The Impact of Agricultural Technology Adoption of Income Inequality in Rural China

This study analyzes the impact of government efforts to increase agricultural incomes on income inequality in rural China. It collects and analyzes survey data from 473 households in Yunnan, China in 2004. In particular, it investigates the effects of government efforts to promote improved upland rice technologies. Our analysis shows that farmers who adopted these technologies had incomes approximately 32 percent higher than non-adopters. While much of this came from increased incomes from the selling of upland rice, adopters also enjoyed higher incomes from other cash crops. We attribute this to technology spillovers. Despite substantial increases associated with the adoption of improved upland rice technologies, we estimate that the impact on income inequality was relatively slight. This is primarily due to the fact that low income farmers had relatively high rates of technology adoption.Rural economic development; Chinese economic development; upland rice; rural-urban income inequality; agricultural income policy

Numerical Simulation on Jet Formation of Shaped Charge with Different Liner Materials

In this paper, the effect of liner material of the shaped charge on jet formation and its penetration capability is investigated by experimental and numerical methods. Liner materials investigated in this paper are copper, steel, and aluminium, respectively. Pulse X-ray photographic technology to shoot the formation of jet is employed to obtain the tip velocity and the diameter of jet. A two-dimensional multi-material code is designed to simulate the entire process from jet formation to penetrating a target. A markers on cell lines method is utilised to treat the multi-material interface. The results show that aluminium jet has the highest velocity with the poorest penetration capability. Copper jet has the strongest penetration capability with a velocity higher than that of steel jet, but lower than that of aluminium jet. The simulated results agree with the experimental results very well. It also indicates that the code developed can not only address large distortion problems but also track the variation of multi-material interfaces. It is favourable to simulate the explosive loading on thin-wall structure such as shaped charge. It is proved that authors’ method is feasible and reliable for optimising the structure of shaped charge jet to dramatically improve its tip velocity and penetration capability, and provides an important theoretic basis for designing high explosive anti-tank warhead.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 279-286, DOI: http://dx.doi.org/10.14429/dsj.65.864

Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images

Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety of visual scenes. However, their direct use in many Remote Sensing (RS) applications is often unsatisfactory due to the special imaging characteristics of RS images. In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs). We employ the visual encoder of FastSAM, an efficient variant of the SAM, to extract visual representations in RS scenes. To adapt FastSAM to focus on some specific ground objects in the RS scenes, we propose a convolutional adaptor to aggregate the task-oriented change information. Moreover, to utilize the semantic representations that are inherent to SAM features, we introduce a task-agnostic semantic learning branch to model the semantic latent in bi-temporal RSIs. The resulting method, SAMCD, obtains superior accuracy compared to the SOTA methods and exhibits a sample-efficient learning ability that is comparable to semi-supervised CD methods. To the best of our knowledge, this is the first work that adapts VFMs for the CD of HR RSIs

Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models

Data poisoning attacks manipulate training data to introduce unexpected behaviors into machine learning models at training time. For text-to-image generative models with massive training datasets, current understanding of poisoning attacks suggests that a successful attack would require injecting millions of poison samples into their training pipeline. In this paper, we show that poisoning attacks can be successful on generative models. We observe that training data per concept can be quite limited in these models, making them vulnerable to prompt-specific poisoning attacks, which target a model's ability to respond to individual prompts. We introduce Nightshade, an optimized prompt-specific poisoning attack where poison samples look visually identical to benign images with matching text prompts. Nightshade poison samples are also optimized for potency and can corrupt an Stable Diffusion SDXL prompt in <100 poison samples. Nightshade poison effects "bleed through" to related concepts, and multiple attacks can composed together in a single prompt. Surprisingly, we show that a moderate number of Nightshade attacks can destabilize general features in a text-to-image generative model, effectively disabling its ability to generate meaningful images. Finally, we propose the use of Nightshade` and similar tools as a last defense for content creators against web scrapers that ignore opt-out/do-not-crawl directives, and discuss possible implications for model trainers and content creators

Surface structure and multigap superconductivity of V3Si (111) revealed by scanning tunneling microscopy

V3Si, a classical silicide superconductor with relatively high TC (~16 K), is promising for constructing silicon-based superconducting devices and hetero-structures. However, real space characterization on its surfaces and superconducting properties are still limited. Here we report the first low-temperature scanning tunnelling microscopy (STM) study on cleaned V3Si (111) single crystal surface. We observed a r3 by r3 superstructure which displays mirror symmetry between adjacent terraces, indicating the surface is V-terminated and reconstructed. The tunneling spectrum shows full superconducting gap with double pairs of coherence peaks, but has a relatively small gap size with comparing to bulk TC. Impurity induced in-gap state is absent on surface defects but present on introduced magnetic adatoms. Upon applying magnetic field, a hexagonal vortex lattice is visualized. Interestingly, the vortex size is found to be field dependent, and the coherence length measured from single vortex at low field is significantly larger than estimated value from bulk H_c2. These results reflect V3Si is a multi-band, s- wave superconductor

Influence of Oil on Heat Transfer Characteristics of R410A Flow Boiling in Conventional and Small Size Microfin Tubes

Compact heat exchangers for refrigeration and air-conditioning systems are beneficial to reduce cost, charge inventory and leakage of refrigerant, and to improve energy efficiency and safety. Using small diameter microfin tubes is one way to decrease the size of heat exchangers. Currently, small diameter micofin tubes with outside diameter (O.D.) of 5.0 mm and 4.0 mm O.D. begin to be applied in newly developed R410A air conditioners instead of conventional size tubes (e.g. 7.0 mm O.D. microfin tubes). With the decrease of the tube diameter, the pressure drop becomes much larger, resulting in the decrease of the heat exchanger performance. In order to avoid such performance decrease, the heat exchanger should be redesign based on clearly understanding the difference of the heat transfer characteristics between conventional size microfin tubes and small diameter micofin tubes. Therefore, the heat transfer characteristics of R410A flow boiling inside both conventional size microfin tubes and small diameter microfin tubes should be known. Under real working conditions of R410A air conditioner, some amount of oil inevitably circulates with the refrigerant and has a significant impact on refrigerant evaporation heat transfer characteristics (Shen and Groll, 2005; Thome, 1996). Therefore, the influence of oil on heat transfer characteristics of R410A flow boiling inside microfin tubes with different diameters covering from conventional size to small size should be investigated. Experiments of R410A-oil mixture flow boiling inside microfin tubes with different outside diameters of 4.0~7.0 mm were performed. The experimental results show that, for 7.0 mm microfin tube, the influence factor of oil on the heat transfer characteristics are larger than 1.0 under the conditions of low vapor qualities (xr,o \u3c 0.4), presenting the enhancement effect of oil on heat transfer characteristics; with the increase of vapor quality, the enhancement becomes smaller, and is smaller than 1.0 under the conditions of low vapor qualities (xr,o \u3e 0.65), showing the deterioration effect of oil on heat transfer characteristics. As the tube diameter decreases from 7.0 mm to 4.0~5.0 mm, the deterioration effect of oil is weakened, especially at intermediate and high vapor qualities; for 4.0-5.0 mm tubes, the presence of oil shows the enhancement effect on heat transfer characteristics under the conditions of intermittent vapor quality (0.4 \u3c xr,o \u3c 0.8), which is not the same as the deterioration effect for 7.0 mm tubes. The comparison of heat transfer coefficient for two 5.0 mm microfin tubes with different fin structures shows that, larger fin height and contact area of liquid with tube wall may enhance the heat transfer for oil-free R410A, but result in smaller enhancement effect of oil at low vapor qualities and smaller deterioration effect of oil at intermediate and high vapor qualities. Based on the experimental data for conventional and small size microfin tubes, a general heat transfer correlation for R410A-oil mixture flow boiling inside microfin tubes was developed, and it agrees with 94% of the experimental data of R410A-oil mixture in 4.0 mm ~ 7.0 mm microfin tubes within a deviation of ±30%

Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images

Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary Change Detection (BCD) since it enables detailed change analysis in the observed areas. Previous works established triple-branch Convolutional Neural Network (CNN) architectures as the paradigm for SCD. However, it remains challenging to exploit semantic information with a limited amount of change samples. In this work, we investigate to jointly consider the spatio-temporal dependencies to improve the accuracy of SCD. First, we propose a Semantic Change Transformer (SCanFormer) to explicitly model the 'from-to' semantic transitions between the bi-temporal RSIs. Then, we introduce a semantic learning scheme to leverage the spatio-temporal constraints, which are coherent to the SCD task, to guide the learning of semantic changes. The resulting network (SCanNet) significantly outperforms the baseline method in terms of both detection of critical semantic changes and semantic consistency in the obtained bi-temporal results. It achieves the SOTA accuracy on two benchmark datasets for the SCD

蛋白质多肽氨基端乙酰化酶NatB介导底物特异性乙酰化反应的分子基础

文章简介蛋白质多肽氨基端乙酰化(N-terminal acetylation)发生在蛋白质或多肽的氨基端第一个氨基酸的(N端)α氨基上,是真核生物中一种最常见的蛋白质翻译后修饰方式。该修饰是由6类N端乙酰转移酶(NAT)来完成的(Nat A至Nat F),而每一种都只作用于其特异的蛋白国家自然科学基金委;;科技部的经费支