Explore the vast range of titles available.

As a result of the COVID-19 pandemic, whether and when the world can reach herd immunity and return to normal life and a strategy for accelerating vaccination programmes constitute major concerns. We employed Metropolis–Hastings sampling and an epidemic model to design experiments based on the current vaccinations administered and a more equitable vaccine allocation scenario. The results show that most high-income countries can reach herd immunity in less than 1 year, whereas low-income countries should reach this state after more than 3 years. With a more equitable vaccine allocation strategy, global herd immunity can be reached in 2021. However, the spread of SARS-CoV-2 variants means that an additional 83 days will be needed to reach global herd immunity and that the number of cumulative cases will increase by 113.37% in 2021. With the more equitable vaccine allocation scenario, the number of cumulative cases will increase by only 5.70% without additional vaccine doses. As SARS-CoV-2 variants arise, herd immunity could be delayed to the point that a return to normal life is theoretically impossible in 2021. Nevertheless, a more equitable global vaccine allocation strategy, such as providing rapid vaccine assistance to low-income countries/regions, can improve the prevention of COVID-19 infection even though the virus could mutate.

River basin cyberinfrastructure with the Internet of Things (IoT) as the core has brought watershed data science into the big data era, greatly improving data acquisition and sharing efficiency. However, challenges in analyzing, processing, and applying very large quantities of observational data remain. Given the observational needs in watershed research, we studied the construction of river basin cyberinfrastructure and developed an integrated observational data control system (IODCS). The IODCS is an important platform for processing large quantities of observational data, including automated collection, storage, analysis, processing, and release. This paper presents various aspects of the IODCS in detail, including the system’s overall design, function realization, big data analysis methods, and integrated models. We took the middle reaches of the Heihe River Basin (HRB) as the application research area to show the performance of the developed system. Since the system began operation, it has automatically received, analyzed, and stored more than 1.4 billion observational data records, with an average of more than 14 million observational data records processed per month and up to 21,011 active users. The demonstrated results show that the IODCS can effectively leverage the processing capability of massive observational data and provide a new perspective for facilitating ecological and hydrological scientific research on the HRB.

Continuous land-surface temperature (LST) observations from ground-based stations are an important reference dataset for validating remote-sensing LST products. However, a lack of evaluations of the representativeness of station observations limits the reliability of validation results. In this study, a new practical validation scheme is presented for validating remote-sensing LST products that includes a key step: assessing the spatial representativeness of ground-based LST measurements. Three indicators, namely, the dominant land-cover type (DLCT), relative bias (RB), and average structure scale (ASS), are established to quantify the representative levels of station observations based on the land-cover type (LCT) and LST reference maps with high spatial resolution. We validated MODIS LSTs using station observations from the Heihe River Basin (HRB) in China. The spatial representative evaluation steps show that the representativeness of observations greatly differs among stations and varies with different vegetation growth and other factors. Large differences in the validation results occur when using different representative level observations, which indicates a large potential for large error during the traditional T-based validation scheme. Comparisons show that the new validation scheme greatly improves the reliability of LST product validation through high-level representative observations.

The complex and variable sea ice environment constrains the safety and efficiency of Arctic shipping. To address the limitations of existing methods in balancing long-term planning with dynamic route adjustment ahead of the vessel, this paper develops an Arctic route decision-making system based on strategic–tactical coordination. The system establishes a “Global Guidance–Local Correction” collaborative mechanism: the strategic module utilizes medium-range climate forecast data to generate a set of daily baseline routes over a 30-day forecast window, providing macro-scale route guidance and feasibility assessment for the voyage; meanwhile, the tactical module integrates high-spatiotemporal-resolution sea ice concentration (SIC) data to screen local high-SIC areas that constrain navigation and execute tactical route correction ahead of the vessel. Experimental results demonstrate the feasibility of integrating long-term global planning with dynamic route adjustment. This provides a feasible route planning framework for integrating long-term strategic guidance with tactical-level dynamic adjustment under complex Arctic ice conditions, offering technical support for future intelligent Arctic navigation systems.

The Tibetan Plateau, known as the world’s “Third Pole” due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole–related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming “big data” era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.

Snow Water Equivalent (SWE) is a key variable for evaluating hydrological processes and the impacts of climate change in mountainous regions such as the Qilian Mountains. Passive microwave remote sensing provides large-scale SWE estimates, but its coarse spatial resolution and coverage gaps pose limitations, particularly in complex terrain with heterogeneous snow distribution. This study integrates multi-source data from 2018 to 2024, combining ground-based observations with multiple meteorological factors to develop a high-resolution SWE reconstruction model tailored to the Qilian Mountains. Eight machine learning algorithms—Support Vector Machine (SVM), CatBoost, LightGBM, XGBoost, Random Forest, AdaBoost, ElasticNet, and Bayesian Ridge Regression—were systematically compared, with LightGBM achieving the best performance on the test set. During feature selection, Granger causality inference was applied to screen input variables, resulting in an optimized reconstruction model with a mean absolute error (MAE) of only 1.984 mm, a root mean square error (RMSE) of 4.656 mm, and a coefficient of determination (R2) of 0.973. Model interpretability was enhanced using SHAP (Shapley Additive Explanations), which revealed that snow depth, surface soil temperature and moisture, and precipitation were the primary driving factors, with varying contributions to the model. The model generates SWE reconstruction sequences at 30 min intervals. This high-resolution dataset provides crucial support for studying snow dynamics in complex mountainous regions and contributes to improved water resource management and climate change assessments in the Qilian Mountains.

Assimilating the latest epidemic data can improve the predictions of epidemic dynamics compared with those using only dynamic models. However, capturing the nonlinear spatiotemporal heterogeneity remains challenging. We propose a data assimilation method to simultaneously update the parameters and states with respect to their spatiotemporal variation intervals by (1) developing a susceptible-infected-removed-vaccinated model by considering vaccination strategy and quarantine periods and (2) assimilating real-time epidemic data using an ensemble Kalman filter for daily updates of the state variables and Metropolis–Hastings sampling for weekly parameter estimation. Synthetic experiments and a WebGIS-based global prediction system demonstrate the sufficient nowcasting accuracy of this method. An analysis of the system outcomes shows that modeling vaccination details, embedding reasonable model and observation errors, using up-to-date parameters, and avoiding the prediction of sporadic cases can increase the correlation coefficient and coefficient of determination by more than 31.35% and 161.19%, respectively, and decrease the root mean square error by more than 54.17%. Our prediction system has been working well for more than 700 days. Its worldwide nowcasting accuracies have been continuously improved, where the overall correlation coefficients, coefficient of determination, and threat percent score exceed 0.7, 0.5 and 65%, respectively. The proposed method lays promising groundwork for the real-time spatiotemporal prediction of infectious diseases.

The ongoing spread of coronavirus disease 2019 (COVID-19) in most South and Southeast Asian countries has led to severe health and economic impacts. Evaluating the performance of nonpharmaceutical interventions in reducing the number of daily new cases is essential for policy designs. Analysis of the growth rate of daily new cases indicates that the value (5.47%) decreased significantly after nonpharmaceutical interventions were adopted (1.85%). Vaccinations failed to significantly reduce the growth rates, which were 0.67% before vaccination and 2.44% and 2.05% after 14 and 28 d of vaccination, respectively. Stringent nonpharmaceutical interventions have been loosened after vaccination drives in most countries. To predict the spread of COVID-19 and clarify the implications to adjust nonpharmaceutical interventions, we build a susceptible–infected–recovered–vaccinated (SIRV) model with a nonpharmaceutical intervention module and Metropolis–Hastings sampling in three scenarios (optimistic, neutral, and pessimistic). The daily new cases are expected to decrease rapidly or increase with a flatter curve with stronger nonpharmaceutical interventions, and the peak date is expected to occur earlier (5–20 d) with minimum infections. These findings demonstrate that adopting stringent nonpharmaceutical interventions is the key to alleviating the spread of COVID-19 before attaining worldwide herd immunity.

Dual emissions at ~700 and 800 nm have been achieved from a single NIR-AZA fluorophore 1 by establishing parameters in which it can exist in either its isolated molecular or aggregated states. Dual near infrared (NIR) fluorescence color lymph node (LN) mapping with 1 was achieved in a large-animal porcine model, with injection site, channels and nodes all detectable at both 700 and 800 nm using a preclinical open camera system. The fluorophore was also compatible with imaging using two clinical instruments for fluorescence guided surgery. Methods: An NIR-AZA fluorophore with hydrophilic and phobic features was synthesised in a straightforward manner and its aggregation properties characterised spectroscopically and by TEM imaging. Toxicity was assessed in a rodent model and dual color fluorescence imaging evaluated by lymph node mapping in a large animal porcine models and in ex-vivo human tissue specimen. Results: Dual color fluorescence imaging has been achieved in the highly complex biomedical scenario of lymph node mapping. Emissions at 700 and 800 nm can be achieved from a single fluorophore by establishing molecular and aggregate forms. Fluorophore was compatible with clinical systems for fluorescence guided surgery and no toxicity was observed in high dosage testing. Conclusion: A new, biomedical compatible form of NIR-dual emission wavelength imaging has been established using a readily accessible fluorophore with significant scope for clinical translation.