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Dynamic simulation analysis of molten salt reactor-coupled air-steam combined cycle power generation system

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Nuclear Science and Techniques 2024年第2期35卷 222-233页

作者： Jing-Lei huang Guo-Bin Jia Li-Feng han Wen-Qian Liu Li huang Zheng-han Yang Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China

A nonlinear dynamic simulation model based on coordinated control of speed and flow rate for the molten salt reactor and combined cycle systems is proposed here to ensure the coordination and stability between the molten salt reactor and power system.This model considers the impact of thermal properties of fluid variation on accuracy and has been validated with Simulink.This study reveals the capability of the control system to compensate for anomalous situations and maintain shaft stability in the event of perturbations occurring in high-temperature molten salt tank outlet parameters.Meanwhile,the control system’s impact on the system’s dynamic characteristics under molten salt disturbance is also analyzed.The results reveal that after the disturbance occurs,the controlled system benefits from the action of the control,and the overshoot and disturbance amplitude are positively correlated,while the system power and frequency eventually return to the initial values.This simulation model provides a basis for utilizing molten salt reactors for power generation and maintaining grid stability.

关键词： Molten salt reactor Combined cycle Dynamic characteristic Control

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

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Neural Regeneration Research 2024年第7期19卷 1553-1558页

作者： Sihong huang Jungong han Hairong Zheng Mengjun Li Chuxin huang Xiaoyan Kui Jun Liu Department of Radiology The Second Xiangya HospitalCentral South UniversityChangshaHunan ProvinceChina Department of Computer Science Aberystwyth UniversityAberystwythCeredigionUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong ProvinceChina School of Computer Science and Engineering Central South UniversityChangshaHunan ProvinceChina Department of Radiology Quality Control Center of Hunan ProvinceChangshaHunan ProvinceChina Clinical Research Center for Medical Imaging of Hunan Province ChangshaHunan ProvinceChina

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal ch

关键词： cognitive function cross-section follow-up functional connectivity graph theory longitudinal study mild traumatic brain injury prediction small-worldness structural connectivity subnetworks whole brain network

Learning feature alignment and dual correlation for few‐shot image classification

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 303-318页

作者： Xilang huang Seon han Choi Department of Electronic and Electrical Engineering Ewha Womans UniversitySeoulSouth Korea Graduate Program in Smart Factory Ewha Womans UniversitySeoulSouth Korea

Few‐shot image classification is the task of classifying novel classes using extremely limited labelled samples.To perform classification using the limited samples,one solution is to learn the feature alignment(FA)information between the labelled and unlabelled sample features.Most FA methods use the feature mean as the class prototype and calculate the correlation between prototype and unlabelled features to learn an alignment strategy.However,mean prototypes tend to degenerate informative features because spatial features at the same position may not be equally important for the final classification,leading to inaccurate correlation calculations.Therefore,the authors propose an effective intraclass FA strategy that aggregates semantically similar spatial features from an adaptive reference prototype in low‐dimensional feature space to obtain an informative prototype feature map for precise correlation computation.Moreover,a dual correlation module to learn the hard and soft correlations was developed by the authors.This module combines the correlation information between the prototype and unlabelled features in both the original and learnable feature spaces,aiming to produce a comprehensive cross‐correlation between the prototypes and unlabelled features.Using both FA and cross‐attention modules,our model can maintain informative class features and capture important shared features for classification.Experimental results on three few‐shot classification benchmarks show that the proposed method outperformed related methods and resulted in a 3%performance boost in the 1‐shot setting by inserting the proposed module into the related methods.

关键词： image classification machine learning metric learning

Novel milestones for early esophageal carcinoma:From bench to bed

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World Journal of Gastrointestinal Oncology 2024年第4期16卷 1104-1118页

作者： Ji-han Qi Shi-Ling huang Shi-Zhu Jin Department of Gastroenterology and Hepatology The Second Affiliated Hospital of Harbin Medical UniversityHarbin 150086Heilongjiang ProvinceChina

Esophageal cancer(EC)is the seventh most common cancer worldwide,and esophageal squamous cell carcinoma(ESCC)accounts for the majority of cases of EC.To effectively diagnose and treat ESCC and improve patient prognosis,timely diagnosis in the initial phase of the illness is necessary.This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs.Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quanti-tative polymerase chain reaction(qPCR),high-throughput sequencing technology(next-generation sequencing),and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis,treatment,and prognosis of diseases.The invest-igation of the microbiome is a swiftly progressing area in human cancer research,and microorganisms with complex functions are potential components of the tumor microenvironment.The intratumoral microbiota was also found to be connected to tumor progression.The application of endoscopy as a crucial technique for the early identification of ESCC has been essential,and with ongoing advancements in technology,endoscopy has continuously improved.With the advancement of artificial intelligence(AI)technology,the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent.The implementation of AI can effectively resolve the discrepancies among observers,improve the detection rate,assist in predicting the depth of invasion and differentiation status,guide the pericancerous margins,and aid in a more accurate diagnosis of ESCC.

关键词： Early esophageal squamous cell carcinoma Epidemiology and risk factors Molecular biology Epigenetic Microbiology Endoscopy and artificial intelligence

An Interpolation Method for Karhunen-Loève Expansion of Random Field Discretization

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工程与科学中的计算机建模(英文) 2024年第1期138卷 245-272页

作者： Zi han Zhentian huang Department of Engineering Mechanics Hohai UniversityNanjing211100China College of Civil and Transportation Engineering Shenzhen UniversityGuangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and UtilizationInstitute of Deep Earth Sciences and Green EnergyShenzhenChina

In the context of global mean square error concerning the number of random variables in the representation,the Karhunen–Loève(KL)expansion is the optimal series expansion method for random field discretization.The computational efficiency and accuracy of the KL expansion are contingent upon the accurate resolution of the Fredholm integral eigenvalue problem(IEVP).The paper proposes an interpolation method based on different interpolation basis functions such as moving least squares(MLS),least squares(LS),and finite element method(FEM)to solve the IEVP.Compared with the Galerkin method based on finite element or Legendre polynomials,the main advantage of the interpolation method is that,in the calculation of eigenvalues and eigenfunctions in one-dimensional random fields,the integral matrix containing covariance function only requires a single integral,which is less than a two-folded integral by the Galerkin method.The effectiveness and computational efficiency of the proposed interpolation method are verified through various one-dimensional examples.Furthermore,based on theKL expansion and polynomial chaos expansion,the stochastic analysis of two-dimensional regular and irregular domains is conducted,and the basis function of the extended finite element method(XFEM)is introduced as the interpolation basis function in two-dimensional irregular domains to solve the IEVP.

关键词： Random field discretization KL expansion IEVP MLS FEM stochastic analysis

Single-Nucleus RNA Sequencing Reveals Cardiac Macrophage Landscape in Hypoplastic Left Heart Syndrome

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Congenital Heart Disease 2024年第2期19卷 233-246页

作者： Xiaozhuo Xu Yilin huang Xu han First Clinical Medical College Affiliated Hospital of Nanjing University of Chinese MedicineNanjing210008China Department of Geriatrics Affiliated Hospital of Nanjing University of Chinese MedicineNanjing210029China

Background:Hypoplastic left heart syndrome(HLHS)is one of the most challenging congenital heart diseases in clinical treatment.In cardiac tissues,resident macrophages fulfill critical functions in maintaining a stable cardiac state and have strong regenerative capacity and organ specificity.However,the molecular mechanisms of macro-phages in HLHS remained unclear.Methods:Single-nucleus RNA sequencing(snRNA-seq)data of HLHS and healthy control(donors)samples obtained from the Gene Expression Omnibus(GEO)database were normalized and clustered using the Seurat package.The“FindMarkers”function was used to screen differentially expressed genes(DEGs)between the HLHS and donor groups and to analyze the functional enrichment of the set of genes of interest.Finally,cell-cell communication,pseudotime,and single-cell regulatory network inference and cluster-ing(SCENIC)analyses were used to study the mechanisms of macrophages in HLHS.Results:Based on the snRNA-seq data of HLHS and donors,we identified a total of 9 cell clusters,among which the proportion of macrophages was significantly less in the HLHS group than in the control group.Subdivision of macrophage subpopulations(Macrophages 1,2,and 3)showed that Macrophages 1 was mainly involved in nervous system development,angiogenesis,and apoptotic processes.In addition,analysis of communication between Macro-phages 1 and cardiomyocytes revealed that ligand-acceptor pairs such as GAS6/AXL,IL6,IGF1,THY1,and L1CAM were present only in the donor group.Finally,pesudotime and SCENIC analyses demonstrated that FOXO3 and ELF2 played a critical role for Macrophages 1 to maintain cardiac function in patients with HLHS.Conclusion:Our study improved the current understanding of the molecular mechanisms of macrophage devel-opment in HLHS,showing that manipulating the regulatory role of macrophages in the heart can be a novel treat-ment for HLHS.

关键词： Hypoplastic left heart syndrome single-nucleus RNA sequencing macrophage cellular subpopulation transcription factor

Evaluation of Auto-Ignition Characteristics of Diesel Surrogate Fuels with the Same Derived Cetane Numbers at Changed Thermodynamic Environments

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Journal of Thermal Science 2024年第4期33卷 1590-1606页

作者： ZhanG Cong GUAN Yuanhang huang Zhen han Dong College of Smart Energy Shanghai Jiao Tong University Shanghai Non-carbon Energy Conversion and Utilization Institute Key Laboratory for power Machinery and Engineering Ministry of EducationShanghai Jiao Tong University

The ignition tendency of diesel fuels is highly sensitive to ambient conditions and fuel properties.In this study,the ignition characteristics of different diesel surrogate fuels with the same derived cetane numbers(DCN) were measured and compared in varied thermodynamic and oxidizing environments.The combustion pressures,heat release rates,ignition delays,and combustion delays of the test fuels were compared.The experimental results showed that the diesel surrogate fuels with the same DCNs exhibit similar ignition propensity at standard DCN test conditions.Further,for the test conditions of high cetane fuels,high ambient temperatures,and suficient oxygen concentrations,surrogate fuels with the same DCN have similar ignition behaviors,and using the DCN to evaluate fuel ignition tendency is appropriate.However,for the test conditions of low cetane fuels,low ambient temperatures,and reduced oxygen concentrations,different ignition behaviors are observed for the surrogate fuels with the same DCN,so at these conditions using DCN as the evaluation index for fuel ignition tendency may lead to higher uncertainty.

关键词： diesel fuels surrogate fuels derived cetane numbers constant volume combustion chamber

Two-dimensional precise figuring of 500 mm-long X-ray mirror using one-dimensional ion beam system

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Advances in Manufacturing 2024年第1期12卷 177-184页

作者： Qiu-Shi huang han-Dan huang Qiao-Yu Wu Jun Yu Zhong Zhang Zhan-Shan Wang MOE Key Laboratory of Advanced Micro-Structured Materials Institute of Precision Optical Engineering(IPOE)School of Physics Science and EngineeringTongji UniversityShanghai200092People’s Republic of China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Tongji UniversityShanghai200092People’s Republic of China Shanghai Frontiers Science Center of Digital Optics Tongji UniversityShanghai200092People’s Republic of China

In this study,a new method was developed to realize two-dimensional(2D)figure correction of grazing-incidence X-ray mirrors using a one-dimensional(1D)ion-beam figuring system.A mask of holes was specifically designed to generate removal functions at different widths and extend the figuring capability over a wide area.Accordingly,a long mirror could be manufactured.Using this method,the surface height root-mean-square(RMS)error of the center area of 484 mm×16 mm was reduced from 11.49 nm to 2.01 nm,and the 1D meridional RMS error reached 1.0 nm.The proposed method exhibits high precision and cost effectiveness for production of long X-ray mirrors.

关键词： Long-size mirrors Ion beam figuring Two-dimensional figure correction Mask

Novel intelligent reasoning system for tool wear prediction and parameter optimization in intelligent milling

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Advances in Manufacturing 2024年第1期12卷 76-93页

作者： Long-Hua Xu Chuan-Zhen huang Zhen Wang han-Lian Liu Shui-Quan huang Jun Wang School of Mechanical Engineering Yanshan UniversityQinhuangdao066004HebeiPeople’s Republic of China Center for Advanced Jet Engineering Technologies(CaJET) Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinan250061People’s Republic of China Institute of Manufacturing Technology Guangdong University of TechnologyGuangzhou510006People’s Republic of China

Accurate intelligent reasoning systems are vital for intelligent manufacturing.In this study,a new intelligent reasoning system was developed for milling processes to accurately predict tool wear and dynamically optimize machining parameters.The developed system consists of a self-learning algorithm with an improved particle swarm optimization(IPSO)learning algorithm,prediction model determined by an improved case-based reasoning(ICBR)method,and optimization model containing an improved adaptive neural fuzzy inference system(IANFIS)and IPSO.Experimental results showed that the IPSO algorithm exhibited the best global convergence performance.The ICBR method was observed to have a better performance in predicting tool wear than standard CBR methods.The IANFIS model,in combination with IPSO,enabled the optimization of multiple objectives,thus generating optimal milling parameters.This paper offers a practical approach to developing accurate intelligent reasoning systems for sustainable and intelligent manufacturing.

关键词： Improved particle swarm optimization(IPSO)algorithm Improved case-based reasoning(ICBR)method Adaptive neural fuzzy inference system(ANFIS)model Tool wear prediction Intelligent manufacturing