Assessing the responses of ecosystem patterns, structures and functions to drought under climate change in the Yellow River Basin, China.

Understanding how ecosystems respond and adapt to drought has become an urgent issue as drought stress intensifies under climate change, yet this topic is not fully understood. Currently, conclusions on the response of ecosystems in different regions to drought disturbance are inconsistent. Based on long MODIS data and observed data, this study systematically explored the relationships between ecosystem patterns, structures and functions and drought, taking a typical climate change-sensitive area and an ecologically fragile area-the Yellow River Basin-as a case study. Drought assessment results revealed that the Yellow River Basin has experienced meteorological and hydrological drought during most of the last two decades, predominantly characterized by medium and slight droughts. The ecosystem patterns and structures changed dramatically as the grassland decreased and the landscape fragmentation index (F) increased with increasing wetness. The annual gross primary productivity (GPP) increased, the water use efficiency (WUE) declined and ecosystem service value (ESV) exhibited a W-shaped increase at the watershed scale, but there were significant regional differences. There were positive correlations between F, GPP, ESV and drought indices, while there was a negative correlation between WUE and drought indices at the watershed scale. Under drought stress, the ecosystem structure in the basin was disrupted, the GPP and ESV decreased, but the WUE increased. Notably, approximately 106 %, 20 %, and 1 % of the maximum reductions in F, GPP, and ESV, respectively, were caused by drought, while the maximum 4 % of WUE increased. Responses of some functions in the wetland and grassland to drought vary from those in other ecosystems. The mechanisms underlying ecosystem responses to drought were further investigated. This study enhances the understanding of these responses and will help stakeholders formulate drought mitigation policies and protect ecosystem health.

Analyzing correlations between GNSS retrieved precipitable water vapor and land surface temperature after earthquakes occurrence.

Earthquake is a common and destructive natural disaster. The enormous amount of energy released from seismic events can result in anomalous land surface temperature (LST) and catalyze the accumulation of water vapor in the atmosphere. The majority of previous works are not consensual concerning precipitable water vapor (PWV) and LST after the earthquake. Here, we utilized multi-source data to analyze the changes of PWV and LST anomaly after three Ms 4.0-5.3 crustal earthquakes at low depth (8-9 km) that occurred in Qinghai-Tibet Plateau. Firstly, PWV retrieval using Global Navigation Satellite System (GNSS) technology is performed, showing that its root mean square error (RMSE) is less than 1.8 mm against radiosonde (RS) data or European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) PWV data. The PWV change derived from the nearest GNSS stations around the hypocenter during the earthquakes shows anomalies, and the results reveal that PWV anomalies occurred after the earthquakes, mainly obeying a trend of increasing first and then decreasing. In addition, LST increases three days before PWV peak with a thermal anomaly of 12 °C higher than that of previous days. Robust Satellite Technique (RST) algorithm and ALICE index on Moderate Resolution Imaging Spectroradiometer (MODIS) LST products are introduced to analyze the correlation between the abnormality of LST and PWV. Based on ten-year background field data (2012-2021), the results show that LST during the earthquake has more thermal anomaly occurrences than in previous years. The more severe the LST thermal anomaly is, the higher the probability of a PWV peak occurring.

Helmert Variance Component Estimation for Multi-GNSS Relative Positioning.

The Multi-constellation Global Navigation Satellite System (Multi-GNSS) has become the standard implementation of high accuracy positioning and navigation applications. It is well known that the noise of code and phase measurements depend on GNSS constellation. Then, Helmert variance component estimation (HVCE) is usually used to adjust the contributions of different GNSS constellations by determining their individual variances of unit weight. However, HVCE requires a heavy computation load. In this study, the HVCE posterior weighting was employed to carry out a kinematic relative Multi-GNSS positioning experiment with six short-baselines from day of year (DoY) 171 to 200 in 2019. As a result, the HVCE posterior weighting strategy improved Multi-GNSS positioning accuracy by 20.5%, 15.7% and 13.2% in east-north-up (ENU) components, compared to an elevation-dependent (ED) priori weighting strategy. We observed that the weight proportion of both code and phase observations for each GNSS constellation were consistent during the entire 30 days, which indicates that the weight proportions of both code and phase observations are stable over a long period of time. It was also found that the quality of a phase observation is almost equivalent in each baseline and GNSS constellation, whereas that of a code observation is different. In order to reduce the time consumption of the HVCE method without sacrificing positioning accuracy, the stable variances of unit weights of both phase and code observations obtained over 30 days were averaged and then frozen as a priori information in the positioning experiment. The result demonstrated similar ENU improvements of 20.0%, 14.1% and 11.1% with respect to the ED method but saving 88% of the computation time of the HCVE strategy. Our study concludes with the observations that the frozen variances of unit weight (FVUW) could be applied to the positioning experiment for the next 30 days, that is, from DoY 201 to 230 in 2019, improving the positioning ENU accuracy of the ED method by 18.1%, 13.2% and 10.6%, indicating the effectiveness of the FVUW.

Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation.

Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber's equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.

Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.

BACKGROUND: DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs. RESULTS: Our results suggest that some sequence features can significantly differentiate DSBs and SSBs. Evaluated by 10 fold cross-validation on the benchmark datasets, our prediction method can achieve the accuracy of 88.7% and AUC (area under the curve) of 0.919. Moreover, our method has good performance in independent testing. CONCLUSIONS: Using various sequence-derived features, a novel method is proposed to distinguish DSBs and SSBs accurately. The method also explores novel features, which could be helpful to discover the binding specificity of DNA-binding proteins.

Tightly Coupled Integration of GPS Ambiguity Fixed Precise Point Positioning and MEMS-INS through a Troposphere-Constrained Adaptive Kalman Filter.

Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height component are improved by 30.36%, 16.95% and 24.07% for three different convergence times, i.e., 60, 50 and 30 min, respectively. It shows that the ambiguity-fixed horizontal positioning accuracy has been significantly improved. When compared with the conventional PPP solution, it can be seen that position accuracies are improved by 19.51%, 61.11% and 23.53% for the north, east and height components, respectively, after one hour convergence through the troposphere constraint fixed PPP/INS with adaptive covariance model.

GNSS Precise Kinematic Positioning for Multiple Kinematic Stations Based on A Priori Distance Constraints.

When applying the Global Navigation Satellite System (GNSS) for precise kinematic positioning in airborne and shipborne gravimetry, multiple GNSS receiving equipment is often fixed mounted on the kinematic platform carrying the gravimetry instrumentation. Thus, the distances among these GNSS antennas are known and invariant. This information can be used to improve the accuracy and reliability of the state estimates. For this purpose, the known distances between the antennas are applied as a priori constraints within the state parameters adjustment. These constraints are introduced in such a way that their accuracy is taken into account. To test this approach, GNSS data of a Baltic Sea shipborne gravimetric campaign have been used. The results of our study show that an application of distance constraints improves the accuracy of the GNSS kinematic positioning, for example, by about 4 mm for the radial component.

An ensemble feature selection technique for cancer recognition.

Correlation-based feature selection (CFS) using neighborhood mutual information (NMI) and particle swarm optimization (PSO) are combined into an ensemble technique in this paper. Based on this observation, an efficient gene selection algorithm, denoted by NMICFS-PSO, is proposed. Several cancer recognition tasks are gathered for testing the proposed technique. Moreover, support vector machine (SVM), integrated with leave-one-out cross-validation and served as a classifier, is employed for six classification profiles to calculate the classification accuracy. Experimental results show that the proposed method can reduce the redundant features effectively and achieve superior performance. The classification accuracy obtained by our method is higher in five out of the six gene expression problems as compared with that of other classifi cation methods.