides, 2008), and emphasise the need for further studies, particularly in cross-cultural contexts.

This is the first study to evaluate teaching behaviours of secondary mathematics teachers through students’ perceptions in Bangladesh. It contributes important insights about teaching behaviours to inform the development of evidence-based policy and practice on quality teaching in the region.

This paper aims to investigate potential Muslim tourists’ visit intentions to halal tourism destinations in Japan.

Data were acquired from 397 respondents of potential Muslim tourists in Indonesia. These data were analyzed with variance-based structural modeling to assess the relationships between religiosity, halal awareness, destination image, destination trust and visit intention.

This study disclosed that halal awareness and destination trust could directly affect visit intention. Meanwhile, destination image could influence visit intention after mediated by destination trust. Religiosity was proven to be the antecedent of halal awareness and destination image.

This study exhibits the ways to optimize the development of halal tourism, especially in Muslim minority countries, by shaping the visit intention of potential Muslim tourists. The providers should concentrate on building the destination trust of potential Muslim tourists. They should innovatively exhibit their sincere commitment to fulfilling halal needs of potential tourists, and having a favorable destination image is only one of the ways. Besides that, the providers can promote halal tourism to potential Muslim tourists with more vital halal awareness.

This is an initial study to examine the visit intention of potential Muslim tourist for halal tourism destination in a Muslim minority country.