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1. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Author

DeepSeek-AI, Guo, Daya, Yang, Dejian, Zhang, Haowei, Song, Junxiao, Zhang, Ruoyu, Xu, Runxin, Zhu, Qihao, Ma, Shirong, Wang, Peiyi, Bi, Xiao, Zhang, Xiaokang, Yu, Xingkai, Wu, Yu, Wu, Z. F., Gou, Zhibin, Shao, Zhihong, Li, Zhuoshu, Gao, Ziyi, Liu, Aixin, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Feng, Bei, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, Dai, Damai, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Dai, Fucong, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Bao, Han, Xu, Hanwei, Wang, Haocheng, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Qu, Hui, Li, Hui, Guo, Jianzhong, Li, Jiashi, Wang, Jiawei, Chen, Jingchang, Yuan, Jingyang, Qiu, Junjie, Li, Junlong, Cai, J. L., Ni, Jiaqi, Liang, Jian, Chen, Jin, Dong, Kai, Hu, Kai, Gao, Kaige, Guan, Kang, Huang, Kexin, Yu, Kuai, Wang, Lean, Zhang, Lecong, Zhao, Liang, Wang, Litong, Zhang, Liyue, Xu, Lei, Xia, Leyi, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Meng, Wang, Miaojun, Li, Mingming, Tian, Ning, Huang, Panpan, Zhang, Peng, Wang, Qiancheng, Chen, Qinyu, Du, Qiushi, Ge, Ruiqi, Zhang, Ruisong, Pan, Ruizhe, Wang, Runji, Chen, R. J., Jin, R. L., Chen, Ruyi, Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Ye, Shengfeng, Wang, Shiyu, Yu, Shuiping, Zhou, Shunfeng, Pan, Shuting, Li, S. S., Zhou, Shuang, Wu, Shaoqing, Yun, Tao, Pei, Tian, Sun, Tianyu, Wang, T., Zeng, Wangding, Zhao, Wanjia, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Yu, Wenqin, Zhang, Wentao, Xiao, W. L., An, Wei, Liu, Xiaodong, Wang, Xiaohan, Chen, Xiaokang, Nie, Xiaotao, Cheng, Xin, Liu, Xin, Xie, Xin, Liu, Xingchao, Yang, Xinyu, Li, Xinyuan, Su, Xuecheng, Lin, Xuheng, Li, X. Q., Jin, Xiangyue, Shen, Xiaojin, Chen, Xiaosha, Sun, Xiaowen, Wang, Xiaoxiang, Song, Xinnan, Zhou, Xinyi, Wang, Xianzu, Shan, Xinxia, Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Yang, Xu, Yanhong, Li, Yao, Zhao, Yao, Sun, Yaofeng, Wang, Yaohui, Yu, Yi, Zhang, Yichao, Shi, Yifan, Xiong, Yiliang, He, Ying, Piao, Yishi, Wang, Yisong, Tan, Yixuan, Ma, Yiyang, Liu, Yiyuan, Guo, Yongqiang, Ou, Yuan, Wang, Yuduan, Gong, Yue, Zou, Yuheng, He, Yujia, Xiong, Yunfan, Luo, Yuxiang, You, Yuxiang, Liu, Yuxuan, Zhou, Yuyang, Zhu, Y. X., Huang, Yanping, Li, Yaohui, Zheng, Yi, Zhu, Yuchen, Ma, Yunxian, Tang, Ying, Zha, Yukun, Yan, Yuting, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Xu, Zhean, Xie, Zhenda, Zhang, Zhengyan, Hao, Zhewen, Ma, Zhicheng, Yan, Zhigang, Wu, Zhiyu, Gu, Zihui, Zhu, Zijia, Liu, Zijun, Li, Zilin, Xie, Ziwei, Song, Ziyang, Pan, Zizheng, Huang, Zhen, Xu, Zhipeng, Zhang, Zhongyu, and Zhang, Zhen

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.

Published
2025

2. DeepSeek-V3 Technical Report

Author

DeepSeek-AI, Liu, Aixin, Feng, Bei, Xue, Bing, Wang, Bingxuan, Wu, Bochao, Lu, Chengda, Zhao, Chenggang, Deng, Chengqi, Zhang, Chenyu, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Dai, Fucong, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Bao, Han, Xu, Hanwei, Wang, Haocheng, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Wang, Jiawei, Chen, Jin, Chen, Jingchang, Yuan, Jingyang, Qiu, Junjie, Li, Junlong, Song, Junxiao, Dong, Kai, Hu, Kai, Gao, Kaige, Guan, Kang, Huang, Kexin, Yu, Kuai, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Wang, Litong, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Wang, Qiancheng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Zhang, Ruisong, Pan, Ruizhe, Wang, Runji, Xu, Runxin, Zhang, Ruoyu, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Pan, Shuting, Wang, T., Yun, Tao, Pei, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, Zhao, Wanjia, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Yu, Wenqin, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Zhang, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Cheng, Xin, Liu, Xin, Xie, Xin, Liu, Xingchao, Yu, Xingkai, Song, Xinnan, Shan, Xinxia, Zhou, Xinyi, Yang, Xinyu, Li, Xinyuan, Su, Xuecheng, Lin, Xuheng, Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhu, Y. X., Zhang, Yang, Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Yu, Yi, Zheng, Yi, Zhang, Yichao, Shi, Yifan, Xiong, Yiliang, He, Ying, Tang, Ying, Piao, Yishi, Wang, Yisong, Tan, Yixuan, Ma, Yiyang, Liu, Yiyuan, Guo, Yongqiang, Wu, Yu, Ou, Yuan, Zhu, Yuchen, Wang, Yuduan, Gong, Yue, Zou, Yuheng, He, Yujia, Zha, Yukun, Xiong, Yunfan, Ma, Yunxian, Yan, Yuting, Luo, Yuxiang, You, Yuxiang, Liu, Yuxuan, Zhou, Yuyang, Wu, Z. F., Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Xu, Zhean, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Zhang, Zhengyan, Hao, Zhewen, Gou, Zhibin, Ma, Zhicheng, Yan, Zhigang, Shao, Zhihong, Xu, Zhipeng, Wu, Zhiyu, Zhang, Zhongyu, Li, Zhuoshu, Gu, Zihui, Zhu, Zijia, Liu, Zijun, Li, Zilin, Xie, Ziwei, Song, Ziyang, Gao, Ziyi, and Pan, Zizheng

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

Published
2024

3. UDD: Dataset Distillation via Mining Underutilized Regions

Author

Wang, Shiguang, Zhang, Zhongyu, and Cheng, Jian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Dataset distillation synthesizes a small dataset such that a model trained on this set approximates the performance of the original dataset. Recent studies on dataset distillation focused primarily on the design of the optimization process, with methods such as gradient matching, feature alignment, and training trajectory matching. However, little attention has been given to the issue of underutilized regions in synthetic images. In this paper, we propose UDD, a novel approach to identify and exploit the underutilized regions to make them informative and discriminate, and thus improve the utilization of the synthetic dataset. Technically, UDD involves two underutilized regions searching policies for different conditions, i.e., response-based policy and data jittering-based policy. Compared with previous works, such two policies are utilization-sensitive, equipping with the ability to dynamically adjust the underutilized regions during the training process. Additionally, we analyze the current model optimization problem and design a category-wise feature contrastive loss, which can enhance the distinguishability of different categories and alleviate the shortcomings of the existing multi-formation methods. Experimentally, our method improves the utilization of the synthetic dataset and outperforms the state-of-the-art methods on various datasets, such as MNIST, FashionMNIST, SVHN, CIFAR-10, and CIFAR-100. For example, the improvements on CIFAR-10 and CIFAR-100 are 4.0\% and 3.7\% over the next best method with IPC=1, by mining the underutilized regions., Comment: PRCV2024

Published
2024

5. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Author

DeepSeek-AI, Liu, Aixin, Feng, Bei, Wang, Bin, Wang, Bingxuan, Liu, Bo, Zhao, Chenggang, Dengr, Chengqi, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Xu, Hanwei, Yang, Hao, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Chen, Jin, Yuan, Jingyang, Qiu, Junjie, Song, Junxiao, Dong, Kai, Gao, Kaige, Guan, Kang, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Pan, Ruizhe, Xu, Runxin, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Zheng, Size, Wang, T., Pei, Tian, Yuan, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Liu, Xin, Xie, Xin, Yu, Xingkai, Song, Xinnan, Zhou, Xinyi, Yang, Xinyu, Lu, Xuan, Su, Xuecheng, Wu, Y., Li, Y. K., Wei, Y. X., Zhu, Y. X., Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Zheng, Yi, Zhang, Yichao, Xiong, Yiliang, Zhao, Yilong, He, Ying, Tang, Ying, Piao, Yishi, Dong, Yixin, Tan, Yixuan, Liu, Yiyuan, Wang, Yongji, Guo, Yongqiang, Zhu, Yuchen, Wang, Yuduan, Zou, Yuheng, Zha, Yukun, Ma, Yunxian, Yan, Yuting, You, Yuxiang, Liu, Yuxuan, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Hao, Zhewen, Shao, Zhihong, Wen, Zhiniu, Xu, Zhipeng, Zhang, Zhongyu, Li, Zhuoshu, Wang, Zihan, Gu, Zihui, Li, Zilin, and Xie, Ziwei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.

Published
2024

9. The crystal structure of (3-chlorothiophene-2-carboxylato-κ2O, O′)-(2,2′-dipyridyl-κ2N,N′)lead(II), C20H12Cl2N2O4S2Pb

Author

Zhang Zhongyu, Fu Chunhua, Sun Wan, and Dong Chunmeng

Subjects
2372851, Physics, QC1-999, Crystallography, QD901-999
Abstract

C20H12Cl2N2O4S2Pb, monoclinic, P21/c (no. 14), a = 13.2710(4) Å, b = 16.7810(4) Å, c = 9.6857(2) Å, β = 96.5550(1)°, V = 2142.91(9) Å3, Z = 4, Rgt(F) = 0.0314, wRref(F2) = 0.0807, T = 150.0 K.

Published
2024
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10. Distilling Object Detectors With Global Knowledge

Author

Tang, Sanli, Zhang, Zhongyu, Cheng, Zhanzhan, Lu, Jing, Xu, Yunlu, Niu, Yi, and He, Fan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations, which is the instance-level knowledge only from the teacher model, i.e., the local knowledge. However, the empirical studies show that the local knowledge is much noisy in object detection tasks, especially on the blurred, occluded, or small instances. Thus, a more intrinsic approach is to measure the representations of instances w.r.t. a group of common basis vectors in the two feature spaces of the teacher and the student detectors, i.e., global knowledge. Then, the distilling algorithm can be applied as space alignment. To this end, a novel prototype generation module (PGM) is proposed to find the common basis vectors, dubbed prototypes, in the two feature spaces. Then, a robust distilling module (RDM) is applied to construct the global knowledge based on the prototypes and filtrate noisy global and local knowledge by measuring the discrepancy of the representations in two feature spaces. Experiments with Faster-RCNN and RetinaNet on PASCAL and COCO datasets show that our method achieves the best performance for distilling object detectors with various backbones, which even surpasses the performance of the teacher model. We also show that the existing methods can be easily combined with global knowledge and obtain further improvement. Code is available: https://github.com/hikvision-research/DAVAR-Lab-ML., Comment: Accepted by ECCV2022

Published
2022

26. Gut microbial co-metabolite 2-methylbutyrylcarnitine exacerbates thrombosis via binding to and activating integrin α2β1

Author

Huang, Kan, Li, Zilun, He, Xi, Dai, Jun, Huang, Bingding, Shi, Yongxia, Fan, Dongxiao, Zhang, Zefeng, Liu, Yunchong, Li, Na, Zhang, Zhongyu, Peng, Jiangyun, Liu, Chenshu, Zeng, Renli, Cen, Zhipeng, Wang, Tengyao, Yang, Wenchao, Cen, Meifeng, Li, Jingyu, Yuan, Shuai, Zhang, Lu, Hu, Dandan, Huang, Shuxiang, Chen, Pin, Lai, Peilong, Lin, Liyan, Wen, Jielu, Zhao, Zhengde, Huang, Xiuyi, Yuan, Lining, Zhou, Lifang, Wu, Haoliang, Huang, Lihua, Feng, Kai, Wang, Jian, Liao, Baolin, Cai, Weiping, Deng, Xilong, Li, Yueping, Li, Jianping, Hu, Zhongwei, Yang, Li, Li, Jiaojiao, Zhuo, Youguang, Zhang, Fuchun, Lin, Lin, Luo, Yifeng, Zhang, Wei, Ni, Qianlin, Hong, Xiqiang, Chang, Guangqi, Zhang, Yang, Guan, Dongxian, Cai, Weikang, Lu, Yutong, Li, Fang, Yan, Li, Ren, Meng, Li, Linghua, and Chen, Sifan

Published
2024
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27. The crystal structure of hexakis(3-thiophenecarboxylato-κ2O,O″)-bis(1,10-phenanthroline-κ2N,N′) trimanganese(II), C54H34N4O12S6Mn3

Author

Zhou Lili, Zhang Zhongyu, Men Xiaoxia, and Zhang Yaxuan

Subjects
2248088, Physics, QC1-999, Crystallography, QD901-999
Abstract

C54H34N4O12S6Mn3, monoclinic, P21/c (no. 14), a = 11.0140(4) Å, b = 22.3222(9) Å, c = 11.2886(5) Å, β = 105.436(1)°, Z = 2, V = 2675.27(19) Å3, Rgt(F) = 0.0361, wRref(F2) = 0.0846, T = 273 K.

Published
2023
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33. The crystal structure of diaqua-methanol-κ1 O- (3-thiophenecarboxylato-κO)-(2,2′-dipyridyl-κ2 N,N′)manganese(II) 3-thiophenecarboxylate, C21H22N2O7S2Mn

Author

Shi Zheng, Zhang Zhongyu, Han Bing, and Li Yunyun

Subjects
2225147, Physics, QC1-999, Crystallography, QD901-999
Abstract

C21H22N2O7S2Mn, monoclinic, P21/c (no. 14), a = 14.544(6) Å, b = 9.636(4) Å, c = 17.087(7) Å, β = 96.664(6)° $96.664(6){}^{\circ}$ , V = 2378.5(17) Å3, Z = 4, Rgt ${R}_{\mathit{g}\mathit{t}}$ (F) = 0.0349, wRref $w{R}_{\mathit{r}\mathit{e}\mathit{f}}$ (F 2) = 0.1036, T = 273.15 K.

Published
2023
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35. Extraction Behaviors of Vanadium(V) with Unacidified and Acidified N1923 from a Real Leachate of Vanadium-Titanomagnetite

Author

Liu, Lei, Liu, Zhaobo, Pu, Nianwen, Fu, Yunfeng, Zhang, Zhongyu, Du, Shangchao, Du, Guoshan, Sun, Ninglei, Wang, Dehua, Li, Xiaoyan, Ouchi, Takanari, editor, Azimi, Gisele, editor, Forsberg, Kerstin, editor, Kim, Hojong, editor, Alam, Shafiq, editor, Neelameggham, Neale R., editor, Baba, Alafara Abdullahi, editor, and Peng, Hong, editor

Published
2022
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42. Syntheses, crystal structure, thermal behavior, and anti-tumor activity of three ternary metal complexes with 2-chloro-5-nitrobenzoic acid and heterocyclic compounds

Author

Zhang Zhongyu, Chen Meng, Tong Mingqiong, Sun Wan, Dong Pingxuan, Song Xinfeng, and Wang Xiaoyue

Subjects
metal complexes, 2-chloro-5-nitrobenzoic acid, 1,10-phenanthroline, 2,2′-dipyridyl, anti-tumor activity, Organic chemistry, QD241-441
Abstract

Three complexes, namely complex (1), complex (2), and complex (3), were synthesized and characterized by X-ray diffraction, thermogravimetric study, and elemental study. Complex (1) comprises discrete binuclear clusters, where two oxygen atoms of 2-chloro-5-nitrobenzoic acid bridge the two copper atoms. Complex (2) is a six-coordination structure consisting of four nitrogen atoms and two oxygen atoms in 2-chloro-5-nitrobenzoic acid and 1,10-phenanthroline to furnish a twisted octahedron. Complex (3) is a six-coordination structure consisting of four oxygen atoms and two nitrogen atoms from the 2-chloro-5-nitrobenzoic acid, methanol, and 2,2′-dipyridyl to furnish a distorted octahedral geometry. Metal complexes’ anti-tumor activity was also investigated by the MTT assay. Of the complexes tested, complex (1) could induce apoptosis in these A549 lung cancer and Caco-2 colon adenocarcinoma cells and complex (2) could induce apoptosis in Caco-2 colon adenocarcinoma cells. CCDC for complex (1) was 1543354, CCDC for complex (2) was 1546991, and CCDC for complex (3) was 1543417.

Published
2022
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44. Experimental study on modal analysis and vibration response of tunnel structures under different damage conditions due to subway train loads.

Author

Hou, Rui, Li, Tao, Zhang, Zhongyu, Li, Lingfeng, Liu, Bo, and Ren, Dongwei

Subjects
SUBWAY tunnels, MODAL analysis, SUBWAYS, POLYNOMIALS, PROTOTYPES, STRUCTURAL health monitoring, TUNNELS
Abstract

Based on a prototype of the Beijing subway tunnel, this research conducts large-scale model experiments to systematically investigate the vibration response patterns of tunnels with different damage levels under the influence of measured train loads. Initially, the polynomial fitting modal identification method (Levy) and the model test preparation process are introduced. Then, using time-domain peak acceleration, frequency response function, frequency-domain modal frequency, and modal shape indicators, a detailed analysis of the tunnel's dynamic response is conducted. The results indicate that damage significantly amplifies vibration acceleration, with the amplification increasing with the severity of the damage. When the crack lengths are 2 cm, 4 cm, and 6 cm, the peak acceleration increases by 25.12%, 36.35%, and 50.29%, respectively, while adjacent segments show increases of 13%, 29%, and 45%. Damage decreases the tunnel structure's modal frequency, with the first two modal frequencies showing the most significant reductions of 9.87% and 7.34%, respectively. The adjacent segments show reductions of 7.7% and 4.2%. As the severity of the damage increases, the amplitude of the modal shape at the damaged location also increases, with the first modal shape rising by 43.37% for 4 cm damage compared to 2 cm damage and by 72.21% for 6 cm damage. The second modal shape increases by 9.04% and 26.51%, respectively. Additionally, the effectiveness of the polynomial fitting modal identification method (Levy) for tunnel structural damage detection was validated. Finally, based on the methods outlined above, the tunnel responses measured on-site in the Beijing metro were also analyzed. The findings of this study provide important theoretical support for the assessment and routine maintenance of metro tunnels. [ABSTRACT FROM AUTHOR]

Published
2025
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45. Screening for retinopathy of prematurity in China: a five-year cohort study in seven screening centers.

Author

Zhong, Yusheng, Lin, Xiacheng, Yang, Yating, He, Liying, Li, Na, Lu, Jun, Zhou, Yu, Zhang, Rong, Wang, Yacong, Zhang, Zhongyu, Yang, Mochi, Yin, Hong, Zhao, Mingwei, Li, Xiaoxin, Liang, Jianhong, and Cheng, Yong

Abstract

Background: To analyze the demographic characteristics of retinopathy of prematurity (ROP) in China, attempting to propose optimized screening criteria and hopefully providing valuable information for future updates to the ROP guideline. Methods: A multicenter, retrospective-cohort study was conducted. The study included infants born between January 1, 2018, and July 31, 2023, who underwent ROP screening and were diagnosed with ROP at seven screening centers in China. Examinations were carried out in accordance with the ROP guidelines in 2014: infants with a gestational age (GA)<32 weeks and/or birth weight (BW)<2000 g, or infants who were suspected to be at risk of ROP. ROP treatment followed the recommendations of the Early Treatment for Retinopathy of Prematurity Cooperative Group. We utilized receiver operating characteristic (ROC) curves to determine the optimal predictive model, and conducted internal validation as well as compared the model to current standards. Results: Among the 4770 infants diagnosed with ROP after fundus screening, 1330 (27.9%) infants received treatment. The mean GA at birth for all enrolled infants was 29.67 ± 2.45 weeks, with a mean BW of 1295.89 ± 403.64 g. This study proposed the optimization of guidelines to be ≤ 30 weeks of GA and ≤ 1600 g of BW, achieving a sensitivity of 99.4%, as high as the current standard, with an 18.0% reduction in screening requirements. Conclusion: Considering the decrease in both GA and BW among the population requiring ROP treatment in China, it is imperative to contemplate updating the ROP screening guideline. [ABSTRACT FROM AUTHOR]

Published
2025
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