Since the implementation of policies such as the “ Ten Rules of the Atmosphere, ” particulate matter like PM2. 5 has been better controlled in China, while ozone concentration has shown a rising trend, and has become an important pollutant in China after PM2. 5 . Given that high temperatures can further catalyze ozone production, this study examines the characteristics of temperature and ozone concentration and their interrelationship in South China. Using temperature data from the ERA5 reanalysis data ( ERA5Land Hourly) provided by the European Centre for Medium Range Weather Fore casts ( ECMWF) and ozone concentration data from ground level air quality monitoring stations, we investigated the spatial and temporal distribution patterns of temperature and ozone, their correlation, and the variation of ozone concentration with distance from the coast in 2015 and 2020. The findings reveal the following: ①Spatially, both temperature and ozone concentrations exhibit spatial autocorrelation, with distinct cold and hot spot patterns; temporally, both variables display seasonal variations, peaking in summer and fall and decreasing in winter and spring. ②A positive correlation exists be tween temperature and ozone concentration, with the relationship being most pronounced on an hourly scale ( r = 0.97 in 2015 and r = 0. 96 in 2020) . ③Ozone concentration varies with coastal proximity, peaking within 10 - 50 km of the coast and displaying a fluctuating decline in the 50 - 300 km range.④Ozone suppression occurs at a cutoff temperature of 28 ℃ in South China. This study provides valuable insights for ozone mitigation and regional environmental protection efforts by exploring the spatial temporal characteristics of temperature and ozone concentration, their correlations, and the influence of coastal distance on ozone levels.

The Three Gorges Reservoir area is the hardest hit area for landslide occurrence in China, and the dams water storage and flood discharge accelerated the slope activities along the Yangtze River and its tributaries, reactivating a large number of ancient landslides. The Songjiawuchang landslide occurred in Dongdukou Town, Badong County. In this paper, the time series deformation of this land slide was obtained by collecting 15 views of ALOS2 / PALSAR2 images from April 2020 to September 2021, and the area was divided according to the stratigraphic distribution and the slip pattern, with an emphasis on analysing the characteristics and causes of the slip. In addition, the reliability of the de formation results was verified by statistical stability region information. The results show that: ① During the monitoring period, the Songjiawuchang landslide showed an ‘ upper section middle section lower section stepped slip, with a bias towards the right side, and the largest deformation appeared in the right side of the middle section ( Q2R) , with a rate of - 193 9 mm / yr. ② Through the extraction of the profile lines and feature points, the temporal and spatial morphology of the landslide deformation was further explained, and it was found that the stepped slip was consistent with the stratigraphic distribution; ③ Using the Pearson correlation test, it is concluded that the overall slip rate of the landslide is jointly affected by the cumulative precipitation and water level changes.

The vertical column densities ( VCD) of tropospheric formaldehyde ( HCHO) observed by satellites have become an important means for studying the spatiotemporal evolution of volatile organic compounds ( VOCs) at the regional scale. Exploring the spatiotemporal evolution of HCHO VCD in the troposphere is of great significance for the coordinated control of PM2. 5 and O3 pollution in the Guangdong-Hong Kong-Macao Greater Bay Area ( GBA) and even in Guangdong Province. To explore the spatiotemporal evolution of tropospheric HCHO VCD in the GBA, this research processed Level 2 formaldehyde observations from Ozone Monitoring Instrument ( OMI) using a self-developed oversampling algorithm to achieve kilometer-level monitoring of tropospheric HCHO VCD in the GBA from 2013 to 2022. The influencing factors of tropospheric HCHO VCD in the GBA were diagnosed from the perspectives of natural and anthropogenic sources. Research has found that the HCHO VCD of GBA showed a rising trend from 2013 to 2022. Compared with 2013, HCHO VCD increased by 11 3% in 2022. Low HCHO VCD are mainly distributed in the surrounding areas of the GBA with higher terrain and coastal areas, while high VCD are mainly located in the central area of the GBA, with Guangzhou, Foshan, Dongguan as the high value center region, as well as parts in Zhaoqing, Huizhou, and Jiangmen. The average VCD in GBA shows a periodic pattern of winter < spring < autumn < summer. Cities such as Zhaoqing, Dongguan, Guangzhou, and Foshan exhibit winter < spring< autumn < summer, while coastal cities including Jiangmen, Zhongshan, Shenzhen, Hong Kong, and Zhuhai present winter < spring < summer < autumn. The increase in vegetation in the GBA holds a positive contribution to the rise in tropospheric HCHO VCD, but it is not very significant. The correlation coefficient between nighttime light radiation intensity and tropospheric HCHO VCD is not high, while the annual GDP of GBA shows a significant positive correlation with tropospheric HCHO VCD, indicating that GDP growth plays an important role in the ascension of tropospheric HCHO VCD.

Metal organic frameworks ( MOFs) , with their diverse chemical structures, exhibit broad application potential in fields such as gas storage and separation, catalysis, and drug storage and de livery. With the rapid expansion of MOFs varieties and application domains, traditional experimental methods and molecular simulations can no longer sufficiently evaluate the performance of new MOFs in a short time. Given the vast number of MOFs and the enormous amount of data related to their structures and properties, integrating machine learning methods into the design and development of MOFs will undoubtedly bring significant benefits. By constructing machine learning models, the complex structure property relationships of MOFs can be effectively elucidated, accelerating the performance prediction and material design processes. In this review, we comprehensively summarize and analyze research on MOFs adsorption and separation utilizing machine learning methods. First, various MOFs databases, feature descriptors, algorithms, and evaluation metrics suitable for machine learning work flows are discussed. Next, the role of machine learning in facilitating high throughput computational screening and accelerating research on the adsorption and separation of gases such as CH4 , CO2 , and H2 in MOFs are explored. Finally, this paper discusses the opportunities and challenges faced by ma chine learning in supporting big databased computational simulations of MOFs gas adsorption, separation, and storage. Through this comprehensive review and analysis, researchers can better understand and apply machine learning and big data mining to accelerate the design and development of MOFs, providing new research directions and technical support for related fields.

In order to improve the catalytic activity of catalysts for the catalytic oxidation of CO, a batch of xMn / CeO2-L ( nanowire) catalysts with different manganese ( Mn) loading amounts were prepared by hydrothermal method and wet impregnation method. The effect of different Mn loading amounts on the catalytic performance of cerium based catalysts was investigated. According to the evaluation of CO activity, 0. 75Mn / CeO2-L catalyst showed the best catalytic performance ( T50 = 129℃ , T90 = 179 ℃ ) , which was about 221 ℃ and 171 ℃ lower than that of CeO2-L catalyst, significantly improving the CO catalytic activity of cerium based catalysts. X-ray powder diffraction( XRD) , N2 adsorption desorption determination ( BET) , X-ray photoelectron spectroscopy ( XPS) ,Raman and H2 temperature reduction procedure ( H2-TPR) characterization were carried out. There sults showed that 0.75Mn / CeO2-L catalyst had higher oxygen vacancy content, indicating that an appropriate amount of Mn loading could enhance the oxygen vacancy of CeO2-L catalyst. In addition, the electrical conductivity of the catalyst was tested by the four-probe method, and it was found that there was a linear correlation between the electrical conductivity of the catalyst and the catalytic activity ( R2 ( T50 ) = 0.998 and R2 ( T 90) = 0.998) and the oxygen vacancy ( R2 ( I D/ I F2g) = 0.999) . The results show that for pure CeO2-L catalysts, loading Mn can effectively increase the electrical conductivity of xMn / CeO2-L catalysts, thereby promoting the ion migration rate on the catalyst surface and enhancing the catalytic activity of xMn / CeO2-L. This experiment is conducive to the realization of low-temperature and high-efficiency catalysis in the vehicle exhaust gas treatment system and reducing CO pollution.

Anti-homomorphism is the map reversing operation which plays the same role with homo- morphism, it is essential in the study of structures and properties of algebraic systems. According to the isomorphism theorems of homomorphism in rings, this paper parallels the anti-homomorphism to the structure of ring theory and gives isomorphism theorems under anti-homomorphism in order to characterize more results about the structure and properties of rings.

This paper studies the automorphism group of the complex representation ring r( D4 ) of the dihedral group D4 of order 8 and all the ring automorphisms of r( D4 ) are computed. It is shown that the automorphism group of r( D4 ) is isomorphic to the direct product of the symmetric group of degree 3 and the cyclic group of order 2.

This article mainly utilizes the recursive relation of tensor product decomposition formulas for all irreducible complex representations of a dihedral group, and obtains the adjoint subring of the complex representation ring over r( Dn ) . By studying the indecomposable Z+-sub-bimodules over the adjoint subring, the universal grading structure of the fusion ring r( Dn ) is characterized.

In physical engineering, due to the different materials of objects, there is an object composed of two different materials, and the structure of different areas in an object will be different. For this reason, the eigenvalue problem of two phase elliptic partial differential equation is discussed when there are two different materials in one object. In one dimension, it is equivalent to the eigen value problem of ordinary differential equation. In two dimensions, the system is transformed into one dimension eigenvalue problem by the method of separating variables. According to the characteristics of boundary conditions, the eigenvalues and eigenfunctions are obtained by using block regions.

In laser soldering, the Precise control of the temperature of the weld joint is critical to the quality of the weld. Existing soldering temperature control for laser soft brazing is realized based on Proportional-Integral-Differential ( PID) control. The traditional PID control method cannot effectively deal with the problem of brazing material specific heat capacity and laser absorption rate changing with temperature due to its fixed parameters. In the process of using the traditional PID to control the temperature, the response speed and control accuracy of the system are reduced, the error value between the actual output temperature and the target temperature is large, and the output power fluctuation is also large. For this reason, this paper proposes a temperature control method based on Model-Predictive Control ( MPC) , which can dynamically adjust the control parameters and reduce the hysteresis or overshoot of PID when facing temperature changes by predicting the future temperature changes using a process model. Experimental results show that the MPC method significantly improves the temperature control accuracy of the solder joints, with a temperature error variance value of 32% of that of PID control and a maximum absolute error of 67% of that of PID control. These improvements effectively enhance the wettability and fluidity of the brazing material, improve the uniformity and solder quality of the solder joints, and significantly enhance the yield of laser soft brazing.

Abstract: Endocrine disruptors are difficult to remove from wastewater due to their stable chemical structure, and their continuous release in the aqueous environment remains a challenge of great concern in water treatment. In this paper, a copper-doped graphene like catalyst ( Cu@ IANC) synthesized from algae was constructed with Cu-centered electron-rich regions and Cu-centered electron-poor regions. In the reactive aqueous solution containing bisphenol A ( BPA) and 10 mM H2O2 , the degradation of BPA by Cu@ IANC exhibited excellent activity and stability. In the reactive aqueous solution, BPA was complexed in the electron-poor region on the Cu@ IANC surface by π-π and hydrogen bonding, while H2O2 was adsorbed in the electron-rich region on the Cu@ IANC surface, resulting in the formation of a multipoint adsorption pattern on the Cu@ IANC surface. In this adsorption mode, BPA was electronically delocalized by the Cu-π electrostatic force and captured by H2O2 to generate hydroxyl radicals ( · OH) . The generated · OH was free in aqueous solution to attack BPA and its intermediates. The synergistic interaction of H2O2 and Cu-π electrostatic forces over biochar-based Fen- ton-like catalysts is a low-cost option for the effective degradation of environmental endocrine.