Higher Root Mean Square Error Research Articles

Thigh Accelerometer Placement Accuracy for Physical Activity and Energy Expenditure Prediction in a Semi-Structured Setting

An inclinometer-based accelerometer (PAL) worn on the thigh, coupled with proprietary software, may provide accurate estimates of sedentary time. However, its accuracy for estimating other physical activity (PA) intensities and energy expenditure (EE) is unknown. PURPOSE: To evaluate accuracy of the PAL software for prediction of time spent in PA intensities [sedentary, light, and moderate-to-vigorous (MVPA)] and EE and compare its accuracy to that of a machine learning model (ANN) developed from raw PAL data. METHODS: Participants (n=39 [19 male]; age=22.1±4.3) completed a 90-min, semi-structured protocol in which they performed 13 activities for 3-10 min each, choosing activity order, duration, and intensity. Participants wore a PAL accelerometer on the right thigh and a portable metabolic analyzer (OXY). Time spent in sedentary, light, and MVPA was determined using MET values of ≤1.5, 1.6-2.9, and ≥3.0, respectively, calculated from OXY. Estimated times in each PA intensity from the PAL software and ANN were compared with OXY using difference scores and 95% confidence intervals; non-overlap of confidence intervals with zero indicated significant differences from OXY. Window-by-window EE prediction was assessed using correlations and root mean square error (RMSE). RESULTS: PAL software-predicted sedentary time was not different from OXY (-1.6 min; 95% CI: -3.6 - 0.4 min), but light PA was over-predicted (6.2 min; 95% CI: 4.1 - 8.3 min) and MVPA was under-predicted (-4.6 min; 95% CI: -6.5 - -2.7 min). ANN-predicted sedentary time and light PA were not different from OXY, (-0.7 min, 95% CI: -2.3 - 0.8 min; -0.9 min; 95% CI: -2.8 - 1.1 min, respectively), but MVPA was over-predicted (1.6 min; 95% CI: 0.1 - 3.2 min). For EE prediction, the PAL software had lower correlations (r=0.76 vs. r =0.89) and higher RMSE (1.74 vs. 1.07 METs) than the ANN. Under-prediction by the PAL software was more pronounced at intensities >5.0 METs. CONCLUSIONS: The PAL software distinguished between sedentary and non-sedentary activities but had high error for EE prediction, especially for higher-intensity PA. The ANN had high accuracy for prediction of sedentary and light PA and EE prediction, indicating strong potential for raw data from a thigh-worn PAL to be used in PA assessment. Supported by Blue Cross Blue Shield of Michigan Foundation.

Measuring quality of life in opioid-induced constipation: mapping EQ-5D-3L and PAC-QOL.

BackgroundIn health economic evaluations, quality of life should be measured with preference-based utilities, such as the EuroQol 5 Dimension 3-level (EQ-5D-3 L). Non-preference-based instruments (often disease-specific questionnaires) are commonly mapped to utilities. We investigated if the relationship observed between the Patient Assessment of Constipation Quality of Life (PAC-QOL) and the EQ-5D-3 L in patients with chronic idiopathic constipation (CIC) also applies in opioid-induced constipation (OIC).MethodsEQ-5D-3 L patient-level data from a clinical study of lubiprostone in OIC (n = 439) were scored using the UK tariff. A published mapping between the PAC-QOL and the EQ-5D-3 L was tested using these data. New mapping formulas were analysed, including PAC-QOL total and subscale scores. The root mean square error (RMSE), the adjusted R2 and predicted/observed plots were used to test the fit.ResultsThe utility measured with the EQ-5D-3 L was 0.450 ± 0.329, with a distinctly bimodal distribution. This significantly improved if patients responded to treatment (defined as an increase of three spontaneous bowel movements per week, with no rescue medication taken). The published mapping in CIC performed poorly in this OIC population, and the PAC-QOL could not be reliably mapped on to the EQ-5D-3 L even when re-estimating coefficients. This was shown in our two mappings (using PAC-QOL total score, and subscale scores) by a high RMSE (0.317 and 0.314) and a low R2 (0.068 and 0.080), with high utilities underestimated and low utilities overestimated.ConclusionsPatients with OIC have a low quality of life which does improve with the resolution of symptoms. However the PAC-QOL cannot be used to estimate the EQ-5D-3 L utility – potentially as the PAC-QOL does not capture the all relevant aspects of the patients quality of life (for example the cause of the opioid use).

Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland

ABSTRACTNDVI (Normalized Difference Vegetation Index) time-series have been used for permitting a land surface phenology retrieval but these time series are affected by clouds and aerosols, which add noise to the signal sensor. In this sense, several smoothing functions are used to remove noise introduced by undetected clouds and poor atmospheric conditions, but a comparison between methods is still necessary due to disagreements about its performance in the literature. The application of a smoothing function is a necessarily previous step to describe land surface phenology in different ecosystems. The aims of this research were to evaluate the consistency of different smoothing functions from TIMESAT software and their impacts on phenological attributes of temperate grassland – a complex mosaic of land uses with natural vegetated and agricultural regions using NDVI-MODIS time series. An adaptive Savitzky–Golay (SG) filter, Asymmetric Gaussian (AG) and Double Logistic (DL) functions to fitting NDVI data were used and their performances were assessed using the measures root mean square error (RMSE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and bias. Besides, differences on the estimation of the start of the growing season (SOS) and the length of the growing season (LOS) were obtained. High and low RMSE over croplands and grassland were observed for the three smoothing functions; in the rest of the region, the SG filter showed more reliable results. Patterns of difference on the estimation of SOS and LOS between SG filter and the other two models were randomly distributed, where differences of 20–50 days were found. This study demonstrated that methods from TIMESAT software are robust and spatially consistent but must be carefully used.

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors

Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR) soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF) is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI) scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE), while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

Performance of Decadal Prediction in Coupled Model Intercomparisson Project Phase 5 (CMIP5) on Projecting Climate in Tropical Area

Skill of BCC-CSM1.1, MPI-ESM-LR and IPSL-CM5A-LR (climate models which are collected together in Coupled Model Intercomparison Project Phase 5) on projecting precipitation, air temperature and sea surface temperature (SST) in tropical region were analyzed during December-January-February (DJF) and June-July-August (JJA) using Empirical Orthogonal Functions (EOF), climatology, bias and Root Mean Square Error (RMSE) parameters. These models were compared with the reanalysis/observation data. The results showed that dominant modes (EOF1) of all models for air temperature were similar with the mode of reanalysis data. In general, EOF1 of all models for precipitation are similar with mode of the observation which is dominated by positive (negative) anomaly in northern (southern) equator. Climatology of precipitation, air temperature and sea surface temperature showed that high values were located in southern equator during DJF and shifted to the north during JJA. The highest air temperature bias was located in Africa (around 6°C during DJF and JJA) for MPI-ESM-LR. In addition all models captured the ITCZ signal. The highest RMSE and bias of precipitation (> 9 and 5mm/day) were located in western Pacific in BCC-CSM1.1 during DJF and IPSL-CM5A-LR during JJA. BCC-CSM1.1 had higher RMSE (> 9 mm/day) in projecting precipitation in Papua and Borneo, MPI-ESM-LR detected higher bias in Sumatra, Java, Nusa Tenggara, while IPSL-CM5A-LR in Sumatra. The highest RMSE for SST (more than 2°C) was located in the eastern Pacific.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03–0.06) and TME (0.2–0.8). In principal component regression, low value of adjusted R2 and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models.

Improved Runoff Estimation Using Event-Based Rainfall-Runoff Models

Event-based rainfall-runoff models are effective tools in operational hydrological forecasting and preparedness for extreme events. In the current study, the popular Natural Resources Soil Conservation curve number (NRCS-CN) model and the proposed simple nonlinear models were employed for runoff estimation. The runoff prediction capability of the NRCS model for the CN values obtained from tables was very poor in comparison to those calculated from the measured rainfall-runoff (storm-events) data. The proposed models were calibrated based on the rank-order, measured rainfall-runoff data (1,005 events) from 25 watersheds and validated in six watersheds for runoff estimation (170 events). The quantitative models’ performances were evaluated and compared based on the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS). Using tabulated CNs, the NRCS model exhibited comparatively insignificant results in the maximum number of watersheds (high RMSE, low NSE, and statistically poor PBIAS values). Using storm-event based calibrated CNs, the NRCS model showed improvement for runoff estimation. Furthermore, the proposed models without the CN concept were superior (with comparatively low RMSE, high NSE, and statistically significant PBIAS values) for depicting improved performance in almost all of the watersheds.

Optimizing internal wave drag in a forward barotropic model with semidiurnal tides

A global tuning experiment for the semidiurnal tide is performed with a barotropic model. The model is forced with the M2 equilibrium tide and accounts for the self-attraction and loading (SAL) term. In addition to a quadratic drag, various linear internal wave drag terms adjusted by a scale factor of O(1) are applied. The drag terms include the original Nycander (2005) tensor scheme, the Nycander tensor scheme reduced at supercritical slopes, and their scalar sisters, a Nycander scalar scheme computed for additional abyssal hill roughness, and the Jayne and St. Laurent (2001) scalar scheme. The Nycander scheme does not have a tunable parameter, but to obtain the best tidal solutions, it is demonstrated that some tuning is unavoidable. It is shown that the scalar Nycander schemes yield slightly lower root-mean square (RMS) elevation errors vs. the data-assimilative TPXO tide model than the tensor schemes. Although the simulation with the optimally tuned original Nycander scalar yields dissipation rates close to TPXO, the RMS error is among the highest. The RMS error is lowered for the reduced schemes, which place relatively more dissipation in deeper water. The inclusion of abyssal hill roughness improves the regional agreement with TPXO dissipation rates, without changing the RMS errors. It is difficult to have each ocean basin optimally tuned with the application of a constant scale factor. The relatively high RMS error in the Atlantic Ocean is reduced with a spatially varying scale factor with a larger value in the Atlantic. Our best global mean RMS error of 4.4cm for areas deeper than 1000m and equatorward of 66° is among the lowest obtained in a forward barotropic tide model.

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (Corg) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial Corg distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total Corg stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest Corg stocks. While vegetation Corg has very high RMSEs, outcomes for soil and total Corg stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations.

A note on arterial to venous oxygen saturation as reference for NIRS-determined frontal lobe oxygen saturation in healthy humans

A note on arterial to venous oxygen saturation as reference for NIRS-determined frontal lobe oxygen saturation in healthy humans

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

Comparison of observed TEC values with IRI-2007 TEC and IRI-2007 TEC with optional foF2 measurements predictions at an equatorial region, Chumphon, Thailand

In this research, as part of working towards improving the IRI over equatorial region, the total electron content (TEC) derived from GPS measurements and IRI-2007 TEC predictions at Chumphon station (10.72°N,99.37°E), Thailand, during 2004–2006 is analyzed. The seasonal variation of the IRI-2007 TEC predictions is compared with the TEC from the IRI-2007 TEC model with the option of the actual F2 plasma frequency (foF2) measurements as well as the TEC from the GPS and International GNSS service (IGS). The Chumphon station is located at the equatorial region and the low latitude of 3.22°N. For a declining phase of the solar cycle (2004–2006), the study shows that the IRI-2007 TEC underestimates the IRI-2007 TEC with the foF2 observation at the nighttime by about 5 TECU. The maximum differences are about 15 TECU during daytime and 5 TECU during nighttime. The overestimation is more evident at daytime than at nighttime. When compared in terms of the root-mean square error (RMSE), we find that the highest RMSE between GPS TEC and IRI 2007 TEC is 14.840 TECU at 1230 LT in 2004 and the lowest average between them is 1.318 TECU at 0630 LT in 2006. The noon bite-out phenomena are clearly seen in the IRI-2007 TEC with and without optional foF2 measurements, but not on the GPS TEC and IGS TEC. The IRI TEC with optional foF2 measurements gives the lowest RMSE values between IRI TEC predicted and TEC measurement. However, the TEC measurements (GPS TEC and IGS TEC) are more correct to use at Chumphon station.

Satellite-derived UV irradiance for a region with complex morphology and meteorology: comparison against ground measurements in Santiago de Chile

Ground-based measurements of ultraviolet (UV) irradiance, carried out by a four-channel UV radiometer in Santiago de Chile from October 2004 to December 2011, have been used to estimate daily values of the UV index (UVI). These ground-based data have been compared with UVI estimates retrieved from the Ozone Measurement Instrument (OMI) on board the Aura spacecraft. Since the widely used OMI-gridded UVI data may not be suitable for the complex local morphology and meteorology, a careful screening of overpass OMI data was applied. Nevertheless, we found that OMI-derived UVI data overestimate ground-based values; depending on cloud-cover conditions, the mean bias (MB) and the root mean square error (RMSE) range from 34.53% to 30.29% and from 35.22% to 43.50%, respectively, with the lowest MB (and the highest RMSE) values occurring under overcast conditions. Moreover, the difference between satellite-derived and ground-based UVI data exhibits a limited seasonality with somewhat larger differences in the fall season. The detected overestimation seems to be linked with the boundary layer aerosol absorption that is not accounted for by the OMI algorithm. Indeed, we found that the difference in UVI increases with the aerosol concentration (which in Santiago shows seasonal variations). Ceilometer profiles of backscatter intensities, directly related to aerosol concentrations, and PM10 concentrations correlate with UVI differences (correlation coefficient r of approximately 0.6 and 0.4, respectively) under cloud-free conditions for time scales ranging from months to years. Additional comparisons were performed between UVI estimates retrieved from our ground-based measurements in Santiago and from the Tropospheric Emission Monitoring Internet Service (TEMIS) Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Under cloudless conditions, also TEMIS-derived data overestimate ground-based UVI estimations (by about 31%) and exhibit a small seasonality.

Comparative Evaluation of US Food and Drug Administration and Pharmacologically Guided Approaches to Determine the Maximum Recommended Starting Dose for First-in-Human Clinical Trials in Adult Healthy Men

The authors compared US Food and Drug Administration (FDA) and 9 pharmacologically guided approaches (PGAs; simple allometry, maximum life span potential [MLP], brain weight, rule of exponent [ROE], two 2-sp methods and 3 one-sp methods) to determine the maximum recommended starting dose (MRSD) for first-in-human clinical trials in adult healthy men using 10 drugs. The ROE method as suggested by Mahmood and Balian1 gave the best prediction accuracy for a pharmacokinetic (PK) parameter. Values derived from clearance were consistently better than volume of distribution (Vd)-based methods and had lower root mean square error (RMSE) values. A pictorial method evaluation chart was developed based on fold errors for simultaneous evaluation of various methods. The one-sp method (rat) and the US FDA methods gave the highest prediction accuracy and low RMSE values, and the 2-sp methods gave the least prediction accuracy with high RMSE values. The ROE method gave more consistent predictions for PK parameters than other allometric methods. Despite this, the MRSD predictions were not better than US FDA methods, probably indicating that across-species variation in clearance may be higher than variation in no observed adverse effect level (NOAEL) and that PGA methods may not be consistently better than the NOAEL based methods.

Estimating impervious surfaces from medium spatial resolution imagery: a comparison between fuzzy classification and LSMA

Impervious surfaces are important environmental indicators and are related to many environmental issues, such as water quality, stream health and the urban heat island effect. Therefore, detailed impervious surface information is crucial for urban planning and environment management. To extract impervious surfaces from remote sensing imagery, many algorithms and techniques have been developed. However, there are still debates over the strengths and limitations of linear versus nonlinear algorithms in handling mixed pixels in the urban landscapes. In the meantime, although many previous studies have compared various techniques, few comparisons were made between linear and nonlinear techniques. The objective of this study is to compare the performance between nonlinear and linear methods for impervious surface extraction from medium spatial resolution imagery. A linear spectral mixture analysis (LSMA) and a fuzzy classifier were applied to three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images acquired on 5 April 2004, 16 June 2001 and 3 October 2000, which covered Marion County, Indiana, United States. An aerial photo of Marion County with a spatial resolution of 0.14 m was used for validation of estimation results. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R 2) were calculated to indicate the accuracy of impervious surface maps. The results show that the fuzzy classification outperformed LSMA in impervious surface estimation in all seasons. For the June image, LSMA yielded a result with an RMSE of 13.2%, while the fuzzy classifier yielded an RMSE of 12.4%. For the April image, LSMA yielded an accuracy of 21.1% and the fuzzy classifier yielded 17.0%. For the October image, LSMA yielded a result with an RMSE of 19.8%, but the fuzzy classifier yielded an RMSE of 17.5%. Moreover, a subset image of the commercial, high-density and low-density residential areas was selected in order to compare the effectiveness of the developed algorithms for estimating impervious surfaces of different land use types. The result shows that the fuzzy classification was more effective than LSMA in both high-density and low-density residential areas. These areas prevailed with mixed pixels in the medium resolution imagery, such as ASTER. The results from the tested commercial area had a very high RMSE value due to the prevalence of shade in the area. It is suggested that the fuzzy classifier based on the nonlinear assumption can handle mixed pixels more effectively than LSMA.

Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE)

The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure–Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructures under identical C- or log P-judgements in KATE. However, in the case of the Daphnia end-point this was not so, and the group of chemicals with reactive substructures did not always have higher RMSEs: the Schiff base mechanism did not function as a high error detector. In addition to the RMSE findings, the presence of outliers suggested that the KATE classification rules needs to be reconsidered, particularly for the amine group. Examination of the dependency of the organism on the toxic action of chemicals in fish and Daphnia revealed that some of the reactive substructures could be applied to the improvement of the KATE system. It was concluded that the reaction mechanistic domains of toxic action for skin sensitisation could provide useful complementary information in predicting acute aquatic ecotoxicity, especially at the fish end-point.

Representing the precipitation regime by means of Fourier series

AbstractWe propose the use of Fourier series for representing the precipitation regime in a certain location and predicting it in ungauged locations, allowing for map production. We analyse monthly average precipitation data of 2043 gauging stations covering the Italian territory. The Fourier series allows to represent a curve as a sum of different sinusoidal components characterized by their period, amplitude and phase. Being the different harmonics not correlated, it is possible to fit them with stepwise multiple linear regressions. The Fourier series allows for a parsimonious representation of the regime, being usually the 12‐ and 6‐month harmonics able to reproduce the observed values with little residuals [in this exercise the fitting gave an average monthly root mean square error (RMSE) of 9.21 mm and a correlation coefficient of 0.979]. Once the at‐station harmonics parameters are obtained, it is possible to map them for predicting the regime in ungauged locations. Here we use ordinary kriging and the leave‐one‐out validation scheme for evaluating the amplitudes and phases of the harmonics of the 12‐ and 6‐month periods and reconstructing the precipitation regime. We use the same scheme for the interpolation of the station data on a month‐by‐month basis, whose results are used as a benchmark. The analyses provide similar results, with overall RMSEs of 17.53 and 15.97 mm and correlation coefficients of 0.909 and 0.921, respectively. The spatial patterns of the reconstruction error are similar for the two cases. The stations having higher RMSE are clustered in the areas presenting high precipitation gradients, such as in the Appennines, or where major precipitation regime changes occur. For demonstrating that the Fourier series approach is more suitable for regionalization purposes, a k‐means cluster analysis on the Fourier parameters was performed and the effect of such stratification on the mapping of the precipitation regime by applying regression kriging was assessed. Copyright © 2010 Royal Meteorological Society

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

The studies of impervious surfaces are important because they are related to many environmental problems, such as water quality, stream health, and the urban heat island effect. Previous studies have discussed that the self-organizing map (SOM) can provide a promising alternative to the multi-layer perceptron (MLP) neural networks for image classification at both per-pixel and sub-pixel level. However, the performances of SOM and MLP have not been compared in the estimation and mapping of urban impervious surfaces. In mid-latitude areas, plant phenology has a significant influence on remote sensing of the environment. When the neural networks approaches are applied, how satellite images acquired in different seasons impact impervious surface estimation of various urban surfaces (such as commercial, residential, and suburban/rural areas) remains to be answered. In this paper, an SOM and an MLP neural network were applied to three ASTER images acquired on April 5, 2004, June 16, 2001, and October 3, 2000, respectively, which covered Marion County, Indiana, United States. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination ( R 2) were calculated to indicate the accuracy of impervious surface maps. The results indicated that the SOM can generate a slightly better estimation of impervious surfaces than the MLP. Moreover, the results from three test areas showed that, in the residential areas, more accurate results were yielded by the SOM, which indicates that the SOM was more effective in coping with the mixed pixels than the MLP, because the residential area prevailed with mixed pixels. Results obtained from the commercial area possessed very high RMSE values due to the prevalence of shade, which indicates that both algorithms cannot handle the shade problem well. The lowest RMSE value was obtained from the rural area due to containing of less mixed pixels and shade. This research supports previous observations that the SOM can provide a promising alternative to the MLP neural network. This study also found that the impact of different map sizes on the impervious surface estimation is significant.

Accuracy of Respiration Rate Trends from an Implanted CRM Device

Purpose: Elevated respiratory rate (RespR) is a sign of acute decompensated heart failure (ADHF). Accurate ambulatory monitoring of RespR with an implanted device in HF patients may provide early warning of ADHF. Some implanted CRM devices include intra-thoracic impedance (ITZ) sensing, which may be used to monitor RespR. Methods: Retrospective analysis of data from 2 clinical studies was performed to determine the accuracy of RespR derived from ITZ. RespR was monitored using both ITZ (RespRz) and external respiration sensors (RespRe). In study 1 (20 pacemaker patients, age 69±10, 55% male), RespRe was derived from a pneumotach during various posture and respiratory maneuvers and a sub-maximal exercise test. In study 2 (15 pacemaker patients with sleep apnea, age 68±12, 100% male), RespRe was derived from respiratory bands during a sleep study. Median RespRz and RespRe values were computed for each of three sets of analysis epochs: 10 min, 30 min, and 8 hours (study 2 only). Accuracy was evaluated using the coefficient of determination (R2), bias (mean difference between RespRz and RespRe), and the root-mean-square error (RMSE) between RespRz and RespRe. Summary: Excellent agreement was obtained between RespRz and RespRe for all analysis epochs, indicated by high R2 (> 0.84), low bias (< 0.6 br/min, breaths per minute) and low RMSE (< 1.6 br/min) (see table). For the 10-min epochs, over 90% of RespRz values were within 3 br/min of RespRe. For the 30-min and 8-hour epochs, over 97% were within 3 br/min. Conclusions: Accurate RespR trends can be derived from an ITZ sensor over a variety of challenging posture, activity, and respiratory conditions, including exercise and sleep apnea. Monitoring RespR with implanted devices may prove to be a useful tool for managing ambulatory HF patients.

Development of a Wheelchair Virtual Driving Environment: Trials With Subjects With Traumatic Brain Injury

Spaeth DM, Mahajan H, Karmarkar A, Collins D, Cooper RA, Boninger ML. Development of a wheelchair virtual driving environment: trials with subjects with traumatic brain injury. Objective To develop and test a wheelchair virtual driving environment that can provide quantifiable measures of driving ability, offer driver training, and measure the performance of alternative controls. Design A virtual driving environment was developed. The wheelchair icon is displayed in a 2-dimensional, bird's eye view and has realistic steering and inertial properties. Eight subjects were recruited to test the virtual driving environment. They were clinically evaluated for range of motion, muscle strength, and visual field function. Driving capacity was assessed by a brief trial with an actual wheelchair. During virtual trials, subjects were seated in a stationary wheelchair; a standard motion sensing joystick (MSJ) was compared with an experimental isometric joystick by using a repeated-measures design. Setting Subjects made 2 laboratory visits. The first visit included clinical evaluation, tuning the isometric joystick, familiarization with virtual driving environment, and 4 driving tasks. The second visit included 40 trials with each joystick. Participants Subjects (n=8; 7 men, 1 woman) with a mean age of 22.65±2y and traumatic brain injury, both ambulatory and nonambulatory, were recruited. Interventions The MSJ used factory settings. A tuning program customized the isometric joystick transfer functions during visit 1. During the second visit, subjects performed 40 trials with each joystick. Main Outcome Measure The root mean square error (RMSE) was defined as the average deviation from track centerline (in meters) and speed (in m/s). Results Data analysis from the first 8 subjects showed no statistically significant differences between joysticks. RMSE averaged .12 to .21m; speed averaged .75m/s. For all tasks and joysticks, driving in reverse resulted in a higher RMSE and more virtual collisions than forward driving. RMSE rates were greater in left and right turns than straight and docking tasks. Conclusions Testing with instrumented real wheelchairs can validate the virtual driving environment and assess whether virtual driving skills transfer to actual driving.