Reduced Root Mean Square Research Articles

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system (GNSS) technique has irreplaceable advantages in the continuous monitoring of surface deformation. Reducing noise to improve the signal-to-noise ratio (SNR) and extract the concerned signals is of great significance. As an improved algorithm of empirical mode decomposition (EMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm has better signal processing ability. Using the CEEMDAN algorithm, the height time series of 29 GNSS stations in Chinese mainland were analyzed, and good denoising effects and extraction from periodic signals were achieved. The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis, and large differences in the long-term signals were found between the stations at different locations in Chinese mainland. With respect to data denoising, compared with the EMD and wavelet denoising algorithms, the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%, increased the correlation coefficient by 8.67% and 11.96%, and reduced root mean square error (RMSE) by 44.68% and 43.48%, indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms. In addition, the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity. The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm, which indicated that the CEEMDAN algorithm had advantages over frequency aliasing. In conclusion, the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization

This study aims to enhance the effectiveness of automobile air conditioning (AAC) systems through the use of composite nano-lubricants and fuzzy modeling optimization techniques. Composite nano-lubricants, which consist of varied metal oxide ingredients and content ratios, are projected to surpass single-component nano-lubricants in terms of improving the performance of AAC systems. Fuzzy modeling is used to simulate the AAC system based on experimental data using three input parameters: volume concentration of nano-lubricants (%), the refrigerant charge (g), and compressor speed (rpm). The output performance of the AAC system is measured using four parameters: cooling capacity (CC) in kW, compressor work (CW) in kJ/kg, coefficient of performance (COP), and power consumption (PC) in kW. Optimization is performed using the marine predators algorithm (MPA) to identify the best values for the input control parameters. The objective function is to minimize CW, COP, and PC while simultaneously maximizing CC and COP. Results showed that the performance of the AAC system improved from 85% to 88% compared to the experimental dataset, highlighting the potential benefits of using composite nano-lubricants and fuzzy modeling optimization for improving the energy efficiency of AAC systems. Furthermore, a comprehensive comparison with ANOVA was performed to demonstrate the superiority of the fuzzy modeling approach. The results indicate that the fuzzy model outperforms ANOVA, as evidenced by a reduced root mean square error (RMSE) for all data, from 0.412 using ANOVA to 0.0572 using fuzzy. Additionally, the coefficient of determination for training increased from 0.9207 with ANOVA to 1.0 with fuzzy, further substantiating the success of the fuzzy modeling phase.

Effects of Preoperative Sarcopenia-Related Parameters on Cardiac Autonomic Function in Women with Obesity Following Bariatric Surgery: A One-Year Prospective Study.

Investigate changes in blood pressure (BP) and heart rate variability (HRV) in women with and without sarcopenia-related parameters who underwent bariatric surgery (BS) during a one-year follow-up. Women were separated into obesity (OB, n = 20) and women with obesity displaying sarcopenia-related parameters (SOP, n = 14) and evaluated before BS and 3, 6, and 12 months after BS. SOP was defined as low handgrip strength (HS) and/or low appendicular skeletal mass adjusted for weight (ASM/wt × 100, %) in the lowest quartile of the sample. ASM/wt × 100, % and HS were significantly lower in SOP than OB over a one-year follow-up of BS (p < 0.05). There was a reduction in diastolic BP, heart rate (HR), SDHR, LF, and the LF/HF ratio (p < 0.05) and an increase in the HF band in both groups during the follow-up period (p < 0.05). SOP women had reduced root mean square differences of successive RR intervals (RMSSD) and HF band and an increased LF band and SD2/SD1 ratio compared to the OB group during the one-year follow-up (p < 0.05). ASM/wt × 100, % was negatively associated with the LF band (r = -0.24, p = 0.00) and positively associated with the HF band (r = 0.22, p = 0.01). Conversely, HS had no association with LF (r = -0.14, p = 0.09) and HF (r = 0.11, p = 0.19). ASM/wt × 100, % and HS were negatively associated with the LF/HF ratio (p < 0.05). Women who underwent BS had an improved HRV over a one-year follow-up. However, the improvement in HRV variables was less pronounced in women with low muscle mass and/or HS during the follow-up period.

Improved flux-surface parameterization through constrained nonlinear optimization

Parameterization of magnetic flux-surfaces is often used for magnetohydrodynamic stability analysis and microturbulence modeling in tokamaks. Shape parameters for such local parameterization of a (numerical) equilibrium are traditionally computed analytically using geometrically derived quantities. However, often the shape is approximated by the average of values for different sections of the flux-surface contour or a truncated series, which does not guarantee an optimal fit. Here, instead nonlinear least squares optimization is used to compute these parameters, with a weighted sum of squared error cost function that is robust to outliers. This method results in a lower total absolute error for both the parameterization of the flux-surface contour and the poloidal magnetic field density than current methods for several parameterizations based on the well-known “Miller geometry.” Furthermore, rapid convergence of shape parameters is achieved, no approximate geometric measurements of the contour are needed, and the method is applicable to any analytical shape parameterization. Validation with local, linear gyrokinetic simulations using these optimized shape parameters showed reduced root mean square errors in both the growth rate and frequency spectra when compared with simulations based on numerical equilibria. In particular, the popular Turnbull–Miller parameterization benefits from this approach, extending its usability closer toward the last-closed flux-surface for cases with minor up-down asymmetry.

Impact of Surface Roughness on the Impingement of Urea–Water Solution Droplets

The understanding of impingement processes is crucial for optimizing automotive selective catalytic reduction (SCR) systems. An accurate description of this behavior helps design exhaust systems and increases the validity of modeling approaches. A component test bench was set up, featuring a droplet chain generator for producing droplet sizes typically found in the urea–water solution sprays of SCR systems. A heatable impingement plate with an interchangeable surface enabled investigation of the influence of surface roughness. Data were acquired using a high-speed camera and image postprocessing. The droplet–wall interaction could be described using different regimes. An approach to characterizing impingement behavior based on weighted-regime superposition enabled gradual transitions between regimes, instead of step-like changes. It was observed that the surface roughness increased the droplet–solid contact area and generated thermal-induced secondary droplets at lower temperatures. A region of enhanced mechanical disintegration of the droplet was found, caused by peaks of the surface shearing off parts of the droplet. The probability of a droplet rebounding from the wall was reduced on a rough surface, due to the interference of the surface spikes with the droplet’s spreading and contracting motion. Additionally, the influence of surface topography was investigated using a shot-peened surface. Caused by this surface’s reduced root mean square slope, the aforementioned enhancement of mechanical disintegration was not observed.

Alternative muscle synergy patterns of upper limb amputees.

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.

A noise autocovariance model for SAR altimeter measurements with implications for optimal sampling

Earlier work has empirically demonstrated some advantages of an increased posting rate of Synthetic Aperture Radar (SAR) altimeters beyond the expected ground resolution of about 320 m in Delay-Doppler (unfocused SAR, UFSAR) processing, corresponding to ∼20 Hz sampling. Higher posting rates of 40–80 Hz were shown to prevent spectral aliasing of the signal, enable to measure swell wave related signal distortions and may lead to a reduced root mean square error of 1 Hz estimates of Sea Surface Heights (SSH), radar cross section (sigma0) and Significant Wave Heights (SWH) from current SAR altimeters. These improvements were explained by the narrow noise autocovariance function of the waveform signal’s power speckle noise in along-track direction on one hand, and frequency doubling by power detection (squaring of the signal) on the other. It has not been explained, however, why the power speckle noise decorrelates faster than anticipated by the predicted Doppler resolution, and whether this decorrelation depends on the altimeter and processing configuration. Also, it has not been shown explicitly that the estimates of SSH, SWH and sigma0 decorrelate in the same way. Describing the noise autocovariance function – or equivalently the noise power spectral density via the Wiener-Kintchin theorem – is necessary on two counts: Knowing the noise autocovariance allows to apply optimal filtering strategies that maximize precision on one hand, while the noise power spectral density predicts the frequencies contained in the noise (and signals), which in turn determines the required sampling frequency according to the Nyquist theorem. Using a newly derived analytic noise autocovariance model for UFSAR-processed altimeter data, we show that the swift signal decorrelation is mainly due to the observation geometry. Furthermore, our results demonstrate that the noise autocovariance functions of power speckle, SSH, SWH and sigma0 estimates in along-track direction are different and depend on the sea state. On top of that, the noise autocovariance functions are strongly dependent on the number of Doppler beams used for multilooking, the used retracker, and the processing choices such as antenna gain pattern compensation and windowing within the UFSAR processing (Level-1b). We validated our noise autocovariance model with segments of 42 Sentinel-3B overpasses. Our findings are in accordance to all earlier work, but indicate that the reported precision improvements with respect to 20 Hz may have been too optimistic and that the SSH, SWH and sigma0 generally decorrelate slower than the power speckle noise. We found that the required posting rate is always higher or equal to 40 Hz. Our results will potentially enable improved spectral analysis and optimal filtering of any UFSAR altimetry data. More importantly, our results can be used to trade off different aspects for determining an optimal posting rate in UFSAR altimeter processing in different sea states and with changing processing parameters, which is necessary in view of strict precision requirements of existing and future SAR altimetry missions.

Innovations at the Nexus of Sustainability and Industry 4.0: Data-Driven Approach for Preemptive Equipment Management in Smart Factories

The convergence of Industry 4.0 and sustainability has brought forth a new era of manufacturing, where data-driven approaches play a pivotal role in achieving operational efficiency while minimizing environmental impact. This paper presents an innovative framework for sustainable smart manufacturing through data-driven predictive maintenance planning. By integrating advanced analytics and machine learning, we propose a preemptive equipment management approach that not only optimizes production processes but also fosters environmental responsibility. Our methodology combines the power of Long Short-Term Memory (LSTM) networks for pattern modeling and the Sea Lion Optimization Algorithm for feature selection. We demonstrate the effectiveness of our approach through a comprehensive empirical analysis conducted on a real case study, where the results indicate significant improvements over baseline studies, as evidenced by reduced Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), along with higher R-squared (R2) values. Our findings emphasize the synergy between technological innovation and sustainability imperatives, positioning our approach as a catalyst for reshaping modern manufacturing practices.

Study on Optical Fiber Gas-Holdup Meter Signal Denoising Using Improved Threshold Wavelet Transform

The optical fiber probe gas-holdup meter is a new logging tool, which can be used to measure the gas-holdup of oil-gas-water three-phase flow during production logging. In this paper, the wavelet transform is used to denoise the measured gas-holdup signal of the optical fiber probe. For the wavelet transform threshold de-noising method, the existing hard and soft threshold functions have certain defects. So we propose a wavelet transform denoising method with an improved threshold function, and use this method to denoise the measured gas-holdup signal of the optical fiber probe. The signal-to-noise ratio, root mean square error, smoothness, and correlation coefficient are used as the evaluation indexes of signal denoising effect. The improved threshold function is between the hard threshold function and the soft threshold function. It has the advantages of both hard and soft threshold functions, can effectively overcome the discontinuity of the hard threshold function and the constant deviation of the soft threshold function, and improves the denoising efficiency of the gas-holdup signal. The proposed method is applied to the synthetic data and the real gas-holdup signal of the optical fiber probe respectively. Compared with the traditional soft and hard threshold functions, the experiment results show that the improved threshold wavelet denoising method is feasible. And it is effective in the gas-holdup signal denoising, improving the signal-to-noise ratio, reducing root mean square error, and increasing the correlation coefficient. The proposed method preserves well the original features of the measured gas-holdup signal, which provides a reference for the signal processing and dynamic monitoring analysis of the subsequent oil-well downhole development.

Experimental validation of a hybrid 1-D multi-node model of a hot water thermal energy storage tank

Hot water-based thermal energy storage (TES) tanks are extensively used in heating applications to provide operational flexibility. Simple yet effective one-dimensional (1-D) tank models are desirable to simulate and design efficient energy management systems. However, the standard multi-node modelling approach struggles to reproduce the dynamics of highly thermally stratified tanks due to their artificial numerical diffusion. In this paper, a novel 1-D multi-node modelling approach is introduced for accurately simulating water tanks with a high extent of thermal stratification. A non-linear, hybrid continuous–discrete time model able to capture the sudden temperature change within the tank is presented. The modelling approach was adopted to simulate a commercial TES tank, with the model being implemented in MATLAB/Simulink. Results from experimental tests were compared with simulation results, demonstrating that a hybrid continuous–discrete 12-node model accurately estimates the temperatures of the tank. It is also shown that the hybrid model avoids the numerical diffusion exhibited by standard multi-node models. This has been evidenced by the reduced root mean square and mean absolute errors exhibited by the hybrid model when compared with the experimental data.

Multi-step forecast of PM2.5 and PM10 concentrations using convolutional neural network integrated with spatial–temporal attention and residual learning

Accurate and reliable forecasting of PM2.5 and PM10 concentrations is important to the public to reasonably avoid air pollution and for the governmental policy responses. However, the prediction of PM2.5 and PM10 concentrations has great uncertainty and instability because of the dynamics of atmospheric flows, making it difficult for a single model to efficiently extract the spatial–temporal dependences. This paper reports a robust forecasting system to achieve accurate multi-step ahead forecasting of PM2.5 and PM10 concentrations. First, correlation analysis is adopted to screen the spatial information on pollution and meteorology that may facilitate the prediction of concentrations in a target city. Then, a spatial–temporal attention mechanism is used to assign weights to original inputs from both space and time dimensions to enhance the essential information. Subsequently, the residual-based convolutional neural network with feature extraction capabilities is employed to model the refined inputs. Finally, five accuracy metrics and two additional statistical tests are applied to comprehensively assess the performance of the proposed forecasting system. In addition, experimental studies of three major cities in the Yangtze River Delta urban agglomeration region indicate that the forecasting system outperforms various prevalent baseline models in terms of accuracy and stability. Quantitatively, the proposed STA-ResCNN model reduces root mean square error by 5.595 %-15.247 % and 6.827 %-16.906 % for the average of 1–4 h ahead predictions in three major cities of PM2.5 and PM10, respectively, compared to baseline models. The applicability and generalization of the proposed forecasting system are further verified by the extended applications in the other 23 cities in the entire region. The results prove that the forecasting system is promising in the early warning, regional prevention, and control of air pollution.

Corneal morphological and structural changes measured with pentacam induced by pterygium surgery

AbstractIntroduction: Pterygium has been related to the induction of astigmatic and optical aberrations and surgery could induce a reduction in these corneal characteristics.Purpose: Study main objective was to evaluate corneal changes and optical aberrations that occura after primary pterygium surgery, and as a secondary objective, to evaluate changes depending on the used surgical technique (simple excision vs. conjunctival plasty).Methods: Data collection was performed through Scheimpflug camera system for anterior segment analysis (Pentacam®): corneal astigmatism in diopters (K) and curvature radius and corneal aberration (Root Mean Square (RMS) Low order aberrations (LOA) and RMS High Order aberrations HOA) in a total of 18 eyes of 18 patients from the Hospital Universitario de Jerez (HUJ) who need pterigion surgery. Variables measured were analysed before surgery and 3 months later.Results: Results showed that there is no statistically significant change in astigmatism after pterygium surgery, although there is a statistically significant reduction in total RMS of the anterior cornea (p = 0.02), RMS LOA of the anterior cornea (p = 0.03) and RMS HOA of total cornea (p = 0.007), anterior cornea (p = 0.03) and posterior cornea (p = 0.03). There are not statistically significant differences between both groups when comparing the surgical technique used.Conclusions: No statistically significant changes in astigmatism after pterygium surgery have been found, but there is a significant decrease in optical aberrations, especially in the anterior cornea. It cannot be concluded that one surgical technique is superior to another.

Design of field programmable gate array-based data processing system for multi global positioning system receiver

&lt;span&gt;A global positioning system (GPS) sensor is needed for a ballistic/moving object to do position tracking. In previous study, a multi GPS processing system was made using several microcontrollers and data processing cannot be done simultaneously. Therefore, it was considered as ineffective system. In this research, field programmable gate array (FPGA)-based data processing system for multi-GPS receiver was proposed. The proposed system was designed to reduce root mean square error (RMSE). There are two main processes in the proposed system which work in parallel, i.e. data parsing and data processing. Raw data from GPS receiver was collected and calculated to get average value, then sent it through serial communication to show result. Experimental results &lt;span&gt;confirm the RMSE value of the proposed system is smaller than the conventional one. The RMSE for latitude, longitude, and altitude decrease by 38.46%, 58.28%, and 24.&lt;/span&gt;80%, respectively.&lt;/span&gt;

Prediction of Air Pollutant Concentrations via RANDOM Forest Regressor Coupled with Uncertainty Analysis—A Case Study in Ningxia

Air pollution has not received much attention until recent years when people started to understand its dreadful impacts on human health. According to air pollution and the meteorological monitoring data from 1 January 2016 to 31 December 2017 in Ningxia, we analyzed the impact of ground surface temperature, air temperature, relative humidity and the power of wind on air pollutant concentrations. Meanwhile, we analyze the relationships between air pollutant concentrations and meteorological variables by using the mathematical model of decision tree regressor (DTR), feedforward artificial neural network with back-propagation algorithm (FFANN-BP) and random forest regressor (RFR) according to air-monitoring station data. For all pollutants, the RFR increases R2 of FFANN-BP and DTR by up to 0.53 and 0.42 respectively, reduces root mean square error (RMSE) by up to 68.7 and 41.2, and MAE by up to 25.2 and 17. The empirical results show that the proposed RFR displays the best forecasting performance and could provide local authorities with reliable and precise predictions of air pollutant concentrations. The RFR effectively establishes the relationships between the influential factors and air pollutant concentrations, and well suppresses the overfitting problem and improves the accuracy of prediction. Besides, the limitation of machine learning for single site prediction is also overcame.

A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China

Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations.

Effects of fixed versus variable task prioritization during short-term dual task practice on motor and cognitive task performance in young adults.

ObjectiveIt has been shown that variable compared to fixed task prioritization during dual task practice more effectively improves motor (i.e., postural control) and cognitive (i.e., memory) performance in older adults. However, it is unclear whether this finding is also valid in young adults. Thus, the present study examined the effect of fixed (allocate equal priority on both tasks) versus variable (vary priority between both tasks) priority during short-term motor-cognitive dual task practice on single and dual task performance in healthy young adults (age range: 20–30 years).ResultsDuring two days of practice, significant improvements of motor (i.e., balance task: reduced root mean square error; p < 001, ηp2 = .72) and cognitive (i.e., arithmetic task: increased serial three subtractions; p < .001, ηp2 = .78) task performance were observed and that was irrespective of group (“fixed priority” and “variable priority”). Further, the statistical analysis of post-practice single and dual task performance revealed no significant differences between groups, irrespective of task (i.e., motor or cognitive). This indicates that in young as opposed to old adults, single and dual task performance improvements are independent of task prioritization (i.e., fixed or variable priority) during short-term motor-cognitive dual task practice.

Optimal Current Ripple PWM for Three-Level Inverter With Common Mode Voltage Reduction

Three-level inverter has been widely used in electric drives and renewable power generation system. However, the common mode voltage (CMV) and output current ripple reduction of three-level inverter should be carefully regulated for the appropriate operation. As these two targets are mutually interrelated, the conventional space vector modulation (SVM) scheme cannot cope with them decently. To overcome this drawback, this paper proposes a novel modified SVM scheme to realize CMV and current ripple reduction simultaneously. The generating mechanism of output current ripple is analyzed, and the relationship between current ripple and utilized vectors is quantitatively described. Based on the analyses, the proposed optimal current ripple and CMV reduction pulse width modulation (OCR-CMVRPWM) method adopts four voltage vectors (large, medium, small and zero vectors) to reduce root mean square (RMS) value of current ripple by allocating the sequence and dwell times of utilized vectors. Compared to the existing PWM methods, the proposed OCR-CMVRPWM scheme can reduce the peak of CMV to half of value using the conventional SVM, and obtain a significant reduction of output current total harmonic distortion (THD). The effectiveness of the proposed method is validated by both simulation and experimental results.

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

Dosimetric assessment of patient dose calculation on a deep learning-based synthesized computed tomography image for adaptive radiotherapy.

PurposeDose computation using cone beam computed tomography (CBCT) images is inaccurate for the purpose of adaptive treatment planning. The main goal of this study is to assess the dosimetric accuracy of synthetic computed tomography (CT)‐based calculation for adaptive planning in the upper abdominal region. We hypothesized that deep learning‐based synthetically generated CT images will produce comparable results to a deformed CT (CTdef) in terms of dose calculation, while displaying a more accurate representation of the daily anatomy and therefore superior dosimetric accuracy.MethodsWe have implemented a cycle‐consistent generative adversarial networks (CycleGANs) architecture to synthesize CT images from the daily acquired CBCT image with minimal error. CBCT and CT images from 17 liver stereotactic body radiation therapy (SBRT) patients were used to train, test, and validate the algorithm.ResultsThe synthetically generated images showed increased signal‐to‐noise ratio, contrast resolution, and reduced root mean square error, mean absolute error, noise, and artifact severity. Superior edge matching, sharpness, and preservation of anatomical structures from the CBCT images were observed for the synthetic images when compared to the CTdef registration method. Three verification plans (CBCT, CTdef, and synthetic) were created from the original treatment plan and dose volume histogram (DVH) statistics were calculated. The synthetic‐based calculation shows comparatively similar results to the CTdef‐based calculation with a maximum mean deviation of 1.5%.ConclusionsOur findings show that CycleGANs can produce reliable synthetic images for the adaptive delivery framework. Dose calculations can be performed on synthetic images with minimal error. Additionally, enhanced image quality should translate into better daily alignment, increasing treatment delivery accuracy.

Spatial changes of nutrients and metallic contaminants in topsoil with multi-geostatistical approaches in a large-size watershed

Appropriate assessment on concerned soil contaminants spatially is of importance for decision-makers and stakeholders to make efficient mitigation countermeasures. In this study, we applied multiple geostatistical approaches to explore soil nutrient and metallic contaminant distributions in a large river watershed in Thailand, and to compare their performances in predicting spatial distribution patterns of the concerned soil contaminants under suitable application scenarios. The total carbon, nitrogen and phosphorous in surface soils over the whole watershed were measured with their maximum concentrations up to 131.47, 9.24, 5.33 g·kg−1, respectively, while the concentrations of eight metallic elements (Cu, Zn, Pb, Cd, Hg, As, Cr, and Ni) were 933.00, 6862.50, 373.00, 6.22, 1.15, 178.53, 761.11, and 372.44 mg·kg−1, respectively. It was found that the conditional interpolation approaches such as land use stratified inverse distance weighted and land use stratified original kriging provided better boundary details than original interpolations, with substantially reduced root mean square errors (up to 28% for nutrients and 54% for specific metals) and mean relative errors (up to 38% for nutrients and specific metals respectively) in predicting the spatial patterns of soil nutrients and several land use specific metals (Cu, Zn, Cd, and Pb). The global accuracies were equivalent or higher than those of geographically weighted regression. Nonetheless, the prediction accuracy for Cr, Ni, As, and Hg could not be improved using the land use stratified interpolation because their sources and pathways were not significantly correlated with land use types in the watershed, as reflected by the results of analysis of variance with post hoc test (p ≤ 0.05) and principal component analysis.