Measurement Data Research Articles

Estimates and measurements of radiofrequency exposures in smart-connected homes.

The aim of this research was to quantify the levels of radiofrequency electromagnetic energy (RF-EME) in a residential home/apartment equipped with a range of wireless devices, often referred to as internet of things (IoT) devices or smart devices and subsequently develop a tool that could be useful for estimating the levels of RF-EME in a domestic environment. Over the course of 3 years measurements were performed in peoples' homes on a total of 43 devices across 16 device categories. Another 12 devices were measured in detail in a laboratory setup. In all a total of 55 individual devices across 23 device categories were measured. Based on this measurement data we developed predictive software that showed that even with a single device in 23 device categories operating near maximum they would, in total, produce exposures at a distance of 1 m of 0.17% of the ICNIRP (2020) public exposure limits. Measurements were also made in two separate smart apartments-one contained over 50 IoT devices and a second with over 100 IoT devices with the devices driven as hard as could reasonably be achieved. The respective 6-min average exposure level recorded were 0.0077% and 0.44% of the ICNIRP (2020) 30-min average public exposure limit.

A framework for studying environmental statistics in developmental science.

Psychologists tend to rely on verbal descriptions of the environment over time, using terms like "unpredictable," "variable," and "unstable." These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying "unpredictability" in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different "unpredictability statistics" across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Modeling the unphysical pseudomode model with physical ensembles: Simulation, mitigation, and restructuring of non-Markovian quantum noise

The influence of a Gaussian environment on a quantum system can be described by effectively replacing the continuum with a discrete set of ancillary quantum and classical degrees of freedom. This defines a pseudomode model which can be used to classically simulate the reduced system dynamics. Here we consider an alternative point of view and analyze the potential benefits of an analog or digital quantum simulation of the pseudomode model itself. Superficially, such a direct experimental implementation is, in general, impossible due to the unphysical properties of the effective degrees of freedom involved. However, we show that the effects of the unphysical pseudomode model can still be reproduced using measurement results over an ensemble of physical systems involving ancillary harmonic modes and an optional stochastic driving field. This is done by introducing an extrapolation technique whose efficiency is limited by stability against imprecision in the measurement data. We examine how such a simulation would allow us to (i) perform a quantum simulation of the effects of complex nonperturbative and non-Markovian environments in regimes that are challenging for classical simulation; (ii) conversely, mitigate potential unwanted non-Markovian noise present in quantum devices; and (iii) restructure some of the properties of a given physical bath, such as its temperature. Published by the American Physical Society 2024

Fuzzy Learning-Based Electric Measurement Data Circulation Monitoring and Security Risk Anomaly Evaluation

With the circulation of massive electric measurement data, data anomaly caused by security attacks imposes security risks on reliable operation of smart grid. Long short-term memory (LSTM) based data circulation monitoring and security risk anomaly evaluation has been intensively studied. However, some issues remain unsolved, including learning overfitting and large prediction error. In this paper, we investigate fuzzy learning to infer the abnormal level of security risk. In particular, an adaptive grey wolf optimization-LSTM-fuzzy petri network (AGWO-LSTM-FPN) based electrical measurement data circulation monitoring and security risk anomaly evaluation algorithm is proposed. Specifically, AGWO is utilized to optimize LSTM parameter updating and improve traffic prediction accuracy. Furthermore, FPN is combined with multi-dimensional monitoring indicators to enhance anomaly level evaluation. Simulation results illustrate the excellent performance of AGWO-LSTM-FPN.

Reflections on the challenges of conducting an international multicentre randomized controlled trial of balance training in addition to pulmonary rehabilitation and its impact on fall incidence in people with COPD

BackgroundPulmonary rehabilitation (PR) is accepted as standard care for individuals with COPD. We conducted an international, multi-centred randomized controlled trial (RCT) to determine if adding balance training to PR would reduce the incidence of falls in people with COPD. While there have been many trials investigating the effectiveness of PR, few have involved international collaboration. Successful execution of rehabilitation trials requires a significant investment of time, staffing, and resources. With the recent completion of the Balance Training for Fall Reduction in COPD RCT, we report on the design, implementation, and execution of our trial using project management phases. We also highlight our lessons learned for consideration in future multi-centre rehabilitation trials.MethodsThis was a retrospective review of the planning, preparation, timelines, and personnel training involved in the execution of this study using four of the five project management phases described by Farrell et al. in 2010: (1) initiation, (2) planning, (3) execution, and (4) monitoring and controlling. We report descriptive statistics as percentages and counts and summarize our lessons learned.ResultsTen outpatient PR programs in three continents participated. Thirty-one personnel worked on the trial across all sites. Enrolment began in January 2017 and was suspended in March 2020 due to the COVID-19 pandemic. Approximately 1275 patients were screened, 455 (36%) were eligible, 258 (57%) consented, 243 (53%) participated, and 130 (61%) completed the 12-month follow-up assessment. Lessons learned through our experience included (1) ensuring awareness of funder policies and considering the impact on collaborating sites; (2) preparing for the possibility of human resource and program disruptions; (3) anticipating site dropout and having a contingency plan in place; (4) planning and monitoring process measure data before, during, and after trial initiation; (5) ensuring frequent and consistent communication with and between collaborating sites; (6) maximizing features of database platform to ensure data set completeness and controlled data access; and (7) identifying strategies for increasing patient engagement in a high-demand study.ConclusionsWe identify seven lessons learned through our experience conducting an international, multicentre rehabilitation-based RCT. These lessons can provide guidance to other trialists conducting studies with similar logistics and may assist with future trial planning and implementation.

Outcomes and Conclusions from the 2022 AM Bench Measurements, Challenge Problems, Modeling Submissions, and Conference

AbstractThe Additive Manufacturing Benchmark Test Series (AM Bench) provides rigorous measurement data for validating additive manufacturing (AM) simulations for a broad range of AM technologies and material systems. AM Bench includes extensive in situ and ex situ measurements, simulation challenges for the AM modeling community, and a corresponding conference series. In 2022, the second round of AM Bench measurements, challenge problems, and conference were completed, focusing primarily upon laser powder bed fusion (LPBF) processing of metals, and both material extrusion processing and vat photopolymerization of polymers. In all, more than 100 people from 10 National Institute of Standards and Technology (NIST) divisions and 21 additional organizations were directly involved in the AM Bench 2022 measurements, data management, and conference organization. The international AM community submitted 138 sets of blind modeling simulations for comparison with the in situ and ex situ measurements, up from 46 submissions for the first round of AM Bench in 2018. Analysis of these submissions provides valuable insight into current AM modeling capabilities. The AM Bench data are permanently archived and freely accessible online. The AM Bench conference also hosted an embedded workshop on qualification and certification of AM materials and components.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

Exploration on the protective effect of liraglutide on renal injury in diabetic nephropathy rats based on ROS-NLRP3 inflammasomes

Objective: To investigate the effect of liraglutide on diabetic nephropathy in rats and its regulatory mechanism. Methods: The diabetic nephropathy rat model was constructed with high-glucose-high-fat diet in combination with STZ, and was randomly divided into normal saline group and liraglutide group. The rats in liraglutide group were given sc 200 μg/kg of liraglutide, and the rats in normal saline group were given sc 20 mg/kg of normal saline twice a day for 4 weeks. The normal control group was not treated with any treatment. The biochemical indexes such as rat body weight, 24-hour urine total protein quantification (UTP), fasting blood glucose (FPG), triglyceride (TG), cholesterol (TC), blood urea nitrogen (BUN) and serum creatinine (Scr) were measured. HE staining, Masson staining and PAS staining were used to observe the pathologically morphological changes in renal tissues. Western-blot was used to detect the expression of NLRP3 inflammasome-related protein in renal tissues. Elisa was used to measure the serum levels of interleukin-18 (IL-18) and IL-1β. SPSS 26.0 statistical software and Graph Prism 9.0 software were used for analysis and mapping. The t-test was used for the comparison of measurement data between two groups, one-way ANOVA was used for the comparison among multiple groups, and Tukey’s test was used for the comparison in the same group. Results: Compared with the normal saline group, FBG, UTP, BUN, Scr, TC and TG in the liraglutide group were significantly decreased (p < .01), the glomerular basement membrane was slightly thickened, with the tubular lumen slightly dilated and the lesion damage alleviated; the expression levels of NLRP3, ASC and caspase-1 inflammasome-related proteins were decreased (p < .01), and the levels of IL-18 and IL-1β were decreased (p < .01).Conclusion: Liraglutide can inhibit the activation of NLRP3 inflammasome mediated by oxidative stress in renal tissues through ROS-NLRP3 inflammasome pathway, thereby inhibiting the inflammation, and finally playing an anti-diabetic nephropathy renal injury role.

Iterative Stress Reconstruction Algorithm to Estimate Three-Dimensional Residual Stress Fields in Manufactured Components

Abstract Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ∼2.5 MPa in the Al plate, and ∼10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ∼2.3 MPa in the Al plate and ∼45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution.

Joint k-ω Space Image Reconstruction and Data Fitting for Chemical Exchange Saturation Transfer Magnetic Resonance Imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k-ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω, while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively.

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

AbstractThe Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench.

Shedding Light on Luminescence Lifetime Measurement and Associated Data Treatment

Luminescence lifetime is a crucial parameter in photophysical studies that bears essential physical and chemical information and that is used to quantify a variety of phenomena, from the determination of quenching mechanisms to temperature sensing and bioimaging. The current perception of lifetime measurement is that it is a trivial and fast experiment. However, despite this apparent simplicity, measuring luminescence decay and fitting the obtained data to a suitable model can be far more intricate. In this perspective, the influence of experimental parameters and fitting procedures on the determination of lifetimes are investigated and, through carefully chosen examples, it is shown that large variations, up to 10%, can be induced by varying parameters such as the data acquisition time, the baseline evaluation, or the mathematical fitting model. In order to present to a wider audience, detailed mathematical descriptions are kept out of the manuscript.

Assessing wayside traffic noise levels using long-term noise monitoring and available Department of Transportation traffic data - worst-case noise traffic conditions, effects of repaving, and methods for comparing traffic noise data sets

The availability of automated continuous traffic data from State Departments of Transportation (DOTs), such as the California Department of Transportation (Caltrans) Performance Measurement System (PeMS), provides increased opportunities for the analysis of highway noise. Traffic data that is free to access and downloadable via the internet can be matched with unattended long-term noise measurement data to provide large datasets for analysis, without requiring field staff to be continuously on site. A 2018 ICF/Caltrans paper documented how this approach was used on an operating freeway to determine the volume of traffic that results in the highest one-hour energy average traffic noise level. At the time of those measurements, the subject freeway had Portland cement concrete pavement. The freeway has since been repaved with an open-graded rubberized asphalt concrete pavement overlay. The current study uses new (2023) traffic noise measurements and PeMS data to investigate how worst-noise conditions have changed and the effect of the repaving on wayside noise levels. Changes in traffic patterns since 2018 required the authors to normalize the measurement data for comparison. Opportunities were identified to take advantage of the large measurement data sets to match before-and-after data, and those methods are also described in this paper.

Investigation of methods to estimate NLR of residential structures for airport sound insulation programs

This paper investigates methods to estimate outdoor-to-indoor noise level reduction (NLR) for single family residential structures before and after the installation of sound insulation treatments to improve the structure's NLR. Acoustical modeling results and Outdoor-Indoor Transmission Class (OITC) ratings are evaluated in the context of treatment design for airport sound insulation programs and assessment of soundproofing product performance. Overall, post-construction NLR predictions are observed to have a high degree of accuracy when the results are generated using a combination of pre-construction measurement data and acoustical modeling techniques. The composite OITC rating of the noise exposed building façade is also shown to be an effective metric for predicting NLR values.

Observational study of missing SOFA score data frequency in RCTs relative to ICU length of stay

The Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review being referred to included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5–10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.

A shared-aperture pentaband antenna with high impedance surface for CubeSat application

A circular polarized (CP) pentaband antenna based on the aperture-in-aperture (AIA) concept is presented for CubeSat applications. This AIA consists of five different bands ranging from L-band to Ka-band. Four different antennas, each operating at a specific frequency band, namely 12 GHz, 18.5 GHz, 26 GHz, and 32 GHz, were incorporated into an L-band (viz. 1.5 GHz) antenna. Notably, the five antennas can operate simultaneously for a CubeSat downlink operation with a frequency ratio of 21.3:1. The antenna structure shows a realized gain of 5–10 dBi with good CP bandwidth (< 3 dB) across the overall operational frequency range. That is, the realized gain of L-band (1.5 GHz), X-band (12.5 GHz), K-band1 (18.5 GHz), K-band2 (26 GHz), and Ka-bands (32 GHz) are 5.05 dBi, 8.21 dBi, 7.33 dBi, 7.97 dBi, and 8.56 dBi. A high impedance surface (HIS) is incorporated with the Ka-band antenna to mitigate the ripples in the radiation pattern created by the interference of surface waves. A prototype was fabricated and tested. The measurement data agrees well with the simulation.

Application of a Laser Profile Sensor for the Full-Field Measurement of the Continuous Icing Process of Rotating Blades.

With the advancing energy transition, icing is a growing problem in the wind turbine sector. The development of systems to detect and mitigate icing makes it necessary to understand its basic behavior and characteristics. This paper proposes a method for the continuous and full-field measurement of the icing process of rotating blades, using a single line laser profile scanner. Inside of a climate chamber, a rotor is driven by a motor, while a system of nozzles provides a fine water dust, which leads to ice accumulating on simple NACA blades, which in turn is measured by a triangulation laser. The measurement data are cleared from outliers and presented as a surface in 3D space. An alpha shape is used to reconstruct and extract the volume of the ice between a reference and a measurement surface, using the corresponding Matlab function. Appropriate input parameters for the function and offsetting of the reference surface to improve the results are compared and discussed. The resulting system is able to detect small changes in the ice layer thickness in the sub-millimeter range.

An accelerated inexact Newton regularization scheme with a learned feature-selection rule for non-linear inverse problems

With computational inverse problems, it is desirable to develop an efficient inversion algorithm to find a solution from measurement data through a mathematical model connecting the unknown solution and measurable quantity based on the first principles. However, most of mathematical models represent only a few aspects of the physical quantity of interest, and some of them are even incomplete in the sense that one measurement corresponds to many solutions satisfying the forward model. In this paper, in light of the recently developed iNETT method in (2023 Inverse Problems 39 055002), we propose a novel iterative regularization method for efficiently solving non-linear ill-posed inverse problems with potentially non-injective forward mappings and (locally) non-stable inversion mappings. Our approach integrates the inexact Newton iteration, the non-stationary iterated Tikhonov regularization, the two-point gradient acceleration method, and the structure-free feature-selection rule. The main difficulty in the regularization technique is how to design an appropriate regularization penalty, capturing the key feature of the unknown solution. To overcome this difficulty, we replace the traditional regularization penalty with a deep neural network, which is structure-free and can identify the correct solution in a huge null space. A comprehensive convergence analysis of the proposed algorithm is performed under standard assumptions of regularization theory. Numerical experiments with comparisons with other state-of-the-art methods for two model problems are presented to show the efficiency of the proposed approach.

An Alternative Non-Invasive Screening Model for Liver Fibrosis among US Adults at Risk of MASLD.

Background and Aims: Screening for liver fibrosis presents a clinical challenge. This study aimed to explore a useful alternative method for assessing fibrosis risk among US adults at risk of metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: A liver stiffness score (LSS) model was proposed and tested using data from 3976 participants at possible risk of MASLD, obtained from the US National Health and Nutrition Examination Survey (NHANES). Results: The LSS model was developed using liver stiffness measurements, blood biochemistry, and body measurement data from 2414 NHANES participants at risk of MASLD, sampled between 2017 and 2020: LSS = exp(0.007035 × bodyweightkg - 0.1061 × raceblack1,0 + 0.183221 × diabetes1,0 + 0.008539 × ASTIU/L - 0.0018 × plateletcount1000cell/UL - 0.21011 × albuming/dL + 2.259087). The probability (P) of having fibrosis F3 + F4 is calculated as follows: P = 0.0091 × LSS2 - 0.0791 × LSS + 0.1933. The developed LSS model was tested on 1562 at-risk participants from the 2017-2018 cycle. The results showed that the LSS model achieved AUROC values of 0.79 and 0.78 for diagnosing cirrhosis (F4) and advanced fibrosis (F3 + F4) in the US population, respectively. It outperformed existing models such as NFS, FIB-4, SAFE, and FIB-3. For screening F3 + F4 fibrosis, the LSS model's top decile outperformed the NFS and FIB-4 models by 37.7% and 42.6%, respectively. Additionally, it showed superior performance compared to the waist circumference classification method by 29.5%. Conclusions: derived from an ethnically diverse population dataset, the LSS screening model, along with its probability equation, may offer clinicians a valuable alternative method for assessing the risk of liver fibrosis in the at-risk adult population.

Sub-Aperture Synthetic Aperture Radar Imaging of Fixed-Platform Beam-Steering Radar for Blast Furnace Burden Surface Detection.

Due to the scheme of fixed-platform beam-steering radar and the space of the blast furnace being subjected to harsh environmental influences, the traditional detection methods of burden surface are limited by geometric distortion, noncoherent clutter, and noise interference, which leads to an increase in the image entropy value and the equivalent number of views, makes the density distribution of burden surface show a diffuse state, and greatly affects the stability and accuracy. In this paper, a new fixed-platform beam-steering radar synthetic aperture radar imaging method (FPBS-SAR) is proposed in the sensory domain of the blast furnace environment. From the perspective of fixed-platform beam-steering radar motion characteristics, the target range-azimuth coupled distance history model under the sub-aperture is established, the azimuthal Doppler variation characteristics of the fixed-platform beam-steering process are analyzed, and the compensation function of the transform domain for geometric disturbance correction is proposed. For noncoherent noise suppression in blast furnaces, the trimmed geometric mean-order-likelihood CFAR method is proposed to take into account the information of burden surface and clutter suppression. To verify the method, point target simulation and imaging for the industrial field measurement data are carried out. The results indicate that geometric distortion is well eliminated, the image entropy value and the equivalent number of views have decreased, and noncoherent noise in blast furnaces is suppressed.