A Study of the Merger History of the Galaxy Group HCG 62 Based on X-Ray Observations and SPH Simulations

We choose the bright compact group HCG 62, which was found to exhibit both excess X-ray emission and high Fe abundance to the southwest of its core, as an example to study the impact of mergers on chemical enrichment in the intragroup medium. We first reanalyze the high-quality Chandra and XMM-Newton archive data to search for the evidence for additional SN II yields, which is expected as a direct result of the possible merger-induced starburst. We reveal that, similar to the Fe abundance, the Mg abundance also shows a high value in both the innermost region and the southwest substructure, forming a high-abundance plateau, meanwhile all the SN Ia and SN II yields show rather flat distributions in $>0.1r_{200}$ in favor of an early enrichment. Then we carry out a series of idealized numerical simulations to model the collision of two initially isolated galaxy groups by using the TreePM-SPH GADGET-3 code. We find that the observed X-ray emission and metal distributions, as well as the relative positions of the two bright central galaxies with reference to the X-ray peak, can be well reproduced in a major merger with a mass ratio of 3 when the merger-induced starburst is assumed. The `best-match' snapshot is pinpointed after the third pericentric passage when the southwest substructure is formed due to gas sloshing. By following the evolution of the simulated merging system, we conclude that the effects of such a major merger on chemical enrichment are mostly restricted within the core region when the final relaxed state is reached.Comment: Accepted for publication in the Astrophysical Journa

Exploration of Teaching Reform in Environmental Protection Equipment and Engineering Design Course under the Background of New Engineering

Environmental protection equipment and engineering design is an important foundational course for environmental majors in higher education institutions, which combines systematic theory with strong practicality. The article is based on research on learning situations and pain points in educational reform. Through a series of educational reform measures, a learning community is established, scientific research is strengthened to support teaching, teaching content is optimized, teaching cases are enriched, and diversified teaching practices are carried out; Expand the second classroom, build a teaching platform, establish a mentorship system for undergraduate students, and organize all students to participate in innovation and entrepreneurship competitions; Integrating ideological and political education into the curriculum, cultivating students' scientific thinking, and solving practical environmental problems. Since the implementation of this innovative model, it has broadened students' horizons and improved the quality of teaching; We have established a comprehensive and full-time education model, enhancing students' practical and innovative abilities; It cultivates students' scientific thinking and exercises their ability to solve practical environmental problems, which has certain promotion and reference significance for comprehensively promoting the reform of the environmental chemistry curriculum system. Keywords: New engineering, Environmental protection equipment and engineering design, Scientific thinking DOI: 10.7176/JEP/15-11-03 Publication date: October 30th 2024

3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions

3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years, owing to its low cost of data acquisition and availability for large-scale applications. However, existing methods rely on expensive 3D-annotated samples for fully-supervised training, restricting their application to large-scale cross-city scenarios. In this work, we propose MLS-BRN, a multi-level supervised building reconstruction network that can flexibly utilize training samples with different annotation levels to achieve better reconstruction results in an end-to-end manner. To alleviate the demand on full 3D supervision, we design two new modules, Pseudo Building Bbox Calculator and Roof-Offset guided Footprint Extractor, as well as new tasks and training strategies for different types of samples. Experimental results on several public and new datasets demonstrate that our proposed MLS-BRN achieves competitive performance using much fewer 3D-annotated samples, and significantly improves the footprint extraction and 3D reconstruction performance compared with current state-of-the-art. The code and datasets of this work will be released at https://github.com/opendatalab/MLS-BRN.git.Comment: accepted by CVPR 202

Search for the Lepton Flavor Violation Process J/ψ→eμJ/\psi \to e\mu at BESIII

We search for the lepton-flavor-violating decay of the $J/\psi$ into an electron and a muon using $(225.3\pm2.8)\times 10^{6}$ $J/\psi$ events collected with the BESIII detector at the BEPCII collider. Four candidate events are found in the signal region, consistent with background expectations. An upper limit on the branching fraction of $\mathcal{B}(J/\psi \to e\mu)< 1.5 \times 10^{-7}$ (90% C.L.) is obtained

Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression

Content-adaptive compression is crucial for enhancing the adaptability of the pre-trained neural codec for various contents. Although these methods have been very practical in neural image compression (NIC), their application in neural video compression (NVC) is still limited due to two main aspects: 1), video compression relies heavily on temporal redundancy, therefore updating just one or a few frames can lead to significant errors accumulating over time; 2), NVC frameworks are generally more complex, with many large components that are not easy to update quickly during encoding. To address the previously mentioned challenges, we have developed a content-adaptive NVC technique called Group-aware Parameter-Efficient Updating (GPU). Initially, to minimize error accumulation, we adopt a group-aware approach for updating encoder parameters. This involves adopting a patch-based Group of Pictures (GoP) training strategy to segment a video into patch-based GoPs, which will be updated to facilitate a globally optimized domain-transferable solution. Subsequently, we introduce a parameter-efficient delta-tuning strategy, which is achieved by integrating several light-weight adapters into each coding component of the encoding process by both serial and parallel configuration. Such architecture-agnostic modules stimulate the components with large parameters, thereby reducing both the update cost and the encoding time. We incorporate our GPU into the latest NVC framework and conduct comprehensive experiments, whose results showcase outstanding video compression efficiency across four video benchmarks and adaptability of one medical image benchmark.Accepted by ACM MM 2024, Melbourne, Australi