scholarly journals DNA-based constitutional dynamic networks as functional modules for logic gates and computing circuit operations

2021 ◽  
Vol 12 (15) ◽  
pp. 5473-5483
Author(s):  
Zhixin Zhou ◽  
Jianbang Wang ◽  
R. D. Levine ◽  
Francoise Remacle ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Logic Gates ◽  
Functional Modules ◽  
Logic Operations

A nucleic acid-based constitutional dynamic network (CDN) provides a single functional computational module for diverse input-guided logic operations and computing circuits.

scholarly journals Digital logic gates in soft, conductive mechanical metamaterials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Charles El Helou ◽  
Philip R. Buskohl ◽  
Christopher E. Tabor ◽  
Ryan L. Harne
Keyword(s):  
Integrated Circuits ◽  
Electronic Materials ◽  
Polymer Networks ◽  
Conductive Polymer ◽  
Network Connectivity ◽  
Logic Gates ◽  
Boolean Logic ◽  
Digital Logic ◽  
Mechanical Metamaterials ◽  
Logic Operations

AbstractIntegrated circuits utilize networked logic gates to compute Boolean logic operations that are the foundation of modern computation and electronics. With the emergence of flexible electronic materials and devices, an opportunity exists to formulate digital logic from compliant, conductive materials. Here, we introduce a general method of leveraging cellular, mechanical metamaterials composed of conductive polymers to realize all digital logic gates and gate assemblies. We establish a method for applying conductive polymer networks to metamaterial constituents and correlate mechanical buckling modes with network connectivity. With this foundation, each of the conventional logic gates is realized in an equivalent mechanical metamaterial, leading to soft, conductive matter that thinks about applied mechanical stress. These findings may advance the growing fields of soft robotics and smart mechanical matter, and may be leveraged across length scales and physics.

scholarly journals Integration of photocatalytic and dark-operating catalytic biomimetic transformations through DNA-based constitutional dynamic networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chen Wang ◽  
Michael P. O’Hagan ◽  
Ehud Neumann ◽  
Rachel Nechushtai ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Energy Harvesting ◽  
Information Transfer ◽  
Dynamic Networks ◽  
Research Direction ◽  
Cellular Biology ◽  
Dynamic Feedback ◽  
Dynamic Coupling ◽  
Metabolic Processes ◽  
Catalytic Functions

AbstractNucleic acid-based constitutional dynamic networks (CDNs) have recently emerged as versatile tools to control a variety of catalytic processes. A key challenge in the application of these systems is achieving intercommunication between different CDNs to mimic the complex interlinked networks found in cellular biology. In particular, the possibility to interface photochemical ‘energy-harvesting’ processes with dark-operating ‘metabolic’ processes, in a similar way to plants, represents an up to now unexplored yet enticing research direction. The present study introduces two CDNs that allow the intercommunication of photocatalytic and dark-operating catalytic functions mediated by environmental components that facilitate the dynamic coupling of the networks. The dynamic feedback-driven intercommunication of the networks is accomplished via information transfer between the two CDNs effected by hairpin fuel strands in the environment of the system, leading to the coupling of the photochemical and dark-operating modules.

An experimental evaluation and guideline for path finding in weighted dynamic network

2021 ◽  
Vol 14 (11) ◽  
pp. 2127-2140
Author(s):  
Mengxuan Zhang ◽  
Lei Li ◽  
Xiaofang Zhou
Keyword(s):  
Shortest Path ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Real Life ◽  
Dynamic Network ◽  
Problem Space ◽  
Network Applications ◽  
Complete Problem ◽  
Path Computation ◽  
Shortest Path Computation

Shortest path computation is a building block of various network applications. Since real-life networks evolve as time passes, the Dynamic Shortest Path (DSP) problem has drawn lots of attention in recent years. However, as DSP has many factors related to network topology, update patterns, and query characteristics, existing works only test their algorithms on limited situations without sufficient comparisons with other approaches. Thus, it is still hard to choose the most suitable method in practice. To this end, we first identify the determinant dimensions and constraint dimensions of the DSP problem and create a complete problem space to cover all possible situations. Then we evaluate the state-of-the-art DSP methods under the same implementation standard and test them systematically under a set of synthetic dynamic networks. Furthermore, we propose the concept of dynamic degree to classify the dynamic environments and use throughput to evaluate their performance. These results can serve as a guideline to find the best solution for each situation during system implementation and also identify research opportunities. Finally, we validate our findings on real-life dynamic networks.

Data Mining Techniques for Communities’ Detection in Dynamic Social Networks

Data Mining ◽  
2013 ◽  
pp. 719-733
Author(s):  
Céline Robardet
Keyword(s):  
Dynamic Networks ◽  
Dynamic Network ◽  
Added Value ◽  
Strong Interactions ◽  
Time Stamp ◽  
Efficient Computation ◽  
Real World Data ◽  
Positive Side ◽  
Dynamic Social Networks ◽  
Temporal Events

Social network analysis studies relationships between individuals and aims at identifying interesting substructures such as communities. This type of network structure is intuitively defined as a subset of nodes more densely linked, when compared with the rest of the network. Such dense subgraphs gather individuals sharing similar property depending on the type of relation encoded in the graph. In this chapter we tackle the problem of identifying communities in dynamic networks where relationships among entities evolve over time. Meaningful patterns in such structured data must capture the strong interactions between individuals but also their temporal relationships. We propose a pattern discovery method to identify evolving patterns defined by constraints. In this paradigm, constraints are parameterized by the user to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. In the proposed approach, dense and isolated subgraphs, defined by two user-parameterized constraints, are first computed in the dynamic network restricted at a given time stamp. Second, the temporal evolution of such patterns is captured by associating a temporal event types to each subgraph. We consider five basic temporal events: the formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next one. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible thanks to efficient pruning patterns strategies. Experimental results on real-world data confirm the practical feasibility of our approach. We evaluate the added-value of the method, both in terms of the relevancy of the extracted evolving patterns and in terms of scalability, on two dynamic sensor networks and on a dynamic mobility network.

scholarly journals Implementation of Unbalanced Ternary Logic Gates with the Combination of Spintronic Memristor and CMOS

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 542 ◽  
Author(s):  
Haifeng Zhang ◽  
Zhaowei Zhang ◽  
Mingyu Gao ◽  
Li Luo ◽  
Shukai Duan ◽  
...  
Keyword(s):  
High Efficiency ◽  
Load Capacity ◽  
Complementary Metal Oxide Semiconductor ◽  
Logic Gates ◽  
Logic Circuit ◽  
Oxide Semiconductor ◽  
Ternary Logic ◽  
Logic Operations ◽  
Almost All ◽  
Logic Functions

A memristor is a nanoscale electronic element that displays a threshold property, non-volatility, and variable conductivity. Its composite circuits are promising for the implementation of intelligence computation, especially for logic operations. In this paper, a flexible logic circuit composed of a spintronic memristor and complementary metal-oxide-semiconductor (CMOS) switches is proposed for the implementation of the basic unbalanced ternary logic gates, including the NAND, NOR, AND, and OR gates. Meanwhile, due to the participation of the memristor and CMOS, the proposed circuit has advantages in terms of non-volatility and load capacity. Furthermore, the input and output of the proposed logic are both constant voltages without signal degradation. All these three merits make the proposed circuit capable of realizing the cascaded logic functions. In order to demonstrate the validity and effectiveness of the entire work, series circuit simulations were carried out. The experimental results indicated that the proposed logic circuit has the potential to realize almost all basic ternary logic gates, and even some more complicated cascaded logic functions with a compact circuit construction, high efficiency, and good robustness.

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Community Structures ◽  
Block Model ◽  
Real World Data ◽  
Stochastic Block Model ◽  
Dynamic Social Networks ◽  
Influential Nodes

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

scholarly journals The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 212
Author(s):  
Zhiwei Yang ◽  
Weigang Wu
Keyword(s):  
Network Topology ◽  
Information Dissemination ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Models ◽  
Point Of View ◽  
Path Model ◽  
Dynamic Network Models ◽  
Topology Changes ◽  
Practical Design

A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

scholarly journals Dynamic network prediction

2020 ◽  
Vol 8 (4) ◽  
pp. 574-595
Author(s):  
Ravi Goyal ◽  
Victor De Gruttola
Keyword(s):  
Social Relationships ◽  
Network Structure ◽  
Broad Class ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Simulation Studies ◽  
Population Composition ◽  
Statistical Framework ◽  
Network Properties ◽  
Network Prediction

AbstractWe present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network (DTEQT) using machine learning

2019 ◽  
Vol 18 (01) ◽  
pp. 1941020
Author(s):  
S. Ananth ◽  
A. M. Kalpana ◽  
R. Vijayarajeswari
Keyword(s):  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Wireless Sensor ◽  
Software Defined Network ◽  
Mobile Nodes ◽  
Energy Constrained

Wireless Sensor Network (WSN) is the interconnection between things or objects embedded with hardware and software. In WSN, small end devices (like sensors) and high end devices (like servers) are connected to the Internet. For WSN enabled in Software-Defined Network (SDN), the routers are controlled using a controller server node. It is a dynamic network due to the presence of mobile nodes and energy constrained nodes. The routing is the process of detecting route from source to target. In dynamic networks like WSN, routing is a challengeable task. This paper is to provide a routing solution for backboneless SDN-enabled WSN. The proposed work enhances routing Quality of Service (QoS) in WSN. The paths are dynamically reallocated to reduce the packet loss.

scholarly journals Dynamic network partnerships and social contagion drive cooperation

2018 ◽  
Vol 285 (1893) ◽  
pp. 20181973 ◽  
Author(s):  
Roslyn Dakin ◽  
T. Brandt Ryder
Keyword(s):  
Repeated Measures ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Social Contagion ◽  
Evolution Of Cooperation ◽  
Cooperative Behaviour ◽  
Data Logging ◽  
Sources Of Variation ◽  
Positive Correlations ◽  
Multiple Membership

Both reciprocity and positive assortment (like with like) are predicted to promote the evolution of cooperation, yet how partners influence each other's behaviour within dynamic networks is not well understood. One way to test this question is to partition phenotypic variation into differences among individuals in the expression of cooperative behaviour (the ‘direct effect’), and plasticity within individuals in response to the social environment (the ‘indirect effect’). A positive correlation between these two sources of variation, such that more cooperative individuals elicit others to cooperate, is predicted to facilitate social contagion and selection on cooperative behaviour. Testing this hypothesis is challenging, however, because it requires repeated measures of behaviour across a dynamic social landscape. Here, we use an automated data-logging system to quantify the behaviour of 179 wire-tailed manakins, birds that form cooperative male–male coalitions, and we use multiple-membership models to test the hypothesis that dynamic network partnerships shape within-individual variation in cooperative behaviour. Our results show strong positive correlations between a bird's own sociality and his estimated effect on his partners, consistent with the hypothesis that cooperation begets cooperation. These findings support the hypothesis that social contagion can facilitate selection for cooperative behaviour within social networks.

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