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Optimizing mechanical properties of HIPS fabricated with low-cost desktop 3D printers:investigating the impact of process parameters

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Advances in Manufacturing 2024年第2期12卷 379-395页

作者： Jin-Ting xu Guang-wei Zhang Man-Man Chen School of Automotive Engineering Dalian University of TechnologyDalian116024People’s Republic of China

Recently,low-cost desktop three-dimensional(3D)printers,employing the fused deposition modeling(FDM)technique,have gained widespread ***,most users cannot test the strength of printed parts,and little information is available about the mechanical properties of printed high-impact polystyrene(HIPS)parts using desktop 3D *** this study,the user-adjustable parameters of desktop 3D printers,such as crisscross raster orientation,layer thickness,and infill density,were *** experimental plans were designed using the Box-Behnken method,and tensile,3-point bending,and compression tests were carried out to determine the mechanical responses of the printed *** prediction models of the process parameters were regressed to produce the optimal combination of process *** experimental results showcase that the crisscross raster orientation has significant effects on the flexural and compression strengths,but not on the tensile *** an increase in the layer thickness,the tensile,flexural,and compression strengths first decreased and then increased,reaching their minimum values at approximately 0.16 mm layer *** addition,they all increased with an increase of infill *** was demonstrated that when the raster orientation,layer thickness,and infill density were 13.08°/–76.92°,0.09 mm,and 80%,respectively,the comprehensive mechanical properties of the printed HIPS were *** results can help end-users of desktop 3D printers understand the effects of process parameters on the mechanical properties,and offer practical suggestions for setting proper printing parameters for fabricating HIPS parts.

关键词： Desktop three-dimensional(3D)printers Fused deposition modeling(FDM) High-impact polystyrene(HIPS) Process parameters

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Honokiol Prevents Intestinal Barrier Dysfunction in Mice with Severe Acute Pancreatitis and Inhibits JAK/STAT1 Pathway and Acetylation of HMGB1

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Chinese Journal of Integrative Medicine 2024年第6期30卷 534-542页

作者： LI Jie CHEN Ya-feng GAO Lei LI Yi-jie feng Dian-xu Department of General Surgery Putuo HospitalShanghai University of Traditional Chinese MedicineShanghai200062China

Objective To investigate the effect of honokiol(HON)and the role of high-mobility group protein B1(HMGB1)on the pathogenesis of severe acute pancreatitis(SAP).Methods Thirty mice were numbered according to weight,and randomly divided into 5 groups using a random number table,including control,SAP,SAP and normal saline(SAP+NS),SAP and ethyl pyruvate(SAP+EP),or SAP+HON groups,6 mice in each *** of pancreas,intestine,and blood were collected 12 h after SAP model induction for examination of pathologic changes,immune function alterations by enzyme linked immunosorbent assay(ELISA),and Western *** vitro experiments,macrophages were divided into 5 groups,the control,lipopolysaccharide(LPS),LPS+DMSO(DMSO),LPS+anti-HMGB1 monoclonal antibody(mAb),and LPS+HON *** tight connection level was determined by transmission electron microscopy and fluorescein *** location and acetylation of HMGB1 were measured by Western ***,pyridone 6 and silencing signal transducer and activator of the transcription 1(siSTAT1)combined with honokiol were added to determine whether the Janus kinase(JAK)/STAT1 participated in the regulation of honokiol on *** protein expression levels of HMGB1,JAK,and STAT1 were detected using Western *** Mice with SAP had inflammatory injury in the pancreas,bleeding of intestinal tissues,and cells with disrupted *** in the SAP+HON group had significantly fewer pathological *** with SAP also had significant increases in the serum levels of amylase,lipase,HMGB1,tumor necrosis factor-α,interleukin-6,diamine oxidase,endotoxin-1,and *** in the SAP+HON group did not show these abnormalities(P<0.01).Studies of Caco-2 cells indicated that LPS increased the levels of occludin and claudin-1 as well as tight junction permeability,decreased the levels of junctional adhesion molecule C,and elevated intercellular permeability(P<0.01).HON treatment blocked these *** of

关键词： severe acute pancreatitis intestinal barrier dysfunction honokiol high-mobility group protein B1 Januskinase signal transducerand activatorof transcription1

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A two-step multi-frequency receiver function inversion method for shallow crustal S-wave velocity structure and its application across the basin-mountain range belts in Northeast China

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Science China Earth Sciences 2024年第3期67卷 687-703页

作者： Ruihao YANG xu WANG Ling CHEN Mingye feng Qifu CHEN State Key Laboratory of Lithospheric Evolution Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Earth and Planetary Physics Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China

A shallow crustal velocity structure(above 10 km depth) is essential for understanding the crustal structures and deformation and assessing the exploration prospect of natural resources, and also provides priori information for imaging deeper crustal and mantle structure. Passive-source seismic methods are cost-effective and advantageous for regional-scale imaging of shallow crustal structures compared to active-source methods. Among these passive methods, techniques utilizing receiver function waveforms and/or body-wave amplitude ratios have recently gained prominence due to their relatively high spatial resolution. However, in basin regions, reverberations caused by near-surface unconsolidated sedimentary layers often introduce strong non-uniqueness and uncertainty, limiting the applicability of such methods. To address these challenges, we propose a two-step inversion method that uses multi-frequency P-RF waveforms and P-RF horizontal-to-vertical amplitude ratios. Synthetic tests indicate that our two-step inversion method can mitigate the non-uniqueness of the inversion and enhance the stability of the results. Applying this method to teleseismic data from a linear seismic array across the sedimentary basins in Northeast China, we obtain a high-resolution image of the shallow crustal S-wave velocity structure along the array. Our results reveal significant differences between the basins and mountains. The identification of low-velocity anomalies(<2.8 km s^(-1)) at depths less than 1.0 km beneath the Erlian Basin and less than 2.5 km beneath the Songliao Basin suggests the existence of sedimentary layers. Moreover, the high-velocity anomalies(~3.4–3.8 km s^(-1)) occurring at depths greater than 7 km in the Songliao Basin may reflect mafic intrusions emplaced during the Early Cretaceous. Velocity anomaly distribution in our imaging result is consistent with the location of the major faults, uplifts, and sedimentary depressions, as well as active-source seismic res

关键词： Receiver function Frequency dependence Two-step inversion Shallow crustal velocity structures Unconsolidated sedimentary basins

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Genetically predicted fatty liver disease and risk of psychiatric disorders: A mendelian randomization study

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World Journal of Clinical Cases 2024年第14期12卷 2359-2369页

作者： wei-Ming xu Hai-Fu Zhang Yong-Hang feng Shuo-Jun Li Bi-Yun Xie Department of Medicine The First People's Hospital of FuyangHangzhou 311400Zhejiang ProvinceChina Department of Internal Medicine The First People's Hospital of FuyangHangzhou 311400Zhejiang ProvinceChina

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)and alcohol-related liver disease(Ar-LD)constitute the primary forms of chronic liver disease,and their incidence is progressively increasing with changes in lifestyle *** studies have do-cumented a correlation between the occurrence and development of prevalent mental disorders and fatty *** To investigate the correlation between fatty liver and mental disorders,thus ne-cessitating the implementation of a mendelian randomization(MR)study to elu-cidate this *** Data on NAFLD and ArLD were retrieved from the genome-wide association studies catalog,while information on mental disorders,including Alzheimer's disease,schizophrenia,anxiety disorder,attention deficit hyperactivity disorder(ADHD),bipolar disorder,major depressive disorder,multiple personality dis-order,obsessive-compulsive disorder(OCD),post-traumatic stress disorder(PTSD),and schizophrenia was acquired from the psychiatric genomics consor-tium.A two-sample MR method was applied to investigate mediators in signifi-cant *** After excluding weak instrumental variables,a causal relationship was identified between fatty liver disease and the occurrence and development of some psychia-tric ***,the findings indicated that ArLD was associated with a significantly elevated risk of developing ADHD(OR:5.81,95%CI:5.59-6.03,P<0.01),bipolar disorder(OR:5.73,95%CI:5.42-6.05,P=0.03),OCD(OR:6.42,95%CI:5.60-7.36,P<0.01),and PTSD(OR:5.66,95%CI:5.33-6.01,P<0.01).Meanwhile,NAFLD significantly increased the risk of developing bipolar disorder(OR:55.08,95%CI:3.59-845.51,P<0.01),OCD(OR:61.50,95%CI:6.69-565.45,P<0.01),and PTSD(OR:52.09,95%CI:4.24-639.32,P<0.01).CONCLUSION Associations were found between genetic predisposition to fatty liver disease and an increased risk of a broad range of psychiatric disorders,namely bipolar disorder,OCD,and PTSD,highlighting the significance of preven-tive measures against psy

关键词： Non-alcoholic fatty liver disease Alcohol-related liver disease Psychiatric disorders Mendelian randomization Single nucleotide polymorphisms

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Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

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Synthetic and Systems Biotechnology 2024年第1期9卷 33-42页

作者： Debiao Wu feng xu Yaying xu Mingzhi Huang Zhimin Li Ju Chu State Key Laboratory of Bioreactor Engineering East China University of Science and Technology130 Meilong RoadShanghai200237People’s Republic of China

Metabolic modeling and machine learning(ML)are crucial components of the evolving next-generation tools in systems and synthetic biology,aiming to unravel the intricate relationship between genotype,phenotype,and the ***,the comprehensive exploration of integrating these two frameworks,and fully harnessing the potential of fluxomic data,remains an unexplored *** this study,we present,rigorously evaluate,and compare ML-based techniques for data *** hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol ***,we investigated the influence of succinate dehydrogenase(SDH)on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask *** findings indicate a noticeable increase in ethanol yield,ranging from 6%to 10%,in SDH subunit gene knockout strains compared to the wild-type ***,in pursuit of a high-yielding strain for ethanol production,dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase(GPD)and *** results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1,Δsdh5Δgpd1,Δsdh6Δgpd1,Δsdh4Δgpd2,Δsdh5Δgpd2,and Δsdh6Δgpd2,with improvements of 21.6%,27.9%,and 22.7%,***,the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic *** addition,the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes,thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

关键词： Metabolic modeling Machine learning Flux balance analysis Biosystems design Saccharomyces cerevisiae Succinate dehydrogenase

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LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences

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Frontiers of Computer Science 2024年第4期18卷 39-47页

作者： Ziwang FU feng LIU Qing xu Xiangling FU Jiayin QI School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Beijing 100876China Shanghai International School of Chief Technology Officer East China Normal UniversityShanghai 200062China School of Computer Science and Technology East China Normal UniversityShanghai 200062China School of Cyberspace Security Guangzhou UniversityGuangdong 510006China

Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse language,visual,and audio ***,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not *** this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal ***,we first perform feature extraction for the three modalities respectively to obtain the local structure of the ***,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention *** addition,we splice the fused features with the original features to classify the emotions of the ***,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and *** experimental results show the superiority and efficiency of our proposed method in both *** with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.

关键词： modality-fused representations cross-model blocks multimodal emotion recognition unaligned multimodal sequences computational affection

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Hybrid Analytical and Data-Driven Model Based Instance-Transfer Method for Power System Online Transient Stability Assessment

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1664-1675页

作者： feng Li Qi Wang Yi Tang Yan xu Jie Dang School of Electrical Engineering Southeast UniversityNanjing 210000China Center for Power Engineering(CPE) School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore 639798Singapore Central China Branch of State Grid Corporation of China Wuhan 430077China

Data-driven methods are widely recognized and generate conducive results for online transient stability ***,the tedious and time-consuming process of sample collection is often *** functioning of power systems involves repetitive sample collection due to the constant variations occurring in the operation mode,thereby highlighting the importance of collection *** a means to achieve high sample collection efficiency following the operation mode change,we propose a novel instance-transfer method based on compression and matching strategy,which facilitates the direct acquisition of useful previous samples,used for creating the new sample ***,we present a hybrid model to ensure rationality in the process of sample similarity comparison and selection,where features of analytical modeling with special significance are introduced into data-driven *** the same time,a data-driven method can also be integrated in the hybrid model to achieve rapid error correction of analytical models,enabling fast and accurate post-disturbance transient stability *** a paradigm,we consider a scheme for online critical clearing time estimation,where integrated extended equal area criterion and extreme learning machine are employed as analytical model part and data-driven error correction model part,*** results validate the credible efficacy of the proposed method.

关键词： Critical clearing time extreme learning machine instance-transfer method transient stability assessment

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Enhanced High-Temperature Energy Storage Performance of All-Organic Composite Dielectric via Constructing Fiber-Re in forced Structure

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Energy & Environmental Materials 2024年第2期7卷 299-307页

作者： Mengjia feng Yu feng Changhai Zhang Tiandong Zhang xu Tong Qiang Gao Qingguo Chen Qingguo Chi Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China School of Electrical and Electronic Engineering Harbin University of Science and TechnologyHarbin 150080China

Optimizing the high-temperature energy storage characteristics of energy storage dielectrics is of great significance for the development of pulsed power devices and power control *** a polymer with a higher glass transition temperature(T_(g))as the matrix is one of the effective ways to increase the upper limit of the polymer operating ***,current high-T_(g)polymers have limitations,and it is difficult to meet the demand for high-temperature energy storage dielectrics with only one *** example,polyetherimide has high-energy storage efficiency,but low breakdown strength at high *** has high corona resistance,but low high-temperature energy storage *** this work,combining the advantages of two polymer,a novel high-T_(g)polymer fiber-reinforced microstructure is *** is designed as extremely fine fibers distributed in the composite dielectric,which will facilitate the reduction of high-temperature conductivity loss for *** the same time,due to the high-temperature resistance and corona resistance of polyimide,the high-temperature breakdown strength of the composite dielectric is *** the polyimide content with the best high-temperature energy storage characteristics is determined,molecular semiconductors(ITIC)are blended into the polyimide fibers to further improve the high-temperature ***,excellent high-temperature energy storage properties are *** 0.25 vol%ITIC-polyimide/polyetherimide composite exhibits high-energy density and high discharge efficiency at 150℃(2.9 J cm^(-3),90%)and 180℃(2.16 J cm^(-3),90%).This work provides a scalable design idea for high-performance all-organic high-temperature energy storage dielectrics.

关键词： all-organic energy storage density high-temperature high-temperature breakdown strength

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Coexistence of antiferromagnetism and unconventional superconductivity in a quasi-one-dimensional flat-band system:Creutz lattice

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Chinese Physics B 2024年第3期33卷 583-588页

作者：徐峰张磊 School of Physics and Telecommunication Engineering Shaanxi University of TechnologyHanzhong 723001China

We study the coexistence of antiferromagnetism and unconventional superconductivity on the Creutz lattice which shows strictly flat bands in the noninteracting *** famous renormalized mean-field theory is used to deal with strong electron-electron repulsive Hubbard interaction in the effective low-energy t-J model,the superfluid weight of the unconventional superconducting state has been calculated via the linear response *** unconventional superconducting state with both spin-singlet and staggered spin-triplet pairs emerges beyond a critical antiferromagnetic coupling interaction,while antiferromagnetism accompanies this *** superconducting state with only spin-singlet pairs is dominant with paramagnetic *** A phase is analogous to the pseudogap phase,which shows that electrons go to form pairs but do not cause a *** also show the superfluid behavior of the unconventional superconducting state and its critical *** is proven directly that the flat band can effectively raise the critical temperature of *** is implementable to simulate and control strongly-correlated electrons'behavior on the Creutz lattice in the ultracold atoms experiment or other artificial *** results may help the understanding of the interplay between unconventional superconductivity and magnetism.

关键词： flat-band unconventional superconductivity antiferromagnetism strong electron-electron interaction superfluid weight

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Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

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Environmental Science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo xu Haofei Yu Zongbo Shi Jinxing Liu Yuting wei Zhongcheng Zhang Yanqi Huangfu Han xu Yue Li Linlin Zhang Yinchang feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA School of Geography Earth and Environment Sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic Technology(Tianjin)Co. LtdTianjin300072China College of Computer Science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

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