Lake volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019

Lake storage change serves as a unique indicator of natural climate change on the Tibetan Plateau (TP). However, comprehensive lake storage data, especially for lakes smaller than 10 km2, are still lacking in the region. In this dataset, we completed a census of annual relative lake volume (RLV) for 976 lakes, which are larger than 1 km2, on the endorheic basin of the Tibetan Plateau (EBTP) during 1989–2019 using Landsat imagery and digital terrain models. Our method first identifies individual lakes, determines their analysis extents and calculates annual lake area from Landsat imagery. It then derives lake area-elevation relationship, estimates lake surface elevation, and calculates RLV. Validation and comparison with several existing datasets indicate our data are more reliable and comprehensive. Our study complements existing lake datasets by providing a complete and long-term lake water volume change data for the region

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics - A Review

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning

DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning

Online Class-Incremental (OCI) learning has sparked new approaches to expand the previously trained model knowledge from sequentially arriving data streams with new classes. Unfortunately, OCI learning can suffer from catastrophic forgetting (CF) as the decision boundaries for old classes can become inaccurate when perturbated by new ones. Existing literature have applied the data augmentation (DA) to alleviate the model forgetting, while the role of DA in OCI has not been well understood so far. In this paper, we theoretically show that augmented samples with lower correlation to the original data are more effective in preventing forgetting. However, aggressive augmentation may also reduce the consistency between data and corresponding labels, which motivates us to exploit proper DA to boost the OCI performance and prevent the CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the augmented samples and their labels simultaneously, which is shown to enhance the sample diversity while maintaining strong consistency with corresponding labels. Further, to solve the class imbalance problem, we design an Adaptive Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples from both old and new classes and dynamically adjusting the label mixing ratio. Our approach is demonstrated to be effective on several benchmark datasets through extensive experiments, and it is shown to be compatible with other replay-based techniques.Comment: 10 pages, 7 figures and 3 table

Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations

The baiji, or Yangtze River dolphin (Lipotes vexillifer), is a flagship species for the conservation of aquatic animals and ecosystems in the Yangtze River of China; however, this species has now been recognized as functionally extinct. Here we report a high-quality draft genome and three re-sequenced genomes of L. vexillifer using Illumina short-read sequencing technology. Comparative genomic analyses reveal that cetaceans have a slow molecular clock and molecular adaptations to their aquatic lifestyle. We also find a significantly lower number of heterozygous single nucleotide polymorphisms in the baiji compared to all other mammalian genomes reported thus far. A reconstruction of the demographic history of the baiji indicates that a bottleneck occurred near the end of the last deglaciation, a time coinciding with a rapid decrease in temperature and the rise of eustatic sea level

Distance Measurement for the Indoor WSN Nodes Using WTR Method

Time reversal (TR), which leads various multipath signals to focus spatiotemporally on arbitrarily complex propagation environments, can resist signal multipath delay and attenuation which is caused by inhomogeneous complex environments. In this paper, the windowed time reversal (WTR) method for distance measurement between the wireless sensor network (WSN) nodes is proposed based on TR. In the WTR, the main lobe of the channel response is captured by a window. WTR not only takes advantage of the spatiotemporal focus features of TR, but also compensates the multipath effect to eliminate various factors from the environment. WTR can recover the favorable symmetry of the main lobe of channel response, thereby accurately measuring the times-of-arrival (TOA) of the electromagnetic wave and distance between WSN nodes. By analyzing the characteristics of the time reversal operator, the theoretical basis of the WTR is given in this paper. An algorithm for node distance measurement with WTR in indoor environments is described and a large number of simulations with various environments are carried out. The errors for the proposed WTR have been analyzed. Simulation results show that ranging errors by WTR are less than 1% no matter how many obstacles are in the indoor environment

High-resolution imaging algorithm based on temporal focal characteristic of time-reversed signal

Although the high-resolution materials can improve the resolution of the conventional time-reversal imaging (TRI) algorithms, they also limit the applications of TRI. In this paper, a new TRI algorithm with high-resolution is presented. Since the proposed algorithm utilizes multiple time reversal operation steps to improve resolution, it can realize high-resolution without invoking any high-resolution materials. The results show the resolution of the proposed algorithm is superior to that of the conventional TRI

Viscosity Monitoring During Hemodiluted Blood Coagulation Using Optical Coherence Elastography

Rapid and accurate clot diagnostic systems are needed for the assessment of hemodiluted blood coagulation. We develop a real-time optical coherence elastography (OCE) system, which measures the attenuation coefficient of a compressional wave induced by a piezoelectric transducer (PZT) in a drop of blood using optical coherence tomography (OCT), for the determination of viscous properties during the dynamic whole blood coagulation process. Changes in the viscous properties increase the attenuation coefficient of the sample. Consequently, dynamic blood coagulation status can be monitored by relating changes of the attenuation coefficient to clinically relevant coagulation metrics, including the initial coagulation time and the clot formation rate. This system was used to characterize the influence of activator kaolin and the influence of hemodilution with either NaCl 0.9% or hydroxyethyl starch (HES) 6% on blood coagulation. The results show that PZT-OCE is sensitive to coagulation abnormalities and is able to characterize blood coagulation status based on viscosity-related attenuation coefficient measurements. PZT-OCE can be used for point-of-care testing for diagnosis of coagulation disorders and monitoring of therapies

Negative Pressure Waves Based High Resolution Leakage Localization Method Using Piezoceramic Transducers and Multiple Temporal Convolutions

The negative pressure wave (NPW) signals generated by a pipeline leakage often have a long signal duration. When these signals are utilized to compute the leakage position, the long signal duration will result in a large area being considered as leakage area. The localization resolution is low. A novel high-resolution localization algorithm is developed for pipeline leakage detection using piezoceramic transducers in this paper. The proposed algorithm utilizes multiple temporal convolutions to decrease the localization functional values at the points close to the leakage, in order to reduce the range of the leakage area revealed by the proposed algorithm. As a result, the localization resolution is improved. A measured experiment was conducted to study the proposed algorithm. In the experiment, the proposed algorithm was used to monitor a 55.8 m pressurized pipeline with two controllable valves and two Lead Zirconate Titanate (PZT) sensors. With the aid of the piezoceramic sensor, the experimental results show that the proposed algorithm results in a resolution which is better than that of the traditional method

Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau

Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes greater than 1 km2 in the endorheic basins of the Tibetan Plateau (EBTP) using Landsat images from 1990 to 2019, and analysed the relationships between lake area and local and large-scale climate variables at different geographic scales. The results show that lake area in the EBTP has increased significantly from 1990 to 2019 at a rate of 432.52 km2·year−1. In the past 30 years, lake area changes in the EBTP have mainly been affected by local climate variables such as precipitation and temperature. At a large scale, Indian Summer Monsoon (ISM) has correlations with lake area in western sub-regions in the Inner Basin (IB). While Atlantic Multidecadal Oscillation (AMO) has a significant connection with lake area, the North Atlantic Oscillation (NAO) does not. We also found that abnormal drought (rainfall) brought by the El Niño/La Niña events are significantly correlated with the lake area change in most sub-regions in the IB

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity