A Continuum Model for Dislocation Climb

Dislocation climb plays an important role in understanding plastic deformation of metallic materials at high temperature. In this paper, we present a continuum formulation for dislocation climb velocity based on densities of dislocations. The obtained continuum formulation is an accurate approximation of the Green's function based discrete dislocation dynamics method (Gu et al. J. Mech. Phys. Solids 83:319-337, 2015). The continuum dislocation climb formulation has the advantage of accounting for both the long-range effect of vacancy bulk diffusion and that of the Peach-Koehler climb force, and the two longrange effects are canceled into a short-range effect (integral with fast-decaying kernel) and in some special cases, a completely local effect. This significantly simplifies the calculation in the Green's function based discrete dislocation dynamics method, in which a linear system has to be solved over the entire system for the long-range effect of vacancy diffusion and the long-range Peach-Koehler climb force has to be calculated. This obtained continuum dislocation climb velocity can be applied in any available continuum dislocation dynamics frameworks. We also present numerical validations for this continuum climb velocity and simulation examples for implementation in continuum dislocation dynamics frameworks

Molecular oxygen-assisted in defect-rich ZnO for catalytic depolymerization of polyethylene terephthalate

Polyethylene terephthalate (PET) is the most produced polyester plastic; its waste has a disruptive impact on the environment and ecosystem. Here, we report a catalytic depolymerization of PET into bis(2-hydroxyethyl) terephthalate (BHET) using molecule oxygen (O2)−assisted in defect-rich ZnO. At air, the PET conversion rate, the BHET yield, and the space-time yield are 3.5, 10.6, and 10.6 times higher than those in nitrogen, respectively. Combining structural characterization with the results of DFT calculations, we conclude that the (100) facet of defect-rich ZnO nanosheets conducive to the formation of reactive oxygen species (∗O2−) and Zn defect, promotes the PET breakage of the ester bond and thus complete the depolymerization processed. This approach demonstrates a sustainable route for PET depolymerization by molecule-assisted defect engineering.publishedVersio

Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

An Azimuth Aware Deep Reinforcement Learning Framework for Active SAR Target Recognition

Achieving automatic target recognition in synthetic aperture radar (SAR) imagery is a long-standing difficulty because of the limited training samples and its sensitivity to imaging condition. Active target recognition methods can offer an innovative perspective to improve recognition accuracy compared to their passive counterparts. Although prevailing in the optical imagery area, the active target recognition in SAR image processing remains underexplored. This article proposes an active SAR target recognition framework based on deep reinforcement learning for the first time, where we design a simple view-matching task and model it as a Markov decision process. The proximal policy optimization algorithm is used to help the agent learn how to alter the observing azimuth to seek more discriminative target images for the classifier. Furthermore, the single-view feature extractor is trained with the contrastive learning method to help distinguish the target images under different azimuths, allowing the agent to successfully learn the active data collection policy in the training environment and transfer it to the test environment. Lastly, the effectiveness and advancement of the proposed framework are verified on the SAMPLE dataset. When the training samples for the classifier are very scarce, it could bring around 10% more gain in target recognition rate compared to existing active target recognition frameworks

Strategies for improved isopropanol–butanol production by a Clostridium strain from glucose and hemicellulose through consolidated bioprocessing

Abstract Background High cost of traditional substrates and formation of by-products (such as acetone and ethanol) in acetone–butanol–ethanol (ABE) fermentation hindered the large-scale production of biobutanol. Here, we comprehensively characterized a newly isolated solventogenic and xylanolytic Clostridium species, which could produce butanol at a high ratio with elimination of ethanol and conversion of acetone to more value-added product, isopropanol. Ultimately, direct butanol production from hemicellulose was achieved with efficient expression of indigenous xylanase by the novel strain via consolidated bioprocessing. Results A novel wild-type Clostridium sp. strain NJP7 was isolated and characterized in this study, which was capable of fermenting monosaccharides, e.g., glucose into butanol via a fermentative acetone–isopropanol–butanol pathway. With enhancement of buffering capacity and alcohol dehydrogenase activities, butanol and isopropanol titer by Clostridium sp. strain NJP7 was improved to 12.21 and 1.92 g/L, respectively, and solvent productivity could be enhanced to 0.44 g/L/h. Furthermore, with in situ extraction with biodiesel, the amount of butanol and isopropanol was finally improved to 25.58 and 5.25 g/L in the fed-batch mode. Meanwhile, Clostridium sp. strain NJP7 shows capability of direct isopropanol–butanol production from hemicelluloses with expression of indigenous xylanase. 2.06 g/L of butanol and 0.54 g/L of isopropanol were finally achieved through the temperature-shift simultaneous saccharification and fermentation, representing the highest butanol production directly from hemicellulose. Conclusion The co-production of isopropanol with butanol by the newly isolated Clostridium sp. strain NJP7 would add on the economical values for butanol fermentation. Furthermore, the high isopropanol-butanol production with in situ extraction would also greatly enhance the economic feasibility for fermentative production of butanol–isopropanol in large scale. Meanwhile, its direct production of butanol–isopropanol from polysaccharides, hemicellulose through secretion of indigenous thermostable xylanase, shows great potential using lignocellulosic wastes for biofuel production

Overexpression of PIK3R1 promotes hepatocellular carcinoma progression

Abstract Background Phosphoinositide-3-kinase, regulatory subunit 1 (PIK3R1) could regulate cancer cell proliferation important for cancer cell proliferation; however, its role in Hepatocellular carcinoma (HCC) remains largely unknown. Here, we investigated the role of PIK3R1 in HCC and examined the underlying molecular mechanisms. Methods The expression of PIK3R1 was evaluated by immunohistochemistry and qRT-PCR in a series of HCC tissues. The mRNA and protein expression of PIK3R1 was used by qRT-PCR and western blot assays in a series of human HCC cell lines, and then we choose MHCC97H and HCCLM3 cells as a model to investigate the effect of PIK3R1 on HCC progression. The effects of PIK3R1 knowdown on cell proliferation, migration, apoptosis of HCC were assessed by the MTT assay, clonogenic assays, wound healing assay and flow cytometry in vitro. Western blot assay was performed to assess the expression changes of PI3K/AKT/mTOR signaling pathway. Results Our results found that PIK3R1 was highly expressed in HCC tissues compared with adjacent normal tissues. Knockdown of PIK3R1 inhibited the proliferation, migration and promoted apoptosis of HCC cell lines. In addition, we proved that knockdown of PIK3R1 downregulated p-PI3K, p-AKT, and p-mTOR expressions in MHCC97H and HCCLM3 cells. Conclusions In conclusion, PIK3R1 providing potential novel targets for the treatment of HCC