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A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

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computer Modeling in Engineering & Sciences 2024年第8期140卷 1975-1996页

作者： Huile zhang Shikang li Yurui Wu pengpeng Zhi wei wang Zhonglai wang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization Huzhou CollegeHuzhou313000China SAIC Motor Corporation limited Passenger Vehicle Co. Shanghai201804China School ofMechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive ***,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance *** study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive ***,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python *** enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s *** Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process *** studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

关键词： Multiscale reliability-based design optimization carbon-fabric-reinforced composite drive shaft

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

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World Journal of Gastroenterology 2024年第11期30卷 1609-1620页

作者： Jun-Tao li Hong-Mei zhang wei wang Dong-Qing wei college of Mathematics and Information Science Henan Normal UniversityXinxiang 453007Henan ProvinceChina college of computer and Information Engineering Henan Normal UniversityXinxiang 453007Henan ProvinceChina State Key Laboratory of Microbial Metabolism and School of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China

BACKGROUND liver cancer is one of the deadliest malignant tumors *** has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual *** immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival *** several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately *** To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy *** Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related *** active immune cells were identified by intersecting the highly active cell groups with B cells and T *** significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related *** genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression ***,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene *** The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell *** effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltrat

关键词： liver cancer Cell-cell communication Gene signature Prognosis Immunotherapy Single-cell RNA sequencing

Phase-Dislocation-Mediated High-Dimensional Fractional Acoustic-Vortex Communication

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Research 2024年第3期2023卷 575-584页

作者： ruijie Cao Gepu Guo wei Yue Yang Huang Xinpeng li Chengzhi Kai Yuzhi li Juan Tu Dong zhang peng Xi Qingyu Ma School of computer and Electronic Information Nanjing Normal UniversityNanjing 210023China Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijing 100871China nstitute of Acoustics Nanjing UniversityNanjng 210093China National Biomedical Imaging Center Peking UniversityBeijing 100871China

With unlimited topological modes in mathematics,the fractional orbital angular momentum(FOAM)demonstrates the potential to infinitely increase the channel capacity in acoustic-vortex(AV)***,the accuracy and stability of FOAM recognition are still limited by the nonorthogonality and poor anti-interference of fractional AV *** popular machine learning,widely used in optics based on large datasets of images,does not work in acoustics because of the huge engineering of the 2-dimensional point-by-point ***,we report a strategy of phase-dislocation-mediated high-dimensional fractional AV communication based on pair-FOAM multiplexing,circular sparse sampling,and machine *** unique phase dislocation corresponding to the topological charge provides important physical guidance to recognize FOAMs and reduce sampling points from theory to practice.A straightforward convolutional neural network considering turbulence and misalignment is further constructed to achieve the stable and accurate communication without involving experimental *** experimentally present that the 32-point dual-ring sampling can realize the 10-bit information transmission in a limited topological charge scope from±0.6 to±2.4 with the FOAM resolution of 0.2,which greatly reduce the divergence in AV *** infinitely expanded channel capacity is further verified by the improved FOAM resolution of *** with other milestone works,our strategy reaches 3-fold OAM utilization,4-fold information level,and 5-fold OAM *** of the extra advantages of high dimension,high speed,and low divergence,this technology may shed light on the next-generation AV communication.

关键词： infinitely topological fractional

Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma li zhang Shuo Xiao Dan-Dan zhang Shi-Jun Dang ru-Shuang Zhao Pei wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng wei-An li Si-Yao li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China college of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTru2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTru2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

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Advances in Atmospheric Sciences 2024年第7期41卷 1326-1341页

作者： Lu li Yongjiu DAI Zhongwang wei wei SHANGGUAN Nan wei Yonggen zhang Qingliang li Xian-Xiang li School of Atmospheric Sciences Sun Yat-sen UniversityGuangzhou 510275China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Guangzhou 510275China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Guangzhou 510275China Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China college of computer Science and Technology Changchun Normal UniversityChangchun 130123China

Accurate soil moisture(SM)prediction is critical for understanding hydrological ***-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface *** addition to PB models,deep learning(DL)models have been widely used in SM predictions ***,few pure DL models have notably high success rates due to lacking physical ***,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM *** this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM *** performances of the proposed hybrid models were investigated and compared with two existing hybrid *** results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid ***,the ensemble model performed best among all hybrid models at all forecast time scales and different soil *** is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day *** findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

关键词： soil moisture forecasting hybrid model deep learning ConvLSTM attention mechanism

A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

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Chinese Physics C 2024年第9期48卷 176-187页

作者： Ming-Xiang Fu Yu Song Jia-Meng Lv liang Cao peng Jia Nan li Xiang-ru li Ji-Feng liu A-li Luo Bo Qiu Shi-Yin Shen liang-Ping Tu li-li wang Shou-lin wei Hai-Feng Yang Zhen-Ping Yi Zhi-Qiang Zou National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China college of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Key lab of Space Astronomy and Technology National Astronomical ObservatoriesBeijing 100101China School of computer Science South China Normal UniversityGuangzhou 510631China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China university of Chinese Academy of Sciences NanjingNanjing 211135China university of Science and Technology Beijing Beijing 100083China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of computer and Information Dezhou UniversityDezhou 253023China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 30024China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China nanjing university of posts&telecommunications Nanjing 210023China School of Science University of Science and Technology LiaoningAnshan 114051China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the ***,effectively analyzing this vast amount of data poses a significant *** response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate *** overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and *** the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images *** proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging *** particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 *** addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

关键词： artificial intelligence large vision model human-in-the-loop astronomy galaxies

computer-assisted three-dimensional individualized extreme liver resection for hepatoblastoma in proximity to the major liver vasculature

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World Journal of Gastrointestinal Surgery 2024年第4期16卷 1066-1077页

作者： Wen-li Xiu Jie liu Jing-li zhang Jing-Miao wang Xue-Feng wang Fei-Fei wang Jie Mi Xi-wei Hao NanXia Qian Dong Department of Pediatric Surgery The Affiliated Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Department of Pediatric Surgery Yijishan Hospital of Wannan Medical CollegeWuhu 241000Anhui ProvinceChina Shandong Provincial Key Laboratory of Digital Medicine and computer-assisted Surgery Shandong College Collaborative Innovation Center of Digital Medicine Clinical Treatment and Nutrition HealthQingdao UniversityQingdao 266003Shandong ProvinceChina

BACKGROUND The management of hepatoblastoma(HB)becomes challenging when the tumor remains in close proximity to the major liver vasculature(PMV)even after a full course of neoadjuvant chemotherapy(NAC).In such cases,extreme liver resection can be considered a potential *** To explore whether computer-assisted three-dimensional individualized extreme liver resection is safe and feasible for children with HB who still have PMV after a full course of *** We retrospectively collected data from children with HB who underwent surgical resection at our center from June 2013 to June *** then analyzed the detailed clinical and three-dimensional characteristics of children with HB who still had PMV after a full course of *** Sixty-seven children diagnosed with HB underwent surgical *** age at diagnosis was 21.4±18.8 months,and 40 boys and 27 girls were ***-nine(88.1%)patients had a single tumor,39(58.2%)of which was located in the right lobe of the liver.A total of 47 patients(70.1%)had PRE-TEXT III or ***-nine patients(58.2%)underwent delayed *** a full course of NAC,16 patients still had close PMV(within 1 cm in two patients,touching in 11 patients,compressing in four patients,and showing tumor thrombus in three patients).There were 6 patients of tumors in the middle lobe of the liver,and four of those patients exhibited liver anatomy *** 16 children underwent extreme liver resection after comprehensive preoperative *** procedures were performed according to the preoperative plan,and the operations were successfully ***,the 3-year event-free survival of 67 children with HB is 88%.Among the 16 children who underwent extreme liver resection,three experienced recurrence,and one died due to multiple *** Extreme liver resection for HB that is still in close PMV after a full course of NAC is both safe and *** approach not only reduces th

关键词： Children Hepatoblastoma Surgery Three-dimensional computer-assisted

The Four Most Basic Elements In Machine Cognition

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Data Intelligence 2024年第2期6卷 297-319页

作者： Deyi li Jialun Yin Tianlei zhang wei Han Hong Bao Chinese Association for Artificial Intelligence Beijing 100101China Department of computer Science and Technology Tsinghua UniversityBeijing 100084China Trunk Beijing 100101China AIForceTech Beijing 100101China college of Robotics Beijing Union UniversityBeijing 100101China

The widespread use of ChatGPT has normalized the dialogue Turing *** meet this challenge,china's major national development strategy suggests that for a new generation of artificial intelligence,it is first necessary to answer the big questions raised by Turing in 1950 from the perspective of cognitive physics:Can machines think?How do machines think?How do machines cognize?Whether it is carbon-based human cognition or silicon-based machine cognition,it is an interaction between complex constructs composed of the four most basic elements:matter,energy,structure,and *** humans and machines depend on negative entropy for living,and time is the cornerstone of *** and time are parasitic on matter and energy in physical space,forming hard-structured *** soft-structured ware in cognitive space is mind,which is parasitic on the hard-structured ware or other existing soft-structured ware,and constitutes a rich hierarchy of multi-scale feelings,concepts,information,and ***"abstraction"from the symbolic school of artificial intelligence,"association"from the connectionist school,and"interaction"from the behaviorist school,the core of cognition is established on the shoulders of such scientific giants such as Schrodinger,Turing and *** and hard-structured ware *** machine can comprise heterogeneous hard-structured ware,such as field programmable gate arrays(FPGAs),data processing units(DPUs),central processing units(CPUs),graphics processing units(GPUs),tensor processing units(TPUs),and *** can also be implanted with the"Baby Cognitive Nucleus"which is hard-structured ware genetically inherited and naturally evolved to form the embodied *** the hard-structured ware is parasitized by rich,multi-scale soft-structured *** regulating matter and energy through soft-structured ware,machines produce orderly events,form coordinated and orderly thinking *** heterogeneous sensors configur

关键词： Cognitive physical science Negentropy Soft-structured ware Baby cognitive nucleus Intense thinking Embodied turing test

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

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Acta Pharmaceutica Sinica B 2024年第2期14卷 623-634页

作者： Jiacheng Xiong Rongrong Cui Zhaojun li wei zhang runze zhang Zunyun Fu Xiaohong liu Zhenghao li Kaixian Chen Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China university of Chinese Academy of Sciences Beijing 100049China college of computer and Information Engineering Dezhou UniversityDezhou 253023China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215000China Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and TechnologyShanghaiTech UniversityShanghai 200031China State Key Laboratory of Pharmaceutical Biotechnology Nanjing UniversityNanjing 210023China

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other ***-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic *** models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism *** this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated *** integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both *** significantly outperformed the benchmark methods in both cross-validation and external *** AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro ***,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://***.

关键词： Drugmetabolism Aldehyde oxidase Transferlearning Graph neural network Kinase inhibitor