Absolute Coefficients Research Articles

Measuring The Myopic Loss Aversion Premium: An Experimental Approach

ABSTRACT This paper proposes new methods to assess the myopic loss aversion premium (the expected return-differential) based on an experimental design by eliciting both willingness-to-pay and allocation percentage figures and also by inferring and comparing absolute risk aversion coefficients. Our results indicate a high level of loss aversion and support the existence of the myopic loss aversion bias.

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study

PurposeTo develop a deep learning (DL) algorithm for predicting anterior chamber depth (ACD) from smartphone-acquired anterior segment photographs.MethodsFor algorithm development, we included 4,157 eyes from 2,084 Chinese primary school students (aged 11–15 years) from Mojiang Myopia Progression Study (MMPS). All participants had with ACD measurement measured with Lenstar (LS 900) and anterior segment photographs acquired from a smartphone (iPhone Xs), which was mounted on slit lamp and under diffuses lighting. The anterior segment photographs were randomly selected by person into training (80%, no. of eyes = 3,326) and testing (20%, no. of eyes = 831) dataset. We excluded participants with intraocular surgery history or pronounced corneal haze. A convolutional neural network was developed to predict ACD based on these anterior segment photographs. To determine the accuracy of our algorithm, we measured the mean absolute error (MAE) and coefficient of determination (R2) were evaluated. Bland Altman plot was used to illustrate the agreement between DL-predicted and measured ACD values.ResultsIn the test set of 831 eyes, the mean measured ACD was 3.06 ± 0.25 mm, and the mean DL-predicted ACD was 3.10 ± 0.20 mm. The MAE was 0.16 ± 0.13 mm, and R2 was 0.40 between the predicted and measured ACD. The overall mean difference was −0.04 ± 0.20 mm, with 95% limits of agreement ranging between −0.43 and 0.34 mm. The generated saliency maps showed that the algorithm mainly utilized central corneal region (i.e., the site where ACD is clinically measured typically) in making its prediction, providing further plausibility to the algorithm's prediction.ConclusionsWe developed a DL algorithm to estimate ACD based on smartphone-acquired anterior segment photographs. Upon further validation, our algorithm may be further refined for use as a ACD screening tool in rural localities where means of assessing ocular biometry is not readily available. This is particularly important in China where the risk of primary angle closure disease is high and often undetected.

Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms

The accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developing traditional theoretical methods. Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. Three machine-learning algorithms (i.e., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination ( R 2 ), are selected to evaluate the prediction performance. Results demonstrated that the filtering and selection of model parameters is vitally important to the accuracy of model prediction. Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata.

Image Fusion by Using Bi-Orthogonal Wavelets in Discrete Wavelet Transform (DWT) Domain

Image fusion can be widely explained as merging more than one photograph or a few in their factors right into a single photo without the creation of distortion or lack of information. Image fusion pastimes mix complementary as properly as approximate statistics for greater than one photograph to create fused photo output. Hence, the output picture achieved must have to include an extra accurate representation of the position than any of the man or woman supply pix and is greater fantastic for human visible, and pc understanding or in addition picture processing and comparison duties. . The notably used fusion rule is the most willpower scheme. This handy scheme chooses the biggest absolute wavelet coefficient at each location from the given snapshots because of the coefficient on the area within the fused photo. After that, the fused photo is obtained with the aid of using the inverse DWT for the corresponding wavelet coefficient. The well-known significantly used pixel-primarily based fusion rule is the aforementioned maximum favored scheme. This approach can choose the salient elements from the furnished photographs; however, it's miles touchy with noise and artifacts as they have been supposed to have better contrast. When the 2 photographs are degraded with the aid of using the wavelet transform, the approximation picture (low–frequency band) and thing image (high-frequency band) may additionally have one-of-a-type bodily meaning. In this project, a new fusion rule is proposed to feature a wavelet coefficient that treats low-frequency and high-frequency bands with high-quality fusion schemes.

Primary Healthcare Quality in Conflict and Fragility: a subnational analysis of disparities using Population Health surveys

BackgroundRecent global reports highlighted the importance of addressing the quality of care in all settings including fragile and conflict-affected situations (FCS), as a central strategy for the attainment of sustainable development goals and universal health coverage. Increased mortality burden in FCS reflects the inability to provide routine services of good quality. There is also paucity of research documenting the impact of conflict on the quality of care within fragile states including disparities in service delivery. This study addresses this measurement gap by examining disparities in the quality of primary healthcare services in four conflict-affected fragile states using proxy indicators.MethodsA secondary analysis of publicly available data sources was performed in four conflict-affected fragile states: Cameroon, the Democratic Republic of Congo, Mali, and Nigeria. Two main databases were utilized: the Demographic Health Survey and the Uppsala Conflict Data Program for information on components of care and conflict events, respectively. Three equity measures were computed for each country: absolute difference, concentration index, and coefficients of mixed-effects logistic regression. Each computed measure was then compared according to the intensity of organized violence events at the neighborhood level.ResultsOverall, the four studied countries had poor quality of PHC services, with considerable subnational variation in the quality index. Poor quality of PHC services was not only limited to neighborhoods where medium or high intensity conflict was recorded but was also likely to be observed in neighborhoods with no or low intensity conflict. Both economic and educational disparities were observed in individual quality components in both categories of conflict intensity.ConclusionEach of the four conflict-affected countries had an overall poor quality of PHC services with both economic and educational disparities in the individual components of the quality index, regardless of conflict intensity. Multi-sectoral efforts are needed to improve the quality of care and disparities in these settings, without a limited focus on sub-national areas where medium or high intensity conflict is recorded.

Italian validation of the short form of the Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12)

Introduction and hypothesisThe aim of this study was to translate the English short form of Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12) and evaluate its validity, internal consistency, and test-retest reliability.MethodsThe questionnaire was translated into Italian by standardized procedural steps, and the final version was submitted to women referred to urogynecological outpatient care for genital prolapse or urinary incontinence reporting sexual disorders (cases) or not (controls). For the test-retest evaluation, cases received the questionnaire 2 weeks later. The Wilcoxon test (non-parametric) was used to assess differences between cases and controls. Convergent validity was tested with the Italian version of the FSFI-19. The internal consistency was tested using Cronbachʼs alpha. The degree of concordance/agreement was measured with Cohenʼs kappa. The absolute agreement of test-retest results was tested with the intraclass correlation coefficient (ICC).ResultsSixty women were recruited for the study and answered the questionnaire. The overall rate of missing items was 1.3%. Construct validity was demonstrated, as the questionnaire discriminated significantly between patients with and without symptoms. Convergent validity with FSFI-19 was tested, and a linear correlation between scores was demonstrated (F < 0.001). Internal consistency reliability evaluated with Cronbach's alpha was satisfactory (0.54–0.81). Cohenʼs kappa values as absolute agreement coefficients were between 0.59 and 0.80 (good agreement). Intraclass correlation coefficients ranged between 0.88 and 0.94 (very satisfactory agreement) for each functional domain.ConclusionsThe Italian version of the PISQ-12 is reliable, valid, and consistent.

Research on COD Soft Measurement Technology Based on Multi-Parameter Coupling Analysis Method

This paper presents a soft measurement technique for COD (Chemical Oxygen Demand) based on the multiparameter coupling analysis method. First, through mechanism analysis and correlation analysis of historical data during the measurement process, water quality parameters, such as hydrogen potential (PH), dissolved oxygen (DO), turbidity (TU), and electrical conductivity (EC), can be used to estimate COD values. To further improve the estimation accuracy of the water quality parameter model, we adopted a modeling method combining a BP neural network and support vector machine, which showed an average relative error of 6.13% and an absolute coefficient of up to 0.9605. Finally, experiments in a lake environment demonstrate that this method shows excellent performance, with highly reliable and accurate prediction results.

Modelling dissolved oxygen and biochemical oxygen demand using data-driven techniques

Precise quantification of Biochemical oxygen demand (BOD) and Dissolved oxygen (DO) are critically important for water quality assessment as well as for development of various management policies. To calculate BOD and DO for any water sample, standard technique Winkler-Azide method is used which is cumbersome and prone to measurement error. Therefore, there is a need to device alternate Data Driven Technique (DDT). In the present study, three different DDT: Artificial Neural Network (ANN), Multi Gene Genetic Programming (MG-GP) and M5 Model Tree (M5T) have been used for DO as well as BOD prediction for 3 separate stretches of Mula-Mutha River situated in Pune, India. Additionally, attempt has been made to predict BOD using modelled DO; which shows possibility of using modelled parameter in development of another model. Performance of the models was assessed through, root mean square error (RMSE); mean absolute relative error (MARE) and coefficient of correlation (R). Results based on 3 stations indicate that ANN and MGGP both outperformed with R above 0.85 and RMSE below 1 mg/L for 2 stations out of 3. MGGP and M5T can grasp the influence parameter which can be seen from the input frequency distribution in MGGP and coefficient of input parameters in M5T.

Thunderstorm wind load evaluation on storm shelters using wind tunnel testing

In Canada, there is a growing number of thunderstorms capable of generating intensive gust fronts and tornadoes, which drastically affect people living in rural areas. Currently, the influence of windstorms is not sufficiently accounted for in building codes such as the National Building Code of Canada (NBCC 2015). Moreover, people in rural areas are more prone to those weather-related fatalities due to the lack of suitable storm shelters. The current study aims at studying the aerodynamic behavior of storm shelters subjected to non-synoptic wind (i.e., downbursts). Wind tunnel tests were conducted at Ryerson University for the three commonly used storm shelter shapes in Canada for different angles of attack. Mean force and moment coefficients were found and benchmarked against similar data for the same models obtained from a turbulent boundary layer wind tunnel test. Afterwards, the maximum absolute design force and moment coefficients resulting from all angles of attack were identified, and a design example for storm shelters in Canada was given to compare between downburst and boundary layer design loads.

Experimental investigations of channel profile and surface roughness on PMMA substrate for microfluidic devices with mathematical modelling

Poly-methyl-methacrylate (PMMA) microchannel based devices are gaining popularity, particularly in medical applications. Experiments were carried out on a PMMA based microchannel profile of thickness 10 mm through a 100-Watt CO2 laser machine by varying laser power, scanning speed and number of passes of the laser machine. The full factorial design of the experiment was implemented to investigate the microchannel profile, depth and surface roughness of the microchannel. The analytical model was developed to predict the microchannel profile and depth for the prescribed number of laser passes. The developed analytical model has been compared with the experimental investigations and also with the previously developed models available in the literature. Results of the analytical model predicted the fabricated profile with appreciable accuracy. Moreover, the close agreement of the results with the available literature models validates the acceptability of the proposed analytical model. The surface roughness predictive modeling using linear regression, polynomial regression of order 2,3 and 4, exponential Box-Lucas method and sigmoid Boltzmann models have been compared. The statistical analysis of all the models was performed, with the analysis of variance, mean squared error, Root Mean Square Error, mean absolute percentage error and coefficient of determination. Based on the comparative assessment, the best fitted and accurate model obtained was polynomial regression of order 3. Thus, the improved surface roughness generated with the laser machining would be a time and cost technique for the fabrication of microfluidic devices.

Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts.

Application of a metaheuristic gradient‐based optimizer algorithm integrated into artificial neural network model in a local geoid modeling with global navigation satellite systems/leveling measurements

AbstractIn the present article, the efficiency of a new hybrid learning method, named artificial neural network with gradient‐based optimizer algorithm (ANN‐GBO), is investigated to determine a local geoid. The outcomes of the assessed method are compared with classical ANN (without GBO), some metaheuristic‐based ANN models and other study results (interpolation methods). Four commonly used performance metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and coefficient of determination () have been used to assess the applied methods. Assessment of predictions revealed that ANN‐GBO yielded the best results (RMSE: 10.51 cm, MAE: 8.26 cm, MARE: 0.27 cm, and : 0.9622). The outcomes of the work clearly show that the ANN‐GBO approach has the lowest prediction error compared with ANN. It can be said that the GBO algorithm enhances the ANN capability in local geoid modeling. It may be recommended to optimize the weights of the ANN with GBO for high prediction accuracy. This research can be extended to other regions.

Forecasting Mortality Trend of Indonesian Old Aged Population with Bayesian Method

From around the nineteenth until the beginning of the twenty-first century, mortality rates show a declining trend. However, recent data on the United States population shows that the rate of decline started to slow down in the 2010s. Insurance companies need to be prepared in both ways: either mortality rates continue to decline, or there will be a turning point, and mortality rates start to increase. In this paper, we aim to get the whole picture of the mortality trend of Indonesian males, detect the possibility of a turning point in the mortality rates, and forecast mortality rates in the future. To reach this aim, we propose adjustments to the Makeham mortality model by including period and cohort information of the population via quadratic function. We also propose using the Bayesian method to estimate the parameters for the Indonesian old-aged males' population, where some adjustments were made in determining the priors, and the estimates were sampled from the posterior distribution using the Gibbs sampling algorithm. We found that our forecasting accuracy is satisfactory by considering the mean absolute percentage error values and coefficient of determination (R2). We found that mortality rates are declining in the long term, but the probability of a turning point in the future is statistically significant. We identified two risks, longevity risk because of more centenarians in the future and mortality risk before their children complete compulsory education.

Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning

The rapid and accurate detection of diesel multiple properties is an important research topic in petrochemical industry that is conducive to diesel quality assessment and environmental pollution mitigation. To that end, this paper developed a new machine learning model for near infrared (NIR) spectroscopy capable of simultaneously determining diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics. The model combined improved XY co-occurrence distance (ISPXY) and differential evolution-gray wolf optimization support vector machine (DEGWO-SVM) to attain the goal of rapidity and accuracy. Experimental results indicated that the average recovery, mean square error, mean absolute percentage error and determination coefficient of the presented method outperformed those of the existing machine learning methods. The proposed hybrid model provides superior solution to the problem of low efficiency and high cost of diesel quality detection, and has the potential to be utilized as a promising tool for diesel routine monitoring.

Methodological approaches for imputing missing data into monthly flows series

Missing data is one of the main difficulties in working with fluviometric records. Database gaps may result from fluviometric stations components problems, monitoring interruptions and lack of observers. Incomplete series analysis generates uncertain results, negatively impacting water resources management. Thus, proper missing data consideration is very important to ensure better information quality. This work aims to analyze, comparatively, missing data imputation methodologies in monthly river-flow time series, considering, as a case study, the Doce River, located in Southeast Brazil. Missing data were simulated in 5%, 10%, 15%, 25% and 40% proportions following a random distribution pattern, ignoring the missing data generation mechanisms. Ten missing data imputation methodologies were used: arithmetic mean, median, simple and multiple linear regression, regional weighting, spline and Stineman interpolation, Kalman smoothing, multiple imputation and maximum likelihood. Their performances were compared through bias, root mean square error, absolute mean percentage error, determination coefficient and concordance index. Results indicate that for 5% missing data, any methodology for imputing can be considered, recommending caution for arithmetic mean method application. However, as the missing data proportion increases, it is recommended to use multiple imputation and maximum likelihood methodologies when there are support stations for imputation, and the Stineman interpolation and Kalman Smoothing methods when only the studied series is available.&#x0D; Keywords: Doce river, imputation, missing data.

Contractile State Dependent Sarcomere Length Variability in Isolated Guinea-Pig Cardiomyocytes.

Cardiomyocytes contract keeping their sarcomere length (SL) close to optimal values for force generation. Transmural heterogeneity in SL across the ventricular wall coordinates the contractility of the whole-ventricle. SL heterogeneity (variability) exists not only at the tissue (macroscale) level, but also presents at the level of a single cardiomyocyte (microscale level). However, transmural differences in intracellular SL variability and its possible dependence on the state of contraction (e.g. end-diastole or end-systole) have not been previously reported. In the present study, we studied three aspects of sarcomere-to-sarcomere variability in intact cardiomyocytes isolated from the left ventricle of healthy guinea-pig: 1) transmural differences in SL distribution between subepi- (EPI) and subendocardial (ENDO) cardiomyocytes; 2) the dependence of intracellular variability in SL upon the state of contraction; 3) local differences in SL variability, comparing SL distributions between central and peripheral regions within the cardiomyocyte. To characterize the intracellular variability of SL, we used different normality tests for the assessment of SL distributions, as well as nonparametric coefficients to quantify the variability. We found that individual SL values in the end-systolic state of contraction followed a normal distribution to a lesser extent as compared to the end-diastolic state of contraction (∼1.3-fold and ∼1.6-fold in ENDO and EPI, respectively). The relative and absolute coefficients of sarcomere-to-sarcomere variability in end-systolic SL were significantly greater (∼1.3-fold) as compared to end-diastolic SL. This was independent of both the transmural region across the left ventricle and the intracellular region within the cardiomyocyte. We conclude that the intracellular variability in SL, which exists in normal intact guinea-pig cardiomyocytes, is affected by the contractile state of the myocyte. This phenomenon may play a role in inter-sarcomere communication in the beating heart.

Prediction of Refractive Error Based on Ultrawide Field Images With Deep Learning Models in Myopia Patients.

SummaryUltrawide field fundus images could be applied in deep learning models to predict the refractive error of myopic patients. The predicted error was related to the older age and greater spherical power.PurposeTo explore the possibility of predicting the refractive error of myopic patients by applying deep learning models trained with ultrawide field (UWF) images.MethodsUWF fundus images were collected from left eyes of 987 myopia patients of Eye and ENT Hospital, Fudan University between November 2015 and January 2019. The fundus images were all captured with Optomap Daytona, a 200° UWF imaging device. Three deep learning models (ResNet-50, Inception-v3, Inception-ResNet-v2) were trained with the UWF images for predicting refractive error. 133 UWF fundus images were also collected after January 2021 as an the external validation data set. The predicted refractive error was compared with the “true value” measured by subjective refraction. Mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient (R2) value were calculated in the test set. The Spearman rank correlation test was applied for univariate analysis and multivariate linear regression analysis on variables affecting MAE. The weighted heat map was generated by averaging the predicted weight of each pixel.ResultsResNet-50, Inception-v3 and Inception-ResNet-v2 models were trained with the UWF images for refractive error prediction with R2 of 0.9562, 0.9555, 0.9563 and MAE of 1.72(95%CI: 1.62–1.82), 1.75(95%CI: 1.65–1.86) and 1.76(95%CI: 1.66–1.86), respectively. 29.95%, 31.47% and 29.44% of the test set were within the predictive error of 0.75D in the three models. 64.97%, 64.97%, and 64.47% was within 2.00D predictive error. The predicted MAE was related to older age (P < 0.01) and greater spherical power(P < 0.01). The optic papilla and macular region had significant predictive power in the weighted heat map.ConclusionsIt was feasible to predict refractive error in myopic patients with deep learning models trained by UWF images with the accuracy to be improved.

Athree-dimensional method to calculate mechanical advantage in mandibular function : Intra- and interexaminer reliability study.

Masticatory muscles are physically affected by several skeletal features. The muscle performance depends on muscle size, intrinsic strength, fiber direction, moment arm, and neuromuscular control. To date, for the masticatory apparatus, only atwo-dimensional cephalometric method for assessing the mechanical advantage, which is ameasure for the ratio of the output force to the input force in asystem, is available. This study determined the reliability and errors of athree-dimensional (3D) mechanical advantage calculation for the masticatory system. Using cone-beam computed tomography images from teenage patients undergoing orthodontic treatments, 36craniofacial landmarks were identified, and the moment arms for seven muscles and their load moment arms (biomechanical variables) were determined. The 3D mechanical advantage for each muscle was calculated. This procedure was repeated by three examiners. Reliability was verified using the intraclass correlation coefficient (ICC) and the errors by calculating the absolute differences, variance estimator and coefficient of variation (CV). Landmark coordinates demonstrated excellent intra- and interexaminer reliability (ICC 0.998-1.000; p < 0.0001). Intraexaminer data showed errors < 1.5 mm. Unsatisfactory interexaminer errors ranged from 1.51-5.83 mm. All biomechanical variables presented excellent intraexaminer reliability (ICC 0.919-1.000, p < 0.0001; CV < 7%). Interexaminer results were almost excellent, but with lower values (ICC 0.750-1.000, p < 0.0001; CV < 10%). However, the muscle moment arm and 3D mechanical advantage of the lateral pterygoid muscles had ICCs < 0.500 (p < 0.05) and CV < 30%. Intra- and interexaminer errors were ≤ 0.01 and ≤ 0.05, respectively. Both landmarks and biomechanical variables showed high reliability and acceptable errors. The proposed method is viable for the 3D mechanical advantage measure.

In-Flight Relative Radiometric Calibration of a Wide Field of View Directional Polarimetric Camera Based on the Rayleigh Scattering over Ocean

The directional polarimetric camera (DPC) is a Chinese satellite sensor with a large field of view (FOV) (±50° both along-track and cross-track) and a high spatial resolution (about 3.3 km at nadir) that operates in a sun-synchronous orbit. It is a difficult task to calibrate the in-flight relative radiometric variation of the sensors with such a wide FOV. In this study, a new method based on Rayleigh scattering over the ocean is developed to estimate the radiometric sensitivity variation over the whole FOV of DPC. Firstly, the theoretical uncertainty of the method is analyzed to calibrate the relative radiometric response. The calibration uncertainties are about 2–6.9% (depending on the wavelength) when the view zenith angle (VZA) is 0° and decrease to about 1–3.8% when VZA increases to 70°. Then, the method is applied to evaluate the long-term radiometric drift of the DPC. It is found that the radiometric response of DPC/GaoFen-5 over the whole FOV is progressively drifting over time. The sensitivity at shorter bands decreases more strongly than longer bands, and at the central part of the optics decreases more strongly than the marginal part. During the 14 months (from March 2019 to April 2020) of operational running in-orbit, the DPC radiometric responses of 443 nm, 490 nm, 565 nm, and 670 nm bands drifted by about 4.44–23.08%, 4.75–16.22%, 3.86–9.81%, and 4.7–16.86%, respectively, from the marginal to the central part of the FOV. The radiometric sensitivity has become more stable since January 2020. The monthly radiometric drift is separated into the relative radiometric part and the absolute radiometric part. The relative radiometric drift of DPC is found to be smoothly varying with VZA, which can be parameterized as a polynomial function via VZA. At last, the temporal radiometric drift of DPC/GaoFen-5 is corrected by combining the relative and absolute radiometric coefficients. The correction is convincing by cross calibration with MODIS/Aqua observation over the desert sites and improving the aerosol retrievals. The Rayleigh method in this study is efficient for the radiometric sensitivity calibration of wide FOV satellite sensors.