Site response analysis of liquefiable soil employing continuous wavelet transform

Propagation of the earthquake motion towards the ground surface alters both the acceleration and frequency content of the motion. Acceleration time record and Fourier amplitude spectrum of the motion reveal changes in the acceleration and frequency content. However, Fourier amplitude spectrum fails to give frequency-time variation. Wavelet transform overcomes this difficulty. In the present study, site response analysis of a liquefiable soil domain has been investigated employing wavelet transform. Three earthquake motions with distinct predominant frequencies are considered. It is revealed that the moment soil undergoes initial liquefaction, it causes a spike in the acceleration time history. Frequency of the spikes is found to be greater than the predominant frequency of the acceleration-time history recorded at the ground surface from the analysis. Interestingly, the spikes belong to the sharp tips of the shear stress-shear strain curve. Immediately after the spike, acceleration deamplification is observed. Post-liquefaction deamplification (filtering) of the frequency components is also observed.

Echocardiographic probability of pulmonary hypertension: a validation study

BackgroundAccording to current guidelines, the diagnosis of pulmonary hypertension (PH) relies on echocardiographic probability followed by right heart catheterization. How echocardiography predicts PH recently re-defined by a mean pulmonary artery pressure (mPAP) >20 mmHg instead of ≥25 mmHg and pulmonary vascular disease defined by a pulmonary vascular resistance (PVR) >3 or >2 Wood units has not been established.MethodsA total of 278 patients referred for PH underwent a comprehensive echocardiography followed by a right heart catheterization. Fifteen patients (5.4%) were excluded because of insufficient quality echocardiography.ResultsWith PH defined by a mPAP >20 mmHg, 23 patients had no PH, 146 had pre-capillary and 94 post-capillary PH. At univariate analysis, maximum velocity of tricuspid regurgitation (TRV) ≥2.9 and ≤3.4 m s−1, left ventricle (LV) eccentricity index >1.1, right ventricle (RV) outflow tract (OT) notching or acceleration time <105 ms, RV-LV basal diameter >1 and PA diameter predicted PH, whereas inferior vena cava diameter and right atrial area did not. At multivariable analysis, only TRV ≥2.9 m s−1 independently predicted PH. Additional independent prediction of PVR >3 Wood units was offered by LV eccentricity index >1.1 and RVOT acceleration time <105 ms and/or notching, but with no improvement of optimal combination of specificity and sensibility or positive prediction.ConclusionsEchocardiography as recommended in current guidelines can be used to assess the probability of re-defined PH in a referral center. However, the added value of indirect signs is modest and sufficient quality echocardiographic signals may not be recovered in some patients.

Acceleration time to Ejection time ratio in fetal pulmonary artery system can predict neonatal respiratory disorders in gestational diabetic mellitus women

BACKGROUND: Few researches studied fetal pulmonary pulse wave doppler and the clinical end point disorders in gestational diabetic mellitus (GDM) cohort. OBJECTIVE: To investigate fetal pulmonary artery acceleration time to ejection time ratio (PATET) in the prediction of neonatal respiratory disorders (NRD). METHODS: 238 pregnant women diagnosed with GDM who attended our hospital between February 2018 and March 2020 were retrospectively included. Fetal pulmonary artery Doppler wave measurements were recorded, including main, left and right pulmonary artery blood flow, and left and right peripheral pulmonary artery blood flow. Acceleration time (At)/ejection time (Et) were calculated. RESULTS: 183 GDM pregnant women and neonates were divided into NRD(+)(n = 42) and NRD(–) group (n = 141). 16 cases were neonatal pneumonia (NP) within 28 days after birth in NRD(+) group. The area under curve (AUC) of left peripheral pulmonary artery acceleration time to ejection time (LPPATET) was 0.967 (95% CI: 0.927∼1.000, P < 0.001) and the AUC of right peripheral pulmonary artery acceleration time to ejection time (RPPATET) was 0.967 (95% CI: 0.927∼1.000, P < 0.001), indicating that LPPATET and RPPATET are both predictors for NRD. The results of interobserver variabilty and intraobserver variability showed a good consistency. CONCLUSIONS: The At/Et of fetal peripheral pulmonary artery (PPA) in GDM women may be predictors for NRD, and the indicator can provide assistance in clinical management of diabetes pregnant patients.

A Possible Role for Arterial Duplex and Hand Acceleration Time in Diagnosing and Managing Shock

Shock represents a state when arterial perfusion is inadequate to supply the needs of cellular respiration, leading to anerobic metabolism, acidosis, and cell death. Although typically described in terms of blood pressure and heart rate, these measures can both lead to delayed recognition of shock and under appreciation of the severity of end-organ malperfusion. Recently, there has been increased interest in monitoring peripheral perfusion both to detect early shock and monitor the response to treatment. However, current methods are variable and, in some cases, require specialized equipment. We present a case in which duplex ultrasound assessment of peripheral palmar acceleration time identified a post-hemorrhagic shock state before it was clinically apparent. Bedside arterial duplex and hand acceleration time may provide a simple tool to assess the degree of shock and response to intervention.

372 Machine learning and pulmonary hypertension

Aims Pulmonary hypertension (IP) characterized by an average resting pulmonary pressure ≥20 mmHg can sustain various clinical conditions that differ in physiopathological, haemodynamic, and therefore therapeutic aspects. The goal of our work was to apply a machine learning algorithm that could accurately distinguish pre- and post-heart pulmonary hypertension through non-invasive methods (medical history, clinical, and echocardiographic data). Methods and results In order to achieve our goal we used the ‘decision tree’ machine learning algorithm implemented in the C5.0 package of the R development environment. The first step was the preparation of the data. The dataset of patients with IP was composed of 85 patients divided into XX precapillary IP (1) and YY postcapillary (2). Each patient is described by 11 features: some comorbidities (arterial hypertension and atrial fibrillation), BMI, right axial deviation on ECG, DLCO, and some echocardiographic measurements (e/e′, right atrial area, S wave at TDI, acceleration time on the pulmonary, inferior vena cava, diameters of the right ventricle). The dataset was divided into a data.train training subset (45 patients) and an evaluation subset (40 patients), maintaining the proportion between classes. Starting from the training dataset, the C5.0 algorithm generated the decision tree shown in Figure 1. The root node was made up of the mitral pattern e/e′, followed by the right axis deviation on the ECG and the acceleration rate on the lung that the algorithm considered the best discriminated features. The model was then validated in the validation dataset and through the Caret package and the Confusion matrix function we calculated the performance metrics of the algorithm obtaining an accuracy of 0.87, a kappa statistic of 0.742, a sensitivity of 0.913, and a specificity of 0.823. The true positive rate was 0.87 while the true negative rate was 0.87. The performance of the model was also measured using the ROC curve, obtaining an area under the curve of 0.916. Conclusions Our results show that the ‘decision tree’ algorithm starting from echocardiographic data and the ECG has a good ability to discriminate between the precapillary and postcapillary IP. In particular, the decision chain consisting of: mitral pattern and / and ratio ≤8, right axial deviation on the ECG and acceleration time on the lung ≤80 ms seems to predict the IP class with reasonable accuracy. Our results confirm that the probability of prediction and the prediction itself depend, however, on what degree of purity the partitions learned during the decision tree construction process are made up. To improve the estimation of the algorithm’s performance and thus generalize the results obtained, we believe to evaluate this approach on larger datasets also considering different machine learning algorithms. 372 Figure

Relationship Between the Ratio of Acceleration Time/Ejection Time and Mortality in Patients With High‐Gradient Severe Aortic Stenosis

Background The ratio of acceleration time/ejection time (AT/ET) is a simple and reproducible echocardiographic parameter that integrates aortic stenosis severity and its consequences on the left ventricle. No study has specifically assessed the prognostic impact of AT/ET on outcome in patients with high‐gradient severe aortic stenosis (SAS) and no or mild symptoms. We sought to evaluate the relationship between AT/ET and mortality and determine the best predictive AT/ET cutoff value in these patients. Methods and Results A total of 353 patients (median age, 79 years; 46% women) with high‐gradient (mean pressure gradient ≥40 mm Hg and/or aortic peak jet velocity ≥4 m/s) SAS, left ventricular ejection fraction ≥50%, and no or mild symptoms were studied. The impact of AT/ET ≤0.35 or >0.35 on all‐cause mortality was retrospectively studied. During a median follow‐up of 39 (25th–75th percentile, 23–62) months, 70 patients died. AT/ET >0.35 was associated with a considerable increased mortality risk after adjustment for established prognostic factors in SAS under medical and/or surgical management (adjusted hazard ratio [HR], 2.54; 95% CI, 1.47–4.37; P <0.001) or conservative management (adjusted HR, 3.29; 95% CI, 1.70–6.39; P <0.001). Moreover, AT/ET >0.35 improved the predictive performance of models including established risk factors in SAS with better global model fit, reclassification, and discrimination. After propensity matching, increased mortality risk persisted when AT/ET >0.35 (adjusted HR, 2.10; 95% CI, 1.12–3.90; P <0.001). Conclusions AT/ET >0.35 is a strong predictor of outcome in patients with SAS and no or only mild symptoms and identifies a subgroup of patients at higher risk of death who may derive benefit from earlier aortic valve replacement.

Design and Wheel Torque Performance Test of the Electric Racing Car Concept E-Falco

To reduce the use of fossil fuels in vehicles and reduce exhaust emissions, it is necessary to use electric vehicle technology. Solidworks software is used in designing and manufacturing an electric car and a simulation is carried out using CFD (Computation Fluid Dynamic) software to determine the strength of the frame structure and air drag when the electric car is running. The performance test of the motor by using the dyno test to determine the acceleration time, power, and torque of the motor. The results of the simulation show that at a speed of 10 km/h the air drag is 6.24 N, a speed of 20 km/h is 24.64 N, and a speed of 40 km/h is 93.92 N. The results of the dyno test shows that the acceleration time with full acceleration from a speed of 0-70 km/h is 13.63 seconds, the maximum power output by the motor is 14.17 hp occurs at a speed of 36-53 km/h and the amount of peak torque released by the motor occurs at a speed of 13 km/h at 228 Nm.

MATHEMATICAL MODELING OF THE ENGINE PARAMETERS INFLUENCE ON VEHICLE ACCELERATION DYNAMICS

The throttle response of a vehicle determines its dynamic properties and is characterized by an acceleration time from 0 to 100 km/h. An experimental study of the influence of vehicle parameters on its throttle response is associated with significant material and labor costs. At the stage of sketching the design of the vehicle, preliminary determination of design parameters and settings, it is rational to use mathematical models. In the existing models of the vehicles movement dynamics, the engine power, as a rule, is set by empirical dependencies and does not take into account the possibility of changing its parameters and characteristics. The paper proposes a mathematical model that combines models of the engine workflow and the dynamics of vehicle acceleration. The mathematical model of the engine workflow is a quasi-stationary thermodynamic model, in which combustion is described by the Vibe equation, and heat transfer with the walls is described by the Woschni equation. To check its adequacy, an experimental study of the VAZ-2108 engine was carried out to obtain external speed, load and control characteristics. Good agreement between the calculated and experimental data is shown. Vehicle acceleration simulation was carried out according to the method of E.A. Chudakov. The parameters of the VAZ-2108 car and the resistance forces during acceleration from 0 to 100 km / h have been determined. It is shown that the car accelerates from 0 to 100 km / h in 18.3 s, which corresponds to the experimental data and indicates the adequacy of the chosen techniques. The influence of changing the parameters and settings of the engine on the dynamics of vehicle acceleration has been investigated. It is shown that in order to achieve better dynamics of motion, the cylinder diameter and compression ratio must be maximized. The ignition timing, intake valve closing angle and excess air ratio have extremes. The efficiency of using a 16-valve cylinder head instead of an 8-valve one is shown. Based on the results of the studies, it was proposed to apply a set of engine parameters, which made it possible to reduce the acceleration time of the VAZ-2108 from 18.3 s to 13.2 s. Thus, the developed mathematical model makes it possible to quantitatively evaluate the influence of engine parameters on the dynamics of vehicle acceleration, to optimize the parameters and settings of the power plant and the vehicle as a whole.