Unified Wind Wave Growth and Spectrum Functions for All Water Depths: Field Observations and Model Results

Wind wave development is governed by the fetch- or duration-limited growth principle that is expressed as a pair of similarity functions relating the dimensionless elevation variance (wave energy) and spectral peak frequency to fetch or duration. Combining the pair of similarity funtions the fetch or duration variable can be removed to form a dimensionless function of elevation variance and spectral peak frequency, which is interepreated as the wave enegry evolution with wave age. The relationship is initially developed for quasi-neural stability and quasi-steady wind forcing conditions. Further analyses show that the same fetch, duration, and wave age similarity functions are applicable to unsteady wind forcing conditions, including rapidly accelerating and decelerating mountain gap wind episodes and tropical cyclone (TC) wind fields. Here it is shown that with the dimensionless frequency converted to dimensionless wavenumber using the surface wave dispersion relationship, the same similarity function is applicable in all water depths. Field data collected in shallow to deep waters and mild to TC wind conditions, and synthetic data generated by spectrum model computations are assembled to illustrate the applicability. For the simulation work, the finite-depth wind wave spectrum model and its shoaling function are formulated for variable spectral slopes. Given wind speed, wave age, and water depth, the measrued and spectrum-computed significant wave heights and the associated growth parameters are in good agreement in forcing conditions from mild to TC winds and in all depths from deep ocean to shallow lake.

Flaw Detection in Highly Scattering Materials Using a Simple Ultrasonic Sensor Employing Adaptive Template Matching

Ultrasonic sensors have been extensively used in the nondestructive testing of materials for flaw detection. For polycrystalline materials, however, due to the scattering nature of the material, which results in strong grain noise and attenuation of the ultrasonic signal, accurate detection of flaws is particularly difficult. In this paper, a novel flaw-detection method using a simple ultrasonic sensor is proposed by exploiting time-frequency features of an ultrasonic signal. Since grain scattering mostly happens in the Rayleigh scattering region, it is possible to separate grain-scattered noise from flaw echoes in the frequency domain employing their spectral difference. We start with the spectral modeling of grain noise and flaw echo, and how the two spectra evolve with time is established. Then, a time-adaptive spectrum model for flaw echo is proposed, which serves as a template for the flaw-detection procedure. Next, a specially designed similarity measure is proposed, based on which the similarity between the template spectrum and the spectrum of the signal at each time point is evaluated sequentially, producing a series of matching coefficients termed moving window spectrum similarity (MWSS). The time-delay information of flaws is directly indicated by the peaks of MWSSs. Finally, the performance of the proposed method is validated by both simulated and experimental signals, showing satisfactory accuracy and efficiency.

Potential demographic dividend for India, 2001 to 2061: A macro-simulation projection using the spectrum model

This paper projects potential demographic dividend for India for the period from 2001 to 2061 by using simulation modelling software, Spectrum 5.753 which integrates demographic and socio-economic changes. Two key findings, after checking their robustness, from the simulation modelling are: First, the effective demographic windows of opportunity for India is available for the period between 2011 and 2041, giving India roughly 30 years of demographic bonus. It is the period where the maximum of the first demographic dividend can be reaped before the ageing burden starts. Second, favourable demographic changes alone provide a demographic dividend of over 165,000 rupees (almost an additional 43 percentage) in terms of GDP per capita by 2061 when integrated with supporting socio-economic policy environment in terms of investment in human capital, family planning, decent employment opportunities, the rapid pace of urbanization, and agricultural growth.

Turbulent model validations with CFD/wind tunnel test and application to SEA for wind noise prediction

Wind noise is becoming to have a higher priority in automotive industry. Several past studies investigated whether SEA can be utilized to predict wind noise by applying a turbulent spectrum model as the input. However, there are many kinds of turbulent models developed and the appropriate model for input to SEA is still unclear. Due to this, this paper focuses on clarifying an appropriate turbulent model for SEA simulation. First, the input turbulent pressure spectrum from five models are validated with wind tunnel tests and CFD. Next, a conventional numerical approach is used to validate models from the aspect of response accuracy. Finally, turbulent models are applied to an SEA model developed for a wind tunnel, and the SEA response is validated with test data. From those input/response validations, an appropriate turbulent model is investigated.

Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms

Aims Spectrum bias can arise when a diagnostic test is derived from study populations with different disease spectra than the target population, resulting in poor generalizability. We used a real-world artificial intelligence-derived algorithm to detect severe aortic stenosis to experimentally assess the effect of spectrum bias on test performance. Methods and Results All adult patients at the Mayo Clinic between January 1st, 1989 to September 30th, 2019 with transthoracic echocardiograms within 180 days after electrocardiogram were identified. Two models were developed from two distinct patient cohorts: a whole-spectrum cohort comparing severe AS to any non-severe AS and an extreme-spectrum cohort comparing severe AS to no AS at all. Model performance was assessed. Overall, 258,607 patients had valid ECG and echocardiograms pairs. The area under the receiver operator curve was 0.87 and 0.91 for the whole-spectrum and extreme-spectrum models respectively. Sensitivity and specificity for the whole-spectrum model was 80% and 81% respectively, while for the extreme-spectrum model it was 84% and 84% respectively. When applying the AI-ECG derived from the extreme-spectrum cohort to patients in the whole-spectrum cohort, the sensitivity, specificity and AUC dropped to 83%, 73% and 0.86 respectively. Conclusion While the algorithm performed robustly in identifying severe AS, this study shows that limiting datasets to clearly positive or negative labels leads to overestimation of test performance when testing an artificial intelligence algorithm in the setting of classifying severe AS using ECG data. While the effect of the bias may be modest in this example, clinicians should be aware of the existence of such a bias in AI-derived algorithms.