scholarly journals Visibility Graph Analysis of IOTA and IoTeX Price Series: An Intentional Risk-Based Strategy to Use 5G for IoT

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2282
Author(s):  
Alberto Partida ◽  
Regino Criado ◽  
Miguel Romance
Keyword(s):  
Time Series ◽  
Information Security ◽  
Complex Networks ◽  
Directed Acyclic Graph ◽  
Price Volatility ◽  
Price Series ◽  
Visibility Graphs ◽  
Iot Platforms ◽  
Visibility Graph Analysis ◽  
The Impact

The transformation of time series into complex networks through visibility graphs is an innovative way to study time-based events. In this work, we use visibility graphs to transform IOTA and IoTeX price volatility time series into complex networks. Our aim is twofold: first, to better understand the markets of the two most capitalised Internet of Things (IoT) platforms at the time of writing. IOTA runs on a public directed acyclic graph (DAG) and IoTeX on a blockchain. Second, to suggest how 5G can improve information security in these two key IoT platforms. The analysis of the networks created by the natural and horizontal visibility graphs shows, first, that both IOTA and IoTeX are still at their infancy in their development, with IoTex seemingly developing faster. Second, both IoT tokens form communities in a hierarchical structure, and third, 5G can accelerate their development. We use intentional risk management as a lever to understand the impact of 5G on IOTA and IoTeX. Our results lead us to provide a set of design recommendations that contribute to improving information security in future 5G-based IoT implementations.

An empirical comparison of sales time series for online and offline channels for commodities in China

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
JINLONG WANG ◽  
CAN WEN ◽  
XIAOYI WANG
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Power Law ◽  
Constant Parameter ◽  
Power Law Distribution ◽  
Empirical Comparison ◽  
Visibility Graphs ◽  
Offline Channels ◽  
Power Law Distributions

In this paper we make an empirical comparison of sales time series for online and offline channels. In particular, we analyse the sales dynamic and fluctuation level underlying the sales time series in different channels. The accumulative daily sales distributions of commodities are analysed statistically and the daily sales series are also studied from the perspective of complex networks. We find that most of the commodities' accumulative sales distributions can be fitted by power-law distributions. Visibility graphs are constructed for the daily sales series, and the accumulative degree distributions are also investigated – it is found that they also almost follow power-law distribution. The constant parameter α indicates that different specifications of the same goods have different sales characteristics, and different forms of packaging of commodities, either special offer or ordinary, also show distinctive sales fluctuation levels. The differences show that the direction of these relationships is opposite for online and offline channels.

Impact of COVID-19 lockdown on prices of potato and onion in metropolitan cities of India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kuldeep Rajpoot ◽  
Saurav Singla ◽  
Abhishek Singh ◽  
Shashi Shekhar
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Extensive Study ◽  
Series Data ◽  
Content Type ◽  
Analysis Techniques ◽  
Series Analysis ◽  
Price Series ◽  
The Impact

PurposeThis study focuses on accessing the impact of lockdown implemented to curb the pandemic of coronavirus disease 2019 (COVID-19) on prices of potato and onion crops using the time series analysis techniques.Design/methodology/approachThe present study uses secondary price series data for both crops. Along with the study of percent increase or decrease, the time series analysis techniques of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), as well as machine learning; neural network autoregressive (NNAR) models were used to model the prices. For the purpose of comparison, the data from past years were taken as the period of normalcy. The behaviour of the forecasts for the normal periods and during the pandemic based on respective datasets was compared.FindingsThe results show that there was an unprecedented rise in prices during the months of lockdown. It could be attributed to the decline in arrivals due to several reasons like issues with transportation and labour availability. Also, towards the end of lockdown (May 2020), the prices seemed to decrease. Such a drop could be attributed to the relaxations in lockdown and reduced demand. The study also discusses that how some unique approaches like e-marketing, localized resource development for attaining self-sufficiency and developing transport chain, especially, for agriculture could help in such a situation of emergency.Research limitations/implicationsA more extensive study could be conducted to mark the factors specifically that caused the increase in price.Originality/valueThe study clearly marks that the prices of the crops increased more than expectations using time series methods. Also, it surveys the prevailing situation through available resources to link up the reasons behind it.

How Bad is it? – A Branching Activity Model to Estimate the Impact of Information Security Breaches

2013 ◽  
Author(s):  
Russell Cameron Thomas ◽  
Marcin Antkiewicz ◽  
Patrick Florer ◽  
Suzanne Widup ◽  
Matthew Woodyard
Keyword(s):  
Information Security ◽  
Activity Model ◽  
Security Breaches ◽  
The Impact

River-Basin Planning for Control of Nitrate Pollution

1982 ◽  
Vol 14 (4-5) ◽  
pp. 245-252 ◽  
Author(s):  
C S Sinnott ◽  
D G Jamieson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Water Resource ◽  
Potable Water ◽  
Potential Problem ◽  
Nitrate Pollution ◽  
Optimal Mix ◽  
Regional Basis ◽  
The Impact

The combination of increasing nitrate concentrations in the River Thames and the recent EEC Directive on the acceptable level in potable water is posing a potential problem. In assessing the impact of nitrates on water-resource systems, extensive use has been made of time-series analysis and simulation. These techniques are being used to define the optimal mix of alternatives for overcoming the problem on a regional basis.

Hospital Informatization in China During the COVID-19 Pandemic: A Cross-Sectional Internet-Based Survey Study (Preprint)

2020 ◽  
Author(s):  
Ke Zeng ◽  
Weiguo Zhu ◽  
Caiyou Wang ◽  
Liyan Zhu
Keyword(s):  
Information Technology ◽  
Information Security ◽  
Medical Information ◽  
Survey Study ◽  
Chinese Government ◽  
Qr Code ◽  
Cross Sectional ◽  
Network Information ◽  
Tertiary Hospitals ◽  
The Impact

BACKGROUND The rapid spread of COVID-19 has created a severe challenge to China’s healthcare system. Hospitals across the country reacted quickly under the leadership of the Chinese government and implemented a range of informatization measures to effectively respond to the COVID-19. OBJECTIVE To understand the impact of the pandemic on the medical business of Chinese hospitals and the difficulties faced by hospital informatization construction. To discuss the application of hospital informatization measures during the COVID-19 pandemic. To summarize the practical experience of hospitals using information technology to fight the pandemic. METHODS Performing a cross-sectional on-line questionnaire survey in Chinese hospitals, of which the participants are invited including hospital information staff, hospital administrators, medical staff, etc. Statistical analyzing the collected data by using SPSS version 24. RESULTS A total of 804 valid questionnaires (88.45%) are collected in this study from 30 provinces in mainland China, of which 731 (90.92%) were filled out by hospital information staff. 473 (58.83%) hospitals are tertiary hospitals while the remaining 331 (41.17%) are secondary hospitals. The majority hospitals (82.46%) had a drop in their business volume during the pandemic and a more substantial drop is found in tertiary hospitals. 70.40% (n=566) of hospitals have upgraded or modified their information systems in response to the epidemic. The proportion of tertiary hospitals that upgraded or modified systems is significantly higher than that of secondary hospitals. Internet hospital consultation (70.52%), pre-check and triage (62.56%), telemedicine (60.32%), health QR code (57.71%), and telecommuting (50.87%) are the most used informatization anti-pandemic measures. There are obvious differences in the application of information measures between tertiary hospitals and secondary hospitals. Among these measures, most of them (41.17%) are aiming at serving patients and most of them (62.38%) are universal which continue to be used after pandemic. The informatization measures are mostly used to control the source of infection (48.19%), such as health QR Code, etc. During the pandemic, the main difficulties faced by the hospital information department are “information construction projects are hindered” (58.96%) and “increased difficulty in ensuring network information security” (58.58%). There are significant differences in this issue between tertiary hospitals and secondary hospitals. The shortcomings of hospital informatization that should be made up for are “shorten patient consultation time and optimize consultation process” (72.51%), “Ensure network information security” (72.14%) and “build internet hospital consultations platform” (59.95%). CONCLUSIONS A significant number of innovative medical information technology have been used and played a significant role in all phases of COVID-19 prevention and control in China. Since the COVID-19 brought many challenges and difficulties for informatization work, hospitals need to constantly improve their own information technology skills to respond to public health emergencies that arise at any moment.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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