The advantages of complementing MT profiles in 3-D environments with geomagnetic transfer function and interstation horizontal magnetic transfer function data: results from a synthetic case study

Changes in the underlying conductivity around hypocenters are generally considered one of the promising mechanisms of seismo-electromagnetic anomaly generation. Parkinson vectors are indicators of high-conductivity materials and were utilized to remotely monitor conductivity changes during the MW 6.5 Jiuzhaigou earthquake (103.82°E, 33.20°N) on 8 August 2017. Three-component geomagnetic data recorded in 2017 at nine magnetic stations with epicenter distances of 63–770 km were utilized to compute the azimuths of the Parkinson vectors based on the magnetic transfer function. The monitoring and background distributions at each station were constructed by using the azimuths within a 15-day moving window and over the entire study period, respectively. The background distribution was subtracted from the monitoring distribution to mitigate the effects of underlying inhomogeneous electric conductivity structures. The differences obtained at nine stations were superimposed and the intersection of a seismo-conductivity anomaly was located about 70 km away from the epicenter about 17 days before the earthquake. The anomaly disappeared about 7 days before and remained insignificant after the earthquake. Analytical results suggested that the underlying conductivity close to the hypocenter changed before the Jiuzhaigou earthquake. These changes can be detected simultaneously by using multiple magnetometers located far from the epicenter. The disappearance of the seismo-conductivity anomaly after the earthquake sheds light on a promising candidate of the pre-earthquake anomalous phenomena.

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Modelling Multi-Input-Single-Output (MISO) Production Process Using Transfer Function: A Case Study of a Brewery

Research Journal of Applied Sciences Engineering and Technology ◽

10.19026/rjaset.12.2308 ◽

2016 ◽

Vol 12 (1) ◽

pp. 100-107

Author(s):

Stanley Okiy ◽

Chidozie Chukwuemeka Nwobi-Okoye ◽

Anthony Clement Igboanugo

Keyword(s):

Transfer Function ◽

Production Process ◽

Single Output

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CASE STUDY EXEMPLAR OF DETECTING SEVERE DIASTOLIC DYSFUNCTION USING BALLISTOCARDIOGRAM

Innovation in Aging ◽

10.1093/geroni/igz038.339 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S88-S89

Author(s):

Laurel A Despins ◽

Giovanna Guidoboni ◽

Marjorie Skubic ◽

Lorenzo Sala ◽

Moein Enayati ◽

...

Keyword(s):

Cardiac Function ◽

Diastolic Function ◽

Hospital Readmissions ◽

Clinical Problem ◽

Physiological Model ◽

Function Data ◽

Great Vessels ◽

New Diagnosis ◽

Pressure Changes

Abstract The specific aim of this case study was to describe how monitoring ballistocardiogram (BCG) waveforms can detect early heart failure (HF) changes. HF significantly impairs quality of life and is the principal cause for hospital readmissions in older adults. HF prevalence in American adults aged 65 years and older is expected to increase over 70% by 2030. Detecting worsening HF is challenging. Invasive arterial waveforms display blood pressure changes with each heartbeat; BCG waveforms display repetitive body motions resulting from ejection of blood into the great vessels. BCG waveforms change as cardiac function changes. Currently, BCG signals can be captured non-invasively using sensors placed under a bed mattress and provide heart and respiratory rates. We have developed a new way to analyze the BCG waveform using an innovative closed-loop physiological model of the cardiovascular system. The subject, a 94-year old female with hypertension, presented to her physician with symptoms associated with a new diagnosis of acute mixed congestive HF. Mean heart and respiratory rate trends obtained from her bed sensor in the prior two months did not indicate HF. We simulated cardiac cycles using normal cardiac function data, mildly impaired diastolic function data, and the subject’s echocardiography data. The results demonstrated BCG waveform changes that correlated with decreasing cardiac output related to worsening diastolic function. New methods for clinically interpreting BCG waveforms present a significant opportunity for improving early HF detection and improving outcomes. Working on a clinical problem from an engineering perspective merges two disciplines, creating a new methodology.

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Applications of dispersion relations to the geomagnetic transfer function

Physics of The Earth and Planetary Interiors ◽

10.1016/j.pepi.2004.08.016 ◽

2005 ◽

Vol 150 (1-3) ◽

pp. 85-91 ◽

Cited By ~ 8

Author(s):

A. Marcuello ◽

P. Queralt ◽

J. Ledo

Keyword(s):

Transfer Function ◽

Dispersion Relations ◽

Geomagnetic Transfer Function

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Forecasting Exchange Rate Across Countries with Gold Price as Exogenous Variable Using Transfer Function and VARI-X Model

MATEMATIKA ◽

10.11113/matematika.v36.n3.1211 ◽

2020 ◽

Vol 36 (3) ◽

pp. 181-196

Author(s):

Alhassan Sesay ◽

Suhartono Suhartono ◽

Dedy Dwi Prastyo

Keyword(s):

South Africa ◽

Exchange Rate ◽

Transfer Function ◽

Exchange Rates ◽

Arima Model ◽

Gold Price ◽

Savings Accounts ◽

Daily Data ◽

The Relationship

Investors and collectors hold gold as protection for their savings and wealth atlarge. Gold does not pay interest like treasure bonds or savings accounts, but current goldprices often reflect increases and decreases of an asset. This research aims to provide amodel for the relationship between the exchange rate, which is vital in exporting gold, andgold prices across countries. The Australia, Brazil, and South Africa exchange rates areused as a case study against the gold price. The ARIMA model is used for forecasting goldprice as an input for the Transfer Function and VARIX models. The Transfer Functionmodel only considers the relationship between gold prices as input with the exchange ratein each country, whereas the VARIX model also considers the interrelationship betweenexchange rates in these countries. Daily data is used for the period 1st June 2010 to the28th February 2018. The RMSE and MAPE are used as criteria for selecting the bestmodel. The results show that VARIX is the best model for forecasting the Australianexchange rate, while the Transfer function is the best model for forecasting South Africanand Brazilian exchange rates.

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Intervention analysis using control series and exogenous variables in a transfer function model: A case study

International Journal of Forecasting ◽

10.1016/0169-2070(89)90060-5 ◽

1989 ◽

Vol 5 (1) ◽

pp. 21-27 ◽

Cited By ~ 6

Author(s):

Lakshman Krishnamurthi ◽

Jack Narayan ◽

S.P. Raj

Keyword(s):

Transfer Function ◽

Intervention Analysis ◽

Transfer Function Model ◽

Exogenous Variables ◽

Function Model ◽

Control Series

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Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200046 ◽

2020 ◽

Vol 24 (3) ◽

pp. 243-251

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Transfer Function ◽

Mean Square Error ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coefficient Of Determination ◽

Hydraulic Structures ◽

Mean Square ◽

Feed Forward Back Propagation

Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

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