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Constraints on what controls millennial-scale basin-wide denudation across mountain belts, Asia

science China Earth sciences 2025年第1期68卷 74-93页

作者： Ye YANG Steven A.BINNIE Sheng XU Cong-Qiang LIU Tibor J.DUNAI institute of Surface-Earth System science School of Earth System Science Tianjin University institute of Geology and Mineralogy University of Cologne

The terrestrial cosmogenic nuclide dataset consisting of 123310Be measurements of fluvial sediment samples across the Asian mountain belts is used to constrain the influence of climate,tectonics and topography on Earth-surface denudation processes.10Be-derived basin-wide denudation rates were recalculated using a Monte Carlo simulation approach that relied on pixel-by-pixel production *** rates derived span four orders of magnitude,from 7.4±0.7 mm kyr-1（1σ;Central Tibetan Plateau） to 9,646744+777mm kyr-1（1σ;Southern Tibetan Plateau）,with a median of 186 mm *** our results with the traditional basin-wide production rate scaling model using a centroid coordinate and mean elevation,suggests that 96.6%of production rates will be underestimated using the latter method,especially for basins with a large *** bias between both methods reaches as large as 40%,caused by basin hypsometry and the nonlinear scaling of production rate with elevation and *** of the correlations between uniformly recalculated denudation rates and tectonics,climate,topography and rock lithology will facilitate the extrapolation of denudation rates in unknown basins using available *** the perspective of local scales,precipitation coupled with neotectonics-driven landslides is likely the most influential factor in subtropical Taiwan Island,while topography primarily constrains surface denudation rates in the eastern Tibetan Plateau within a narrow range of *** highland regions with glacier coverage,such as the Pamir,central and southern Tibetan Plateau high denudation rates are affected by glaciation,whereas lower precipitation and high erosion base level may limit river incision and evolving relief in the Central Tibetan *** contrast to local-scale findings highlighting a dominant factor constraining denudation rate,our large-scale quantitative analyses across the Asian mountain belts finds that （1） topographic metrics including

关键词： 10Be-derived denudation rate Climate change Tectonic uplift Topographic gradient Asian mountains

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Sex differences in aging and injured brain

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Neural Regeneration Research 2025年第10期20卷 2901-2902页

作者： Jordan N.Williamson Yuan Yang University of Illinois Urbana-Champaign Grainger College of EngineeringDepartment of BioengineeringUrbanaILUSA Carle Foundation Hospital Stephenson Family Clinical Research InstituteClinical Imaging Research CenterUrbanaILUSA University of Illinois Urbana-Champaign Beckman Institute for Advanced Science and TechnologyUrbanaILUSA Northwestern University Department of Physical Therapy and Human Movement SciencesChicagoILUSA

Background:The prevalence,age of onset,and symptomatology of traumatic brain injury,stroke,and neurodegenerative diseases(such as Alzheimer’s disease(AD),Parkinson’s disease,amyotrophic lateral sclerosis,and Huntington’s disease)differ substantially between males and *** higher prevalence of these brain disorders has been attributed to females having a greater longevity compared with *** one of the greatest risk factors of acquired brain injury(such as stroke,traumatic brain injury caused by fall)and neurodegenerative disease is age.

关键词： Huntington prevalence traumatic

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Metal-organic frameworks with dinuclear metal centers for synergistically boosting CO2 photoreduction

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science China Chemistry 2025年第1期68卷 201-208页

作者： Hui-Feng Wang Wen-Jie Shi Yu-Xin Yang Bao-Xia Dong Tong-Bu Lu Di-Chang Zhong School of Chemistry and Chemical Engineering Yangzhou University institute for New Energy Materials and Low Carbon Technologies School of Materials Science and Engineering Tianjin University of Technology

Dinuclear metal synergistic catalysis （DMSC） has been proved powerful in boosting CO2reduction,while this has not been achieved in heterogeneous catalysts with long-range order *** this study,we,by introduction of metal catalytic centers into an indium-based metal-organic framework （MOF）,obtained a series of isostructural In-MOFs,including three MOFs with mononuclear metal catalytic center （M-In）,three MOFs with dinuclear homometallic catalytic centers （MM-In）,and three MOFs with dinuclear heterometallic catalytic centers (MM’-In,M/M’=Co（II）,Ni（II）,Zn（II）).It was found that all these MOFs can serve as catalysts for photochemical CO2reduction to *** with M-In,the catalytic activities of MM-In were obviously enhanced,and that of MM’-In was further significantly *** example,the CO yield of Co Zn-In reaches a record value of 12,700μmol g-1h-1,which is 4.0,8.1,9.4,and 18.6-fold higher than that of Co Co-In,Zn Zn-In,Co-In,and Zn-In,***,the catalytic activities of Co Ni-In and Ni Zn-In also show different fold increase than those of corresponding dinuclear homometallic and mononuclear metallic *** results of control experiments and theoretical calculation reveal that the higher catalytic activities of MM-In over M-In can be ascribed to the DMSC effect,and the higher catalytic activity of MM’-In over MM-In can be ascribed to the strengthened DMSC *** work reveals the relationship between DMSC effect and bimetal centers in CO2reduction,which provides new insights into the DMSC and paves a new way to design highly active catalysts for photochemical CO2reduction.

关键词： metal-organic frameworks dinuclear metal centers synergistic catalysis CO2 reduction photocatalysis

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movement analysis in the diagnosis and management of Parkinson’s disease

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Neural Regeneration Research 2025年第2期20卷 485-486页

作者： Johannes Burtscher Nicolas Bourdillon Jules MJanssen Daalen Aurélien Patoz Julien FBally Martin Kopp Davide Malatesta Bastiaan RBloem institute of Sport sciences University of LausanneLausanneSwitzerland Radboud University Medical Center Department of NeurologyDonders Institute for BrainCognition and BehaviorCenter of Expertise for Parkinson and Movement DisordersNijmegenThe Netherlands Radboud University Medical Center Department of Medical BioSciencesNijmegenThe Netherlands Research and Development Department Volodalen Swiss Sport LabAigleSwitzerland Service of Neurology Department of Clinical NeurosciencesLausanne University Hospital(CHUV)and University of Lausanne(UNIL)LausanneSwitzerland Department of Sport science University of InnsbruckInnsbruckAustria

Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-motor symptoms also constitute a major part of the overall ***,this disease cannot be diagnosed reliably until a large part of the vulnerable dopaminergic neurons has been irretrievably lost,and the disease progresses *** biological criteria for PD have been proposed recently and might eventually improve early diagnosis,but they require further validation,and their use will initially be restricted to a research environment(Darweesh et al.,2024).

关键词： diagnosis Clinical eventually

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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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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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Numerical Simulation of Radiation Transport to Improve Microalgae Cultivation in an Air-Lift Photobioreactor

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Journal of Thermal science 2025年第1期34卷 62-76页

作者： ZHANG Xianbin LI Mingjia HUNG Tzuchen ZHANG Zixun Key Laboratory of Thermo-Fluid science and Engineering of Ministry of Education School of Energy and Power EngineeringXi'an Jiaotong University School of Mechanical Engineering Beijing Institute of Technology Department of Mechanical Engineering Taipei University of Technology

A comprehensive numerical model is developed to simulate the growth of microalgae under light/dark cycling *** purpose of this study is to predict the growth rate of Chlorella vulgaris cultivated in photobioreactors(PBRs) in order to improve the light conditions for microalgae and enhance the photosynthetic *** fluid dynamics(CFD) is used to simulate its internal hydrodynamic *** Lagrangian method is employed to track the movement of microalgae *** radiative transfer equation(RTE)is used to obtain light intensity *** combination of light radiation field and microalgae cell motions is used to construct the light history and they are integrated into the model of the photosynthetic units(PSU) to calculate the microalgae growth *** numerical results demonstrate that enhanced light/dark cycling frequency with ordered mixing can promote efficient microalgae *** effect of the vortex flow field generated by the baffles in an air-lift PBR is analyzed for increasing microalgae growth *** using the 1:1 baffle spacing,the biomass production of microalgae is increased by 41.8% compared to the original PBR.

关键词： light distribution particle tracking photobioreactor structure optimization

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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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Computer Modeling in Engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of Computer science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu institute of science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

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Computers, Materials & Continua 2025年第1期82卷 307-325页

作者： Ao Shen Zhiquan Lai Dongsheng Li Xiaoyu Hu National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha410073China Strategic Assessments and Consultation institute Academy of Military ScienceBeijing100091China

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning *** this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader ***-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational *** these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA *** these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains *** study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM *** investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do *** insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized ***,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller *** study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM

关键词： Large-scale Language Model Parameter-Efficient Fine-Tuning parameter quantization key variable trainable parameters experimental analysis

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer science Chongqing University State Key Laboratory of Computer science Institute of Software Chinese Academy of Sciences University of Chinese Academy of sciences School of Software Technology Zhejiang University School of Computer science and Engineering Nanyang Technological University

Partial-label learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

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