Accuracy Rate Research Articles

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.

Evaluation on Network Social Media Named Entity Recognition Model Based on Active Learning

The medical security privacy and named entity recognition (NER) technology under the blockchain technology has been a hot topic in all walks of life. As a typical representative of medical security risks and NER, the NER model of online social media based on active learning has attracted worldwide attention. NER is an important part of natural language processing. Traditional recognition technology usually requires a lot of external information, and through the manual identification of its features, which costs a lot of time and energy. In order to solve the shortcomings of traditional recognition algorithms and the lack of feature extraction in network media NER, a new active learning model was introduced in this paper. In the information age, people are increasingly demanding a large amount of text information, and NER technology came into being. Its main function is to accurately identify important information from text and provide useful information for high-level work. The initial design of NER system is mainly based on the recognition of rules, so as to realize the recognition of named entities. However, in a complex network environment, it takes a lot of time and energy to establish rules without conflicts, and it has poor mobility. In recent years, with the continuous development of computer technology, the use of machine learning to actively learn the unknown information in the target area reduces the workload of manual annotation, thus realizing the active learning of large amounts of data. The research showed that the recognition accuracy under the traditional NER was low, and the information processing speed was slow; the accuracy rate of NER based on active learning was as high as 97%, and the speed of information processing had also been greatly improved, which had solved many problems under the traditional mode. User satisfaction could be as high as 95%, which showed that the latter had broad prospects. The progress of the new era cannot be separated from the support of new technologies. The research of this article has important guiding significance for medical security privacy and the application of NER under blockchain technology.

Improving the safety of radiotherapy treatment processes via incident-driven FMEA feedback loops.

Failure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates. A novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes. Twenty-three pre-identified failure modes of our external beam radiotherapy process, covering the process steps from patient admission to treatment delivery, were proffered on dedicated FMEA feedback and incident reporting terminals generated by the IRS. The clinical setting involved a computed tomography scanner, dosimetry, and five linacs. Incoming reports were used as basis to identify additional failure modes or confirm initial ones. The Kruskal-Wallis H test was applied to compare the risk priorities of the retrospective and prospective failure modes. Wald's sequential probability ratio test was used to investigate the correctness of the experts' occurrence ratings by means of the number of incoming reports. Over a 15-month period, 304 reports were submitted. There were 0.005 (confidence interval [CI], 0.0014-0.0082) reported incidents per imaging study and 0.0006 (CI, 0.0003-0.0009) reported incidents per treatment fraction. Sixteen additional failure modes could be identified, and their risk priorities did not differ from those of the initial failure modes (p=0.954). One failure mode occurrence rating could be increased, whereas the other 22 occurrence ratings could not be disproved. Our approach is suitable for validating FMEAs and deducing additional failure modes on a continual basis. Accurate error rates can only be provided if a sufficient number of reports is available.

TO EAT, OR NOT TO EAT? A PSYCHOANALYTIC VIEW ON THE FOOD‐PARENT

Research shows eating disorders increase the mortality rate: anorexia nervosa has the highest mortality rate and yet an accurate morbidity rate of bulimia nervosa remains hidden. However, research on the endopsychic structural dynamics that perpetuate in patients with eating disorders is scant. This essay depicts the use of Fairbairn's theory of endopsychic personality structure in understanding anorexia nervosa, binge eating disorder and bulimia nervosa. Fairbairn, of the independent group of British object relations theorists, provides a picture of the endopsychic structure based on the conscious and unconscious psychodynamics between partial ego/part‐object dyads. Using three case histories, the following pages illustrate the incessantly present endopsychic permutations of the aforementioned dynamics and the possibility of the entrapment in one of these or swinging from one to the other in eating disorders. The essay also shows that early traumatic experiences are present in the case history of individuals with any one of these eating disorders and despite their manifested behavioural differences, they result from the widening of fissures in the universal split in the psyche due to emotional and/or physical abuse. Finally, using Fairbairn's theory, the analysis in the essay explains the comorbidity of certain eating disorders and borderline personality structure.

Recognition of domestic <em>Felis Catus</em> vocalisations using a computational associative model

The cat (Felis catus) lives around or with humans and is considered one of the most popular pets in the world. Domestic cats have an extensive and complex vocalisation repertoire, and little is known about the meaning of their vocalisations; these animal sounds have been studied to determine their behavioural characteristics. This work implemented a computational model for cat vocalisation as a signal process to characterise it. Additionally, a recognition model is proposed to identify and associate domestic cat vocalisations with cat intentions. Domestic cat vocalisations were studied by obtaining sounds from audio and video sources stored in the web cloud and processing signals (distress, hiss, howl, meow, fright, purr, sexual call, and trill vocalisations) that belong to some cat situations (soliciting attention, complaining, surprise, threatening, welcoming, fighting, feeling hungry, mating, etc.). In measured signals, environmental noise was removed using a computational algorithm to increase the effectiveness of the recognition process. As a first approach, cat vocalisation patterns were extracted using Welch's method for spectral density estimation, appreciating vocalisation frequency characteristics and statistics. Additionally, a similarity function to compare cat vocalisation signals was developed, excluding useless patterns that could introduce computational problems in the learning phase of the recognition model. Then, an associative memory, the Lernmatrix Thresholded model, was used to recognise the domestic cat vocalisation needed, and pair associations between sounds and moods related to the recognition process result were established, i.e., identifying cat intention through vocalisation by analysing the signals emitted. The experimental results show the performance of the thresholded Lernmatrix among different pattern recognition models, where accurate vocalisation recognition rates are obtained. This computational model emerges as a suitable tool that could be helpful in animal care and ethology science to characterise the behaviour of animals through their vocalisation.

Development of sensitive biomass xylan-based carbon dots fluorescence sensor for quantification detection Cu2+ in real water and soil

Copper ions (Cu2+) pose significant risks to both human health and the environment as they tend to accumulate in soil and water. To address this issue, an innovative method using biomass-derived fluorescent carbon dots (D-CDs) synthesized via a hydrothermal process, with xylan serving as the carbon source was developed. D-CDs solution exhibited remarkable sensitivity and selectivity as a fluorescence sensor for Cu2+, boasting a low detection threshold of 0.64 μM. In order to facilitate real-time monitoring of Cu2+, solid-state fluorescent nanofiber membrane (NFD-CDs) through electrospinning was engineered. Additionally, D-CDs demonstrated successful Cu2+ detection in various real water samples, including those sourced from Xuanwu Lake, the Yangtze River, tap water, and bottled water, with accurate recovery rates observed. As a result, this research introduces a dual-mode analytical system for onsite detection of Cu2+ in real scenarios. By harnessing biomass-derived fluorescent CDs materials and solid-state fluorescence sensors, this approach offers a promising solution for addressing the challenges associated with Cu2+ contamination.

Combining Bourgoyne and Young Equations by Bagging Tree Regression to Predict Rate of Penetration in a Southern Iraqi Field, Case Study

Abstract Achieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain knowledge and capturing non-linear relationships. Its ensemble nature also mitigates the impact of outliers. This approach combines physics-based equations with machine learning to enable more accurate ROP predictions in drilling operations. It enhances drilling efficiency, reduces expenses, and improves decision-making in the oil and gas sector. Extensive testing on actual drilling datasets has demonstrated outstanding performance compared to the multiple linear regression (MLR) method. With the increased R2 and zero P-value. positive findings show that this tool can benefit precise future ROP prediction in southern Iraqi oil well drilling.

Final state rescattering effects in axio-hadronic η and η′ decays

It has been long-understood that final state rescattering effects provide O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(1) corrections to hadronic meson decays rates, such as η → πππ and η′ → ηππ. Hence, one would expect that such effects would be just as important in axio-hadronic η and η′ decays, such as η(′) → ππa, where a is an axion or axion-like particle (ALP). And indeed they are, as we show in this paper by using the treatment of dispersion relations to include the effects of strong final state interactions in several axio-hadronic processes, namely, η(′) → π0π0a, η(′) → π+π−a, and η′ → ηπ0a. We also compute the perturbative, leading order decay rates for multiple ALP emission, such as in η(′) → π0aa, η′ → ηaa and η(′) → aaa, and briefly discuss the expected corrections from strong interactions and the processes that must be considered for an accurate rate estimation of these multi-ALP decay channels.

3D DenseNet with temporal transition layer for heart rate estimation from real-life RGB videos.

Deep learning has demonstrated superior performance over traditional methods for the estimation of heart rates in controlled contexts. However, in less controlled scenarios this performance seems to vary based on the training dataset and the architecture of the deep learning models. In this paper, we develop a deep learning-based model leveraging the power of 3D convolutional neural networks (3DCNN) to extract temporal and spatial features that lead to an accurate heart rates estimation from RGB no pre-defined region of interest (ROI) videos. We propose a 3D DenseNet with a 3D temporal transition layer for the estimation of heart rates from a large-scale dataset of videos that appear more hospital-like and real-life than other existing facial video-based datasets. Experimentally, our model was trained and tested on this less controlled dataset and showed heart rate estimation performance with root mean square error (RMSE) of 8.68 BPM and mean absolute error (MAE) of 3.34 BPM. Moreover, we show that such a model can also achieve better results than the state-of-the-art models when tested on the VIPL-HR public dataset.

Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island, Indonesia

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

Highly accurate heat release rate marker detection in NH3–CH4 cofiring through machine learning and domain knowledge-based selection integration

Ammonia emerges as a promising substitute for traditional fuels, offering a potential reduction in fossil fuel consumption and the associated emissions. Given its weak reactivity, an effective strategy to harness the potential of ammonia involves cofiring it with methane, to establish a self-sustaining combustion process. The understanding of heat release rate (HRR) in the cofiring process of ammonia and methane is crucial for burner design. This study introduces novel HRR indicators through a machine learning-based approach, focusing on species significantly contributing to HRR. Kinetic analysis is carried out utilizing a one-dimensional freely propagating flame model, applying a mechanism including 59 species and 356 reactions. Domain knowledge-based feature selection narrows down the search space, enhancing computational efficiency during HRR indicator identification. A random forest model is then employed to identify radical/radical combinations and their respective reaction orders, representing HRR optimally based on mean decrease impurity. The proposed HRR markers are [CH4]0·76[OH]1.08, [HCO]0·65[NO]0.24, [CH3]0.65[O]0.63, [NH2][O]0.59, and [NH3]0·93[OH]0.96. The root mean square error for each marker was extracted and compared with literature data, demonstrating higher reliability of the proposed indicators over those previously suggested. Additionally, the paper concludes with a discussion of the feasibility of measuring these markers from an experimental perspective.

Evaluation of daily precipitation modeling performance from different CMIP6 datasets: A case study in the Hanjiang River basin

To effectively compare and analyze daily precipitation modeling capabilities across different CMIP6 datasets, our study introduces a novel method to compare daily precipitation models across CMIP6 datasets in the Hanjiang River Basin (HRB). We quantify indicators such as precipitation distribution, temporal correlation, wet-dry detection, extreme value error, and spatio-temporal variability, enabling a comprehensive rating of precipitation accuracy. It has been found that while both CMIP6(CP6) and NEX-GDDP-CMIP6 (GCP6) models show similar simulation accuracy, GCP6 excels in several aspects like distribution, temporal correlation, extreme value simulation, and spatial variability, yet lags in wet-dry detection and temporal change. Notably, using the comprehensive rating score (CRS) analysis, significant differences in precipitation simulation accuracy exist between models, particularly CP6, with variations of up to 0.22 (51.2%) between the highest and lowest scores. Among the top ten models, GCP6 occupies four positions such as MRI-ESM2-0, GFDL-CM4, MPI-ESM1-2-HR, and TaiESM1, while CP6 holds the remaining six like CanESM5, EC-Earth3-Veg, MPI-ESM1-2-HR, GFDL-CM4, MRI-ESM2-0, and IPSL-CM6A-LR. These findings not only offer a clear understanding of the simulation performance of CMIP6 datasets across various precipitation characteristics, but also quantitatively compare the modeling capabilities of different models for watershed precipitation through CRS. This aids climate adaptation research, hydrological forecasting, and flood management in the basin.

Research on Abnormity Detection based on Big Data Analysis of Smart Meter

There are over five hundred million smart meters in China. The current standard for the use of smart meters is physical inspection of meter dismantling within 8 years. The method leads to many issues including high cost of testing, low sampling rate, unknown meter status huge waste of resources etc. Searching for non- dismantling meter detection solution is necessary. Although the smart grid can be managed much better with the increasing use of smart meters, the current standard brings many issues. To solve the problems like a huge waste of resources, detecting inaccurate smart meters and targeting them for replacement must be done. Based on the big data analysis of smart meters, abnormity can be predicted and diagnosed. For this purpose, the method is based on Long Short-Term Memory (LSTM) and a modified Convolutional Neural Network (CNN) to predict electricity usage patterns based on historical data. In this process, LSTM is used to fit the trend prediction of smart meters, and recurrence plot is used to detect the abnormality of smart meter. Both LSTM and recurrence plot method is the first time to be used in smart meter detection. In actual research, many methods including Elastic Net, GBR, LSTM and etc. are used to predict the trend of smart meters. Through the best method LSTM, the accurate rate of the trend prediction of smart meters can arrive at about 96%. Similarly many methods are used to detect the abnormality of smart meters. In single-input modeling, there are sequence-input and matrix-input methods. In dual-input modeling, there are TS-RP CNN, VGG+BiLSTM, ResNet50+1D-CNN and ResNet50+BiLSTM etc. Eventually based on the most successful method recurrence plot, the abnormity testing and failure recognition can be got at 82% roughly. This is the breakthrough in the electricity power domain. With the success of the solution, the service time of a normal meter can be prolonged by abnormity detection. This will lead to saving a lot of resources on smart meter applications.

A New Method for Estimating Oil Well Production Rates Based on Enthalpy Balance Utilizing Temperature Data

Abstract The fundamental objective of this study is to utilize a temperature dataset to achieve accurate rate estimation. This study introduces a graphical method, incorporating suitable assumptions, for predicting flow rates in single-phase vertical oil flow. The method takes into consideration the fouling factor and addresses the steady-state flow of heat in the wellbore, while also accounting for the transient heat conduction in the formation. To evaluate and validate the proposed model, two field cases are examined, and associated uncertainties are discussed. The model's predictions are compared with those obtained from Hassan and Kabir's approach, as well as actual separator flow rate data. The comparisons reveal that the model exhibits a high level of accuracy within its specified range of application. Statistical analyses indicate that most of the models perform comparably, although the suggested graphical model stands out for its higher accuracy and user-friendly nature. The performance of uncertainty is found to be more reliant on the accuracy of data measurement rather than the features of the model itself.

Numerical Simulation and Experimental Comparison of System Analysis Module 1D Mixing Model for Cold Shock Transients in the Gallium Thermal-Hydraulic Mixing Facility

Liquid metals are being investigated as coolants in many advanced reactor designs because of their high thermal conductivity and effectiveness at high temperatures. However, they often pose challenges to reactor operation and safety because of the complex thermal mixing and stratification in the plenum of pool-type reactor designs. The advanced system analysis code System Analysis Module (SAM) currently under development at Argonne National Laboratory aims to develop and implement thermal mixing models to accurately capture these complex thermal fluid behaviors. In this study, the SAM thermal mixing model was compared against experimental data from the Gallium Thermal-Hydraulic Experiment facility, a scaled liquid metal test facility that uses gallium as a surrogate fluid to investigate the stratification and thermal mixing of low-Prandtl-number fluids in the upper plenum of a liquid metal–cooled reactor. Two cold shock transient cases were used: one with stable stratified flow (Ri = 32) and one with stronger thermal mixing (Ri = 0.5). The resultant temperatures were then compared with the experimental temperatures over the entire plenum to assess the ability of the mixing models to capture the thermal behavior and to better correspond mixing parameters to various flow scenarios. Generally, the zero-dimensional mixing model was more capable of capturing the bulk temperature of the component modeled assuming that an accurate mass flow rate was provided, but it was inherently unable to capture thermal gradients in space. The one-dimensional mixing model was capable of capturing that the thermal gradients provided accurate selection of the mixing coefficients. The temperature at the outlet junction was compared over time for each of the mixing models with the recorded experimental temperature. The implemented mixing models demonstrated the ability to effectively capture the overall thermal behavior for stronger mixing scenarios but struggled with more stably stratified flows. It was found that a system analysis code’s covering of the entire range of different operating conditions still remains a challenging task, and it is suggested that further model and closure improvements are necessary to accurately capture complex thermal mixing and stratification phenomena.

Efficient Fetal Health Prediction using Machine Learning

The growth of technology in our day-to-day enterprise with advanced machines are outstanding through machine learning involving both machine learning and deep learning all over the world. Fetal monitoring during pregnancy time is the most important to save the life of the mother as well as the child. In this project, we present a ML technique that is used to measure the fetal heart rate during the time of pregnancy. The major component used for this detection is Fetal Digital stethoscope sensor which is to be placed on the abdomen of the pregnant and the signals are processed by the micro-controller used and the accurate fetal heart rate. This system is very flexible and low cost helps the patient to monitor the fetal heart rate in home. We will use ML method for our project. In this paper Fetal health is predicted by algorithms namely Decision Tree (DT) as existing and Recurrent Neural Network (RNN) as proposed and compared in terms of accuracy. From our work we can prove that our proposed RNN works better than other existing DT algorithm.

Quantitative determination of SO2 flux from industrial chimney through machine vision with plume model verification

Accurate emission rate data are essential for assessing the environmental impact of industrial emissions and calibrating the effectiveness of desulphurization equipment. However, accurate calculation of SO2 emissions is difficult to realize due to the environmental conditions, the time-varying and spatial non-uniformity of pollution source emissions. In this paper, a Dual TV Plus machine vision algorithm is proposed, calculated and validated with the conventional Farneback optical flow algorithm in terms of industrial stack emission rates. In addition, the effect of turbulence on the inversion of SO2 emission under complex environmental conditions is evaluated by combining simulations and outdoor experiments. The results show that the Dual TV Plus algorithm performs better than the Farnebäck algorithm and the Dual TV-L1 algorithm, with 50% and 30% improvement in accuracy and 5% and 8% improvement in immunity to interference, respectively. This work is important for promoting the engineering applications of remote sensing instruments for environmental monitoring.

Integrative studies of Microtus hartingi and Microtus guentheri (Cricetidae, Arvicolinae) and taxonomic problem of Microtus elbeyli

In this study, Taxonomic differences between Microtus guentheri and Microtus hartingi, which are similar in terms of external morphological features, were determined using linear morphometric, geometric morphometric, karyological and molecular analyses methods. Through classical morphometric (mean values) and geometric morphometric analyses of craniodental and mandible characters, influenced by various genetic, developmental, and functional processes, it was found that M. hartingi and M. guentheri exhibited notable differences in both mean linear measurements and shape. The Jackknife in classical morphometric analyses of samples, which underwent molecular and karyological species diagnoses, was higher than 88 % for each character. In geometric morphometric analyses, the Procrustes distance for shape follows the order Ventral < Dorsal < Mandible < M3<M2<M1. Based on cross-validation, the accurate classification rate is between 73 and 100 %. In both mean linear measurements and centroid size, M. hartingi specimens exhibited significantly larger mandibles and craniodentals compared to individuals of M. guentheri. According to PLS results, there was a significant relationship between the shape of M1, M2 and M3 and environmental variables, while the relationship between their size and environmental variables was not significant. However, neither the shape nor the size of the other parts had an sufficient relationship with environmental variables. Both species exhibited karyological similarity with a chromosome number of 2n = 54, but display polymorphism in chromosome arm number. There were differences in the C-banding of autosomal chromosomes and X chromosome. The classification of species in classical and geometric morphometric analyses, diverges from the outcomes of karyological and molecular assessments. In both species, the intraspecific genetic distance remained below 1.35 %. The cyt-b exhibits significant differentiation between the two species (K2P = 6.55 %). The divergence time for M. guentheri from the clade containing M. hartingi + M. qazvinensis + Microtus dogramacii was estimated to be 0.73 million years ago. Since no sample of Microtus elbeyli with karyological and cyt-b characteristics was found in the samples taken from the type locality, the taxonomic status of M. elbeyli remains unclear.

Not Just Small Adults: Considerations for Pediatric Chronic Kidney Disease

Chronic kidney disease (CKD), including pediatric CKD, is a global public health concern. Pediatric CKD has lasting effects into adulthood. In this review, we focus on the etiology of pediatric CKD and unique aspects that should be considered in treating a child with CKD, such as ensuring adequate nutrition and assessing growth hormone axis dysregulation. We review risk factors for CKD progression and how clinical surveillance can be used to address modifiable factors. We address the issues of accurate glomerular filtration rate (GFR) estimation, cardiovascular disease, immunization, neurodevelopment, and planned transition to adult care. We also cover kidney failure preparation and global CKD care disparities affecting children worldwide.

Analytic Prediction of Rotor Broadband Noise with Serrated Trailing Edges with Applications to Urban Air Mobility Aircraft

Theoretical predictions for broadband noise generated by rotors with serrated trailing edges are presented. This study extends Lyu and Ayton’s model for serrated trailing-edge noise to account for factors such as finite span length, the accurate acoustic attenuation rate of trailing-edge noise, modified spanwise wavenumber, and the proper scattering factor. This new model is implemented in the rotorcraft broadband noise tool, UCD‐QuietFly. The validation results demonstrate a satisfactory level of agreement with the experimental data for serrated trailing‐edge noise of a wing section, a hovering rotor, and a forward‐flight rotor. Next, the effects of serration parameters, such as the number of serrations, serrations starting radial location, serration geometry, and serration height, on rotor broadband noise are investigated. For example, it is observed that sine-wave, chopped peak, or sawtooth serration shapes provide the most significant noise reduction among the various shapes considered. Finally, broadband noise reduction through serrations is applied to two urban air mobility aircraft scenarios: a six‐passenger quadrotor and a quiet helicopter. For the quadrotor, serrated trailing edges show a noise reduction potential of over 9 dB, with higher reductions observed at higher tip speeds and fewer blades. A quiet helicopter equipped with serrated blades in forward flight achieves a maximum noise reduction of over 10 dB in the forward direction.