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This paper aims to present a distributed Bayesian approach with connectivity maintenance to manage a multi-agent network search for a target on a two-dimensional plane.

The Bayesian framework is used to compute the local probability density functions (PDFs) of the target and obtain the global PDF with the consensus algorithm. An inverse power iteration algorithm is introduced to estimate the algebraic connectivity λ₂ of the network. Based on the estimated λ₂, the authors design a potential field for the connectivity maintenance. Then, based on the detection probability function, the authors design a potential field for the search target. The authors combine the two potential fields and design a distributed gradient-based control for the agents.

The inverse power iteration algorithm can distributed estimate the algebraic connectivity by the agents. The agents can efficient search the target with connectivity maintenance with the designed distributed gradient-based search algorithm.

Previous study has paid little attention to the multi-agent search problem with connectivity maintenance. Our algorithm guarantees that the strongly connected graph of the multi-agent communication topology is always established while performing the distributed target search problem.

A method for qualitative estimation of reliability of large and complex mechanical and hydraulic systems is presented. It is especially useful for comparison and optimum selection of the structure at the conceptual stage of design when no other information about the salient features or parameters of the system is known. The method permits the identification and analysis of critical paths, loops and subsystems causing failure under different causes and modes. The method is based on graph theory and the graph variants proposed as reliability measures are also modified to yield realistic and useful results. The concept of system graph introduced in the article for dealing with large systems appears to be the most appropriate for analysis, comparison, selection and reliability estimates at the beginning of the system′s design.

The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.

The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.

Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.

To the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.

This paper seeks to develop experimental laboratory biological techniques for approximation of existing road networks, optimizing transport links, and designing alternative optimal solutions to current transport problems. It studies how slime mould of Physarum polycephalum approximate highway networks of Brazil.

The 21 most populous urban areas in Brazil are considered and represented with source of nutrients placed in the positions of slime mould growing substrate corresponding to the areas. At the beginning of each experiment slime mould is inoculated in São Paulo area. Slime mould exhibits foraging behavior and spans sources of nutrients (which represent urban areas) with a network of protoplasmic tubes (which approximate vehicular transport networks). The structure of transport networks developed by slime mould are analyzed and compared with families of known proximity graphs. The paper also imitates slime‐mould response to simulated disaster.

It was found that the plasmodium of P. polycephalum develops a minimal approximation of a transport network spanning urban areas. Physarum‐developed network matches man‐made highway network very well. The high degree of similarity is preserved even when high‐demand constraints are placed on repeatability of links in the experiments. Physarum approximates almost all major transport links. In response to a sudden disaster, gradually spreading from its epicenter, the Physarum transport networks react by abandoning transport links affected by disaster zone, enhancement of those unaffected directly by the disaster, massive sprouting from the epicenter, and increase of scouting activity in the regions distant to the epicenter of the disaster.

Experimental methods and computer analysis techniques presented in the paper lay a foundation of novel biological laboratory approaches to imitation and prognostication of socio‐economical developments.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

A simultaneous solution to the localization and mapping problem of a graph‐like environment by a swarm of robots requires solutions to task coordination and map merging. The purpose of this paper is to examine the performance of two different map‐merging strategies.

Building a representation of the environment is a key problem in robotics where the problem is known as simultaneous localization and mapping (SLAM). When large groups of robots operate within the environment, the SLAM problem becomes complicated by issues related to coordination of the elements of the swarm and integration of the environmental representations obtained by individual swarm elements. This paper considers these issues within the formalism of a group of simulated robots operating within a graph‐like environment. Starting at a common node, the swarm partitions the unknown edges of the known graph and explores the graph for a pre‐arranged period. The swarm elements then meet at a particular time and location to integrate their partial world models. This process is repeated until the entire world has been mapped. A correctness proof of the algorithm is presented, and different coordination strategies are compared via simulation.

The paper demonstrates that a swarm of identical robots, each equipped with its own marker, and capable of simple sensing and action abilities, can explore and map an unknown graph‐like environment. Moreover, experimental results show that exploration with multiple robots can provide an improvement in exploration effort over a single robot and that this improvement does not scale linearly with the size of the swarm.

The paper represents efforts toward exploration and mapping in a graph‐like world with robot swarms. The paper suggests several extensions and variations including the development of adaptive partitioning and rendezvous schedule strategies to further improve both overall swarm efficiency and individual robot utilization during exploration.

The novelty associated with this paper is the formal extension of the single robot graph‐like exploration of Dudek et al. to robot swarms. The paper here examines fundamental limits to multiple robot SLAM and does this within a topological framework. Results obtained within this topological formalism can be readily transferred to the more traditional metric representation.

Large-scale urbanisation has become a significant barrier to the natural migration of tree species, which is being exacerbated by accelerated climate change. Within this context, improving the permeability of urban landscapes is expected to be an effective strategy to facilitate the process of forest migration through cities. The purpose of this paper is to develop a method to assess the permeability of urban green spaces as stepping stones for forest migration, from the perspective of seed dispersal.

The proposed method combines a least-cost path (LCP) model and a graph theory-based approach. The LCP model is applied to map the potential pathways of seed dispersal at multiple spatial and temporal scales, based on which graph theory-based indices are used to quantify the accessibility of urban landscapes for seed dispersers. This method is demonstrated by a case study in the Greater Manchester area, UK. Eurasian jay, Eurasian siskin, coal tit and grey squirrel are selected as the main seed dispersers in the study area.

The results provide a comparison of the landscape permeability maps generated from different seed dispersers and identify key areas likely to facilitate the process of forest migration. Recommendations regarding landscape management for improving permeability are also discussed.

This method allows designers to re-visualise highly modified and fragmented urban landscapes as stepping stones for seed dispersal, which in turn allows for a more piecemeal form of landscape design to optimise urban landscapes for climate adaptation.

With the rapid development of the indoor spaces positioning technologies such as the radio-frequency identification (RFID), Bluetooth and WI-FI, the locations of indoor spatial objects (static or moving) constitute an important foundation for a variety of applications. However, there are many challenges and limitations associated with the structuring and querying of spatial objects in indoor spaces. The purpose of this study is to address the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces. Also it addresses the related features of indoor spaces such as indoor structures, positioning technologies and others.

In this paper, the author focuses on understanding the aspects and challenges of spatial database managements in indoor spaces. The author explains the differences between indoor spaces and outdoor spaces. Also examines the issues pertaining to indoor spaces positioning and the impact of different shapes and structures within these spaces. In addition, the author considers the varieties of spatial queries that relate specifically to indoor spaces.

Most of the research on data management in indoor spaces does not consider the issues and the challenges associated with indoor positioning such as the overlapping of Wi-Fi. The future trend of the indoor spaces includes included different shapes of indoors beside the current 2D indoor spaces on which the majority of the data structures and query processing for spatial objects have focused on. The diversities of the indoor environments features such as directed floors, multi-floors cases should be considered and studied. Furthermore, indoor environments include many special queries besides the common ones queries that used in outdoor spaces such as KNN, range and temporal queries. These special queries need to be considered in data management and querying of indoor environments.

To the best of the author’s knowledge, this paper successfully addresses the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces.

Let H be a connected subgraph of a connected graph G. The H-structure connectivity of the graph G, denoted by κ(G;H), is the minimum cardinality of a minimal set of subgraphs F={H1′,H2′,…,Hm′} in G, such that every H′i∈F is isomorphic to H and removal of F from G will disconnect G. The H-substructure connectivity of the graph G, denoted by κs(G;H), is the minimum cardinality of a minimal set of subgraphs F={J1′,J2′,…,Jm′} in G, such that every Ji′∈F is a connected subgraph of H and removal of F from G will disconnect G. In this paper, we provide the H-structure and the H-substructure connectivity of the circulant graph Cir(n,Ω) where Ω={1,…,k,n−k,…,n−1},1≤k≤⌊n2⌋ and the hypercube Qn for some connected subgraphs H.

This paper aims to present a novel approach for assessing the resilience of transportation road infrastructure against different failure scenarios based on the topological properties of the network. The approach is implemented in the context of developing countries where data scarcity is the norm, taking the capital city of Beirut as a case study.

The approach is based on the graph theory concepts and uses spatial data and urban network analysis toolbox to estimate the resilience under random and rank-ordering failure scenarios. The quantitative approach is applied to statistically model the topological graph properties, centralities and appropriate resilience metrics.

The research approach is able to provide a unique insight into the network configuration in terms of resilience against failures. The road network of Beirut, with an average nodal degree of three, turns to act more similarly to a random graph when exposed to failures. Topological parameters, connectivity and density indices of the network decline through disruptions while revealing an entire dependence on the state of nodes. The Beirut random network responds similarly to random and targeted removals. Critical network components are highlighted following the approach.

The approach is limited to an undirected and weighted specific graph of Beirut where the capacity to collect and process the necessary data in such context is limited.

Decision-makers are better able to direct and optimize resources by prioritizing the critical network components, therefore reducing the failure-induced downtime in the functionality.

The resilience of Beirut transportation network is quantified uniquely through graph theory under various node removal modes.