Combination Of Parameters Research Articles

A combination of clinical, electrocardiographic, and echocardiographic parameters predicts pulmonary hypertension occurrence in patients with end-stage renal disease

BackgroundPulmonary hypertension (PH) in patients with end-stage renal disease (ESRD) has a high incidence rate and mortality and its early identification is critical. However, whether a combination of clinical, electrocardiographic, and echocardiographic parameters can predict the occurrence of PH in patients with ESRD remains to be elucidated. Herein, we evaluated the predictive value of the combined score of these parameters.MethodsData from 370 patients with newly diagnosed ESRD who underwent routine echocardiography and electrocardiography between May 2016 and May 2017 were retrospectively evaluated. The incidence of PH during a 60-month follow-up period was investigated. Twenty-one patients were excluded due to incomplete data among other reasons. Finally, 349 patients were included in the analysis, of whom, 158 (45%) developed PH.ResultsAnalysis of electrocardiogram reports suggested that a corrected Q-T interval (QTc) of >438.5 ms was associated with PH. Echocardiographic reports suggest that left atrial diameter (LAD), interventricular septum thickness in end-diastole (IVSd), stroke volume (SV), and pericardial effusion are also associated with PH development. Results of multivariate Cox analysis showed that LAD >3.785 cm, IVSd >1.165 cm, SV >79.5 ml, QTc >438.5 ms, and pericardial effusion were independent predictors of PH in patients with ESRD. The incidence of new-onset PH increased significantly with increasing composite scores, that is, the sum of risk scores determined using hazard ratios.ConclusionsA total score that includes a combination of parameters such as LAD >3.785 cm, IVSd>1.165 cm, SV >79.5 ml, QTc >438.5 ms, and pericardial effusion can help describe the risk of new-onset PH.

Efficient Recovery of Vascular Sap from Aphid Infested Brassica sps. Depends on Parameter Combinations Namely, Number of Considered Leaves and Duration of Exudation

Efficient Recovery of Vascular Sap from Aphid Infested Brassica sps. Depends on Parameter Combinations Namely, Number of Considered Leaves and Duration of Exudation

Effects of Coated Carbide Insert on Surface Finish of Multiple Hardened EN31 Alloy Steel Using Regression, ANN, and RSM

Objectives: Surface finish plays an essential role in every machining process. The current study mainly focuses on multiple hardened EN31 alloy steel, using various machining parameters and optimizing using regression, ANN, and RSM. Methods: The experimentation was performed using a commercial CNC machine and a surface roughness tester, using machining parameters such as tool nose radius (re), cutting speed (v), feed (f), depth of cut (d), and hardness (H) of the material ranging from 0.4 to 1.2 mm, 160 to 260 m/min, 0.1 to 0.20 mm/rev, 0.05 to 0.15 mm, and 45 to 49 HRC, respectively. Findings: The study revealed that cutting speed and feed rate significantly affect the surface finish, followed by tool nose radius. With the help of optimization techniques, the surface roughness predicted through RSM was 99.91%, followed by Regression and ANN. The contour plot predicts Ra using the best possible combinations of machining parameters. Novelty: The current research compares the surface finish results of multiple hardened EN31 alloy steels predicted through regression, RSM, and ANN with experimental values. The generated correlations will undoubtedly assist researchers in determining surface finish for the defined ranges of machining parameters. Keywords: Hard Turning, Coated Carbide, EN31 Steel, Surface Roughness, Optimization, RSM

Multi-objective optimization in EDM of functionally graded Fe-Al using grey relational analysis

Abstract Functionally Graded Materials (FGMs) are multipurpose materials with a specific goal of managing changes in structural, thermal, or functional qualities. They feature a spatial variation in microstructure and/or composition. The current work prepared three layers of sample with different chemical compositions of Fe-Al FGM (50 Al-50 Fe, 45 Al-55 Fe, and 40 Al-60 Fe) at. % produced by powder metallurgy, then studied the machining behavior of these samples. The literature on the machining behavior of FGM was reviewed, and the influence of electrical discharge machine (EDM) process parameters such as voltage (V), current (I), pulse-on time, and pulse-off time on performance characteristics (material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra)) was investigated. The optimal machining settings for Fe-Al FGM samples will be ascertained by use of Grey Relational Analysis (GRA), which is based on the Taguchi approach, after an experimental investigation of the L18 orthogonal array design of trials. The GRA findings verify that V 140 volts, Ip10 A, Ton 100 μs, and Toff 75 μs is the optimal combination of process parameters. It has been found that the voltage is more significantly affected than the rest of the input parameters to obtain greater material removal rate (MRR) and lower tool wear rate (EWR) and surface roughness (Ra) through the response table. According to the study, choosing the right process parameters can improve the multi-performance feature.

Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data

Predicting wave-induced liquefaction around submarine pipelines is crucial for marine engineering safety. However, the complex of interactions between ocean dynamics and seabed sediments makes rapid and accurate assessments challenging with traditional numerical methods. Although machine learning approaches are increasingly applied to wave-induced liquefaction problems, the comparative accuracy of different models remains under-explored. We evaluate the predictive accuracy of four classical machine learning models: Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF). The results indicate that the GB model exhibits high stability and accuracy in predicting wave-induced liquefaction, due to its strong ability to handle complex nonlinear geological data. Prediction accuracy varies across output parameters, with higher accuracy for seabed predictions than for pipeline surroundings. The combination of different input parameters significantly influences model predictive accuracy. Compared to traditional finite element numerical methods, employing machine learning models significantly reduces computation time, offering an effective tool for rapid disaster assessment and early warning in marine engineering. This research contributes to the safety of marine pipeline protections and provides new insights into the intersection of marine geological engineering and artificial intelligence.

Experimental investigation on in-situ laser-assisted mechanical ruling of single crystal silicon

Abstract In this paper, response surface methodology (RSM) was employed as a robust and convenient predictive tool to establish the correlation between process parameters of in situ laser-assisted mechanical ruling and the ductile-to-brittle transition of single-crystal silicon. The interaction effects among three factors laser power, ruling speed, and negative rake angle on the ductile-to-brittle transition of single-crystal silicon were investigated. The optimal combination of process parameters was determined to be a laser power of 30W, a ruling speed of 0.25 mm s−1, and a negative rake angle of 58°. Utilizing a self-assembled setup and the optimal process parameters, multiple processing experiments were conducted. The average error between the experimental and predicted values was found to be 2.8%.

Closed-Form Solutions of Injection-Driven Flow and Heat Transfer Inside an Inclined Horizontal Filter Chamber

Abstract The closed-form solutions of models provide operators and designers with a better understanding of how the systems perform practically, thus improving critical industrial production operations. Due to this importance, the case study at hand seeks to find closed-form solutions of the internal momentum and temperature variation during the filtration process to advance fluid purification. Lie group analysis is used to transform a system of equations representing the flow and heat transfer into a solvable system without changing the dynamics of the case study. The transformed solvable system is then integrated to find closed-form solutions of the internal velocity (momentum) and temperature variation. The obtained closed-form solutions are then used to analyze the effects of physical parameters arising from the process dynamics to find combinations of parameters that yield maximum permeates outflow. The analysis conveys that the internal fluid velocity increases when enhancing the permeation parameter and minimizing Reynolds number, wave speed parameter, and chamber height.

Pregnancy dating: Determining gestational age through fetal kidney length-what do we observe?

Accurate gestational age is crucial for obstetric care, pregnancy management, and preterm labor decisions. Ultrasound, based on fetal biometric parameters, is a reliable method for this purpose. This study aims to estimate gestational age in the third trimester using ultrasonographic methods, focusing on fetal kidney length. Gestational age is estimated in the third trimester by measuring fetal kidney length and comparing it with gestational age derived from other biometric parameters (bi-parietal diameter, head circumference, abdominal circumference, and femur length) and the last menstrual period (LMP). A nine-month prospective study was conducted Kozhikode, Kerala, from September 2016 to May 2017. The study involved 200 women in their third trimester of pregnancy, utilizing third-trimester ultrasound to measure and document kidney length along with other biometric parameters (BPD, HC, AC, and FL). Subsequently, the collected data was reviewed and analyzed.: The study revealed a strong positive correlation between fetal kidney lengths (FKL) measured in millimeters and gestational age in weeks during the third trimester. The correlation was significant, with a Pearson's correlation coefficient of 0.87 (p < 0.001). Additionally, FKL exhibited positive correlations with other variables, including abdominal circumference (r = 0.832), head circumference (r = 0.845), bi-parietal diameter (r = 0.838), and femur length (r = 0.837). These findings demonstrated a strong Pearson’s correlation (r= 0.87-0.96, P <0.05) for all the biometric parameters including FKL. A linear relationship exists between fetal kidney length (FKL) and gestational age, allowing FKL to serve as an effective parameter for estimating gestational age during the third trimester. Given its positive correlation with other fetal biometric parameters such as bi-parietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL), incorporating FKL alongside these measurements enhances the accuracy of gestational dating in the third trimester. This combination of parameters improves the prediction of gestational age during this stage of pregnancy.

Optimization of working parameters of high-pressure roller mill based on entropy weight method and response surface method.

In order to improve the crushing efficiency of high-pressure roller mill and reduce energy consumption, the optimal parameter combination of high-pressure roller mill is sought, GM160-140 high-pressure roller mill is taken as the research object, and the discrete element simulation calculation model of high-pressure roller mill is established to simulate the crushing process. Taking the roll surface pressure, productivity, and the proportion of discharge particle diameter as the performance evaluation indexes, the simulation data are used to analyze the influence of each working condition parameter of the high-pressure roller mill on its performance law. Through the entropy weighting method to determine the weights of high-pressure roller mill productivity, roll surface pressure, the proportion of discharge particle diameter, and through the extreme difference analysis to get the optimal parameter combination. Minitab software is used to carry out the response surface method (RSM) experimental design, the optimal parameter combinations are derived from the optimization using the response surface apparatus, the optimal parameter combinations derived from the two optimization methods are compared and analyzed, and ultimately it is concluded that the optimization of the response surface is the global optimal, and the optimal parameter combinations are as follows: the roller speed is 2.50rad/s, the roller gap is 28.87mm, and the feed size is 31.97mm, Under the above optimal parameters, the roll surface pressure is 150450.00N, the productivity is 1268.00t/h, the percentage of the material less than 6mm is 58.14%, so as to achieve the purpose of optimizing the parameters of the high-pressure roller mill, It is of significance to save resources and respond to the national green development and intelligent development.

Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach

Summary The spatial correlation of coseismic slip is a necessary input for generating stochastic seismic rupture models, which are commonly employed in seismic and tsunami hazard assessments. To date, the spatial correlation of individual earthquakes is characterized using finite fault models by finding the combination of parameters of a von Kármán autocorrelation function that best fits the observed autocorrelation function of the finite fault model. However, because a priori spatial correlation conditions (i.e., not in the data) are generally applied in finite fault model generation, the results obtained using this method may be biased. Additionally, robust uncertainty estimates for spatial correlations of coseismic slip are generally not performed. Considering these limitations in the classic method, here, a method is developed based on a Bayesian formulation of Finite Fault Inversion (FFI) with positivity constraints. This method allows for characterizing the spatial correlation of coseismic slip and its uncertainties for an earthquake by using samples of coseismic slip from a posterior probability density function (PDF). Furthermore, a Bayesian model selection criterion called Akaike Bayesian Information Criterion (ABIC) is applied to objectively choose between different prior spatial correlation schemes before computing the posterior, to reduce subjectivity due to this prior condition. The ABIC is calculated using an approximate analytical expression of Bayesian evidence. The method is applied to simulated P-waves, demonstrating that model selection allows for objectively estimating the most suitable prior spatial correlation scheme in FFI. Additionally, the target spatial correlation of coseismic slip is accurately recovered using samples from the posterior PDF, as well as their uncertainties. Moreover, in the simulated experiment, it is shown that a non-robust choice of the prior spatial correlation scheme can significantly bias the estimated spatial correlations of coseismic slip. We apply our method to observed P-waves from the 2015, Illapel earthquake (Mw = 8.3), finding that the spatial correlation of coseismic slip of this earthquake is better described by a von Kármán ACF, with mean correlation lengths of around 47 km and Hurst parameter of 0.58. We conclude that using our method reduces biases associated with prior spatial correlation conditions and allows for robust estimation of spatial correlations of coseismic slip and their uncertainties.

Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model

Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to estimate the solar irradiance of typical irrigation areas. The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as input parameters. In the model testing, the RMSE and MAE of WOA-XGBoost are 2.142 MJ·m−2·d−1 and 1.531 MJ·m−2·d−1, respectively, while those of XGBoost are 2.298 MJ·m−2·d−1 and 1.598 MJ·m−2·d−1. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model. The model developed in this study can be applied to forecast solar irradiance in different regions. By inputting the meteorological parameter data specific to a given area, this model can effectively produce accurate solar irradiance predictions for that region. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

Experimental Study on the Pelleting and Coating Performance of Red Clover Seeds

This study aimed to optimize the pelleting and coating process for red clover seeds, addressing the issue of low pelleting success rates. Through theoretical analysis and experimental research, coating pan fill rate, powder supply quantity, and pelleting time were identified as key factors influencing the pelleting success rate. Single-factor experiments were conducted to investigate the effects of these parameters on the quality of red clover seed pelleting and coating. Based on these results, orthogonal trials were carried out, and response surface analysis was employed to reveal the influence patterns and interactions of each factor. The research results indicate that the factors affecting the pelleting success rate, ranked in order of importance, are coating pan fill rate, pelleting time, and powder supply quantity. Through mathematical model optimization, the optimal combination of process parameters was determined to be coating pan fill rate of 35.9%, powder supply quantity of 160.2 g, and pelleting time of 6.9 s. Under these conditions, a pelleting success rate of 94.3% was achieved in validation experiments. This study provides a theoretical foundation and practical guidance for optimizing the pelleting and coating process of red clover seeds, which is significant for improving seed coating quality and promoting red clover cultivation.

The role of corneal epithelial thickness map in detecting early keratoconus.

To detect subtle changes in early keratoconus by evaluating corneal epithelial thickness differences among patients with binocular very asymmetric ectasia (VAE) and normal subjects. Corneal epithelial thickness was measured using the Fourier-domain AS-OCT system RTVue® 100 (Optovue, Fremont, CA, USA). 152 eyes from 76 patients were divided into three groups: Very asymmetry ectasia-ectasia (VAE-E, n = 38), Very asymmetry ectasia-normal topography (VAE-NT, n = 38), and Normal control (NC, n = 76). Discrimination capacity was assessed using areas under the curve (AUC) of receiver operator characteristic (ROC) curves. In the keratoconus group, the epithelial Min (minimum), central, midperipheral I (inferior), midperipheral IT (inferior-temporal), peripheral IT, and midperipheral T (temporal) were thinner (all P < 0.05). The topography normal group had thinner midperipheral IN (inferior-nasal), peripheral IN, midperipheral T, and peripheral T, and larger Max-Min and Std. Dev (P < 0.05). For diagnosing typical keratoconus, Std. Dev (AUC = 0.982, sensitivity 97.4%, specificity 92.1%) had the highest diagnostic efficiency. Combining four variables (Minimum, Max-Min, Midperipheral IT, and Midperipheral I) performed well in distinguishing topography normal eyes (AUC = 0.896, sensitivity 76.3%, specificity 89.5%). Multivariable analysis using epithelial parameters combined with Pentacam random forest index (PRFI) yielded the best results (AUC = 0.951, sensitivity 90.6%, specificity 89.5%). The corneal epithelial parameters play an important auxiliary role in the diagnosis of keratoconus and the screening of subclinical keratoconus. Combination of epithelial parameters and tomographic parameters can improve the sensitivity of early stage keratoconus detection.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Parametric Study of Rainfall-Induced Instability in Fine-Grained Sandy Soil

This study investigates the stability of fine-grained sandy soil slopes under varying rainfall intensities, durations, and geotechnical properties using a parametric analysis within GeoStudio. A total of 4416 unique parameter combinations were analyzed, incorporating variations in unit weight, cohesion, friction angle, slope inclination, slope height, rainfall intensity, and duration. Results reveal that rainfall intensity is the most influential variable on the factor of safety (FS), with higher intensities (e.g., 360 mm/h) on steeper slopes (e.g., 45°) leading to critical FS values below 1, indicating an imminent risk of failure. Under moderate conditions (e.g., 9 mm/h rainfall on slopes of 26.6°), the FS remains above 2. This dataset provides a valuable foundation for training machine learning models to predict slope stability under diverse environmental conditions, contributing to the development of early warning systems for rainfall-induced landslides.

Abstract 4125587: Vasomotor function and its regulation by perivascular adipose tissue in metabolic syndrome vary depending on severity and combination of metabolic parameters

Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular disease. Because perivascular adipose tissue (PVAT) regulates arterial tone by releasing vasorelaxing/vasocontracting factors, the effects of PVAT on vasomotor function have been heterogeneously reported. Various PVAT functions can result from the severity or combination of metabolic parameters in MetS. Therefore, we compared the effects of mesenteric arterial PVAT on the nitric oxide-dependent vasorelaxation response among the three metabolic syndrome (MetS) model strains. We compared male OLETF, Zucker fatty (ZF), and SHRSPZF (SPZF) rats aged 20 weeks to lean controls, LETO, ZF +/+ , and Wistar-Kyoto (WKY) rats. To determine the effects of PVAT on vasodilators, we used organ bath techniques to assay nitroprusside-induced relaxation of isolated mesenteric arterial ring preparations with intact or removed PVAT. The waist circumference to length ratio in OLETF and ZF rats was higher than that in SPZF rats, and the systolic blood pressure was higher in SPZF rats than in the other groups. In the serum, there was no significant difference in triglyceride levels. Still, glucose and thiobarbituric acid reactive substance levels were higher in OLETF and SPZF rats than in ZF rats. Relaxation in ZF rats did not change compared to that in ZF +/+ rats and the presence of PVAT. In contrast, relaxation in OLETF rats was lower than in LETO rats, and there was no difference between the presence and absence of PVAT. However, relaxation in SPZF rats was lower than in WKY rats, and relaxation increased to the same level as in WKY rats in the presence of PVAT. This study unveils novel insights, suggesting that high serum glucose and oxidative stress cause arterial dysfunction, whereas high blood pressure induces PVAT functional changes. These findings underscore the complex interplay between metabolic disorders and vascular health, potentially paving the way for more targeted interventions in patients with MetS.

Parametric analysis of 3D printing parameters on stiffness and hysteresis characteristics of pediatric prosthetic foot coupon samples

Background: This research paper presents a comprehensive parametric study that investigates the influence of various 3D printing parameters on the mechanical properties of a pediatric prosthetic keel using coupon samples. Objective: To analyze how 3D printing parameters impact upon on 2 crucial mechanical properties: stiffness and hysteresis. Methods: Key 3D printing parameters including the amount of continuous carbon fiber layers, fiber distribution, and matrix fill pattern, are systematically varied, and mechanically tested through compression to analyze the results. Results: The results demonstrate the substantial impact that printing parameters have on the mechanical characteristics of 3D-printed pediatric prosthetic feet. Notably, the selection of these parameters for the prosthetic keel plays a pivotal role in shaping the overall performance and functionality of the prosthetic foot, emphasizing the need for precise parameter optimization in pediatric prosthetic design, if 3D printing is the manufacturing process. Conclusions: The findings of this study contribute to a better understanding of the manufacturing process for pediatric prosthetic feet via 3D printing and offer valuable insights for optimizing their design. By identifying the ideal combination of 3D printing parameters that yield the desired stiffness and minimize hysteresis, we aim to enhance the performance and comfort of pediatric prosthetic devices, ultimately improving the quality of life for young users.

Abstract 4138417: A Novel Echocardiography Risk Score Predicted Mortality In Patients With Heart Failure With Preserved Ejection Fraction.

Introduction: Patients with heart failure with preserved ejection fraction (HFpEF) exhibit several echocardiographic abnormalities, including diastolic dysfunction, left ventricular (LV) hypertrophy, left atrial enlargement or pulmonary hypertrophy. It is not fully understood how these abnormalities are related with prognosis in HFpEF patients. Hypothesis: A combination of echocardiography parameters can predict clinical outcome in patients with HFpEF. Aims: This study aimed to develop a model to predict all-cause mortality in patients with HFpEF using echocardiography parameters. Methods: We analyzed data from 952 patients (81±9 years, 43% male) registered in the PURSUIT-HFpEF (Prospective, Multicenter, Observational Study of Patients with Heart Failure with Preserved Ejection Fraction) study, a multicenter registry of patients hospitalized for acute decompensated HFpEF. Comprehensive echocardiography was performed before discharge. Patients were randomly assigned to the derivation and validation cohorts (2:1 ratio). Stepwise model selection was performed on age, sex and 26 echocardiography parameters to identify independent predictors of mortality. These predictors were used to construct a risk model for mortality. The accuracy of the risk score was validated using time-dependent ROC curves in both the derivation cohort (n=629) and in the validation cohort (n=323). Results: During the follow-up period (720 days, IQR: 376, 1100 days), all-cause death was observed in 248 patients (26.1%). Age, sex, diastolic (DWS), E/e’ ratio, LV mass index, LV posterior wall thickness (PWth), relative wall thickness (RWT), tricuspid regurgitant pressure gradient (TRPG) were selected as independent predictors of all-cause death. The score shown in the Table was applied to each factor, and the sum of the scores was calculated as the risk score. The AUC of the risk score for predicting 1-year mortality in the derivation cohort was 0.71 and the optimal cut-off value of 4.5 provided a sensitivity and specificity of 62.8% and 69.1%, respectively. The AUC for 1-year mortality in the validation cohort was 0.67. Conclusions: The novel echocardiography score effectively predicted mortality in HFpEF. This model could be a valuable tool for risk stratification.

Nickel-based alloy efficient milling with ceramic tool and experimental verification in closed impeller

Nickel-based alloys are a class of materials that possess exceptional properties, including superior corrosion, oxidation, and fatigue resistance. They are increasingly utilized in high-performance critical components, particularly in the fields of aerospace engines and gas turbines. Nickel-based alloys pose significant challenges during machining due to their high cutting forces and work hardening, leading to low machining efficiency, severe tool wear, and subpar surface quality of the machined parts. To address these challenges, an orthogonal cutting experiment was conducted on GH4061 alloy to evaluate the machining performance by using monolithic ceramic milling tools. The optimal cutting parameters combination for dry cutting of nickel-based alloys was determined. The surface quality was tested and analyzed, including surface micro structure, residual stress, and surface roughness. The machining experiment was conducted on GH4061 closed impeller. The results demonstrated that high-speed cutting was more effective than low-speed cutting in improving the surface quality of the machined part. In the rough processing, monolithic ceramic milling tools was used to quickly remove the excess material. The adhesive and diffusion effects mainly contributing to the wear of the monolithic ceramic tool in GH4061 milling. Average surface roughness 1.41 μm can be obtained through dry milling using a monolithic ceramic tool with cutting speed 602.88 m/min, cutting depth 0.3 mm, cutting width 6 mm and feed per tooth 0.03 mm/z, along with metal removal rate 5184 mm3/min. High cutting speed will resulting in relatively low residual stress in the surface layer as well as a lower thickness of the surface alternation layer. Finally, a physical GH4061 closed impeller was machined to verify the proposed milling method.

EXPERIMENTAL EVALUATION OF THE PERFORMANCE OF DYNAMIC VIBRATION ABSORBERS FOR VIBRATION MITIGATION IN BEAM STRUCTURES

In this study, experiments are used to evaluate the effectiveness of dynamic vibration absorbers (DVAs) in minimizing vibrations in beam structures. The dynamic vibration absorbers are modest additions to a structure that employ a mass-spring system tuned to the natural frequency of the structure to lower vibration levels. These absorbers were added to a beam construction as part of the experimental investigation, and the vibration levels under various conditions were measured. Under pinned-free boundary, the dynamic behavior of a beam is experimentally investigated with various combinations of the design parameters (mass and spring) and locations of the dynamic vibration absorbers. The beam is subjected to external vibrations, and both with and without the absorbers, its amplitude is measured. According to the results, adding DVAs to the beam structure significantly reduced vibration levels, particularly closer to the natural frequency of the beam. The dynamic response is greatly reduced by mass and stiffness (from, for example, 0.018m to 0.00052m). However, depending on the DVA location, this effect can change. The minimal requirements of the DVA parameters can better reduce the dynamic response if the DVA is positioned at the point of maximum displacement for each corresponding mode.