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Establishment of geological model in volcanic area is challenging owing to lack of borehole data and the effect of volcanic activity on rock distribution. Taking full advantage of the existing complete volcanic landforms and recognized for seven eruptive cycles in Wudalianchi volcanic area, here we apply a layered approach to build geological models for meeting rapid development of agriculture and understanding the evolution of regional geological structure. Based on the volcanic eruption cycle, the stratas in Wudalianchi volcanic area are divided into four layers. UGrid (unstructured grid in GMS) is used combining with DEM data to hierarchically build 3D geological structure model of volcanic area, which realize the visualization of regional stratigraphic distribution, and the reliability of the model is verified by the formation mechanisms of different types springs. The stratified modeling provides a scientific and effective mean for the reconstruction of geological structure in volcanic areas where the data are short and the stratigraphic distribution is complex. The 3D geological structure model established can lay a foundation for the prediction, evaluation and sustainable use of regional groundwater, geothermy, mineral water and mineral mud resources.

Background The aim of this study is to assess the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 patients and to determine the association of acute kidney injury (AKI) with the severity and prognosis of COVID-19 patients. Methods The electronic database of Embase and PubMed were searched for relevant studies. A meta-analysis of eligible studies that reported the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 was performed. The incidences of AKI were compared between severe versus non-severe patients and survivors versus non-survivors. Results A total of 24 studies involving 4963 confirmed COVID-19 patients were included. The proportions of patients with elevation of sCr and BUN levels were 9.6% (95% CI 5.7–13.5%) and 13.7% (95% CI 5.5–21.9%), respectively. Of all patients, 57.2% (95% CI 40.6–73.8%) had proteinuria, 38.8% (95% CI 26.3–51.3%) had proteinuria +, and 10.6% (95% CI 7.9–13.3%) had proteinuria ++ or +++. The overall incidence of AKI in all COVID-19 patients was 4.5% (95% CI 3.0–6.0%), while the incidence of AKI was 1.3% (95% CI 0.2–2.4%), 2.8% (95% CI 1.4–4.2%), and 36.4% (95% CI 14.6–58.3%) in mild or moderate cases, severe cases, and critical cases, respectively. Meanwhile, the incidence of AKI was 52.9%(95% CI 34.5–71.4%), 0.7% (95% CI − 0.3–1.8%) in non-survivors and survivors, respectively. Continuous renal replacement therapy (CRRT) was required in 5.6% (95% CI 2.6–8.6%) severe patients, 0.1% (95% CI − 0.1–0.2%) non-severe patients and 15.6% (95% CI 10.8–20.5%) non-survivors and 0.4% (95% CI − 0.2–1.0%) survivors, respectively. Conclusion The incidence of abnormal urine analysis and kidney dysfunction in COVID-19 was high and AKI is closely associated with the severity and prognosis of COVID-19 patients. Therefore, it is important to increase awareness of kidney dysfunction in COVID-19 patients.

Magnetic core–shell Fe 3 O 4 @SiO 2 nanoparticles were synthesized by sol–gel method. Based on the characterization and experimental results, the adsorbent was found to have an average particle size of approximately 120 nm, a pore size range of 2–5 nm and superparamagnetic properties. It exhibited electrostatic and hydrogen bonding interactions during adsorption of methyl orange (MO). The adsorption of MO on the magnetic Fe 3 O 4 @SiO 2 nanoparticles exhibited pseudo-second-order kinetics, the adsorption process is a spontaneous endothermic adsorption process, which conforms to the Langmuir adsorption isotherm model. he maximum amount of MO was adsorbed at pH = 2, T = 45 °C and t = 30 min, and the highest adsorption capacity was 182.503 mg/g; The unit adsorption capacity of the Fe 3 O 4 @SiO 2 nanoparticles still reached 83% of the original capacity after 5 cycles, so the material was reusable and met the requirements of environmental protection. This study reveals the great potential of magnetic mesoporous nanoparticles for removal of dyes from wastewater.

In the current paper the decentralized output regulation problem of a new class of interconnected uncertain nonlinear systems is considered. A novel decentralized high-gain input driven filter is proposed such that the output feedback based control law can be designed. Moreover, a robust multi-input changing supply function technique is presented such that the stability analysis can be performed by the non-quadratic Lyapunov functions. Therefore, the assumptions on the interconnection terms can be removed. Finally the proposed decentralized control laws are applied to the interconnected mass-spring systems immersed in the liquid and the simulation results illustrate the effectiveness of the proposed control scheme.

Neutrophils are essential components of the innate immune system that defend against the invading pathogens, such as bacteria, viruses, and fungi, as well as having regulatory roles in various conditions, including tissue repair, cancer immunity, and inflammation modulation. The function of neutrophils is strongly related to their mode of cell death, as different types of cell death involve various cellular and molecular alterations. Apoptosis, a non-inflammatory and programmed type of cell death, is the most common in neutrophils, while other modes of cell death, including NETOsis, necrosis, necroptosis, autophagy, pyroptosis, and ferroptosis, have specific roles in neutrophil function regulation. Immunometabolism refers to energy and substance metabolism in immune cells, and profoundly influences immune cell fate and immune system function. Intercellular and intracellular signal transduction modulate neutrophil metabolism, which can, in turn, alter their activities by influencing various cell signaling pathways. In this review, we compile an extensive body of evidence demonstrating the role of neutrophil metabolism in their various forms of cell death. The review highlights the intricate metabolic characteristics of neutrophils and their interplay with various types of cell death.

Since the 2019 novel coronavirus disease (COVID-19) outbreak originated from Wuhan, Hubei Province, China, at the end of 2019, it has become a clinical threat to the general population worldwide. Among people infected with the novel coronavirus (2019-nCoV), the intensive management of the critically ill patients in intensive care unit (ICU) needs substantial medical resource. In the present article, we have summarized the promising drugs, adjunctive agents, respiratory supportive strategies, as well as circulation management, multiple organ function monitoring and appropriate nutritional strategies for the treatment of COVID-19 in the ICU based on the previous experience of treating other viral infections and influenza. These treatments are referable before the vaccine and specific drugs are available for COVID-19.

Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting.

The past decade witnessed remarkable success in synthetic molecular nanographenes. Encouraged by the widespread application of chiral nanomaterials, the design, and construction of chiral nanographenes is a hot topic recently. As a classic nanographene unit, hexa- peri -hexabenzocoronene generally serves as the building block for nanographene synthesis. This review summarizes the representative examples of hexa- peri -hexabenzocoronene-based chiral nanographenes.

Integrating superconductivity with high optical transparency is critical for advancing quantum technologies, yet remains fundamentally challenging due to photon damping at conventional superconductor interfaces. Here, we report the integrated circuit process compatible and scalable fabrication of transparent superconducting tin-doped indium oxide (ITO) thin films by RF magnetron sputtering without post-treatment. Combining Aslamazov-Larkin fluctuation theory and Ginzburg-Landau analysis, a superconducting transition temperature T c of 1.43 K and the zero-temperature coherence length of 18.14 nm are determined. The two-dimensional nature of superconductivity is corroborated by a Berezinskii-Kosterlitz-Thouless transition near 0.8 K. These ITO films exhibit high transmittance across visible and near-infrared wavelengths, meeting the demands of quantum photonic applications. Comparative magneto-transport studies reveal that disorder suppresses electron-phonon coupling, thereby reducing T c or even quenching superconductivity, while superconductivity itself suppresses weak localization (WL) signatures. Based on these observations, we propose a four-stage model describing the evolution from a normal metallic state to a phase-coherent superconducting phase through an interactive regime and a pre-pairing fluctuation regime. This work provides a simple and easy growth method for transparent ITO superconducting films and paves the way of exploring transparent superconductor for promising quantum material platforms.

Concerning the vision of achieving carbon neutral and peak carbon goals, wind energy is extremely important as a renewable and clean energy source. However, existing research ignores the implicit features of the data preprocessing technique and the role of the internal mechanism of the optimization algorithm, making it difficult to achieve high-accuracy prediction. To fill this gap, this study proposes a wind speed forecasting model that combines data denoising techniques, optimization algorithms, and machine learning algorithms. The model discusses the important parameters in the data decomposition technique, determines the best parameter values by comparing the model’s performance, and then decomposes and reconstructs the wind speed time series. In addition, a novel optimization algorithm is used to optimize the parameters of the machine learning algorithm using a waiting strategy and an aggressive strategy to improve the effectiveness of the model. Several control experiments were designed and implemented using 10-min wind speed data from three sites in Penglai, Shandong Province. Based on the numerical comparison results and the discussion of the proposed model, it is concluded that the developed model can obtain high accuracy and reliability of wind speed prediction in the short term relative to other comparative models and can have further applications in wind power plants.