Mean Root Mean Square Error Research Articles

Noncontact Monitoring of Respiratory Rate in Newborn Infants Using Thermal Imaging.

Monitoring of respiratory rate (RR) is very important for patient assessment. In fact, it is considered one of the relevant vital parameters in critical care medicine. Nowadays, standard monitoring relies on obtrusive and invasive techniques, which require adhesive electrodes or sensors to be attached to the patient's body. Unfortunately, these procedures cause stress, pain, and frequently damage the vulnerable skin of preterm infants. This paper presents a "black-box" algorithm for remote monitoring of RR in thermal videos. "Black-box" in this context means that the algorithm does not rely on tracking of specific anatomic landmarks. Instead, it automatically distinguishes regions of interest in the video containing the respiratory signal from those containing only noise. To examine its performance and robustness during physiological (phase A) and pathological scenarios (phase B), a study on 12 healthy volunteers was carried out. After a successful validation on adults, a clinical study on eight newborn infants was conducted. A good agreement between estimated RR and ground truth was achieved. In the study involving adult volunteers, a mean root-mean-square error (RMSE) of ( 0.31 ±0.09)breaths/min and ( 3.27 ±0.72)breaths/min was obtained for phase A and phase B, respectively. In the study involving infants, the mean RMSE hovered around ( 4.15 ±1.44)breaths/min. In brief, this paper demonstrates that infrared thermography might become a clinically relevant alternative for the currently available RR monitoring modalities in neonatal care.

Toward Long-Term Aquatic Science Products from Heritage Landsat Missions

This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (Rrs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and -5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived Rrs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in Rrs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal Rrs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in Rrs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with Rrs(660) > 0.004 [1/sr], which is equivalent of ~1.2% reflectance. Overall, end-users may utilize heritage Rrs products with “fitness-for-purpose” concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.

Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies, but the influence of their limited surface areas, vegetation growth, and rapid flood dynamics on long-term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimize the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these small water bodies. Extensive hydrometric field data (1999–2014) for seven small reservoirs within the Merguellil catchment in central Tunisia and SPOT imagery are used to calibrate the method and explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7, and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2=0.9) and a mean root mean square error (RMSE) of 9300 m2. Comparison with published global water datasets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (Normalised RMSE = 27 %). Interferences from clouds and scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines. Combining Landsat observations with 10 m pansharpened Sentinel-2 imagery further reduces RMSE to 5200 m2, displaying the increased opportunities for surface water monitoring in small water bodies after 2015.

Identifying representative sites to simultaneously predict hillslope surface and subsurface mean soil water contents

Many approaches have been proposed to identify the representative sampling sites for estimating the spatial mean soil water contents. However, comparisons on these approaches have seldom been conducted to simultaneously predict the surface and subsurface mean soil water contents. In this study, five approaches were evaluated in identifying representative sites to estimate the surface and subsurface mean soil water contents on a typical hillslope in Taihu Lake Basin, China. They were temporal stability analysis (TSA), k-means clustering with environmental factors as inputs (EFs), combinations of TSA and EFs (EFs + TSA), k-means clustering with surface soil water contents as inputs (Theta), and combinations of TSA and Theta (Theta+TSA). The correlation coefficient (r) and root mean squared error (RMSE) between estimated and measured mean soil water contents were used to evaluate the accuracies during the calibration period (the first 25 dates) and validation period (the last 18 dates). Results showed the optimal number of representative sites on this hillslope was six. When >6 representative sites were selected, the TSA had the lowest accuracies for estimating both surface and subsurface mean soil water contents during validation period (mean RMSE ≥ 0.011 m3 m−3). The Theta and Theta + TSA had better accuracies in estimating surface mean soil water contents during both calibration and validation periods (mean RMSE < 0.007 m3 m−3). However, to estimate surface and subsurface mean soil water contents simultaneously, the EFs and EFs + TSA were more promising (mean RMSE < 0.011 m3 m−3 during validation period), especially the EFs which only required one-time collection of environmental factors. These findings will be beneficial for choosing proper approach to calibrate and validate the remote sensed soil water contents.

Aylık Yağışın Konumsal Dağılımının Modellenmesinde Farklı Enterpolasyon Yöntemlerinin Karşılaştırmalı Analizi

For many water resources planning and management studies such as water budget and hydrological modeling, it is very important to estimate areal precipitation from point observation stations. There are many deterministic and geostatistical methods for determining the spatial distribution of precipitation. In this study, the most widely used methods, inverse distance weighting (IDW), Simple Kriging (SK) and Co-Kriging (CK) are applied. It is the main objective of the study that Geographic Information Systems (GIS) techniques are used to compare widely preferred interpolation methods and to model the spatial distribution of monthly precipitation values for prediction in ungauged areas in Akarcay Sinanpasa and Suhut sub-basins, Turkey. At the same time, the effects of number of stations, basin area, characteristics and secondary data usage such as elevation on model performance are investigated. The IDW, a deterministic method and the SK-CK, geostatistical methods are compared with each other by cross validation technique and the applicability of the interpolation techniques for the study areas is analyzed. According to the cross validation test results of IDW, SK and CK methods, the mean RMSE (root mean square error) values of Sinanpasa sub-basin are respectively 13,76 mm, 9,32 mm and 8,72 mm while these values are 9,43 mm, 7,82 mm and 7,90 mm for Suhut sub-basin. Then, uncertainty analysis by means of PSE (prediction standard error) is applied to SK-CK methods with clear advantages over the IDW method and with the close RMSE values. In consideration of the results of the uncertainty analysis, the SK method with the mean PSE values 10,30 mm and 8,54 mm has a little superiority to the CK method whose average PSE values are 11,03 mm and 9,02 mm for both Sinanpasa and Suhut sub-basins, respectively. When the findings are evaluated, it can be seen that all three methods can be used for the study areas. The determination of the spatial distribution of precipitation in this way is considered to be beneficial for many water resources engineering studies in areas of ungauged/sparsely gauged.

Evaluation of a portable low-budget three-dimensional stereophotogrammetry system for nasal analysis

BackgroundThree-dimensional (3D) photogrammetry has reached high standards and accuracy but is mainly conducted with stationary and expensive systems. The purpose of this study was to evaluate the accuracy of a low-budget portable system with special regard to the gracile and challenging nasal region. Material and Methods3D models of the perinasal area were acquired by impression-taking and the scanning of the generated plaster models (3Shape D500) or with a portable low-budget 3D stereophotogrammetry (FUEL3D® SCANIFY®) system. Four examiners analysed defined landmarks of the generated Standard Tessellation Language files with regard to accuracy and interobserver reliability by using 3dMDvultus™ software. A semi-automatic 3D best-fit analysis of both models was performed by using Geomagic® and the Root Mean Squared (RMS) errors were calculated. Results41 volunteers were included, with 22 perinasal and perioral landmarks, 15 3D distances and eight 3D angles being analysed per data set. In a point-based analysis the mean spreads were partially smaller in the plaster model scans. Most measurements showed very high (>0.8) to excellent (>0.9) intraclass correlation coefficients, the lowest being found for columella length (0.686) and left nostril width (0.636). Overall, the mean RMS error between the superimposed surfaces was 0.89 ± 0.22 mm in the best-fit analysis. ConclusionsThe corresponding software program was operator-friendly. The findings indicate that the analysed, affordable and portable system is a feasible solution for 3D image acquisition with comparable accuracy reported in the literature. Further studies will analyse the feasibility in neonates.

Evaluation and analysis of real-time precise orbits and clocks products from different IGS analysis centers

To meet the increasing demands from the real-time Precise Point Positioning (PPP) users, the real-time satellite orbit and clock products are generated by different International GNSS Service (IGS) real-time analysis centers and can be publicly received through the Internet. Based on different data sources and processing strategies, the real-time products from different analysis centers therefore differ in availability and accuracy. The main objective of this paper is to evaluate availability and accuracy of different real-time products and their effects on real-time PPP. A total of nine commonly used Real-Time Service (RTS) products, namely IGS01, IGS03, CLK01, CLK15, CLK22, CLK52, CLK70, CLK81 and CLK90, will be evaluated in this paper. Because not all RTS products support multi-GNSS, only GPS products are analyzed in this paper. Firstly, the availability of all RTS products is analyzed in two levels. The first level is the epoch availability, indicating whether there is outage for that epoch. The second level is the satellite availability, which defines the available satellite number for each epoch. Then the accuracy of different RTS products is investigated on nominal accuracy and the accuracy degradation over time. Results show that Root-Mean-Square Error (RMSE) of satellite orbit ranges from 3.8 cm to 7.5 cm for different RTS products. While the mean Standard Deviations of Errors (STDE) of satellite clocks range from 1.9 cm to 5.6 cm. The modified Signal In Space Range Error (SISRE) for all products are from 1.3 cm to 5.5 cm for different RTS products. The accuracy degradation of the orbit has the linear trend for all RTS products and the satellite clock degradation depends on the satellite clock types. The Rb clocks on board of GPS IIF satellites have the smallest degradation rate of less than 3 cm over 10 min while the Cs clocks on board of GPS IIF have the largest degradation rate of more than 10 cm over 10 min. Finally, the real-time kinematic PPP is carried out to investigate the effects of different real-time products. The CLK90 has the best performance and mean RMSE of 26 globally distributed IGS stations in three components are 3.2 cm, 6.6 cm and 8.5 cm. And the second-best positioning results are using IGS03 products.

Viscoelastic features based compensation technique for polymer optical fiber curvature sensors

Polymer optical fiber (POF) curvature sensors present some advantages over conventional technologies for angle assessment, which are compactness, lightweight, multiplexing capabilities and electromagnetic fields immunity. However, POF curvature sensors may present high hysteresis. Since the polymer is a viscoelastic material, which present a hysteretic response with stress or strain, this paper presents a compensation technique for the sensor hysteresis based on the POF viscoelastic features. The sensor response is modelled as a Prony series and the compensation is made by comparing the sensor response with a response without hysteresis obtained on the sensor analytical model. Results show that this technique not only reduces the hysteresis, but also generally reduces the sensor root mean squared error (RMSE) and increases the sensor linearity. The mean hysteresis obtained with the proposed technique is 0.09%, whereas the mean RMSE obtained is 0.80°.

A Robust Calibration Method for Continental‐Scale Soil Water Content Measurements

Core Ideas We present a semiautomated framework for large‐scale calibration of capacitance soil moisture sensors. Soil‐specific calibrations are needed for accurate moisture measurements in nearly all soil types. Transparent, traceable, and complete uncertainty estimates provided for entire approach. Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large‐scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental‐scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil‐specific calibration coefficients for a capacitance sensor. We found that the manufacturer's nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm3 cm−3 (1σ) relative to reference standard measurements. We derived soil‐specific coefficients, and when used with the manufacturer's nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm3 cm−3 (±1σ). A logistic calibration function further reduced the mean RMSE to ±0.016 cm3 cm−3 (±1σ) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer's function. However, the uncertainty of the reference standard was notable (±0.022 cm3 cm−3), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm3 cm−3 regardless of the calibration function used.

Water requirements of short rotation poplar coppice: Experimental and modelling analyses across Europe

Poplars are among the most widely used short rotation woody coppice (SRWC) species but due to their assumed high water use, concerns have been raised with respect to large-scale exploitation and potentially detrimental effects on water resources. Here we present a quantitative analysis of the water requirements of poplar SRWC using experimental data and a soil water balance modelling approach at three different sites across Europe. We used (i) eddy covariance (EC) measurements (2004–2006) at an irrigated SRWC grown on a previous rice paddy in northern Italy, (ii) Bowen ratio and energy balance (BREB) measurements (2008–2015) and EC (2011–2015) at a SRWC in rain-fed uplands in the Czech Republic, and (iii) EC measurements (2010–2013) at a SRWC on a previous agricultural land with a shallow water table in Belgium. Without any calibration against water balance component measurements, simulations by the newly developed soil water balance model R-4ET were compared with evapotranspiration (ET) measurements by EC and BREB with a resulting mean root mean square error (RMSE) of 0.75 mm day−1. In general, there was better agreement between EC and the model (RMSE = 0.66 mm day−1) when EC data were adjusted for lack of energy balance closure. A comparison of the simulated and measured soil water content yielded a mean RMSE of 0.03 m3 m−3. The mean annual crop coefficient, i.e. the ratio between actual and reference ET, was 0.82 (ranging from 0.65 to 0.95) while the monthly maxima reached 1.16. These values indicated that ET of poplar SRWC was significantly lower than ET of a well-watered grass cover at the annual time scale, but exceeded ET of the reference cover at shorter time scales during the growing season. We show that the model R-4ET is a simple, yet reliable tool for the assessment of water requirements of existing or planned SRWC. For very simple assessments on an annual basis, using a crop coefficient of 0.86 (adjusted to a sub-humid climate), representing an average value across the three sites in years with no evident drought stress, is supported by this analysis.

Image quality of four-dimensional cone-beam computed tomography obtained at various gantry rotation speeds for liver stereotactic body radiation therapy with fiducial markers

In this study, qualities of 4D cone-beam CT (CBCT) images obtained using various gantry rotation speeds (GRSs) for liver stereotactic body radiation therapy (SBRT) with fiducial markers were quantitatively evaluated. Abdominal phantom containing a fiducial marker was moved along a sinusoidal waveform, and 4D-CBCT images were acquired with GRSs of 50–200° min−1. We obtained the 4D-CBCT projection data from six patients who underwent liver SBRT and generated 4D-CBCT images at GRSs of 67–200° min−1, by varying the number of projection data points. The image quality was evaluated based on the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and structural similarity index (SSIM). The fiducial marker positions with different GRSs were compared with the setup values and a reference position in the phantom and clinical studies, respectively. The root mean square errors (RMSEs) were calculated relative to the reference positions. In the phantom study, the mean SNR, CNR, and SSIM decreased from 37.6 to 10.1, from 39.8 to 10.1, and from 0.9 to 0.7, respectively, as the GRS increased from 50 to 200° min−1. The fiducial marker positions were within 2.0 mm at all GRSs. Similarly, in the clinical study, the mean SNR, CNR, and SSIM decreased from 50.4 to 13.7, from 24.2 to 6.0, and from 0.92 to 0.73, respectively. The mean RMSEs were 2.0, 2.1, and 3.6 mm for the GRSs of 67, 100, and 200° min−1, respectively. We conclude that GRSs of 67 and 85° min−1 yield images of acceptable quality for 4D-CBCT in liver SBRT with fiducial markers.

Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly Near Surface Air Temperature in China

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 °C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 °C for the Kriging model, 1.74 °C for the IDW model, and 2.39 °C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models.

A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion

Monitoring pulse oxygen saturation (SpO2) and heart rate (HR) using photoplethysmography (PPG) signal contaminated by a motion artifact (MA) remains a difficult problem, especially when the oximeter is not equipped with a 3-axis accelerometer for adaptive noise cancellation. In this paper, we report a pioneering investigation on the impact of altering the frame length of Molgedey and Schuster independent component analysis (ICAMS) on performance, design a multi-classifier fusion strategy for selecting the PPG correlated signal component, and propose a novel approach to extract SpO2 and HR readings from PPG signal contaminated by strong MA interference. The algorithm comprises multiple stages, including dual frame length ICAMS, a multi-classifier-based PPG correlated component selector, line spectral analysis, tree-based HR monitoring, and post-processing. Our approach is evaluated by multi-subject tests. The root mean square error (RMSE) is calculated for each trial. Three statistical metrics are selected as performance evaluation criteria: mean RMSE, median RMSE and the standard deviation (SD) of RMSE. The experimental results demonstrate that a shorter ICAMS analysis window probably results in better performance in SpO2 estimation. Notably, the designed multi-classifier signal component selector achieved satisfactory performance. The subject tests indicate that our algorithm outperforms other baseline methods regarding accuracy under most criteria. The proposed work can contribute to improving the performance of current pulse oximetry and personal wearable monitoring devices.

Using foliar spectral properties to assess the effects of drought on plant water potential.

Drought frequency is predicted to increase in future environments. Leaf water potential (ΨLW) is commonly used to evaluate plant water status, but traditional measurements can be logistically difficult and require destructive sampling. We used reflectance spectroscopy to characterize variation in ΨLW of Quercus oleoides Schltdl. & Cham. under differential water availability and tested the ability to predict pre-dawn ΨLW (PDΨLW) using spectral data collected hours after pressure chamber measurements on dark-acclimated leaves. ΨLW was measured with a Scholander pressure chamber. Leaf reflectance was collected at one or both of two time points: immediately (ΨLW) and ~5 h after pressure chamber measurements (PDΨLW). Predictive models were constructed using partial least-squares regression. Model performance was evaluated using coefficient of determination (R2), root-mean-square error (RMSE), bias, and the percent RMSE of the data range (%RMSE). ΨLW and PDΨLW were well predicted using spectroscopic models and successfully estimated a wide variation in ΨLW (light- or dark-acclimated leaves) as well as PDΨLW (dark-acclimated leaves only). Mean ΨLWR2, RMSE and bias values were 0.65, 0.51 MPa and 0.09, respectively, with a %RMSE between 8% and 20%, while mean PDΨLWR2, RMSE and bias values were 0.60, 0.44 MPa and 0.01, respectively, with a %RMSE between 9% and 20%. Estimates of PDΨLW produced similar statistical outcomes when analyzing treatment effects on PDΨLW as those found using reference pressure chamber measurements. These findings highlight a promising approach to evaluate plant responses to environmental change by providing rapid measurements that can be used to estimate plant water status as well as demonstrating that spectroscopic measurements can be used as a surrogate for standard, reference measurements in a statistical framework.

Constructing a long‐term monthly climate data set in central Asia

ABSTRACTWe compiled and merged in situ observation data from several sources, creating a comprehensive unified monthly air temperature and precipitation data set with 457 stations in central Asia (CA). Stations with a valid data rate higher than 80% were selected, and the remaining gaps in selected station time series were filled with an iterative‐principal component analysis (PCA) gap‐filling method. The result is a gap‐filled station data set for the period 1951–2010, with 369 and 381 stations for air temperature and precipitation, respectively. The cross‐validation shows that the iterative‐PCA gap‐filling algorithm provides stable and trustworthy estimations of gaps, with mean root mean squared error (RMSE) of 0.03 °C (in the range of 0.01–0.13 °C) for air temperature, and mean RMSE of 0.60 mm (in the range of 0.10–1.99 mm) for precipitation. A gridded data set was created by interpolating the gap‐filled station data set with the geographically weighted regression method. Comparison of the gridded data set with the National Centers for Environmental Prediction (NCEP) reanalysis data set shows that though both data sets present the long‐term mean climate situation similarly, the gridded data set exhibits less annual and monthly variability. And the gridded data set has stronger correlations with stations time series than the reanalysis data set (mean correlations are 0.994 vs 0.975 for air temperature, and 0.787 vs 0.515 for precipitation), especially for precipitation in high mountain stations. The gridded data set is more suitable for climate and hydrological studies in CA, especially in high mountains regions.

Improving representation of canopy temperatures for modeling subcanopy incoming longwave radiation to the snow surface

AbstractA comprehensive analysis of canopy surface temperatures was conducted around a small and large gap at a forested alpine site in the Swiss Alps during the 2015 and 2016 snowmelt seasons (March–April). Canopy surface temperatures within the small gap were within 2–3°C of measured reference air temperature. Vertical and horizontal variations in canopy surface temperatures were greatest around the large gap, varying up to 18°C above measured reference air temperature during clear‐sky days. Nighttime canopy surface temperatures around the study site were up to 3°C cooler than reference air temperature. These measurements were used to develop a simple parameterization for correcting reference air temperature for elevated canopy surface temperatures during (1) nighttime conditions (subcanopy shortwave radiation is 0 W m−2) and (2) periods of increased subcanopy shortwave radiation &gt;400 W m−2 representing penetration of shortwave radiation through the canopy. Subcanopy shortwave and longwave radiation collected at a single point in the subcanopy over a 24 h clear‐sky period was used to calculate a nighttime bulk offset of 3°C for scenario 1 and develop a multiple linear regression model for scenario 2 using reference air temperature and subcanopy shortwave radiation to predict canopy surface temperature with a root‐mean‐square error (RMSE) of 0.7°C. Outside of these two scenarios, reference air temperature was used to predict subcanopy incoming longwave radiation. Modeling at 20 radiometer locations throughout two snowmelt seasons using these parameterizations reduced the mean bias and RMSE to below 10 W m s−2 at all locations.

Cochlear Implantation in Cases of Unilateral Hearing Loss: Initial Localization Abilities.

The present study evaluated early auditory localization abilities of cochlear implant (CI) recipients with normal or near-normal hearing (NH) in the contralateral ear. The goal of the study was to better understand the effect of CI listening experience on localization in this population. Twenty participants with unilateral hearing loss enrolled in a prospective clinical trial assessing outcomes of cochlear implantation (ClinicalTrials.gov Identifier: NCT02203305). All participants received the MED-EL Standard electrode array, were fit with an ear-level audio processor, and listened with the FS4 coding strategy. Localization was assessed in the sound field using an 11-speaker array with speakers uniformly positioned on a horizontal, semicircular frame. Stimuli were 200-msec speech-shaped noise bursts. The intensity level (52, 62, and 72 dB SPL) and sound source were randomly interleaved across trials. Participants were tested preoperatively, and 1, 3, and 6 months after activation of the audio processor. Performance was evaluated in two conditions at each interval: (1) unaided (NH ear alone [NH-alone] condition), and (2) aided, with either a bone conduction hearing aid (preoperative interval; bone conduction hearing aid + NH condition) or a CI (postoperative intervals; CI + NH condition). Performance was evaluated by comparing root-mean-squared (RMS) error between listening conditions and between measurement intervals. Mean RMS error for the soft, medium, and loud levels were 66°, 64°, and 69° in the NH-alone condition and 72°, 66°, and 70° in the bone conduction hearing aid + NH condition. Participants experienced a significant improvement in localization in the CI + NH condition at the 1-month interval (38°, 35°, and 38°) as compared with the preoperative NH-alone condition. Localization in the CI + NH condition continued to improve through the 6-month interval. Mean RMS errors were 28°, 25°, and 28° in the CI + NH condition at the 6-month interval. Adult CI recipients with normal or near-normal hearing in the contralateral ear experienced significant improvement in localization after 1 month of device use, and continued to improve through the 6-month interval. The present results show that binaural acclimatization in CI users with unilateral hearing loss can progress rapidly, with marked improvements in performance observed after only 1 month of listening experience.

Diurnal Air Temperature Modeling Based on the Land Surface Temperature

The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the land surface temperature (LST) DTC. The air DTC model parameters were estimated from LST DTC model parameters by a regression analysis. Here, the LST obtained from the INSAT-3D geostationary satellite and the air temperature extracted from weather stations were used within the time frame of 4 March 2015 to 22 May 2017 across Iran. Constant parameters of the air DTC model for each weather station were estimated based on an experimental approach over the time period. Results showed these parameters decrease as elevation increases. The mean absolute error (MAE) and the root mean square error (RMSE) for three hours sampling were calculated. The MAE and RMSE ranges were between [0.1, 4] °C and [0.1, 3.3] °C, respectively. Additionally, 95% of MAEs and RMSEs were less than 2.9 °C and 2.4 °C values, correspondingly. The range of the mean values of MAEs and RMSEs for a three-hour sampling time were [−0.29, 0.6] °C and [2, 2.11] °C. The DTC model results showed a meaningful statistical fitting in both air DTCs modeled from LST and weather station-based DTCs. The variability of mean error and RMSE in different land covers and elevation classes were also investigated. In spite of the complex behavior of the environmental variables in the study area, the model error bar did not show significantly biased estimations for various classes. Therefore, the developed model was less sensitive to variations of land covers and elevation changes. It can be conclude that the coefficients of regression between LST and air DTC could model properly the environmental factors.

A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.

Thermal performance analysis of an inclined passive solar still using agricultural drainage water and artificial neural network in arid climate

In this study, a model based on artificial neural network (ANN) was developed in order to predict the thermal performance of an inclined passive solar still in an arid climate, in which the thermal performance of the still was expressed as instantaneous thermal efficiency (ITE). Agricultural drainage water (AWD) was used as a feed for the desalination process, and this is considered a non-conventional water source. Appropriate meteorological variables, viz., ambient air temperature, relative humidity, wind speed, and solar radiation were used alongside the key operational variables, viz., flow rate, temperature, and total dissolved solids of feed water were used as input variables. The results revealed that an ANN with six neurons and a hyperbolic tangent transfer function was the most appropriate model for ITE prediction. Consequently, the optimal ANN model had a 7–6–1 architecture. The results also indicated that the optimal ANN model forecast the ITE accurately, with a mean root mean square error (RMSE) of just 1.933% and a mean coefficient of determination (CD) of 0.949. To create a sensible comparison, a multiple linear regression (MLR) model was also developed. It was found that the ANN model performed better than the MLR model, which displayed a mean RMSE of 4.345% and a mean CD of 0.739. The mean relative errors of forecasted ITE values within the ANN model were mostly in the region of +8% to −6%. One major output of this research is a comprehensive assessment of the ANN modeling technique for the ITE of a solar still, which adds a new perspective to system analysis, design and modeling of the potential productivity of solar stills in terms of the AWD desalination process.