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Investigation of the Effect of Process Parameters on Energy Consumption in Incremental Sheet forming

Procedia CIRP 2019年 80卷 50-55页

作者： Yanle Li Fuyuan Liu Changxu Xu Weidong Zhai Lirong Zhou Fangyi Li Jianfeng Li Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education School of Mechanical Engineering Shandong University Jinan 250061 China College of Big Data Statistics Guizhou University of Finance and Economics Guiyang 550000 China

The selection of machine tool and process parameters plays a crucial role in the energy consumption in manufacturing processes such as incremental sheet forming (ISF) process. The analysis of how the process parameters influence the energy required during ISF processes will not only provide opportunity to reduce the environmental consequences, but also reduce economic cost. To this end, an empirical power model is presented with the aim of understanding energy consumption composition during ISF performed on a milling machine. The effects of process variables (e.g., material strength, step down, sheet thickness, tool diameter, and feed rate) on the power, forming time, and energy required for ISF processes are experimentally investigated. In particular, experimental results are in-depth analyzed to understand energy required breakdown by a machine tool during the ISF. Further, the mechanism on how the process parameters affect the energy required for the sheet plastic deformation is revealed. Based on the analysis above, useful energy-saving suggestions on selecting process parameters are provided.

关键词： Energy consumption composition incremental sheet forming green manufacturing

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Discovery of Novel Mammalian target of rapamycin (mTOR) Inhibitors by Support Vector Machine

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IOP Conference Series: Earth and Environmental Science 2021年第3期692卷

作者： Panpan Wang Xiaobo Xu Yinghong Li Bo Li Qinglan Pei Pei Yu Chenxi Jing Meng Lu College of Chemistry and Pharmaceutical Engineering Huanghuai University Zhumadian China Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China College of Life Sciences Chongqing Normal University Chongqing China

Mammalian target of rapamycin (mTOR) is a protein serine/threonine kinase playing the central downstream role in multiple mitogenic signalling pathways. As a c entral regulator of cell growth, proliferation, differentiation and survival, mTOR has b een reported to modulate proliferation and angiogenesis in neoplastic processes. Curre ntly, sirolimus and its analogues the only five mTOR inhibitors approved for clinical u se, which shows a great capacity in anticancer therapy. However, endocrine resistance in cancer therapy has been observed in sirolimus analogues, and the unavailability of n ew mTOR inhibitor besides similar structure of sirolimus analogues makes the resistan ce even worse. It is urgent to discovery new mTOR inhibitors as candidates for develo pment of effective anticancer drugs. In this study, support vector machine (SVM) as a virtual screening strategy was proposed. SVM models of mTOR inhibitors were constr ucted by training data published before 2012, and the ones published after 2012 as test set were used to verify according to cross validation. The selected model performed thi n false hit rates of 0.12% and 0.46% by screening PubChem and MDDR chemical libr aries respectively. As results, 9 novel novel scaffolds for mTOR were identified, and 6 of them have been reported their anticancer-related therapeutic capacity. In summary, SVM performed its ability to identify novel mTOR inhibitors, which can supply some candidates for mTOR anticancer drugs, and supply effective method for anticancer dru g discovery in future.

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HSCS:a hybrid shared cache scheduling scheme for multiprogrammed workloads

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Frontiers of Computer Science 2018年第6期12卷 1090-1104页

作者： Jingyu ZHANG Chentao WU Dingyu YANG Yuanyi CHEN Xiaodong MENG Liting XU Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004China School of Electronics and Information Shanghai Dianji UniversityShanghai 200240China

The traditional dynamic random-access memory (DRAM) storage medium can be integrated on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in multicore systems. Compared with static random-access memory (SRAM), DRAM is larger but slower. In the existing research, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together in shared cache systems, ranging from SRAM structure improvement to optimizing cache tags and data access. However, little attention has been paid to designing a shared cache scheduling scheme for multiprogrammed workloads with different memory footprints in multicore systems. Motivated by this, we propose a hybrid shared cache scheduling scheme that allows a multicore system to utilize SRAM and 3D-stacked DRAM efficiently, thus achieving better workload performance. This scheduling scheme employs (1) a cache monitor, which is used to collect cache statistics; (2) a cache evaluator, which is used to evaluate the cache information during the process of programs being executed; and (3) a cache switcher, which is used to self-adaptively choose SRAM or DRAM shared cache modules. A cache data migration policy is naturally developed to guarantee that the scheduling scheme works correctly. Extensive experiments are conducted to evaluate the workload performance of our proposed scheme. The experimental results showed that our method can improve the multiprogrammed workload performance by up to 25% compared with state-of-the-art methods (including conventional and DRAM cache systems).

关键词： multicore system shared cache workload performance

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Theory-screened Prussian blue analogues-based nanozymes for promoting diabetic wound healing via ferroptosis inhibition

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Materials Today Bio 2025年 32卷 101839页

作者： Dong, Qingrong Shen, Xiaomei Fang, Ge Shi, Jia Zhu, Xiafeng Du, Jiangfeng Zhang, Hui Ge, Cuicui Department of Medical Imaging First Hospital of Shanxi Medical University Taiyuan030001 China State Key Laboratory of Radiation Medicine and Protection School of Radiation Medicine and Protection Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions Soochow University Suzhou215123 China College of Chemistry and Materials Jiangxi Normal University Nanchang330022 China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province Taiyuan030001 China College of Pharmacy Academy of Medical Sciences Shanxi Medical University Jinzhong030619 China

Diabetes mellitus (DM) induced wound healing impairment remains a serious health problem, which can lead to limb amputation and even shorten life span. Inhibiting ferroptosis of endothelial cells has shown promise in promoting tissue repair and regeneration. However, a majority of known ferroptosis inhibitors belong to either antioxidants or iron-chelators but with poor pharmacological adherence and even serious side effects. Herein, we construct a series of Prussian blue analogues-based nanozymes as ferroptosis nano-inhibitors to simultaneously achieve highly efficient intracellular iron capture and antioxidant properties. Through computational and experimental methods, the optimized Prussian blue analogues (MPBs) with highest iron chelating efficiency and efficacy (CuPBs) are screened out and both the CuPBs and the post-chelated products have been demonstrated to exhibit reactive oxygen species (ROS)-scavenging activities. As expected, the CuPBs successfully inhibit ferroptosis in high glucose-cultured skin repair cells, thereby repairing their proliferation, migration and angiogenesis. Mechanistically, the CuPBs regulate ferroptosis by inhibiting iron accumulation and improving the antioxidant capacity of the Xc−-GPX4 system. Moreover, in a murine diabetic wound model, the CuPBs remarkably promote the wound healing by inhibiting ferroptosis and increasing the M2/M1 macrophage ratio, showing a 2-fold higher wound closure rate than deferoxamine (DFO). Collectively, our study presents a general design strategy on developing ferroptosis nano-inhibitors and provides a promising approach for treating ferroptosis-related diseases. © 2025 The Authors

关键词： Endothelial cells

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An automatic detection scheme of acute Stanford type A aortic dissection based on DCNNs in CTA images 19

An automatic detection scheme of acute Stanford type A aorti...

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4th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2019

作者： Xu, Xin He, Zhiqiang Niu, Kai Zhang, Yijie Tang, Hao Tan, Ling Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China Guizhou Provincial Engineering Research Center for Livelihood Big Data Comprehensive Application China Department of Cardiovascular Surgery Second Xiangya Hospital Central South University Changsha Hunan China

ISBN: (纸本)9781450371711

As a severely life-threatening disease, acute Stanford type A aortic dissection brings patients with high potential of death. Computed Tomography Angiography (CTA) images become the effective diagnostic basis of aortic dissection. In this paper, we propose a scheme for automatic patient-level and site-level detection of aortic dissection based on Deep Convolutional Neural Networks (DCNNs) in order to relieve the burden on doctors and diagnose accurately and timely. Firstly, Mask R-CNN is adopted to precisely detect and segment the aorta from the original CTA images. Taking advantage of spatial continuity among CTA images, we put forward an area-wise screening method to remove a handful of unsatisfactory segmentation results. Then we process the segmented aorta images to extract the edges by Canny edge detector. Finally, two ResNets are employed in processed aorta images for aortic dissection detection. Specially, we can detect not only the ascending and descending aorta, but also the aortic arch, which may be the site of onset. We tested on over 3500 images, achieving an accuracy of 98.66% in slice-level detection, 96.51% in patient-level detection and 91.47% in site-level detection. © 2019 Association for Computing Machinery.

关键词： Deep neural networks

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF

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Frontiers of Information Technology & Electronic engineering 2019年第6期20卷 872-884页

作者： Ya QIN Guo-wei SHEN Wen-bo ZHAO Yan-ping CHEN Miao YU Xin JIN College of Computer Science and Technology Guizhou University Guiyang 550025 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang 550025 China Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China

By network security threat intelligence analysis based on a security knowledge graph(SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template(FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

关键词： Network security entity Security knowledge graph (SKG) Entity recognition Feature template Neural network

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Daily CO_(2) Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China

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The Innovation 2020年第3期1卷 24-36页

作者： Rong Wang Yuankang Xiong Xiaofan Xing Ruipu Yang Jiarong Li Yijing Wang Junji Cao Yves Balkanski Josep Peñuelas Philippe Ciais Didier Hauglustaine Jordi Sardans Jianmin Chen Jianmin Ma Tang Xu Haidong Kan Yan Zhang Tomohiro Oda Lidia Morawska Renhe Zhang Shu Tao Department of Environmental Science and Engineering Fudan UniversityShanghai 200438P.R.China Institute of Atmospheric Sciences Fudan UniversityShanghai 200438P.R.China Big Data Institute for Carbon Emission and Environmental Pollution Fudan UniversityShanghai 200438P.R.China Key Laboratory of Aerosol Chemistry and Physics Institute of Earth EnvironmentChinese Academy of SciencesXi'an 710075P.R.China Laboratoire des Sciences du Climat et de l’Environnement CEA CNRS UVSQ91190 Gif-sur-YvetteFrance CREAF Cerdanyola del Vallès08193 CataloniaSpain CSIC Global Ecology Unit CREAF-CSIC-UABBellaterra08193 CataloniaSpain College of Urban and Environmental Sciences Laboratory for Earth Surface ProcessesPeking University100871 BeijingP.R.China School of Public Health Key Laboratory of Public Health Safety of the Ministry of Education and National Health Commission Key Laboratory of Health Technology AssessmentFudan UniversityShanghai 200438P.R.China Universities Space Research Association ColumbiaMDUSA Global Modeling and Assimilation Office NASA Goddard Space Flight CenterGreenbeltMDUSA Queensland University of Technology 2 George StreetBrisbaneQLD 4001Australia Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092P.R.China

Lockdown measures are essential to containing the spread of coronavirus disease 2019(COVID-19),but they will slow down economic growth by reducing industrial and commercial ***,the benefits of activity control from containing the pandemic have not been examined and *** we use daily carbon dioxide(CO_(2))emission reduction in China estimated from statistical data for energy consumption and satellite data for nitrogen dioxide(NO_(2))measured by the Ozone Monitoring Instrument(OMI)as an indicator for reduced activities consecutive to a *** perform a correlation analysis to show that a 1%day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO_(2) emissions of 0.22%±0.02%using statistical data for energy consumption relative to emissions without COVID-19,or 0.20%±0.02%using satellite data for atmospheric column NO_(2).We estimate that swift action in China is effective in limiting the number of COVID-19 cases<100,000 with a reduction in CO_(2) emissions of up to 23%by the end of February 2020,whereas a 1-week delay would have required greater containment and a doubling of the emission reduction to meet the same *** analyzing the costs of health care and fatalities,we find that the benefits on public health due to reduced activities in China are 10-fold larger than the loss of gross domestic *** findings suggest an unprecedentedly high cost of maintaining activities and CO_(2) emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19.

关键词： COVID-19 PANDEMIC CARBON EMISSION ENERGY CONSUMPTION SOCIAL CARBON COST CONTAINMENT EFFICACY PUBLIC HEALTH CORRELATION ANALYSIS INTEGRATED MODEL

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Deterministic single-photon source in the ultrastrong coupling regime

arXiv

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arXiv 2022年

作者： Peng, Jie Tang, Jianing Tang, Pinghua Ren, Zhongzhou Tian, Junlong Barraza, Nancy Barrios, Gabriel Alvarado Lamata, Lucas Solano, Enrique Albarrán-Arriagada, F. Hunan Key Laboratory for Micro-Nano Energy Materials and Devices School of Physics and Optoelectronics Xiangtan University Hunan411105 China School of Physics Science and Engineering Tongji University Shanghai200092 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China Department of Physics Shanghai University Shanghai200444 China Kipu Quantum Greifswalderstrasse 226 Berlin10405 Germany Departamento de Física Atómica Molecular y Nuclear Universidad de Sevilla Sevilla41080 Spain Instituto Carlos i de Física Teórica y Computacional Granada18071 Spain Ikerbasque Basque Foundation for Science Plaza Euskadi 5 Bilbao48009 Spain Avenida Víctor Jara 3493 Estación Central9170124 Chile Center for the Development of Nanoscience and Nanotechnology Estación Central9170124 Chile

Deterministic single-photon sources are important and ubiquitous in quantum information protocols. However, to the best of our knowledge, none of them work in the ultrastrong light-matter coupling regime, and each excitation process can only emit one photon. We propose a deterministic single-photon source in circuit QED which can work in the ultrastrong coupling regime. Here, two qubits are excited simultaneously in one process and two deterministic single photons can be sequentially emitted with an arbitrary time separation. This happens through two consecutive adiabatic transfers along the one-photon solutions of the two-qubit Rabi and Jaynes-Cummings model, which has constant eigenenergy in the whole coupling regime. Unlike the stimulated Raman adiabatic passage, the system goes back to the initial state of another period automatically after photon emission. Our scheme can approach unity single-photon efficiency, indistinguishability, and purity simultaneously. With the assistance of the Stark shift, a deterministic single photon can be generated within a time proportional to the inverse of the resonator frequency. © 2022, CC BY.

关键词： Photons

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Research and Implementation of Chinese Text Automatic Proofreading System 2

Research and Implementation of Chinese Text Automatic Proofr...

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2018 2nd Annual International Conference on Cloud Technology and Communication engineering, CTCE 2018

作者： Gong, Yonggang Fu, Junying Lian, Xiaoqin Li, Yuying Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China

The news media platform has a huge amount of original news releases every day, it is impractical to use manual review of text typos. This paper designed and implemented a Chinese text automatic proofreading system for large-scale text content and high-speed processing. The proofreading content is first analyzed and classified: typos and sensitive information. Firstly, the system used the n-gram model to statistically analyze the corpus after segmentation to form a 2-gram model library and a contextual context library;secondly, builded a typo confusion set, and then calculated the probability of the target word in the knowledge base to realize automatic error detection and correction of Chinese text. The system has been successfully applied to the error of the content of many government news media platforms, each server can handle one million articles every day. The results show that the recall rate of the article is 78.9% and the accuracy rate is 85.1%. It meets the demand of high speed and accurate processing of massive text error, and has important practical significance and application fields. © Published under licence by IOP Publishing Ltd.

关键词： Errors

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End-to-End Time Series Imputation via Residual Short Paths 10

End-to-End Time Series Imputation via Residual Short Paths

引用

10th Asian Conference on Machine Learning, ACML 2018

作者： Shen, Lifeng Ma, Qianli Li, Sen School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Time series imputation (replacing missing data) plays an important role in time series analysis due to missing values in real world data. How to recover missing values and model the underlying dynamic dependencies from incomplete time series remains a challenge. A recent work has found that residual networks help build very deep networks by leveraging short paths due to skip connections (Veit et al., 2016). Inspired by this, we observe that these short paths can model underlying correlations between missing items and their previous non-missing observations in a graph-like way. Hence, we propose an end-to-end imputation network with residual short paths, called Residual IMPutation LSTM (RIMP-LSTM), a flexible combination of residual short paths with graph-based temporal dependencies. We construct a residual sum unit (RSU), which enables RIMP-LSTM to make full use of previous revealed information to model incomplete time series and reduce the negative impact of missing values. Moreover, a switch unit is designed to detect the missing values and a new loss function is then developed to train our model with time series in the presence of missing values in an end-to-end way, which also allows simultaneous imputation and prediction. Extensive empirical comparisons with other competitive imputation approaches over several synthetic and real world time series with various rates of missing data verify the superiority of our model. © 2018 L. Shen, Q. Ma & S. Li.

关键词： Graphic methods

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