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We have successfully produced porous CDC nanospheres from SiC nanospheres by using a simple molten salt electrochemical etching method which is much safer, less expensive and more environmentally friendly than the chlorination process.

A two-state reactivity scenario is revealed for an energetically high-lying complex growth pathway based on a structural investigation on [TaO3(CO2)n]+ (n = 2–5) complexes.

With the increase in the number of users accessing the 5G network in the future, how to choose the location of 5G base stations to ensure effective network coverage of the service area, so as to provide reliable communication and transmission services, is a key issue to be considered. By establishing an improved genetic algorithm model, the site planning problem is solved. According to the circular coverage area of the base station, the coverage requirement and the minimum cost optimization goal are completed. First, denoising according to business volume, and finally get 35,915 weak coverage points. The sum of the cost of setting up macro base stations and micro base stations is the optimization goal, and the coverage rate of the weak coverage point is more than 90%, is set as the constraint condition. At the same time, the lethal factor is set in the selection process, and the individuals who do not meet the threshold conditions of the original base station are deleted. The improved operator is adopted in the crossover link and mutation link, which speeds up the running speed of the algorithm and ensures the population diversity and the accuracy of the results. Finally, 689 macro base stations and 269 micro base stations are established, with a coverage rate of 93.51% and an optimal cost solution of 7159. By visualizing the results on the coordinate map, it can be clearly seen that most of the weak coverage points have been covered, which shows that the improved genetic algorithm shows fast convergence speed and good optimal value, and verifies the effectiveness of the model.

In the process of steam injection development of heavy oil, due to the serious heterogeneity and interference of steam channeling in the high-permeability layer, there is a lot of oily sludge in the produced liquid of oil wells, which is difficult to deal with. According to existing profile control technology and the related properties of oily sludge, a profile control system was developed by using oily sludge as the main agent and by adding other additives. At the same time, the properties of sludge, the matching relationship between the particle size of sludge and the formation of pore roar, the temperature resistance, the plugging ability, and other contents were studied. The agent featured an adjustable curing time (3 to 300 h) and resistance to pressures greater than 6 MPa and temperatures greater than 350 °C. After the agent was injected into the high-permeability steam channeling, the system was cemented with the formation rock and the sealing rate was more than 85%, without any damage to the low- and medium-permeability layers. According to the different characteristics of the production well and steam injection well, the construction technology was optimized, the oily sludge profile control technology suitable for heavy oil reservoirs was formed, and the field test was carried out. At present, more than 150 wells have been used in the field, with a cumulative oil increase of 10,756 tons. The technology not only solves the environmental pollution caused by oily sludge but also reduces the cost of thermal recovery of heavy oil, with great significance for improving the final recovery efficiency and commercial benefits of the oilfield.

Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern recognition still faces challenges due to the complexity and diversity of river networks. To address this issue, this study proposes a river network pattern recognition method based on graph convolutional networks (GCNs), aiming to achieve accurate classification of different river network patterns. We utilize binary trees to construct a hierarchical tree structure based on river reaches and progressively determine the tree hierarchy by identifying the upstream and downstream relationships among river reaches. Based on this representation, input features for the graph convolutional model are extracted from both spatial and geometric perspectives. The effectiveness of the proposed method is validated through classification experiments on four types of vector river network data (dendritic, fan-shaped, trellis, and fan-shaped). The experimental results demonstrate that the proposed method can effectively classify vector river networks, providing strong support for research and applications in related fields.

To determine the spatial–temporal variations and the factors leading to vegetation recovery in the loess hilly and gully region of China, this study analyzed a two-decade trend in the variation of vegetation cover based on normalized difference vegetation index (NDVI) data from 1998 to 2019 using the Sen + MK test and Hurst index and determined the driving factors using the Geodetector model. The vegetation index in the area was high in the southeast and low in the northwest, with an overall increasing rate of 0.0108/year. The areas with significant improvement in vegetation cover accounted for 95.14%, and the areas with persistent change accounted for 37.36%. Annual precipitation is the most crucial factor driving the NDVI change, and potential evapotranspiration, relative humidity, elevation, land use type, and vegetation type can also explain local variations. The effect of compound factors is significantly greater than that of a single factor. The most effective factors are annual precipitation, potential evapotranspiration, relative humidity and elevation, which varied between 559.4–698.6 mm, 530.6–744.6 mm, 59%–62%, and 2006–2717 m, respectively. The land use, vegetation, and soil types suitable for healthy vegetation growth are forest, coniferous forest, and eluvial soil.

Ischemic stroke (IS) is often comorbid with type 2 diabetes mellitus (T2DM) wherein the determinants of long-term outcomes, beyond the acute stroke phase, are not adequately known. This study identified the determinants of long-term outcomes for diabetic IS patients through a prospective nested case-control study in 624 patients treated with conservative measures (38.60% females, mean age: 63.85 years). After 36-month follow-up, 117 (18.8%) patients with poor outcome were enrolled in the case group. The poor outcome was defined with a modified Rankin Scale (mRS) score ≥3. Meanwhile, 374 (59.9%) patients with good outcome, defined as (mRS score3), were included in the control group. Patients who died (

Nonpharmaceutical interventions (NPIs) taken to combat the coronavirus disease 2019 (COVID-19) pandemic have not only decreased the spread of severe acute respiratory syndrome coronavirus 2 but also have had an impact on the prevalence of other common viruses. This study aimed to investigate the long-term impact of NPIs on common respiratory and enteric viruses among children in Shanghai, China, as NPIs were relaxed after June 2020. The laboratory results and clinical data of outpatient children with acute respiratory tract infections (ARTI) and acute gastroenteritis (AGE) were analyzed and compared between the post-COVID-19 period (from June 2020 to January 2022) and pre-COVID-19 period (from June 2018 to January 2020). A total of 107 453 patients were enrolled from June 2018 to January 2022, including 43 190 patients with ARTI and 64 263 patients with AGE. The positive rates of most viruses decreased during the post-COVID-19 period, with the greatest decrease for influenza A (-0.94%), followed by adenoviruses (AdV) (-61.54%), rotaviruses (-48.17%), and influenza B (-40%). However, the positive rates of respiratory syncytial virus (RSV) and enteric AdV increased during the post-COVID-19 period as the NPIs were relaxed. Besides this, in the summer of 2021, an unexpected out-of-season resurgence of RSV activity was observed, and the resurgence was more prominent among children older than 5 years. The effectiveness of the current relaxed NPIs in control of common respiratory and enteric viruses was variable. Relaxation of NPIs might lead to the resurgence of common viruses.