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Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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Computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul Karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and big data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research center for big data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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Cortical hem signaling center: functions, development, and potential implications for evolution and brain disorders

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Neural Regeneration Research 2025年第4期20卷 1079-1080页

作者： Victor V.Chizhikov Igor Y.Iskusnykh Department of Anatomy and Neurobiology University of Tennessee Health Science CenterMemphisTNUSA

Development of the telencephalon relies upon several signaling centers-localized cellular populations that supply secreted factors to pattern the cortical *** such signaling center is the cortical hem,which arises during embryonic development at the telencephalic dorsal midline,adjacent to the choroid plexus and hippocampal primordium(Figure 1A).While the cortical hem has also been described in reptiles and birds,most of our knowledge about the developmental roles of the cortical hem is derived from the analysis in *** cortical hem produces several types of secreted molecules,including wingless-related integration site(Wnt)and bone morphogenetic(Bmp)*** cortical hem is particularly important for the development of the hippocampus,which is involved in learning and memory,and the neocortex,which is the most complex brain region that mediates multiple types of behavior and higher cognitive functions(Mangale et al.,2008;Dal-Valle-Anton and Borrell,2022).

关键词： epithelium Figure center

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A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of big data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

Decreased levels of phosphorylated synuclein in plasma are correlated with poststroke cognitive impairment

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Neural Regeneration Research 2025年第9期20卷 2598-2610页

作者： Yi Wang Yuning Li Yakun Gu Wei Ma Yuying Guan Mengyuan Guo Qianqian Shao Xunming Ji Jia Liu Department of Clinical Laboratory Beijing Bo’ai HospitalChina Rehabilitation Research CenterCapital Medical UniversityBeijingChina Beijing Institute of Brain Disorders Laboratory of Brain DisordersMinistry of Science and TechnologyCollaborative Innovation Center for Brain DisordersBeijing Advanced Innovation Center for Big Data-based Precision MedicineCapital Medical UniversityBeijingChina Department of Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijingChina

Poststro ke cognitive impairment is a major secondary effect of ischemic stroke in many patients;however,few options are available for the early diagnosis and treatment of this *** aims of this study were to(1)determine the specific relationship between hypoxic andα-synuclein during the occur of poststroke cognitive impairment and(2)assess whether the serum phosphorylatedα-synuclein level can be used as a biomarker for poststro ke cognitive *** found that the phosphorylatedα-synuclein level was significantly increased and showed pathological aggregation around the cerebral infa rct area in a mouse model of ischemic *** addition,neuronalα-synuclein phosphorylation and aggregation were observed in the brain tissue of mice subjected to chronic hypoxia,suggesting that hypoxia is the underlying cause ofα-synuclein-mediated pathology in the brains of mice with ischemic *** phosphorylatedα-synuclein levels in patients with ischemic stroke were significantly lower than those in healt hy subjects,and were positively correlated with cognition levels in patients with ischemic ***,a decrease in serum high-density lipoprotein levels in stroke patie nts was significantly correlated with a decrease in phosphorylatedα-synuclein *** ischemic stroke mice did not show significant cognitive impairment or disrupted lipid metabolism 14 days after injury,some of them exhibited decreased cognitive function and reduced phosphorylatedα-synuclein *** together,our results suggest that serum phosphorylatedα-synuclein is a potential biomarker for poststroke cognitive impairment.

关键词： biomarker high-density lipoprotein ischemic stroke phosphorylatedα-synuclein poststroke cognitive impairment

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Treadmill exercise in combination with acousto-optic and olfactory stimulation improves cognitive function in APP/PS1 mice through the brain-derived neurotrophic factor-and Cygb-associated signaling pathways

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Neural Regeneration Research 2025年第9期20卷 2706-2726页

作者： Biao Xiao Chaoyang Chu Zhicheng Lin Tianyuan Fang Yuyu Zhou Chuxia Zhang Jianghui Shan Shiyu Chen Liping Li Department of Physiology and Pharmacology Health Science CenterNingbo UniversityNingboZhejiang ProvinceChina Ningbo Key Laboratory of Behavioral Neuroscience Health Science CenterNingbo UniversityNingboZhejiang ProvinceChina Key Laboratory of Addiction Research of Zhejiang Province NingboZhejiang ProvinceChina

A reduction in adult neurogenesis is associated with behavioral abnormalities in patients with Alzheimer's ***,enhancing adult neurogenesis represents a promising therapeutic approach for mitigating disease symptoms and ***,nonpharmacological interventions aimed at inducing adult neurogenesis are currently *** individual non-pharmacological interventions,such as aerobic exercise,acousto-optic stimulation,and olfactory stimulation,have shown limited capacity to improve neurogenesis and cognitive function in patients with Alzheimer's disease,the therapeutic effect of a strategy that combines these interventions has not been fully *** this study,we observed an age-dependent decrease in adult neurogenesis and a concurrent increase in amyloid-beta accumulation in the hippocampus of amyloid precursor protein/presenilin 1 mice aged 2-8 *** deposition became evident at 4 months,while neurogenesis declined by 6 months,further deteriorating as the disease ***,following a 4-week multifactor stimulation protocol,which encompassed treadmill running(46 min/d,10 m/min,6 days per week),40 Hz acousto-optic stimulation(1 hour/day,6 days/week),and olfactory stimulation(1 hour/day,6 days/week),we found a significant increase in the number of newborn cells(5'-bromo-2'-deoxyuridine-positive cells),immature neurons(doublecortin-positive cells),newborn immature neurons(5'-bromo-2'-deoxyuridine-positive/doublecortin-positive cells),and newborn astrocytes(5'-bromo-2'-deoxyuridine-positive/glial fibrillary acidic protein-positive cells).Additionally,the amyloid-beta load in the hippocampus *** findings suggest that multifactor stimulation can enhance adult hippocampal neurogenesis and mitigate amyloid-beta neuropathology in amyloid precursor protein/presenilin 1 ***,cognitive abilities were improved,and depressive symptoms were alleviated in amyloid precursor protein/presenilin 1 mice

关键词： acousto-optic stimulation adult neurogenesis Alzheimer's disease amyloid precursor protein/presenilin 1 mice amyloid-beta deposition brain cell apoptosis cognitive impairment depression-like behavior involuntary treadmill exercise olfactory stimulation serum metabolites

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database Resources in big data center:Submission, Archiving, and Integration of big data in Plant science

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Molecular Plant 2019年第3期12卷 279-281页

作者： Shuhui Song Zhang Zhang big data center Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Genome sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China University of Chinese Academy of sciences Beijing 100049China

With the rapid advancement of sequencing technologies and the growing volume of omics data in plants, there is much anticipation in digging out the treasure from such big data and accordingly refining the current agricultural practice to be applied in the near future. Toward this end, database resources that deliver web services for plant omics data submission, archiving, and integration are urgently needed. As a part of Beijing Institute of Genomics (big) of the Chinese Academy of sciences (CAS), big data center (http://***) provides open access to a suite of database resources (Table 1), with the aim of supporting plant research activities for domestic and international users in both academia and industry to translate big data into big discoveries (big data center Members, 2017;big data center Members, 2018;big data center Members, 2019). Here, we give a brief introduction of plant-related database resources in big data center and appeal to plant research com丒 munities to make full use of these resources for plant data submission, archiving, and integration.

关键词： database Resources big data center

An NIR-Ⅱ-emissive Ru(Ⅱ) metallacycle for efficient and safe immunotherapy

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science China Chemistry 2025年第1期68卷 5-7页

作者： Chonglu Li Jianbo Zhan Biao Dong Institute of health Inspection and Testing Hubei Provincial Center for Disease Control and Prevention State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University

Immunotherapy, as a groundbreaking cancer treatment, has gained considerable attention for its utilization of the body’s immune system to combat tumors [1]. This therapy induces tumor cell apoptosis, triggering a sustained anti-tumor immune response and effectively preventing tumor recurrence and metastasis [2]. The fundamental principle lies in inducing tumor cell death through specific treatment modalities, leading to the release of a cascade of signaling molecules and antigens, thereby mobilizing the immune system to actively identify and attack tumor cells [3].

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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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