Jue Hou

Research Institute of Petroleum Exploration and Development

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Roman Yangarber

University of Helsinki

Anisia Katinskaia

University of Helsinki

Anh‐Duc Vu

University of Helsinki

Tianxi Cai

Harvard University

Guping Tang

The University of Texas MD Anderson Cancer Center

Hongzhen Bai

Zhejiang University

Thomas Fang Zheng

Tsinghua University

Jiayi Chen

Zhejiang University

Guoqiao Xu

Zhejiang University

Danning Jiang

Peking University

Cooperative Institutions

Changzhou University

Zhejiang University

Chinese Academy of Sciences

Harvard University

Research Institute of Petroleum Exploration and Development

Peking University

Beijing Institute of Technology

University of Chinese Academy of Sciences

Huazhong University of Science and Technology

Xi'an Jiaotong University

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Stress and Noises of Cable-Tower Anchorage Zone for Sutong Bridge

Zhijian Chen Ningning Zhang Qiang Wang Jue Hou Tao Xue

Conference paper written by Zhijian Chen, Ningning Zhang, Qiang Wang, Jue Hou, Tao Xue and Shanpo Qi presented at IABSE Conference: Information and Communication Technology (ICT) for Bridges, Buildings and Construction Practice, Helsinki, Finland, June 2008.

Bridge (graph theory)

10.2749/222137808796106701

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A collagenase-decorated Cu-based nanotheranostics: remodeling extracellular matrix for optimizing cuproptosis and MRI in pancreatic ductal adenocarcinoma

Journal of Nanobiotechnology (2024)

Yining Wang Qiaomei Zhou Wangping Luo Xiaoyan Yang Jinguo Zhang

Pancreatic ductal adenocarcinoma (PDAC), characterized by a dense extracellular matrix (ECM), presents significant therapeutic challenges due to its poor prognosis and high resistance to chemotherapy. Current chemodrugs and diagnostic agents largely fail to cross the barrier posed by the ECM, which severely limits the PDAC theranostics. This study introduces a novel theranostic strategy using thioether-hybridized hollow mesoporous organosilica nanoparticles (dsMNs) for the co-delivery of copper (Cu) and disulfiram (DSF), aiming to induce cuproptosis in PDAC cells. Our approach leverages the ECM-degrading enzyme collagenase, integrated with dsMNs, to enhance drug penetration by reducing matrix stiffness. Furthermore, the innovative use of a pancreatic cancer cell membrane coating on the nanoparticles enhances tumor targeting and stability (dsMCu-D@M-Co). The multifunctional platform not only facilitates deep drug penetration and triggers cuproptosis effectively but also utilizes the inherent properties of Cu to serve as a T1-weighted magnetic resonance imaging (MRI) contrast agent. In vitro and in vivo assessments demonstrate significant tumor size reduction in PDAC-bearing mice, highlighting the dual functionality of our platform in improving therapeutic efficacy and diagnostic precision. This integrated strategy represents a significant advancement in the management of PDAC, offering a promising new direction for overcoming one of the most lethal cancers.

10.1186/s12951-024-02968-6

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What do Transformers Know about Government?

arXiv (Cornell University) (2024)

Jue Hou Anisia Katinskaia Lari Kotilainen Sathianpong Trangcasanchai Anh‐Duc Vu

This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models.In particular, we explore how BERT encodes the government relation between constituents in a sentence. We use several probing classifiers, and data from two morphologically rich languages. Our experiments show that information about government is encoded across all transformer layers, but predominantly in the early layers of the model. We find that, for both languages, a small number of attention heads encode enough information about the government relations to enable us to train a classifier capable of discovering new, previously unknown types of government, never seen in the training data. Currently, data is lacking for the research community working on grammatical constructions, and government in particular. We release the Government Bank -- a dataset defining the government relations for thousands of lemmas in the languages in our experiments.

Need to know

10.48550/arxiv.2404.14270

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Distributed Control of Hydrogen-Based Microgrids for the Demand Side: A Multiagent Self-Triggered MPC-Based Strategy

Algorithms (2024)

Tingzhe Pan Jue Hou Xin Jin Zhenfan Yu Wei Zhou

With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based on multiagent self-triggered model predictive control (ST-MPC), with the aim of achieving demand-side control of hydrogen-based microgrid systems. This architecture considers a hybrid energy storage system with renewable energy as the main power source, supplemented by fuel cells based on electrolytic hydrogen. The primary objective of this architecture is aiming at the supply and demand balance problem under the supply and demand relationship of microgrid, the service life of hydrogen-based microgrid energy storage equipment can be increased on the basis of realizing demand-side control of hydrogen energy microgrid system. To accomplish this, model predictive controllers are implemented within a self-triggered framework that dynamically adjusts the counting period. The simulation results demonstrate that the ST-MPC architecture significantly reduces the frequency of control action changes while maintaining an acceptable level of set-point tracking. These findings highlight the viability of the proposed solution for microgrids equipped with multiple types of electrochemical storage, which contributes to improved sustainability and efficiency in renewable-based microgrid systems.

Microgrid

Model Predictive Control

10.3390/a17060251

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An Experimental Study on the Blended Teaching Model of Volleyball in Ethnic Academies to Enhance College Students' Interest in Sports Participation

Transactions on Comparative Education (2023)

Jue Hou Jinhao Xu Lei Wang

To improve sports psychology and service delivery among sportspeople, the basics of applied sports psychology and the teaching quality of accounting majors could be the best possible solution. The purpose of this study is to investigate whether blended teaching of volleyball and its derivatives can effectively stimulate students' learning interest in ethnic colleges and universities, and to provide strong evidence and practical reference for the promotion of this model in the teaching of ethnic colleges and universities in China. Using Physical Education Situational Interest Scale (PESIS) to 124 students in ethnic colleges and universities, and research methods such as questionnaire survey method and teaching experiment method were used to study the influence of mixed teaching mode of volleyball on students' interest in sports participation in ethnic colleges and universities. The results showed that blended instruction showed highly significant differences in the dimensions novelty, attention, exploration, pleasure, and overall interest compared to the traditional instructional model, and significant differences in the challenging dimensions. Girls' interest in sports participation increased more significantly in the blended instruction model than boys.

Situational ethics

Promotion (chess)

Physical Education

Sport management

10.23977/trance.2023.050301

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An Oxygen‐Self‐Produced Nanoplatform Based on MnO2 for Relieving Hypoxia

ChemNanoMat (2024)

Guohua Pan Hanxiao Bao Jinguo Zhang Jue Hou Jun Zhou

Manganese dioxide ( MnO2) nanoparticles are increasingly recognized for their potential in biomedical applications, particularly in the realm of hypoxic cancer therapy, due to their unique physicochemical characteristics. This investigation introduces a novel, template‐free synthesis strategy. The reducing groups inherent in bovine serum albumin (BSA) facilitate a redox reaction with potassium permanganate (KMnO4), while the abundance of functional groups in BSA is instrumental in the formation of MnO2. The transmission electron microscopy (TEM) analysis has characterized the synthesized MnO2 nanoparticles, termed BM NPs, as spherical particles with a mean diameter of 210 nm and zeta potential of −40.1 mV measured by dynamic light scattering (DLS). The Fourier transform infrared (FT‐IR) spectrum of BM NPs exhibits the characteristic peak of MnO2 at 1113 cm‐1 and 620 cm‐1. Furthermore, the elemental composition of BM NPs has been ascertained through energy‐dispersive X‐ray (EDX) elemental mapping analysis. Though concentration of BM NPs up to 200 μg/mL, the survival rate of 4T1 cells remained approximately 75%. Given that BM NPs at a concentration of 100 µg/mL significantly alleviate cellular hypoxia, the high oxygen‐generating capacity of these nanoparticles is proved, suggesting their suitability as a drug delivery system, especially in the context of hypoxic tumor microenvironments.

Hypoxia

10.1002/cnma.202400157

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Principle and Experimental Study of Flow Noise Reduction for Underwater Vehicle Hull-Mounted Sonar

Journal of Physics Conference Series (2023)

Renqi Fu Wenmiao Shen Jue Hou Wanxiang Lou

Abstract When the underwater vehicle is sailing at high speed, the noise of flow becomes one of the principal background noises of the hull-mounted sonar, and suppressing the flow noise is an important way to further develop the detection execution of the hull-mounted sonar. In this paper, by establishing a physical model for the analysis of flow noise, The theoretical calculation of the fluctuating level of noise acquired by a plane element sensor imbedded in an rubbery layer is performed, and the impact of different wave-number filter elements such as sensor receiving area and rubbery layer on reducing turbulent noise is discussed, and then, the effectiveness of the flow noise reduction is verified through the measurement in a large-scale flow noise test tunnel.

10.1088/1742-6596/2419/1/012061

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Container Terminal Operation System in Emission Control Area Using Modelling Based on Multi Agents

Frontiers in artificial intelligence and applications (2017)

Jue Hou

Improving operation efficiency of container terminal, is important for terminal operators. Meanwhile, the air pollution problem is increasingly serious in container terminal, with the establishment of the Emission Control Areas (ECAs) of China in 2015, environmental protection pressure is increasing to terminal operators. In order to comply with the ECA regulation, optimizing the operation efficiency, reducing air pollution emissions, which, is an urgent problem to terminal management in China. This paper based on the characteristic of Container Terminal Operation System (CTOS), including berth allocation problem, truck dispatching problem, and yard allocation problem, establishes the architecture of CTOS based on the Multi Agents System. A model and simulation on a port container terminal is built on the JADE platform to support the decision-making of container terminal, which can reduce air pollution emissions in port, and maintain service levels. CTOS allows the container terminal operator to be more flexible in their decision to meet the ECA regulation.

10.3233/978-1-61499-785-6-168

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Projecting named entity recognizers from resource-rich to resource-poor languages without annotated or parallel corpora

Jue Hou

Named entity

Entity linking

Named Entity Recognition

Source

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Semi-supervised Approach to Event Time Annotation Using Longitudinal Electronic Health Records

arXiv (Cornell University) (2021)

Liang Liang Jue Hou Hajime Uno Kelly Cho Yanyuan Ma

Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-$n$ consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration.

Time point

Event data

10.48550/arxiv.2110.09612

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