A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach

This study was supported by the Strategic Priority Research Programs of the Chinese Academy of Sciences (grant nos. XDA20100300 and XDA19070202) and the Swiss National Science Foundation (200021E_177652/1) within the framework of the DFG Research Unit GlobalCDA (FOR2630).Knowledge about the occurrence and characteristics of surge-type glaciers is crucial due to the impact of surging on glacier melt and glacier-related hazards. One of the super-clusters of surge-type glaciers is High Mountain Asia (HMA). However, no consistent region-wide inventory of surge-type glaciers in HMA exists. We present a regionally resolved inventory of surge-type glaciers based on their behaviour across High Mountain Asia between 2000 and 2018. We identify surge-type behaviour from surface velocity, elevation and feature change patterns using a multi-factor remote sensing approach that combines yearly ITS_LIVE velocity data, DEM differences and very-high-resolution imagery (Bing Maps, Google Earth). Out of the ≈95000 glaciers in HMA, we identified 666 that show diagnostic surge-type glacier behaviour between 2000 and 2018, which are mainly found in the Karakoram (223) and the Pamir regions (223). The total area covered by the 666 surge-type glaciers represents 19.5% of the glacierized area in Randolph Glacier Inventory (RGI) V6.0 polygons in HMA. Only 68 glaciers were already identified as "surge type" in the RGI V6.0. We further validate 107 glaciers previously labelled as "probably surge type" and newly identify 491 glaciers, not previously reported in other inventories covering HMA. We finally discuss the possibility of self-organized criticality in glacier surges. Across all regions of HMA, the surge-affected area within glacier complexes displays a significant power law dependency with glacier length.Publisher PDFPeer reviewe

Examining geodetic glacier mass balance in the eastern Pamir transition zone

This research was supported by the Key Research Program of Frontier Sciences CAS [QYZDY-SSW-DQC026] and the National Natural Science Foundation of China [41590853]. SRTM DEM and NASA HMA DEM data were sourced from NASA Earthdata (https://earthdata.nasa.gov/), and the ALOS Global Digital Surface Model (AW3D30) was sourced from JAXA (https://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm). ALOS PRISM level-1B1 products were ordered from http://en.alos-pasco.com/alos/prism/ under a 4th ALOS RA data grant awarded to Quincey (PI No. 1008). Landsat-7 and Landsat-8 images can be freely downloaded from http://glovis.usgs.gov. Randolph Glacier Inventory data were acquired from Global Land Ice Measurements from Space (GLIMS) (RGI Consortium, 2017). Lv acknowledges program B for outstanding PhD candidates of Nanjing University and the support from the Chinese Scholarship Council (CSC) for studying at the University of Leeds.Glaciers in the eastern Pamir have reportedly been gaining mass during recent decades, even though glaciers in most other regions in High Mountain Asia have been in recession. Questions still remain about whether the trend is strengthening or weakening, and how far the positive balances extend into the eastern Pamir. To address these gaps, we use three different digital elevation models to reconstruct glacier surface elevation changes over two periods (2000–09 and 2000–15/16). We characterize the eastern Pamir as a zone of transition from positive to negative mass balance with the boundary lying at the northern end of Kongur Tagh, and find that glaciers situated at higher elevations are those with the most positive balances. Most (67% of 55) glaciers displayed a net mass gain since the 21st century. This led to an increasing regional geodetic glacier mass balance from −0.06 ± 0.16 m w.e. a−1 in 2000–09 to 0.06 ± 0.04 m w.e. a−1 in 2000–15/16. Surge-type glaciers, which are prevalent in the eastern Pamir, showed fluctuations in mass balance on an individual scale during and after surges, but no statistical difference compared to non-surge-type glaciers when aggregated across the region.Publisher PDFPeer reviewe

Futuristic 6G Pervasive On-Demand Services:Integrating Space Edge Computing With Terrestrial Networks

Futuristic 6G technologies will integrate emerging low-Earth orbit (LEO) megaconstellations into terrestrial networks, promising to provide ubiquitous, low-latency and high-throughput network services on-demand. However, several unique characteristics of satellites (e.g., high dynamics and error-prone operational environments) make it very challenging to unleash the potential of megacons-tellations and accomplish these aforementioned promises

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Distinguishing Glaciers between Surging and Advancing by Remote Sensing: A Case Study in the Eastern Karakoram

The Karakoram has had an overall slight positive glacier mass balance since the end of 20th century, which is anomalous given that most other regions in High Mountain Asia have had negative changes. A large number of advancing, retreating, and surging glaciers are heterogeneously mixed in the Karakoram increasing the difficulties and inaccuracies to identify glacier surges. We found two adjacent glaciers in the eastern Karakoram behaving differently from 1995 to 2019: one was surging and the other was advancing. In order to figure out the differences existing between them and the potential controls on surges in this region, we collected satellite images from Landsat series, ASTER, and Google Earth, along with two sets of digital elevation model. Utilizing visual interpretation, feature tracking of optical images, and differencing between digital elevation models, three major differences were observed: (1) the evolution profiles of the terminus positions occupied different change patterns; (2) the surging glacier experienced a dramatic fluctuation in the surface velocities during and after the event, while the advancing glacier flowed in a stable mode; and (3) surface elevation of the surging glacier decreased in the reservoir and increased in the receiving zone. However, the advancing glacier only had an obvious elevation increase over its terminus part. These differences can be regarded as standards for surge identification in mountain ranges. After combining the differences with regional meteorological conditions, we suggested that changes of thermal and hydrological conditions could play a role in the surge occurrence, in addition, geomorphological characteristics and increasing warming climate might also be part of it. This research strongly contributes to the literatures of glacial motion and glacier mass change in the eastern Karakoram through remote sensing

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the performance degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new performance degradation index, which is regarded as the input data to input into KELM, and the degradation trend prediction model is established to realize the performance degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the performance degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods

Research on time series forecasting method of ion concentration in produced fluid

Abstract In the research on strong alkali‐surfactant‐polymer flooding scaling prediction, the variation characteristics of ion concentration in the produced fluid are obviously consistent with those of scale component content. Consequently, studying the variation tendency of ion concentration in produced fluid is helpful for further revealing the scaling tendency. Given the periodicity and chaos characteristics of the ion concentration data of the produced fluid, an echo state network (ESN) is used to realize the relevant time series forecasting. Simultaneously, to cope with the failure of the ESN in selecting suitable reservoir parameters according to different characteristics of time series, a modified discrete particle swarm optimization algorithm based on objective space decomposition is used to optimize the reservoir parameters. The experimental results indicate that the improved ESN presents the lowest error and is the closest to the target value