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In recent years, the ubiquitous nature of Internet-of-Things (IoT) applications as well as the pervasive character of next-generation communication protocols, such as the 5G technology, have become widely evident. In this work, we identify the need for low-cost security in current and next-generation IoT networks and address this demand through the implementation, testing, and validation of an intrinsic low-cost and low-overhead hardware-based security primitive within an inherent network component. In particular, an intrinsic Physical Unclonable Function (PUF) is implemented in the peripheral network module of a tri-band commercial off-the-shelf router. Subsequently, we demonstrate the robustness of this PUF to ambient temperature variations and to limited natural aging, and examine in detail its potential for securing the next generation of IoT networks and other applications. Finally, the security of the proposed PUF-based schemes is briefly assessed and discussed.

© 2020 Annual Reviews Inc.. All rights reserved. Protein folding in the cell is mediated by an extensive network of >1,000 chaperones, quality control factors, and trafficking mechanisms collectively termed the proteostasis network. While the components and organization of this network are generally well established, our understanding of how protein-folding problems are identified, how the network components integrate to successfully address challenges, and what types of biophysical issues each proteostasis network component is capable of addressing remains immature. We describe a chemical biology-informed framework for studying cellular proteostasis that relies on selection of interesting protein-folding problems and precise researcher control of proteostasis network composition and activities. By combining these methods with multifaceted strategies to monitor protein folding, degradation, trafficking, and aggregation in cells, researchers continue to rapidly generate new insights into cellular proteostasis.

Universality is a principle that fundamentally underlies many critical phenomena, ranging from epidemic spreading to the emergence or breakdown of global connectivity in networks. Percolation, the transition to global connectedness on gradual addition of links, may exhibit substantial gaps in the size of the largest connected network component. We uncover that the largest gap statistics is governed by extreme-value theory. This allows us to unify continuous and discontinuous percolation by virtue of universal critical scaling functions, obtained from normal and extreme-value statistics. Specifically, we show that the universal scaling function of the size of the largest gap is given by the extreme-value Gumbel distribution. This links extreme-value statistics to universality and criticality in percolation. Percolation transitions underpin a generic class of phenomena associated with the degree of connectedness in networks. A detailed numerical study now uncovers a universal scaling in the size of the largest cluster identified in such percolation models.

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

This poster describes the design of Mnemosyne, a distributed logger running over Named Data Networking. Mnemosyne utilizes proof of authenticity instead of proof of work. It assures immutability of logged events by interlocking all event records in a DAG mesh. By using a distributed design, Mnemosyne provides both a high logging throughput and system resiliency in face of network component failures.

BACKGROUND Successful partner notification can improve community-level outcomes by increasing the proportion of persons living with human immunodeficiency virus (HIV) who are linked to HIV care and virally suppressed, but it is resource intensive. Understanding where HIV transmission pathways may be undetected by routine partner notification may help improve case finding strategies. METHODS We combined partner notification interview and HIV sequence data for persons diagnosed with HIV in Wake County, NC in 2012 to 2013 to evaluate partner contact networks among persons with HIV pol gene sequences 2% or less pairwise genetic distance. We applied a set of multivariable generalized estimating equations to identify correlates of disparate membership in genetic versus partner contact networks. RESULTS In the multivariable model, being in a male-male pair (adjusted odds ratio [AOR], 16.7; P = 0.01), chronic HIV infection status (AOR, 4.5; P < 0.01), and increasing percent genetic distance between each dyad member's HIV pol gene sequence (AOR, 8.3 per each 1% increase, P < 0.01) were all associated with persons with HIV clustering but not being identified in the partner notification network component. Having anonymous partners or other factors typically associated with risk behavior were not associated. CONCLUSIONS Based on genetic networks, partnerships which may be stigmatized, may have occurred farther back in time or may have an intervening partner were more likely to be unobserved in the partner contact network. The HIV genetic cluster information contributes to public health understanding of HIV transmission networks in these settings where partner identifying information is not available.

This study addresses the voltage control problem of the medium-voltage distribution systems under uncertainty of the network model. A robust voltage control algorithm (RVCA) is developed in order to manage the voltage constraints considering uncertainties associated with the parameters of load, line, and transformer models. The RVCA determines a corrective solution that remains immunised against any realisation of uncertainty associated with the parameters of the network model. To this end, prior to formulating the voltage control problem, Monte Carlo (MC) simulations are used to characterise uncertain parameters of the network component models and load flow (LF) calculations are carried out to evaluate their impacts. The voltage constraints management under the uncertain environment is then formulated as a robust optimisation (RO) problem. The latter is constructed based on the results obtained through the MC simulations and LF calculations. Once the RO is solved, in order to check the robustness of the solution, system voltages are evaluated using the LF calculations considering the new set-points of control variables and uncertainty of network parameters. The simulation results reveal that neglecting model uncertainty in the voltage control problem can lead to infeasible solutions while the proposed RVCA, at an extra cost, determines a corrective solution which remains protected against the studied uncertainties.

Power systems can fail because of various reasons. Often, the effect of smaller failures is limited to the failure and repair of a single network component, as the power system is designed and operated considering \(N-1\,\) redundancy. Serious failures of the power system are therefore caused by combinations of component failures and other influencing factors. In probabilistic reliability analysis, the reliability of the power system is determined based on the reliability of individual components. This chapter discusses how failures of power system components can lead to a large blackout. Failure statistics of components are analyzed and historical blackouts are studied to obtain more insight into this matter.

In this paper, by utilizing fluid approximation, we develop a fluid model for an ICN (Information-Centric Networking) network comprised of multiple entities, multiple routers, and multiple repositories, at the transport level. Since ICN is a more complicated network architecture than IP and the complex interaction among content caching, request packet aggregation, and the behavior of a transport protocol must be considered, ICN requires more complicated modeling approach than IP. In the literature, several mathematical analyses of transport protocol for ICN have been performed. However, these mathematical analyses cannot be utilized to analyze the dynamics of transport protocols in ICN because these studies focus on steady state characteristics. In this paper, we develop a fluid model of a large-scale ICN network, by constructing network component models (i.e., entity, router, and repository) with uniform inputs and outputs (i.e., receiving and sending rate of request packets and content packets).

Demand response (DR) is one of the fundamental components of deregulated power systems which face uncertainty because of unpredictable network component contingencies. In this study, a security-constrained model is proposed to coordinate supply and demand sides in a proper way toward a flexible, secure and economic grid. In the proposed model, generation units are committed to enhance the flexibility by providing up- and down-spinning reserves while an optimal real-time pricing scheme provides demand-side flexibility. Real-time pricing is developed by utilising consumers' behaviour. Different levels of rationality are given by extending demand-price elasticity matrices for different types of consumers. The proposed method can assist independent system operators to schedule generating units a day-ahead in a more reliable manner. Customers also can make the most beneficial plans by joining DR programmes and schedule their consumption optimally according to defined rates in the case of an emergency. The applicability of the proposed model is tested on the IEEE 24-bus reliability test system and its effects on operation cost, technical data of the network and the expected load not served are discussed.

The 3N model of radicalization proposes that violent radicalization is the result of the contribution of needs, networks, and narratives. Although research has mainly been supportive of this perspective, a substantial amount of ground remains uncovered regarding the network component of the model. Within this framework, we examine why individuals living in certain social environments tend to harbor more positive attitudes toward homegrown terrorism than others. Building on prior research, we hypothesized that individuals living in social environments known to be vulnerable (vs. less vulnerable) are more likely to experience a sense of significance loss (i.e., lack of social integration, perceived conflicts between religious groups), find solace in religious social networks (i.e., mosques), and thus adhere to radical narratives (i.e., legitimization of terrorism). A study with 365 young Muslims from different cities in Spain (Almeria, Barcelona, Ceuta, and Melilla)supported these predictions. Theoretical and practical implications for the study of violent extremism are discussed.

The gene regulatory network analysis primary goal is understanding the gene interactions topological order and how the genes influence each other. Network component analysis is a vital technique for build gene regulatory network. However, the network component analysis technique is time consuming and computational intensive. Therefore, parallel techniques are required. PSparseNCA is a parallel network component algorithm. This work present an improved version of PSparseNCA, referred as hPSparseNCA (Hybrid Parallel Computation for Sparse Network Component Analysis). hPSparseNCA uses the hybrid computational model to enhance the performance of PSparseNCA. hPSparseNCA is outperformed PSparseNCA achieving speedup reached 192.77 instead of 36.03 for PSparseNCA on 40 processing nodes. Furthermore, the speedup of the proposed algorithm reached 728.48 when running on 256 processing nodes.

A firefighting system is the most foremost part of any industry and buildings as its aim to protect property, human and industrial equipment. In any pandemic situation like COVID19 it is utmost requirement to increase reliability of all important instruments which are directly involve to human life. In case of fire fighter fight against fire, this required time and skilled man power. Under these pandemic situation chances of infection spreading in firefighters and social distancing will not maintain during this situation. Only one way to resolve this problem is by implementation of fully automated reliable system. In this system we are trying to automate the total system by using hot redundant PLC which is fail safe. It consists of mainly three basic parts - fire storage tank, fire pumps and automated distribution system. Fire storage tank contains the water that used against fire. Fire pumps helps to fill the tank and automated distribution system will be activated during fire and instantly distribute water. There are three pumps one is main electrical pump, second one is diesel backup pump and last one is jockey pump.In redundant configuration there are two PLC acting simultaneously and the entire network component and power supply also has back up so that it will never fail. If one of the PLC fails during operation then second PLC automatically take care of the system within few millisecond. Similarly if power supply failure then UPS supply is also there for uninterrupted power supply.

In the article the problem of finding the maximum multiple flow in the network of any natural multiplicity k is studied. There are arcs of three types: ordinary arcs, multiple arcs and multi-arcs. Each multiple and multi-arc is a union of k linked arcs, which are adjusted with each other. The network constructing rules are described. The definitions of a divisible network and some associated subjects are stated. The important property of the divisible network is that every divisible network can be partitioned into k parts, which are adjusted on the linked arcs of each multiple and multi-arc. Each part is the ordinary transportation network. The main results of the article are the following subclasses of the problem of finding the maximum multiple flow in the divisible network. 1. The divisible networks with the multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in s network parts. In this case the problem can be solved in a polynomial time. 2. The divisible networks with the weak multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in k-1 network parts (1 ≤ s < k − 1) and other parts have at least two such vertices. In that case the multiplicity of the maximum multiple flow problem can be decreased to k - s. 3. The divisible network of the parallel structure. Assume that the divisible network component, which consists of all multiple arcs, can be partitioned into subcomponents, each of them containing exactly one vertex-beginning of a multi-arc. Suppose that intersection of each pair of subcomponents is the only vertex-network source x0. If k=2, the maximum flow problem can be solved in a polynomial time. If k ≥ 3, the problem is NP-complete. The algorithms for each polynomial subclass are suggested. Also, the multiplicity decreasing algorithm for the divisible network with weak multi-arc constraints is formulated.

Epilepsy is one of the most common neurological conditions affecting over 65 million people worldwide. Over one third of people with epilepsy are considered refractory: they do not respond to drug treatments. For this significant cohort of people, surgery is a potentially transformative treatment. However, only a small minority of people with refractory epilepsy are considered suitable for surgery, and long-term seizure freedom is only achieved in half the cases. Recently, several computational approaches have been proposed to support presurgical planning. Typically, these approaches use a dynamic network model to explore the potential impact of surgical resection in silico. The network component of the model is informed by clinical imaging data and is considered static thereafter. This assumption critically overlooks the plasticity of the brain and, therefore, how continued evolution of the brain network post-surgery may impact upon the success of a resection in the longer term. In this work, we use a simplified dynamic network model, which describes transitions to seizures, to systematically explore how the network structure influences seizure propensity, both before and after virtual resections. We illustrate key results in small networks, before extending our findings to larger networks. We demonstrate how the evolution of brain networks post resection can result in a return to increased seizure propensity. Our results effectively determine the robustness of a given resection to possible network reconfigurations and so provide a potential strategy for optimizing long-term seizure freedom.

This paper proposes a quantitative approach for measuring the resilience of the network components and the network itself. This study introduces a conceptual framework featuring the ability of the network system to adopt alternative plans when a component is disrupted. As a first step toward measuring network resilience, the component resilience is defined and quantified as a function of criticality, disruption frequency, disruption impact, and recovery capability. This quantification approach reflects the effect of component level disruption on the network. Hence, it is proposed that the network resilience is measured by the resilience of network component having the lowest resilience index. Efforts to enhance network resilience often require financial resources. Therefore, an optimization model is further introduced to maximize the network resilience under budget constraint through reinforcing the weakest components in the network. The proposed approach can help decision-makers assess the network resilience status and compare with it other networks, identify and improve components with low resilience, evaluate the cost of resilience improvement, and determine the extent of enhancement that can be achieved under a given budget limitation. Numerical experiments are conducted to illustrate our methodology using a supply chain network and a power network. Both examples show that considering the ability of the network to use alternative plans can enhance network resilience and result in lower demand loss when a disruption occurs.

In this paper, we investigate the relationship between regional social capital and corporate payout policies. Using a large sample of US data, we find a positive relationship between regional social capital and both the likelihood and the amount of cash dividend payouts. However, we find that social capital has no bearing on the likelihood and amount of stock repurchases. The results from additional analyses show that the relationship between social capital and dividends is more pronounced for less geographically dispersed firms. We also find that the network component of social capital has a greater effect on dividends than the social norm component. Our results are robust to alternative specifications of dividends and social capital and to the use of a two-stage least squares (2SLS) analysis to alleviate endogeneity concerns. Overall, we document that regional social capital plays an important role in influencing cash dividend payout policies.

State estimation in active distribution networks which is the heart of energy management central is of great importance. Considering the increase in complexity and realtime management of active distribution networks, knowing network information at each time instant is one of the important measures for exploitation, control and protection of these networks. In this paper, state estimation in unbalanced three phase networks is investigated. In order to obtain a high accuracy procedure which is close to reality, elements comprising distribution network should be modelled accurately. In this paper, different components including line, transformer, load and regulator are modelled. In addition, equations regarding real and virtual measurements used in state estimation of three-phase networks are given. Then, network component and measurement modelling in standard IEEE 13-bus network is implemented and simulated.

This paper shows an analysis of the time offset error introduced into a highly accurate packet-based time synchronization link by the most frequently deployed long distance network components. Translating the sub-nanosecond accuracy of a White Rabbit timing link into long distance fiber communication networks is a very demanding task. With the increased distance the need for devices that keep the integrity of the signal and maintain its power across hundreds of kilometers is beyond question. Some of these devices might suffer from large disparities in their environmental conditions due to being placed in distant locations from each other, potentially varying their behavior. The results demonstrate that moderate temperature disparities can lead to timing offset errors of several nanoseconds. This outcome can be seen as the foundation laid for a series of compensation techniques to be developed in future work.

Cancer genomics has produced extensive information on cancer-associated genes, but the number and specificity of cancer-driver mutations remains a matter of debate. We constructed a bipartite network in which 7,665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer genes. We show that about 27% of the tumors can be assigned to statistically supported modules, most of which encompass one or two cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene specificity of driver mutations. Linear regression of the mutational loads in cancer genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers in known cancer genes is approximately two, with a range of one to five. Cancers that are associated with modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

The purpose of this chapter is to investigate the relationship among supply chain innovation and performance in terms of market and operational performance. The chapter is built on empirically data subject to 187 useable responses from a questionnaire-survey among Danish manufacturers. A conceptual model was developed and subsequently two major hypotheses were formulated. Linear regression was performed using SPSS software 22.0 to tests the developed hypotheses. Supply chain innovation is unfolded through the components of business processes, networks structure and technology. Data reveals that supply chain innovation does pay off in terms of improved market and operational performance. The chapter also reveals that the strongest relationship is obtained with supply chain innovation and operational performance. Market performance may be influenced by a number of different factors beyond supply chain innovation. The chapter provides interesting findings of the network component with empirical evidence that it has a positive influence on both market and operation performance. The chapter concludes by suggesting new areas of research including also the relationship to financial performance.

This research seeks to perform an Assessment between ICT Network Component and ICT Storage on Judiciary Performance in Eldoret Law Courts. The questions are: how does ICT Network component influence judiciary performance and does ICT storage influence judiciary performance in Eldoret law courts? The study adopted Correlational research design. The target group was Judges, Magistrates, ICT personnel and Advocates. A sample size of 272 was achieved from a target population of 899 employees in the Judiciary < 0.05 was considered significant. Data will be analyzed by SPSS V 20; regression was used to test the hypotheses. Correlation and multiple regressions were used to measure: Effects. The p

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V.