Specific Nodes Research Articles

Analysis the Special Nodes of E-commerce Platforms Based on the Background of "Double Eleven" Marketing

As a result of the recent outbreak, it would appear that the "Double Eleven" shopping festival has entered its mature phase, with consumer behaviors gradually becoming more rationalized and customer enthusiasm gradually decreasing. The purpose of this study was to analyze how Pinduoduo can reform and improve its marketing campaigns in order to better boost the desire of people to buy and move the economy forward in the midst of a slump. This study came to the conclusion that consumers' willingness to buy will be influenced by quality-assured and diverse products, cost-effective prices, easy-to-play and easily accessible offers, and close user relationships. On the other hand, the proliferation of newly emerged content platforms and live e-commerce will further occupy the e-commerce platform market and squeeze users' space. As a consequence of this, the only way to improve a company's reputation and brand image, as well as improve the process of corporate transformation and upgrading, and increase consumer stickiness, is to consistently attract big brands, expand digital operations, and serve a diverse range of consumer wants.

Joint Shortest Chain and Fair Transmission Design for Energy-Balanced PEGASIS in WSNs

The conventional routing protocol considers several local transmission factors in a single node for the many-to-one packet transmission. These factors lead to rapid energy consumption in some specific nodes, thereby generating energy holes to reduce network lifetime. In this article, a novel energy-balanced power-efficient gathering in sensor information systems (EB-PEGASISs) is proposed to improve energy efficiency and utilization for wireless sensor networks (WSNs). In the original power-efficient gathering in sensor information system (PEGASIS), the protocol constructs a chain-based network with the locally shortest distance rather than the globally optimal network length. To improve this, two construction algorithms are proposed to achieve the shortest network length in the EB-PEGASIS, including centralized formation and distributed construction schemes. In the centralized algorithm, the chain length of each starting node is computed, and the minimum chain length can be subsequently determined in the following packet transmission phase. In order to reduce formation complexity, a distributed method uses the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering algorithm to partition the network into clusters. In particular, it forms a subchain in each cluster and connects each subchain into the final shortest chain. Additionally, to balance the energy consumption among all nodes in the packet transmission phase, Jain’s fairness index for residual battery capacity is designed to minimize the fluctuation of energy consumption among nodes in a network. As a result, not only energy efficiency is achieved with the shortest chain length but also energy utilization is balanced for packet transmission. Ultimately, simulation results validate that the network lifetime of the EB-PEGASIS is about 2.46 times the original PEGASIS and 3.36 times the random projection-polar coordinate-chain (RPC) protocol for WSNs.

Topological synchronization of quantum van der Pol oscillators

To observe synchronization in a large network of classical or quantum systems demands both excellent control of the interactions between the nodes and very accurate preparation of the initial conditions due to the involved nonlinearities and dissipation. This limits the applicability of this phenomenon for future devices. Here, we demonstrate a route towards significantly enhancing the robustness of synchronized behavior in open nonlinear systems that utilizes the power of topology. In a lattice of quantum van der Pol oscillators with topologically motivated couplings, boundary synchronization emerges in the classical mean field as well as the quantum model. In addition to its robustness against disorder and initial state perturbations, the observed dynamics is independent of the underlying topological insulator model provided the existence of zero-energy modes. Our work extends the notion of topology to the general nonlinear dynamics and open quantum system realm with applications to networks where specific nodes need special protection like power grids or quantum networks.

Provision of optimal dispatching scenarios for regional power systems in the face of uncontrollable power shortages

As of 2023, substantiation of the criteria for optimal load balancing of territorial energy islands in the face of uncontrolled power shortages due to unpredictable failure (destruction as a result of military bombardment) of the upper (trunk) level energy infrastructure is relevant. The research aims to substantiate approaches to ensuring the controlled operation of regional power systems under conditions of power shortages. The study applied the theory and methods of mathematical analysis of complex multicomponent systems in the form of mixed Boolean linear programming. A Mathematical Programming Language and modelling methods of the NEOS server based on the Gurobi solver were used to describe the modes of operation of power grids. A proportional power distribution method between load nodes of the post-emergency network configuration was used to determine the direction of electricity transit with the least losses. An algorithm and a program for solving the problem of addressing flows and power losses in multi-node regional power systems are proposed. The optimisation problem is formulated in the form of a mixed Boolean linear programming model with the criterion of minimising the power momentum, considering balance constraints and upper limits on the power between supply and load nodes. The developed transport matrix made it possible to find the optimal power distribution for emergency and post-emergency modes according to the criterion of minimum network losses. Algorithms and scenarios for the response of dispatching services were formed, considering the capacity of power grids and determining temporary power supply schemes, the configuration of which will ensure the “survivability” of energy islands. The levels of efficiency of the Boolean linear programming model associated with the connectivity of the cycles of the transport problem and the order of traversal of its vertices for test examples depending on the constraints imposed on the components of the network structure are established. The process of tracking electricity flows will allow for establishing routes connecting specific electricity supply nodes with load nodes and determining their shares in covering the energy island’s demand under conditions of power shortage

Solving the hierarchical windy postman problem with variable service costs using a math-heuristic algorithm

The Hierarchical Windy Postman Problem (HWPP) is an arc routing problem in which an order relation is imposed on the arcs/edges of the graph, and one has to pass through each edge at least once while adhering to the hierarchical priority relations. The tour starts from and ends at a specific node and the aim is to minimize the length of the tour. We consider a variant of the HWPP in which (i) the precedence order of the edge hierarchies is linear and edges within each hierarchy are connected and (ii) the cost of serving each edge decreases with the number of times it is traversed, and we refer to it as HWPP with variable service costs. An integer non-heuristic linear mathematical formulation is proposed, and a solution approach is designed. Our solution heuristic adapts the layer algorithm of Dror et al. (Networks 17:283–294, 1987) but employs an integer mathematical formulation as a sub-procedure instead of the blossom algorithm to find the least cost path between the nodes of the graph. This choice is based on the fact that the blossom algorithm requires a symmetric cost structure while we deal here with the general case of asymmetric cost structure, which makes our problem a windy variant of the postman problem. It should be noted that our problem is not asymmetric in the sense that there are no opposite arcs with different costs but there are edges which have different costs depending on the traversal direction. In order to compare the performance of our heuristic algorithm with respect to the performance of the mathematical model that is solved by the commercial solver Gurobi, 84 test instances are generated having varying sizes and densities and with different number of hierarchies. These test instances are solved by both methods and the generated results show that the proposed heuristic method is much faster and generates better quality solutions.

The Core Symptoms of Adolescents Online and Offline Gambling in South Korea Using Network Analysis.

Adolescent gambling is rapidly increasing recently. However, little is known about the core feature of adolescent gambling that should be the treatment target for adolescents. Thus, the objective of this study was to determine the core symptom of adolescent gambling using network analysis with large-scale data targeting community indwelling adolescents. We used dataset of the 2018 national survey on youth gambling problems collected by the Korea Center on Gambling Problems to explore symptom networks of gambling in adolescents. Of 17,520 respondents in the dataset of the 2018 national survey on youth gambling problems collected by the Korea Center on Gambling Problems, 5,619 adolescents with experience of gambling were included in the analysis. We computed an association network, a graphical least absolute shrinkage and selection operator, and a directed acyclic graph to model symptom interactions. In each network of online, offline, and all gambling, stealing money or other valuable things in order to gamble or pay off gambling debts was the most centrally situated and skipping practice followed by dropping out of activities. Especially strong connections emerged between stealing money or other valuable things in order to gamble or pay off gambling debts and academic performance degradation due to gambling. Feeling bad due to gamble and skipping hanging out with friends who do not gamble emerged as a highly central node that might be distinctive to adolescents with online gambling. These findings demonstrate central features of adolescent gambling. Different associations among specific network nodes suggest the existence of distinctive psychopathological constructs between online and offline gambling.

Store, share and transfer: Learning and updating sentiment knowledge for aspect-based sentiment analysis

Previous studies have shown that incorporating sentiment knowledge (e.g., sentiment scores) is effective for aspect-based sentiment analysis (ABSA). However, sentiment knowledge is used to create static features, which cannot be propagated over an entire corpus. Unlike previous researchers, we designed a corpus-level sentiment knowledge fusion mechanism with storage, update, and sharing functions, which can help the model to better understand the sentiment information of various opinion words in the dataset. Specifically, we first constructed a dependency graph for each sentence and refined the weights of the edges by the relative distance between the aspect terms and context words. We then introduced two special sentiment knowledge nodes in the graph to establish connections with opinion words by leveraging external sentiment lexicons. We set these two nodes to be globally shared and updatable, which allowed the model to learn corpus-level and domain-specific sentiment knowledge. This knowledge can help the model to generate a better aspect representation that contains rich contextual information and sentiment knowledge. Extensive experiments were conducted on several public datasets, and the experimental results demonstrated the effectiveness of our method. We also analyzed the performance gains from using learned corpus-level sentiment knowledge to transfer across different datasets.

A time sequence coding based node-structure feature model oriented to node classification

Graph data mining is an important method for managing complex systems in artificial intelligence (AI). As an important branch of graph data mining, node classification is widely applied in paper classification in citation networks, user classification in social networks, etc. At present, many graph neural networks (GNNs) have been proposed to realize node classification by obtaining node embeddings encoded by aggregating neighborhood features. Most GNNs use one-hot encoding as the initial node feature when there is no node attribute in the graph. However, one-hot encoding is a global encoding technique that cannot reflect the environmental context of specific nodes. To this end, we proposed a graph structure sequential coding (GSSC) model, including a learnable node-structure feature X, sampler, encoder and classifier, to obtain the structural embeddings which can better reflect the topological structure of non-attribute graphs. For non-attribute graphs, we utilized the learnable feature X as the initial node-structure feature, which could be simultaneously trained with the GSSC model. In addition, we used a decoupling scheme to separate the sampling and encoding processes of GSSC. Therefore, we could use different samplers for sparse and dense graphs. In addition, different sampling orders (i.e., temporality) in node sequences could reflect different semantic associations between the nodes. Therefore, we innovatively employed time sequence models (TSMs) as encoders to encode node sequences with temporality. Based on these TSMs, semantic information of the head node in the node sequence can be captured. The experimental results on five datasets confirmed that our GSSC model performs better than other representative methods in node classification for graphs without node attributes.

Purpose in life promotes resilience to age-related brain burden in middle-aged adults

BackgroundDisease-modifying agents to counteract cognitive impairment in older age remain elusive. Hence, identifying modifiable factors promoting resilience, as the capacity of the brain to maintain cognition and function with aging and disease, is paramount. In Alzheimer’s disease (AD), education and occupation are typical cognitive reserve proxies. However, the importance of psychological factors is being increasingly recognized, as their operating biological mechanisms are elucidated. Purpose in life (PiL), one of the pillars of psychological well-being, has previously been found to reduce the deleterious effects of AD-related pathological changes on cognition. However, whether PiL operates as a resilience factor in middle-aged individuals and what are the underlying neural mechanisms remain unknown.MethodsData was obtained from 624 middle-aged adults (mean age 53.71 ± 6.9; 303 women) from the Barcelona Brain Health Initiative cohort. Individuals with lower (LP; N = 146) and higher (HP; N = 100) PiL rates, according to the division of this variable into quintiles, were compared in terms of cognitive status, a measure reflecting brain burden (white matter lesions; WMLs), and resting-state functional connectivity, examining system segregation (SyS) parameters using 14 common brain circuits.ResultsNeuropsychological status and WMLs burden did not differ between the PiL groups. However, in the LP group, greater WMLs entailed a negative impact on executive functions. Subjects in the HP group showed lower SyS of the dorsal default-mode network (dDMN), indicating lesser segregation of this network from other brain circuits. Specifically, HP individuals had greater inter-network connectivity between specific dDMN nodes, including the frontal cortex, the hippocampal formation, the midcingulate region, and the rest of the brain. Greater functional connectivity in some of these nodes positively correlated with cognitive performance.ConclusionExpanding previous findings on AD pathology and advanced age, the present results suggest that higher rates of PiL may promote resilience against brain changes already observable in middle age. Furthermore, having a purposeful life implies larger functional integration of the dDMN, which may potentially reflect greater brain reserve associated to better cognitive function.

An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks

In a wireless sensor network (WSN), geocasting is a location-based routing protocol used for data collection or information delivery. In geocasting, a target region usually contains many sensor nodes with limited battery capacity, and sensor nodes in multiple target regions need to transmit data to the sink. Therefore, how to use location information to construct an energy efficient geocasting path is a very important issue. FERMA is a geocasting scheme for WSNs based on Fermat points. In this paper, an efficient grid-based geocasting scheme for WSNs, which is called GB-FERMA, is proposed. The scheme uses the Fermat point theorem to search for the specific nodes as Fermat points in a grid-based WSN, and it selects the optimal relay nodes (gateways) in the grid structure to realize energy-aware forwarding. In the simulations, when the initial power 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA can effectively reduce the energy consumption and thus prolong the lifetime of the WSN.

A survey on opportunistic routing protocols in the Internet of Underwater Things

Compared with traditional ocean monitoring technologies, such as cable seabed observation networks, the Internet of Underwater Things (IoUT) can provide real-time underwater environment monitoring and improve efficiency of data collection. Data collection is one of the most essential functions of IoUT, while it is very challenging to achieve efficient data transmission in IoUT. Opportunistic routing is considered to have great advantages and potential in mitigating the drawbacks of underwater communication and improving the performance of data transmission. Therefore, in this paper, the opportunistic routing protocols for IoUT are reviewed and summarized. Taking advantage of the broadcast characteristic of wireless underwater communication, opportunistic routing selects a group of suitable neighbors as potential forwarders instead of selecting only one or a few specific next-hop nodes as in conventional routing, thus improving the success rate of data packet delivery. Critical to routing performance is which nodes participate in data forwarding as forwarders. The nodes act as forwarders are determined by user-defined routing parameters, which are referred to as route metrics in this paper. First, route metrics of opportunistic routing are analyzed and a comprehensive classification of opportunistic routing protocols is discussed based on the route metrics. Then, opportunistic routing protocols for IoUT are further investigated and compared in terms of the adopted communication technique, data delivery ratio, transmission delay efficiency, energy efficiency, buffer efficiency, reliability, etc. Based on the comprehensive classification and comparison, existing problems and future research issues regarding opportunistic routing in IoUT are finally presented.

LncRNA MFSD4A-AS1 promotes lymphangiogenesis and lymphatic metastasis of papillary thyroid cancer.

Lymphatic metastasis is the leading cause responsible for recurrence and progression in papillary thyroid cancer (PTC), where dysregulation of lncRNAs have been extensively demonstrated to be implicated. However, the specific lymphatic node metastatsis-related (LNM) lncRNAs remain not identified in PTC yet. LNM lncRNA, MFSD4A-AS1, were explored in PTC dataset from TCGA, and our clinical samples. The roles of MFSD4A-AS1 in lymphatic metastasis were investigated by in vitro, and in vivo. Bioinformatic analysis, Luciferase assay and RIP assay were performed to identify the potential targets and the underlying pathway of MFSD4A-AS1 in lymphatic metastasis of PTC. MFSD4A-AS1 was specifically upregulated in PTC tissues with lymphatic metastasis. Upregulating MFSD4A-AS1 promoted mesh formation and migration of HUVECs and invasion and migration of PTC cells. Importantly and consistently, MFSD4A-AS1 promoted lymphatic metastasis of PTC cells in vivo by inducing the lymphangiogenic formation and enhancing invasive capability of PTC cells. Mechanistic dissection further revealed that MFSD4A-AS1 functioned as ceRNA to sequester miR-30c-2-3p, miR-145-3p and miR-139-5p to disrupt the miRNAs-mediated inhibition of VEGFA and VEGFC, and further activated TGF-β signaling by sponging miR-30c-2-3p that targeted TGFBR2 and USP15, both of which synergistically promoted lymphangiogenesis and lymphatic metastasis of PTC. Our results unravel a novel dual mechanisms by which MFSD4A-AS1 promotes lymphatic metastasis of PTC, which will facilitate the development of anti-lymphatic metastatic therapeutic strategy in PTC.

Spatio-temporal storm surge emulation using Gaussian Process techniques

Surrogate models (also referenced as metamodels) are recognized as powerful, data-driven, predictive tools for the approximation (emulation) of storm surge. For this application, they are developed using synthetic storm simulations, for example, leveraging databases created during regional flood studies. Once calibrated, they can efficiently emulate the expected storm surge for new storms. These surge predictions pertain to different locations (nodes) of interest and, for this study, to time-series estimation. The developed metamodel approximation in this setting needs to capture both the spatio-temporal variability as well as the variability across the storm features (i.e., the storm parametric description). Two different approaches are examined to address this multi-faceted variability using Gaussian processes as emulation technique. The first approach addresses the spatio-temporal variability through the metamodel output, leveraging the fact that predictions need to always be established for the same nodes and time-series instances. This allows the storm surge for each instance and node to be considered as a separate output, leaving the storm features as the only component determining the surrogate model input. To improve the computational efficiency for the metamodel calibration and predictions, principal component analysis is adopted to address the high dimensionality of the output. The second approach considers all three aforementioned sources of variability as part of the metamodel input, establishing a surrogate model that simultaneously predicts the storm surge (scalar output) across space, time, and storm features. The number of available data points across these three inputs becomes extremely high in this case (product of number of nodes, time-instances, and storms), and in order to establish computational tractability, a separable covariance function is considered for the Gaussian process. Comparison of computational efficiency and accuracy across the two approaches is established utilizing the Coastal Hazards System–North Atlantic (CHS-NA) database. These comparisons show that all approaches perform well, with average root mean squared error of around 5 cm and an average correlation coefficient of over 96% across the majority of the geographic domain. The computational efficiency for establishing predictions is also similar (less than a second per storm), while for the calibration, the computational demand for the approach that addresses spatio-temporal variability through the metamodel input is higher, unless some approximate formulation is adopted. Additionally, the use of distance to landfall (instead of time to landfall) is examined for describing the time-series variability of the surge. It is shown that this distance-based definition can offer a better correlation of the response for specific nodes across storms with different forward speedsbut similar remaining features (size, intensity, and track), something that can ultimately improve the accuracy of the surrogate model calibrated using such a database description.

A Review and Testing of Fault Tolerance Levels of Anti-Poaching Cybersecurity System

The development of anti-poaching networks and systems has created a new environment for animals in game reserves all over the world. Advanced technologies such as heat sensors, drones, and trip wires help prevent poachers from entering the property and therefore, creating a safer environment for animals to roam freely. Radio frequency identification (RFID) systems are used to track the location of animals. These networks are, however, susceptible to being hacked if not properly protected with cybersecurity tools, resulting in cyber-criminals gaining access into the network. Many attacks or threats can be executed on the RFID network due to some exposed vulnerabilities of elements within the anti-poaching network. The purpose of this paper is to explore the empirical methods of common attacks, used by cyber-criminals, to attack the anti-poaching network, and whether these methods used are effective in identifying weaknesses within the network. This will be executed by creating an experimental structure of the anti-poaching system with a specialised focus on the RFID elements, using quantitative research methods to produce findings. GNS3, an open-source software application that has specifically been chosen to conduct this research, is used to build the network simulation in order to analyse the weaknesses of the network. Cybersecurity protocols are implemented to protect the network and aim to protect the animals. The attacks performed, such as Flood and Scapy attacks, have shown that the anti-poaching network is vulnerable to penetration from cyber-criminals. A hypothesis test was conducted to determine whether the attacks had a significant effect on the network, by using the average ping time from specific nodes to Google. It was found that the average ping time increased by 2.0020 units, therefore stating that the elements of the network were successfully attacked. The fault tolerance test shows that the availability of the anti-poaching network is roughly 90 percent which concludes that the network is configured to deliver quality performance and handle failures, should there be any intervention. This will allow game reserves to implement and have information on a better and safer RFID system for the animals.

Analysis of Changes in Brain Region and Connectomics in the Acute Stage of Sudden Sensorineural Hearing Loss in the Resting State via Functional Magnetic Resonance Imaging

To explore the functional changes in the whole brain network in patients with sudden sensorineural hearing loss (SHL) at the acute stage from functional magnetic resonance imaging (fMRI) imaging evaluation results, 80 patients with sudden right SHL were selected as subjects (patient group). In addition, 40 healthy volunteers who underwent physical examination in the hospital during the same period were recruited as a control group. fMRI imaging was performed to analyze functional parameters and core nodes of the whole brain network. It was found that at all thresholds, the fMRI parameters Cp and Lp of the patient group were dramatically superior to those of the control group. The fMRI parameter Eglobal of the patient group was substantially lower than that of the control group ( P &lt; 0.05 ). At most of the thresholds, the fMRI parameter λ in patients was dramatically superior to that in the control group ( P &lt; 0.05 ). There were ten specific network core nodes in patients, including the right parahippocampal gyrus, right supra-occipital gyrus, left suboccipital gyrus, right fusiform gyrus, right parietal lobule, right subparietal lobule, right superior temporal gyrus, left superior marginal gyrus, and right superior temporal gyrus. In summary, the whole brains of patients with sudden SHL still had small-world attributes, but some characteristics of the brain network had changed, and there was a trend of transformation to a regular network. The connection between the auditory brain area and the functional areas related to language and vision was weakened, and the distribution of core nodes changed. This study provides a reference basis for exploring the changes in local brain and connectome levels in patients with sudden sensorineural hearing loss in the acute phase based on resting-state fMRI.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace.

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

Random multi-hooking networks

AbstractWe introduce a broad class of multi-hooking networks, wherein multiple copies of a seed are hooked at each step at random locations, and the number of copies follows a predetermined building sequence of numbers. We analyze the degree profile in random multi-hooking networks by tracking two kinds of node degrees—the local average degree of a specific node over time and the global overall average degree in the graph. The former experiences phases and the latter is invariant with respect to the type of building sequence and is somewhat similar to the average degree in the initial seed. We also discuss the expected number of nodes of the smallest degree. Additionally, we study distances in the network through the lens of the average total path length, the average depth of a node, the eccentricity of a node, and the diameter of the graph.

Optimized Quantum Networks

The topology of classical networks is determined by physical links between nodes, and after a network request the links are used to establish the desired connections. Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests, which can substitute links and allow one to fulfill multiple network requests with the same resource state. We utilize this to design entanglement-based quantum networks tailored to their desired functionality, independent of the underlying physical structure. The kind of entanglement to be stored is chosen to fulfill all desired network requests (i.e. parallel bipartite or multipartite communications between specific nodes chosen from some finite set), but in such a way that the storage requirement is minimized. This can be accomplished by using multipartite entangled states shared between network nodes that can be transformed by local operations to different target states. We introduce a clustering algorithm to identify connected clusters in the network for a given desired functionality, i.e. the required network topology of the entanglement-based network, and a merging algorithm that constructs multipartite entangled resource states with reduced memory requirement to fulfill all desired network requests. This leads to a significant reduction in required time and resources, and provides a powerful tool to design quantum networks that is unique to entanglement-based networks.

A multi-step approach for reliability-based robust design optimization of truss structures

There are two general methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) for the optimal design of structures with non-deterministic variables. In the problem-solving process of both RDO and RBRDO, assessing the probabilistic constraints of the problem and the standard deviation of the structural responses is time-consuming and costly. In this study, by presenting a multi-step approach, for a probable optimal solution, the deterministic constraints are first investigated. Then the limit state functions related to probabilistic constraints are computed based on the mean value of random variables (in the RBRDO problem) and in the step of uncertainties effects simulation on the response of the structure, probabilistic constraints are evaluated (in the RBRDO problem). Designs that not meeting the deterministic constraints or in the unsafe area for the mean values of the random variables are not included in calculating the first and second terms of the objective function and reliability assessment. This leads to reduce the volume of calculations. The Monte Carlo simulation method is used to assess the probabilistic constraints, and the EVPS metaheuristic algorithm is used for the optimization process. Three benchmark trusses of 10, 25, and 72 bars were studied to assess the efficiency of the proposed approach. The first objective function in these problems was the mean weight of trusses and the second objective function was the standard deviation of the displacement of a specific node in a certain direction.

The fault‐tolerant beacon set of hexagonal Möbius ladder network

In localization, some specific nodes (beacon set) are selected to locate all nodes of a network, and if an arbitrary node stops working and still selected nodes remain in the beacon set, then the chosen nodes are called fault‐tolerant beacon set. Due to the variety of metric dimension applications in different areas of sciences, many generalizations were proposed, fault‐tolerant metric dimension is one of them. A resolving (beacon) set is fault tolerant, if for each is also a resolving set; it is also known as a fault‐tolerant beacon set; the minimum cardinality of such a beacon set is known as the fault‐tolerant metric dimension of a graph . In this paper, we find the fault‐tolerant beacon set of hexagonal Möbius ladder network and proved that all the different variations of and in has constant fault‐tolerant metric dimension.