A Facile Chemical Method Enabling Uniform Zn Deposition for Improved Aqueous Zn-Ion Batteries

Rechargeable aqueous Zn-ion batteries (ZIBs) have gained great attention due to their high safety and the natural abundance of Zn. Unfortunately, the Zn metal anode suffers from dendrite growth due to nonuniform deposition during the plating/stripping process, leading to a sudden failure of the batteries. Herein, Cu coated Zn (Cu–Zn) was prepared by a facile pretreatment method using CuSO4 aqueous solution. The Cu coating transformed into an alloy interfacial layer with a high affinity for Zn, which acted as a nucleation site to guide the uniform Zn nucleation and plating. As a result, Cu–Zn demonstrated a cycling life of up to 1600 h in the symmetric cells and endowed a stable cycling performance with a capacity of 207 mAh g−1 even after 1000 cycles in the full cells coupled with a V2O5-based cathode. This work provides a simple and effective strategy to enable uniform Zn deposition for improved ZIBs

Gender and beauty in the financial analyst profession: Evidence from the United States and China

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RSGPT: A Remote Sensing Vision Language Model and Benchmark

The emergence of large-scale large language models, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing (RS), there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for RS applications. In computer vision, recent research has demonstrated that fine-tuning large vision language models on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the RS field. Unlike previous RS datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of RS, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS

The FRIGG project: From intermediate galactic scales to self-gravitating cores

Abridged. Understanding the detailed structure of the interstellar gas is essential for our knowledge of the star formation process. The small-scale structure of the interstellar medium (ISM) is a direct consequence of the galactic scales and making the link between the two is essential. We perform adaptive mesh simulations that aim to bridge the gap between the intermediate galactic scales and the self-gravitating prestellar cores. For this purpose we use stratified supernova regulated ISM magneto-hydrodynamical (MHD) simulations at the kpc scale to set up the initial conditions. We then zoom, performing a series of concentric uniform refinement and then refining on the Jeans length for the last levels. This allows us to reach a spatial resolution of a few $10^{-3}$ pc. The cores are identified using a clump finder and various criteria based on virial analysis. Their most relevant properties are computed and, due to the large number of objects formed in the simulations, reliable statistics are obtained. The cores properties show encouraging agreements with observations. The mass spectrum presents a clear powerlaw at high masses with an exponent close to $\simeq -1.3$ and a peak at about 1-2 $M_\odot$. The velocity dispersion and the angular momentum distributions are respectively a few times the local sound speed and a few $10^{-2}$ pc km s$^{-1}$. We also find that the distribution of thermally supercritical cores present a range of magnetic mass-to-flux over critical mass-to-flux ratio which typically ranges between $\simeq$0.3 and 3.Comment: accepted for publication in A&

Research Progress in the Application of Proteomics andMetabolomics in Bee Products

Bee products are gaining increasing popularity among consumers for their high nutritional value and various biological activities. However, adulteration is becoming a prominent problem in the production and sale of bee products, and the mechanisms underlying their biological activities have not been fully elucidated. Proteomics and metabolomics can provide complete and comprehensive descriptions on the overall characteristics of proteins and small-molecular metabolites. In recent years, these two omics approaches have been widely used in the field of bee products, and become a powerful means to solve the problem of adulteration in bee products and elucidate the mechanisms underlying their biological activities. This paper reviews the research progress in the application of proteomics and metabolomics in bee products. Based on an overview of the advantages of proteomics and metabolomics in simultaneous identification of whole components and screening of characteristic markers, the paper also summarizes their applications in the identification of components, discrimination and authentication, and elucidation of mechanisms for biological activities of bee products in detail. In addition, the existing problems are analyzed and the future research directions are proposed. The paper is expected to provide a reference for extensive and in-depth application of omics technologies in the research of bee products