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Effect of FeSi additive in dual-chamber sample cup on thermal analysis characteristic values and vermiculating rate of compacted graphite iron

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China Foundry 2024年第1期21卷 91-100页

作者： De-quan Shi Ze-yu Liu Gui-li Gao yi-cheng Feng School of Materials and Chemistry University of Shanghai for Science and TechnologyShanghai 200093China School of Materials Science and Chemical Engineering Harbin University of Science&TechnologyHarbin 150040China School of Mechanical and Energy Engineering Shanghai Technical Institute of Electronics and InformationShanghai 201411China

Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.

关键词： FeSi75 additive characteristic value vermiculating rate thermal analysis compacted graphite iron

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A proton birdcage coil integrated with interchangeable single loops for multi-nuclear MRI/MRS

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2024年第2期25卷 168-180页

作者： yi ZHANG Zhiyan quan Feiyang LOU Yujiao FANG Garth J.THOMPSON Gao CHEN Xiaotong ZHANG Department of Neurosurgery the Second Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou310009China Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases Hangzhou310009China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang UniversityHangzhou310058China Interdisciplinary Institute of Neuroscience and Technology College of Biomedical Engineering&Instrument ScienceZhejiang UniversityHangzhou310027China School of Medicine Zhejiang UniversityHangzhou310020China iHuman Institute School of Life Science and TechnologyShanghaiTech UniversityShanghai201210China College of Electrical Engineering Zhejiang UniversityHangzhou310027China

Energy metabolism is fundamental for life.It encompasses the utilization of carbohydrates,lipids,and proteins for internal processes,while aberrant energy metabolism is implicated in many diseases.In the present study,using three-dimensional(3D)printing from polycarbonate via fused deposition modeling,we propose a multi-nuclear radiofrequency(RF)coil design with integrated 1H birdcage and interchangeable X-nuclei(^(2)H,^(13)C,^(23)Na,and^(31)P)single-loop coils for magnetic resonance imaging(MRI)/magnetic resonance spectroscopy(MRS).The single-loop coil for each nucleus attaches to an arc bracket that slides unrestrictedly along the birdcage coil inner surface,enabling convenient switching among various nuclei and animal handling.Compared to a commercial 1H birdcage coil,the proposed 1H birdcage coil exhibited superior signal-excitation homogeneity and imaging signal-to-noise ratio(SNR).For X-nuclei study,prominent peaks in spectroscopy for phantom solutions showed excellent SNR,and the static and dynamic peaks of in vivo spectroscopy validated the efficacy of the coil design in structural imaging and energy metabolism detection simultaneously.

关键词： Energy metabolism Magnetic resonance imaging(MRI) Magnetic resonance spectroscopy(MRS) Multi-nuclear Radiofrequency coil Three-dimensional(3D)printing

Pt nanoclusters modified porous g-C_(3)N_(4)nanosheets to significantly enhance hydrogen production by photocatalytic water reforming of methanol

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Chinese Journal of Chemical Engineering 2024年第2期66卷 40-50页

作者： yi-Fei Liang Jin-Rong Lu Shang-Kun Tian Wen-quan Cui Li Liu Hebei Provincial Key Laboratory of Environmental Photocatalytic and Electrocatalytic Materials College of Chemical EngineeringNorth China University of Science and TechnologyTangshan 063210China

For the use of green hydrogen energy,it is crucial to have efficient photocatalytic activity for hydrogen generation by water reforming of methanol under mild conditions.Much attention has been paid to gC_(3)N_(4)as a promising photocatalyst for the generation of hydrogen.To improve the separation of photogenerated charge,porous nanosheet g-C_(3)N_(4)was modified with Pt nanoclusters(Pt/g-C_(3)N_(4))through impregnation and following photo-induced reduction.This catalyst showed excellent photocatalytic activity of water reforming of methanol fo r hydrogen production with a 17.12 mmol·g^(-1)·h^(-1)rate at room temperature,which was 311 times higher than that of the unmodified g-C_(3)N_(4).The strong interactions of Pt-N in Pt/g-C_(3)N_(4)constructed effective electron transfer channels to promote the separation of photogenerated electrons and holes effectively.In addition,in-situ infrared spectroscopy was used to investigate the intermediates of the hydrogen production reaction,which proved that methanol and water eventually turn into H_(2)and CO_(2)via formaldehyde and formate.This study provides insights for understanding the photocatalytic hydrogen production in the water reforming of methanol.

关键词： Water reforming of methanol Photocatalysis g-C_(3)N_(4) Pt nanoclusters Hydrogen production

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Study on the Performance of the GRANDProto300 Particle Detector Array by Simulation

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Research in Astronomy and Astrophysics 2024年第1期24卷 173-177页

作者： Fu-Lin Dai quan-Bu Gou Xiaoyuan Huang yi-Qing Guo Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China School of Astronomy and Space Science University of Science and Technology of ChinaHefei 230026China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China

The Giant Radio Array for Neutrino Detection(GRAND)is a proposed large-scale observatory designed to detect cosmic rays,gamma-rays,and neutrinos with energies exceeding 100 Pe V.The GRANDProto300 experiment is proposed as the early stage of the GRAND project,consisting of a hybrid array of radio antennas and scintillator detectors.The latter,as a mature and traditional detector,is used to cross-check the nature of the candidate events selected from radio observations.In this study,we developed a simulation software called G4GRANDProto300,based on the Geant4 software package,to optimize the spacing of the scintillator detector array and to investigate its effective area.The analysis was conducted at various zenith angles under different detector spacings,including 300,500,600,700,and 900 m.Our results indicate that,for large zenith angles used to search for cosmic-ray in the GRAND project,the optimized effective area is with a detector spacing of 500 m.The G4GRANDProto300 software that we developed could be used to further optimize the layout of the particle detector array in future work.

关键词： astroparticle physics instrumentation:detectors (ISM:)cosmic rays

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Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 1138-1143页

作者： Jia-Ning ying Hu Li Yan-Yan Zhang Wen-Die Li quan-Yong yi Ningbo Eye Hospital Wenzhou Medical UniversityNingbo 315042Zhejiang ProvinceChina Health Science Center Ningbo UniversityNingbo 315211Zhejiang ProvinceChina

With the advancement of retinal imaging,hyperreflective foci(HRF)on optical coherence tomography(OCT)images have gained significant attention as potential biological biomarkers for retinal neuroinflammation.However,these biomarkers,represented by HRF,present pose challenges in terms of localization,quantification,and require substantial time and resources.In recent years,the progress and utilization of artificial intelligence(AI)have provided powerful tools for the analysis of biological markers.AI technology enables use machine learning(ML),deep learning(DL)and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments.Based on ophthalmic images,AI has significant implications for early screening,diagnostic grading,treatment efficacy evaluation,treatment recommendations,and prognosis development in common ophthalmic diseases.Moreover,it will help reduce the reliance of the healthcare system on human labor,which has the potential to simplify and expedite clinical trials,enhance the reliability and professionalism of disease management,and improve the prediction of adverse events.This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration(AMD),diabetic macular edema(DME),retinal vein occlusion(RVO)and other retinal diseases and presents prospects for their utilization.

关键词： artificial intelligence deep learning hyperreflective foci image analysis

Sequential enantioselective Ugi-4CR/post-Ugi transformation strategy:a precise construction of structurally diverse azaspiro polycyclic scaffolds

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Science China Chemistry 2024年第2期67卷 576-586页

作者： Xiao-Bao Wu Jun-Xiu Shi yi-Ming Ou Hua-Jie Jiang yi-Jun Fang Qi-Ming Wang quan Gao Jie Yu Department of Applied Chemistry Anhui Province Engineering Laboratory for Green Pesticide Development and ApplicationAnhui Province Key Laboratory of Crop Integrated Pest ManagementAnhui Agricultural UniversityHefei230036China

A privileged strategy has been developed for the precise construction of enantioenriched azaspiro polycyclic scaffolds.Structurally diverse azaspiro polycycles bearing multiple contiguous stereocenters are obtained with excellent results(up to 99:1 e.r.,>95:5 d.r.) via sequential enantioselective four-component Ugi reactions/post-Ugi transformations with substrates containing prerequisite functional groups in the presence of anionic stereogenic-at-cobalt(Ⅲ) complexes.

关键词： asymmetric catalysis post-Ugi transformation anionic stereogenic-at-cobalt(Ⅲ)complex azaspiro polycyclic scaffold one-pot synthesis

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Casual associations between blood metabolites and colon cancer

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World Journal of Gastrointestinal Oncology 2024年第5期16卷 1995-2005页

作者： Ke-Yue Hu yi-quan Cheng Zhi-Long Shi Fu-Peng Ren Gang-Feng Xiao Department of Hematology and Oncology Ningbo No.2 HospitalNingbo 315000Zhejiang ProvinceChina

BACKGROUND Limited knowledge exists regarding the casual associations linking blood metabolites and the risk of developing colorectal cancer.AIM To investigate causal associations between blood metabolites and colon cancer.METHODS The study utilized a two-sample Mendelian randomization(MR)analysis to investigate the causal impact of 486 blood metabolites on colorectal cancer.The primary method of analysis used was the inverse variance weighted model.To further validate the results several sensitivity analyses were performed,including Cochran's Q test,MR-Egger intercept test,and MR robust adjusted profile score.These additional analyses were conducted to ensure the reliability and robustness of the findings.RESULTS After rigorous selection for genetic variation,486 blood metabolites were included in the MR analysis.We found Mannose[odds ratio(OR)=2.09(1.10-3.97),P=0.024],N-acetylglycine[OR=3.14(1.78-5.53),P=7.54×10^(-8)],X-11593-O-methylascorbate[OR=1.68(1.04-2.72),P=0.034],1-arachidonoylglycerophosphocholine[OR=4.23(2.51-7.12),P=6.35×10^(-8)]and 1-arachidonoylglycerophosphoethanolamine 4[OR=3.99(1.17-13.54),P=0.027]were positively causally associated with colorectal cancer,and we also found a negative causal relationship between Tyrosine[OR=0.08(0.01-0.63),P=0.014],Urate[OR=0.25(0.10-0.62),P=0.003],N-acetylglycine[0.73(0.54-0.98),P=0.033],X-12092[OR=0.89(0.81-0.99),P=0.028],Succinylcarnitine[OR=0.48(0.27-0.84),P=0.09]with colorectal cancer.A series of sensitivity analyses were performed to confirm the rigidity of the results.CONCLUSION This study showed a causal relationship between 10 blood metabolites and colorectal cancer,of which 5 blood metabolites were found to be causal for the development of colorectal cancer and were confirmed as risk factors.The other five blood metabolites are protective factors.

关键词： Metabolites Colon cancer Mendelian randomization Genome-wide association studies Casual

TSC prediction and dynamic control of BOF steelmaking with state-of-the-art machine learning and deep learning methods

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Journal of Iron and Steel Research(International) 2024年第1期31卷 174-194页

作者： Tian-yi Xie Cai-dong Zhang quan-lin Zhou Zhi-qiang Tian Shuai Liu Han-jie Guo Material Technology Research Institute Hesteel GroupShijiazhuang050023HebeiChina Tangsteel Company Hesteel GroupTangshan063611HebeiChina School of Artificial Intelligence Beijing Technology and Business UniversityBeijing100048China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China

Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC test.To maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.The number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as inputs.First,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time series.Through optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,respectively.Its field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carried out,and dynamic control methods based on pred

关键词： BOF steelmaking In-blow prediction TSC test Machine learning Deep learning Field application

Semiparametric Additive Hazards Model with Doubly Truncated Data

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Acta Mathematica Sinica,English Series 2024年

作者： yi Jin ZHANG Liu quan SUN Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences

Doubly truncated data arise when the survival times of interest are observed only if they fall within certain random intervals. In this paper, we consider a semiparametric additive hazards model with doubly truncated data, and propose a weighted estimating equation approach to estimate the regression coefficients, where the weights are estimated both parametrically and nonparametrically.The asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed estimators perform well in a finite sample. An application to Parkinson’s disease data is provided.

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