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In recent years, the need for the rapid development of rural tourism in China has resulted in a large consumption of ecological resources, which has seriously damaged the ecological balance of travel destinations. Rural tourism in many parts of China is increasingly lacking distinctive local characteristics. It is difficult to adapt to tourists’ requirements for a deeper experience of rural travel. Some measures have been proposed to address these issues. Firstly, the connotation of “green” is reinterpreted based on relevant theoretical knowledge starting from solving the problem of rural tourism community development in China. The questionnaire method is used to screen the indicator system. An empirical analysis is carried out for actual cases. The pictures uploaded by tourists to social media are the main data source. A relatively complete and effective method system for the image research of rural tourism destinations is constructed combined with the visual analysis technology based on deep learning. A dense CNN model producing 10 output parameter combined with various factors is formed to assist the promotion by comparing the differences between the advertising pictures of the tourist destination and the pictures taken by tourists. The results show obvious consistency in the construction scene of the rural travel perception environment. Each tourist destination has its own visual characteristics, as well as differences in various sensory modality types. This research method adopts a combination of subjective and objective and comprehensively examines the scene conspicuousness, distinction, artistic quality, design quality, and time of the pictures. The formed marketing picture research method is more scientific and feasible.

The nanocomposites of poly(o-chloroaniline) with titanium dioxide have been prepared via chemical oxidative polymerization technique using o-chloroaniline monomer and titanium dioxide nanoparticles for photocatalytic application. The different composites were prepared by varying the load percentage of titanium oxide nanoparticles (TiO2 NPs) in polyorthochloroaniline (POCA) matrix. The synthesized composite materials were characterized by Scanning electron microscopy (SEM), X-Rays diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) techniques. The POCA/TiO2 nanocomposites were further applied to evaluate the photocatalytic degradation potential towards direct yellow 50 (DY50) dye in an aqueous medium under ultraviolet radiations.

Different aquatic ecological zones exhibit different gradients present heterogeneous algal composition. The algal community in the water column and sediment with certain environmental factors were screened monthly for twelve months with standard methods, to ascertain disparate in algal distinction. Air and water temperature, conductivity, water depth, dissolved oxygen and nutrients were the primary factors that influenced the algal abundances and diversities. 90% of the algal communities were benthic (33 species) and planktic-benthic (21) with 10% truly planktonic (6) species. Comparatively, the water-column benthic and planktic-benthic surpasses the benthic species associated with the sediment. The PCA on the habitat- type abundance revealed that 99.5% benthic algae in the water column are responsible for the total algal- abundance variance. Species richness and Shannon diversity indices were low, which ANCOVA and Levene tests demonstrated significantly equal and homogeneity of algae respectively between the studied zones. The linear relationships between the algae in the water and sediment zones were non-significant (p>0.05). The Euclidean algorithm confirmed algal community similarity in the water column and sediment. Species diversity limits, a high proportion of benthic algae in the planktonic, and significant homogenous algae in water and sediment. Signatures sediment destruction, breaking and dislodgement and uplifting of associated bottom algae, and the constant mixing of the water column and sediment due to sand dredging in the study area. Machines and human-dredging has exacerbated the abundance of benthic algae in the water column.

The Yellow River Basin is an important economic zone in China and an important base for energy, chemical, raw materials and basic industries. However, the sloppy development pattern of "high consumption and high pollution" in nine provinces along the Yellow River Basin has become an important obstacle to achieving the carbon peaking and carbon neutrality goals. Using the extended STIRPAT model and combining scenario analysis and Monte Carlo simulation to build a prediction model, the future trend of carbon emissions in the nine provinces of the Yellow River Basin was predicted and the optimal path to reach the peak of carbons in the Yellow River Basin was explored. The results show that the nine provincial areas in the Yellow River Basin will not be able to reach the carbon peaking before 2035 under the baseline scenario. By further comparing the carbon peaking conditions of provinces under the low-carbon scenario and the technology breakthrough scenario, it is found that the peaking time, carbon peak value and the number of provinces under the technology breakthrough scenario are significantly better than those under the low-carbon scenario. Under the technology breakthrough scenario, the energy efficiency of the nine provinces in the Yellow River Basin will be greatly improved, the innovation capacity will be significantly enhanced, and the synergistic emission reduction mechanism and related policies between provincial areas will be perfected. Technological breakthroughs will become an important engine for high-quality development in the Yellow River Basin.