Root Mean Square Estimation Research Articles

The validity and reliability of the type 2 diabetes and health promotion scale Turkish version: a methodological study.

A healthy promotion is important for maintaining health and preventing complications in patients with type 2 diabetes. The aim of the present study was to examine the psychometrics of a recently developed tool that can be used to screen for a health-promoting lifestyle in patients with type 2 diabetes. Data were collected from outpatients attending diabetes clinics. The Type 2 Diabetes and Health Promotion Scale (T2DHPS) and a demographic questionnaire were administered to 295 participants. Forward-backward translation of the original English version was used to develop a Turkish version. Internal consistency of the scale was assessed by Cronbach's alpha. An explanatory factor analysis and confirmatory factor analysis used validity of the Type 2 Diabetes and Health Promotion Scale - Turkish version. Kaiser-Meyer-Olkin (KMO) and Bartlett's sphericity tests showed that the sample met the criteria required for factor analysis. The reliability coefficient for the total scale was 0.84, and alpha coefficients for the subscales ranged from 0.57 to 0.92. A six-factor solution was obtained that explained 59.3% of the total variance. The ratio of chi-square statistics to degrees of freedom (χ2 /df) 3.30 (χ2 = 1157.48/SD = 350); error of root mean square approximation (RMSEA) 0.061; GFI value of 0.91 and comparative fit index (CFI) value was obtained as 0.91. Turkish version of The T2DHPS is a valid and reliable tool that can be used to assess patients' health-promoting lifestyle behaviours. Validity and reliability studies in different cultures and regions are recommended.

Effects of pennation angle, electrodes orientation and knee angle on surface electromyography of vastus lateralis during isometric contractions

The effects of pennation angle (PA), electrode orientation (EO) and knee angle (KA) on the amplitude of electromyography signal (EMG) of vastus lateralis muscle (VL) were investigated. Seventeen participants (8 men and 9 women; 26.5 ± 5.1 years; 64.4 ± 12.3 kg; 1.66 ± 0.09 m) accomplished isometric tests at four contraction intensities (COI): 25, 50, 75 and 100% of maximum voluntary contraction (MVC), with KA at 90° and 120°. PA was measured at rest and during contractions. One pair of electrodes was aligned with PA of relaxed VL fibers (pair 1) and another (pair 2) was 20° internally rotated to pair 1. The EMG root mean square (RMS) was calculated and later corrected (RMSC) using equations including PA and 20° angle. PA increased with COI (p 0.05). The PA can be used to improve RMS estimation and further expressed as %MVC maybe promisor in EMG–force related issues. Association between EMG and PA appears to be dependent on their relation to COI.

Build Angle: Does It Influence the Accuracy of 3D-Printed Dental Restorations Using Digital Light-Processing Technology?

The aim of this study was to evaluate the effect of the build orientation/build angle on the dimensional accuracy of full-coverage dental restorations manufactured using digital light-processing technology (DLP-AM). A full dental crown was digitally designed and 3D-printed using DLP-AM. Nine build angles were used: 90, 120, 135, 150, 180, 210, 225, 240, and 270 degrees. The specimens were digitally scanned using a high-resolution optical surface scanner (IScan D104i, Imetric). Dimensional accuracy was evaluated using the digital subtraction technique. The 3D digital files of the scanned printed crowns (test model) were exported in standard tessellation language (STL) format and superimposed on the STL file of the designed crown [reference model] using Geomagic Studio 2014 (3D Systems). The root mean square estimate (RMSE) values were evaluated, and the deviation patterns on the color maps were further assessed. The build angle influenced the dimensional accuracy of 3D-printed restorations. The lowest RMSE was recorded for the 135-degree and 210-degree build angles. However, the overall deviation pattern on the color map was more favorable with the 135-degree build angle in contrast with the 210-degree build angle where the deviation was observed around the critical marginal area. Within the limitations of this study, the recommended build angle using the current DLP system was 135 degrees. Among the selected build angles, it offers the highest dimensional accuracy and the most favorable deviation pattern. It also offers a self-supporting crown geometry throughout the building process.

Validating the shortened Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55) in a sample of children with drug-resistant epilepsy.

The aim of this study was to validate the newly developed shortened Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55) in a sample of children with drug-resistant epilepsy. Data came from 136 children enrolled in the Impact of Pediatric Epilepsy Surgery on Health-Related Quality of Life Study (PEPSQOL), a multicenter prospective cohort study. Confirmatory factor analysis was used to assess the higher-order factor structure of the QOLCE-55. Convergent and divergent validity was assessed by correlating subscales of the KIDSCREEN-27 with the QOLCE-55. Measurement equivalence of the QOLCE-55 was evaluated using multiple-group confirmatory factor analysis of children with drug-resistant epilepsy from PEPSQOL versus children with new-onset epilepsy from HERQULES (Health-Related Quality of Life in Children with Epilepsy Study). The higher-order factor structure of the QOLCE-55 demonstrated adequate fit: Comparative Fit Index (CFI) = 0.948; Tucker-Lewis Index (TLI) = 0.946; Root Mean Square of Approximation (RMSEA) = 0.060 (90% confidence interval [CI] 0.054-0.065); Weighted Root Mean Square Residuals (WRMR) = 1.247. Higher-order factor loadings were strong, ranging from λ = 0.74 to 0.81. Internal consistency reliability was excellent (α = 0.97, subscales α > 0.82). QOLCE-55 subscales demonstrated moderate to strong correlations with similar subscales of the KIDSCREEN-27 (ρ = 0.43-0.75) and weak to moderate correlations with dissimilar subscales (ρ = 0.25-0.42). The QOLCE-55 demonstrated partial measurement equivalence at the level of strict invariance - χ2 (2,823) = 3,727.9, CFI = 0.961, TLI = 0.962, RMSEA = 0.049 (0.044, 0.053), WRMR = 1.834. The findings provide support for the factor structure of the QOLCE-55 and contribute to its robust psychometric profile as a reliable and valid measure. Researchers and health practitioners should consider the QOLCE-55 as a viable option for reducing respondent burden when assessing health-related quality of life in children with epilepsy.

Wheat Hardness Prediction Research Based on NIR Hyperspectral Analysis Combined with Ant Colony Optimization Algorithm

This paper presents a new and improved method that ant colony optimization (ACO) algorithm is combined with the support vector regression for band selection. The method is applied to the prediction research of wheat grain hardness, and tries to detect the feasibility of the forecasting ability. The optimized selection of characteristic wave band is the key link of the near infrared (NIR) hyperspectral analysis technology of wheat hardness. Experimental results showed that eleven characteristic wave band sub-intervals were selected from thirty spectral intervals by the algorithm, including 86 wave points. The selected wave band sub-interval were respectively 902.1 to 931.8 nm, 968.7 to 1027.5 nm, 1119.0 to 1143.4 nm, 1174.1 to 1275.5 nm, 1174.1 to 1275.5 nm, 1626.0 to 1647.6 nm and 1626.0 to 1647.6 nm. After using the optimized parameter in the spectral information forecasts and analyzes by the support vector regression. Prediction performances of regression models are assessed by calculating the estimated root mean square errors of cross-validation(RMSECV) the root mean square errors of prediction (RMSEP) and the correlation coefficient(R). The results showed that the estimated RMSECV and Rcv values were respectively 0.0382, and 0.9810 for the training set, the estimated RMSEP and RP values were respectively 0.0590, and 0.9544 for the validation set. Compared with the full spectrum of partial least squares (PLS), interval partial least squares (IPLS) algorithm, it simultaneously reduces the number of certain variables used in the model and increases in the prediction ability and the precision, and it can better reflect optimization model of the wave band. It is confirmed that the ACO method applied to the prediction research of the grain kernels is feasible.

The Mediating Role of Mental Health in the Relations between Dietary Behaviors and General Health: A Cross-sectional Study.

Background:The aim of this study is to find a pathway to explaining the relations between dietary behaviors with mental health and psychological functioning.Materials and Methods:This cross-sectional study contains 4763 participants from the employees of Isfahan University of Medical Sciences. In this study, four questionnaires were administered which were provided as follows: demographic characteristics, 21-item dietary behaviors questionnaire, Hospital Anxiety and Depression Scale questionnaire, and 12-item General Health Questionnaire (GHQ-12). We started the analysis with the structural equation modeling (SEM) model with 4 unobserved latent and 16 observed variables.Results:The results show that the regression coefficient (standard error) of diet behavior on mental health and mental health on GHQ were − 1 (0.37) and 0.02 (0.01) with their P values as 0.007 and 0.01, respectively. For this model, the estimated root mean square error of approximation is 0.062 with 90% confidence interval of (0.060–0.065). In addition, the comparative fit indices (CFIs) were as 0.866 for incremental fit index and 0.866 for CFI. All these indices represent fairly good fit of the model to the data.Conclusion:We can conclude that “lower scores on diet behavior – higher scores on mental health problems” and “higher mean scores in depression and/or anxiety – higher scores in GHQ domains.” The SEM results showed that dietary behaviors have significance related to depression and/or anxiety and general health status.

Monitoring of wetland inundation dynamics in the Delmarva Peninsula using Landsat time-series imagery from 1985 to 2011

Wetlands provide important ecosystem services, the provision of which is largely controlled by fluctuations in inundation and soil saturation. Inundation is highly dynamic and can vary substantially through time in response to multiple drivers, including precipitation and evapotranspiration. This research focused on developing a practical and effective framework for regional, long-term monitoring of wetland inundation dynamics using airborne LiDAR intensity data (Lang et al., 2013) and Landsat time-series imagery. Subpixel water fraction (SWF) maps indicating the percent of surface water within each 30-m pixel were generated on an annual basis over the entire Delmarva Peninsula on the East Coast of the United States from 1985 to 2011. Comprehensive accuracy assessments of the SWF maps were conducted using historical high-resolution aerial photography to determine the reference condition. The assessment resulted in an estimated root mean square error (RMSE) of 7.78% for the sample of open water areas (mean SWF was ~40% for this region of the map). Moreover, a separate accuracy assessment targeting inundation in wetlands (i.e. presence or absence of water) yielded an overall accuracy of 93%. Accuracies derived indicated that Landsat data can be calibrated to accurately extract long-term water information at the regional scale. Characteristics of inundation were examined with respect to different wetland types defined by water regime and dominant vegetation types, as well as different physical drivers. Results showed that tidal wetlands typically exhibited more intensive inundation than nontidal wetlands, and a higher degree of inundation was associated with emergent wetlands compared to wetland areas dominated by woody vegetation. Analysis of change drivers revealed that tide exerted a statistically significant influence on coastal inundation with r2 values of 32–36% and p<0.01, whereas inundation changes in inland wetland areas were in part driven by precipitation with r2 values of 25–34% and p<0.08. Because an up-to-date archive of Landsat imagery is globally available and LiDAR data are becoming increasingly more affordable, the developed framework can be easily implemented to generate a continuous inundation record in many regions of the globe to assist in ongoing and future studies focused on wetland hydrology and wetland management.

Predictive Modeling of Black Spruce (Picea mariana (Mill.) B.S.P.) Wood Density Using Stand Structure Variables Derived from Airborne LiDAR Data in Boreal Forests of Ontario

Our objective was to model the average wood density in black spruce trees in representative stands across a boreal forest landscape based on relationships with predictor variables extracted from airborne light detection and ranging (LiDAR) point cloud data. Increment core samples were collected from dominant or co-dominant black spruce trees in a network of 400 m2 plots distributed among forest stands representing the full range of species composition and stand development across a 1,231,707 ha forest management unit in northeastern Ontario, Canada. Wood quality data were generated from optical microscopy, image analysis, X-ray densitometry and diffractometry as employed in SilviScan™. Each increment core was associated with a set of field measurements at the plot level as well as a suite of LiDAR-derived variables calculated on a 20 × 20 m raster from a wall-to-wall coverage at a resolution of ~1 point m−2. We used a multiple linear regression approach to identify important predictor variables and describe relationships between stand structure and wood density for average black spruce trees in the stands we observed. A hierarchical classification model was then fitted using random forests to make spatial predictions of mean wood density for average trees in black spruce stands. The model explained 39 percent of the variance in the response variable, with an estimated root mean square error of 38.8 (kg·m−3). Among the predictor variables, P20 (second decile LiDAR height in m) and quadratic mean diameter were most important. Other predictors describing canopy depth and cover were of secondary importance and differed according to the modeling approach. LiDAR-derived variables appear to capture differences in stand structure that reflect different constraints on growth rates, determining the proportion of thin-walled earlywood cells in black spruce stems, and ultimately influencing the pattern of variation in important wood quality attributes such as wood density. A spatial characterization of variation in a desirable wood quality attribute, such as density, enhances the possibility for value chain optimization, which could allow the forest industry to be more competitive through efficient planning for black spruce management by including an indication of suitability for specific products as a modeled variable derived from standard inventory data.

Path Analysis of Campus Walkability/Bikeability and College Students' Physical Activity Attitudes, Behaviors, and Body Mass Index.

The purpose of this study was to assess the relationship between the walkability/bikeability of college campuses and students' body mass index (BMI) with student physical activity (PA) attitudes and behaviors as potential mediators. Cross-sectional. Thirteen university campuses. A total of 1384 student participants. Walkability/bikeability environmental score (ES): 12-item audit assessed an average of 44 path segments per campus. Students were measured for height and weight and completed online surveys. Physical activity stage of change/behavior intentions were assessed using the transtheoretical model. The Cognitive Behavioral Physical Activity Questionnaire assessed outcome expectations, self-regulation, and personal barriers. International Physical Activity Questionnaire assessed walking-, moderate-, and vigorous-intensity PA. Descriptive statistics, zero-order correlations, and path analysis with maximum likelihood estimation. The overall model fit was good with χ2 of 171.388 ( df = 18), P < .001, comparative fit index value of .95, and a root mean square of approximation of .079. After controlling for gender, there was a direct negative association between walkability/bikeability ES and BMI (β = -.085) and positive association between personal barriers and BMI (β = .134). Walkability/bikeability ES was positively associated with walking-intensity PA (β = .010). Self-regulation was positively associated with moderate-intensity PA (β = .213), which, in turn, was negatively associated with BMI (β = -.057). The ease of walking and biking on a campus was related to college students' walking behavior and their BMI. Students' PA behavioral intentions were associated with moderate PA and lower BMI. These results provide evidence to focus on policies and structural supports for walkable/bikeable environments to supplement and enhance interventions encouraging individual behavior change for PA and weight management.

Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds

ABSTRACTTo improve precision management and the cost effectiveness of forest practices, we investigate a pre-harvest airborne laser scanning (ALS) forest inventory with an unmanned aerial vehicle (UAV) acquired post-harvest digital aerial photogrammetry (DAP) inventory to identify the location and residual volume of stands following selection harvesting. ALS data and field measurements collected pre-harvest in 2013 (T1) and UAV imagery collected post-harvest in 2015 (T2) were processed to produce analogous point clouds of the study area near Williams Lake, British Columbia, Canada. Tree height, diameter at breast height (DBH), and species were recorded from systematically located variable radius plots subsequent to ALS and DAP collection. Point cloud metrics and field measurements from each data set were used to create T1 ALS and T2 DAP predictive volume models. Direct and indirect volume change estimates were created from the difference between T1 ALS and T2 DAP model results. The estimated root mean square error (RMSE) for volume was 17.34% and 18.50% for the 2013 ALS and 2015 DAP models, respectively. The indirect and direct models predicting volume change produced errors of 16.65% and 86.56%, respectively. Results achieved from ALS and DAP models indicate strong potential for inventories generated using UAV-acquired DAP to estimate the quantity and location of residual volume after harvest operations, and could be applied in tandem to act as a semi-automated inventory cycling method to improve operational efficiency and cost effectiveness in Canadian forest management.

Ecogeographic variation in black spruce wood properties across Quebec’s boreal forest

Ecogeographic variation in black spruce clear wood properties was investigated for the two main vegetation types of the managed boreal forest of the province of Quebec, Canada. In total, 409 co-dominant and dominant trees from 82 mature stands were sampled. Basic wood density, modulus of elasticity and microfibril angle were measured using SilviScan. Mature fiber length was determined using a high-resolution Fiber Quality Analyzer. Wood growing in pure black spruce stands had longer mature fibers, a significantly denser wood with better mechanical characteristics than the wood growing in mixed stands with balsam fir. All wood properties were clearly influenced by radial growth and species composition.Given the limited number of sample plots for mapping purposes, a two-stage modeling approach was assessed to predict stand-level estimates of black spruce clear wood properties. This scaling-up method, based on field measurements and ring data from 3350 inventory plots, has improved the performance of all models. Stand-level models explained 47%, 57%, 63% and 63% of variance in wood density, modulus of elasticity, microfibril angle and mature fiber length respectively with estimated root mean square errors of 8.9kg/m3, 0.52GPa, 0.60° and 0.06mm.An east-west gradient in black spruce clear wood properties was revealed as possibly the by-product of the change in relative proportions of both studied vegetation types across the study area. The results indicate that the black spruce wood from western regions of the managed boreal forest has a better potential for producing pulp and paper, lumber or engineered products due to its longer mature fibers and higher mechanical properties.

Duty hours and incidents in flight among commercial airline pilots

Introduction. Working long duty hours has often been associated with increased risk of incidents and accidents in transport industries. Despite this, information regarding the intermediate relationship between duty hours and incident risk is limited. This study aimed to test a work hours/incident model to identify the interplay of factors contributing to incidents within the aviation industry. Methods. Nine hundred and fifty-four European-registered commercial airline pilots completed a 30-item survey investigating self-report attitudes and experiences of fatigue. Path analysis was used to test the proposed model. Results. The fit indices indicated this to be a good fit model (χ2 = 11.066, df = 5, p = 0.05; Comparative Fit Index = 0.991; Normed Fit Index = 0.984; Tucker–Lewis Index = 0.962; Root Mean Square of Approximation = 0.036). Highly significant relationships were identified between duty hours and sleep disturbance (r = 0.18, p < 0.001), sleep disturbance and fatigue in the cockpit (r = 0.40, p < 0.001), and fatigue in the cockpit and microsleeps in the cockpit (r = 0.43, p < 0.001). Discussion. A critical pathway from duty hours through to self-reported incidents in flight was identified. Further investigation employing both objective and subjective measures of sleep and fatigue is needed.

Emotional Intelligence and Identity Style as Risk Factors for High-Risk Behavior in Prisoners: The Mediating Role of Resiliency and Social Adjustment

Background: A motivation for the intense scientific interest in high-risk behaviors is to do with firmly held belief that behaviors such as substance abuse and delinquency have ubiquitous, catastrophic outcomes. Objectives: The current study aimed to examine a model of risk factors for high-risk behaviors in prisoners. It was hypothesized that emotional intelligence and identity style have an indirect effect on high-risk behaviors via the mediating role of social adjustment and resiliency. Materials and Methods: The sample consisted of 293 prisoners that were selected via simple random sampling and participated in this research by completing Schutte’s emotional intelligence scale, Berzonsky identity style inventory, social adjustment scale, ConnerDavidson’s resilience scale, and high risk behavior scale; all these instruments are reliable and have been validated. Structural equation modeling (SEM) through SPSS 20 and AMOS 20 were used for data analysis. Results: The results suggested that the model had good fit (root mean square of approximation = 0.07, comparative fit index = 0.95, normed fit index = 0.91, goodness of fit index =0.93) with the data. Accordingly, the indirect effect of resiliency on high-risk behavior via social adjustment and resiliency, and the indirect effect of identity style on high-risk behavior via emotional intelligence and resiliency were approved. Conclusions: Our findings suggest the importance of high-risk behaviors in terms of etiological pathways, maintenance process and treatment interventions.

Monitoring and Prediction of Precipitable Water Vapor using GPS data in Turkey

AbstractAlthough Global Positioning System (GPS) primarily provide accurate estimates of position, velocity and time of the receiver, as the signals pass through the atmoshphere carrying its signatures, thus offers opportunities for atmoshpheric applications. Precipitable water vapor (PWV) is a vital component of the atmosphere and significantly influences atmospheric processes like rainfall and atmospheric temperature. The developing networks of continuously operating GPS can be used to efficiently estimate PWV. The Turkish Permanent GPS Network (TPGN) is employed to monitor PWV information in Turkey. This work primarily aims to derive long-term data of PWV by using atmospheric path delays observed through continuously operating TPGN from November 2014 to October 2015. A least square mathematical approach was then applied to establish the relation of the observed PWV to rainfall and temperature. The modeled PWV was correlated with PWV estimated from GPS data, with an average correlation of 67.10 %–88.60 %. The estimated root mean square error (RMSE) varied from 2.840 to 6.380, with an average of 4.697. Finally, data of TPGN, rainfall, and temperature were obtained for less than 2 months (November 2015 to December 2015) and assessed to validate the mathematical model. This study provides a basis for determining PWV by using rainfall and temperature data.

Near‐Field Source Localization Using Spherical Microphone Arrays

A new method is proposed for joint range and bearing (azimuth and elevation) estimation of multiple near-field acoustic sources using observations collected by a spherical microphone array. First, Spherical Fourier transform (SFT) is used to construct the array signal model in the spherical harmonics domain to decouple range and bearing information. Then the relation among the spherical harmonics of three adjacent degrees is exploited to build the recursive relationship of the signal subspace. Using Eigenvalue decomposition (EVD), bearings are estimated based on the eigenvalues and simultaneously the steering matrix can be represented by the signal subspace. Finally, range is estimated using the energy ratios of the elements of the steering matrix in the spherical harmonics domain. The algorithm can avoid parameter pairing and multi-dimensional searching. It has lower computational complexity than that of the Multiple signal classification (MUSIC) method. The performance is evaluated by Monte-Carlo simulations and the estimation root meansquare errors are compared to the corresponding Cramer-Rao bounds (CRBs) and those of MUSIC range estimates, which demonstrate the validity of the proposed algorithm.

Poor quality of life, depressed mood, and memory impairment may be mediated by sleep disruption in patients with Addison's disease

Standard replacement therapy for Addison's disease (AD) does not restore a normal circadian rhythm. In fact, hydrocortisone replacement in AD patients likely induces disrupted sleep. Given that healthy sleep plays an important role in improving quality of life, optimizing cognition, and ensuring affect regulation, the aim of this study was to investigate whether poor quality of life, mood alterations, and memory complaints reported by AD patients are associated with their disrupted sleep patterns. Sixty patients with AD and 60 matched healthy controls completed a battery of self-report questionnaires assessing perceived physical and mental health (Short-Form 36), mood (Beck Depression Inventory—II), sleep quality (Pittsburgh Sleep Quality Index), and cognition (Cognitive Failures Questionnaire). A latent variable model revealed that although AD had a significant direct effect on quality of life, the indirect effect of sleep was significantly greater. Furthermore, although AD had no direct effect on cognitive functioning, the indirect effect of sleep was significant. The overall model showed a good fit (comparative fit index=0.91, root mean square of approximation=0.09, and standardized root mean square residual=0.05). Our findings suggest that disrupted sleep, and not the disease per se, may induce poor quality of life, memory impairment, and affect dysregulation in patients with AD. We think that improving sleep architecture may improve cognitive, affective, and physical functioning.

Mapping Flooded Rice Paddies Using Time Series of MODIS Imagery in the Krishna River Basin, India

Rice is one of the major crops cultivated predominantly in flooded paddies, thus a large amount of water is consumed during its growing season. Accurate paddy rice maps are therefore important inputs for improved estimates of actual evapotranspiration in the agricultural landscape. The main objective of this study was to obtain flooded paddy rice maps using multi-temporal images of Moderate Resolution Imaging Spectroradiometer (MODIS) in the Krishna River Basin, India. First, ground-based spectral samples collected by a field spectroradiometer, CROPSCAN, were used to demonstrate unique contrasts between the Normalized Difference Vegetation Index (NDVI) and the Land Surface Water Index (LSWI) observed during the transplanting season of rice. The contrast between Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) from MODIS time series data was then used to generate classification decision rules to map flooded rice paddies, for the transplanting seasons of Kharif and Rabi rice crops in the Krishna River Basin. Consistent with ground spectral observations, the relationship of the MODIS EVI vs. LSWI of paddy rice fields showed distinct features from other crops during the transplanting seasons. The MODIS-derived maps were validated against extensive reference data collected from multiple land use field surveys. The accuracy of the paddy rice maps, when determined using field plot data, was approximately 78%. The MODIS-derived rice crop areas were also compared with the areas reported by Department of Agriculture (DOA), Government of India (Government Statistics). The estimated root mean square difference (RMSD) of rice area estimated using MODIS and those reported by the Department of Agriculture over 10 districts varied between 3.4% and 6.6% during 10 years of our study period. Some of the major factors responsible for this difference include high noise of the MODIS images during the prolonged monsoon seasons (typically June–October) and the coarse spatial resolution (500 m) of the MODIS images compared to the small crop fields in the basin. However, this study demonstrates, based on multi-year analysis, that MODIS images can still provide robust and consistent flooded paddy rice extent and areas over a highly heterogeneous large river basin.

Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network

The prediction of volume fractions in order to measure the multiphase flow rate is a very important issue and is the key parameter of multi-phase flow meters (MPFMs). Currently, the gamma ray attenuation technique is known as one of the most precise methods for obtaining volume fractions. The gamma ray attenuation technique is based on the mass attenuation coefficient, which is sensitive to density changes; density is sensitive in turn to temperature and pressure fluctuations. Therefore, MPFM efficiency depends strongly on environmental conditions. The conventional solution to this problem is the periodical recalibration of MPFMs, which is a demanding task. In this study, a method based on dual-modality densitometry and artificial intelligence (AI) is presented, which offers the advantage of the measurement of the oil–gas–water volume fractions independent of density changes. For this purpose, several experiments were carried out and used to validate simulated dual modality densitometry results. The reference density point was established at a temperature of 20 °C and pressure of 1 bar. To cover the full range of likely density fluctuations, four additional density sets were defined (at changes of ±4% and ±8% from the reference point). An annular regime with different percentages of oil, gas and water at different densities was simulated. Four features were extracted from the transmission and scattered detectors and were applied to the artificial neural network (ANN) as inputs. The input parameters included the 241Am full energy peak, 137Cs Compton edge, 137Cs full energy peak and total scattered count, and the outputs were the oil and air percentages. A multi-layer perceptron (MLP) neural network was used to predict the volume fraction independent of the oil and water density changes. The obtained results show that the proposed ANN model achieved good agreement with the real data, with an estimated root mean square error (RMSE) of less than 3.

반도체 센서의 확장칼만필터를 이용한 자세추정

This paper describes about the method for designing an extended Kalman filter to accurately measure the position of the spatial-phase system using a semiconductor sensor. Spatial position is expressed by the correlation of the rotated coordinate system attached to the body from the inertia coordinate system (a fixed coordinate system). To express the attitude, quaternion was adapted as a state variable, Then, the state changes were estimated from the input value which was measured in the gyro sensor. The observed data is the value obtained from the acceleration sensor. By matching between the measured value in the acceleration sensor and the predicted calculation value, the best variable was obtained. To increase the accuracy of estimation, designation of the extended Kalman filter was performed, which showed excellent ability to adjust the estimation period relative to the sensor property. As a result, when a three-axis gyro sensor and a three-axis acceleration sensor were adapted in the estimator, the RMS(Root Mean Square) estimation error in simulation was retained less than 1.7[], and the estimator displayed good property on the prediction of the state in 100 ms measurement period.

A metastatistical approach to rainfall extremes

The traditional statistical theory of extreme events assumes an asymptotic regime in which the number of events per year is large enough for a limiting Generalized Extreme Value distribution to apply. This has been shown not to be applicable to many practical cases. We introduce here a Metastatistical Extreme Value (MEV) approach which is defined in terms of the distribution of the statistical parameters describing “ordinary” daily rainfall occurrence and intensity. The method does not require an asymptotic assumption, and naturally accounts for the influence of the bulk of the distribution of ordinary events on the distribution of annual maximum daily rainfall. Building on existing observations showing the distribution of daily rainfall to be Weibull right-tail equivalent, the MEV approach is then specialized to yield a compact and easily applicable formulation. We apply this formulation to Monte Carlo experiments based on Weibull statistics derived from the 3-century long rainfall time series observed in Padova (Italy). We find an excellent agreement between MEV estimates and the ‘observed’ frequency of occurrence of extreme events in the synthetic time series generated. GEV and Gumbel estimates, on the contrary, exhibit systematic errors. Tests with different rates of occurrence of rainfall events show slight improvements of the GEV and Gumbel estimation bias when the number of events/year is increased. However, a constant bias in GEV and Gumbel estimates is seen for (synthetic) climates where the number of events and the distribution of intensities is varied stochastically. The estimation root mean square error is also larger for the GEV and Gumbel distributions than for the MEV approach. Hence, GEV and Gumbel quantile estimates are more likely to be further away from the actual value than MEV estimates. Finally, the application of the new MEV approach to subsets of the long Padova time series identifies marked variabilities in rainfall extremes at the centennial time scale.