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Insight Into the Separation-of-Variable Methods for the Closed-Form Solutions of Free Vibration of Rectangular Thin Plates

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 329-355页

作者： Yufeng xing Ye Yuan Gen Li Institute of Solid Mechanics Beihang University(BUAA)Beijing100191China

The separation-of-variable(SOV)methods,such as the improved SOV method,the variational SOV method,and the extended SOV method,have been proposed by the present authors and coworkers to obtain the closed-form analytical solutions for free vibration and eigenbuckling of rectangular plates and circular cylindrical *** taking the free vibration of rectangular thin plates as an example,this work presents the theoretical framework of the SOV methods in an instructive way,and the bisection–based solution procedures for a group of nonlinear eigenvalue ***,the explicit equations of nodal lines of the SOV methods are presented,and the relations of nodal line patterns and frequency orders are *** is concluded that the highly accurate SOV methods have the same accuracy for all frequencies,the mode shapes about repeated frequencies can also be precisely captured,and the SOV methods do not have the problem of missing roots as well.

关键词： Separation-of-variable method Rayleigh quotient nodal line eigenvalue equation bisection method

Loss Aware Feature Attention Mechanism for Class and Feature Imbalance Issue

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Computers, Materials & Continua 2025年第1期82卷 751-775页

作者： Yuewei Wu Ruiling Fu Tongtong xing Fulian Yin College of Information and Communication Engineering Communication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China

In the Internet era,recommendation systems play a crucial role in helping users find relevant information from large *** imbalance is known to severely affect data quality,and therefore reduce the performance of recommendation *** to the imbalance,machine learning algorithms tend to classify inputs into the positive(majority)class every time to achieve high prediction *** can be categorized such as by features and classes,but most studies consider only class *** this paper,we propose a recommendation system that can integrate multiple networks to adapt to a large number of imbalanced features and can deal with highly skewed and imbalanced datasets through a loss *** propose a loss aware feature attention mechanism(LAFAM)to solve the issue of feature *** network incorporates an attention mechanism and uses multiple sub-networks to classify and learn *** better results,the network can learn the weights of sub-networks and assign higher weights to important *** propose suppression loss to address class imbalance,which favors negative loss by penalizing positive loss,and pays more attention to sample points near the decision *** on two large-scale datasets verify that the performance of the proposed system is greatly improved compared to baseline methods.

关键词： Imbalanced data deep learning e-commerce recommendation loss function big data analysis

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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Exploring the potential of extrachromosomal DNA as a novel oncogenic driver

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Science China Life Sciences 2025年第1期68卷 144-157页

作者： Huanbo Zhu Longtao Huangfu Junbing Chen Jiafu Ji Xiaofang xing Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing) Division of Gastrointestinal Cancer Translational Research LaboratoryPeking University Cancer Hospital & Institute Department of Gastrointestinal Cancer Center Peking University Cancer Hospital & Institute Department of Liver Surgery & Transplantation Liver Cancer InstituteZhongshan HospitalFudan University Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers Beijing Key Laboratory of Carcinogenesis and Translational ResearchDivision of Gastrointestinal Cancer Translational Research LaboratoryPeking University Cancer Hospital & Institute

Extrachromosomal DNA(ecDNA) is a form of circular DNA mostly found in tumor cells. Unlike the typical chromosomal DNA, ecDNA is circular, self-replicating, and carries complete or partial gene fragments. Although ecDNA occurrence remains a rare event in cancer, recent studies have shown that oncogene amplification on ecDNA is widespread throughout many types of cancer, implying that ecDNA plays a central role in accelerating tumor evolution. ecDNA has also been associated with increased tumor mutation burden, chromosomal instability, and even tumor microenvironment remodeling. ecDNA may be crucial in influencing tumor heterogeneity, drug sensitivity,oncogenic senescence, and tumor immunogenicity, leading to a worsening prognosis for tumor patients. In this way, several clinical trials have been conducted to investigate the importance of ecDNA in clinical treatment. In this review, we summarize the biogenesis, characteristics, and current research methods of ecDNA, discuss the vital role of ecDNA-caused tumor heterogeneity in cancers, and highlight the potential role of ecDNA in cancer therapy.

关键词： extrachromosomal DNA cancer therapy tumor heterogeneity drug resistance tumor evolution

Boosting Oxygen Evolution Reaction Performance on NiFe‑Based Catalysts Through d‑Orbital Hybridization

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Nano-Micro Letters 2025年第1期17卷 281-292页

作者： xing Wang Wei Pi Sheng Hu Haifeng Bao Na Yao Wei Luo State Key Laboratory of New Textile Materials and Advanced Processing Technology Key Laboratory of New Textile Materials and Applications of Hubei ProvinceSchool of Materials Science and EngineeringWuhan Textile UniversityWuhan 430200People’s Republic of China College of Chemistry and Molecular Sciences Wuhan UniversityWuhan 430072People’s Republic of China

Anion-exchange membrane water electrolyzers(AEMWEs)for green hydrogen production have received intensive attention due to their feasibility of using earth-abundant NiFe-based *** introducing a third metal into NiFe-based catalysts to construct asymmetrical M-NiFe units,the d-orbital and electronic structures can be adjusted,which is an important strategy to achieve sufficient oxygen evolution reaction(OER)performance in ***,the ternary NiFeM(M:La,Mo)catalysts featured with distinct M-NiFe units and varying d-orbitals are reported in this *** and theoretical calculation results reveal that the doping of La leads to optimized hybridization between d orbital in NiFeM and 2p in oxygen,resulting in enhanced adsorption strength of oxygen intermediates,and reduced rate-determining step energy barrier,which is responsible for the enhanced OER *** critically,the obtained NiFeLa catalyst only requires 1.58 V to reach 1 A cm^(−2) in an anion exchange membrane electrolyzer and demonstrates excellent long-term stability of up to 600 h.

关键词： NiFe-based catalysts d-orbital coupling Oxygen evolution reaction Anion exchange membrane electrolyzer

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Optimal investment, retirement, and life insurance with consumption ratcheting and time-inhomogeneous utility

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Science China Mathematics 2025年

作者： Jie xing Jingtang Ma School of Big Data Application and Economics Guizhou University of Finance and Economics School of Mathematics Southwestern University of Finance and Economics

In this paper, we study a portfolio selection problem of an investor with a retirement option, who has possibility to buy life insurance and does not tolerate any decline in consumption. The agent's optimization problem can be viewed as a mixed singular control and optimal stopping problem with time-inhomogeneous utility functions. The closed-form optimal solution is not available in general. We use the dual control method to convert the original problem into two classes of optimal stopping problems in finite and infinite *** show that the optimal consumption strategy and the best retirement time depend on the free-boundary functions which satisfy Fredholm and Volterra integral equations. We derive the closed-form formulas for these two free boundaries for some special cases and develop numerical methods to solve the integral equations of the free boundaries for general cases.

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Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(Information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG xing GUO Huaqing LI Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

Enantioselective reductive conjugate alkenylation of α,β-enones with keto alkenyl acetates by nickel catalysis

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Science China Chemistry 2025年第1期68卷 157-162页

作者： Rong-De He Yun-Lei Luo Qiu-Quan Pan Qi-Wei Yao xing-Zhong Shu State Key Laboratory of Applied Organic Chemistry College of Chemistry and Chemical Engineering Lanzhou University

Reactions involving alkenyl acetates offer cost-effective and environmentally friendly routes for alkene synthesis, yet their asymmetric variant remains elusive. Concurrently, asymmetric conjugate alkenylation predominantly centered on nucleophilic addition using alkenyl–M. This manuscript presents an asymmetric reductive conjugate alkenylation reaction involving alkenyl acetates. The method facilitates the enantioselective addition of keto alkenyl groups to α,β-enones, resulting in the formation of unsaturated diketones—a class of useful structural motifs that are challenging to access otherwise. The use of electron-rich Pyroxy ligand is essential for achieving both high reaction efficiency and enantioselectivity.

关键词： nickel alkenylation asymmetric cross-coupling alkenes

Stiffness-tunable biomaterials provide a good extracellular matrix environment for axon growth and regeneration

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Neural Regeneration Research 2025年第5期20卷 1364-1376页

作者： Ronglin Han Lanxin Luo Caiyan Wei Yaru Qiao Jiming Xie Xianchao Pan Juan xing Department of Pathophysiology School of Basic Medical SciencesSouthwest Medical UniversityLuzhouSichuan ProvinceChina Department of Medicinal Chemistry School of PharmacySouthwest Medical UniversityLuzhouSichuan ProvinceChina

Neuronal growth, extension, branching, and formation of neural networks are markedly influenced by the extracellular matrix—a complex network composed of proteins and carbohydrates secreted by cells. In addition to providing physical support for cells, the extracellular matrix also conveys critical mechanical stiffness cues. During the development of the nervous system, extracellular matrix stiffness plays a central role in guiding neuronal growth, particularly in the context of axonal extension, which is crucial for the formation of neural networks. In neural tissue engineering, manipulation of biomaterial stiffness is a promising strategy to provide a permissive environment for the repair and regeneration of injured nervous tissue. Recent research has fine-tuned synthetic biomaterials to fabricate scaffolds that closely replicate the stiffness profiles observed in the nervous system. In this review, we highlight the molecular mechanisms by which extracellular matrix stiffness regulates axonal growth and regeneration. We highlight the progress made in the development of stiffness-tunable biomaterials to emulate in vivo extracellular matrix environments, with an emphasis on their application in neural repair and regeneration, along with a discussion of the current limitations and future prospects. The exploration and optimization of the stiffness-tunable biomaterials has the potential to markedly advance the development of neural tissue engineering.

关键词： alginate axon growth biomaterials extracellular matrix neural repair neurons neuroregeneration polyacrylamide polydimethylsiloxane stiffness

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