The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples

Benefits and Challenges of the Educational Metaverse: Evidence from Quantitative and Qualitative Data

The metaverse, as a more advanced form of virtual reality, has great potential for education because of its unique affordances for enhancing immersion, interaction, and presence. However, because its development is still in its infancy, there are few empirical studies on the application of metaverse in education, with insufficient empirical evidence from the literature regarding its effectiveness and valued design features, as well as its advantages and disadvantages as a technology-enhanced learning environment. Utilizing the case study method, this study designed and implemented a 40-minute psychology course with 31 undergraduate students in the metaverse using the Virbela platform and collected both quantitative and qualitative data to empirically explore the benefits and challenges of the educational metaverse. The quantitative results showed that the students reported good learning experiences in the metaverse, but their learning outcomes were unsatisfactory, just over the passing level. The qualitative results revealed useful design features and common technical challenges of the educational metaverse. Based on the results, several implications for designing and developing effective courses in the educational metaverse were proposed

Recent changes to Arctic river discharge

Arctic rivers drain ~15% of the global land surface and significantly influence local communities and economies, freshwater and marine ecosystems, and global climate. However, trusted and public knowledge of pan-Arctic rivers is inadequate, especially for small rivers and across Eurasia, inhibiting understanding of the Arctic response to climate change. Here, we calculate daily streamflow in 486,493 pan-Arctic river reaches from 1984-2018 by assimilating 9.18 million river discharge estimates made from 155,710 satellite images into hydrologic model simulations. We reveal larger and more heterogenous total water export (3-17% greater) and water export acceleration (factor of 1.2-3.3 larger) than previously reported, with substantial differences across basins, ecoregions, stream orders, human regulation, and permafrost regimes. We also find significant changes in the spring freshet and summer stream intermittency. Ultimately, our results represent an updated, publicly available, and more accurate daily understanding of Arctic rivers uniquely enabled by recent advances in hydrologic modeling and remote sensing

Eckhaus Instability in Laser Cavities with Harmonically Swept Filters

This work was supported in part by National Key R&D Program of China (2019YFB1803904), in part by Science, Technology and Innovation Commission of Shenzhen Municipality (SGDX2019081623060558), in part by Research Grants Council, University Grants Committee of Hong Kong SAR (PolyU152241/18E), and in part by Guangdong Basic and Applied Basic Research Foundation (2021A1515012544) (Corresponding author: Dongmei Huang).Peer reviewedPostprin

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule-guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis. Second, logic rules of different lengths are mined from knowledge graph to learn new entity representations. Finally, knowledge graph inference is performed based on representing entities that converge multi-media features. Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference, demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of “chain-of-pearls” continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples