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Solar membrane distillation offers a highly promising and sustainable solution to the global freshwater crisis. However, its widespread practical application is currently hampered by a key challenge: Pursuing high water production. This bottleneck stems from a mismatch between the evaporation and condensation capacities in existing systems, where vapor may not be condensed in time due to insufficient condensation capacity, or the available condensation capacity may be underutilized when evaporation is inadequate. Here we show an asymmetric tapered multistage solar still that enables ultrahigh water production by introducing a design principle based on optimizing the mass transfer equilibrium between evaporation and condensation. By systematically optimizing the ratio of condensation-to-evaporation areas through a tunable mass transfer gap, the system achieves a state of ultrahigh-production equilibrium, in which evaporation and condensation processes are maximally coupled. Based on this principle, an optimized eight-stage passive solar still device is built to get a total water production of 4.32 L·m −2 ·h −1 and total η c of 81% under 1 kW·m −2 illumination (with 3.1 wt% natural seawater), which ranks among the highest values reported in existing literature. It exhibits stable performance under varying light conditions and salt resistance, producing 34.2 L·d −1 in outdoor tests. Water scarcity is a global challenge due to climate change and population growth. Here, authors developed an asymmetric tapered multistage solar still that optimizes mass transfer equilibrium, achieving ultrahigh water production and efficiency.

Background Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China. Methods We obtained daily confirmed cases of COVID-19, air particulate matter (PM 2.5 , PM 10 ), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM 10 , PM 2.5 and MSI on daily confirmed COVID-19 cases. Results We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m 3 increase in the concentration of PM 10 and PM 2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM 10 and PM 2.5 were at lag 014, and the RRs of each 10 μg/m 3 increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively. Conclusions Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.

Background Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data. Methods A retrospective dataset consisted of PTE patients from 12 hospitals were collected, and 19 physical indicators of patients were included to train the FL-based prognosis assessment model to predict the 30-day death event. Firstly, multiple machine learning methods based on FL were compared to choose the superior model. And then performance of models trained on the independent (IID) and non-independent identical distributed(Non-IID) datasets was calculated and they were tested further on Real-world data. Besides, the optimal model was compared with pulmonary embolism severity index (PESI), simplified PESI (sPESI), Peking Union Medical College Hospital (PUMCH). Results The area under the receiver operating characteristic curve (AUC) of logistic regression(0.842) outperformed convolutional neural network (0.819) and multi layer perceptron (0.784). Under IID, AUC of model trained using FL(Fed) on the training, validation and test sets was 0.852 ± 0.002, 0.867 ± 0.012 and 0.829 ± 0.004. Under Real-world, AUC of Fed was 0.855 ± 0.005, 0.882 ± 0.003 and 0.835 ± 0.005. Under IID and Real-world, AUC of Fed surpassed centralization model(NonFed) (0.847 ± 0.001, 0.841 ± 0.001 and 0.811 ± 0.001). Under Non-IID, although AUC of Fed (0.846 ± 0.047) outperformed NonFed (0.841 ± 0.001) on validation set, it (0.821 ± 0.016 and 0.799 ± 0.031) slightly lagged behind NonFed (0.847 ± 0.001 and 0.811 ± 0.001) on the training and test sets. In practice, AUC of Fed (0.853, 0.884 and 0.842) outshone PESI (0.812, 0.789 and 0.791), sPESI (0.817, 0.770 and 0.786) and PUMCH(0.848, 0.814 and 0.832) on the training, validation and test sets. Additionally, Fed (0.842) exhibited higher AUC values across test sets compared to those trained directly on the clients (0.758, 0.801, 0.783, 0.741, 0.788). Conclusions In this study, the FL based machine learning model demonstrated commendable efficacy on PTE prognostic risk prediction, rendering it well-suited for deployment in hospitals.

The environmental repercussions of extensive carbon dioxide (CO2) emissions on the environment are crucial for policymakers and scholars. The repercussions of and connection between economic growth (ECG), tourism (TOUR), and foreign direct investment (FDI) on CO2 emission mitigation have been measured and argued from empirical and theoretical perspectives by scholars. Notwithstanding, the extant body of knowledge has failed to incorporate and investigate the function of governance in decarbonizing tourism activities and FDI from CO2 emissions to attain a healthy and quality environment in Africa. Hence, this current research investigates governance’s role in the reduction processes of CO2 emissions grounded in environmental Kuznets curve (EKC) conceptual assumptions for panel data spanning 2000 through 2020 for 27 African countries. This research utilized the Westerlund panel cointegration approach for the investigation of the cointegration of the selected variables. This study applied the Driscoll–Kraay regression approach for the long-term estimation. In addition, the dynamic ordinary least squares (DOLS) and the pooled mean group (PMG) were used for robustness checks. The findings of this research indicated that the governance (GOV) indicators employed have a statistically significant effect on the CO2 emission reduction. Besides, this study found that the appreciation of the income of the nations gives credence to the formation of the EKC theory and contributes to the decline in CO2 emissions within the selected African nations. The findings revealed that tourism, FDI, ECG, and GOV are positive and significant factors leading to increased CO2 emissions in Africa. Furthermore, the results showed that effective governance and control of FDI inflows and tourism activities can support decarbonization. These findings suggest the merits of governance in ensuring effective decarbonization policies of the environment, and policy suggestions are accordingly put forward.

Background Based on age, period, and cohort, this study aimed to understand the trend of impairment of activities of daily living (ADL) and its influencing factors in older adults in China and to provide a basis for the development of appropriate interventions. Methods This longitudinal follow-up study was conducted on a sample of 40,748 cases aged ≥ 60 years from the China Family Panel Studies (CFPS) database of 6 periods, using the six-item ADL scale to measure impaired ability to perform daily activities in older adults. Incorporating independent variables affecting ADL impairment in older adults based on the health ecology theory, a stratified age-period-cohort mixed-effects model was used to analyze the factors influencing impaired ADL in older adults. Results Age, different periods, and birth cohort had an independent effect on females suffering from chronic diseases, having lower life satisfaction (dissatisfied, very dissatisfied), living in the western region, residing in rural areas, having per capita household income levels of middle-high, middle-low, lower-middle, lower-middle, and going to specialized and general hospitals were risk factors for ADL impairment in older adults. Smoking, drinking alcohol, exercising, having high life satisfaction (satisfied), being married, having higher education, and having urban/rural/rural residents’ health insurance and employee health insurance were protective factors for ADL impairment in older adults. Conclusions It is important to pay further attention to the current situation of ADL impairment in older adults from the perspective of the whole life cycle and the whole population and to take timely and targeted intervention strategies and preventive measures to improve the health of older adults in different dimensions from the individual to the macro level.

Natural gas hydrate is a promising unconventional natural gas source due to its high energy density and huge global reserves. During exploitation, the drilling fluid may invade the hydrate formation and induce hydrate decomposition, causing reservoir damage. Herein, a novel reservoir protectant made by bio-degradable temporary plugging material (BDTPM) was developed in the form of polymer-ceramic composite microcapsules. As an additive to the drilling fluid, the BDTPM can minimize drilling fluid intrusion by plugging the reservoir during drilling and afterwards maximize permeability recovery by degrading the material. The particle size distribution was in the range of 1–130 μm. The optimal mass ratio between modified ceramic particles, ethyl cellulose and epoxy resin was found to be 4:2:1. The plugging rate was 100% when ethyl cellulose and epoxy resin were mixed to coat the ceramic particles to form BDTPM, and the plugging performance was the best. At a temperature close to the typical hydrate reservoir environment (5 °C), 0.02 wt% low-temperature complex enzyme can degrade BDTPM, and the permeability recovery rate is 64.66%. The efficient reservoir protectant developed in this work could play an important role in the successful drilling of natural gas hydrate reservoirs.

The Hudson Bay Lowlands (HBL) are recognized as the second largest peatland complex in the world. Due to variability in peat thickness across a large and heterogeneous landscape, the existing carbon (C) storage estimates for the HBL may contain large uncertainty. Here, we use geospatial variables that are associated with HBL peat formation, age, accumulation, and occurrence to understand the driving factors for peat depth variability and map peat depth and C storage at 30 m spatial resolution. The estimated average peat depth of HBL is 184(±48) cm with 90% of values falling between 89 and 264 cm. Based on the spatially explicit peat depth, the HBL total C storage is estimated to be 30(±6) Pg. Distance to the coastline is the most important indicator of peat depth where the depth increases with distance further away from Hudson Bay coastline, confirming that the time since peat formation is closely related to peat depth. Plain Language Summary The Hudson Bay Lowlands (HBL) contain the second largest peatland complex in the world. We used spatial data sourced from satellite observations and geospatial information that are associated with peat occurrence, age, formation, and accumulation to estimate peat depth and carbon storage at 30 × 30 m spatial details for the entire HBL. We combined several machine learning models together in a way that improves their ability to work well on new data with a technique called “stacking,” to improve the accuracy of peat depth estimation. The estimated average peat depth was 184 cm while the entire HBL stores 30 billion tonnes of carbon. The peat depth and carbon storage information presented in this study will help monitor and assess the vulnerability of carbon storage to anticipated changes in climate, resource development, land use, and disturbances that are intensifying in the region. They are also crucial for managing and protecting this vital ecosystem, quantifying the carbon cost of resource development, and for developing ecologically sound land management practices in the region. Key Points We use stacking, an ensemble learning technique designed to mitigate overfitting, to estimate peat depth in the Hudson Bay Lowlands (HBL) The average peat depth of HBL is 184 (±48) cm with 90% of depths falling within 89–264 cm HBL stores 30 (±6) Pg carbon (C) with average value of 86 (±35) kg m−2

Constructing high-precision subsidence basins is of paramount importance for mining subsidence monitoring. Traditional unmanned aerial vehicle (UAV) photogrammetry techniques typically construct subsidence basins by directly differencing digital elevation models (DEMs) from different monitoring periods. However, this method often neglects the influence of horizontal displacement on the accuracy of the subsidence basin. Taking a mining area in Ordos, Inner Mongolia, as an example, this study employed the normalized cross-correlation (NCC) matching algorithm to extract horizontal displacement information between two epochs of a digital orthophoto map (DOM) and subsequently corrected the horizontal position of the second-epoch DEM. This ensured that the planar positions of ground feature points remained consistent in the DEM before and after subsidence. Based on this, the vertical displacement in the subsidence area (the subsidence basin) was obtained via DEM differencing, and the parameters of the post-correction subsidence basin were inverted using the probability integral method (PIM). The experimental results indicate that (1) the horizontal displacement was influenced by the gully topography, causing the displacement within the working face to be segmented on both sides of the gully; (2) the influence of the terrain on the subsidence basin was significantly reduced after correction; (3) the post-correction surface subsidence curve was smoother than the pre-correction curve, with abrupt error effects markedly diminished; (4) the accuracy of the post-correction subsidence basin increased by 43.12% compared with the total station data; and (5) comparing the measured horizontal displacement curve with that derived using the probability integral method revealed that the horizontal displacement on the side of an old goaf adjacent to the newly excavated working face shifted toward the advancing direction of the new working face as mining progressed. This study provides a novel approach and insights for using low-cost UAVs to construct high-precision subsidence basins.

Edgecumbe, a dormant volcano located on Kruzof Island in the southeastern part of Alaska, USA, west of the Sitka Strait, has exhibited increased volcanic activity since 2018. To assess the historical and current intensity of this activity and explore its relationship with seismic events in the surrounding region, this study utilized data from the ERS-1/2, ALOS-1, and Sentinel-1 satellites. The Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques were employed to obtain surface deformation data spanning nearly 30 years. Based on the acquired deformation field, the point-source Mogi model was applied to invert the position and temporal volume changes in the volcanic source. Then, by integrating seismic activity data from the surrounding area, the correlation between volcanic activity and earthquake occurrences was analyzed. The results indicate the following: (1) the coherence of interferograms is influenced by seasonal variations, with snow accumulation during the winter months negatively impacting interferometric coherence. (2) Between 1992 and 2000, the surface of the volcano remained relatively stable. From 2007 to 2010, the frequency of seismic events increased, leading to significant surface deformation, with the maximum Line-of-Sight (LOS) deformation rate during this period reaching −26 mm/yr. Between 2015 and 2023, the volcano entered a phase of accelerated uplift, with surface deformation rates increasing to 68 mm/yr after August 2018. (3) The inversion results for the period from 2015 to 2023 show that the volcanic source, located at a depth of 5.4 km, experienced expansion in its magma chamber, with a volumetric increase of 57.8 × 106 m3. These inversion results are consistent with surface deformation fields obtained from both ascending and descending orbits, with cumulative LOS displacement reaching approximately 210 mm and 250 mm in the ascending and descending tracks, respectively. (4) Long-term volcanic surface deformation, changes in magma source volume, and seismic activity suggest that the earthquakes occurring after 2018 have facilitated the expansion of the volcanic magma source and intensified surface deformation. The uplift rate around the volcano has significantly increased.

This study aims to explore the therapeutic mechanisms of Ginkgo Flavone Glycosides (GFGs) delivered via liposomal nanoparticles in treating Diabetic Cardiomyopathy (DCM) by upregulating Sirtuin 1 (SIRT1) to restore energy metabolism and autophagy homeostasis. A DCM mouse model was employed, with groups treated with different doses of GFGs. Various evaluations, including body weight, blood glucose levels, and cardiac function, were performed. Network pharmacology, transcriptomic analysis, and molecular docking studies were conducted to elucidate the key role of SIRT1 in inhibiting DCM progression. In vitro experiments and proteomic sequencing were utilized to validate the regulatory effects of SIRT1. The in vivo animal experiment results demonstrated that treatment with Ginkgo Flavone Glycosides (GFGs) significantly improved cardiac function in diabetic cardiomyopathy mice. Specifically, GFG treatment increased the left ventricular ejection fraction (LVEF) by approximately 81.3% compared to the Model+Lipo group, reduced the left ventricular internal diameter in systole (LVIDs) by approximately 69.2%, and decreased the left ventricular internal diameter in diastole (LVIDd) thickness by approximately 56.1%. Additionally, GFGs alleviated cardiomyocyte apoptosis, further supporting their therapeutic potential for diabetic cardiomyopathy. Bioinformatics analysis supported the regulation of DCM through the SIRT1/FOSL1/TSPAN4 axis. Proteomic data confirmed the beneficial effects of GFGs on diabetic cardiac energy metabolism and autophagy. Liposomal nanoparticles loaded with GFGs significantly extended drug release to 72 hours. In vitro experiments highlighted the role of SIRT1 in modulating FOSL1 and TSPAN4 expression. Proteomic sequencing further validated the regulatory role of the SIRT1/FOSL1/TSPAN4 signaling pathway in DCM and suggested that GFGs might enhance energy metabolism and autophagy in diabetic hearts by activating SIRT1. Liposomal nanoparticle delivery of GFGs was shown to enhance SIRT1 activation, leading to the deacetylation of FOSL1 and suppression of TSPAN4, ultimately improving energy metabolism and autophagy in DCM. This study introduces a novel potential strategy for the treatment of DCM.