Effects of plant-soil feedbacks on the invasion and competitiveness of Aegilops tauschii

The interaction between plants and soil is an important aspect that affects the invasive ability of foreign plants and the invasiveness of ecosystems. The study on plant soil feedback of A. tauschii can provide reference for its invasion mechanism. Firstly, the effects of A. tauschii on the nutrients and enzyme activities of invaded soil were investigated; Secondly, in conjunction with soil sterilization, pot experiments were conducted using the De Wit substitution method to investigate the impact of different degree invasive soils on the development of A. tauschii and its interaction with wheat. The results showed that the invasion of A. tauschii significantly increased soil organic matter, soluble phosphorus and soluble potassium, while also causing a significant decrease in the concentration of nitrate nitrogen. And according to the changes of morphological and biomass indicators of A. tauschii, the results of two-way ANOVAs showed that the invaded soil and its microbiota have a positive feedback effect on the growth of A. tauschii. Finally, it can be seen from the value of the competition balance index, the competition ability of A. tauschii in different invasion degree soil is greater than that of wheat whether the soil invaded by A. tauschii had undergone sterilization treatment or not. In conclusion, the invasion potential of A. tauschii is not only derived from its strong competitiveness, but also may be related to the soil conditions

Boosting Out-of-distribution Detection with Typical Features

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspective of typicality and regard the feature's high-probability region of the deep model as the feature's typical set. We propose to rectify the feature into its typical set and calculate the OOD score with the typical features to achieve reliable uncertainty estimation. The feature rectification can be conducted as a {plug-and-play} module with various OOD scores. We evaluate the superiority of our method on both the commonly used benchmark (CIFAR) and the more challenging high-resolution benchmark with large label space (ImageNet). Notably, our approach outperforms state-of-the-art methods by up to 5.11$\%$ in the average FPR95 on the ImageNet benchmark

Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40$\%$ in some cases.Comment: \copyright 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

The Influencing Factors of Soil Organic Carbon Density in Lanlingxi Watershed in Three Gorges Reservoir Area

To reveal the influencing factors of soil organic carbon (SOC) density in 0-30 cm soil layer of Lanlingxi watershed in Three Gorges Reservoir Area, build the regression equation for soil organic carbon density and adjust carbon sink strategy in this region, soil samples of top soil profile (0-30 cm) in five land use types were selected by the typical method. The SOC density of top soil profile (0-30 cm) and other environmental factors, such as elevation, slope and aspect and soil properties in five land use types, including grassland, scrubland, woodland, land for tea plantation and farmland in the watershed was investigated. The relationship of SOC density with physical properties of soil was also examined. The SOC density of the above five land use types averaged 7.55, 3.83, 6.04, 10.24, 2.83 kg﹒m-2, respectively. There was a significant difference in the SOC density (p0.8, highly correlated) between SOC density and environmental factors was greater than the correlation coefficient between any one independent variable and dependent variable, which fully proved the combined effect of environmental factors on SOC density

CuO and Ag2O/CuO Catalyzed Oxidation of Aldehydes to the Corresponding Carboxylic Acids by Molecular Oxygen

Furfural was oxidized to furoic acid by molecular oxygen under catalysis by 150nm-sized Ag2O/CuO (92%) or simply CuO (86.6%). When 30 nm-size catalyst was used,the main product was a furfural Diels-Alder adduct. Detailed reaction conditions andregeneration of catalysts were investigated. Under optimal conditions, a series of aromaticand aliphatic aldehydes were oxidized to the corresponding acids in good yields

Depth Distribution Pattern of Soil Organic Carbon in Forest from Taowan Basin of Funiu Mountain Area

[Objectives] By testing applicability of SOC depth distribution model in geographical and climatic conditions of Funiu Mountain area, SOC depth distribution model in the region was established and applied. The constructed model was used to estimate SOC mass density in other regions, thereby obtaining SOC abundance distribution chart at different depths. [Methods] 165 soil sampling sites were selected from Quercus variabilis forest, Pinus tabulaeformis forest, mixed forest, and shrub forest in Taowan basin of Funiu Mountain area, to determine SOC content at different depths, study SOC depth distribution pattern of forest in Taowan basin of Funiu Mountain area, and assess SOC reserve at different depths. [Results] Average SOC density of Q. variabilis forest, P. tabulaeformis forest, mixed forest, and shrub forest at the depth of 0-20 cm was 7.92, 8.42, 8.14 and 9.67 kg/m2, and there was significant difference in SOC density between shrub forest and Q. variabilis forest, P. tabulaeformis forest, mixed forest (P<0.05), and SOC density of four kinds of vegetation all abruptly declined with soil depth increased. At the depth of 0-20 cm, correlation between SOC density and vegetation type, canopy density, clay content and sand content was significant, and the correlation with altitude was insignificant. When carbon density at the depth of 0-100 cm was used to describe regional SOC reserve, the estimated value was lower. The established space model could predict SOC density of forest. [Conclusions] The estimation of deep-layer SOC by the established model needed further consideration, and estimation method for special areas needed to be further demonstrated