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The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.

A Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.

The numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.

The research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.

Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.

The paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

Wireless sensor networks are expected to be an integral part of any pervasive computing environment. This implies an ever‐increasing need for efficient energy and resource management of both the sensor nodes, as well as the overall sensor network, in order to meet the expected quality of data and service requirements. There have been numerous studies that have looked at the routing of data in sensor networks with the sole intention of reducing communication power consumption. However, there has been comparatively little prior art in the area of multi‐criteria based routing that exploit both the semantics of queries and the state of sensor nodes to improve network service longevity. In this paper, we look at routing in sensor networks from this perspective and propose an adaptive multi‐criteria routing protocol. Our algorithm offers automated reconfiguration of the routing tree as demanded by variations in the network state to meet application service requirements. Our experimental results show that our approach consistently outperforms, in terms of Network Lifetime and Coverage, the leading semantic‐based routing algorithm which reconfigures the routing tree at fixed periods.

The technique of Critical Path Analysis (CPA) and its applications are described. A simple example is used to illustrate the method. The use of CPA in the implementation of an automated lending system at Teesside Polytechnic Library is described.

Prefabricated construction (PC) will play a vital role in the transformation and upgrading of the construction industry in the future. However, high capital cost is currently one of the biggest obstacles to the application and promotion of PC in China. Clarifying the factors that affect the PC cost from the perspectives of stakeholders and exploring key cost control paths help to achieve effective cost management, but few studies have paid enough attention to this. Therefore, this research aims to explore the critical cost influencing factors (CIFs) and critical stakeholders of PC based on stakeholder theories and propose corresponding strategies for different stakeholders to reduce the cost of PC.

Based on the stakeholder theory and social network theory, literature review and two rounds of expert interviews were used to obtain the stakeholder-associated CIFs and their mutual effects, then the consistency of the data was tested. After that, social network analysis was applied to identify the critical CIFs, critical interaction and key stakeholders in PC cost control and mine the influence conduction paths between CIFs.

The results reveal that the cognition and attitude of developer and relevant standards and codes are the most critical CIFs while the government, developer and contractor are crucial to the cost control of PC. The findings further suggest that measures should be taken to reduce the transaction costs of the developer, and the contractor ought to efficiently apply information technology. Moreover, the collaborative work between designer and manufacturer can avoid unnecessary cost consumption.

This research combines stakeholder management and cost management in PC for the first time and explores the effective cost control paths. The research results can contribute to clarifying the key points of cost management for different stakeholders and improving the cost performance of PC projects.

With multiple-related organizations, worldwide infections, deep economic recession and public disorder, and large consumption amount of anti-epidemic resources, the coronavirus disease 2019 (COVID-19) has been defined as a public health emergency of international concern (PHEIC). Nowadays, Wuhan has recovered from the pandemic disaster and reentered normalization. The purposes of this study are to (1) summarize organization collaboration patterns, successful experience and latent defects under across-stage evolution of Wuhan's cooperation governance mode against the pandemic, and on the basis, (2) reveal how the COVID-19 development trends and organizations' collaborative behaviors affected each other.

Detailed content analysis of online news reports covering COVID-19 prevention and control measures on the website of Wuhan Municipal Government was adopted to identify organizations and their mutual collaborative interrelationships. Four complex network (CN) models of organization collaboration representing the outbreak, preliminary control, recession and normalization stages, respectively, were established then. Time-span-based dynamic parameter analyses of the proposed networks, comprising network cohesiveness analysis and node centrality analysis, were undertaken to indicate changes of global and local characteristics in networks.

First, the definite collaborative status of Wuhan Headquarters for Pandemic Prevention and Control (WHPPC) has persisted throughout the period. Medical institutions and some other administrations were the most crucial participants collaborating with the WHPPC. Construction-industry organizations altered pandemic development trends twice to make the situation controllable. Media, large-scale enterprises, etc. set about underscoring themselves contributions since the third stage. Grassroots cadres and healthcare force, small and medium-sized enterprises (SMEs), financial institutions, etc. were essential collaborated objects. Second, four evolution mechanisms of organization collaboration responding to the COVID-19 in Wuhan has been proposed.

First, universality of Wuhan-style governance experience may be affected. Second, the stage-dividing process may not be the most appropriate. Then, data source was single and link characteristics were not considered when modeling.

This study may offer beneficial action guidelines to governmental agencies, the society force, media, construction-industry organizations and the market in other countries or regions suffering from COVID-19. Other organizations involved could also learn from the concluded organizations' contributions and four evolution mechanisms to find improvement directions.

This study adds to the current theoretical knowledge body by verifying the feasibility and effectiveness of investigating cooperation governance in public emergencies from the perspectives of analyzing the across-stage organization collaboration CNs.

Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.

First, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.

The results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.

The warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.

As globalisation makes supply networks more complex, the risk of material disruptions increases. Many factors have been considered as affecting the reliability of supply networks. However, no empirical research has been carried out to assess and evaluate the impact of each of these factors on the reliability of supply networks. This paper aims to address this issue.

A gap in the literature was identified around the evaluation of the impact of supply network design characteristics on reliability. This gap is addressed by performing a full factorial experimental design considering all the factors described in the literature, and then analysing (by using analysis of variance and linear regression models), thousands of theoretical and extreme structures of supply networks, thus allowing the analysis of the influence of each factor on the overall network resilience.

Results show that network density, node criticality and complexity are significant factors in reducing the reliability of supply networks. In particular, node complexity (i.e. the total number of nodes in the network) was found to have the strongest negative effect on network reliability, while the strongest positive factor was sources criticality (i.e. the level of redundancy of suppliers).

The identification of these factors and their relative impacts on network reliability can serve as a guide for the design of more reliable networks, and to know which are the most important to consider when designed distribution networks.

The paper identifies, from the literature, key factors affecting supply network reliability and evaluates their relative impact. Given the number of factors identified, an extensive Monte Carlo simulation is used for the first time, by considering simple and very complex networks, to allow the testing of the role of each factor in supply network reliability.

The purpose of this paper is to design novel hardened flip-flop using carbon nanotube field effect transistors (CNTFETs).

To design the proposed flip-flop, the Schmitt trigger-based soft error masking and unhardened latches have been used. In the proposed design, the novel mechanism, i.e. hysteresis property is used to enhance the hardness of the single event upset.

To obtain the simulation results, all the proposed circuits are extensively simulated in Hewlett simulation program with integrated circuit emphasis software. Moreover, the results of the proposed latches are compared to the conventional latches to show performance improvements. It is noted that the proposed latch shows the performance improvements up to 25.8%, 51.2% and 17.8%, respectively, in terms of power consumption, area and power delay product compared to the conventional latches. Additionally, it is observed that the simulation result of the proposed flip-flop confirmed the correctness with its respective functions.

The novel hardened flip-flop utilizing ST based SEM latch is presented. This flip-flop is significantly improves the performance and reliability compared to the existing flip-flops.

In light of the COVID-19 pandemic, all business sectors have critical needs. They face multiple challenges to restructuring their operations to build a resilient, cost-effective and sustainable supply chain. Therefore, this paper aims to investigate the practice and the research gaps related to supply chains.

This research paper is influenced by a literature review of the past decade. This review paper incorporates industry challenges of the COVID-19 pandemic, including future steps toward developing resilient supply chains in the new normal economy. The research provides a detailed framework for designing cost-effective survivable supply chains that withstand disruptions for the long term.

The proposed research focuses on the effects of the COVID-19 pandemic on supply chains and attempts to bridge pre and post COVID-19 research and practice gaps. Post-COVID-19 resilient supply chains need to be transformed into survivable supply chains. The survivability of the supply chain can be achieved by combining both supply chain resilience and supply chain viability measures. To the best of the authors’ belief, this is the first study that grounds a theory to provide interconnection of five critical supply chain concepts to manage supply chain risk. This study is uniquely positioned to develop a theoretical framework to design a cost-effective, resilient and sustainable supply chain by establishing the interconnection among these concepts in supply chains. This framework helps practitioners to implement the key strategies at the operational, tactical and strategic levels that enhance maturity in supply chains.

The research findings are based on secondary reports such as industry reports, cases, research papers and expert opinions. The authors tried to consult with many companies. However, they were reluctant to share the recovery plan information from COVID. Also, as COVID still exists in many places in Canada, the authors could not gather every intended information from the companies. However, the authors have successfully shared the outcomes of this research with a reputed retail company in Canada. They recognized the importance of survivability in supply chains. Going forward, business organizations need to design cost-effective, sustainable and survivable supply chains.

The study attempts to unify current research dealing with supply chain resilience. The study concludes with the limitations of the current research. It highlights the prospects of future research and bridges the supply chain practice gaps from the challenges faced by industries due to COVID-19. The study contributes to the literature by identifying gaps to bridge the supply chain practice and reiterating new research directions to develop a cost-effective, survivable and sustainable supply chain.

Application environments of wireless sensor networks (WSNs) include heterogeneous nodes with different packet sizes, transmission abilities and tolerable delay times. This study aims to design a reasonable network topology and transmission timing for these heterogeneous nodes to improve the quality of service (QoS) of networks.

In this paper, the authors treat node urgency and data packets as the basis of network clustering and to extend the network lifetime. The flow, energy consumption and residual energy of a node are included in the cluster head election. We also propose a delay evaluation function.

All the nodes in the network are guaranteed to transmit to the sink nodes efficiently by planning the transmission order in each cluster.

The simulation results show that the proposed method can balance node urgency and data packets path planning, which not only extends the lifetime of the network but also decreases network delay and improves the overall efficiency.