Search Results (7)

Accurate measurement of the infrared spectral emissivity of nickel-based alloys is significant for applications in aerospace. The low thermal conductivity of these alloys limits the accuracy of direct emissivity measurement, especially during the oxidation process. To improve measurement accuracy, a surface temperature correction method based on two thermocouples was proposed to eliminate the effect of thermal conductivity changes on emissivity measurement. By using this method, the infrared spectral emissivity of Inconel 601, Inconel 625, and Inconel 718 alloys was accurately measured during the oxidation process, with a temperature range of 673–873 K, a wavelength range of 3–20 μm, and a zenith angle range of 0–80°. The results show that the emissivity of the three alloys is similar in value and variation law; the emissivity of Inconel 718 is slightly less than that of Inconel 601 and Inconel 625; and the spectral emissivity of the three alloys strongly increases in the first hour, whereafter it grows gradually with the increase in oxidation time. Finally, Inconel 601 has a lower emissivity growth rate, which illustrates that it possesses stronger oxidation resistance and thermal stability. The maximum relative uncertainty of the emissivity measurement of the three alloys does not exceed 2.6%, except for the atmospheric absorption wavebands. Full article

The mechanisms underpinning the soil microbial community assembly are important, particularly in the fragile karst forest ecosystem. Despite such significance, relevant topics remain limited. We investigated a typical karst area, the Maolan National Nature Reserve in China. For this purpose, 30 forest dynamics plots were established on three restoration gradients in degraded karst forests, namely shrub, pioneer tree, and climax communities. Using vegetation surveys, we explored the diversity patterns, driving factors, and community assembly of the soil microbial communities during the restoration of degraded karst forest ecosystems. In addition, the soil physicochemical properties and macrogenomic sequencing data were examined. One-way analysis of variance and principal coordinates analysis showed no significant changes in soil microbial α-diversity during restoration, and the opposite pattern was observed for β-diversity. Variation partitioning analysis revealed that the combined effect of both soil microbial β-diversity and soil was significant (28% and 32% for bacteria and fungi, respectively). Pearson correlation analyses showed that plant species diversity and soil multifunctionality correlated significantly with soil microbial β-diversity. In contrast, the direct effect of plants was smaller (2% and 3% for bacteria and fungi, respectively). According to the dispersal–niche continuum index, stochastic processes were responsible for the assembly of the bacterial and fungal soil microbial communities. During restoration, the dominant influence of stochastic effects on the assembly of bacterial communities intensified. In contrast, the reverse tendency was observed in soil fungi. The investigation of the diversity pattern of soil microbial communities and their assembly can provide theoretical references for the restoration of degraded ecosystems. Full article

The biodiversity–ecosystem function (BEF) relationship is the basis for studying the restoration of degraded ecosystems, and the simultaneous assessment of multi-trophic-level biodiversity and ecosystem multifunctionality relationship is more conducive to unravelling the restoration mechanism of degraded ecosystems, especially for degraded forest ecosystems with harsh habitats and infertile soils such as karst. In this study, we evaluated the biodiversity and soil multifunctionality (SMF) of degraded karst forests (scrub, SB; secondary growth forests, SG; old-growth forests, OG) in the Maolan National Nature Reserve, China, using 30 sample plots. Biodiversity and soil multifunctionality (SMF) at three trophic levels (plant–soil fauna–soil microorganisms), were assessed through vegetation surveys and soil sampling. One-way ANOVA showed that SMF increased with natural restoration, but multi-trophic level biodiversity showed different trends. Pearson’s correlation analysis showed a positive correlation between plant species diversity and SMF (p < 0.001), whereas soil fauna and soil microorganisms were negatively correlated with SMF. Structural equation modeling revealed a cascading effect of the multi-trophic level on the stimulation of the SMF during restoration. Only soil microorganisms exhibited a direct driving effect on SMF (p < 0.001), whereas plants indirectly influenced soil microorganisms through soil fauna, which subsequently affected the SMF. Although we observed the negative effects of increased plant diversity on soil fauna and soil microbial diversity in terms of quantitative relationships, the increase in soil fauna species and the evenness of soil microbial function still contributed to SMF restoration. This study revealed the cascading effects of multi-trophic diversity in promoting SMF restoration and emphasized that soil microbes are key to unraveling restoration mechanisms and processes, whereas soil fauna is an important intermediate link. Full article

Sulfuration-amine flotation is the most commonly used method to separate zinc oxide ore, but its shortcomings such as unstable separation index and poor applicability to high mud-content raw ore limit its application in industry. In this study, the influence mechanism of sodium carbonate on the flotation separation of smithsonite and calcite by the sulfuration-amine method was investigated by chemical analysis of the flotation solution, contact angle measurement, Zeta potential test, and XPS analysis. The results showed that sodium carbonate significantly improved the flotation separation performance of smithsonite and calcite. The chemical analysis of the flotation solution showed that sodium carbonate hindered the dissolution of smithsonite, reducing its negative impact on the flotation of smithsonite. The results of the zeta potential and XPS tests showed that sodium carbonate enhanced the electronegativity of the smithsonite surface, and in an alkaline environment, sodium carbonate was favorable for the adsorption of dodecylamine on the surface of smithsonite, while sodium carbonate and high alkalinity enhanced the inhibitory effect of sodium hexametaphosphate on calcite. The study proved that sodium carbonate could be an effective modifier to promote the flotation separation of smithsonite and calcite using the aulfuration-amine method. Full article

The dual-carbon target brings severe challenges to enterprise financing. Low-carbon supply chain financing has become a key measure by which enterprises break through the financing bottleneck. How to accurately evaluate and optimize the financial efficiency of a low-carbon supply chain is of great significance. We developed a financial behavior scale of the low-carbon supply chain for enterprises and constructed the evaluation index system for financing efficiency of enterprises’ low-carbon supply chain. Based on the qualitative and quantitative data of 205 listed companies, we combine the PCA model with the DEA–Malmquist model to conduct static and dynamic analysis of the financing efficiency of enterprises’ low-carbon supply chain. The results show that the financing efficiency of the culture, sports, and entertainment industries needs to be improved. The pure technical efficiency of information transmission, software, and information technology services is low. The total factor productivity index of water conservancy, environment and public facilities management, and the real estate industry fluctuated significantly. In this regard, enterprises must combine the characteristics of the industry and improve the financing efficiency of the supply chain using low-carbon processes of improvement, selection of low-carbon environmental protection materials, and collaborative supply chain emission reduction to break the financing constraints of enterprises and promote the realization of the national dual-carbon target. Full article

Soil microbial communities link soil and plants and play a key role in connecting above-ground and below-ground communities in terrestrial ecosystems. Currently, how artificial revegetation promotes the restoration of soil microbial community diversity in degraded ecosystems attracts extensive attention. In this study, soil samples were collected from long-term artificially restored mobile sandy lands (aerial seeding sample plots) from 1983 to 2015 in the Mu Us Desert. The second-generation high-throughput sequencing technology was adopted to identify soil microorganisms and analyze the changes in their community structure and diversity. The relationships between changes in microbial diversity and soil nutrients were explored by Pearson correlation analysis and canonical correspondence analysis. In addition, the restoration of subsurface soil microbial communities in this area was evaluated. The results are as follows: (1) The alpha diversity of the soil microorganisms increased significantly with the restoration period, and the composition and diversity of the soil microbial communities in the sample plots in different restoration years varied significantly. (2) Soil nutrient indexes, such as total carbon, total nitrogen and nitrate nitrogen, significantly increased with the restoration period and were significantly positively correlated with soil fungal and bacterial diversity. (3) Key soil fungal and bacterial phyla contributed to nutrient cycling in degraded ecosystems. It can be concluded that afforestation by aerial seeding facilitates the change in community structure and increases the diversity of soil microorganisms in the Mu Us Desert. This paper provides a basis for future measures and policies for restoring degraded lands and ecosystems. Full article

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification. Full article