To enhance market acceptance of construction waste recycling products, the impact of awe on consumers’ purchasing willingness investigated was investigated. Using an emotion assessment scale, purchase intention scores, and fNIRS, along with a virtual purchase experimental setup, the effects of awe induced by nature videos on subjects’willingness to purchase construction waste recycling products were examined. Measurements were conducted to separately assess the emotions induced by nature videos, the willingness to purchase construction waste recycling products, and the changes in brain activity during the viewing of nature videos. The results from the emotion assessment scale revealed that nature videos significantly induced awe emotions. The fNIRS data demonstrated deactivation in the brain's default mode network (DMN), associated with self-processing. This suggests that the experience of awe may be linked to reduced self-consciousness. The scoring data indicated that the awe experienced significantly enhanced the subjects' willingness to purchase construction waste recycling household products, however, the subjects' willingness to purchase construction waste recycling materials were not being significantly influenced by awe. Therefore, in construction waste recycling household product marketing, leveraging awe through natural videos can increase the willingness to purchase construction waste recycling household products, subsequently improving its market acceptance.

The explosive popularity of the new generation of artificial intelligence technologies will profoundly impact the risk experience of perceptual subjects within risk societies. Factor analysis and multiple indicators and multiple causes (MIMIC) model were used to study 12 risk scenarios of generative AI, besides four indicators reflecting the public's risk perception and five dimensions affecting the public's risk perception were explored. The results show that the public's perception of the risks of generative AI can be reflected by expectations of safety, technology, user and corporate regulatory. The public's risk perception is affected by its subjective evaluation of technology risks, macro risks, equity risks, subject risks and application risks, among which both equity risks and macro risks have the most significant impact. It shows that the public's risk perception of generative artificial intelligence is mainly characterized by “self-oriented” and “precautionary”. On this basis, the public's risk perception of generative artificial intelligence from the perspectives of history and culture, risk communication and technology governance was analyzed further, and corresponding countermeasures was put forward.

With the rapid development of industry and agriculture, the situation of Cd pollution in farmland soil is severe. Cd pollution in farmland will directly or indirectly have adverse effects on soil ecological security, crop growth and human health development. Cd has received extensive attention due to its strong biological toxicity and easy migration and accumulation, and effective management and control of Cd pollution in farmland is urgently needed. At present, the remediation technology of Cd pollution in farmland soil has been gradually developed and enriched, but the summary analysis of the remediation technology is relatively lacking. The characteristics and hazards of farmland soil Cd contamination were analyzed, focusing on the current status and sources of farmland Cd pollution. Based on this analysis, the principles, characteristics, and applicable scopes of remediation technologies for Cd-contaminated farmland soil were summarized. Case studies were used to compare the practical application effects of these remediation technologies. Additionally, the advantages, disadvantages, and limitations of various remediation approaches were examined, providing theoretical references for the prevention and remediation of farmland soil Cd pollution and promoting high-quality development of agriculture.

In order to predict perforation velocity of SCS (steel-concrete-steel) slab against missile impact and obtain influence sequence of structural factors of SCS slab on the perforation velocity, a dimensionless equation of the perforation velocity was established and a prediction model was obtained based on dimensional analysis and artificial neural network. Orthogonal experiment design was used to determine finite element calculation, and the influence degree of 7 factors of SCS slab on perforation velocity was quantitatively evaluated by variance analysis. The deviation between the predicted perforation velocity and the actual value is less than 12%, and the quantitative evaluation results of variance analysis show that the thickness of steel plate has a largest effect, followed by the distance of tie bar and the thickness of concrete, the yield strength of steel plate, the yield strength of tie bar and the diameter of tie bar have a smaller effect, and the concrete compressive strength has a smallest effect. The established model solves the prediction problem of perforation velocity from a new perspective with a good prediction effect, which can effectively evaluate the ability of SCS slab against perforation failure of missile impact, and the order of the factors is beneficial to the optimal design of SCS slab resisting perforation from missile impact.

As an important aluminum industrial base in China, the bauxite concentration area in central Guizhou Province is hosted within the Lower Carboniferous Jiujialu Formation, and the deposit type belongs to the sedimentary bauxite. The Caijiaba bauxite deposit is a newly discovered bauxite deposit during the fine investigation of bauxite in central Guizhou Province in recent years. The paleoclimatic conditions, sedimentary environment and metallogenic provenance related to this bauxite deposit in the metallogenic process were studied. The results show that the upper bauxitic claystone of the Al-bearing rock series is mainly pelitomorphic texture and is mainly composed of kaolinite and illite. The middle bauxite ore is mainly clastic and cryptocrystalline textures, followed by a small number of ooidal texture, which is mainly composed of diaspore and illite. The lower ferruginous rock is mainly cryptocrystalline texture and is mainly composed of hematite. The Al-bearing rock series of the Caijiaba bauxite deposit is mainly formed in the continental environment, and the upper bauxitic claystone and middle bauxite ore layers are mainly formed in a relatively oxidized environment, while the lower ferruginous rock layer mainly shows a relatively reduced environment. Meanwhile, the lower ferruginous rock and middle bauxite ore layers are mainly formed in hot and humid paleoclimatic conditions, while the upper bauxitic claystone layer is the product of warm and humid climatic conditions. Research on the sources of ore-forming materials indicates that the Al-bearing rock series of the Caijiaba bauxite deposit is mainly derived from the dolomite and interbedded gray-green claystone of the Lower Cambrian Qingxudong Formation.The results are of great significance in guiding the exploration and prospecting of bauxite deposits in this area.

In order to apply remote sensing technology to uranium exploration in the Mouding area of Yunnan Province, based on ASTER (advanced spaceborne thermal emission and reflection radiometer) remote sensing data, the interference removal and PCA (principal component analysis) method was used to extract the Al-OH, Mg-OH, {\mathrm{CO}}_3^{2-} and iron-stained alteration information in the study area. The lineaments in the study area were automatically extracted by PCA and LINE model in PCI Geomatica software, and the density map of lineaments was created. Finally, combined with geological data, the relationship between uranium mineralization and alteration and linear structures in the study area was analyzed, and a favorable mineralization area was delineated. This study can provide some ideas for subsequent exploration in the area, and also provide some reference for remote sensing technology in mineral exploration in vegetation-covered areas.

In order to analyze the effect of maximum window area and multi-resolution DEM (digital elevation model) on the extraction of optimal area for relief amplitude, explore feasible solutions to quantitatively analyze the optimal area, and classify landforms based on the best results of various types data. Based on three DEM data obtained, including ALOS (advanced land observing satellite), ASTER GDEM.V2 (Version 2 of the advanced spaceborne thermal emission and reflection radiometer global digital elevation model) and SRTM3 (3 arc-seconds shuttle radar topography mission), the influence mechanism was statistically analyzed by calling the Arcpy module and using the mean variation point method, respectively, with the maximum window area and the resolution of the DEM as a single variable. In order to determine the appropriate maximum window area, the traversal method to correlate the relief amplitude classification results with the DEM data were proposed, to determine the optimal area for different data according to the maximum correlation coefficient, and to arbitrate the elevation classification data to obtain the geomorphic distribution map. The results show as follows. The optimal area increases in a stepwise manner with the increase of the maximum window area. There is a negative correlation between the DEM resolution and the optimal area in the same area range, that is, the corresponding area decreases sequentially with the increase of resolution. Based on the correlation analysis, the maximum correlation coefficient of ALOS DEM is 0.785 0, which corresponds to the optimal area of 0.21 km². ASTER GDEM.V2 DEM is 0.764, with an area of 0.32 km², and SRTM3 DEM is 0.782, with an area of 0.40 km². The geomorphological classification maps corresponding to the optimal areas of the three data were obtained, and by comparing them with the spatial distribution of China's 1∶1 million geomorphological types, it is concluded that the distribution of geomorphological features of the experimental data is more reasonable, and the boundaries are more clear. It is concluded that the maximum window area has a greater influence on the optimal area compared to the DEM resolution, and in order to solve the influence of the maximum window area, the correlation analysis can provide a theoretical basis for quantitatively determining the optimal area of relief amplitude.

In order to reveal the association patterns between ferroptosis-related diseases and genes and predict potential Disease-Gene associations, ferroptosis-related research literature was analyzed to extract disease and gene entities, and a disease-gene complex network was constructed. The network's basic characteristics were further analyzed, and the Apriori algorithm was applied to extract strong disease-gene association rules. Link prediction technology was used to identify potential disease-gene associations. The results show as follows. ferroptosis plays a critical role in lethal diseases such as hepatocellular carcinoma, adenocarcinoma, breast cancer, and colorectal cancer. The genes such as GPX4 and ROS play key roles in cell survival or death through the regulation of iron homeostasis, oxidative stress, and lipid peroxidation. GPX4 and ROS are significantly associated with various diseases. The link prediction method revealed potential target genes for adenocarcinoma, lung cancer, colorectal cancer, and breast cancer, and preliminary validation of some predicted results was conducted through literature review.It is concluded that the research methodology employed in this study is both feasible and effective. The findings offer valuable references and suggest future directions for research on the prevention and treatment of ferroptosis-related diseases.

In order to study the relationship between VCI (vascular cognitive impairment) and intracranial and extracranial large artery stenosis, cerebral white matter lesions and brain atrophy. By consecutively enrolling 105 patients with VCI, divided into mild group (n=77) and severe group (n=28), and at the same time selecting patients with normal cognition as the control group (n=71). comparing the differences in cerebrovascular disease risk factors, cerebral white matter lesions, ischemic cerebral infarction, and cerebral atrophy among the 3 groups, and analysing the correlation between the degree of stenosis of the intracranial and extracranial large arteries and VCI. The results show that the differences in the history of ischaemic stroke and the proportion of ≥2 lacunae were statistically significant among the 3 groups (P<0.001).The differences in cerebral white matter high signal, paraventricular white matter, deep white matter Fazekas score, and whole-brain cortical atrophy GCA grading were statistically significant among the 3 groups (P<0.001). In the multivariate ordered logistic regression analysis model, it was found that internal carotid artery segment C1, internal carotid segment C2~C7, and the degree of middle cerebral artery stenosis are the main influencing factors for the severity of VCI. The degree of stenosis of internal carotid artery C1 segment and internal carotid C2~C7 segment is positively correlated with the severity of VCI patients to a low degree, whereas the degree of stenosis of the middle cerebral artery, cerebral white matter lesions, and cerebral atrophy grading are positively correlated with the severity of VCI patients to a moderate degree. It is evident that with increasing cardiovascular risk factors, history of ischaemic stroke and degree of stenosis of the internal carotid and middle cerebral arteries, the risk of VCI in the subjects increased significantly. It suggests that the condition of intracranial and extracranial large arterial lesions can be used as one of the indicators for the detection of VCI, and that there are certain feasible therapeutic directions.

It is a new problem for the prevention and control of non-point source pollution to adsorb pollutants by colloids and assist them to quickly migrate from soil to water during the rainfall-runoff process. Southwest Guizhou Province is one of the important ecological barrier areas in the Pearl River Basin, but some areas sow corn in the middle and late April and enter the rainy season in May. The destruction of soil structure, the significant increase of precipitation and the typical karst landforms in the region lead to high water environmental risks in sloping farmland areas. Undisturbed soil samples are collected from newly ploughed yellow soil slope farmland in karst area. These samples undergo simulated rainfall infiltration experiments. The purpose is to investigate the dynamic release rule of colloids under different rainfall intensities, including the change characteristics of colloid concentration, particle size distribution and its content level with the increase of accumulated rainfall. The results show as follows. The colloid concentration increased with the increase of rainfall intensity and cumulative infiltration, and the study further revealed that the difference of released soil colloid concentration under three rainfall intensities showed three different stages: the colloid concentration is not significantly different when the rainfall intensity is 25 mm/h and 40 mm/h in the 0~100 mm stage, but is significantly higher than 10 mm/h. The colloid concentration in the 100~250 mm stage is quite different under three rainfall intensities. When the accumulated rainfall is more than 250 mm, the difference between them is very small. When the rainfall intensity is 40 mm/h, the characteristic statistics of colloidal particle size show obvious two-stage and sudden drop characteristics. At first, the average particle size of effluent increases with the increase of rainfall intensity, and then decreases slightly with the further increase of rainfall. When the rainfall intensity is 25 mm/h and 10 mm/h, it presents a gradual change characteristic. The change of colloid content with different particle sizes shows a trend of stability, increase and decrease respectively. The research innovatively reveals the stage characteristics of the outflow concentration difference of soil colloids at different flow rates, and quantifies the change trend of colloids with different particle sizes with the cumulative infiltration from the meso-scale, which will provide a reference for further evaluating the regional water environmental risk and driving mechanism in this period.