Based on the 5.7-magnitude earthquake that stroke Songyuan (China) and 172 groups of liquefaction data collected in mainland China, the hyperbolic liquefaction discriminant formula originally proposed by Sun Rui was revised, and a new formula for the liquefaction of sand was put forward. Groups of data derived from the Bachu earthquake in Xinjiang and an earthquake that occurred in New Zealand (47 and 195 groups, respectively) were used to carry out a back-judgment test, then, the results were compared with those of the existing standard method. Overall, the results showed that the new formula for hyperbolic liquefaction discrimination compensates for the conservative liquefaction discrimination of the older formula; moreover, it has a good applicability to different intensities, groundwater levels, and the deep sand layer of the Songyuan site, reflected by a more balanced success rate. Therefore, combining the existing liquefaction discrimination methods and the research results of discrimination, it is necessary to establish a suitable regional identification method through the continuous accumulation of liquefaction data and expanding database.

As part of the Earth Climate Observatory space mission concept for the direct observation from space of the Earth Energy Imbalance, we propose an advanced camera suite for the high resolution observation of the Total Outgoing Radiation of the Earth. For the observation of the Reflected Solar Radiation, we propose the use of two multispectral cameras covering the range from 400 to 950 nm, with a nadir resolution of 1.7 km, combined with a high resolution RGB camera, with a nadir resolution of 0.57 km. For the observation of the Outgoing Longwave Radiation, we propose the use of 6 microbolometer cameras, with each a spectral bandwidth of 1 μm in the range from 8 to 14 μm, and with a nadir resolution of 2.2 km.

To compare The effects of organic and inorganic impregnation on the properties of unmodified, phenol formaldehyde oligomer-modified (PFOMCF), and sodium silicate-modified Chinese fir wood (SSMCF) were compared using samples prepared using the respiratory impregnation method. Impregnation and reinforcement effects and water resistance of PFOMCF and SSMCF were compared and the results was showed that the weight percentage gain, density increase rate, bending strength, and compressive strength of SSMCF were clearly higher than those of PFOMCF and had a lower water absorption rate within 60 h. The impregnation and reinforcement effects and dimensional stability of SSMCF were better than those of PFOMCF. FT-IR, XRD, CONE, and TGA examinations were used to test and analyze the chemical structure, crystalline structure, flame retardancy, and heat resistance of these modified woods. The results indicated that SSMCF possessed more hydrogen bonds than PFOMCF and that Si–O–Si chemical bonding with high bond energy was formed. Meanwhile, the weakened degree of the diffraction peak of SSMCF was much less than that of PFOMCF. These results explained that the mechanical properties and water resistance of SSMCF were better than PFOMCF. Compared with PFOMCF, SSMCF had a lower heat release rate (HRR), peak-HRR, mean-HRR, total heat release, smoke production rate, and total smoke production as well as higher thermal decomposition temperature and residual rate. Inorganic sodium silicate was shown to be a better flame retardant, while SSMCF had good smoke suppression effects, thermal stability, and safety performance in the case of fire.

Plant endophytic fungi and rhizosphere soil fungi are often reported as biocontrol agents against plant pathogens or with plant growth promotion potential. Four treatments were performed in field and greenhouse experiments where cucumber plants were inoculated with Trichoderma harzianum and Fusarium oxysporum in 2022. The roots, stems and leaves of cucumber plants and their rhizosphere soil were collected twice individually from the field and greenhouse for isolation of cucumber endophytic and rhizosphere soil fungi. All 74 genera and 1275 fungal strains were sequenced by rDNA ITS. The PDA media yielded the highest number of genera isolated from cucumber plants, rhizosphere soil and both compared to other media. There were no significant differences among the 4 media for the isolation of cucumber endophytic fungi. The best media for isolating fungi from roots and stems were MRBA and PDA, respectively. PDA and CZA had higher isolation efficiency for the rhizosphere soil fungi than MRBA. The 28 fungal genera had high isolation efficiency, and the endophytic Trichoderma strains were best isolated by MEA. It is suggested that PDA can be used as a basic medium, and different media can be considered for specific fungal genera.

In order to solve the labor-intensive and time-consuming problem in the process of measuring yak body ruler and weight in yak breeding industry in Qinghai Province, a non-contact method for measuring yak body ruler and weight was proposed in this experiment, and key technologies based on semantic segmentation, binocular ranging and neural network algorithm were studied to boost the development of yak breeding industry in Qinghai Province. Main conclusions: (1) Study yak foreground image extraction, and implement yak foreground image extraction model based on U-net algorithm; select 2263 yak images for experiment, and verify that the accuracy of the model in yak image extraction is over 97%. (2) Develop an algorithm for estimating yak body ruler based on binocular vision, and use the extraction algorithm of yak body ruler related measurement points combined with depth image to estimate yak body ruler. The final test shows that the average estimation error of body height and body oblique length is 2.6%, and the average estimation error of chest depth is 5.94%. (3) Study the yak weight prediction model; select the body height, body oblique length and chest depth obtained by binocular vision to estimate the yak weight; use two algorithms to establish the yak weight prediction model, and verify that the average estimation error of the model for yak weight is 10.7% and 13.01% respectively.

When the epipelic algae group growing on the concrete inclined side wall, which is typical of urban rivers and open diversion channels, enters the water flow, it will seriously affect the func-tion of the rivers or the channels. It is urgent to predict the transport capacity of epipelic algae group with water flow. However, the current research on the transport of pollutants is mostly based on mathematical models, and the study of the transport of flake epipelic algae group is extremely rare. A generalized hydrodynamic experimental model of a flume with sidewall epi-pelic algae group was established. Through a series of hydraulic experiments, the physical movement form and transport capacity of the suspended epipelic algae group on the side wall of the channel were studied qualitatively and quantitatively by taking the thickness, sheet size and flow rate of the epipelic algae group as the influencing factors. The key influencing factors of the suspension of epipelic algae are clarified, so as to quantitatively analyze the critical conditions of its suspension. Through flow scheduling, the probability of epipelic algae gathering some-where is increased. It provides a theoretical basis for realizing the long-distance migration of epipelic algae and optimizing the mathematical model.

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).