scholarly journals The Landscape of Risk Perception Research: A Scientometric Analysis

2021 ◽  
Vol 13 (23) ◽  
pp. 13188
Author(s):  
Floris Goerlandt ◽  
Jie Li ◽  
Genserik Reniers
Keyword(s):  
Risk Perception ◽  
Consumer Choice ◽  
Traffic Accidents ◽  
Temporal Dynamics ◽  
Research Output ◽  
Temporal Trends ◽  
Future Research ◽  
Scientometric Analysis ◽  
Research Fronts ◽  
High Level

Risk perception is important in organizational and societal governance contexts. This article presents a high-level analysis of risk perception research using Web of Science core collection databases, scientometrics methods and visualization tools. The focus is on trends in outputs, geographical and temporal trends, and patterns in the associated scientific categories. Thematic clusters and temporal dynamics of focus topics are identified using keyword analysis. A co-citation analysis is performed to identify the evolution of research fronts and key documents. The results indicate that research output is growing fast, with most contributions originating from western countries. The domain is highly interdisciplinary, rooted in psychology and social sciences, but branching into domains related to environmental sciences, medicine, and engineering. Significant research themes focus on perceptions related to health, with a focus on cancer, human immunodeficiency virus, and epidemiology, natural hazards and major disasters, traffic accidents, technological and industrial risks, and customer trust. Risk perception research originated from consumer choice decisions, with subsequent research fronts focusing on understanding the risk perception concept, and on developing taxonomies and measurement methods. Applied research fronts focus on environmental hazards, traffic accidents, breast cancer and, more recently, e-commerce transactions and flood risk. Based on the results, various avenues for future research are described.

scholarly journals A Scientometric Overview of Global Dinoflagellate Research

Publications ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 50
Author(s):  
Carlos Yure B. Oliveira ◽  
Cicero Diogo L. Oliveira ◽  
Marius N. Müller ◽  
Elizabeth P. Santos ◽  
Danielli M. M. Dantas ◽  
...  
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Research Output ◽  
Research Field ◽  
Future Research ◽  
Scientometric Analysis ◽  
Fish Production ◽  
Emerging Trends ◽  
Basic Characteristics ◽  
Number Of Publications

Understanding the evolution of scientific literature is a critical and necessary step for the development and strengthening of a research field. However, an overview of global dinoflagellate research remains unavailable. Herein, global dinoflagellate research output was analyzed based on a scientometric approach using the Scopus data archive. The basic characteristics and worldwide interactions of dinoflagellate research output were analyzed to determine the temporal evolution and new emerging trends. The results confirm that dinoflagellate research output, reflected in the number of publications, is a fast-growing area since the mid-1990s. In total, five research subareas emerged using a bibliometric keywords analysis: (1) “symbiosis with coral reefs”, (2) “phylogeny”, (3) “palynology”, (4) “harmful algal blooms” and (5) “nutrition strategies”. Dinoflagellate publications were modeled by fish production (both aquaculture and fisheries) and economic and social indexes. Finally, directions for future research are proposed and discussed. The presented scientometric analysis confirms that dinoflagellate research is an active and important area with focus on mitigating economic impacts, especially in regard to fish production.

scholarly journals Algorithm for Applying Statistics and Scientometric Analysis to Identify Innovative Areas of Focus in Scientific Knowledge in Labour Economics

2019 ◽  
Vol 26 (2) ◽  
pp. 53-66
Author(s):  
O. E. Bashina ◽  
L. V. Matraeva ◽  
Ye. S. Vasyutina
Keyword(s):  
Scientific Knowledge ◽  
Search Algorithm ◽  
Subject Area ◽  
Future Research ◽  
Scientometric Analysis ◽  
Labour Economics ◽  
Timing Diagram ◽  
Research Fronts ◽  
Principal Points ◽  
Active Research

The modern Digital Universe changes and expands at a speed that every two years double the amount of data. It leads to a situation when huge accumulated flows of information can no longer be covered by traditional scientific search and built into a relevant scientific research framework. The authors argue that there is a need for using modern statistics and scientometric application packages for solving research tasks in the primary trends of the information economy. The article presents a comparative analysis of various scientometric programs and describes a new approach to identifying and visualizing patterns and transient regularities in the scientific literature on the basis of a study of global publication flows in the last 25 years in the subject area of «labour economics» represented in the Web of Science.The authors conceptualize and visualize scientific domain of «labour economics» within the framework of the timing diagram of the evolution of research fronts. They introduce the search algorithm for active research fronts in the global information flow using CiteSpace V.0 and highlight the most critical trends and principal points of research clusters for the past decade on labour economics and its core studies. The paper determines most perspective citation spikes that could potentially become the center of new scientific knowledge in this area and outlines opportunities for future research.

scholarly journals Scientometric Analysis of Disaster Risk Perception: 2000–2020

2021 ◽  
Vol 18 (24) ◽  
pp. 13003
Author(s):  
Tianlong Yu ◽  
Hao Yang ◽  
Xiaowei Luo ◽  
Yifeng Jiang ◽  
Xiang Wu ◽  
...  
Keyword(s):  
Risk Perception ◽  
Web Of Science ◽  
Disaster Risk ◽  
Research Field ◽  
Nuclear Radiation ◽  
Future Research ◽  
Scientometric Analysis ◽  
Graph Analysis ◽  
Analysis Tool ◽  
Three Stages

This paper used 1526 works from the literature on disaster risk perception from 2000 to 2020 in the Web of Science core collection database as the research subject. The CiteSpace knowledge graph analysis tool was used to visual analyze the country, author, institution, discipline distribution, keywords, and keyword clustering mapping. The paper drew the following conclusions. Firstly, disaster risk perception research has experienced three stages of steady development, undulating growth, and rapid growth. Secondly, the field of disaster risk perception was mainly concentrated in the disciplines of engineering, natural science, and management science. Thirdly, meteorological disasters, earthquakes, nuclear radiation, and epidemics were the main disasters in the field of disaster risk perception. Residents and adolescents were the main subjects of research in the field of disaster risk perception. Fourthly, research on human risk behavior and risk psychology and research on disaster risk control and emergency management were two major research hotspots in the field of disaster risk perception. Finally, the research field of disaster risk perception is constantly expanding. There is a trend from theory to application and multi-perspective combination, and future research on disaster risk perception will be presented more systematically. The conclusion can provide a reference for disaster risk perception research, as well as directions for future research.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Questions to the Article of research output and international cooperation among countries during the COVID-19 pandemic (Preprint)

2020 ◽  
Author(s):  
Shu-Chun Kuo ◽  
Tsair-Wei Chien ◽  
Willy Chou
Keyword(s):  
Citation Analysis ◽  
International Cooperation ◽  
Web Of Science ◽  
Research Output ◽  
Visual Representations ◽  
Scientometric Analysis ◽  
Medical Subject Headings ◽  
Pubmed Central ◽  
The Us ◽  
Circle Diagram

UNSTRUCTURED We read with great interest the study by Grammes et al. on research output and international cooperation among countries during the COVID-19 pandemic. The paper is a quantitative study using scientometric analysis instead of a qualitative research using citation analysis. A total of 7,185 publications were extracted from Web of Science Core Collection (WoS) with keywords of “covid19 OR covid-19 OR sarscov2 OR sars-cov-2” as of July 4, 2020. We replicated a citation analysis study to extract abstracts from Pubmed Central(PMC) with similar keywords mentioned above and obtained 35,421 articles relevant to COVID-10 matching their corresponding number of citation in PMC. one hundred top-cited atricles were selected and compared on diagrams. Social network analysis combined with citation numbers in articles was performed to analyze international cooperation among countries. The results were shown on a world map instead of the circle diagram in the previous study. A Sankey diagram was applied to highlight entities(e.g., countries, article types, medical subject headings, and journals) with the most citations. Authors from Chian dominated citations in these 100 top-cited articles rather than the US in publications addressed in the previous study. Both visual representations of the world map and Sankey diagram were provided to readers with a better understanding of the research output and international cooperation among countries during the COVID-19 pandemic

scholarly journals Opportunities and Challenges of Green-Lean: An Integrated System for Sustainable Construction

2020 ◽  
Vol 12 (11) ◽  
pp. 4460 ◽  
Author(s):  
Mohammadsoroush Tafazzoli ◽  
Ehsan Mousavi ◽  
Sharareh Kermanshachi
Keyword(s):  
Cost Savings ◽  
Integrated Approach ◽  
Integrated System ◽  
Waste Minimization ◽  
Sustainable Construction ◽  
Future Research ◽  
Safety Improvement ◽  
The Individual ◽  
High Level ◽  
Industry Professionals

Although the two concepts of lean and sustainable construction have been developed due to different incentives, and they do not pursue the same exact goals, there exists considerable commonality between them. This paper discusses the potentials for integrating the two approaches and their practices and how the resulting synergy from combining the two methods can potentially lead to higher levels of fulfilling the individual goals of each of them. Some limitations and challenges to implementing the integrated approach are also discussed. Based on a comprehensive review of existing papers related to sustainable and lean construction topics, the commonality between the two approaches is discussed and grouped in five categories of (1) cost savings, (2) waste minimization, (3) Jobsite safety improvement, (4) reduced energy consumption, and (5) customers’ satisfaction improvement. The challenges of this integration are similarly identified and discussed in the four main categories of (1) additional initial costs to the project, (2) difficulty of providing specialized expertise, (3) contractors’ unwillingness to adopt the additional requirements, and (4) challenges to establish a high level of teamwork. Industry professionals were then interviewed to rank the elements in each of the two categories of opportunities and challenges. The results of the study highlight how future research can pursue the development of a new Green-Lean approach by investing in the communalities and meeting the challenges of this integration.

scholarly journals Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

2021 ◽  
Vol 18 (8) ◽  
pp. 3922
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann
Keyword(s):  
Recovery Time ◽  
System Of Systems ◽  
Future Research ◽  
Business Networks ◽  
Simulation Experiments ◽  
Huge Impact ◽  
High Level ◽  
Systems Simulation ◽  
Different Characteristics ◽  
The Impact

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

scholarly journals Impact of ambient sound on risk perception in humans: neuroeconomic investigations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elise Payzan-LeNestour ◽  
Lionnel Pradier ◽  
James Doran ◽  
Gideon Nave ◽  
Bernard Balleine
Keyword(s):  
Risk Perception ◽  
Multisensory Perception ◽  
Future Research ◽  
Perception Of Risk ◽  
Asset Risk ◽  
Financial Trading ◽  
Ambient Sound ◽  
General Relevance ◽  
Wide Range ◽  
Professional Traders

AbstractResearch in the field of multisensory perception shows that what we hear can influence what we see in a wide range of perceptual tasks. It is however unknown whether this extends to the visual perception of risk, despite the importance of the question in many applied domains where properly assessing risk is crucial, starting with financial trading. To fill this knowledge gap, we ran interviews with professional traders and conducted three laboratory studies using judgments of financial asset risk as a testbed. We provide evidence that the presence of ambient sound impacts risk perception, possibly due to the combination of facilitatory and synesthetic effects of general relevance to the perception of risk in many species as well as humans. We discuss the implications of our findings for various applied domains (e.g., financial, medical, and military decision-making), and raise new questions for future research.

Probabilistic Rule Learning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-16
Author(s):  
Abdus Salam ◽  
Rolf Schwitter ◽  
Mehmet A. Orgun
Keyword(s):  
Probability Theory ◽  
Rule Learning ◽  
Background Knowledge ◽  
Learning Systems ◽  
Future Research ◽  
Research Directions ◽  
System Architectures ◽  
Probabilistic Rule ◽  
Future Research Directions ◽  
High Level

This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.

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