scholarly journals Employing Fuzzy Logic to Analyze the Structure of Complex Biological and Epidemic Spreading Models

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 977
Author(s):  
Nickie Lefevr ◽  
Andreas Kanavos ◽  
Vassilis C. Gerogiannis ◽  
Lazaros Iliadis ◽  
Panagiotis Pintelas
Keyword(s):  
Fuzzy Logic ◽  
Complex Networks ◽  
Real World ◽  
Biological Networks ◽  
Low Cost ◽  
Epidemic Models ◽  
Disease Spread ◽  
Hiv Test ◽  
Partial Truth ◽  
Model Setup

Complex networks constitute a new field of scientific research that is derived from the observation and analysis of real-world networks, for example, biological, computer and social ones. An important subset of complex networks is the biological, which deals with the numerical examination of connections/associations among different nodes, namely interfaces. These interfaces are evolutionary and physiological, where network epidemic models or even neural networks can be considered as representative examples. The investigation of the corresponding biological networks along with the study of human diseases has resulted in an examination of networks regarding medical supplies. This examination aims at a more profound understanding of concrete networks. Fuzzy logic is considered one of the most powerful mathematical tools for dealing with imprecision, uncertainties and partial truth. It was developed to consider partial truth values, between completely true and completely false, and aims to provide robust and low-cost solutions to real-world problems. In this manuscript, we introduce a fuzzy implementation of epidemic models regarding the Human Immunodeficiency Virus (HIV) spreading in a sample of needle drug individuals. Various fuzzy scenarios for a different number of users and different number of HIV test samples per year are analyzed in order for the samples used in the experiments to vary from case to case. To the best of our knowledge, analyzing HIV spreading with fuzzy-based simulation scenarios is a research topic that has not been particularly investigated in the literature. The simulation results of the considered scenarios demonstrate that the existence of fuzziness plays an important role in the model setup process as well as in analyzing the effects of the disease spread.

SOFT COMPUTING: AN INTRODUCTION

2018 ◽  
Vol 8 (6) ◽  
pp. 63
Author(s):  
Matthew N. O. Sadiku ◽  
Yonghui Wang ◽  
Suxia Cui ◽  
Sarhan M. Musa
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Low Cost ◽  
Computational Techniques ◽  
Inaccurate Information ◽  
Major Benefit ◽  
Partial Truth ◽  
Human Decision

Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

A spectral method to detect community structure based on distance modularity matrix

2017 ◽  
Vol 31 (20) ◽  
pp. 1750129 ◽  
Author(s):  
Jin-Xuan Yang ◽  
Xiao-Dong Zhang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Spectral Method ◽  
Real World ◽  
Biological Networks ◽  
Community Organizations ◽  
Experimental Results ◽  
Time Consumption ◽  
Connected Vertex

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

Relative Assortativity Index: A Quantitative Metric to Assess the Impact of Link Prediction Techniques on Assortativity of Complex Networks

2019 ◽  
Vol 63 (9) ◽  
pp. 1417-1437
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Biological Networks ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Prediction Technique ◽  
Prediction Techniques ◽  
The Impact

Abstract We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

2020 ◽  
Author(s):  
Andrew Fang ◽  
Jonathan Kia-Sheng Phua ◽  
Terrence Chiew ◽  
Daniel De-Liang Loh ◽  
Lincoln Ming Han Liow ◽  
...  
Keyword(s):  
Open Source ◽  
Surveillance System ◽  
Community Care ◽  
Communicable Disease ◽  
Low Cost ◽  
Real Life ◽  
Disease Outbreak ◽  
Disease Spread ◽  
Healthcare Team ◽  
Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals The Role of Movement Patterns in Epidemic Models on Complex Networks

2021 ◽  
Vol 83 (10) ◽  
Author(s):  
Alfonso Ruiz-Herrera ◽  
Pedro J. Torres
Keyword(s):  
Complex Networks ◽  
Network Theory ◽  
Diffusion Rate ◽  
Critical Role ◽  
Movement Patterns ◽  
Epidemic Models ◽  
Simplifying Assumptions ◽  
The Common

AbstractIn this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.

Applications of Operations Research in Human Resource Management

2021 ◽  
Vol 9 (9) ◽  
pp. 1611-1614
Author(s):  
Vanya Aggarwal
Keyword(s):  
Human Resource Management ◽  
Human Resource ◽  
Real World ◽  
Operations Research ◽  
Operational Research ◽  
Low Cost ◽  
External World ◽  
Future Plan ◽  
The Future ◽  
Future Path

Abstract: Operational HR encompasses the highly visible, day-to-day tactical operations required to keep a workforce running. This made us look for strategic approaches essential for most organisations. Be it defining the future path, determining the future plan, mission, vision, planning, objectives and goals of a particular organization. In a nutshell, we wanted to bring out the intricate relationship between HR and operational research especially considering the current dynamics of the external world. The unprecedented changes in HRM made us dig deeper on the importance of the role and applications of operations research to cope with these changes. Finally, we believed our research was complete when we presented real-world examples, and it was demonstrated to us that Operations Research approaches may assist firms in making good HR policy decisions at a low cost

scholarly journals Hobbit: Providing Fall Detection and Prevention for the Elderly in the Real World

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Markus Bajones ◽  
David Fischinger ◽  
Astrid Weiss ◽  
Daniel Wolf ◽  
Markus Vincze ◽  
...  
Keyword(s):  
Real World ◽  
Independent Living ◽  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Field Trials ◽  
Lessons Learned ◽  
Human Robot Interaction ◽  
Service Robot ◽  
Emergency Situations

We present the robot developed within the Hobbit project, a socially assistive service robot aiming at the challenge of enabling prolonged independent living of elderly people in their own homes. We present the second prototype (Hobbit PT2) in terms of hardware and functionality improvements following first user studies. Our main contribution lies within the description of all components developed within the Hobbit project, leading to autonomous operation of 371 days during field trials in Austria, Greece, and Sweden. In these field trials, we studied how 18 elderly users (aged 75 years and older) lived with the autonomously interacting service robot over multiple weeks. To the best of our knowledge, this is the first time a multifunctional, low-cost service robot equipped with a manipulator was studied and evaluated for several weeks under real-world conditions. We show that Hobbit’s adaptive approach towards the user increasingly eased the interaction between the users and Hobbit. We provide lessons learned regarding the need for adaptive behavior coordination, support during emergency situations, and clear communication of robotic actions and their consequences for fellow researchers who are developing an autonomous, low-cost service robot designed to interact with their users in domestic contexts. Our trials show the necessity to move out into actual user homes, as only there can we encounter issues such as misinterpretation of actions during unscripted human-robot interaction.

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