True Value Research Articles

Reliability Of Maximum Heart Rate Estimation Equations In Athletes In The Field Football Base Category

The objective of this study was to verify the reliability of the HRmax prediction equations proposed by Fox, Tanaka and Nikolaidis in field football athletes who belong to the youth categories of a club in the first division of Brazilian league. One hundred and sixty-four players, aged between 14 and 20, took part in this study. In order to determine the athletes' HRmax (bpm), Yo-Yo Endurance Test Level 1 was used and defined as the highest value achieved during the test. Data were expressed as mean, standard deviation (SD) of the mean, minimum and maximum values.Independent Student's t-test and Pearson's correlation coefficient were used in statistical analyses. The results showed that none of the three available equations (Fox, Tanaka and Nikolaidis) provide accurate HRmax values in a sample of youth football players, underestimating or overestimating HRmax values. The equation that provided the most accurate HRmax was the Nikolaidis equation. This result is probably due to the fact that this equation was developed based on an example of a football player.

Rheology Assessment of Mortar Materials for Additive Manufacturing

Abstract: This review article discusses the relevant rheological tests to evaluate the properties of compositions applied to the 3D printing of concrete (3DCP). These materials must rapidly develop rigidity and resistance, avoiding the collapse of the printed structure, with suitable buildability and other state properties, such as extrudability. A good balance must be maintained between properties and rheological parameters, such as yield stress and viscosity. Cohesion, Young's modulus, and thixotropy are also among the parameters used in these evaluations. The rheological tests addressed are the rheometer, direct shear test, uniaxial unconfined compression test, and penetration test. Their limitations must be taken into account to obtain accurate values of the rheological parameters. It was found that the most used test is the rheometer, and the test that needs to be further studied is the penetration test. Hence, it is recommended to search for a more expeditious method related to the rheological assessment to facilitate obtaining the associated parameters in a simple way.

Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling

This study addresses the usage of data from industrial plate mills to calculate the mean flow stress of different steel grades. Accurate flow stress values may optimize rolling technology, but the existing literature often provides coefficients like those in the Hensel–Spittel equation for a limited number of steel grades, whereas in modern production, the chemical composition may vary by thickness, customer requirements, and economic factors, making it necessary to conduct costly and labor-intensive laboratory studies. This research demonstrates that leveraging data from industrial rolling mills and employing machine learning (ML) methods can predict material rheological behavior without extensive laboratory research. Two modeling approaches are employed: Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. The model comprising one GRU layer and two fully connected layers, each containing 32 neurons, yields the best performance, achieving a Root Mean Squared Error (RMSE) of 7.5 MPa for the predicted flow stress of three steel grades in the validation set.

Forecasting Gate-Front Water Levels Using a Coupled GRU–TCN–Transformer Model and Permutation Entropy Algorithm

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity and non-stationarity characteristics of gate-front water level sequences, this paper introduces a gate-front water level forecasting method based on a GRU–TCN–Transformer coupled model and permutation entropy (PE) algorithm. Firstly, an analysis method combining Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to separate the original water level data into different frequency modal components. The PE algorithm subsequently divides each modal component into sequences of high and low frequencies. The GRU model is applied to predict the high-frequency sequence part, while the TCN–Transformer combination model is used for the low-frequency sequence part. The forecasting from both models are combined to obtain the final water level forecasting value. Multiple evaluation metrics are used to assess the forecasting performance. The findings indicate that the combined GRU–TCN–Transformer model achieves a Mean Absolute Error (MAE) of 0.0154, a Root Mean Square Error (RMSE) of 0.0205, and a Coefficient of Determination (R2) of 0.8076. These metrics indicate that the model outperforms machine learning Support Vector Machine (SVM) models, GRU models, Transformer models, and TCN–Transformer combination models in forecasting performance. The forecasting results have high credibility. This model provides a new reference for improving the accuracy of gate-front water level forecasting and offers significant insights for water resource management and flood prevention, demonstrating promising application prospects.

AN INVESTIGATION ON NONLINEAR OPTION PRICING BEHAVIOURS THROUGH A NEW FRÉCHET DERIVATIVE-BASED QUADRATURE APPROACH

Abstract Complicated option pricing models attract much attention in financial industries, as they produce relatively better accurate values by taking into account more realistic assumptions such as market liquidity, uncertain volatility and so forth. We propose a new hybrid method to accurately explore the behaviour of the nonlinear pricing model in illiquid markets, which is important in financial risk management. Our method is based on the Newton iteration technique and the Fréchet derivative to linearize the model. The linearized equation is then discretized by a differential quadrature method in space and a quadratic trapezoid rule in time. It is observed through computations that the accurate solutions for the model emerge using very few grid points and time elements, compared with the finite difference method in the literature. Furthermore, this method also helps to avoid consideration of the convergence issues of the Newton approach applied to the nonlinear algebraic system containing many unknowns at each time step if an implicit method is used in time discretization. It is important to note that the Fréchet derivative supports to enhance the convergence order of the proposed iterative scheme.

Computational approaches to apply the String Edit Algorithm to create accurate visual scan paths

Eye movement detection algorithms (e.g., I-VT) require the selection of thresholds to identify eye fixations and saccadic movements from gaze data. The choice of threshold is important, as thresholds too low or large may fail to accurately identify eye fixations and saccades. An inaccurate threshold might also affect the resulting visual scan path, the time-ordered sequence of eye fixations and saccades, carried out by the participant. Commonly used approaches to evaluate threshold accuracy can be manually laborious, or require information about the expected visual scan paths of participants, which might not be available. To address this issue, we propose two different computational approaches, labeled as “between-participants comparisons” and “within-participants comparisons.” The approaches were evaluated using the open-source Gazebase dataset, which contained a bullseye-target tracking task, where participants were instructed to follow the movements of a bullseye-target. The predetermined path of the bullseye-target enabled us to evaluate our proposed approaches against the expected visual scan path. The approaches identified threshold values (220°/s and 210°/s) that were 83% similar to the expected visual scan path, outperforming a 30°/s benchmark threshold (41.5%). These methods might assist researchers in identifying accurate threshold values for the I-VT algorithm or potentially other eye movement detection algorithms.

Impact of proxymetacaine on the dynamics of cyclopentolate in White 6- to 7-year-olds.

This study compared the efficacy of cyclopentolate hydrochloride at 10-, 20- and 30-min post-instillation in White 6- to 7-year-olds, with and without prior instillation of proxymetacaine hydrochloride. The primary aim was to determine if accurate autorefraction values can be obtained sooner than the current standard of 30-min post-cycloplegia. The secondary aim was to investigate whether proxymetacaine hydrochloride enhances the efficiency of cyclopentolate. Participants were 112 White 6- to 7-year-olds from the Child Eye Health Study. The right eye received 0.5% proxymetacaine hydrochloride and 1.0% cyclopentolate hydrochloride, and the left eye received only 1.0% cyclopentolate hydrochloride. Non-cycloplegic and cycloplegic refractive error (at 0, 10, 20 and 30 min) was measured using a binocular, open-field autorefractometer. Data were analysed through paired t-tests, concordance analysis, linear regression, equivalence testing and Bland-Altman analysis, using the 95% limits of agreement. Mean spherical equivalent refraction (SER) (SD) in the right eye at 0-, 10-, 20- and 30-min post-instillation was 0.62 (1.45) D, 1.52 (1.80) D, 1.64 (1.81) D and 1.72 (1.80) D, respectively. Mean left eye SER (SD) were 0.68 (1.24) D, 1.42 (1.66) D, 1.56 (1.66) D and 1.68 (1.72) D, respectively. Bland-Altman analysis showed a high level of agreement, and equivalence testing confirmed that there was no clinically significant difference in SER at 20 and 30 min in both eyes (within ±0.50 D), with mean differences of 0.08 (0.23) D in the right eye and 0.13 (0.30) D in the left eye (p = 0.21). However, SER at 10 and 30 min were equivalent in the right eye only. Accurate autorefraction values can be obtained 20-min post-instillation of 1.0% cyclopentolate in white children aged 6-7 years, potentially reducing clinical testing times. Proxymetacaine pre-instillation allows for reliable measurements as early as 10-min post-instillation of cyclopentolate. Further research is needed to validate these findings in non-White populations and to determine the safe discharge time post-proxymetacaine instillation.

Charge-coupled device readout by digital-correlated double sampling

Charge-coupled devices (CCDs) play crucial roles in astronomy owing to their widespread use in the optical band, offering high sensibility, low noise, high dynamic range, and high spatial resolution. Recent advancements in CCDs have focused on improving the sub-electron noise levels for particle detection and enhancing readout speeds through simulation studies. The main objective of this study is to replace the analog processing of CCD signals using the digital-correlated double sampling (DCDS) technique. DCDS allows post-acquisition noise correction through intermittent sampling of an internal reference voltage to provide compact and flexible performance. In this study, DCDS was evaluated using two digital processing systems, namely, a 2.5 mega-samples per second (MSPS) 24-bit analog-to-digital converter (ADC) board and a 250 MSPS 16-bit ADC field-programmable-gate-array( FPGA)-based buffer memory board. The readout noise value at 74 kpix/s (7.1 e) was a significant improvement over that obtained with analog processing (9.7 e). The DCDS implementation demonstrates optimal signal-to-noise ratio for a wide range of readout speeds. Parameters such as the sample positions per pixel, number of samples, and number of pixels were identified to be essential in achieving an accurate gain value. Finally, hardware implementation of the DCDS IP core algorithm on a Xilinx Zynq-7000 AP system-on-a-chip (SoC) showed a significant improvement in power dissipation (7.1 W) compared to the analog Monsoon correlated double sample (CDS) circuit (13.6 W). The DCDS IP core implementation also showed a background noise reduction of up to 34% compared to DCDS offline readout processing using a Python algorithm.

Modal Frequency Shifts Measured on Xinjiang Bridge Excited by Traveling Vehicles

To excite sufficient vibrations of bridges for more effective modal experiments to take place, traveling vehicles were utilized in many bridge field inspection campaigns in history. However, the dynamics of a bridge subjected to the traveling vehicles are of changed resonant properties compared with those of the same structure with the ambient excitation in nature. Therefore, the field modal experiments employing vehicle excitations might unfavorably lead to shifted measurement results of low-order natural frequencies for long-span bridges of large mass. Using Xinjiang Bridge (a single-tower pre-stressed concrete cable-stayed bridge with spans [Formula: see text]) as the engineering background, this paper validates this technical notion based on reliable physical experiments and numerical simulations and seeks a practical approach to deal with the issue. It is found that the maximum deviation between the low-order modal frequencies measured on Xinjiang Bridge for the vehicle excitation case and those measured for the ambient excitation case (the accurate values) reaches 0.65[Formula: see text]Hz, and the deviation between the lower limit of the usual vehicle excitation’s energy band and the modal frequency measured (factor 1) and the traveling vehicles’ speed (factor 2) are the two key factors influencing the modal frequency shifts. An empirical model to compensate for modal frequencies’ deviations is formulated considering the two key factors accordingly, which is of high-practical significance in future applications of the modal experiments using vehicle excitations to other long-span bridges.

NHPP Software Reliability Model with Rayleigh Fault Detection Rate and Optimal Release Time for Operating Environment Uncertainty

Software is used in diverse settings and depends on development and testing environments. Software development should improve the reliability, quality, cost, and stability of software, making the software testing period crucial. We proposed a software reliability model (SRM) that considers the uncertainty of software environments and the fault detection rate function as a Rayleigh distribution, with an explicit mean value function solution in the model. The goodness-of-fit of the proposed model relative to several existing nonhomogeneous Poisson process (NHPP) SRMs is presented based on three software application failure datasets. Further, a cost model is also presented that addresses the error-removal risk level and required time. The optimal testing release policy for minimizing the expected total cost (ETC) is also determined for NHPP SRMs. The impact of the software environment is studied by varying it, and the optimal release times and minimum ETCs are compared. The goodness-of-fit comparison confirmed that the proposed model has more accurate prediction values than other models. Further, whereas the existing models applied to the cost model do not change after a certain operation period, the proposed model yields changes in release time even for long operating periods.

Acoustic feature extraction of radiation pressure signal induced by bubble oscillation under dual-frequency acoustic excitation based on CEEMDAN and bubble entropy

Acoustic feature extraction of radiation pressure signals (RPSs) induced by bubble oscillations is a crucial task in the characterization of the properties of underwater objects. In this article, to improve the extraction accuracy, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bubble entropy (BE) algorithms are combined to extract the effective acoustic components of the RPS. For verification, the proposed extraction scheme is applied to a typical simulated RPS under dual-frequency acoustic excitation. Compared with other extraction methods, CEEMDAN can extract richer acoustic feature information from the RPS, including accurate values for the amplitude and period of oscillation. Furthermore, when the components of the simulated RPS become more complex, the CEEMDAN–BE scheme gives better evaluation results than other schemes in terms of three evaluation indices. Under complex conditions, the signal extraction performances of singular value decomposition and ensemble empirical mode decomposition decrease greatly, but CEEMDAN retains its high signal extraction efficiency, which further confirms the effectiveness of the proposed signal extraction scheme.

Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.

Radiation-free systems based on dorsal surface topography can potentially represent an alternative to radiographic examination for early screening of scoliosis, based on the ability of recognizing the presence of deformity or classifying its severity. This study aims to assess the effectiveness of a deep learning model based on convolutional neural networks in directly predicting the Cobb angle from rasterstereographic images of the back surface in subjects with adolescent idiopathic scoliosis. Two datasets, comprising a total of 900 individuals, were utilized for model training (720 samples) and testing (180). Rasterstereographic scans were performed using the Formetric4D device. The true Cobb angle was obtained from radiographic examination. The best model configuration was identified by comparing different network architectures and hyperparameters through cross-validation in the training set. The performance of the developed model in predicting the Cobb angle was assessed on the test set. The accuracy in classifying scoliosis severity (non-scoliotic, mild, and moderate category) based on Cobb angle was evaluated as well. The mean absolute error in predicting the Cobb angle was 6.1° ± 5.0°. Moderate correlation (r = 0.68) and a root-mean-square error of 8° between the predicted and true values was reported. The overall accuracy in classifying scoliosis severity was 59%. Despite some improvement over previous approaches that relied on spine shape reconstruction, the performance of the present fully automatic application is below that of radiographic evaluation performed by human operators. The study confirms that rasterstereography cannot be considered a valid non-invasive alternative to radiographic examination for clinical purposes.

Quantitative estimation of optical properties in bilayer media within the subdiffusive regime using a tilted fiber-optic probe in diffuse reflectance spectroscopy, part 1: a theoretical framework for designing probe geometry.

As biological tissues are highly heterogeneous, there is a great interest in developing non-invasive optical approaches capable of characterizing them in a very localized manner. Obtaining accurate absolute values of the local optical properties from the measured reflectance requires finding a probe geometry, which allows us to solve this inverse problem robustly and reliably despite neglecting the higher-order moments of the scattering phase function. Our goal is to develop a theoretical framework for designing tilted-fiber diffuse reflectance probes that allow quantitative estimation of the optical properties corresponding to limited tissue volume (typically a few cubic millimeters). Relationships among probe geometry, sampled tissue volume, and robustness of the inverse solver to calculate optical properties from reflectance are studied using Monte Carlo simulations. The analysis of the number of scattering events of the collected photons leads to the establishment of relationships among the probe geometry, the sampled tissue volume, and the validity of a subdiffusive regime for the reflectance. A methodology is proposed for the design of new compact probes with tilted fiber geometry that can quantitatively estimate the values of the optical coefficients in a localized manner within living biological tissues by recording diffuse reflectance spectra.

Accuracy and Reproducibility of Lipari-Szabo Order Parameters From Molecular Dynamics.

The Lipari-Szabo generalized order parameter probes the picosecond to nanosecond time scale motions of a protein and is useful for rationalizing a multitude of biological processes such as protein recognition and ligand binding. Although these fast motions are an important and intrinsic property of proteins, it remains unclear what simulation conditions are most suitable to reproduce methyl symmetry axis side chain order parameter data (Saxis2) from molecular dynamics simulations. In this study, we show that, while Saxis2 tends to converge within tens of nanoseconds, it is essential to run 10 to 20 replicas starting from configurations close to the experimental structure to obtain the best agreement with experimental Saxis2 values. Additionally, in a comparison of force fields, AMBER ff14SB outperforms CHARMM36m in accurately capturing these fast time scale motions, and we suggest that the origin of this performance gap is likely attributed to differences in side chain torsional parametrization and not due to differences in the global protein conformations sampled by the force fields. This study provides insight into obtaining accurate and reproducible Saxis2 values from molecular simulations and underscores the necessity of using replica simulations to compute equilibrium properties.

Perancangan Aplikasi Sistem Absensi Guru Dan Karyawan Berbasis Web Sekolah Menengah Kejuruan

The development of information technology is quite extensive and global and has had a changing impact on all aspects ofhuman life. In the field of education, information technology is very necessary in order to support the performance ofeducation so that it can be useful in building an intelligent and competitive generation, one of which is at Global PersadaVocational School. Based on observations, Global Persada Vocational School does not yet have a teacher and employeeattendance system for processing teacher absence data. The obstacles that are often encountered in manual processing ofabsentee data are that it is very slow, ineffective, and makes it difficult for school principals to manage and check data onteachers who are present, and is even prone to losing absenteeism recap data. The aim of this research is to design andbuild a Web-based attendance system for processing teacher and employee attendance data for Global PersadaVocational School to make it easier to process teacher and employee attendance data. In building the teacher andemployee attendance system, the author used the SDLC method with the Waterfall model in designing the system andproduced a system with a simple interface that can help present accurate and relevant value data processing information.

Assessment of right ventricular ejection fraction cut-point for predicting mortality and heart failure hospitalization in asian patients with heart failure and reduced ejection fraction (HFrEF)

Abstract Background Left ventricular ejection fraction (LVEF) has been well established as a predictor of morbidity and mortality in heart failure patients (HF). Meta-analyses have confirmed that right ventricular systolic dysfunction (RVSD) identified by cardiac MR predicts adverse outcomes in HF patients. However, the cut-point and predictive value of right ventricular ejection fraction (RVEF) in Asian patients remain unknown. Objectives To evaluate the RVEF cut-point for RVSD and demonstrate the association between RVSD by cardiac MR and a composite endpoint consisting of HF hospitalisation and/or all-cause mortality in HFrEF patients (LVEF ≤40%). Materials & Methods A retrospective chart review of 370 consecutive HFrEF patients who underwent cardiac MR between 01 June 2009 and 31 December 2013 was done. The primary outcome was defined as all-cause mortality and/or HF hospitalisation. Receiver Operating Characteristic (ROC) analysis was done to explore the most accurate RVEF as a cut-point for RVSD. Multivariate Cox regression analysis was calculated. Results Patients were followed for a total of 1,989 patient-years with an average of 5.4 ± 2.9 years. The primary outcome occurred in 112 patients (30.3%). ROC analysis demonstrated that RVEF <48% provided the most accurate prognostic value (specificity 52%, sensitivity 70%) with an area under the curve of 0.62 (95% CI 0.55 – 0.69). Multivariate Cox analysis showed that RVSD was a significant independent risk factor (hazard ratio 1.71; 95% CI 1.10 – 2.66; p=0.018) for the primary outcome, in addition to age (p<0.001), New York Heart Association functional class (p<0.001), and diabetes mellitus (p=0.014), regardless of LVEF. Conclusions In Asian HFrEF patients, RVSD defined by RVEF <48% by cardiac MR can predict adverse events and provide a prognostic parameter for risk stratification.

Epoxy Value Measurement of E44 Epoxy Resin by Various Chemical Titration Method

AbstractDuring the production of epoxy resins, it is crucial to control or to know the epoxy groups. It is known that epoxy value is the most important analytical index of epoxy resin physical quantity to characterize the number of epoxy groups in epoxy resin. In this paper, two ways for testing the epoxy values of E44 epoxy resin are developed based on chemical titration method: hydrochloric acid‐acetone method and perchloric acid‐tetraethylammonium bromide method. The features of different methods and the influence of approaches on epoxy value are studied systematically. At the significant level of 0.05, for hydrochloric acid‐acetone method, the confidence interval for the mean is (0.454±0.003)eq/100 g, and for perchloric acid‐tetraethylammonium bromide method, the confidence interval for the mean is (0.453±0.003)eq/100 g. It has been proved that the test results of the two methods are basically the same. And the advantages and disadvantages of the two methods have been put forward. In addition, the testing mechanism of hydrochloric acid‐acetone method and perchloric acid‐tetraethylammonium bromide method is illustrated in detail. On the basis of superiority over the two methods, a novel method for testing epoxy resin with rapid and accurate epoxy value can be envisioned.

A Comparison of Bayes Estimators for the parameter of Rayleigh Distribution with Simulation

مقارنة للتوزيعات الاولية المضاعفة مفترضة لمعلمة توزيع رايلي .تضمنة ثلاث مجاميع للتوزيعات المضاعفة للمعلمة المجهولة لتوزيع رايلي. فقد تم افتراض ثلاثة توزيعات اولية مضاعفة : توزيع الجذرالتربيعي لمعكوس كاما (SRIG)- الاولي العائلة المرافقة الطبيعية , توزيع الجذرالتربيعي لمعكوس كاما (SRIG)- التوزيع غير المعلوماتي , توزيع العائلة المرافقة الطبيعية - التوزيع غير المعلوماتي, كدوال معلوماتية مضاعفه. البيانات ولدت من ثلاثة حالات لتوزيع رايلي لحجوم مختلفة من العينات ( الصغيرة والمتوسطة والكبيرة ) . وتم اشتقاق مقدرات بيز للمعلمة باستعمال دالة الخسارة التربيعية والدالة التربيعية الموزونه مع ثلاثة دوال معلوماتية مضاعفة. استعملت المحاكاة للحصول على نتائج هذا البحث. وتحديد افضل مقدر وفقا لاقل قيمة لمعيار متوسط مربعات الخطأ ومتوسط مربعات الخطأ الموزونة. وجدنا بان افضل تقدير للمعلمة لكل حجوم العينات , عندما يكون التوزيع الاولي المضاعف توزيع الجذرالتربيعي لمعكوس كاما (SRIG)- الاولي العائلة المرافقة الطبيعية عند القيم و للقيمتين الحقيقية للمعلمة وعلى التوالي . كذلك حصلنا على افضل تقدير لـ عندما يكون التوزيع الاولي المضاعف توزيع العائلة المرافقة الطبيعية - التوزيع غير المعلوماتي عند القيم للقيمة الحقيقية .

Hemodynamic monitoring during weaning from mechanical ventilation in critically ill pediatric patients: a prospective observational study

BackgroundCardiovascular dysfunction is a significant factor contributing to weaning failure in mechanically ventilated children. Understanding the cardiopulmonary pathophysiological changes that occur during weaning is a prerequisite for the early recognition of weaning failure of cardiovascular origin. This study aimed to assess the effect of weaning trials on central hemodynamics and to identify the indices predictive of cardiac-related weaning failure.MethodsThis prospective observational study was conducted in the Pediatric Intensive Care Unit (PICU) and included mechanically ventilated patients aged between 2 and 30 months who were on minimal ventilatory settings and ready for weaning. Patients who were hemodynamically unstable, diagnosed with neuromuscular diseases, or diagnosed with cardiac diseases were excluded. Hemodynamic parameters were evaluated during weaning from ventilation via echocardiography and noninvasive cardiometry during pressure support (PS) ventilation and at the end of the spontaneous breathing trial (SBT).ResultsThe study included 50 patients, comprising 30 males (60%) and 20 females (40%) with ages ranging from 2 to 30 months. Echocardiography revealed a significant increase in the cardiac index (CI), tricuspid annular plane systolic excursion (TAPSE), and the E/A ratio at the end of SBT. Moreover, right ventricular systolic pressure (RVSP) significantly decreased. Noninvasive cardiometry revealed a significant increase in the index of contractility (ICON) and CI at the end of SBT (p-value = 0.023 and < 0.001, respectively). Of the 12 (25%) patients who failed their first extubation trial, they exhibited a significantly lower CI and TAPSE (p values = 0.001 and 0.001, respectively).ConclusionThis study identified that weaning from mechanical ventilation in children is associated with hemodynamic changes, which can impact weaning success and reveal potential ventricular dysfunction. Bedside echocardiography was found to detect cardiac dysfunctions during weaning, and noninvasive cardiometry was considered a reliable tool that supports echocardiography for detecting changing trends in CI in PICUs. However, accurate values should be confirmed by echocardiography.

MinoritySalMix and adaptive semantic weight compensation for long-tailed classification

In real-world datasets, the widespread presence of a long-tailed distribution often leads models to become overly biased towards majority class samples while ignoring minority class samples. We propose a strategy called MASW (MinoritySalMix and adaptive semantic weight compensation) to improve this problem. First, we propose a data augmentation method called MinoritySalMix (minority-saliency-mixing), which uses significance detection techniques to select significant regions from minority class samples as cropping regions and paste them into the same regions of majority class samples to generate brand new samples, thereby amplifying images containing important regions of minority class samples. Second, in order to make the label value information of the newly generated samples more consistent with the image content of the newly generated samples, we propose an adaptive semantic compensation factor. This factor provides more label value compensation for minority samples based on the different cropping areas, thereby making the new label values closer to the content of the newly generated samples. Improve model performance by generating more accurate new label value information. Finally, considering that some current re-sampling strategies generally lack flexibility in handling class sampling weight allocation and frequently require manual adjustment. We designed an adaptive weight function and incorporated it into the re-sampling strategy to achieve better sampling. The experimental results on three long-tailed datasets show that our method can effectively improve the performance of the model and is superior to most advanced long-tailed methods. Furthermore, we extended MinoritySalMix’s strategy to three balanced datasets for experimentation, and the results indicated that our method surpassed several advanced data augmentation techniques.