scholarly journals Early Detection of the Wood-boring Insect Semanotus Bifasciatus Using Acoustic Detection Technology

2021 ◽  
Author(s):  
qi jiang ◽  
Yujie Liu ◽  
Lili Ren ◽  
Yu Sun ◽  
Youqing Luo
Keyword(s):  
Pulse Duration ◽  
Early Identification ◽  
Prediction Accuracy ◽  
Time Window ◽  
Larval Instar ◽  
Feeding Activity ◽  
Peak Frequency ◽  
Acoustic Detection ◽  
Detection Technology ◽  
Wood Boring

Abstract BACKGROUND: Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood-boring pests of Platycladus trees in East Asia, threatening the protection of ancient cypress species and urban ecological safety. Acoustic detection technology has the advantages of high sensitivity, single wood diagnosis and anti-interference, which can be useful for early identification of cryptic wood boring damage. However, there has been limited research on detection time window and acoustics features that suitable for early detection of forest wood borers. METHODS: In this study, we carried out a manipulated insect infestation experiment by inoculating S. bifasciatus into fresh logs, and the feeding sound signals of S. bifasciatus larvae were recorded in timeseries. Then, nine feature variables were selected to characterize the sounds of larval feeding activity. The best time window for acoustic detection during a single day and the whole larval growth stage was determined. And the optimal models for predicting larval instar and population were established using the stepwise regression (SR) and partial least squares regression (PLSR) approach.RESULTS: (1) The single pulse duration of S. bifasciatus was less than 15 ms, and the peak frequency was approximately 8 kHz; (2) Within a 24-hour day, the feeding sound signals were strongest during 13:00 and 20:00; (3) The feeding activity of larvae was greatest during the 1st to the 3rd instar, declined from the 4th instar, and was lowest at the 5th instar; (4) Weak correlations were found between larval instar and feature variables, r ranging from 0.3 to 0.6. By contrast, the larval population has a strong linear correlation with all variables (r>0.7). Except for Average pulse duration and Peak frequency, there indicated high or severe multicollinearity among other variables (the variance inflation factor, VIF >10); (5) The SR model was optimal for predicting larval instar; its prediction accuracy was R2 = 0.71, RMSEp = 0.42, and RPD = 3.38. Average entropy, Peak frequency, and Average pulse duration had the largest influence on the model. (6) The optimal model for predicting population was the PLSR model, and its prediction accuracy was R2 = 0.97, RMSEp = 61.96, and RPD = 28.87. Except for Peak Freq, the other eight variables had a great impact on the model. CONCLUSION: This study highlighted the suitable detection time window and acoustic feature variables for early identification of S. bifasciatus larvae, and optimal models for predicting its larval instar and population were provided. This work will promote further improvements in the efficiency and accuracy of acoustic detection technology for practical applications, providing a reference for evaluating the early damage of wood-boring pest.

Fish Bioacoustics and Behavior: Passive Acoustic Detection and the Application of a Closed-Circuit Rebreather for Field Study

2001 ◽  
Vol 35 (2) ◽  
pp. 19-28 ◽  
Author(s):  
Phillip S. Lobel
Keyword(s):  
Behavioral Ecology ◽  
Daily Basis ◽  
Open Circuit ◽  
Acoustic Detection ◽  
Ecological Processes ◽  
Detection Technology ◽  
Scientific Goal ◽  
Species Specific ◽  
Passive Acoustic ◽  
And Behavior

The simple thesis of this paper is that using rebreathers to study fish behavioral ecology, especially bioacoustics, is well worth the expense and additional training required. The scientific goal of my bioacoustic research is to determine which fishes produce species-specific sound patterns exclusively with explicit acts of courtship and mating. This provides scientific insight into evolutionary and ecological processes and also provides data necessary to develop the passive acoustic detection technology for monitoring fish reproduction. When used on a daily basis, rebreathers, in my experience, are economical and as practical as open circuit scuba. This is based both on the costs of diving as well as the efficiency of gathering useful data. The use of open circuit SCUBA while conducting acoustic recordings results in a loss of at least 40% of the data due to the bubble noise from a divers breathing. Rebreathers also provide extended bottom time, especially in shallow water, which enhances a diver's ability to observe fish and gather acoustic-behavioral data.

Acoustic Detection Technology Based on Fiber Bragg Grating Fabry-Perot Interferometer in Liquid Medium

2013 ◽  
Vol 33 (9) ◽  
pp. 0906001 ◽  
Author(s):  
张伟超 Zhang Weichao ◽  
赵洪 Zhao Hong ◽  
刘通 Liu Tong ◽  
王国利 Wang Guoli ◽  
李锐海 Li Ruihai
Keyword(s):  
Liquid Medium ◽  
Fiber Bragg Grating ◽  
Bragg Grating ◽  
Acoustic Detection ◽  
Detection Technology ◽  
Fabry Perot

Towards an Automated Acoustic Detection Algorithm for Wood-Boring Beetle Larvae (Coleoptera: Cerambycidae and Buprestidae)

2019 ◽  
Vol 112 (3) ◽  
pp. 1327-1336 ◽  
Author(s):  
Alexander Sutin ◽  
Alexander Yakubovskiy ◽  
Hady R Salloum ◽  
Timothy J Flynn ◽  
Nikolay Sedunov ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Acoustic Detection ◽  
Wood Boring

Early Identification of Reading Difficulties: A Screening Strategy that Adjusts the Sensitivity to the Level of Prediction Accuracy

Dyslexia ◽  
2017 ◽  
Vol 23 (3) ◽  
pp. 251-267 ◽  
Author(s):  
Mads Poulsen ◽  
Anne-Mette Veber Nielsen ◽  
Holger Juul ◽  
Carsten Elbro
Keyword(s):  
Early Identification ◽  
Prediction Accuracy ◽  
Reading Difficulties ◽  
Screening Strategy

scholarly journals Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis (Preprint)

2021 ◽  
Author(s):  
Chengbo Zeng ◽  
Jiajia Zhang ◽  
Zhenlong Li ◽  
Xiaowen Sun ◽  
Bankole Olatosi ◽  
...  
Keyword(s):  
Time Series ◽  
South Carolina ◽  
Prediction Accuracy ◽  
Temporal Relationship ◽  
Time Window ◽  
State Level ◽  
The State ◽  
County Level ◽  
Population Mobility ◽  
Mobility Data

BACKGROUND Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. OBJECTIVE The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. METHODS This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. RESULTS Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. CONCLUSIONS Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

scholarly journals Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

2020 ◽  
Vol 25 (1) ◽  
pp. 33-42
Author(s):  
Isaac Kofi Nti ◽  
Adebayo Felix Adekoya ◽  
Benjamin Asubam Weyori
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Prediction Accuracy ◽  
Time Window ◽  
Market Price ◽  
Investor Sentiment ◽  
Google Trends ◽  
Price Movement ◽  
The Future ◽  
Web News

AbstractPredicting the stock market remains a challenging task due to the numerous influencing factors such as investor sentiment, firm performance, economic factors and social media sentiments. However, the profitability and economic advantage associated with accurate prediction of stock price draw the interest of academicians, economic, and financial analyst into researching in this field. Despite the improvement in stock prediction accuracy, the literature argues that prediction accuracy can be further improved beyond its current measure by looking for newer information sources particularly on the Internet. Using web news, financial tweets posted on Twitter, Google trends and forum discussions, the current study examines the association between public sentiments and the predictability of future stock price movement using Artificial Neural Network (ANN). We experimented the proposed predictive framework with stock data obtained from the Ghana Stock Exchange (GSE) between January 2010 and September 2019, and predicted the future stock value for a time window of 1 day, 7 days, 30 days, 60 days, and 90 days. We observed an accuracy of (49.4–52.95 %) based on Google trends, (55.5–60.05 %) based on Twitter, (41.52–41.77 %) based on forum post, (50.43–55.81 %) based on web news and (70.66–77.12 %) based on a combined dataset. Thus, we recorded an increase in prediction accuracy as several stock-related data sources were combined as input to our prediction model. We also established a high level of direct association between stock market behaviour and social networking sites. Therefore, based on the study outcome, we advised that stock market investors could utilise the information from web financial news, tweet, forum discussion, and Google trends to effectively perceive the future stock price movement and design effective portfolio/investment plans.

scholarly journals Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis

2021 ◽  
Author(s):  
Chengbo Zeng ◽  
Jiajia Zhang ◽  
Zhenlong Li ◽  
Xiaowen Sun ◽  
Bankole Olatosi ◽  
...  
Keyword(s):  
Time Series ◽  
South Carolina ◽  
Disease Surveillance ◽  
Prediction Accuracy ◽  
Temporal Relationship ◽  
Time Window ◽  
State Level ◽  
Population Mobility ◽  
Mobility Data ◽  
Background Population

Background: Population mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19. Objective: To examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC. Methods: This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on their geotagged twitters. Poisson count time series model was employed to carry out the research goals. Results: Population mobility was positively associated with state-level daily COVID-19 incidence and those of the top five counties (i.e., Charleston, Greenville, Horry, Spartanburg, Richland). At the state-level, final model with time window within the last 7-day had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3-, 7-, 14- days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9-, 14-, 28-, 20-, and 9- days, respectively. The 14-day prediction accuracy ranged from 60.3% to 74.5%. Conclusions: Population mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

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