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ImSpiRE: image feature-aided spatial resolution enhancement method

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Science China Life Sciences 2025年第1期68卷 272-283页

作者： Yuwei Hua Yizhi Zhang Zhenming Guo Shan Bian Yong Zhang State key laboratory of Cardiovascular Diseases and Medical Innovation Center Institute for Regenerative MedicineDepartment of NeurosurgeryShanghai East HospitalShanghai Key Laboratory of Signaling and Disease ResearchFrontier Science Center for Stem Cell ResearchSchool of Life Sciences and TechnologyTongji University

The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level, limiting their applications in biological discoveries. Here, we introduce ImSpiRE, an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome. Taking the information stored in histological images, ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution, together with extending the gene expression profiles into unmeasured regions. Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains, signaling communication patterns, and spatiotemporal characterization.

关键词： spatial transcriptome histological images spatial resolution enhancement

Domain generalization with semi-supervised learning for people-centric activity recognition

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Science China(information Sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & technology Fudan University shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Unraveling post-growth mechanisms of monolayer CsPbBr3 nanocubes:Laser-enhanced transformations and cathodoluminescence-electron microscopy correlations

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Journal of Energy Chemistry 2025年第1期100卷 146-156页

作者： Mingxing Li Xiaoge Wang Xiaofan Cao Zhiqun He Chunjun Liang Mingxing Chen Jing Ju Fangtian You key laboratory of Luminescence and Optical information Ministry of Education Institute of Optoelectronic Technology Beijing Jiaotong University Beijing National laboratory for Molecular Sciences (BNLMS) College of Chemistry and Molecular Engineering Peking University

Lead-halide perovskite nanoparticles（LHP NPs） are highly promising materials for next-generation displays and solid-state lighting due to their exceptional optical properties. However, their inherent instability presents a significant challenge. Recent advances have demonstrated that optoelectronic devices based on monolayer nanoparticle films exhibit both high luminescence efficiency and long-term *** research demonstrates that mobility limitations and anisotropic alignments in CsPbBr3nanocube monolayer films are key to their stabilization, hindering spontaneous growth through face-to-face fusion and resulting in the formation of connecting necks in a diagonal direction. Introducing laser irradiation confirmed this by significantly accelerating nanocubes growth, increasing mobility, and enhancing local structural ordering, leading to larger and more regularly shaped nanosheets. Fourier transform infrared spectroscopy and energy dispersive spectroscopy line-scan analyses indicated that laser irradiation did not disrupt the ligand structure. Transmission electron microscopy and correlative cathodoluminescence electron microscopy revealed the effects of post-growth and heterogeneous structures, including enhanced luminescence and inhomogeneous intensity in the nanosheets. These findings deepen the understanding of the post-growth mechanism of monolayer nanoparticles and the structure-emission correlation and highlight the unique role of laser irradiation in directing the formation of well-defined and regular nanostructures.

关键词： Energy dispersive spectroscopy

Decentralized fault diagnosis of discrete-event systems with unreliable sensors using linear temporal logic

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Science China(information Sciences) 2025年第1期68卷 322-341页

作者： Weijie DONG Shaoyuan LI Xiang YIN key laboratory of System Control and information Processing Ministry of EducationDepartment of Automation Shanghai Jiao Tong University

In this paper,we investigate a decentralized diagnosis problem of a discrete-evnt system(DES) subject to unreliable sensors,where the sensor observations of local diagnosers may be non-deterministic as a result of possible *** studies on decentralized robust diagnosis can only deal with different types of sensor failures separately,e.g.,all sensors suffer from the same type of sensor failures such as intermittent sensor failures or permanent sensor ***,since sensors of different local diagnosers may face different external environments and have different internal characteristics,sensors corresponding to different local diagnosers may also have their own fault *** this paper,we propose a flexible framework of decentralized diagnosis for DES subject to unreliable sensors such that sensors of different local diagnosers are permitted to have different types of sensor *** this end,we extend the existing decentralized diagnosis framework to the case where there exist common sensors broadcasting their observations to all local *** apply linear temporal logic(LTL) to constrain infinite behaviors of private sensors of local diagnosers and common ***,a new notion of φ-codiagnosability is proposed as the necessary and sufficient condition for the existence of a decentralized diagnoser that works correctly under sensors,satisfying LTL-based sensor ***,we provide an effective approach to verify the φ-codiagnosability.

关键词： discrete event systems decentralized diagnosis codiagnosability automata

Poly(lactic acid)/Poly(butylene adipate-co-terephthalate)films with simultaneous high oxygen barrier and fast degradation properties

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Green Energy & Environment 2025年第1期10卷 1-10页

作者： Mengjing Yang Yuxi Mao Penghui Zhang Jie Li Zeming Tong Zhenguo Liu Yanhui Chen Institute of Flexible Electronics Northwestern Polytechnical UniversityXi'an710072China key laboratory of Flexible Electronics of Zhejiang Province Ningbo Institute of Northwestern Polytechnical University218 Qingyi RoadNingbo315103China School of Chemistry and Chemical Engineering Shaanxi Key Laboratory of Macromolecular Science and TechnologyKey Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi'an 710072China

Although poly(lactic acid)(PLA)is a good environmentally-friendly bio-degradable polymer which is used to substitute traditional petrochemical-based polymer packaging films,the barrier properties of PLA films are still insufficient for high-barrier packaging *** this study,oxygen scavenger hydroxyl-terminated polybutadiene(HTPB)and cobalt salt catalyst were incorporated into the PLA/poly(butylene adipate-co-terephthalate)(PLA/PBAT),followed by melting extrusion and three-layer co-extrusion blown film process to prepare the composite *** oxygen permeability coefficient of the composite film combined with 6 wt%oxygen scavenger and 0.4 wt%catalyst was decreased significantly from 377.00 cc·mil·m^(-2)·day^(-1)·0.1 MPa^(-1) to 0.98 cc·mil·m^(-2)·day^(-1)·0.1 MPa^(-1),showing a remarkable enhancement of 384.69 times compared with the PLA/PBAT composite ***,the degradation behavior of the composite film was also accelerated,exhibiting a mass loss of nearly 60%of the original mass after seven days of degradation in an alkaline environment,whereas PLA/PBAT composite film only showed a mass loss of 32%.This work has successfully prepared PLA/PBAT composite films with simultaneously improved oxygen barrier property and degradation behavior,which has great potential for high-demanding green chemistry packaging industries,including food,agricultural,and military packaging.

关键词： Barrier property Oxygen scavenging Blow molding Eco-packaging Degradable composite film

Unraveling the significance of cobalt on transformation kinetics,crystallography and impact toughness in high-strength steels

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 380-390页

作者： Yishuang Yu Jingxiao Zhao Xuelin Wang Hui Guo Zhenjia Xie Chengjia Shang Collaborative Innovation Center of Steel technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China State key laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing 100083China

This work reveals the significant effects of cobalt(Co)on the microstructure and impact toughness of as-quenched highstrength steels by experimental characterizations and thermo-kinetic *** results show that the Co-bearing steel exhibits finer blocks and a lower ductile-brittle transition temperature than the steel without ***,the Co-bearing steel reveals higher transformation rates at the intermediate stage with bainite volume fraction ranging from around 0.1 to *** improved impact toughness of the Co-bearing steel results from the higher dense block boundaries dominated by the V1/V2 variant ***,the addition of Co induces a larger transformation driving force and a lower bainite start temperature(BS),thereby contributing to the refinement of blocks and the increase of the V1/V2 variant *** findings would be instructive for the composition,microstructure design,and property optimization of high-strength steels.

关键词： high-strength steel cobalt transformation kinetics crystallography impact toughness

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Understanding the local structure and thermophysical behavior of Mg-La liquid alloys via machine learning potential

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 439-449页

作者： Jia Zhao Taixi Feng Guimin Lu National Engineering Research Center for Integrated Utilization of Salt Lake Resource East China University of Science and TechnologyShanghai 200237China Joint International laboratory for Potassium and Lithium Strategic Resources East China University of Science and TechnologyShanghai 200237China

The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.

关键词： magnesium-lanthanum liquid alloys local structure macroscopic properties thermodynamic behavior deep potential mo-lecular dynamic simulation

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Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance

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Science China(information Sciences) 2025年第1期68卷 292-305页

作者： Tao SHI Wei-Wei CHE School of Mechatronic Engineering and Automation Shanghai University State key laboratory of Synthetical Automation for Process Industries Northeastern University College of information Science and Engineering Northeastern University

This article proposes a distributed dynamic event-triggered data-driven iterative learning control(DET-DDILC)scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent systems(MASs). An improved dynamic linearization technique is utilized to convert the nonlinear MASs into an iterative linear data model. First,a peer-to-peer mapping function is introduced to map the constrained distributed system output homeomorphism to an unconstrained one. In addition, a DET mechanism based on a time-iteration-varying function is devised to conserve network communication resources. Based on the unconstrained transformation and the designed DET mechanism, the DET-DDILC algorithm is devised to ensure that the bipartite tracking performance of MASs can be within the preset range. Finally, the effectiveness and feasibility of the designed control scheme are demonstrated via a simulation case by a comparison.

关键词： bipartite tracking control prescribed performance event-triggered iterative learning control multiagent systems

Underground laboratories · Deep underground observation ·Scientific questions—Insights from observations of multi-physic fields in deep underground labs

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Science China Earth Sciences 2025年

作者： Huiqi REN Yun WANG Chang CHEN Guangyu FU Longqing QIU Lianghui GUO Chengliang XIE Yongsheng HE Heping SUN Jiwen TENG Institute of Defense Engineering Academy of Military Sciences State Kay laboratory of Geological Processes and Mineral Resources Key Laboratory of Intraplate Volcanoes and Earthquakes Ministry of Education China University of Geosciences (Beijing) shanghai Institute of Microsystem and information technology Chinese Academy of Sciences Innovation Academy for Precision Measurement Science and technology Chinese Academy of Sciences Institute of Geology and Geophysics Chinese Academy of Sciences

This paper presents a comprehensive review on recent development and research conducted in domestic and international underground laboratories. We first introduce the differences in three environments—surface, mountain tunnel cavities and underground coal mine tunnels—by examining cosmic ray background, ambient noises related to gravity and seismic measurement, and electromagnetic noises in magnetic and magnetotelluric measurements. We highlight potential misuse of the term Underground Lab or Deep Underground Lab when describing observations in different physical fields. We introduce unique features of underground coal mine tunnels in China, such as large spaces, ultra-quiet conditions, and ultra-clean environments. When comparing with mountain tunnel cavities and borehole observations, coal mine tunnel observations have superior long-term stability and high precision. Through observations and comparisons of multi-physic fields at surface and the deep underground, we find that the higher SNR seismic observations conducted in deep underground tunnels in coal mines are beneficial to improve velocity tomography of the solid earth. The gravity observation with a Superconducting Quantum Interference Device(SQUID) makes it possibly to capture slow earthquake, which has not been observed previously in the Chinese mainland. SQUID magnetic observations can detect fluctuations as weak as femto-Tesla(fT), enabling us to explore the attenuation of Schumann Resonance down to the solid Earth. This opens opportunities to investigate the connections between the Earth's magnetic field and the interactions within the human brain and heart. To improve the precision of quantum measurement,we should consider the possible effects of weak magnetic disturbances in deep underground environments. Finally, we discuss the importance of deep underground laboratories, observing facilities and techniques deployed in these laboratories, and their possible connection with respect to “deep space” an

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