Entropy Theory for Streamflow Forecasting

Entropy spectral analysis is developed for monthly streamflow forecasting, which contains the use of configurational entropy and relative entropy. Multi-channel entropy spectral analysis is developed for long-term drought forecasting with climate indicators. The configurational entropy spectral analysis (CESA) is developed with both spectral power and frequency as random variables. With spectral power as a random variable, the configurational entropy spectral analysis (CESAS) identical to the original Burg entropy spectral analysis (BESA) when the underlying process is Gaussian. Through examination using monthly streamflow from the Mississippi Watershed, CESAS and BESA yield the same results and two methods are considered equivalent or as one method. With frequency as a random variable, the configurational entropy spectral analysis (CESAF) is developed and tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CESAF captures streamflow seasonality and satisfactorily forecasts both high and low flows. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately than downstream streamflow. Minimum relative entropy spectral analysis (MRESA) is developed under two conditions: spectral power as a random variable (RESAS) and frequency as a random variable (RESAF). The exponential distribution was chosen as a prior probability in the RESAS theory, and in the RESAF theory, the prior is chosen from the periodicity of streamflow. Both MRESA theories were evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflow shows higher reliability in the Upper Mississippi than in the Lower Mississippi. The proposed univariate entropy spectral analyses are generally recommended over the classical autoregressive (AR) process for higher reliability and longer forecasting lead time. By comparing two MRESA theories with the two maximum entropy spectral analyses (MESA) (BESA and CESA), it is found that MRESA provided higher resolution in spectral estimation and more reliable streamflow forecasting, especially for multi-peak flow conditions. The MRESA theory is more accurate in forecasting streamflow for both peak and low flow values with longer lead time than MESA. Besides, choosing frequency as a random variable shows advantages over choosing spectral power. Spectral density estimated by the RESAF or CESAF theory shows higher resolution than the RESAS or BESA theory, respectively, and streamflow forecasted by RESAF or CESAF is more reliable than that by RESAS or BESA, respectively. Finally, multi-channel entropy spectral analysis (MCESA) is developed for bivariate or multi-variate time series forecasting. MCESA theory is verified by forecasting long-term standardized streamflow index with El Nino Southern Oscillation (ENSO) indicator. SSI was successfully forecasted using multi-channel spectral analysis with ENSO as an indicator. The monthly drought series is forecasted for lead times of 4-6 years by MCESA

Sediment Graphs Based on Entropy Theory

Using the entropy theory, this paper derives an instantaneous unit sediment graph (IUSG or USG) to determine sediment discharge and the relation between sediment yield and runoff volume. The derivation of IUSG requires an expression of the effective sediment erosion intensity whose relation with rainfall is revisited. The entropy theory provides an efficient way to estimate the parameters involved in the derivations. Sediment discharge is also computed using the instantaneous unit hydrograph (IUH), which can also be derived using the entropy theory. This method works as well as the IUSG method, especially when the peak sediment discharge and peak runoff occur at the same time. The entropy theory yields the probability distribution of sediment yield and of sediment discharge, which can then be used to estimate uncertainty in sediment yield prediction

Estimation of Velocity Distribution and Suspended Sediment Discharge in Open Channels Using Entropy

In hydraulics, velocity distribution is needed to determine flow characteristics, like discharge, sediment discharge, head loss, energy coefficient, moment coefficient, and scour. However, the complicated interaction between water and sediment causes great difficulties in the measurement of flow and sediment discharge. Thus, the development of a method which can simulate the velocity distribution and sediment discharge in open channels is designable. Traditional methods for the estimation of velocity distribution, such as the Prandtl-von Karman logarithmic velocity and of sediment concentration distribution, such as the Rouse equation, are generally invalid at or near the channel bed and are inaccurate at the water surface. Considering the limitations of traditional methods, entropy based models have been applied, yet the assumption on the cumulative distribution function made in these methods limits their application. The objective of this research is to develop an efficient method to estimate velocity distribution and suspended sediment discharge in open channels using the Tsallis entropy. This research focuses on a better-organized hypothesis on the cumulative probability distribution function under more applicable coordinates, which should be transformable in different dimensions. Velocity distribution and sediment distribution are derived using the Tsallis entropy under the hypothesis that the cumulative probability distribution follows a non-linear function, in which the value of the exponent is shown to be related to the width-depth ratio of channel cross-section. Three different combinations of entropy and empirical methods for velocity and sediment concentration distribution are applied to compute suspended sediment discharge. Then advantages and disadvantages of each method are discussed. The velocity distribution derived using the Tsallis entropy is expected to be easy to apply and valid throughout the whole cross-section of the open channel. This research contributes to the application of entropy theory and shows its advantages in hydraulic engineering

Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos

Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic

Tsallis Entropy-Based Flow Duration Curve

The flow duration curve (FDC) is employed for addressing a multitude of problems in water resources engineering, such as prediction of the distribution of future flows, forecasting of future recurrence frequencies, comparison of watersheds, construction of load duration curves, and determination of low flow thresholds. Usually, the FDC is constructed empirically for a given set of flow data, and the FDC so constructed is found to vary from one year to another and from one gauging station to another within the same watershed. This article attempts to analytically derive the FDC by maximizing the Tsallis entropy based on the knowledge that the mean discharge is known, thus obviating the need for any fitting. The mean discharge is found to be strongly related to the drainage area. The Tsallis entropy-based FDC is tested using field data and is found to be in agreement with the observed curve. The entropy method permits a probabilistic characterization of the FDC and hence a quantitative assessment of its uncertainty. With this method, the flow duration curve can also be forecasted for different recurrence intervals. The entropy is found to monotonically increase with the increase in time interval, indicating that the flow system becomes more complex but the degree of complexity decreases with increasing time interval after a certain time, eventually reaching a constant value, reflecting a reduced influence of land use change and other human influences on the flow regime

QMRPF-UKF Master-Slave Filtering for the Attitude Determination of Micro-Nano Satellites Using Gyro and Magnetometer

In this paper, the problem of estimating the attitude of a micro-nano satellite, obtaining geomagnetic field measurements via a three-axis magnetometer and obtaining angle rate via gyro, is considered. For this application, a QMRPF-UKF master-slave filtering method is proposed, which uses the QMRPF and UKF algorithms to estimate the rotation quaternion and the gyro bias parameters, respectively. The computational complexicity related to the particle filtering technique is eliminated by introducing a multiresolution approach that permits a significant reduction in the number of particles. This renders QMRPF-UKF master-slave filter computationally efficient and enables its implementation with a remarkably small number of particles. Simulation results by using QMRPF-UKF are given, which demonstrate the validity of the QMRPF-UKF nonlinear filter

Gray Matter Atrophy in Parkinson’s Disease and the Parkinsonian Variant of Multiple System Atrophy: A Combined ROI- and Voxel-Based Morphometric Study

OBJECTIVES: Parkinson’s disease (PD) and the parkinsonian variant of multiple system atrophy (MSA-P) are distinct neurodegenerative disorders that share similar clinical features of parkinsonism. The morphological alterations of these diseases have yet to be understood. The purpose of this study was to evaluate gray matter atrophy in PD and MSA-P using regions of interest (ROI)-based measurements and voxel-based morphometry (VBM). METHODS: We studied 41 patients with PD, 20 patients with MSA-P, and 39 controls matched for age, sex, and handedness using an improved T1-weighted sequence that eased gray matter segmentation. The gray matter volumes were measured using ROI and VBM. RESULTS: ROI volumetric measurements showed significantly reduced bilateral putamen volumes in MSA-P patients compared with those in PD patients and controls (po0.05), and the volumes of the bilateral caudate nucleus were significantly reduced in both MSA-P and PD patients compared with those in the controls (po0.05). VBM analysis revealed multifocal cortical and subcortical atrophy in both MSA-P and PD patients, and the volumes of the cerebellum and temporal lobes were remarkably reduced in MSA-P patients compared with the volumes in PD patients (po0.05). CONCLUSIONS: Both PD and MSA-P are associated with gray matter atrophy, which mainly involves the bilateral putamen, caudate nucleus, cerebellum, and temporal lobes. ROI and VBM can be used to identify these morphological alterations, and VBM is more sensitive and repeatable and less time-consuming, which may have potential diagnostic value

Treatment patterns and clinical outcomes in 157 patients with extensive-stage small cell lung cancer: real-world evidence from a single-center retrospective study

BackgroundImmune checkpoint inhibitors (ICIs) have changed the therapeutic options for extensive-stage small-cell lung cancer (ES-SCLC). In this real-world study, we analyzed the treatment patterns in patients with ES-SCLC and evaluated the efficacy of chemotherapy combined with immunotherapy as first-line therapy.MethodsA retrospective analysis was performed on patients with ES-SCLC who received treatment at China-Japan Friendship Hospital (Beijing, China) between August 1, 2020, and April 30, 2023. The treatment patterns appeared in the form of Sunburst Chart and Sankey diagram. The survival analyses were conducted by Kaplan-Meier curves.ResultsA total of 157 patients with ES-SCLC were retrospectively included. According to first-line therapy, patients were divided into the chemotherapy (CT) group (n=82) and chemo-immunotherapy (CIT) group (n=75). The median treatment lines were 2[1, 2] and cycles were 8[5, 12], respectively. 82 patients received the second line of therapy, followed by 37 for the third, 15 for the fourth, 11 for the fifth, and 5 for the sixth. Overall, the treatment patterns involved 11 options including 12 chemotherapy regimens, 11 ICIs, and 4 targeted agents. The second-line treatment pattern had the most options (9) and regimens (43). In the first 3 lines, chemotherapy was the largest proportion of treatment options. The addition of ICIs prolonged progression-free survival from 6.77 (95% confidence interval [CI], 6.00-7.87) to 7.33 (95% CI, 6.03-9.80) months (hazard ratio [HR]=0.67, 95% CI, 0.47-0.95; P=0.025), overall survival from 12.97 (10.90-23.3) to 14.33 (12.67-NA) months without statistically significant difference (HR=0.86, 95% CI, 0.55-1.34; P=0.505).ConclusionThe treatment options of patients with ES-SCLC are more diversified. Combination therapy is the current trend, where chemotherapy is the cornerstone. Meanwhile, ICIs participate in almost all lines of treatment. However, the clinical efficacy remains barely satisfactory. We are urgently expecting more breakthrough therapies except immunology will be applied in the clinic