A PSO Enable Multi-Hop Clustering Algorithm for VANET

VANET (Vehicle Ad-hoc Network) is an emerging technology in today’s intelligent transport system. In VANET, there are many moving nodes which are called the vehicle running on the road. They communicate with each other to provide the information to driver regarding the road condition, traffic, weather and parking. VANET is a kind of network where moving nodes talk with each other with the help of equipment. There are various other things which also make complete to VANET like OBU (onboard unit), RSU (Road Aside Unit) and CA (Certificate authority). In this paper, a new PSO enable multi-hop technique is proposed which helps in VANET to Select the best route and find the stable cluster head and remove the malicious node from the network to avoid the false messaging. The false can be occurred when there is the malicious node in a network. Clustering is a technique for making a group of the same type node. This proposed work is based on PSO enable clustering and its importance in VANET. While using this approach in VANET, it has increased the 20% packet delivery ratio.

Ligand-Based Virtual Screening Based on the Graph Edit Distance

Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these components. In this case, pharmacophore-type node descriptions are represented by nodes and chemical bounds by edges. If we want to obtain the bioactivity dissimilarity between two chemical compounds, a distance between attributed graphs can be used. The Graph Edit Distance allows computing this distance, and it is defined as the cost of transforming one graph into another. Nevertheless, to define this dissimilarity, the transformation cost must be properly tuned. The aim of this paper is to analyse the structural-based screening methods to verify the quality of the Harper transformation costs proposal and to present an algorithm to learn these transformation costs such that the bioactivity dissimilarity is properly defined in a ligand-based virtual screening application. The goodness of the dissimilarity is represented by the classification accuracy. Six publicly available datasets—CAPST, DUD-E, GLL&GDD, NRLiSt-BDB, MUV and ULS-UDS—have been used to validate our methodology and show that with our learned costs, we obtain the highest ratios in identifying the bioactivity similarity in a structurally diverse group of molecules.

Temporal Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the whole evolution process and capturing all temporal dynamics. In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). THINE not only uses attention mechanism and meta-path to preserve structures and semantics in HIN but also combines the Hawkes process to simulate the evolution of the temporal network. Our extensive evaluations with various real-world temporal HINs demonstrate that THINE achieves state-of-the-art performance in both static and dynamic tasks, including node classification, link prediction, and temporal link recommendation.

Study of Reversible Platelet Aggregation Model by Nonlinear Dynamics

Blood cell platelets form aggregates upon vessel wall injury. Under certain conditions, a disintegration of the platelet aggregates, called “reversible aggregation”, is observed in vitro. Previously, we have proposed an extremely simple (two equations, five parameters) ordinary differential equation-based mathematical model of the reversible platelet aggregation. That model was based on mass-action law, and the parameters represented probabilities of platelet aggregate formations. Here, we aimed to perform a nonlinear dynamics analysis of this mathematical model to derive the biomedical meaning of the model’s parameters. The model’s parameters were estimated automatically from experimental data in COPASI software. Further analysis was performed in Python 2.7. Contrary to our expectations, for a broad range of parameter values, the model had only one steady state of the stable type node, thus eliminating the initial assumption that the reversibility of the aggregation curve could be explained by the system’s being near a stable focus. Therefore, we conclude that during platelet aggregation, the system is outside of the influence area of the steady state. Further analysis of the model’s parameters demonstrated that the rate constants for the reaction of aggregate formation from existing aggregates determine the reversibility of the aggregation curve. The other parameters of the model influenced either the initial aggregation rate or the quasi-steady state aggregation values.

Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening

Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

A Model Approach to Analytically Identify Stuck Pipe and Its Mechanism in Wellbore

Stuck pipe incidents translate to non-productive time. There is a need to mitigate stuck pipe incidents which can be achieved by conforming to recommended practices. Also, quick diagnosis is necessary in order to free a stuck pipe. Trial-and-error method can further complicate the situation. This work aims at diagnosing stuck pipe mechanisms and recommend practices to free a stuck pipe. spANALYZE also estimates the axial force and torque needed to free a stuck pipe caused by differential sticking. spANALYZE is a thick desktop client application developed in C# using the Microsoft Visual Studio 2019 development environment. It is an object-oriented .NET application that utilizes the Windows Presentation Foundation (WPF) architecture for its user interface. Each of the analyzers within spANALYZE were implemented generically as a list of nodes, representing the concept of a flow chart. New analyzers can easily be added simply by programmatically defining each node in the flow chart. Each node has a node identifier, a node type, node text, and the node identifiers of each answer – yes, no and restricted. spANALYZE presents the following benefits: quick and early detection of stuck pipe mechanisms, propose recommended action steps to free pipe, calculate stuck pipe depth, compute the torque and axial force needed to free a stuck string.

Multi-Type Node Detection in Network Communities

Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types on diverse complex systems. However, most of the existing methods identify only either disjoint nodes or overlapping nodes. Many of these methods rarely identify disjunct nodes, even though they could play significant roles on networks. In this paper, a new method, which distinctly identifies disjoint nodes (node clusters), disjunct nodes (single node partitions) and overlapping nodes (nodes binding overlapping communities), is proposed. The approach, which differs from existing methods, involves iterative computation of bridging centrality to determine nodes with the highest bridging centrality value. Additionally, node similarity is computed between the bridge-node and its neighbours, and the neighbours with the least node similarity values are disconnected. This process is sustained until a stoppage criterion condition is met. Bridging centrality metric and Jaccard similarity coefficient are employed to identify bridge-nodes (nodes at cut points) and the level of similarity between the bridge-nodes and their direct neighbours respectively. Properties that characterise disjunct nodes are equally highlighted. Extensive experiments are conducted with artificial networks and real-world datasets and the results obtained demonstrate efficiency of the proposed method in distinctly detecting and classifying multi-type nodes in network communities. This method can be applied to vast areas such as examination of cell interactions and drug designs, disease control in epidemics, dislodging organised crime gangs and drug courier networks, etc.

Development of an Automated Real-Time System for Soil Temperature and Moisture Measurement

This trial goes along with irrigation systems based on the development and use of free software and hardware for direct measurements of soil moisture and temperature throughout the plant cycle. Thus, irrigation systems can optimize water use during the process at lower cost regarding TDR application. Four humidity sensors were used: one was resistive, and three capacitors were interconnected in a mesh network system. Thus, this research was carried out in laboratory and the studied soil was characterized as a typical dystroferric Red Latosol (Oxisol) with very clayey texture (66%). Soil clods were undone and dried in a greenhouse, then divided in 20 containers with addition of known volumes of water in each one. A network of mesh-type node sensors has been developed based on Arduino technology to read and transmit data to a single gateway. The sensor node was designed and built with Arduino Nano, radio NRF24L01, capacitive sensors of type SHT20 and DHT22, in addition to FC-28 that is resistive. The system also featured a Real Time Clock DS1302, three photovoltaic cells and circuit battery charger. Domoticz software was used to store data and make them available on a server connected to the internet. Cubic modeling was one of the results of the relation among each sensor, TDR and the greenhouse method. The resistive sensor showed very close values to the TDR in its model as well as the set of the monitoring system showed low cost in relation to TDR.