LONG WAVE EFFECTS ON BREAKING WAVES OVER FRINGING REEFS

Modeling of wave energy transformation and breaking on fringing reefs is inherently difficult due to their unique topography. Prior methods of determining dissipation are based on empirical data from gently sloping beaches and offer only bulk energy dissipation estimates over the entire spectrum. Methods for deducing a frequency dependent dissipation have been limited to hypothesized linkages between dissipation and wave shape in the surf, and have used bulk dissipation models as a constraint on the overall dissipation for mild sloping beaches. However, there is no clear indication that the constraint on the overall level of dissipation is suitable for the entire reef structure. Using these constraints the frequency dependent dissipation rate can be deduced from laboratory data, taken at the Coastal and Hydraulics Laboratory, of wave transformation over reefs. The frequency dependent dissipation rate can then be integrated over the spectrum to derive an empirically-based counterpart to energy flux dissipation. Comparing the bulk energy dissipation estimates for the reef system to the frequency based method allows for the modification of wave breaking parameters in the frequency estimation, to better estimate total dissipation. Since this method is based on the Fourier transform of the time series data, it allows the dissipation to be found as a function of the frequency. This analysis shows that there is a correlation between the amount of energy in the low frequencies of the wave spectrum and certain characteristics of the frequency dependent dissipation coefficient.

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Laboratory studies of the role of bandwidth in surface transport and energy dissipation of deep-water breaking waves

Journal of Fluid Mechanics ◽

10.1017/jfm.2021.734 ◽

2021 ◽

Vol 927 ◽

Author(s):

James T. Sinnis ◽

Laurent Grare ◽

Luc Lenain ◽

Nick Pizzo

Keyword(s):

Energy Dissipation ◽

Wave Packet ◽

Deep Water ◽

Dissipation Rate ◽

Laboratory Experiments ◽

Linear Prediction ◽

Laboratory Data ◽

Length Scale ◽

Surface Transport ◽

Horizontal Length

This paper presents laboratory measurements of surface transport due to non-breaking and breaking deep-water focusing surface wave packets and examines the dependence of the transport on the wave packet bandwidth, $\varDelta$ . This extends the work of Deike et al. (J. Fluid Mech., vol. 829, 2017, pp. 364–391) and Lenain et al. (J. Fluid Mech., vol. 876, 2019, p. R1), where similar numerical and laboratory experiments were conducted, but the bandwidth was held constant. In this paper, it is shown that the transport is strongly affected by the bandwidth. A model for the horizontal length scale of the breaking region is proposed that incorporates the bandwidth, central frequency, the linear prediction of the slope at focusing and the breaking threshold slope of the wave packet. This is then evaluated with data from archived and new laboratory experiments, and agreement is found. Furthermore, the horizontal length scale of the breaking region implies modifications to the model of the energy dissipation rate from Drazen et al. (J. Fluid Mech., vol. 611, 2008, pp. 307–332). This modification accounts for differing trends in the dissipation rate caused by the bandwidth in the available laboratory data.

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SERRE GREEN-NAGHDI MODELLING OF WAVE TRANSFORMATION BREAKING AND RUN-UP USING A HIGH-ORDER FINITE-VOLUME FINITE-DIFFERENCE SCHEME

Coastal Engineering Proceedings ◽

10.9753/icce.v32.waves.13 ◽

2011 ◽

Vol 1 (32) ◽

pp. 13 ◽

Cited By ~ 3

Author(s):

Marion Tissier ◽

Philippe Bonneton ◽

Fabien Marche ◽

Florent Chazel ◽

David Lannes

Keyword(s):

Energy Dissipation ◽

Finite Difference ◽

Finite Volume ◽

Wave Breaking ◽

Ad Hoc ◽

Laboratory Data ◽

Breaking Waves ◽

Wave Transformation ◽

Finite Volume Schemes ◽

Boussinesq Model

In this paper, a fully nonlinear Boussinesq model is presented and applied to the description of breaking waves and shoreline motions. It is based on Serre Green-Naghdi equations, solved using a time-splitting approach separating hyperbolic and dispersive parts of the equations. The hyperbolic part of the equations is solved using Finite-Volume schemes, whereas dispersive terms are solved using a Finite-Difference method. The idea is to switch locally in space and time to NSWE by skipping the dispersive step when the wave is ready to break, so as the energy dissipation due to wave breaking is predicted by the shock theory. This approach allows wave breaking to be handled naturally, without any ad-hoc parameterization for the energy dissipation. Extensive validations of the method are presented using laboratory data.

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Separation of emitted and photochemical formaldehyde in Mexico City using a statistical analysis and a new pair of gas-phase tracers

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-5-11583-2005 ◽

2005 ◽

Vol 5 (6) ◽

pp. 11583-11615 ◽

Cited By ~ 2

Author(s):

A. R. García ◽

R. Volkamer ◽

L. T. Molina ◽

M. J. Molina ◽

J. Samuelson ◽

...

Keyword(s):

Mexico City ◽

Time Series Data ◽

Control Strategies ◽

Laboratory Data ◽

Photochemical Oxidation ◽

Series Data ◽

Statistical Regression ◽

Photochemical Pollution ◽

Secondary Sources ◽

Primary Emissions

Abstract. Photochemical pollution control strategies require an understanding of photochemical oxidation precursors, making it important to distinguish between primary and secondary sources of HCHO. Estimates for the relative strengths of primary and secondary sources of formaldehyde (HCHO) were obtained using a statistical regression analysis with time series data of carbon monoxide (CO) and glyoxal (CHOCHO) measured in the Mexico City Metropolitan Area (MCMA) during the spring of 2003. Differences between Easter week and more typical weeks are evaluated. The use of CO-CHOCHO as HCHO tracers is more suitable for differentiating primary and secondary sources than CO-O3. The application of the CO-O3 tracer pair to mobile laboratory data suggests a potential in-city source of background HCHO. A significant amount of HCHO observed in the MCMA is associated with primary emissions.

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Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients

Journal of Clinical Medicine ◽

10.3390/jcm9061668 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1668 ◽

Cited By ~ 10

Author(s):

Fu-Yuan Cheng ◽

Himanshu Joshi ◽

Pranai Tandon ◽

Robert Freeman ◽

David L Reich ◽

...

Keyword(s):

Machine Learning ◽

Time Series Data ◽

Laboratory Data ◽

Vital Signs ◽

Predictive Performance ◽

Series Data ◽

Care Providers ◽

Operating Characteristics ◽

Risk Prioritization ◽

Icu Transfer

Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers’ efforts and help hospitals plan their flow of operations. Methods: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. Results: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2–81.1%) sensitivity, 76.3% (95% CI: 74.7–77.9%) specificity, 76.2% (95% CI: 74.6–77.7%) accuracy, and 79.9% (95% CI: 75.2–84.6%) area under the receiver operating characteristics curve. Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.

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Separation of emitted and photochemical formaldehyde in Mexico City using a statistical analysis and a new pair of gas-phase tracers

Atmospheric Chemistry and Physics ◽

10.5194/acp-6-4545-2006 ◽

2006 ◽

Vol 6 (12) ◽

pp. 4545-4557 ◽

Cited By ~ 99

Author(s):

A. R. Garcia ◽

R. Volkamer ◽

L. T. Molina ◽

M. J. Molina ◽

J. Samuelson ◽

...

Keyword(s):

Mexico City ◽

Time Series Data ◽

Control Strategies ◽

Laboratory Data ◽

Photochemical Oxidation ◽

Series Data ◽

Statistical Regression ◽

Photochemical Pollution ◽

Secondary Sources ◽

Primary Emissions

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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