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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.

The purpose of this paper is to study the correlation structure of the credit spreads.

The minimal spanning tree is used to find the risk center node and the basic correlation structure of the credit spreads. The dynamic copula and pair copula models are applied to capture the dynamic and non-linear correlation structure.

The authors take the enterprise bond with trading data from January 2013 to December 2013 as the research sample. The empirical study of minimum spanning tree shows that the credit risk of corporate bonds forms a network structure with a center node. Meanwhile, the correlation between credit spreads shows dynamic characteristics. Under the framework of dynamic copula, the lower tail dependence is less than the upper tail dependence, thus, in economic boom period, the dynamic correlation is more significant than in recession period. The authors also find that the centrality of credit risk network is not significant according to the pair copula and Granger causality test. The empirical study shows that the goodness-of-fit of D vine is superior to Canonical vine, and the Granger causality test additionally proves that the center node has influence on few other nodes in the risk network, thus the center node captured by the minimum spanning tree is a weak center node, and this characteristic of credit risk network indicates that the risk network of credit spreads is generated mostly by the external shocks rather than the internal risk contagion.

This paper provides new ideas for investors and researchers to analyze the credit risk correlation or contagion.

Material handling (MH) is an important facility in any manufacturing system. It facilitates the transport of in-process material from one workstation (WS) to another. MH devices do imply incurring capital costs and, therefore, minimizing their deployment without compromising on smooth material flow will ensure savings in addition to the optimal use of productive shop floor space and, avoid space cluttering. The purpose of this paper is to evaluate the minimal network that connects all the WSs, therefore ensuring economic and safe manufacturing operations.

Graph theoretical approach and Prim’s algorithm for minimal spanning tree is used to evaluate the minimal span of the MH devices. The algorithm is initialized by translating the graph of WSs into a distance matrix to evaluate the minimal MH network.

The minimal length of the MH devices is evaluated for a typical case study.

The step-by step methodology explained in the manuscript acts as a good guide for practicing operational managers. The shortcoming of the methodology is that, it presumes the use of modular MH devices that will need to be reconfigured based on dynamic changes to the manufacturing system.

The methodology is explained in detail to enable the practicing managers to use it for designing their MH networks in any manufacturing system.

There is no evidence to indicate the use of minimal spanning tree algorithm for design of MH networks in a manufacturing system. This paper attempts to fill this gap.

Many synchronization approaches are based on low-level time capturing, causing a tight integration with the Media Access Control (MAC) layer. Alternatively, this study aims to present a hybrid approach combining both receiver–receiver and sender–receiver schemes to reduce the variation of two-way message exchange durations, in heavy-load networks. To achieve network-wide synchronization, a variant of Prim’s algorithm (Cormen et al., 2009) is used to build a spanning tree, guaranteeing the minimum number of ancestors and limited error propagation. The simulation results show that the proposed approach is very competitive with a set of the most-cited synchronization protocols. In addition, a new synchronization simulator SynSim was developed using C++ language

To achieve network-wide synchronization, a variant of Prim's algorithm (Cormen et al., 2009) is used to build a spanning tree, guaranteeing the minimum number of ancestors and limited error propagation.

Simulation results show that the proposed approach is very competitive with a set of the most-cited synchronization protocols. In addition, a new synchronization simulator SynSim was developed using in C++ language.

It can be concluded from the experiments that MDSP is suitable for WSNs especially if MAC layer timestamping is not possible. So, the mean delays synchronization protocol (MSDP) is suitable to achieve time synchronization in single-hop and multi-hop networks without the MAC layer timestamping in large wireless sensor network (WSN) deployments.

A future enhancement of MDSP could be switching between the traditional timestamping and the new proposed timestamping based on a given threshold, which is the number of nodes in the neighborhood and the load of the network. It will be also interesting to test it in a prototype. The proposed solution can be used in practice to implement the Time-division multiple access (TDMA) protocol in a WSN. In addition, the proposed simulator can be used in a computer network synchronization protocols course.

To the best of authors’ knowledge, this study’s contribution is original. In addition, the authors implemented a new synchronization simulator

This paper aims to analyze the dependence structure between the Green Sukuk Spread in Malaysia and uncertainty factors from January 1, 2017, to May 23, 2023, covering two main periods: the pre-COVID-19 and the COVID-19 periods.

This study contributes to the current literature by explicitly modeling nonlinear dependencies using the Regular vine copula approach to capture asymmetric characteristics of the tail dependence distribution. This study used the Archimedean copula models: Student’s-t, Gumbel, Gaussian, Clayton, Frank and Joe, which exhibit different tail dependence structures.

The empirical results suggest that Green Sukuk and various uncertainty variables have the strongest co-dependency before and during the COVID-19 crisis. Due to external uncertainties (COVID-19), the results reveal that global factors, such as the Infect-EMV-index and the higher financial stress index, significantly affect the spread of Green Sukuk. Interestingly, in times of COVID-19, its dependence on Green Sukuk and the news sentiment seems to be a symmetric tail dependence with a Student’s-t copula. This result is relevant for hedging strategies, as investors can enhance the performance of their portfolio during the COVID-19 crash period.

This study contributes to a better understanding of the dependency structure between Green Sukuk and uncertainty factors. It is relevant for market participants seeking to improve their risk management for Green Sukuk.

The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.

In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.

Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.

The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.

The state-of-the-art methods designed for overlapping community detection are limited by their high execution time as in CPM or the need to provide some parameters like the number of communities in Bigclam and Nise_sph, which is a nontrivial information. Hence, there is a need to develop the accuracy that represents the primordial goal, where the actual state-of-the-art methods do not succeed to achieve high correspondence with the ground truth for many instances of networks. The paper aims to discuss this issue.

The authors offer a new method that explore the union of all maximum spanning trees (UMST) and models the strength of links between nodes. Also, each node in the UMST is linked with its most similar neighbor. From this model, the authors extract local community for each node, and then they combine the produced communities according to their number of shared nodes.

The experiments on eight real-world data sets and four sets of artificial networks show that the proposed method achieves obvious improvements over four state-of-the-art (BigClam, OSLOM, Demon, SE, DMST and ST) methods in terms of the F-score and ONMI for the networks with ground truth (Amazon, Youtube, LiveJournal and Orkut). Also, for the other networks, it provides communities with a good overlapping modularity.

In this paper, the authors investigate the UMST for the overlapping community detection.

The purpose of this paper is to develop a new rectilinear branch pipe‐routing algorithm for automatic generation of rectilinear branch pipe routes in constrained spaces of aero‐engines.

Rectilinear branch pipe routing that connects multiple terminals in a constrained space with obstacles can be formulated as a rectilinear Steiner minimum tree with obstacles (RSMTO) problem while meeting certain engineering rules, which has been proved to be an NP‐hard and discrete problem. This paper presents a discrete particle swarm optimization (PSO) algorithm for rectilinear branch pipe routing (DPSO‐RBPRA) problems, which adopts an attraction operator and an energy function to plan the shortest collision‐free connecting networks in a discrete graph space. Moreover, this paper integrates several existing techniques to evaluate particles for the RSMTO problem in discrete Manhattan spaces. Further, the DPSO‐RBPRA is extended to surface cases to adapt to requirements of routing pipes on the surfaces of aero‐engines.

Pipe routing numeral computations show that, DPSO‐RBPRA finds satisfactory connecting networks while considering several engineering rules, which demonstrates the effectiveness of the proposed method.

This paper applies the Steiner tree theory and develops a DPSO algorithm to plan the aero‐engine rectilinear branch pipe‐routing layouts.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

In this study, we analyze the power of the individual return-to-volatility security performance heuristic (ri/stdi) to simplify the identification of securities to buy and, consequently, to form the optimal no short sales mean–variance portfolios. The heuristic ri/stdi is powerful enough to identify the long and shorts sets. This is due to the positive definiteness of the variance–covariance matrix – the key is to use the heuristic sequentially. At the investor level, the heuristic helps investors to decide what securities to consider first. At the portfolio level, the heuristic may help us find out whether it is a good idea to invest in equity to begin with. Our research may also help to integrate individual security analysis into portfolio optimization through improved security rankings.