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The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction. [ABSTRACT FROM AUTHOR]

Ionospheric oblique backscatter sounding is an effective means of monitoring the ionosphere which can be used as a frequency selection system to serve HF communication and ensure its quality and stability. But how to obtain effective information from the oblique backscatter ionogram is still a hot issue. Due to this situation, a frequency selecting method for HF communication based on ionospheric oblique backscatter sounding is proposed in this study. After obtaining the ionograms, pattern recognition is used to separate the vertical echoes and the oblique backscatter echoes. Next, the leading edge of the oblique backscatter echoes are extracted, and then a two-dimensional electron density profile can be reconstructed. Then, with the help of ray tracing, the usable frequency range can be estimated. Finally, according to the signal-to-noise ratio reflected by the ionograms, several optimal communication frequencies can be selected. In order to verify this method, oblique ionograms are obtained through oblique sounding experiments to evaluate its accuracy. The result indicates that the usable frequency range and the selected frequencies are in accordance with the echo of the oblique ionogram, so the practicability and accuracy of the method are validated. Eventually, the maximum usable frequencies (MUFs) obtained from oblique backscatter sounding are compared with the MUFs from the oblique sounding ionogram; its Mean Absolute Percentage Error (MAPE) is 7.8% and its root mean squared error (RMSE) is 1.34 MHz. [ABSTRACT FROM AUTHOR]

Monitoring three-dimensional (3D) deformation in underground mining areas is crucial for the prevention and control of mining-induced disasters. Differential interferometric synthetic aperture radar (D-InSAR) is limited to detecting one-dimensional (1D) deformation along the line of sight (LOS). This paper proposes a new method for extracting 3D mining-induced deformation based on the differentiability of D-InSAR LOS deformation fields. The method approximates the D-InSAR LOS deformation field in underground mining areas as a differentiable function and constructs a 3D deformation extraction model utilizing directional derivatives of this function. The least squares method is used for estimating and evaluating the 3D deformation. Simulation and real data experiments have been used to verify the feasibility of the method in extracting mining-induced 3D deformation. The simulation results show relative root mean square errors (RRMSES) of 1.24%, 6.05%, 0.97%, and 11.47% for vertical and horizontal displacements along the east–west and south–north directions, respectively. The real data experiments using Sentinel-1 images show that the root mean square errors (RMSES) of the up–down, south–north, and east–west directions are 14.06 mm, 7.37 mm, and 11.56 mm, respectively. Experimental results show that the method can provide a certain basis for 3D surface deformation monitoring of mining subsidence. [ABSTRACT FROM AUTHOR]

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. [ABSTRACT FROM AUTHOR]

This paper proposes a novel precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI). This method facilitates the spatiotemporal matching of 125 features derived from the multi-temporal and multi-channel data of the FY-4B satellite with precipitation data at stations. Subsequently, a precipitation model was constructed using the light gradient boosting machine (LGBM) algorithm. A comparative analysis of FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 revealed the following: (1) The results demonstrate that FY-4B_AI is more accurate than GPM/IMERG-L. Six of the eight evaluation indices exhibit superior performance for FY-4B_AI in comparison to GPM/IMERG-L. These indices include the mean absolute error (MAE), root mean square error (RMSE), relative error (RE), correlation coefficient (CC), probability of detection (POD), and critical success index (CSI). As for the MAE, the results are 1.67 (FY-4B_AI) and 1.92 (GPM/IMERG-L), respectively. The RMSEs are 3.68 and 4.07, respectively. The REs are 17.72% and 26.28%, respectively. The CCs are 0.44 and 0.36, respectively. The PODs are 61.84% and 47.31%, respectively. The CSIs are 0.30 and 0.27, respectively. However, with regard to the mean errors (MEs) and false alarm rates (FARs), FY-4B_AI (−0.88 and 62.85%, respectively) displays a slight degree of inferiority in comparison to GPM/IMERG-L (−0.80 and 62.21%, respectively). (2) An evaluation of two strong weather events to represent the spatial distribution of precipitation in different climatic zones revealed that both FY-4B_AI and GPM/IMERG-L are equally capable of accurately representing these phenomena, irrespective of whether the region in question is humid, as is the case in the southeast, or dry, as is the case in the northwest. [ABSTRACT FROM AUTHOR]

Detecting and characterizing continuous changes on Earth's surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites' images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models' performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin's Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R2). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R2 and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets. [ABSTRACT FROM AUTHOR]

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model's maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments. [ABSTRACT FROM AUTHOR]

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event. [ABSTRACT FROM AUTHOR]

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms. [ABSTRACT FROM AUTHOR]

The BeiDou Navigation Satellite System (BDS-3) has officially provided services worldwide since July 2020. BDS-3 has added new signals for B1C, B2a and B2b based on old BDS-2 B1I and B3I signals, which brings opportunities for achieving high-precision time transfer. In this research, the BDS-3/BDS-2 combined penta-frequency common-view (CV) single-differenced (SD) precise time transfer model is established with B1I, B3I, B2I, B1C, B2a and B2b signals, including dual-, triple-, quad- and penta-frequency (abbreviated as DF, TF, QF and PF) ionosphere-free (IF) combination CV SD models. Taking four long baseline time links (from 637.6 km to 1331.6 km) as examples, the accuracy and frequency stability of the BDS-3/BDS-2 combined DF, TF, QF and PF SD time transfer models were evaluated. The experimental results show that the frequency stability of the TF, QF and PF SD models were improved by 2.5%, 5.3% and 8.5%, on average, over the DF SD model. Compared with the traditional DF (B1I/B3I IF combination) SD model, the standard deviation (STD) of the multi-frequency SD model was reduced by 5.9%, on average, and the frequency stability was improved by 4.0% on average, which had the most apparent effect on the improvement of short-term frequency stability. Specifically, the DF1 (B1C and B2a DF IF combination), TF1 (B1C, B2a and B2b TF IF combination), QF1 (B1C, B1I, B2a and B2b QF IF combination) and PF4 (B1C, B1I, B2a, B2b and B3I PF IF combination) SD models had better performance in timing, and the PF4 SD model had the best performance. Considering that the PF4 (one PF signal IF combination) SD model does not require an estimated inter-frequency bias and that its noise factor is minor compared with the PF1 (four DF signal IF combination), PF2 (three TF signal IF combination) and PF3 (two QF signal IF combination) SD models, we recommend the PF4 SD model for multi-frequency time transfer and the use of the PF2, PF2 or PF3 SD model to supplement the PF4 SD model in cases of penta-frequency observation loss. [ABSTRACT FROM AUTHOR]

The existing models for estimating cation exchange capacity (CEC) from easy‐to‐measure hygroscopic water content (θh) were based on a single water activity (aw) value rather than on the processes that govern soil water vapor adsorption for a distinct aw range. Here, we present a new CEC estimation model based on θh data of 119 soils with different clay mineralogy (i.e., illitic [IL], montmorillonitic [ML], and kaolinitic [KA] samples) and organic carbon (OC) contents for the aw range from 0.23 to 0.57 (Δθ0.23–0.57) and validate its performance. Based on the hypothesis that multilayer adsorption exhibits a higher correlation with CEC than monolayer adsorption and capillary condensation, the aw range from 0.23 to 0.57 was chosen with CEC calculated as CEC = k × Δθ0.23–0.57. The performance of the new model is compared to the Arthur (2017) model and the Torrent (2015) model, which considers a single θh value. The average proportionality coefficient (k) varied with the dominant clay mineralogy of the investigated soils. For soils dominated by 2:1 clay minerals (i.e., IL and ML), the new model showed a good estimation accuracy (Nash‐Sutcliffe model efficiency [E] ≥ 0.85; root mean squared error [RMSE] ≤ 4.18 cmol(+) kg−1). The new model performed better for IL and ML samples than for KA samples, and yielded more accurate CEC estimations than the Arthur model and Torrent model for soils with 2:1 clay minerals. For soil with high OC content (>23.2 g kg−1), the new model slightly underestimated CEC (E = 0.66; RMSE = 5.87). Core Ideas: A new model for estimating cation exchange capacity (CEC) from hygroscopic water content (θh) is introduced.The new model is established based on θh for a distinct water activity (aw) range.The new model had higher prediction accuracy than a single‐point θh CEC model. [ABSTRACT FROM AUTHOR]

Respiratory rate (RR) is an important indicator of the health and welfare status of dairy cows. In recent years, progress has been made in monitoring the RR of dairy cows using video data and learning methods. However, existing approaches often involve multiple processing modules, such as region of interest (ROI) detection and tracking, which can introduce errors that propagate through successive steps. The objective of this study was to develop an end-to-end computer vision method to predict RR of dairy cows continuously and automatically. The method leverages the capabilities of a state-of-the-art Transformer model, VideoMAE, which divides video frames into patches as input tokens, enabling the automated selection and featurization of relevant regions, such as a cow's abdomen, for predicting RR. The original encoder of VideoMAE was retained, and a classification head was added on top of it. Further, the weights of the first 11 layers of the pre-trained model were kept, whereas the weights of the final layer and classifier were fine-tuned using video data collected in a tiestall barn from 6 dairy cows. Respiratory rates measured using a respiratory belt for individual cows were serving as the ground truth (GT). The evaluation of the developed model was conducted using multiple metrics, including mean absolute error (MAE) of 2.58 breaths per minute (bpm), root mean squared error (RMSE) of 3.52 bpm, root mean squared prediction error (RMSPE; as a proportion of observed mean) of 15.03%, and Pearson r of 0.86. Compared with a conventional method involving multiple processing modules, the end-to-end approach performed better in terms of MAE, RMSE, and RMSPE. These results suggest the potential to implement the developed computer vision method for an end-to-end solution, for monitoring RR of dairy cows automatically in a tiestall setting. Future research on integrating this method with other behavioral detection and animal identification algorithms for animal monitoring in a freestall dairy barn can be beneficial for a broader application. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

The widespread use of milk mid-infrared (MIR) spectroscopy for phenotype prediction has urged the application of prediction models across regions and countries. Spectra standardization is the most effective way to reduce the variability in the spectral signal provided by different instruments and labs. This study aimed to develop different standardization models for MIR spectra collected by multiple instruments, across 2 provinces of China, and investigate whether the standardization method (piecewise direct standardization, PDS, and direct standardization, DS), testing scenario (standardization of spectra collected on the same day or after 7 mo), infrared prediction model accuracy (high or low), and instrument (6 instruments from 2 brands) affect the performance of the standardization model. The results showed that the determination coefficient (R2) between absorbance values at each wavenumber provided by the primary and the secondary instruments increased from less than 0.90 to nearly 1.00 after standardization. Both PDS and DS successfully reduced spectra variation among instruments, and performed significantly better than nonstandardization. However, DS was more prone to overfitting than PDS. Standardization accuracy was higher when tested using spectra collected on the same day compared with those collected 7 mo after, but great improvement in model transferability was obtained for both scenarios compared with the nonstandardized spectra. The less accurate infrared prediction model (for C8:0 and C10:0 content) benefited the most from spectra standardization compared with the more accurate model (for total fat and protein content). For spectra collected 7 mo after standardization, after PDS the root mean square error between predictions obtained by different machines decreased on average by 86% and 94% compared with the values before standardization for C8:0 and C10:0, respectively. The secondary instrument had no significant effect on the R2 between predictions. The variation in the spectral signal provided by different instruments was successfully reduced by standardization across 2 provinces in China. This study lays the foundations for developing a national MIR spectra database to provide consistent predictions across provinces to be used in dairy farm management and breeding programs in China. Additionally, this provides opportunities for data exchange and cooperation at international levels. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

In an analysis of 1,825 state or provincial election outcomes in five federal democracies the rate of decay of incumbency (K) serves to partition the distribution of the vote for the incumbent party, the party of the head of government, between those who win a subsequent term and those who do not. In conjunction with the mean and standard deviation of the distribution, the weighted mean of the vote in re-election and defeat is identified. The model's predictions are generally within 2–3 percentage points of the actual outcome. [ABSTRACT FROM AUTHOR]

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta‐analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z‐scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual‐level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non‐convergence. [ABSTRACT FROM AUTHOR]

The performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x‐variables depends on the type of scaling of the x‐variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with P = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least‐squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model. [ABSTRACT FROM AUTHOR]

Background In December 2021, the BIOFISH 300 SUL method for the determination of total sulfites in shrimp was adopted as a First Action Official Method of AnalysisSM by the AOAC INTERNATIONAL. Objective A collaborative study was conducted in February 2023 in order to test the reproducibility of the method. Methods The method is based on the use of a benchtop biosensor device that relates the concentration of sulfite to a quantifiable electric current signal. The sensing element, the Biotest, harbors an enzyme that specifically oxidizes sulfite, and the reaction products are electrochemically detected by the device in less than 3 min. The sulfite is extracted from the solid using an aqueous-based buffer solution, which ensures that all sulfite is present as a free anion. Results Eleven collaborators participated in the study of nine different shrimp samples. Values of repeatability and reproducibility relative standard deviation (RSDr and RSDR) obtained from the statistical analysis of valid data ranged from 2.1–8.1% and 7.5–14.3%, respectively, for shrimp samples above the quantification limit of the method, set at 7 mg/kg. Conclusion These results showed good repeatability and reproducibility of the method, even at concentrations below the legal threshold for sulfite in food, where the reference optimised Monier-Williams (OMW) method shows relatively high imprecision. Highlights On the basis of these results, the enzymatic amperometric biosensor method developed by BIOLAN Microbiosensores was adopted as Final Action Official Method in September 2023. [ABSTRACT FROM AUTHOR]

This study aims to assess the relationships between SOC content and main soil-forming factors and identify key factors explaining the spatial distribution of SOC. The research was conducted in the Southern Ural Mountains throughout 420 km from north to south in the Republic of Bashkortostan. The predominant soil types are mountainous gray forest (Eutric Retisols (Loamic, Cutanic, Humic)), dark gray forest (Luvic Retic Greyzemic Someric Phaeozems (Loamic)) soils, and gray-humus lithozems (Eutric Leptosols (Loamic, Humic)). Forest stands are mainly composed of birch (Betula pendula), pine (Pinus sylvestris), spruce (Picea obovata Ledeb.), and fir (Abies sibirica Ledeb.). A data set of 306 soil samples taken from the top layer (0–20 cm) was studied using the "random forest" machine learning method. Ninety four spatial environmental covariates were used as explanatory variables, including remote sensing data, climate (temperature, precipitation, cloudiness, etc.), digital elevation model and its derivatives, land uses, bioclimatic zones, etc. The results showed that the SOC content varied widely from 0.8 to 32%. The random forest predictive model explained 55% of SOC variation (R2) with a root mean squared error (RMSE) of 1.35%. Key variables included surface temperature, absolute elevation, precipitation, and cloudiness, which together reflect the Dokuchaev vertical and horizontal zonality laws. The findings emphasize the importance of considering multiple environmental factors in subsequent research focused on assessing the spatial distribution of SOC. [ABSTRACT FROM AUTHOR]

Background: Testicular volume is a marker of male pubertal development. Various clinical conditions and their treatments may influence testicular growth. Objectives: To create ruler‐based age‐dependent pubertal testicular volume references that enable calculation of standard deviation (SD) scores. Materials and Methods: Study cohort comprised 65 boys who attended clinical examination twice a year from the age of 8.5 years until the attainment of final testicular size. Forty‐nine (75.4%) boys completed the follow‐up and 16 (24.6%) boys dropped out before the attainment of final post‐pubertal testicular size. At each follow‐up visit testicular size was measured with a ruler, orchidometer, and ultrasonography. LMS or LMSP method served as the technique for creating reference growth curves for testicular volumes. Using the novel references for ruler measurements, development of SD scores was assessed in a cohort of boys with unilateral cryptorchidism. Results: Reference growth curves were constructed separately for ruler, orchidometer, and ultrasonography measurements. Median orchidometer volume of 4 mL, marker of male pubertal onset, occurred at the age of 11.7 years, whereas +2SD curve surpassed 4 mL at 10.2 years and −2SD curve at 13.7 years. Modeled ages at the attainment of 4 mL testicular volume based on ruler measurements were 9.7 years for +2SD curve, 11.5 years for median curve, and 13.6 years for −2SD curve. Ultrasonography‐based volume of 1.3 mL corresponded with the median modeled orchidometer‐based volume of 4 mL. In boys with unilateral cryptorchidism, ruler‐based SD scores decreased during puberty in undescended testes, but not in descended testes. Discussion and Conclusion: The present study provides reference values for pubertal testicular volume measured with a ruler enabling an age‐dependent assessment of testicular size. Comparison with measurements by an orchidometer and ultrasonography is also presented. [ABSTRACT FROM AUTHOR]

Recently, traditional manufacturing industries have faced two serious gaps and problems in line with effective product-line sales forecasting methods to balance the negative impacts on the performance of the subjective experience, including (1) arbitrary judgment, such as growth rate of expectancy, manager's experiences, and historical sales data, may cause inaccurately predictive results and severe negative effects, and (2) sales forecasting is a key priority and challenge in the context of considerable product lines that have different properties and need specific models for supporting decision analytics. This study is motivated to propose an advanced hybrid model to utilize the advantages of variation for methods of fuzzy time series (FTS), exponential smoothing (ES), moving average (MA), and regression analysis (RA). To analyze the four product lines—stably growing product (SGP), declining product (DP), irregularly growing product (IGP), and special sales product (SSP)—this study is based on empirical sales data from a leading traditional manufacturer to accurately identify the high potentials of decisive key factors and objectively evaluate the model. Two evaluation standards—the mean absolute percentage error (MAPE) and root mean square error (RMSE), a parameter sensitivity analysis, and comparative analysis—are measured. After implementing the data from the case study, four key reports were conclusively identified. (1) Purely for the RMSE, the best one (10.32) is the ES method in the SGP line. (2) In the DP line, the better one is the RA(2) method, with a relatively low MAPE of 17.78% and RMSE of 26.46. (3) The FTS method is the best choice (MAPE 12.41% and RMSE 18.98) for the IGP line. (4) For the SSP line, the better one (MAPE 24.05 and RMSE 29.34) is the MA method. According to the above reports, although the proposed hybrid model has a general performance for the SSP line, it still has a superior predictor when compared to manager subjective prediction. Interestingly, the proposed model is rarely used, has a new trial with an innovative solution for the traditional manufacturer, and thus realizes applicable values. The study concludes with acceptable and satisfactory results and yields seven important findings and three managerial implications that significantly contribute to decision-making reference for complete sales-production planning for interested parties. Thus, this study benefits and values a conventional industry upgrade from novel application techniques. [ABSTRACT FROM AUTHOR]

The quality and the security of preparations in hospital centres are essential to guarantee the patient's safety. The development of fast and efficient analytical methods is required to control the finished product before use. In this context, the study aimed to compare the performance and interchangeability of two spectral analytical methods: the flow injection analysis (FIA) with UV detection and the Raman spectroscopy for the quality control of preparation before use to quantify the remdesivir as a SARS‐CoV‐2 drug. A quantitative study of remdesivir was performed using clinically relevant concentration solutions ranging from 0.25 to 1.625 mg.mL−1 in 0.9% NaCl. Samples were analysed by FIA‐UV at 245 nm and by a handheld Raman spectroscopy at 785 nm. Quantitative models were developed using a calibration set (n = 45 samples) and optimized using a validation set (n = 27). An external validation test set (n = 58) was used to compare the two methods by a Bland–Altman plot. Partial least square regression was used to analyse Raman spectra, while univariate analysis was performed at 245 nm for FIA‐UV. The regression coefficient was higher than 0.990 for both methods, and the root mean square error of prediction was 0.031 mg.mL−1 for Raman spectroscopy. The Bland–Altman plot confirmed the interchangeability of the two methods and the potential of Raman spectroscopy to control remdesivir during clinical preparation in the hospital. [ABSTRACT FROM AUTHOR]

In the present study, a modified carbon nano-onion and β–cyclodextrin composite was utilized in micro-solid phase extraction of atenolol and metoprolol from human plasma samples. The synthesized sorbent structure and morphology were evaluated. In this method, a proper amount of the sorbent was contacted with the diluted plasma sample under vortexing (to promote the sorbent contact area with the solution). The separated particles by centrifugation were contacted with a proper eluent to desorb the target analytes. After that, the eluent was separated by centrifugation and used during analysis of the analytes by high-performance liquid chromatograph. Several effective extraction parameters such as the sorbent amount, type and elution solvent volume, and extraction time were evaluated and optimized. The validated factors showed that detection and quantification were 0.09 and 0.01 and 0.29 and 0.03 ng mL–1, for metoprolol and atenolol, respectively. The method precision, considered as relative standard deviation, was ≤ 2.5 and ≤ 3.6% for intra- (n = 5) and inter-day (n = 4) precisions, respectively. The extraction recovery values were obtained 84 and 63% for metoprolol and atenolol, respectively. Lastly, the developed method was utilized successfully on the plasma samples of patients treated with metoprolol and atenolol. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of specialization, urbanization, and diversification externalities on the dynamics of tourism employment in Brazilian microregions. We use a dynamic panel data model for the 2006–2019 period. The location quotient, population density and the inverse of the Hirschman–Herfindahl index, proxies for specialization, urbanization, and diversification, respectively, positively affect tourism employment in the long run. Based on the estimated long‐run elasticities, the specialization externality produces the strongest influence on tourism employment after a permanent increase of one standard deviation. [ABSTRACT FROM AUTHOR]

Blunt force trauma remains a serious threat to many populations and is commonly seen in motor vehicle crashes, sports, and military environments. Effective design of helmets and protective armor should consider biomechanical tolerances of organs in which they intend to protect and require accurate measurements of deformation as a primary injury metric during impact. To overcome challenges found in velocity and displacement measurements during blunt impact using an integrated accelerometer and two-dimensional (2D) high-speed video, three-dimensional (3D) digital image correlation (DIC) measurements were taken and compared to the accepted techniques. A semispherical impactor was launched at impact velocities from 14 to 20 m/s into synthetic ballistic gelatin to simulate blunt impacts observed in behind armor blunt trauma (BABT), falls, and sports impacts. Repeated measures Analysis of Variance resulted in no significant differences in maximum displacement (p = 0.10), time of maximum displacement (p = 0.21), impact velocity (p = 0.13), and rebound velocity (p = 0.21) between methods. The 3D-DIC measurements demonstrated equal or improved percent difference and low root-mean-square deviation compared to the accepted measurement techniques. Therefore, 3D-DIC may be utilized in BABT and other blunt impact applications for accurate 3D kinematic measurements, especially when an accelerometer or 2D lateral camera analysis is impractical or susceptible to error. [ABSTRACT FROM AUTHOR]

Tegoprazan is a novel potassium‐competitive acid blocker used for gastroesophageal reflux disease. It is administered as the (S)‐ and (R)‐enantiomers are considered impurities that should be controlled. The objective of this work was to establish a chiral analytical method for the determination of the enantiomeric impurity of tegoprazan in the drug substance by high‐performance liquid chromatography (HPLC). Enantioselective separation was performed on a Chiralpak alpha1‐acid glycoprotein (AGP) column (4.6 mm × 250 mm, 5.0 µm) at 30°C utilizing a mobile phase consisting of 5 mmol L−1 ammonium acetate buffer (pH 6.0) and 2‐propanol (93:7, v/v) at a flow rate of 0.7 mL min−1 with a detection wavelength of 220 nm. The effects of chromatographic conditions, including the type and concentration of organic modifiers, buffer pH, buffer concentration, flow rate, and column temperature, were thoroughly investigated. Under the optimized conditions, the resolution between (S)‐ and (R)‐tegoprazan was greater than 3.0. Moreover, the developed method was validated in terms of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy, precision, and stability. Each enantiomer showed a good linear relationship over the concentration range of 1.0–100 µg mL−1, with the correlation coefficient of 0.9999. The LOD and LOQ were 0.1 and 0.3 µg mL−1, respectively. In addition, the recoveries ranged from 94.10% to 99.39% with a relative standard deviation (RSD) of 1.9%. Application of this proposed method to determine the enantiomeric impurity in tegoprazan drug substance proved satisfactory. The quantitative results were about 2.0%, demonstrating that enantiomeric impurity remained in the tegoprazan drug substance and should be controlled in the quality criteria. [ABSTRACT FROM AUTHOR]

An easy, safe, simple, and efficient pH‐switchable hydrophobic deep eutectic solvent (DES)‐based liquid‐phase microextraction was developed for the extraction of chlorobenzenes (CBs) from water samples followed by a gas chromatography–electron capture detector (GC–ECD). The switchability of the synthesized DESs was tested by changing the pH. The DES synthesized from thymol:octanoic acid (OA) in a molar ratio of 2:1 had the highest efficiency in extracting CBs from water samples. 1,4‐Dibromobenzene was used as internal standard (IS). The optimal conditions were established to be 20.0 µL of thymol:OA (2:1), 100 µL of 5 mol L−1 KOH, 100 µL of 5 mol L−1 HCl, 60 s of extraction time, and without salt addition. Under the optimum conditions, the calibration graphs were linear in the range of 0.005–150 µg L−1, and limits of detection were in the range of 0.001–0.03 µg L−1. The relative standard deviations, including intraday and inter‐day for 5.0 µg L−1 of dichlorobenzene isomers, 2.0 µg L−1 of trichlorobenzene and tetrachlorobenzene isomers, and 0.50 µg L−1 of pentachlorobenzene and hexachlorobenzene isomers in water with using IS, were in the range of 0.6%–3.2% (n = 7) and 3.8%–6.7% (n = 7), respectively. The accuracies, including inter‐day and intraday, were in the range of 93.5%–106.3% and 91.7%–109.4%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: According to previous biomechanical studies, the success of meniscus root repair depends on the suture–meniscus interface and optimisation of this procedure seems to be critical. A progressive, reliable and adjustable knot has numerous advantages in meniscal repair since the surgeon can adapt and meticulously tune the final strength of the fixation. We hypothesised that a single passage of one tape at two different points of the posterior meniscal root with a modified Nice knot configuration may allow similar or superior fixation for root repair compared to the cinch stitch suture technique. Methods: Posterior root repair of medial and lateral meniscus was performed on 26 porcine knees. In group (A), two simple cinch stitches were applied, and in group (B), a modified Nice knot was used in a crossmatch configuration. For both groups, two passages through the meniscus with a 2‐mm braided tape were used, and a single transosseous tibial tunnel technique was performed and tested in pull‐out conditions. Results: The modified Nice knot showed an improved biomechanical performance considering the maximum failure load for both the medial (600.7 ± 77.5 N) and lateral (686.1 ± 83.5 N) (p = 0.006) posterior root fixation when compared to a double cinch stitch (558.0 ± 123.9 N) and (629.0 ± 110.2 N) (p = 0.178) for medial and lateral fixation, respectively. The maximum stiffness was also higher for the modified Nice knot configuration for both medial (17.1 ± 1.5 vs. 13.3 ± 1.6 N/mm) and lateral meniscus (20.0 ± 2.6 vs. 13.8 ± 2.3 N/mm), being this difference statistically significative (p = 0.001). Conclusions: The modified Nice knot allowed better adaptation in the pull‐out tests and presented higher fixation strength, stiffness and reproducibility, with lower standard deviation, being at the same time economically advantageous, since only one tape is needed. Level of Evidence: Level III. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is an excellent technology for the rapid analysis of heavy metal (Cu) contaminated Tegillarca granosa. It is well known that LIBS typically contains thousands of wavelengths, but most of these signals are composed of background or irrelevant components that lack desired information. In multivariate data analysis, these redundant signals affect the model's stability and accuracy. Therefore, a strategy is proposed to screen out variables that behave differently from the majority of variables by unsupervised kernel minimum regularized covariance determinant (KMRCD). The KMRCD algorithm with optimized parameters was used to select 50 variables from the LIBS spectra. The partial least squares model constructed with these 50 selected variables demonstrated good performance with a determination coefficient of prediction of 0.806 and a root mean square error of prediction of 16.496 mg kg−1. The obtained results indicate that the unsupervised KMRCD method can effectively eliminate wavelengths that do not provide available metal information from complex LIBS more efficiently than general variable selection methods. This study provides a good reference for identifying informative variables and measuring other constituents in LIBS. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to assess support vector machine (SVM) models' predictive ability to estimate half-cell potential (HCP) values from input parameters by using Bayesian optimization, grid search and random search. Design/methodology/approach: A data set with 1,134 rows and 6 columns is used for principal component analysis (PCA) to minimize dimensionality and preserve 95% of explained variance. HCP is output from temperature, age, relative humidity, X and Y lengths. Root mean square error (RMSE), R-squared, mean squared error (MSE), mean absolute error, prediction speed and training time are used to measure model effectiveness. SHAPLEY analysis is also executed. Findings: The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency. Originality/value: This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity and dimensionality reduction methods such as PCA. [ABSTRACT FROM AUTHOR]

Background: Adaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real‐time or online ART workflow. In response to this challenge, approaches to auto‐segmentation involving deformable image registration, atlas‐based segmentation, and deep learning‐based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model. Purpose: To address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto‐segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)‐IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation. Methods: The PHL‐IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL‐IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients. Results: Implementing the PHL‐IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.81±$ \pm $0.05 with the general model, 0.83±0.04$ \pm 0.04$ for the continual model, 0.83±0.04$ \pm 0.04$ for the conventional IDOL model to an average of 0.87±0.03$ \pm 0.03$ with the PHL‐IDOL model. Similarly, the Hausdorff distance decreased from 3.06±0.99$ \pm 0.99$ with the general model, 2.84±0.69$ \pm 0.69$ for the continual model, 2.79±0.79$ \pm 0.79$ for the conventional IDOL model and 2.36±0.52$ \pm 0.52$ for the PHL‐IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL‐IDOL model. Conclusion: The PHL‐IDOL framework applied to the auto‐segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient‐specific prior information in a task central to online ART workflows. [ABSTRACT FROM AUTHOR]

Background: Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit. Purpose: The aim of this study was to present a novel simulation‐based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities. Methods: Twelve VMAT plans for different treatment sites (prostate‐, head & neck‐, lung‐, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi‐leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D‐spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom. Results: The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values. Conclusions: The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high‐dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high‐dose region was more profound for plans of higher complexity. [ABSTRACT FROM AUTHOR]

AbstractTime-of-flight mass spectrometry (TOF-MS) was used to unravel the composition of commercial polyglycerol polyricinoleate (PGPR) samples, by identifying the various molecular species present. To cover the broad range of molecular weights for the present species, a combination of three ionisation conditions was used. Species exceeding the molecular weight of pentaglycerol hexaricinoleate were difficult to detect. Over 100 molecular species were observed and identified in the analysed samples, including free polyglycerols, ricinoleates, and PGPR-esters. Commercial PGPR samples were shown to be mainly composed of esterification products of di-, tri-, and tetraglycerol, while the esterification degree mainly varied from 1 to 5. The TOF-MS analysis was proven to be reproducible with a relative standard deviation (RSD) below 2.86% for three independent measurements on different days. The method proved to be very suitable to evaluate batch-to-batch variations and to compare the composition of different types of commercial PGPR’s. Moreover, this method can be applied to monitor the quality of PGPR products during the synthesis process. Furthermore, it can also provide fundamental knowledge for optimizing PGPR composition to improve its functionality. [ABSTRACT FROM AUTHOR]

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and environmental challenges, including chemotherapy, cause a cell state transition, which is accompanied by a continuous morphological alteration that is often extremely difficult to recognize even by direct microscopic inspection. To determine whether deep learning-based image analysis enables the detection of cell shape reflecting a crucial cell state alteration, we used the oral cancer cell line resistant to chemotherapy but having cell morphology nearly indiscernible from its non-resistant parental cells. We then implemented the automatic approach via deep learning methods based on EfficienNet-B3 models, along with over- and down-sampling techniques to determine whether image analysis of the Convolutional Neural Network (CNN) can accomplish three-class classification of non-cancer cells vs. cancer cells with and without chemoresistance. We also examine the capability of CNN-based image analysis to approximate the composition of chemoresistant cancer cells within a population. We show that the classification model achieves at least 98.33% accuracy by the CNN model trained with over- and down-sampling techniques. For heterogeneous populations, the best model can approximate the true proportions of non-chemoresistant and chemoresistant cancer cells with Root Mean Square Error (RMSE) reduced to 0.16 by Ensemble Learning (EL). In conclusion, our study demonstrates the potential of CNN models to identify altered cell shapes that are visually challenging to recognize, thus supporting future applications with this automatic approach to image analysis. [ABSTRACT FROM AUTHOR]

Variable radius plot surveys can require updated estimates when the quality of previous estimates have declined over time. We developed a statistical estimator for this purpose that is the product of two estimators, one estimator is for the updated volume-to-basal-area ratio and the other estimator is for the updated basal area. Our estimator relies only on the surveyor identifying previously measured trees and does not require the identification of ingrowth, which simplifies its application. We also propose a variance estimator that relies on the bootstrap method. We investigate the point and variance estimators under repeated sampling of stem-mapped populations that include five measurements over a period of approximately 20 years. Based on 144 sampling scenarios, our estimator demonstrated relative biases ranging between −0.3% and 0.8%. The bootstrap variance estimator tended to be deflated, with relative absolute biases ranging between −8% and 4%. When revisiting one third of the initially installed plots, our estimator obtained relative root mean square errors within 1.6% of an independent resurvey effort that does not use the prior survey. This implies that our estimator can substantially reduce field work while producing surveys of approximately the same quality when updating prior surveys. [ABSTRACT FROM AUTHOR]

Background: Calibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown. Methods: This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces. Results: On average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 0.53 vs. 0.07). SynX yielded calibrated Hill-type muscle–tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to "gold standard" calibrated model parameter values. Conclusions: These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments. [ABSTRACT FROM AUTHOR]

A comparison of error rates between studies in forensic handwriting examination has been made in response to the lack of knowledge on error in this field. Fifteen indicators have been used to describe and compare error rates between studies. The results of each study have been processed to determine these indicators. Parameters related to the participants, conclusion scale, amount of time allocated to the task, and the task itself are also reported. The error rate indicators are provided for each study, and then combined across studies using values of mean and standard deviation. Experts were found to perform better than laypeople. For handwritten texts, absolute error rate for experts ranges from 0.32% to 5.85% (with a mean of 2.84 ± 2.33%), and for laypeople from 11.43% to 28.72% (with a mean of 21.40 ± 8.94%). For signatures, absolute error rate for experts ranges from 0% to 4.86% (with a mean of 2.50 ± 1.55%), and for laypeople from 10.68% to 28% (with a mean of 19.55 ± 7.05%). Overall, experts have an absolute error rate of 2.63 ± 1.73% (against 20.16 ± 7.20% for laypeople). Experts are also more likely to give inconclusive answers than laypeople. Overall, the rate of absolute inconclusive answers for experts is 21.96 ± 23.15% (against 8.13 ± 7.96% for laypeople). The comparative review of error rates presented in the present article contributes to validating the discipline by showing how research has been devoted to meeting the criteria of testing to be considered scientific. [ABSTRACT FROM AUTHOR]

Traditional methods for measuring single-cell mechanical characteristics face several challenges, including lengthy measurement times, low throughput, and a requirement for advanced technical skills. To overcome these challenges, a novel machine learning (ML) approach is implemented based on the convolutional neural networks (CNNs), aiming at predicting cells' elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels. In the present study, the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus, incorporating three widely-used constitutive models that characterize the cellular mechanical behavior, i.e., the Mooney-Rivlin (M-R), Neo-Hookean (N-H), and Kelvin-Voigt (K-V) models. Utilizing this dataset, a multi-input convolutional neural network (MI-CNN) algorithm is developed by incorporating cellular deformation data as well as the time and positional information. This approach accurately predicts the cell elastic modulus, with a coefficient of determination R2 of 0.999, a root mean square error of 0.218, and a mean absolute percentage error of 1.089%. The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R2 of 0.99, even when the stochastic noise is added to the simulated data. One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied. The model accurately and reliably predicts single-cell mechanical properties, showcasing a robust ability to generalize. We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and generalization. This algorithm presents a possibility for high-throughput, highly automated, real-time, and precise characterization of single-cell mechanical properties. [ABSTRACT FROM AUTHOR]

The impact of the Madden-Julian Oscillation (MJO) on India's intraseasonal and interannual variability of rainfall has been investigated in this study. Anomalous MJO amplitude points from 1979 to 2021 are classified into post-, pre- and normal onset years of the Indian Summer Monsoon (ISM). High MJO amplitudes, particularly those exceeding three standard deviations from the mean, correlate with significant rainfall anomalies, affecting both the timing and intensity of the monsoon. Specific MJO phases contribute differently to rainfall patterns, with phases 3, 4, and 5 generally suppressing rainfall, while phases 1 and 8 enhance it. Pre-onset years show significant rainfall anomalies driven by strong MJO activity, particularly during phases 3, 4, and 5. Normal onset years exhibit notable suppression and enhancement patterns, with significant fluctuations observed in 1996 and 2020. Post-onset years have the highest frequency of anomalies, with phases 3 and 4 prominently influencing rainfall variability. Overall, this research underscores the critical role of MJO dynamics in modulating rainfall variability in India. The findings suggest that understanding MJO phases can improve rainfall predictability and monsoon onset forecasting, aiding in better climate prediction and water resource management. This study contributes to the broader climatological discourse by elucidating the interplay between MJO activity and regional precipitation patterns. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of extreme rainfall is vital for planning effective adaptation and mitigation measures. Thus, this study aims to evaluate the performances of 16 general circulation models (GCMs) of the coupled model intercomparison project (8 CMIP5 and 8 CMIP6) in reproducing observed extreme rainfall indices (ERFIs) and monthly rainfall in the Upper Blue Nile (UBN) basin of Ethiopia (1981–2005). The observed ERFIs were computed based on rainfall estimates of the random forest merging (RF-MERGE) algorithm, which combines ground-based rainfall with three gridded rainfall products (GRFPs). The GCMs were evaluated using statistical performance measures such as the Pearson correlation coefficient (R), root mean square error (RMSE), and percent bias (PBIAS). Using the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS), the GCMs were ranked from least skilled to most skilled. Accordingly, MIROC5 of the CMIP5 and MPI-ESM1-2-LR of the CMIP6 models were found to be the most suitable models. Furthermore, the top-ranked models were selected and bias corrected using quantile mapping (QM), and their ensembles (CMIP5-ensemble and CMIP6-ensemble) were used for projecting future extremes under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5), respectively, for the periods 2031–2055 and 2056–2080. Most of the ERFIs exhibited high variability and inconsistent trends for both the observations and future periods. The findings of this study provide valuable insights the the impacts of climate change on ERFIs, and the developed framework can serves as a useful reference for future research. [ABSTRACT FROM AUTHOR]

Objectives: To compare the image quality of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients, with a specific focus on the degree of susceptibility artifacts. Methods: This single-center retrospective study included post-hip arthroplasty patients who underwent 1.5T prostate MRIs between 2021 and 2023, as well as comparative 3T prostate MRIs. Three blinded abdominal radiologists retrospectively reviewed their diffusion-weighted imaging (DWI, 50 s/mm2), T2-weighted imaging (T2WI), and dynamic contrast-enhanced imaging (DCE) to evaluate the image quality. The degree of susceptibility artifacts was categorized using a three-point scale, with 3 indicating the least artifact and 1 indicating the most. Image quality was also evaluated using Prostate Imaging Quality (PI-QUAL) version 2. The median of the three raters' scores was compared between 1.5T and 3T prostate MRIs using the Wilcoxon signed-rank test. The inter-rater agreement was evaluated using the multi-rater generalized kappa. Results: Twenty pairs of 1.5T and 3T prostate MRI examinations from 20 unique patients were included. The DWI susceptibility artifact score at 1.5T was significantly higher than at 3T (mean score ± standard deviation, 2.80 ± 0.41 vs. 2.35 ± 0.93, p = 0.014). In contrast, no significant differences were observed in the susceptibility artifact scores in T2WI and DCE, or in the PI-QUAL score. The inter-reader agreement in the susceptibility artifact score was moderate (multi-rater generalized kappa: 0.60) in DWI, perfect in T2WI (not applicable), and substantial (0.65) in DCE. The inter-reader agreement was fair (0.27) in the PI-QUAL score. Conclusion: Using 1.5T scanners may be preferable to reduce susceptibility artifacts from hip prostheses in DWI. [ABSTRACT FROM AUTHOR]

Purpose: The Reporting and Data System (RADS) has proven successful in various medical settings, but a standardized reporting system for abdominal emergencies is lacking. In this study, the Abdominal Emergency Reporting and Data System (AEM-RADS) for urgent findings on abdominal CT scans is introduced to address the need for consistency in emergency radiology. Methods: In this prospective observational study, conducted over a six-month period, the urgency of abdominal CT scans was assessed using the proposed AEM-RADS scoring system. The committee developed a scale ranging from AEM-RADS 1 (normal) to AEM-RADS 5 (urgent disease). Interobserver agreement between two observers with different experience was evaluated, and robust AEM-RADS reference values were established by radiologists who were not observers. Statistical analysis used mean, standard deviations and Kendall's tau analysis for interobserver agreement. Results: Among 2656 patients who underwent CT for abdominal emergencies, the AEM-RADS distribution was 17.50% AEM-RADS 1, 28.57% AEM-RADS 2, 7.22% AEM-RADS 3, 35.61% AEM-RADS 4, and 11.06% AEM-RADS 5. Interobserver agreement was high, especially for urgent and emergent cases (p < 0.0001). Notable discrepancies were observed in AEM-RADS categories 2C–D and 3B–C, emphasizing the influence of radiologists' experience on interpretation. However, the interobserver agreement for both AEM-RADS 2C–D and 3B–C were statistically significant (p < 0.001). Conclusions: AEM-RADS showed promising reliability, particularly in identifying urgent and emergent cases. Despite some inter-observer discrepancies, the system showed potential for standardized emergency workups. AEM-RADS could significantly enhance diagnostic accuracy in abdominal emergencies and provide a structured framework for shared decision-making between clinicians and radiologists.0 [ABSTRACT FROM AUTHOR]

Objectives: To compare the repeatability and interrelation of various late gadolinium enhancement (LGE) assessment techniques for monitoring fibrotic changes in myocarditis follow-up. Materials and methods: LGE extent change between baseline and 3-month cardiovascular magnetic resonance (CMR) was compared in patients with acute myocarditis using the full width at half maximum (FWHM), gray-scale thresholds at 5 and 6 standard deviations (SD5 and SD6), visual assessment with threshold (VAT) and full manual (FM) techniques. In addition, visual presence score (VPS), visual transmurality score (VTS), and a simplified visual change score (VCS) were assessed. Intraclass-correlation (ICC) was used to evaluate repeatability, and methods were compared using Spearman's correlation. Results: Forty-seven patients (38 male, median age: 27 [IQR: 21; 38] years) were included. LGE extent change differed among quantitative techniques (p < 0.01), with variability in the proportion of patients showing LGE change during follow-up (FWHM: 62%, SD5: 74%, SD6: 66%, VAT: 43%, FM: 60%, VPS: 53%, VTS: 77%, VCS: 89%). Repeatability was highest with FWHM (ICC: 0.97) and lowest with SD5 (ICC: 0.89). Semiquantitative scoring had slightly lower values (VPS ICC: 0.81; VTS ICC: 0.71). VCS repeatability was excellent (ICC: 0.93). VPS and VTS correlated with quantitative techniques, while VCS was positively associated with VPS, VTS, VAT, and FM, but not with FWHM, SD5, and SD6. Conclusion: FWHM offers the least observer-dependent LGE follow-up after myocarditis. VPS, VTS, and VCS are practical alternatives, showing reliable correlations with quantitative methods. Classification of patients exhibiting either stable or changing LGE relies on the assessment technique. Clinical relevance statement: This study shows that LGE monitoring in myocarditis is technique-dependent; the FWHM method yields the most consistent fibrotic tracking results, with scoring-based techniques as reliable alternatives. Key Points: Recognition of fibrotic changes during myocarditis follow-up is significantly influenced by the choice of the quantification technique employed. The FWHM technique ensures highly repeatable tracking of myocarditis-related LGE changes. Segment-based visual scoring and the simplified visual change score offer practical, reproducible alternatives in resource-limited settings. [ABSTRACT FROM AUTHOR]

Purpose: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ( V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods: The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results: The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion: Machine learning models predict V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number: DRKS00031401 (6 March 2023, retrospectively registered). [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures. Methods: Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model. Results: Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5–23.6 and 0.05–0.44 for AM PRS and HRV change, respectively). Conclusion: At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

The aerosol optical depth, as an aerosol optical property, is a key physical quantity used to characterize the degree of atmospheric turbidity. The radiance of the integrating sphere light source after radiometric calibration is relatively stable. Compared with the original use of city lights to invert the night-time aerosol optical depth (AOD), the use of the integrating sphere can effectively reduce the change of the emission source and improve the inversion accuracy. A model to retrieve night-time AOD based on combined measurements of satellite low light channels and ground-based integrating spheres was established by the night-time artificial observation experiment in the Dunhuang radiation correction field (DRCF) for 38 consecutive days from October to November 2018. After excluding cloud pollution and moonlight pollution, the calculated results are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Visible/Infrared Imaging Radiometer Suite (VIIRS) night-time aerosol data. The average root mean square error of the processed data is 0.11 and 0.23, and the correlation coefficient reaches 0.85 and 0.46, respectively. The experimental results preliminarily show that the retrieval AOD is in good agreement with the satellite data, and it is feasible to retrieve AOD based on combined measurements of satellite low light channels and ground-based integrating spheres. [ABSTRACT FROM AUTHOR]

Despite significant progress, a comprehensive analysis of Machine learning algorithms for detecting delamination and crack damage in composite laminate plates is noticeably absent in the literature. This study aims to bridge this gap by meticulously help spectrum of six regression algorithms. By utilizing simulated displacement-time responses from a 16-layer laminated composite plate structure subjected to a random force, a robust dataset is constructed to evaluate the efficacy of these machine learning paradigms. Performance assessments rely on critical metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]

Purpose: This study aims to assess the prevalence of mild and moderate hearing loss spanning three decades, from 1990 to 2019, and to project the anticipated trends from 2020 to 2030 among adolescents, young adults, middle-aged adults, and age-standardised groups in Malaysia. Methods: This study involved secondary data analysis of mild and moderate hearing loss prevalence over 30 years among the Malaysian population aged 15–19, 25–29, 35–39, 45–49, and age-standardised groups. Subsequently, three time-series models were evaluated and the best models with the minimal Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) were selected for projecting the prevalence of hearing loss until 2030. Results: A relatively stable trend of mild hearing loss prevalence and gradual decline of moderate hearing loss were observed across all age groups throughout the study period. The prevalence of mild hearing loss was consistently higher than moderate hearing loss across all age groups, with its prevalence increasing with age. The projected prevalence of hearing loss exhibits a gradual declining trend in the future for all age groups, except for mild hearing loss for the 15-19-year-old group. Conclusion: Over the past 30 years, there has been a relatively stable and slightly declining trend in the prevalence of mild and moderate hearing loss among the Malaysian population, respectively with projections showing a slow reduction in the future. These findings highlighted the need for identifying the best intervention and vulnerable age groups, directing increased resources and prioritization towards them. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter reflecting the interaction between land and atmosphere. Currently, thermal infrared (TIR) quantitative remote sensing technology is the only means to obtain large-scale, high spatial resolution LST. Accurately retrieving high spatial resolution mountainous LST (MLST) plays an important role in the study of mountain climate change. The complex terrain and strong spatial heterogeneity in mountainous areas change the geometric relationship between the surface and satellite sensors, affecting the radiation received by the sensors, and rendering the assumption of planar parallelism invalid. In this study, considering the influence of complex terrain in mountainous areas on atmospheric downward radiation and the thermal radiation contribution of adjacent pixels, a mountainous TIR radiative transfer model based on the sky view factor was developed. Combining with the atmospheric radiative transfer model MODTRAN 5.2, a nonlinear generalized split-window algorithm suitable for high spatial resolution MLST retrieval was constructed and applied to Landsat-9 TIRS-2 satellite TIR remote sensing data. The analysis results indicate that neglecting the topographic and adjacency effects would lead to significant discrepancies in LST retrieval, with simulated data showing LST differences of up to 2.5 K. Furthermore, due to the lack of measured MLST in the field, the MLST accuracy obtained by this retrieval method was indirectly validated using the currently recognized highest-accuracy forward 3D radiative transfer model DART. The MLST and emissivity were input into the DART model to simulate the brightness temperature at the top of the atmosphere (TOA) of Landsat-9 band 10, and compared with the brightness temperature at TOA of Landsat-9 band 10. The RMSE (Root Mean Square Error) for the two subregions was 0.50 and 0.61 K, respectively, indicating that the method proposed can retrieve high-precision MLST. [ABSTRACT FROM AUTHOR]

Background: This study examines the association between the coefficient of variation (%CV) of lithium levels and episode risk and frequency in bipolar patients maintaining serum lithium levels within the therapeutic range. Methods: We retrospectively reviewed patients with bipolar disorder under care from 2018 to 2022. Inclusion criteria were at least 2 years of follow-up, a minimum of three annual lithium level measurements within the therapeutic range. Patients were categorized based on seizure status. We calculated mean lithium levels, standard deviation (SD), and %CV. Results: The study included 75 patients (patients with-without episodes, 39–36). Demographic data revealed no significant differences. While mean lithium levels showed no significant disparity between groups, SD and %CV were notably higher in patients with episodes (P <.05). ROC analysis demonstrated AUC values of 0.722 (95% CI: 0.607–0.836 P =.001) for %CV and 0.709 (95% CI: 0.593–0.826; P =.002) for SD. The optimal %CV cutoff was 17.39, with 67% sensitivity and 69% specificity. A weak correlation was found between %CV and the number of episodes (P =.001, r = 0.376). The post-hoc power analysis for this study was 0.78. Conclusions: Despite acceptable lithium levels, patients with recent episodes exhibited significant lithium level fluctuations. Integrating %CV with real-time lithium measurements during bipolar disorder follow-up may enhance clinical monitoring and seizure prediction. [ABSTRACT FROM AUTHOR]