Network Location Research Articles

A comprehensive ultrastructural analysis of the Toxoplasma gondii cytoskeleton

The invasive nature of Toxoplasma gondii is closely related to the properties of its cytoskeleton, which is constituted by a group of diverse structural and dynamic components that play key roles during the infection. Even if there have been numerous reports about the composition and function of the Toxoplasma cytoskeleton, the ultrastructural organization of some of these components has not yet been fully characterized. This study used a detergent extraction process and several electron microscopy contrast methods that allowed the successful isolation of the cytoskeleton of Toxoplasma tachyzoites. This process allowed for the conservation of the structures known to date and several new structures that had not been characterized at the ultrastructural level. For the first time, characterization was achieved for a group of nanofibers that allow the association between the polar apical ring and the conoid as well as the ultrastructural characterization of the apical cap of the parasite. The ultrastructure and precise location of the peripheral rings were also found, and the annular components of the basal complex were characterized. Finally, through immunoelectron microscopy, the exact spatial location of the subpellicular network inside the internal membrane system that forms the pellicle was found. The findings regarding these new structures contribute to the knowledge concerning the biology of the Toxoplasma gondii cytoskeleton. They also provide new opportunities in the search for therapeutic strategies aimed at these components with the purpose of inhibiting invasion and thus parasitism.

A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk

ABSTRACTThe presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.

The Influence of Interlocking Director Network Location on Enterprise Performance—Adjustment based on Redundant Resources and Regional Development Level

This study examines the impact of network of interlocking directors’ network location on enterprise performance using panel dataset drawn from 1856 firm-year observations during the period 2013-2018 in China’s listed companies. The moderating effects of external development level and internal absorbed slack and unabsorbed slack resources on the impact were investigated. We find that network centrality has positive effect on firm performance；absorbed slack negatively moderates the relationship between network centrality and enterprise performance, unabsorbed slack have positive moderating effect, but regional development level have no significant moderating effect. Further analysis showed that redundant resources and regional development level have bidirectional adjusting effect, the variation of interlocking director network location has negative moderating on enterprise performance. Compared with state-owned enterprises, network centrality of non-state-owned enterprises has a positive moderating on enterprise performance, compared with the central and western regions, the network position of interlocking directors in the eastern region has a positive moderating on corporate performance.

Multiple Faults Detection and Location in Bus-Shaped Cable Networks by Distributed Time-Domain Reflectometry

Cables are the primary transmission media for electric power and communication in modern systems. Faults in cable networks affect the system’s reliability and have serious consequences, leading to significant interruptions, failures, and accidents. In this letter, we propose a new method for detecting and locating multiple faults in bus-shaped cable networks based on distributed time-domain reflectometry. The proposed method can detect multiple faults existing simultaneously within one or more network branches. We use analytical modeling and adjacency matrix to categorize reflections measured at the terminations of a bus-shaped network. The proposed method can recognize redundant reflections by distinguishing the first reflection of each error. It detects the faults and identifies their corresponding network branches. The method has been validated on a network with two junctions, one soft fault, one open circuit, and one short circuit. It achieves a maximal relative position deviation of 1.05% and a maximal absolute deviation of 0.98 m.

An aerial ammunition ad hoc network collaborative localization algorithm based on relative ranging and velocity measurement in a highly-dynamic topographic structure

In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of munitions with an aerial three-dimensional (3D) highly-dynamic topographic structure under a satellite denied environment. As for aerial networked munitions, the measurement of munitions is objectively incomplete due to the degenerated and interrupted link of munitions. For this reason, a cluster-oriented collaborative localization method is put forward in this paper. Multidimensional scaling (MDS) was first integrated with a trilateration localization method (TLM) to construct a relative localization algorithm for determining the relative location of a mobile cluster network. The information related to relative velocity was then combined into a collaborative localization framework to devise a TLM-vMDS algorithm. Finally, an iterative refinement algorithm based on scaling by majorizing a complicated function (SMACOF) was employed to effectively eliminate the influence of incomplete link observation on localization accuracy. Compared with the currently available advanced algorithms, the proposed TLM-vMDS algorithm achieves higher localization accuracy and faster convergence for a cluster of extensively networked munitions, and also offers better numerical stability and robustness for high-speed motion models.

The Application of Artificial Intelligence Oriented to the Internet of Things Technology in the Complexity of Digital Crossborder Trade

With the continuous update of technologies such as cloud computing, big data, AI, and 5G, various IoT services have also been developed. Various IoT terminals have emerged one after another, and the number of access terminals has also risen sharply. With the development and popularization of a new generation of artificial intelligence technology, the Internet of Things mainly connects various objects to each other in an agreed manner through various sensing devices and conducts wireless communication and data exchange to achieve object-to-object communication, positioning, traceability, intelligent identification, and management. The Internet of Things has broad application prospects in many aspects such as intelligent robots, transportation, and intelligent buildings. Accurate node positioning is considered to be the focus of the application of the Internet of Things in geographic and network location services. More and more experts and scholars have begun to worry about the future development of artificial intelligence technology. While artificial intelligence technology provides convenience to the society, it may also cause huge hidden dangers to the public safety of the entire society. Therefore, it is necessary to make decisions about the technical risks of artificial intelligence. Philosophical analysis has certain historical, practical significance, and theoretical value. Internet technology innovation and digital technology innovation continue to emerge. Digital trade has become a typical feature of the current economic structural transformation, and it is also a new driving force for countries to participate in the division of labor in the global value chain. At present, the scale of global digital trade continues to expand, but there are still many restrictions on digital trade rules. Digital trade tariff barriers and nontariff barriers in digital trade such as crossborder data flow restrictions, intellectual property infringements, and personal information protection are bound to have an impact on the global value chain division of labor system. IoT technology-oriented artificial intelligence accelerates the logistics speed and tracking efficiency of digital cross-border trade, improves consumers’ willingness to spend, and promotes the development of the entire e-commerce trade field.

Flow rule‐based routing protocol management system in software‐defined IoT sensor network for IoT applications

SummaryIn this article, a software‐defined (SDN)‐based Internet of things (IoT) sensor network (S‐ISN) architecture is proposed to fulfill the IoT applications in real time. The proposed system's architecture contains three planes, namely, data plane, control plane, and application plane, to enrich SDN in IoT applications. An IoT application‐based SDN controller is designed exclusively with two management rules—IoT application device management and IoT application‐based topology management. IoT application device management engages in controlling the functions of physical devices that lie on the data plane—path formation of the sensor (proactive, reactive, and hybrid) and reporting scheme management (query, time, and event) of the IoT sensor node. Likewise, IoT application topology management facilitates controlling network topology‐related issues using network structure management (data, location, group, and hierarchical) and next‐hop selection management (broadcast, location, and probabilistic) of the IoT sensor network. The above said management policies are achieved in real time to meet the applications of IoT. Hence, this S‐ISN scheme focuses on IoT application‐related issues with great flexibility in real time. Experimental results show that the network metrics have been improved by the proposed approach as compared with existing methods.

Light weight object detector based on composite attention residual network and boundary location loss

The object detector based on deep learning has received extensive attention, but the high computational cost has become an obstacle to its large-scale application. It is a great challenge for object detection to further reduce the hardware requirements on the premise of ensuring high detection accuracy. We propose a one-stage lightweight object detector and a new regression loss. In this method, ResNet is improved and combined with attention mechanism to ensure the maximum integrity of feature information with fewer parameters; The multi-scale feature fusion network is improved to reduce the reasoning complexity of the structure. In addition, the bounding box regression loss is improved, and the specific position of the bounding box is adjusted by considering the balance of multiple factors in the regression process. The experimental results show that: 1) the combination of most detectors and improved loss can further improve the performance of detectors; 2) As a whole, our improved network and loss can give consideration to both speed and accuracy on Pascal VOC and COCO; 3) in the addition of other new training tricks such as DropBlock and Mosaic, we can achieve better overall performance on the coco test development set, 38.42 AP (average accuracy) at 40.3 FPS.

Indoor Positioning with CNN and Path-Loss Model Based on Multivariable Fingerprints in 5G Mobile Communication System.

Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.

A Novel Energy-Aware Routing in Wireless Sensor Network Using Clustering Based on Combination of Multiobjective Genetic and Cuckoo Search Algorithm

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self-organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near-optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

Private and Mobile inter-network routing for Wireless Sensor Networks and Internet of Things

In the last few years, using the Internet of Things has been expanded in many areas, such as environmental monitoring, industries, and smart home. Since the Internet of Things has a direct relation to human life, its security is of paramount importance. Therefore, the communication between the nodes should be secured and the valuable private information should be kept private so that the attacker cannot detect the network structure. This article provides a protocol that can handle routing privately. To do this, we use the data structure called Spatial Bloom Filter (SBF). In addition, the proposed protocol uses random identifiers instead of IP addresses, so that an attacker cannot collect network structure information and location of nodes from IP addresses. Using a homomorphic encryption scheme, the protocol prevent attackers from retrieving valuable network information, if they can infiltrate to one or more network nodes. Also, since almost all nodes in the internet of things are mobile, the structure of networks and subnets is constantly changing. The proposed protocol has the ability to manage to route in networks with a dynamic structure.

A multi-objective integrated optimisation model for facility location and order allocation problem in a two-level supply chain network

A multi-objective integrated optimisation model for facility location and order allocation problem in a two-level supply chain network

Financing constraints and innovation performance: the moderating role of the network location of cross-border innovation cooperation among Internet enterprises

PurposeThis study aims to explore the relationship between financing constraints and the innovation performance of Internet enterprises in the cross-border innovation cooperation network. The study also analyzes the moderating effect of the location of the cross-border innovation cooperation network.Design/methodology/approachThe authors selected patent data, related transaction data and other data of A-share listed companies on Shanghai and Shenzhen stock exchanges from 2014 to 2019. The generalized moment estimation method of instrumental variables (IV-GMM) method was used to analyze the relationship between financing constraints and the innovation performance of Internet firms and the moderating effect of the cross-border innovation cooperation network location. The threshold value of the moderating effect of the network structure hole was calculated with the threshold model.FindingsThe empirical results show a significant inverted U-shaped relationship between financing constraints and the innovation performance in the cross-border innovation cooperation network of Internet enterprises. Network centrality positively moderates this relationship. There is a threshold for the adjustment effect of network-structural holes, and the adjustment intensity of structural holes changes before and after the threshold.Originality/valueThis study provides a new perspective for Internet firms in innovation cooperation networks to alleviate the negative impact of financing constraints on innovation performance. The inverted U-shaped relationship between financing constraints and the innovation performance of Internet enterprises is in two stages. The moderating range of network centrality and the structural hole besides the threshold of the moderating effect of a structural hole are detailed.

Fault section location in urban distribution network based on fault marking

The goal is to propose an effective method for locating a fault segment in urban power distribution networks. Urban distribution networks have multiple outgoing lines, switches with multiple connections and have variable topology characteristics. It is found that the fault location method based on matrix algorithm has low adaptive capability and fault tolerance when dealing with complex and variable topology. Therefore, this paper proposes an efficient fault segment location method based on special fault indicators, which can significantly improve the accuracy and reliability of fault location. Accordingly, to improve the accuracy and reliability of fault location, a fault segment location method based on fault marking is proposed. The approach proposed in the paper relies on the analysis of the incident matrix, which describes the relationship between nodes and branches, and allows the use of graph theory. The branch state vector is added to obtain the adjacency matrix, which allows to describe the state of change in the dynamics of the distribution network topology. In the next step, a set of nodes and branches, which reflect the incoming and outgoing interconnections of the nodes, is established based on the selected direction of the network binding. According to the direction of the node fault current, the suspicious branches are identified and labeled to indicate the fault. By cumulative calculation and analysis of the labels, the target branches are screened out and the faulty sections of the city power supply network are identified. The results of the case study conducted in the paper show that the proposed method has good adaptability to the variable topology and increases the fault tolerance and accuracy of the developed matrix algorithm. The topological operating state of the network can be changed by controlling the switches to optimize the operation and improve the reliability of the power supply. Thus, the algorithm for fast and accurate fault location is of great importance for improving the safety and quality of urban power supply.

Weathering the storm: Impacts of hurricane-induced noise on the probability of detecting cetaceans

Hurricanes Dorian (Category 5; 2019), Florence (Category 4; 2018), and Humberto (Category 3; 2019) impacted the soundscape as observed at the Atlantic Deepwater Ecosystem Observatory Network (ADEON) locations in the US Mid- and South Atlantic Outer Continental Shelf. Passive acoustic data obtained from bottom-mounted hydrophones at the ADEON locations were examined to assess changes in ambient sound before, during, and afterhurricane presence. Ambient sound level increased up to 25 dB in the 0.1–7 kHz band which could impact the detectability of cetaceans vocalizing at those frequencies. The probability of detection of fin whales, minke whales, and pilot whales was estimated using empirical ambient sound levels, modelled propagation loss, and pre-defined detection parameters. Detection area, as defined by range of minimum detectability of the cetacean from the receiver, was estimated before, during, and after hurricane presence at each ADEON location. Detection area changed considerably during hurricane presence with site-specific impacts for each of the cetaceans, which may affect estimates of their abundance from passive acoustic recordings. [Study concept, oversight, and funding were provided by ONR Award N00014-16-1-2594 and BOEM under Contract No. M16PC00003, in partnership with ONR and NOAA. Funding for ship time was provided under separate contracts by ONR, Code 32.]

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

AbstractA unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

A Novel Variable Pseudonym Scheme for Preserving Privacy User Location in 5G Networks

Due to the development in 5G mobile communications, user privacy becomes the main challenge, especially with the multiplicity of services and applications that can be accessed. Location privacy is related to the user privacy in terms of the possibility of tracking and unwanted advertisements, as well as the possibility of exposure to suspicious activities and terrorist attacks based on the user location. Accordingly, previous mobile systems use pseudonyms instead of a permanent identity to preserving the user’s location privacy in mobile networks, by what is known as the Cellular Radio Network Temporary Identifiers (C-RNTIs). The C-RNTI protects the user privacy relatively, but it faces some problems due to the clear text of the user in CRNI exchange, which will make the user easily trackable by man-in-the-middle attack. This article aims at proposing a new algorithm that improves the user’s location privacy and enhances the capabilities of the 5G infrastructure in terms of confidentiality and privacy. The idea is based on the use of a novel variable pseudonym (V-RNTI) algorithm that acts as a radio channel identifier for the user, which improves the allocation of pseudonyms to identify users. The proposed algorithm uses different V-RNTI values by the UE and can be changed frequently to improve the pseudonym allocation procedure. This approach can be implemented in 3GPP standard architecture by upgrading UEs and eNB by minor modifications. During this study, the proposed 5G V-RNTI authentication protocol model was built. And then, the automated analysis of the protocol model is performed by using ProVerif Model Checker. The results showed that the model works well without any noticeable problems.

Analysis of protein phosphorylation sites in the hypothalamus tissues of pubescent goats

Protein phosphorylation plays an important role in animal reproduction. However, its role in the onset of puberty in goats remains largely unexplored. Accordingly, in the present study, the molecular changes controlling the onset of puberty in goats were investigated by identifying the differentially phosphorylated proteins (DPPs) and phosphorylation sites (DPSs) in the hypothalami of prepubertal and pubertal female goats using LC–MS/MS and tandem mass tag labelling. A total of 3265 phosphopeptides corresponding to 1628 phosphoproteins were identified, including 234 upregulated and 342 downregulated phosphopeptides. The DPSs HTT, MAP1B, CAMKK1, MAP2, DNAJC5, and GAP43 were identified. These DPPs are enriched in the endocytosis, cAMP signaling, Rap1 signaling, melanogenesis, and insulin secretion pathways. These pathways are related to gonadotropin-releasing hormone and puberty. In particular, glucose-6-phosphate isomerase, fructose-bisphosphate aldolase C, and fructose-bisphosphate aldolase A occupy important locations in the protein-protein interaction network. These data provide evidence for a complex interaction network in goat hypothalamus proteins that affects puberty. Furthermore, they may help identify new puberty-regulating candidates and/or serve as an important resource for exploring the physiological mechanism of puberty onset in mammals. SignificanceThis study provides evidence for a complex interaction network in goat hypothalamus proteins that affects puberty. Furthermore, our data may help identify new puberty-regulating candidates and/or serve as an important resource for exploring the physiological mechanism of puberty onset in mammals.

Relationship Strength Affects Firms' Technological Innovation Performance: Study of the Conditional Process

In this paper, we empirically analyze the influence mechanism of innovation performance of relationship strength firms through the panel data of patents and standards of 15 firms in China's OLED industry from 2014 to 2020, using hierarchical regression and bias-corrected percentile Bootstrap method for testing. The findings show that: relationship strength positively affects firm innovation performance; relationship strength positively affects firm innovation performance through the mediating effect of network location; the effect of network location on innovation performance is moderated by technological diversification; the mediating effect of network location is also moderated by technological diversification, and the higher the degree of technological diversification, the stronger the mediating effect of network location. The findings enrich the research on innovation performance and R&D networks, and provide theoretical guidance for Chinese firms to build effective R&D networks and technology diversification strategies.

Monitoring COVID-19 spread in Prague local neighborhoods based on the presence of SARS-CoV-2 RNA in wastewater collected throughout the sewer network

Many reports have documented that the presence of SARS-CoV-2 RNA in the influents of municipal wastewater treatment plants (WWTP) correlates with the actual epidemic situation in a given city. However, few data have been reported thus far on measurements upstream of WWTPs, i.e. throughout the sewer network. In this study, the monitoring of the presence of SARS-CoV-2 RNA in Prague wastewater was carried out at selected locations of the Prague sewer network from August 2020 through May 2021. Various locations such as residential areas of various sizes, hospitals, city center areas, student dormitories, transportation hubs (airport, bus terminal), and commercial areas were monitored together with four of the main Prague sewers. The presence of SARS-CoV-2 RNA was determined by reverse transcription – multiplex quantitative polymerase chain reaction (RT-mqPCR) after the precipitation of nucleic acids with PEG 8,000 and RNA isolation with TRIzol™ Reagent. The number of copies of the gene encoding SARS-CoV-2 nucleocapsid (N1) per liter of wastewater was compared with the number of officially registered COVID-19 cases in Prague. Although the data obtained by sampling wastewater from the major Prague sewers were more consistent than those obtained from the small sewers, the correlation between wastewater-based and clinical-testing data was also good for the residential areas with more than 7,000 registered inhabitants. It was shown that monitoring SARS-CoV-2 RNA in wastewater sampled from small sewers could identify isolated occurrences of COVID-19-positive cases in local neighborhoods. This can be very valuable while tracking COVID-19 hotspots within large cities.