# api_taskdetail

author: Liqu Wen;Wei Zhang;Xiaohong Jin;Ziqiang Liu
date: 2019
original_link: https://ieeexplore.ieee.org/document/8950768/
site: ieeeXplore
title: Detection of Research Front Topic Based on Data of NSF Artificial Intelligence Project
created_at: 2025-09-30 11:57:31.428912
summary:
  Artificial intelligence in the United States has been the world's leading, and the detection of research front topic based on the data of NSF artificial intelligent projects is helpful for us to obtain quantitative information for prioritized technologies that could be used for technology management and decision making for research funding and technology investment in the field of artificial intelligent. PLDA model was used to identify topic contained in the NSF project data in the field of artificial intelligence from 2013 to 2018. The funding time, funding amount and centrality indexes of the topic were taken as three indexes to judge research front topics. Threshold value was set according to Pareto Law. And 17 research front topics in the artificial intelligence field were determined. The results showed that 13 aspects, including intelligent system and intelligent community, quantum technology, molecular programming, intelligent simulation, underwater communication and positioning, big data analysis and so on, are the main research front contents concerned in the field of artificial intelligence in the United State.

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date: 29 September, 2025
original_link: https://arxiv.org/abs/2509.25189
pdf_url: https://arxiv.org/pdf/2509.25189
site: arxiv
title: InfoAgent: Advancing Autonomous Information-Seeking Agents
created_at: 2025-09-30 11:57:31.430566
author:
  Gongrui Zhang;Jialiang Zhu;Ruiqi Yang;Kai Qiu;Miaosen Zhang;Zhirong Wu;Qi Dai;Bei Liu;Chong Luo;Zhengyuan Yang;Linjie Li;Lijuan Wang;Weizhu Chen;Yuan Zhang;Xin Li;Zhaoyi Liu;Xin Geng;Baining Guo

summary:
  Abstract:Building Large Language Model agents that expand their capabilities by interacting with external tools represents a new frontier in AI research and applications. In this paper, we introduce InfoAgent, a deep research agent powered by an innovative data synthesis pipeline and orchestrated web search tools. To construct challenging, hard-to-find queries,we build entity trees and apply sub-tree sampling with entity fuzzification to systematically increase question difficulty. Unlike prior work that relies heavily on commercial search tools, we develop a dedicated self-hosted search infrastructure, enhancing transparency of agent environments and facilitating further advancement of agent capacity. We evaluate the effectiveness of our data pipeline by measuring the average number of tool calls required to correctly answer a question, and also show that our agent yields better performance when equipped with our tools. Our \mbox{InfoAgent} is post-trained from Qwen3-14B using a two-stage recipe: cold-start supervised finetuning to instill long-horizon search behaviors, followed by reinforcement learning which significantly improves reasoning-driven tool use. With our methods, InfoAgent achieves 15.3\% accuracy on BrowseComp, 29.2\% on BrowseComp-ZH, and 40.4\% on Xbench-DS, outperforming prior open-source deep research agents such as WebSailor-72B and DeepDive-32B.

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author: 黄彩梅
keywords: 智库;人工智能政策;以人为本”人工智能研究所
source: 智库理论与实践
site: 维普
title: “以人为本”人工智能研究所对美国人工智能政策的影响
created_at: 2025-09-30 11:57:31.433521
original_link:
  https://www.cqvip.com/doc/journal/3509787429?sign=71d7f63d659f34d5053f482c71d7541b3438bb8a9a64e47ef586d317b94309b2&expireTime=1790740628884&resourceId=3509787429

summary:
  [目的/意义]智库是影响美国人工智能政策的一股重要力量。研究“以人为本”人工智能研究所对美国人工智能政策的影响,可以更好地洞悉智库在美国政治制度运作中的作用。[方法/过程]本文基于政策过程理论考察“以人为本”人工智能研究所对美国人工智能政策的具体影响,基于知识运用理论和社会分层理论考察“以人为本”人工智能研究所实现政策影响力的方式。前者注重考察其实现政策影响力的内部能力,后者则注重考察考察其实现政策影响力的外部能力。[结果/结论]“以人为本”人工智能研究所对美国人工智能政策的影响主要体现为推动美国人工智能研究资源建设。从政策过程视角来看,“以人为本”人工智能研究所参与了政策问题、政策议程和政策制定三个阶段,并产生了以“线型影响为形式、政策合法化为结果”的影响。从知识运用视角来看,“以人为本”人工智能研究所的影响根植于其政策研究和政策叙事中。从社会网络视角来看,“以人为本”人工智能研究通过构建和运用核心网路、中心网路和边缘网络向受众投递政策建议。

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author: 肖宇昕;张雨桐;魏义航;张昊楠;曹苇杭
date: 2025-09-29
keywords: 人工智能;;无托槽隐形矫治;;正畸;;诊断;;预测;
source: 中国实用口腔科杂志
site: 知网
title: 人工智能在无托槽隐形矫治中的应用研究进展
created_at: 2025-09-30 12:13:21.953494
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzTFvarT73MENhCY5PwtS1ImSnwzV7quCNbXnM3zaQq98WjsD8pO5FH2jd472WoVaHRRs_6_122qX9fb_5IFOXB52JDz1BlWnUU1sQO8NMIMcwXY96Sm89e9nVFYY4dA8TtO76OSTu_QNIx_WI0Za_9Jyr0i5fiu9YOqWV-55P3IRQ==&uniplatform=NZKPT&language=CHS

summary:
  无托槽隐形矫治技术是一种利用透明可摘矫治器进行非固定式矫正的现代正畸方法。人工智能数字化技术的快速发展为无托槽隐形矫治技术注入了新的发展动力，在诊断、方案设计、远程监控及患者沟通方面展现出巨大潜力。文章就人工智能在无托槽隐形矫治技术中的应用优势及存在的局限与挑战做一综述，并展望其未来发展方向。

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author: 叶梦梦;周陶胡;葛艳明
date: 2025年7期
keywords: 前列腺癌;人工智能;磁共振成像;影像分割;个性化治疗
quote: 0
source: 《磁共振成像》
site: 万方
title: 前列腺癌的人工智能研究进展
created_at: 2025-09-30 12:13:21.960422
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmNnemN4MjAyNTA3MDMxGgh5Y3I2eGEyYQ%3D%3D

summary:
  全球男性所患恶性肿瘤中,前列腺癌(prostate cancer,PCa)发病率排第二,其精准诊断与治疗决策方面亟需更精准的辅助工具.人工智能(artificial intelligence,AI)技术的兴起,为PCa的早期诊断和精准治疗带来了前所未有的机遇.本文系统综述了AI在PCa三个核心领域中的现状:(1)诊断与预后评估——回顾了当下传统PCa诊断手段的应用情形,并重点介绍AI在多模态成像技术的应用进展;(2)分子机制研究——探讨了AI在基因组学、蛋白组学等高通量组学数据中的应用模式,包括生物标志物筛选与药物疗效预测,揭示疾病发生发展的关键分子机制;(3)治疗决策优化——阐述了AI在手术规划与术中导航、靶向治疗方案个性化设计及术后动态监测中的创新实践,凸显了AI在提升疗效、减少并发症风险上的潜在价值.本文介绍了临床级的PCa专用AI系统,分析其在提高诊疗效率和精度方面的优势.针对AI在PCa应用中面临的数据来源单一、模型泛化能力不足、"黑箱"特性以及多模态数据标准化缺失等挑战,未来研究应聚焦于构建跨中心、多模态标准化数据库,并引入联邦学习等隐私计算技术应用;开发可解释性AI框架,以增强临床信任度;持续优化算法性能,提高模型的实用性与可靠性.本综述旨在总结AI在PCa领域的最新应用进展和挑战,为未来的研究方向提供指导,以助力AI技术与PCa临床研究与实践的深度融合.

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author: 封帅;薛世锟
date: 2025-09-25 15:59
keywords: 开源人工智能;;DeepSeek;;技术平权;;国际政治;;全球南方;
source: 东南亚研究
site: 知网
title: 开源人工智能与国际政治变革
created_at: 2025-09-30 12:13:21.962069
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzS-AcGHH1A797eH-qJ_x2Uv5F1L3FJh-Hixyxj09XZJ_AgZE5aHGceGyBxZUWNkW24jeGpeS8CD-Sp0EsQu3KHAFoJOy11hNc2RbMRsEazqxVePeOSTCCR9h0wuGGwQeLc0i7jYLx6luX88SBr3K3jcReFnwrxx8bumjjrIUtby3g==&uniplatform=NZKPT&language=CHS

summary:
  人工智能技术的发展正在出现微妙的方向调整,以DeepSeek为代表的开源人工智能逐渐走近技术发展舞台的中央。开源人工智能不仅重塑这场技术革命的底层逻辑,而且开启了全球技术平权进程。与所有前沿技术的发展轨迹一样,从封闭走向开放,从垄断走向共享,实现普惠目标一直是人工智能技术发展的内在诉求。开源生态的崛起是打破技术和成本壁垒,走向更平等世界的必由之路。开源人工智能一定程度上打开了黑箱,推动人工智能技术逐渐向社会公共产品转化,使人类社会看到了弥合“数字鸿沟”的微弱曙光。但开源人工智能的普及也将不可避免地对既有秩序体系、治理模式乃至人机关系构成严重冲击,带来极其复杂的社会影响。在这样的时代背景下,只有充分领悟人类命运共同体理念,才能在开源人工智能的发展进程中找到国际政治变革和发展的合理路径。

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author: 孙宏
date: 2025年8期
keywords: 开放;临床实验室;人工智能;前沿技术
quote: 0
source: 《科技风》
site: 万方
title: 基于人工智能前沿技术的开放临床医学教学实验室管理模式探索
created_at: 2025-09-30 12:13:21.963796
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDGtqZjIwMjUwODA1NBoIcDk0cHJyNjY%3D

summary:
  随着医学教育的改革,临床实践技能培养已经成为医学教育改革的重要内容,实践技能培养是临床医学生培养动手能力的重要手段,是打好基础走向临床的第一步.因此,开放临床教学实验室给学生提供自主实践训练的平台,是加强实践技能培养的重要举措.在开放临床教学实验室的探索过程中,实验室管理方面的问题不容小觑.本文旨在探索通过人工智能前沿技术,力求化解开放实验室面临的管理问题,进而构建智能化、数字化的开放实验室平台,助力医学教育质量的提升.

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author: Meijing Song;Xinjian Chen
date: 2021
original_link: https://ieeexplore.ieee.org/document/9718926/
site: ieeeXplore
created_at: 2025-09-30 12:13:21.965314
summary:
  In this era of rapid development, artificial intelligence has gradually entered our life, as a will be applied to the global technology, artificial intelligence in many fields have broad application prospects, artificial intelligence as the product of social development and technological innovation, has become a new round of scientific and technological innovation and the core driving force, is on the world economy, social progress and people’s life has had an extremely profound impact. The commercial application of artificial intelligence will change the internal operation and production and operation process of enterprises, and brings many challenges and reform opportunities for enterprise management. Article for the impact of artificial intelligence on the enterprise business management, analyze the application of enterprise business management and the impact of the technology application to the enterprise, and combined with some possible problems, in order to make artificial intelligence technology can be better applied in the enterprise management, put forward part of the corresponding solutions.

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title:
  Construction of Enterprise Business Management Analysis Framework in the Development of Artificial Intelligence


author: Haolei Xu;Xinyu Mei;Yuchen Yan;Rui Zhou;Wenqi Zhang;Weiming Lu;Yueting Zhuang;Yongliang Shen
date: 29 September, 2025
original_link: https://arxiv.org/abs/2509.25175
pdf_url: https://arxiv.org/pdf/2509.25175
site: arxiv
title: EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
created_at: 2025-09-30 12:13:21.966881
summary:
  Abstract:Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM's optimized inference engine, EasySteer achieves 5.5-11.4\times speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models.

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author: Nathan Inkawhich
date: 25 September, 2025
original_link: https://arxiv.org/abs/2509.21722
pdf_url: https://arxiv.org/pdf/2509.21722
site: arxiv
title: On the Status of Foundation Models for SAR Imagery
created_at: 2025-09-30 12:13:21.968266
summary:
  Abstract:In this work we investigate the viability of foundational AI/ML models for Synthetic Aperture Radar (SAR) object recognition tasks. We are inspired by the tremendous progress being made in the wider community, particularly in the natural image domain where frontier labs are training huge models on web-scale datasets with unprecedented computing budgets. It has become clear that these models, often trained with Self-Supervised Learning (SSL), will transform how we develop AI/ML solutions for object recognition tasks - they can be adapted downstream with very limited labeled data, they are more robust to many forms of distribution shift, and their features are highly transferable out-of-the-box. For these reasons and more, we are motivated to apply this technology to the SAR domain. In our experiments we first run tests with today's most powerful visual foundational models, including DINOv2, DINOv3 and PE-Core and observe their shortcomings at extracting semantically-interesting discriminative SAR target features when used off-the-shelf. We then show that Self-Supervised finetuning of publicly available SSL models with SAR data is a viable path forward by training several AFRL-DINOv2s and setting a new state-of-the-art for SAR foundation models, significantly outperforming today's best SAR-domain model SARATR-X. Our experiments further analyze the performance trade-off of using different backbones with different downstream task-adaptation recipes, and we monitor each model's ability to overcome challenges within the downstream environments (e.g., extended operating conditions and low amounts of labeled data). We hope this work will inform and inspire future SAR foundation model builders, because despite our positive results, we still have a long way to go.

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author: Qin S, Xiao W, Zhou C, Pu Q, Deng X, Lan L, Liang H, Song X, Wu M.
original_link: https://pubmed.ncbi.nlm.nih.gov/35752612/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9233671/pdf/41392_2022_Article_1056.pdf
site: pubmed
created_at: 2025-09-30 12:13:21.969509
summary:
  Pseudomonas aeruginosa (P. aeruginosa) is a Gram-negative opportunistic pathogen that infects patients with cystic fibrosis, burn wounds, immunodeficiency, chronic obstructive pulmonary disorder (COPD), cancer, and severe infection requiring ventilation, such as COVID-19. P. aeruginosa is also a widely-used model bacterium for all biological areas. In addition to continued, intense efforts in understanding bacterial pathogenesis of P. aeruginosa including virulence factors (LPS, quorum sensing, two-component systems, 6 type secretion systems, outer membrane vesicles (OMVs), CRISPR-Cas and their regulation), rapid progress has been made in further studying host-pathogen interaction, particularly host immune networks involving autophagy, inflammasome, non-coding RNAs, cGAS, etc. Furthermore, numerous technologic advances, such as bioinformatics, metabolomics, scRNA-seq, nanoparticles, drug screening, and phage therapy, have been used to improve our understanding of P. aeruginosa pathogenesis and host defense. Nevertheless, much remains to be uncovered about interactions between P. aeruginosa and host immune responses, including mechanisms of drug resistance by known or unannotated bacterial virulence factors as well as mammalian cell signaling pathways. The widespread use of antibiotics and the slow development of effective antimicrobials present daunting challenges and necessitate new theoretical and practical platforms to screen and develop mechanism-tested novel drugs to treat intractable infections, especially those caused by multi-drug resistance strains. Benefited from has advancing in research tools and technology, dissecting this pathogen's feature has entered into molecular and mechanistic details as well as dynamic and holistic views. Herein, we comprehensively review the progress and discuss the current status of P. aeruginosa biophysical traits, behaviors, virulence factors, invasive regulators, and host defense patterns against its infection, which point out new directions for future investigation and add to the design of novel and/or alternative therapeutics to combat this clinically significant pathogen.

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title:
  Pseudomonas aeruginosa: pathogenesis, virulence factors, antibiotic resistance, interaction with host, technology advances and emerging therapeutics.


author: 叶梦梦;周陶胡;葛艳明;范丽
keywords: 前列腺癌;个性化治疗;磁共振成像;影像分割;人工智能
source: 磁共振成像
site: 维普
title: 前列腺癌的人工智能研究进展
created_at: 2025-09-30 12:13:21.970801
original_link:
  https://www.cqvip.com/doc/journal/3506560274?sign=3f7935fd6206af8c321711ce05509d5c35848cb1ba5878dc109c0e1ae66eb9e9&expireTime=1790741548515&resourceId=3506560274

summary:
  全球男性所患恶性肿瘤中,前列腺癌(prostate cancer,PCa)发病率排第二,其精准诊断与治疗决策方面亟需更精准的辅助工具。人工智能(artificial intelligence,AI)技术的兴起,为PCa的早期诊断和精准治疗带来了前所未有的机遇。本文系统综述了AI在PCa三个核心领域中的现状:(1)诊断与预后评估——回顾了当下传统PCa诊断手段的应用情形,并重点介绍AI在多模态成像技术的应用进展;(2)分子机制研究——探讨了AI在基因组学、蛋白组学等高通量组学数据中的应用模式,包括生物标志物筛选与药物疗效预测,揭示疾病发生发展的关键分子机制;(3)治疗决策优化——阐述了AI在手术规划与术中导航、靶向治疗方案个性化设计及术后动态监测中的创新实践,凸显了AI在提升疗效、减少并发症风险上的潜在价值。本文介绍了临床级的PCa专用AI系统,分析其在提高诊疗效率和精度方面的优势。针对AI在PCa应用中面临的数据来源单一、模型泛化能力不足、“黑箱”特性以及多模态数据标准化缺失等挑战,未来研究应聚焦于构建跨中心、多模态标准化数据库,并引入联邦学习等隐私计算技术应用;开发可解释性AI框架,以增强临床信任度;持续优化算法性能,提高模型的实用性与可靠性。本综述旨在总结AI在PCa领域的最新应用进展和挑战,为未来的研究方向提供指导,以助力AI技术与PCa临床研究与实践的深度融合。

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author: 孙宏
keywords: 开放;人工智能;临床实验室;前沿技术
source: 科技风
site: 维普
title: 基于人工智能前沿技术的开放临床医学教学实验室管理模式探索
created_at: 2025-09-30 12:13:21.972083
original_link:
  https://www.cqvip.com/doc/journal/3480216175?sign=3423360f41b2afcd657f06fb4b4945b0269621434ff9698b51954e9588aadf67&expireTime=1790741579872&resourceId=3480216175

summary:
  随着医学教育的改革,临床实践技能培养已经成为医学教育改革的重要内容,实践技能培养是临床医学生培养动手能力的重要手段,是打好基础走向临床的第一步。因此,开放临床教学实验室给学生提供自主实践训练的平台,是加强实践技能培养的重要举措。在开放临床教学实验室的探索过程中,实验室管理方面的问题不容小觑。本文旨在探索通过人工智能前沿技术,力求化解开放实验室面临的管理问题,进而构建智能化、数字化的开放实验室平台,助力医学教育质量的提升。

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author: 叶梦梦;周陶胡;葛艳明
date: 2025年7期
keywords: 前列腺癌;人工智能;磁共振成像;影像分割;个性化治疗
quote: 0
source: 《磁共振成像》
site: 万方
title: 前列腺癌的人工智能研究进展
created_at: 2025-09-30 12:14:14.693019
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmNnemN4MjAyNTA3MDMxGgh5Ym15OWhwbQ%3D%3D

summary:
  全球男性所患恶性肿瘤中,前列腺癌(prostate cancer,PCa)发病率排第二,其精准诊断与治疗决策方面亟需更精准的辅助工具.人工智能(artificial intelligence,AI)技术的兴起,为PCa的早期诊断和精准治疗带来了前所未有的机遇.本文系统综述了AI在PCa三个核心领域中的现状:(1)诊断与预后评估——回顾了当下传统PCa诊断手段的应用情形,并重点介绍AI在多模态成像技术的应用进展;(2)分子机制研究——探讨了AI在基因组学、蛋白组学等高通量组学数据中的应用模式,包括生物标志物筛选与药物疗效预测,揭示疾病发生发展的关键分子机制;(3)治疗决策优化——阐述了AI在手术规划与术中导航、靶向治疗方案个性化设计及术后动态监测中的创新实践,凸显了AI在提升疗效、减少并发症风险上的潜在价值.本文介绍了临床级的PCa专用AI系统,分析其在提高诊疗效率和精度方面的优势.针对AI在PCa应用中面临的数据来源单一、模型泛化能力不足、"黑箱"特性以及多模态数据标准化缺失等挑战,未来研究应聚焦于构建跨中心、多模态标准化数据库,并引入联邦学习等隐私计算技术应用;开发可解释性AI框架,以增强临床信任度;持续优化算法性能,提高模型的实用性与可靠性.本综述旨在总结AI在PCa领域的最新应用进展和挑战,为未来的研究方向提供指导,以助力AI技术与PCa临床研究与实践的深度融合.

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author: 叶梦梦;周陶胡;葛艳明;范丽
keywords: 前列腺癌;个性化治疗;磁共振成像;影像分割;人工智能
source: 磁共振成像
site: 维普
title: 前列腺癌的人工智能研究进展
created_at: 2025-09-30 12:14:14.694872
original_link:
  https://www.cqvip.com/doc/journal/3506560274?sign=501dd280e6339d756472a339f034d8361c000b41b462a1452557a109c94d4cd6&expireTime=1790741615803&resourceId=3506560274

summary:
  全球男性所患恶性肿瘤中,前列腺癌(prostate cancer,PCa)发病率排第二,其精准诊断与治疗决策方面亟需更精准的辅助工具。人工智能(artificial intelligence,AI)技术的兴起,为PCa的早期诊断和精准治疗带来了前所未有的机遇。本文系统综述了AI在PCa三个核心领域中的现状:(1)诊断与预后评估——回顾了当下传统PCa诊断手段的应用情形,并重点介绍AI在多模态成像技术的应用进展;(2)分子机制研究——探讨了AI在基因组学、蛋白组学等高通量组学数据中的应用模式,包括生物标志物筛选与药物疗效预测,揭示疾病发生发展的关键分子机制;(3)治疗决策优化——阐述了AI在手术规划与术中导航、靶向治疗方案个性化设计及术后动态监测中的创新实践,凸显了AI在提升疗效、减少并发症风险上的潜在价值。本文介绍了临床级的PCa专用AI系统,分析其在提高诊疗效率和精度方面的优势。针对AI在PCa应用中面临的数据来源单一、模型泛化能力不足、“黑箱”特性以及多模态数据标准化缺失等挑战,未来研究应聚焦于构建跨中心、多模态标准化数据库,并引入联邦学习等隐私计算技术应用;开发可解释性AI框架,以增强临床信任度;持续优化算法性能,提高模型的实用性与可靠性。本综述旨在总结AI在PCa领域的最新应用进展和挑战,为未来的研究方向提供指导,以助力AI技术与PCa临床研究与实践的深度融合。

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author: Nadine Y. Fares;Manar Jammal
date: 2024
original_link: https://ieeexplore.ieee.org/document/10833589/
site: ieeeXplore
created_at: 2025-09-30 12:14:14.696038
summary:
  The rapid integration of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies holds significant promise for enhancing human-centric applications, particularly in the domain of law enforcement. This paper explores the application of AI and ML in crime prevention and resource allocation, pivotal areas of concern for law enforcement agencies (LEAs) globally. By utilizing historical crime data, sensory inputs, and advanced analytics, this study contributes to the evolving discourse on smart policing and proactive crime strategies. Our objective is to facilitate the transformation of LEAs from primarily reactive entities into proactive crime preventers through the adoption of predictive analytics, facial recognition enhanced surveillance, and natural language processing for efficient data analysis. We emphasize that collaborative efforts with AI experts ensure the responsible use of technology, meticulously balancing security imperatives with privacy concerns.

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title:
  Decoding Justice: The Synergy of Artificial Intelligence and Machine Learning in the Legal Landscape


author: Marco Bronzini;Carlo Nicolini;Bruno Lepri;Jacopo Staiano;Andrea Passerini
date: 29 September, 2025
original_link: https://arxiv.org/abs/2509.25045
pdf_url: https://arxiv.org/pdf/2509.25045
site: arxiv
title: Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
created_at: 2025-09-30 12:14:14.697305
summary:
  Abstract:Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Current interpretability methods, such as direct logit attribution (DLA) and sparse autoencoders (SAEs), provide restricted insight due to limitations such as the model's output vocabulary or unclear feature names. This work introduces Hyperdimensional Probe, a novel paradigm for decoding information from the LLM vector space. It combines ideas from symbolic representations and neural probing to project the model's residual stream into interpretable concepts via Vector Symbolic Architectures (VSAs). This probe combines the strengths of SAEs and conventional probes while overcoming their key limitations. We validate our decoding paradigm with controlled input-completion tasks, probing the model's final state before next-token prediction on inputs spanning syntactic pattern recognition, key-value associations, and abstract inference. We further assess it in a question-answering setting, examining the state of the model both before and after text generation. Our experiments show that our probe reliably extracts meaningful concepts across varied LLMs, embedding sizes, and input domains, also helping identify LLM failures. Our work advances information decoding in LLM vector space, enabling extracting more informative, interpretable, and structured features from neural representations.

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author: Rahman MA, Yilmaz I, Albadri ST, Salem FE, Dangott BJ, Taner CB, Nassar A, Akkus Z.
original_link: https://pubmed.ncbi.nlm.nih.gov/37760142/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10525684/pdf/bioengineering-10-01041.pdf
site: pubmed
title: Artificial Intelligence Advances in Transplant Pathology.
created_at: 2025-09-30 12:14:14.698400
keywords:
  artificial intelligence; digital pathology; heart transplant; kidney transplant; liver transplant; lung transplant; transplant pathology.

summary:
  Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.

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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 12:20:09.179596
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzThgvgP2jxF0HLAexJIB-uUgWQcl9Xa7Tqo4VqgoDiEwmLv0iGFJlRSdy0QuPvzHp6Hw4Er7Gbeoc9nSuYCtKs8z9ALq4Dzv-4GEpOmS0MJuDmlVbdTOPEUIppawecZQgCoOcupPuBknKWsE0xDX60k1PSm5OsLIvg0DLMcPwkSKw==&uniplatform=NZKPT&language=CHS

summary:
  煤矿安全生产一直是安全管理的重点领域,而隐患排查与治理则是预防煤矿事故发生的关键环节。随着大型语言模型技术的快速发展,DeepSeek作为一种先进的大规模预训练语言模型,为煤矿安全领域知识体系的构建提供了新的技术路径。通过探讨基于DeepSeek模型构建煤矿隐患查询知识库的方法与实践,整合多源异构数据,利用DeepSeek的知识理解与推理能力,构建了一套完整的煤矿隐患查询知识库系统。研究从知识库构建的关键技术入手,详细阐述了基于DeepSeek的知识获取、组织与推理方法,提出了一种融合领域知识与深度学习的煤矿隐患查询模型,并通过实际应用验证了系统的有效性。试验结果表明,基于DeepSeek构建的煤矿隐患查询知识库在隐患识别准确率、查询响应速度和多模态信息处理等方面均表现出显著优势,为煤矿安全管理提供了高效的技术支持。

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 12:20:09.182067
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoINXgyaDlscGQ%3D

summary:
  深度学习是人工智能领域的热门研究方向之一,它通过构建多层人工神经网络模仿人脑对数据的处理机制.大型语言模型(large language model,LLM)基于深度学习的架构,在无需编程指令的情况下,能通过分析大量数据以获得理解和生成人类语言的能力,被广泛应用于自然语言处理、计算机视觉、智慧医疗、智慧交通等诸多领域.文章总结了 LLM在医疗领域的应用,涵盖了 LLM针对医疗任务的基本训练流程、特殊策略以及在具体医疗场景中的应用.同时,进一步讨论了 LLM在应用中面临的挑战,包括决策过程缺乏透明度、输出准确性以及隐私、伦理问题等,随后列举了相应的改进策略.最后,文章展望了 LLM在医疗领域的未来发展趋势,及其对人类健康事业发展的潜在影响.

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author: 黄凡
date: 2025-02-25
keywords: 自然语言处理;;智能对话系统;;深度学习;;强化学习;;用户画像;
quote: 1
source: 信息与电脑
site: 知网
title: 基于自然语言处理的智能对话系统研究
created_at: 2025-09-30 12:20:09.183539
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzT8fZ3YXY7OrXMqHkLJQCh4pYN05wMApMzgRFvqvDx3gmA1YwT1p7KKDQysSco-ZjaZxjPOdPIti-0JEKfO78iIZSefyuzxCqJqHjPHkEzuOZBmc8-Zotb4Jf8tYXFUNyRuMBxaMLbQJrJq5FfLti5b-B4v1Hb5dag0K8mGJr80QQ==&uniplatform=NZKPT&language=CHS

summary:
  随着人工智能技术的快速发展，自然语言处理和智能对话系统已成为研究热点。文章概述了自然语言处理的基础知识和智能对话系统的架构，分析了当前智能对话系统存在的主要问题，包括语义理解的局限性、上下文连贯性的缺失以及个性化服务的不足。针对这些问题，文章提出了基于新技术的优化策略，包括利用深度学习增强语义理解，采用强化学习优化对话策略，以及通过用户画像驱动个性化服务。

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author: 任芳
date: 2024年3期
keywords: 递归网络;Q学习;卡尔曼滤波;语音增强;语音交互
quote: 1
source: 《自动化与仪器仪表》
site: 万方
title: 基于深度强化学习的对话机器人人机交互系统优化设计
created_at: 2025-09-30 12:20:09.184795
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXpkaHl5cXliMjAyNDAzMDQwGgg3eHlxbHB1bA%3D%3D

summary:
  为了提高对话机器人人机交互的准确率,提出一种基于协作递归网络的语音增强方法对语音分析模块进行优化.方法首先基于广义最小绝对偏差方法构建语音信号的AR参数估计模型,并采用深度递归Q网络对模型进行求解;再根据所求参数,通过卡尔曼滤波器递归网络依次还原语音信号数据.实验证明,在语音增强测试中,采用所提方法进行语音消噪,相较于改进谱减法、YW估计自适应卡尔曼滤波法和MG自适应卡尔曼滤波法等常用语音增强方法,可以更好地还原语音信号,尤其是在短视语音的消噪上,减小了语音失真,大幅提高了语音的信噪比.在人机交互测试中,基于所提语音增强方法优化的人机交互系统,对话识别准确率达到了 93.33％,相较于未优化的系统,提高了 16.66％,性能优越性明显,更满足对话机器人人机交互需求.

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author: 官群;张冲;姚茹
keywords: 易学灵;P300;英语学困;脑机制
source: 中国特殊教育
site: 维普
title: “易学灵”人机对话系统优化英语学困生脑机制的研究:来自事件相关电位P300的证据
created_at: 2025-09-30 12:20:09.186016
original_link:
  https://www.cqvip.com/doc/journal/929536220?sign=df68f3c66e613ef29504598e46c58b1074e42b1d5b5e2216358157203f6bfcfe&expireTime=1790741883080&resourceId=929536220

summary:
  英语学习困难在我国存在普遍性和严重性,对英语学困生的干预和矫治已成为研究者亟待研究的课题。本研究使用国家专利产品"易学灵"(Enjoy Learning)人机对话系统对英语学困生进行为期三个月的干预。这一人机对话系统是国内第一个将计算机游戏与行为认知能力提升结合人机对话系统,获得我国技术专利,专利号:ZL2012 2 0181187.4。本研究采用实验组对照组前测后测设计,将32名初一英语学困生分为同质的两组,训练组接受历时一个学期的每天30分钟的"易学灵"人机对话系统训练,对照组则只进行常规学习生活,不接受训练。训练前后,两组被试均接受视觉oddball任务的注意力集中的脑电测试。在行为测验进步显著基础上,进一步以事件相关电位P300成分为脑电指标评估脑机制干预效果。结果发现,训练组的后测P300潜伏期比前测显著缩短,而对照组前后测P300潜伏期则无显著差异,表明"易学灵"人机对话系统能够提高英语学困生的信息加工效率。本研究利用P300来衡量认知水平经过干预研究之后是如何提升的。这是脑神经认知科学与人机交互课程培训的最新结合创新型的研究。

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author: Penghao Wu;Yushan Zhang;Haiwen Diao;Bo Li;Lewei Lu;Ziwei Liu
date: 29 September, 2025
original_link: https://arxiv.org/abs/2509.25190
pdf_url: https://arxiv.org/pdf/2509.25190
site: arxiv
title: Visual Jigsaw Post-Training Improves MLLMs
created_at: 2025-09-30 12:20:09.188090
summary:
  Abstract:Reinforcement learning based post-training has recently emerged as a powerful paradigm for enhancing the alignment and reasoning capabilities of multimodal large language models (MLLMs). While vision-centric post-training is crucial for enhancing MLLMs' intrinsic understanding of visual signals, current post-training paradigms are predominantly text-centric, where dense visual inputs are only leveraged to extract sparse cues for text-based reasoning. There exist a few approaches in this direction, however, they often still rely on text as an intermediate mediator or introduce additional visual generative designs. In this work, we introduce Visual Jigsaw, a generic self-supervised post-training framework designed to strengthen visual understanding in MLLMs. Visual Jigsaw is formulated as a general ordering task: visual inputs are partitioned, shuffled, and the model must reconstruct the visual information by producing the correct permutation in natural language. This naturally aligns with reinforcement learning from verifiable rewards (RLVR), requires no additional visual generative components, and derives its supervisory signal automatically without any annotations. We instantiate Visual Jigsaw across three visual modalities, including images, videos, and 3D data. Extensive experiments demonstrate substantial improvements in fine-grained perception, temporal reasoning, and 3D spatial understanding. Our findings highlight the potential of self-supervised vision-centric tasks in post-training MLLMs and aim to inspire further research on vision-centric pretext designs. Project Page: this https URL

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author: Sungjin Lee;Maxine Eskenazi
date: 2012
original_link: https://ieeexplore.ieee.org/document/6424198/
site: ieeeXplore
title: POMDP-based Let's Go system for spoken dialog challenge
created_at: 2025-09-30 12:20:09.191504
summary:
  This paper describes a POMDP-based Let's Go system which incorporates belief tracking and dialog policy optimization into the dialog manager of the reference system for the Spoken Dialog Challenge (SDC). Since all components except for the dialog manager were kept the same, component-wise comparison can be performed to investigate the effect of belief tracking and dialog policy optimization on the overall system performance. In addition, since unsupervised methods have been adopted to learn all required models to reduce human labor and development time, the effectiveness of the unsupervised approaches compared to conventional supervised approaches can be investigated. The result system participated in the 2011 SDC and showed comparable performance with the base system which has been enhanced from the reference system for the 2010 SDC. This shows the capability of the proposed method to rapidly produce an effective system with minimal human labor and experts' knowledge.

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author: Alba Maria Marmol-Romero;Manuel Garcia-Vega;Miguel Angel Garcia-Cumbreras;Arturo Montejo-Raez
date: 24 September, 2025
original_link: https://arxiv.org/abs/2509.19861
pdf_url: https://arxiv.org/pdf/2509.19861
site: arxiv
title: SINAI at eRisk@CLEF 2025: Transformer-Based and Conversational Strategies for Depression Detection
created_at: 2025-09-30 12:20:09.192675
summary:
  Abstract:This paper describes the participation of the SINAI-UJA team in the eRisk@CLEF 2025 lab. Specifically, we addressed two of the proposed tasks: (i) Task 2: Contextualized Early Detection of Depression, and (ii) Pilot Task: Conversational Depression Detection via LLMs. Our approach for Task 2 combines an extensive preprocessing pipeline with the use of several transformer-based models, such as RoBERTa Base or MentalRoBERTA Large, to capture the contextual and sequential nature of multi-user conversations. For the Pilot Task, we designed a set of conversational strategies to interact with LLM-powered personas, focusing on maximizing information gain within a limited number of dialogue turns. In Task 2, our system ranked 8th out of 12 participating teams based on F1 score. However, a deeper analysis revealed that our models were among the fastest in issuing early predictions, which is a critical factor in real-world deployment scenarios. This highlights the trade-off between early detection and classification accuracy, suggesting potential avenues for optimizing both jointly in future work. In the Pilot Task, we achieved 1st place out of 5 teams, obtaining the best overall performance across all evaluation metrics: DCHR, ADODL and ASHR. Our success in this task demonstrates the effectiveness of structured conversational design when combined with powerful language models, reinforcing the feasibility of deploying LLMs in sensitive mental health assessment contexts.

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original_link: https://pubmed.ncbi.nlm.nih.gov/40205050/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12158756/pdf/41586_2025_Article_8866.pdf
site: pubmed
title: Towards conversational diagnostic artificial intelligence.
created_at: 2025-09-30 12:20:09.193894
author:
  Tu T, Schaekermann M, Palepu A, Saab K, Freyberg J, Tanno R, Wang A, Li B, Amin M, Cheng Y, Vedadi E, Tomasev N, Azizi S, Singhal K, Hou L, Webson A, Kulkarni K, Mahdavi SS, Semturs C, Gottweis J, Barral J, Chou K, Corrado GS, Matias Y, Karthikesalingam A, Natarajan V.

summary:
  At the heart of medicine lies physician-patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust1,2. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians' expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self-play-based3 simulated environment with automated feedback for scaling learning across disease conditions, specialties and contexts. We designed a framework for evaluating clinically meaningful axes of performance, including history-taking, diagnostic accuracy, management, communication skills and empathy. We compared AMIE's performance to that of primary care physicians in a randomized, double-blind crossover study of text-based consultations with validated patient-actors similar to objective structured clinical examination4,5. The study included 159 case scenarios from providers in Canada, the United Kingdom and India, 20 primary care physicians compared to AMIE, and evaluations by specialist physicians and patient-actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 30 out of 32 axes according to the specialist physicians and 25 out of 26 axes according to the patient-actors. Our research has several limitations and should be interpreted with caution. Clinicians used synchronous text chat, which permits large-scale LLM-patient interactions, but this is unfamiliar in clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 12:20:09.195104
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=77c6f3bee03c8b1278542ad46c19127f47a32dc8c7ca57a6cc6d50c8a757320c&expireTime=1790741966974&resourceId=3509810251

summary:
  大型语言模型(Large Language Model,LLM)在处理常规语言指令方面表现出色,但是处理印刷领域相关专业问题的能力还有待提升。本研究通过构建高质量的印刷领域微调数据集对开源LLM进行微调优化,利用清晰的微调提示词引导模型生成符合期望的回答。基于此,设计了一个针对印刷领域应用场景的服务型LLM,借助定制化训练提高其在印刷领域的表现能力。该过程主要涉及两项关键工作:通过收集、清洗、标注和扩增数据等方法,构建一个印刷领域微调数据集;选择Qwen-7B-Chat作为基座模型进行监督式微调,结合LoRA方法以实现参数高效的任务适应,并借助AdamW优化器策略对LLM的微调训练过程进行优化。验证结果表明,微调后的Qwen-7B-Chat模型相较原模型在回答长度上提升了约302.92%,并在回答质量评估环节保持了更高的满意率。

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author: 宋雪芬;王琳
date: 2025-08-15
keywords: 柔性电子;;近代物理学;;课程改革;
source: 科教导刊
site: 知网
title: 近代物理学在柔性电子发展中的前沿科学导向作用分析
created_at: 2025-09-30 12:36:06.479628
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzSZOX_WR2yI6dPzueziq2mBAurDvN_0wQrL4rceAVmTJcidWf_Yx1fJZU5tnuo7MApqgqqr-oW4oEdWB96Eesbww9HYENanbNDid2KMMOJ_v6wRXxe5IqF-cibAgGFlZA2PQDSx4N2ieQS1BCczKf_ngCOL5FxDzy-xcMepL0STUg==&uniplatform=NZKPT&language=CHS

summary:
  柔性电子技术作为未来科技的关键发展方向，已在人工智能、健康医疗、可穿戴设备等多个领域展现出广泛的应用潜力。材料物理学、量子力学等基础学科作为近代物理学的重要组成部分，为柔性电子技术的进步提供了重要的理论支持。然而，目前近代物理学在柔性电子技术中的应用仍存在一定的融入度不足问题。文章分析了近代物理学与柔性电子学相互赋能的内在发展关系，探讨了近代物理基础理论对柔性电子材料设计、性能优化以及器件功能实现的深远影响。同时，文章还讨论了近代物理学课程改革的方向，提出加强实验教学和项目驱动的教学模式，强调跨学科知识的融合，以培养具有创新能力的科技人才。

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 12:36:06.481649
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1Ggg5aGN2bmd4ZA%3D%3D

summary:
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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-30 12:36:06.483044
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=3652238a6e388ee17b57d2ef81fe771b6288bb7c10a12561fde3e035baed8e9c&expireTime=1790742921995&resourceId=3465772587

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author: 宋雪芬;王琳
date: 2025-08-15
keywords: 柔性电子;;近代物理学;;课程改革;
source: 科教导刊
site: 知网
title: 近代物理学在柔性电子发展中的前沿科学导向作用分析
created_at: 2025-09-30 12:55:38.395142
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzReJl_MmV-PG8KUyUKYpyVOoLfYNWqz21ykaCvw8IjFGhIXMcikq_sIEBDKfJHFVl1JE-gv0daML9yeHyAFQDDXq6YOziWKFOWd3KLjwbr3ddmzRwO5HGqlmAkBhWXVLTYM0uv-YefpNOnnlEUZRIPlbw9hmDifC80UYdBCMxdkQA==&uniplatform=NZKPT&language=CHS

summary:
  柔性电子技术作为未来科技的关键发展方向，已在人工智能、健康医疗、可穿戴设备等多个领域展现出广泛的应用潜力。材料物理学、量子力学等基础学科作为近代物理学的重要组成部分，为柔性电子技术的进步提供了重要的理论支持。然而，目前近代物理学在柔性电子技术中的应用仍存在一定的融入度不足问题。文章分析了近代物理学与柔性电子学相互赋能的内在发展关系，探讨了近代物理基础理论对柔性电子材料设计、性能优化以及器件功能实现的深远影响。同时，文章还讨论了近代物理学课程改革的方向，提出加强实验教学和项目驱动的教学模式，强调跨学科知识的融合，以培养具有创新能力的科技人才。

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 12:55:38.397610
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1Ggg0dWxxYmx2NQ%3D%3D

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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-30 12:55:38.401501
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=24b6626c6036d2faccb641df8d13875c3ae465660754344fe47b45f0831a01cb&expireTime=1790744117416&resourceId=3465772587

summary:
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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 12:58:52.225426
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzSnc5P5R20nHxEFlxFFhxpXAFTMenIrUpymI9qzD2h8enB3A1TMUfxFmhS21h8IG4z6OPRZ4IFTspHzqizYJJvtdldwYeRz02oSvZqS9h5cK7C61xf56qlsgWMIq-nzsf4y9uwR-1TxR7609s7lDWaSvGukiDMp2GBbPf-uupgxtw==&uniplatform=NZKPT&language=CHS

summary:
  煤矿安全生产一直是安全管理的重点领域,而隐患排查与治理则是预防煤矿事故发生的关键环节。随着大型语言模型技术的快速发展,DeepSeek作为一种先进的大规模预训练语言模型,为煤矿安全领域知识体系的构建提供了新的技术路径。通过探讨基于DeepSeek模型构建煤矿隐患查询知识库的方法与实践,整合多源异构数据,利用DeepSeek的知识理解与推理能力,构建了一套完整的煤矿隐患查询知识库系统。研究从知识库构建的关键技术入手,详细阐述了基于DeepSeek的知识获取、组织与推理方法,提出了一种融合领域知识与深度学习的煤矿隐患查询模型,并通过实际应用验证了系统的有效性。试验结果表明,基于DeepSeek构建的煤矿隐患查询知识库在隐患识别准确率、查询响应速度和多模态信息处理等方面均表现出显著优势,为煤矿安全管理提供了高效的技术支持。

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 12:58:52.227329
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoIMWlvMmZhZmg%3D

summary:
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author: 黄凡
date: 2025-02-25
keywords: 自然语言处理;;智能对话系统;;深度学习;;强化学习;;用户画像;
quote: 1
source: 信息与电脑
site: 知网
title: 基于自然语言处理的智能对话系统研究
created_at: 2025-09-30 12:58:52.228552
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzTwiPKdIwxOgnqwcZ9w8Jwyj-8FjE3fBkmQ6au9aDG3uxpVYHbR0vY_btM_mZQt4pni79cHSS4rAQ81OTcRmh2u39jHzIh8gWwvYGoVjhGXOJfgmJjoFocVQzVyynEseCln1o-vjGsE1eZgBfv3nnHnAoDiSUCSQoxWVA2gAkYTVQ==&uniplatform=NZKPT&language=CHS

summary:
  随着人工智能技术的快速发展，自然语言处理和智能对话系统已成为研究热点。文章概述了自然语言处理的基础知识和智能对话系统的架构，分析了当前智能对话系统存在的主要问题，包括语义理解的局限性、上下文连贯性的缺失以及个性化服务的不足。针对这些问题，文章提出了基于新技术的优化策略，包括利用深度学习增强语义理解，采用强化学习优化对话策略，以及通过用户画像驱动个性化服务。

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author: 任芳
date: 2024年3期
keywords: 递归网络;Q学习;卡尔曼滤波;语音增强;语音交互
quote: 1
source: 《自动化与仪器仪表》
site: 万方
title: 基于深度强化学习的对话机器人人机交互系统优化设计
created_at: 2025-09-30 12:58:52.229704
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXpkaHl5cXliMjAyNDAzMDQwGghldG12NmVxZQ%3D%3D

summary:
  为了提高对话机器人人机交互的准确率,提出一种基于协作递归网络的语音增强方法对语音分析模块进行优化.方法首先基于广义最小绝对偏差方法构建语音信号的AR参数估计模型,并采用深度递归Q网络对模型进行求解;再根据所求参数,通过卡尔曼滤波器递归网络依次还原语音信号数据.实验证明,在语音增强测试中,采用所提方法进行语音消噪,相较于改进谱减法、YW估计自适应卡尔曼滤波法和MG自适应卡尔曼滤波法等常用语音增强方法,可以更好地还原语音信号,尤其是在短视语音的消噪上,减小了语音失真,大幅提高了语音的信噪比.在人机交互测试中,基于所提语音增强方法优化的人机交互系统,对话识别准确率达到了 93.33％,相较于未优化的系统,提高了 16.66％,性能优越性明显,更满足对话机器人人机交互需求.

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 12:58:52.234429
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=c80ebe8d799f64bd1f4b1c6896e65f1727dcf4586d73d2383da2b333397951b1&expireTime=1790744258659&resourceId=3509810251

summary:
  大型语言模型(Large Language Model,LLM)在处理常规语言指令方面表现出色,但是处理印刷领域相关专业问题的能力还有待提升。本研究通过构建高质量的印刷领域微调数据集对开源LLM进行微调优化,利用清晰的微调提示词引导模型生成符合期望的回答。基于此,设计了一个针对印刷领域应用场景的服务型LLM,借助定制化训练提高其在印刷领域的表现能力。该过程主要涉及两项关键工作:通过收集、清洗、标注和扩增数据等方法,构建一个印刷领域微调数据集;选择Qwen-7B-Chat作为基座模型进行监督式微调,结合LoRA方法以实现参数高效的任务适应,并借助AdamW优化器策略对LLM的微调训练过程进行优化。验证结果表明,微调后的Qwen-7B-Chat模型相较原模型在回答长度上提升了约302.92%,并在回答质量评估环节保持了更高的满意率。

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author: 官群;张冲;姚茹
keywords: 易学灵;P300;英语学困;脑机制
source: 中国特殊教育
site: 维普
title: “易学灵”人机对话系统优化英语学困生脑机制的研究:来自事件相关电位P300的证据
created_at: 2025-09-30 12:58:52.235618
original_link:
  https://www.cqvip.com/doc/journal/929536220?sign=0fb5274b71f0018845c7fd6a6ea6af0d8e28b66e7fe61a6cbf2e158d7848b51c&expireTime=1790744310704&resourceId=929536220

summary:
  英语学习困难在我国存在普遍性和严重性,对英语学困生的干预和矫治已成为研究者亟待研究的课题。本研究使用国家专利产品"易学灵"(Enjoy Learning)人机对话系统对英语学困生进行为期三个月的干预。这一人机对话系统是国内第一个将计算机游戏与行为认知能力提升结合人机对话系统,获得我国技术专利,专利号:ZL2012 2 0181187.4。本研究采用实验组对照组前测后测设计,将32名初一英语学困生分为同质的两组,训练组接受历时一个学期的每天30分钟的"易学灵"人机对话系统训练,对照组则只进行常规学习生活,不接受训练。训练前后,两组被试均接受视觉oddball任务的注意力集中的脑电测试。在行为测验进步显著基础上,进一步以事件相关电位P300成分为脑电指标评估脑机制干预效果。结果发现,训练组的后测P300潜伏期比前测显著缩短,而对照组前后测P300潜伏期则无显著差异,表明"易学灵"人机对话系统能够提高英语学困生的信息加工效率。本研究利用P300来衡量认知水平经过干预研究之后是如何提升的。这是脑神经认知科学与人机交互课程培训的最新结合创新型的研究。

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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 13:02:19.784371
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzQ9VPMvb5SV7WoPX2nzNzyvO9qnALpcMzH5xumS5DbW2GyJfoM-wApCk3fAysx5A_86f1NeynKr9phrXmg28Mhb9gO2L9k6-gTYULLeNnR5X4Jd_doC0cKLRzGd9FhRBP6btUj6feIgiS5VIIiELYtDNc8Em9CDw6Bh5wb7AX42zg==&uniplatform=NZKPT&language=CHS

summary:
  煤矿安全生产一直是安全管理的重点领域,而隐患排查与治理则是预防煤矿事故发生的关键环节。随着大型语言模型技术的快速发展,DeepSeek作为一种先进的大规模预训练语言模型,为煤矿安全领域知识体系的构建提供了新的技术路径。通过探讨基于DeepSeek模型构建煤矿隐患查询知识库的方法与实践,整合多源异构数据,利用DeepSeek的知识理解与推理能力,构建了一套完整的煤矿隐患查询知识库系统。研究从知识库构建的关键技术入手,详细阐述了基于DeepSeek的知识获取、组织与推理方法,提出了一种融合领域知识与深度学习的煤矿隐患查询模型,并通过实际应用验证了系统的有效性。试验结果表明,基于DeepSeek构建的煤矿隐患查询知识库在隐患识别准确率、查询响应速度和多模态信息处理等方面均表现出显著优势,为煤矿安全管理提供了高效的技术支持。

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 13:02:19.786226
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoIdWVja3JmYjc%3D

summary:
[cell repeats an earlier one in this file: 323 characters]

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author: 黄凡
date: 2025-02-25
keywords: 自然语言处理;;智能对话系统;;深度学习;;强化学习;;用户画像;
quote: 1
source: 信息与电脑
site: 知网
title: 基于自然语言处理的智能对话系统研究
created_at: 2025-09-30 13:02:19.787605
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzTmgNDfJ8ZoJSkcYtvHE9TnksV2iDaOQ4a8q_FJV3qSZpsDGmWMptM64GDyEoff_IuBwGwyO8jSbzt8hofFATcfBLnM2q4efFWnWJB_V07NbC1qF516_gv6sYh-VMZmT1tsmsSlWFLj45KtW5OQ8DcmReyfT1vpafmJ788v5eYKig==&uniplatform=NZKPT&language=CHS

summary:
  随着人工智能技术的快速发展，自然语言处理和智能对话系统已成为研究热点。文章概述了自然语言处理的基础知识和智能对话系统的架构，分析了当前智能对话系统存在的主要问题，包括语义理解的局限性、上下文连贯性的缺失以及个性化服务的不足。针对这些问题，文章提出了基于新技术的优化策略，包括利用深度学习增强语义理解，采用强化学习优化对话策略，以及通过用户画像驱动个性化服务。

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author: 任芳
date: 2024年3期
keywords: 递归网络;Q学习;卡尔曼滤波;语音增强;语音交互
quote: 1
source: 《自动化与仪器仪表》
site: 万方
title: 基于深度强化学习的对话机器人人机交互系统优化设计
created_at: 2025-09-30 13:02:19.788803
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXpkaHl5cXliMjAyNDAzMDQwGghkamlzd3hrYw%3D%3D

summary:
  为了提高对话机器人人机交互的准确率,提出一种基于协作递归网络的语音增强方法对语音分析模块进行优化.方法首先基于广义最小绝对偏差方法构建语音信号的AR参数估计模型,并采用深度递归Q网络对模型进行求解;再根据所求参数,通过卡尔曼滤波器递归网络依次还原语音信号数据.实验证明,在语音增强测试中,采用所提方法进行语音消噪,相较于改进谱减法、YW估计自适应卡尔曼滤波法和MG自适应卡尔曼滤波法等常用语音增强方法,可以更好地还原语音信号,尤其是在短视语音的消噪上,减小了语音失真,大幅提高了语音的信噪比.在人机交互测试中,基于所提语音增强方法优化的人机交互系统,对话识别准确率达到了 93.33％,相较于未优化的系统,提高了 16.66％,性能优越性明显,更满足对话机器人人机交互需求.

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 13:02:19.794871
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=8d73c9621fb7bbfe5683474820a145a3a4482cc26e2867ff63253da61edb2548&expireTime=1790744465252&resourceId=3509810251

summary:
  大型语言模型(Large Language Model,LLM)在处理常规语言指令方面表现出色,但是处理印刷领域相关专业问题的能力还有待提升。本研究通过构建高质量的印刷领域微调数据集对开源LLM进行微调优化,利用清晰的微调提示词引导模型生成符合期望的回答。基于此,设计了一个针对印刷领域应用场景的服务型LLM,借助定制化训练提高其在印刷领域的表现能力。该过程主要涉及两项关键工作:通过收集、清洗、标注和扩增数据等方法,构建一个印刷领域微调数据集;选择Qwen-7B-Chat作为基座模型进行监督式微调,结合LoRA方法以实现参数高效的任务适应,并借助AdamW优化器策略对LLM的微调训练过程进行优化。验证结果表明,微调后的Qwen-7B-Chat模型相较原模型在回答长度上提升了约302.92%,并在回答质量评估环节保持了更高的满意率。

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author: 官群;张冲;姚茹
keywords: 易学灵;P300;英语学困;脑机制
source: 中国特殊教育
site: 维普
title: “易学灵”人机对话系统优化英语学困生脑机制的研究:来自事件相关电位P300的证据
created_at: 2025-09-30 13:02:19.796088
original_link:
  https://www.cqvip.com/doc/journal/929536220?sign=be1f6f03491200422eb773025386b76ef660733ed3c2cd229765357eb21baa95&expireTime=1790744518068&resourceId=929536220

summary:
  英语学习困难在我国存在普遍性和严重性,对英语学困生的干预和矫治已成为研究者亟待研究的课题。本研究使用国家专利产品"易学灵"(Enjoy Learning)人机对话系统对英语学困生进行为期三个月的干预。这一人机对话系统是国内第一个将计算机游戏与行为认知能力提升结合人机对话系统,获得我国技术专利,专利号:ZL2012 2 0181187.4。本研究采用实验组对照组前测后测设计,将32名初一英语学困生分为同质的两组,训练组接受历时一个学期的每天30分钟的"易学灵"人机对话系统训练,对照组则只进行常规学习生活,不接受训练。训练前后,两组被试均接受视觉oddball任务的注意力集中的脑电测试。在行为测验进步显著基础上,进一步以事件相关电位P300成分为脑电指标评估脑机制干预效果。结果发现,训练组的后测P300潜伏期比前测显著缩短,而对照组前后测P300潜伏期则无显著差异,表明"易学灵"人机对话系统能够提高英语学困生的信息加工效率。本研究利用P300来衡量认知水平经过干预研究之后是如何提升的。这是脑神经认知科学与人机交互课程培训的最新结合创新型的研究。

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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 13:05:51.396414
original_link:
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summary:
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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 13:05:51.398971
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoINGVubTFoemM%3D

summary:
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author: 张苏挺
date: 2025-09-29
keywords: 华山松;;育苗技术;;病虫害防治;
source: 种子世界
site: 知网
title: 华山松育苗科学开展路径及病虫害防治有效解决策略分析
created_at: 2025-09-30 13:05:51.400863
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzSstPxwmyAOBCMTGZDSZ9I1mFYDhB4cjhuvIpOqDSce_Q8eglCM8z8XywqW6tEPiStZ1SwohC6m9mAc8PdbM0V4HBb6rjMgPzPGzg7tVknrSskDEsLTGfeo3YOiOLkOrCxRFHLPH3csMjmXOVX0516umhpQ4mATDUst4oNBpF7GrQ==&uniplatform=NZKPT&language=CHS

summary:
  华山松是山西省重要的经济林种，其因优良的木材质量和生态价值而备受重视。育苗技术的进步对华山松产业的长久发展具有关键意义。然而，在育苗过程中，病虫害的发生严重影响了成苗率和林木健康。基于此，本文详细阐述了华山松的育苗技术，同时对华山松常见的病虫害种类、发生特点及防治措施进行了深入分析，旨在为华山松的科学育苗和病虫害有效防治提供参考。

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author: 赵鸿
date: 2025年17期
keywords: 体验;问题解决策略;一一列举;教学设计
quote: 0
source: 《教学月刊（小学版）》
site: 万方
title: 基于体验的问题解决策略的形成
created_at: 2025-09-30 13:05:51.402677
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWp4eWsteHhiMjAyNTE3MDExGgg2ZGFoNml0NA%3D%3D

summary:
  问题解决的策略是对问题解决方法的抽象,更具一般意义.学生在问题解决过程中掌握的是方法,形成的是策略,内化的是数学思想.学生要掌握问题解决的策略,将其转化为素养,关键是体验.以苏教版教材五年级上册"一一列举的策略"教学为例,学生经历"直面问题,体验条件与问题之间的关联;亲历探索,体验问题解决的思路;多维交互,体验问题解决的方法;反思抽象,体验问题解决的策略;变式应用,体验策略背后所蕴含的数学思想"等过程,从而形成问题解决策略.

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author: Xu Hongbo;Jiang Jianhua;Zeng Wenquan;He Yongjun
date: 2016
original_link: https://ieeexplore.ieee.org/document/7726200/
site: ieeeXplore
title: Medical Image Transmission Strategy Based on Bandwidth Sensitivity and Multi-resolution
created_at: 2025-09-30 13:05:51.409420
summary:
  On the basis of traditional image transmission technology, the medical image transmission strategy based on bandwidth sensitivity and multi-resolution is proposed in mobile transmission environment. According to current network bandwidth, remaining battery power of mobile terminal equipment, screen resolution of mobile device supporting and real-time transmission requirements, the strategy selects the image replica with the optimal transmission resolution as the transfer object. Experiments show that the strategy is effective.

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date: 29 September, 2025
original_link: https://arxiv.org/abs/2509.24986
pdf_url: https://arxiv.org/pdf/2509.24986
site: arxiv
title: Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes
created_at: 2025-09-30 13:05:51.411058
author:
  Yuhan Wang;Weikai Chen;Zeyu Hu;Runze Zhang;Yingda Yin;Ruoyu Wu;Keyang Luo;Shengju Qian;Yiyan Ma;Hongyi Li;Yuan Gao;Yuhuan Zhou;Hao Luo;Wan Wang;Xiaobin Shen;Zhaowei Li;Kuixin Zhu;Chuanlang Hong;Yueyue Wang;Lijie Feng;Xin Wang;Chen Change Loy

summary:
  Abstract:In user-generated-content (UGC) applications, non-expert users often rely on image-to-3D generative models to create 3D assets. In this context, primitive-based shape abstraction offers a promising solution for UGC scenarios by compressing high-resolution meshes into compact, editable representations. Towards this end, effective shape abstraction must therefore be structure-aware, characterized by low overlap between primitives, part-aware alignment, and primitive compactness. We present Light-SQ, a novel superquadric-based optimization framework that explicitly emphasizes structure-awareness from three aspects. (a) We introduce SDF carving to iteratively udpate the target signed distance field, discouraging overlap between primitives. (b) We propose a block-regrow-fill strategy guided by structure-aware volumetric decomposition, enabling structural partitioning to drive primitive placement. (c) We implement adaptive residual pruning based on SDF update history to surpress over-segmentation and ensure compact results. In addition, Light-SQ supports multiscale fitting, enabling localized refinement to preserve fine geometric details. To evaluate our method, we introduce 3DGen-Prim, a benchmark extending 3DGen-Bench with new metrics for both reconstruction quality and primitive-level editability. Extensive experiments demonstrate that Light-SQ enables efficient, high-fidelity, and editable shape abstraction with superquadrics for complex generated geometry, advancing the feasibility of 3D UGC creation.

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author: Burgers TCQ, Vlijm R.
original_link: https://pubmed.ncbi.nlm.nih.gov/37000292/
pdf_url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10356683/pdf/412_2023_Article_792.pdf
site: pubmed
title: Fluorescence-based super-resolution-microscopy strategies for chromatin studies.
created_at: 2025-09-30 13:05:51.412726
keywords:
  Chromatin labelling; Chromatin organisation; STimulated Emission Depletion microscopy (STED); STochastic Optical Reconstruction Microscopy (STORM); Super-resolution microscopy.

summary:
  Super-resolution microscopy (SRM) is a prime tool to study chromatin organisation at near biomolecular resolution in the native cellular environment. With fluorescent labels DNA, chromatin-associated proteins and specific epigenetic states can be identified with high molecular specificity. The aim of this review is to introduce the field of diffraction-unlimited SRM to enable an informed selection of the most suitable SRM method for a specific chromatin-related research question. We will explain both diffraction-unlimited approaches (coordinate-targeted and stochastic-localisation-based) and list their characteristic spatio-temporal resolutions, live-cell compatibility, image-processing, and ability for multi-colour imaging. As the increase in resolution, compared to, e.g. confocal microscopy, leads to a central role of the sample quality, important considerations for sample preparation and concrete examples of labelling strategies applicable to chromatin research are discussed. To illustrate how SRM-based methods can significantly improve our understanding of chromatin functioning, and to serve as an inspiring starting point for future work, we conclude with examples of recent applications of SRM in chromatin research.

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 13:05:51.414224
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=a993d8ba0ffcdccc4c8f14a4782d6f3ecd02b2fbf3a6ea03c5a8465a4c632f10&expireTime=1790744675159&resourceId=3509810251

summary:
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author: 宋亮亮;梁颢严;李顺清;邱报;刘兆平
keywords: 正极材料;改性策略;锂离子电池;合成优化;富锂层状氧化物
source: 物理化学学报
site: 维普
title: 超高比能电池高锰富锂层状氧化物正极材料面临的挑战与解决策略
created_at: 2025-09-30 13:05:51.415740
original_link:
  https://www.cqvip.com/doc/journal/3505817613?sign=b325eedd8ded8de20448aec650e122fb890af8acf6eed28e55243ff3576a0a9d&expireTime=1790744729364&resourceId=3505817613

summary:
  得益于过渡金属和晶格氧共同参与氧化还原反应,富锂层状氧化物(LLOs)具有大于250 mAh∙g−1的比容量,因而成为下一代商用锂离子电池的潜在候选正极材料。为进一步提高理论比容量并减少对环境和健康有害的钴、镍元素依赖,开发高锰富锂层状氧化物(HM-LLOs)成为一种可行的策略。通过引入更多的Li–O–Li构型,可以促进更多晶格氧参与氧化还原反应,从而提升理论比容量。然而,锰含量的增加也带来了如活化困难和不可逆氧释放等挑战,显著限制了HM-LLOs理论比容量的实际利用。基于此,本文首先探讨了HM-LLOs高理论比容量的来源,随后深入分析了高锰特性引发的结构变化及其对实际比容量利用的限制,最后系统总结了从合成到活性材料改性的多种优化策略,并展望了可能提升HMLLOs实际比容量的未来方向。

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author: 张林英
date: 2025-09-30
keywords: 人工智能;;泌尿系统疾病;;临床护理;;护理管理;;挑战;
source: 中国急救复苏与灾害医学杂志
site: 知网
title: 基于人工智能技术的泌尿外科临床护理中应用研究与进展
created_at: 2025-09-30 13:07:09.188342
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzT9G3fEOS3KH-6Mm60pzdhmiUxd1dL-_BWZ6VIzsta4QuG4ijehza7PLOcOi-utSh_VhGolAM6JbYgRc-aoPpX1toxYTaBAZ5pQGYVti3AM21eUHOul5s6fi7HhlYBL9e4-szJkheHaRbkSu2C3vwB8z9Qib1e7cZkMf8Z0tv5ppQ==&uniplatform=NZKPT&language=CHS

summary:
  人工智能技术的快速发展为泌尿外科临床护理的智能化、精准化与高效化提供了革命性机遇。本研究围绕护理实践核心环节，系统探讨了人工智能技术在泌尿外科患者评估、教育、干预、监测及管理中的应用现状、效果评估、关键挑战及未来发展方向。当前人工智能护理应用面临数据质量与隐私、算法可解释性、法规伦理及护理—技术复合型人才短缺等核心挑战。针对上述问题，未来需重点构建护理数据安全与共享机制，研发符合护理工作逻辑的可视化工具与决策支持系统，健全适应人工智能护理的法规伦理框架，并大力推动护理信息学与跨学科人才培养。通过深化护理场景驱动的人工智能技术研究、完善标准体系及促进多领域协作，人工智能技术将深度赋能泌尿外科护理全流程，显著提升护理质量、效率及患者健康结局。

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date: 29 September, 2025
original_link: https://arxiv.org/abs/2509.24855
pdf_url: https://arxiv.org/pdf/2509.24855
site: arxiv
created_at: 2025-09-30 13:07:09.191847
author:
  Fangchen Yu;Junchi Yao;Ziyi Wang;Haiyuan Wan;Youling Huang;Bo Zhang;Shuyue Hu;Dongzhan Zhou;Ning Ding;Ganqu Cui;Lei Bai;Wanli Ouyang;Peng Ye

summary:
  Abstract:Physics is central to understanding and shaping the real world, and the ability to solve physics problems is a key indicator of real-world physical intelligence. Physics Olympiads, renowned as the crown of competitive physics, provide a rigorous testbed requiring complex reasoning and deep multimodal understanding, yet they remain largely underexplored in AI research. Existing approaches are predominantly single-model based, and open-source MLLMs rarely reach gold-medal-level performance. To address this gap, we propose PhysicsMinions, a coevolutionary multi-agent system for Physics Olympiad. Its architecture features three synergistic studios: a Visual Studio to interpret diagrams, a Logic Studio to formulate solutions, and a Review Studio to perform dual-stage verification. The system coevolves through an iterative refinement loop where feedback from the Review Studio continuously guides the Logic Studio, enabling the system to self-correct and converge towards the ground truth. Evaluated on the HiPhO benchmark spanning 7 latest physics Olympiads, PhysicsMinions delivers three major breakthroughs: (i) Strong generalization: it consistently improves both open-source and closed-source models of different sizes, delivering clear benefits over their single-model baselines; (ii) Historic breakthroughs: it elevates open-source models from only 1-2 to 6 gold medals across 7 Olympiads, achieving the first-ever open-source gold medal in the latest International Physics Olympiad (IPhO) under the average-score metric; and (iii) Scaling to human expert: it further advances the open-source Pass@32 score to 26.8/30 points on the latest IPhO, ranking 4th of 406 contestants and far surpassing the top single-model score of 22.7 (ranked 22nd). Generally, PhysicsMinions offers a generalizable framework for Olympiad-level problem solving, with the potential to extend across disciplines.

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title:
  PhysicsMinions: Winning Gold Medals in the Latest Physics Olympiads with a Coevolutionary Multimodal Multi-Agent System


author: Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y.
original_link: https://pubmed.ncbi.nlm.nih.gov/39952597/
site: pubmed
created_at: 2025-09-30 13:07:09.193577
keywords:
  Artificial intelligence; Clinical decision support; Gastrointestinal cancer; Histopathology; Medical imaging.

summary:
  With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.

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title:
  Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications.


author: 叶梦梦;周陶胡;葛艳明;范丽
keywords: 前列腺癌;个性化治疗;磁共振成像;影像分割;人工智能
source: 磁共振成像
site: 维普
title: 前列腺癌的人工智能研究进展
created_at: 2025-09-30 13:07:09.195189
original_link:
  https://www.cqvip.com/doc/journal/3506560274?sign=5f1cbe4c51f5b56a4b12dbe9e43a9bfdc399a0cab037ab03bd437ae043d68212&expireTime=1790744806134&resourceId=3506560274

summary:
  全球男性所患恶性肿瘤中,前列腺癌(prostate cancer,PCa)发病率排第二,其精准诊断与治疗决策方面亟需更精准的辅助工具。人工智能(artificial intelligence,AI)技术的兴起,为PCa的早期诊断和精准治疗带来了前所未有的机遇。本文系统综述了AI在PCa三个核心领域中的现状:(1)诊断与预后评估——回顾了当下传统PCa诊断手段的应用情形,并重点介绍AI在多模态成像技术的应用进展;(2)分子机制研究——探讨了AI在基因组学、蛋白组学等高通量组学数据中的应用模式,包括生物标志物筛选与药物疗效预测,揭示疾病发生发展的关键分子机制;(3)治疗决策优化——阐述了AI在手术规划与术中导航、靶向治疗方案个性化设计及术后动态监测中的创新实践,凸显了AI在提升疗效、减少并发症风险上的潜在价值。本文介绍了临床级的PCa专用AI系统,分析其在提高诊疗效率和精度方面的优势。针对AI在PCa应用中面临的数据来源单一、模型泛化能力不足、“黑箱”特性以及多模态数据标准化缺失等挑战,未来研究应聚焦于构建跨中心、多模态标准化数据库,并引入联邦学习等隐私计算技术应用;开发可解释性AI框架,以增强临床信任度;持续优化算法性能,提高模型的实用性与可靠性。本综述旨在总结AI在PCa领域的最新应用进展和挑战,为未来的研究方向提供指导,以助力AI技术与PCa临床研究与实践的深度融合。

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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 13:09:01.212605
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzTHs_uIFnM5jj26Vd-TFRSb3_qU-uh-l4LmHkm2KdBCHh0NB_QOKWJlPxTdqcDYXebz6aiW8OjeFctS2gKgDG-Rz_ha0ICxc7OQ9h8XMNBjeLxaAtCtV0EjxmGu04mKYDkdqY2SqG71csOXT7qjiqRImbkmaTGMQYhvguLWUOVMlA==&uniplatform=NZKPT&language=CHS

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 13:09:01.214420
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoIcWlrdXBiYnE%3D

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 13:09:01.215668
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=15d89c6dc99910e64e1695b9d1e97f3e7c956f5c1fbdde997a29cd73ab9187a1&expireTime=1790744843799&resourceId=3509810251

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author: 宋雪芬;王琳
date: 2025-08-15
keywords: 柔性电子;;近代物理学;;课程改革;
source: 科教导刊
site: 知网
title: 近代物理学在柔性电子发展中的前沿科学导向作用分析
created_at: 2025-09-30 13:20:06.294775
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzT3MNitYYa_vbk_v5TA3QBIBHEllHaW1eLNzyGPT-DIiPJTmhlauUjFcEVSCBMOEEp5q64zYqGTBIon69IycDWFuByaQEfidd3PADUD3IqeR9uYLyXvxvpaZSpkC2GzTqW95nEhG_qERveaiJ5-UVKgxG8dFlgBXFOvptlHRgTiVw==&uniplatform=NZKPT&language=CHS

summary:
  柔性电子技术作为未来科技的关键发展方向，已在人工智能、健康医疗、可穿戴设备等多个领域展现出广泛的应用潜力。材料物理学、量子力学等基础学科作为近代物理学的重要组成部分，为柔性电子技术的进步提供了重要的理论支持。然而，目前近代物理学在柔性电子技术中的应用仍存在一定的融入度不足问题。文章分析了近代物理学与柔性电子学相互赋能的内在发展关系，探讨了近代物理基础理论对柔性电子材料设计、性能优化以及器件功能实现的深远影响。同时，文章还讨论了近代物理学课程改革的方向，提出加强实验教学和项目驱动的教学模式，强调跨学科知识的融合，以培养具有创新能力的科技人才。

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-09-30 13:20:06.296940
original_link:
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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-09-30 13:20:06.299281
original_link:
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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 13:23:53.372592
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzRNRupA-upbwif7fQ-K4afgIb89_zveIzk-k6yWlfDQABk0d8ekIFNBQ3maTSd-gZtrHdTQJWiTeQK-LNhsWkQaSu7PXuTmIfei_QutHZ-_XetaxWXVm02mZwhAItIOBmFX6S3Fx5rH53c0N7SnCqbmIF20fjCBaS0JY2Qo2nejfQ==&uniplatform=NZKPT&language=CHS

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 13:23:53.374615
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoIYTZiYzhhNjM%3D

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author: 张苏挺
date: 2025-09-29
keywords: 华山松;;育苗技术;;病虫害防治;
source: 种子世界
site: 知网
title: 华山松育苗科学开展路径及病虫害防治有效解决策略分析
created_at: 2025-09-30 13:23:53.375946
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzQ9W7cb6n_cCI0VFLh_4a8LAiK7_K4s2-hn0PInqx16-lKHVysmDwcgUkSM-4Enl8h-tAf6Pmp5J6rFwm0PAcDEfXi0Nm8GcCYc1b1w274wHKu959t7HSUIVE-xa32YTFDvahEpthSB6JUZaZHX6-s1HRQtUFpqF7LNlkJpwG9PFw==&uniplatform=NZKPT&language=CHS

summary:
  华山松是山西省重要的经济林种，其因优良的木材质量和生态价值而备受重视。育苗技术的进步对华山松产业的长久发展具有关键意义。然而，在育苗过程中，病虫害的发生严重影响了成苗率和林木健康。基于此，本文详细阐述了华山松的育苗技术，同时对华山松常见的病虫害种类、发生特点及防治措施进行了深入分析，旨在为华山松的科学育苗和病虫害有效防治提供参考。

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author: 赵鸿
date: 2025年17期
keywords: 体验;问题解决策略;一一列举;教学设计
quote: 0
source: 《教学月刊（小学版）》
site: 万方
title: 基于体验的问题解决策略的形成
created_at: 2025-09-30 13:23:53.377280
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEWp4eWsteHhiMjAyNTE3MDExGgg5bGRhM2Q0Mg%3D%3D

summary:
  问题解决的策略是对问题解决方法的抽象,更具一般意义.学生在问题解决过程中掌握的是方法,形成的是策略,内化的是数学思想.学生要掌握问题解决的策略,将其转化为素养,关键是体验.以苏教版教材五年级上册"一一列举的策略"教学为例,学生经历"直面问题,体验条件与问题之间的关联;亲历探索,体验问题解决的思路;多维交互,体验问题解决的方法;反思抽象,体验问题解决的策略;变式应用,体验策略背后所蕴含的数学思想"等过程,从而形成问题解决策略.

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 13:23:53.386172
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=3b8b29f03d62052bbec116fca903c369181598c1c228253fa90579634c0d4bb9&expireTime=1790745759564&resourceId=3509810251

summary:
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author: 宋亮亮;梁颢严;李顺清;邱报;刘兆平
keywords: 正极材料;改性策略;锂离子电池;合成优化;富锂层状氧化物
source: 物理化学学报
site: 维普
title: 超高比能电池高锰富锂层状氧化物正极材料面临的挑战与解决策略
created_at: 2025-09-30 13:23:53.387906
original_link:
  https://www.cqvip.com/doc/journal/3505817613?sign=f0d64fc893e4d4e747ddebffd1615d8e91a49fff67bc03fc4b888272142b0a94&expireTime=1790745811685&resourceId=3505817613

summary:
  得益于过渡金属和晶格氧共同参与氧化还原反应,富锂层状氧化物(LLOs)具有大于250 mAh∙g−1的比容量,因而成为下一代商用锂离子电池的潜在候选正极材料。为进一步提高理论比容量并减少对环境和健康有害的钴、镍元素依赖,开发高锰富锂层状氧化物(HM-LLOs)成为一种可行的策略。通过引入更多的Li–O–Li构型,可以促进更多晶格氧参与氧化还原反应,从而提升理论比容量。然而,锰含量的增加也带来了如活化困难和不可逆氧释放等挑战,显著限制了HM-LLOs理论比容量的实际利用。基于此,本文首先探讨了HM-LLOs高理论比容量的来源,随后深入分析了高锰特性引发的结构变化及其对实际比容量利用的限制,最后系统总结了从合成到活性材料改性的多种优化策略,并展望了可能提升HMLLOs实际比容量的未来方向。

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author: 王勇;王伟;王健;邱磊
date: 2025-09-26
keywords: 煤矿安全;;DeepSeek;;隐患查询;;知识库构建;;大型语言模型;
source: 采矿技术
site: 知网
title: 基于DeepSeek的煤矿隐患查询知识库构建研究
created_at: 2025-09-30 13:27:27.848749
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzRgaim1JCrNsGBYN3cz25vmofim5gsqJQ0IkbSg_K0CrR2PoX6p_RCcpd_L-N0kI3ZqIFkKM4MNoGGugF0q1Ro7IhvuvvyqE0dROFJHupBsp2uNJUpvz0HUppzIQHTyB1Dv2gMqBxLHLVK5abAmfpP4G3lHx8wKrxn7qG68sD8RTA==&uniplatform=NZKPT&language=CHS

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author: 肖建力;许东舟;王浩
date: 2025年3期
keywords: 人工智能;深度学习;Transformer;大型语言模型;智慧医疗;数据分析;图像处理;计算机视觉
quote: 0
source: 《智能系统学报》
site: 万方
title: 医疗领域的大型语言模型综述
created_at: 2025-09-30 13:27:27.850465
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISD3hka2p5YzIwMjUwMzAwMhoINnptNGh1Z24%3D

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author: 黄凡
date: 2025-02-25
keywords: 自然语言处理;;智能对话系统;;深度学习;;强化学习;;用户画像;
quote: 1
source: 信息与电脑
site: 知网
title: 基于自然语言处理的智能对话系统研究
created_at: 2025-09-30 13:27:27.851674
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=fvwDbTJIyzRXrjbPwMQlDHbWHJW2_-vN0KFytmDJ-1KPvzgEis6TSVpt5QY7uSKH9c27qWcdseU3XeYZXF6Ha2T2Hdsugy2a3kH6z4Mm7fbfmiMchtfoRPuHSMlvsTJH2-n47SzAjgojCEW41hr6wSl7bGfApQxUZ5kP_EZtsswa3cQ7t72x9A==&uniplatform=NZKPT&language=CHS

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author: 任芳
date: 2024年3期
keywords: 递归网络;Q学习;卡尔曼滤波;语音增强;语音交互
quote: 1
source: 《自动化与仪器仪表》
site: 万方
title: 基于深度强化学习的对话机器人人机交互系统优化设计
created_at: 2025-09-30 13:27:27.852870
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXpkaHl5cXliMjAyNDAzMDQwGghmYnI1cWtyaw%3D%3D

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author: 程朝;李金栋;张国伟
keywords: 垂直领域大模型;印刷领域;LoRA;AdamW;监督式微调
source: 印刷与数字媒体技术研究
site: 维普
title: 印刷领域专用服务型大型语言模型的研究与应用
created_at: 2025-09-30 13:27:27.859831
original_link:
  https://www.cqvip.com/doc/journal/3509810251?sign=5fe08bc75f565c9098ded2fcd92aac2fc4ab874bc4995f4f483dfcf95c03c338&expireTime=1790745972369&resourceId=3509810251

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author: 官群;张冲;姚茹
keywords: 易学灵;P300;英语学困;脑机制
source: 中国特殊教育
site: 维普
title: “易学灵”人机对话系统优化英语学困生脑机制的研究:来自事件相关电位P300的证据
created_at: 2025-09-30 13:27:27.860982
original_link:
  https://www.cqvip.com/doc/journal/929536220?sign=319824041de74af123ca319de5313cf23eab6acf37d8ad9d2ffc06b7897423b3&expireTime=1790746026160&resourceId=929536220

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author: 张硕
date: 2025-09-22 09:25
keywords: 政务人工智能;;幻觉;;技术与场景耦合;;风险分级;;准入规制;
source: 电子政务
site: 知网
title: 政务人工智能的幻觉应对：基于技术与场景耦合风险分级的梯度化准入规制
created_at: 2025-10-09 17:14:37.064004
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=MsI1aVwi7T_vnaCRDPZMJRf5UUS4LwbRqAOyA0e_ums93Oct8enFysE38jGAwj7NvGqsmVvHx8H-w5tXJMMx6SRHXh547s5TwNFNFRUZKNj12yVLrSbfTgTG7jvbdDMOnYO140I933Fj9TB93-VzqhkYFoshVTMyJzyxv7MY92L9yaIiuHjP1g==&uniplatform=NZKPT&language=CHS

summary:
  当下，人工智能与政务系统的结合已经成为一种超越效能工具的制度重构与价值再造，其在驱动数字政府转型的同时，亦由生成式技术的自主性衍生出从信息失准到决策失真的新型幻觉风险。对于多元政务场景中人工智能技术的应用，事中的监管与事后的追责并不能有效预防和抑制幻觉所带来的损害，有必要突破单一静态风险观，建构基于人工智能技术与政务系统场景双重维度的“技术-场景”耦合式风险分级框架，并遵循包容审慎的规制理念衔接匹配严格程度差异化的准入措施。技术风险分级基于数据依赖性、技术可控性及系统复杂性，场景风险分级根据系统所容纳之行政行为对公共利益与公民权利的影响程度，二者耦合评定出高、中、低三级风险政务人工智能，分别匹配许可、认证、备案三种准入规制措施。

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author: 张帅
date: 2025年16期
keywords: 人工智能幻觉;治理路径;技术优化;法律与监管
quote: 0
source: 《科技和产业》
site: 万方
title: 人工智能幻觉的成因及其治理路径
created_at: 2025-10-09 17:14:37.078013
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmtqaGN5MjAyNTE2MDEzGghwaGF1NWt3cg%3D%3D

summary:
  人工智能幻觉是指人工智能模型生成的内容看似逻辑自洽但缺乏现实依据的现象,其产生有着复杂的技术原因和社会根源.其不仅会造成其用户直接损失、加剧信息过载,还可能会阻碍人工智能技术的长期健康发展.应当综合运用技术、法律等手段,通过改进人工智能技术、制定人工智能系统的开发与应用标准、制定《人工智能法》、设立人工智能监管机关等措施,减轻人工智能幻觉现象的发生及其带来的负面影响,促进人工智能技术的健康发展.

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author: 苏本跃;刘文瑶;宗文杰;王保乾;盛敏
date: 2025-09-29 16:25
keywords: 运动意图识别;;智能下肢假肢;;惯性测量单元;;动态时间规整;;自适应时序对齐;;帧间差分;;单一步;
source: 中国康复理论与实践
site: 知网
title: 基于自适应时序对齐的智能下肢假肢运动意图识别
created_at: 2025-10-09 17:14:37.080105
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=MsI1aVwi7T_mXDzLXjawZDajmuAVKutKTX_eUoINYbzN5JhF6Z6X8E1nR66x8SxhFd4wHVnjOtsuc_z9x5jBg0Yu67m-0gjL3iasaY1Vu1KLyZLlQYWzplsmDQmwVz3_oQF1hcya7DRp4jAUo4cCzgNdmPsSUTjip7t46pKsf0NFIsiojaS6Rg==&uniplatform=NZKPT&language=CHS

summary:
  目的 针对智能下肢假肢运动意图识别中因个体步态差异和固定时间窗提取数据导致的运动误分类问题，提出一种基于自适应时序对齐的运动意图识别方法。方法 在下肢运动中，对于连续两个步态周期数据，基于不同稳态模式下的类间差异性，通过跨周期帧间差分检测步态模式一致性。针对检测为单一稳态模式的样本，引入动态时间规整算法将相邻周期运动序列进行对齐，以减小个体差异。提取Haar小波4层分解低频系数构建特征向量，最后通过支持向量机实现分类。试验方案设计为：利用3个惯性测量单元采集测试对象在13种运动模式中下肢的加速度和角速度信息，测试对象为10例健康受试者和1例经胫骨截肢受试者，13种运动模式包括5种稳态模式(平地行走、上楼、下楼、上坡和下坡)和8种转换模式(平地行走与上楼、下楼、上坡、下坡的相互转换)。结果 经过对10例健康人的模拟测试和1例截肢者的测试，5种稳态模式的识别准确率分别为99.24%和100%,13种运动模式的识别准确率分别为98.51%和89.11%。结论 本研究提出了一种自适应时序对齐的运动意图识别方法，该方法有效减小个体步态差异对特征表征的干扰，增强了步态特征的一致性与判别性，最终实现...

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author: 谌文佳;杨琳;李金林
date: 2025年2期
keywords: 意图识别;医疗问答;语义相似度分类;深度学习
quote: 2
source: 《数据分析与知识发现》
site: 万方
title: 嵌入意图识别的医疗健康问答文本语义分类模型
created_at: 2025-10-09 17:14:37.081968
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISEXhkdHNxYmpzMjAyNTAyMDAzGghyZDJ6a2h6Mg%3D%3D

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[cell repeats an earlier one in this file: 452 characters]

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author: Waqar Hussain
date: 2025
original_link: https://ieeexplore.ieee.org/document/11029428/
site: ieeeXplore
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title: Mitigating Values Debt in Generative AI: Responsible Engineering with Graph RAG
created_at: 2025-10-09 17:14:37.083896
summary:
  Generative AI technologies are rapidly transforming industries such as healthcare, education, and transportation. However, this progress often incurs a Values Debt-ethical and operational deficits due to insufficient ethical considerations during development. This paper examines Values Debt in Gen-erative AI and introduces the Helpful, Honest, Harmless (HHH) framework to align AI systems with human values. In developing GRAISE, a Graph RAG-based chatbot for aviation safety, the HHH framework is applied to integrate ethical practices through-out the development process. This case study demonstrates how the HHH framework addresses ethical challenges and provides reliable contextual information to enhance pilot communication, exemplifying responsible AI engineering. These findings advocate for the broader adoption of ethical AI frameworks across various sectors, promoting trust and integrity in AI applications.


author: Jonggeun Lee;Woojung Song;Jongwook Han;Haesung Pyun;Yohan Jo
date: 8 October, 2025
original_link: https://arxiv.org/abs/2510.07248
pdf_url: https://arxiv.org/pdf/2510.07248
site: arxiv
title: Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models
created_at: 2025-10-09 17:14:37.085546
summary:
  Abstract:Small language models (SLMs) offer significant computational advantages for tool-augmented AI systems, yet they struggle with tool-use tasks, particularly in selecting appropriate tools and identifying correct parameters. A common failure mode is schema misalignment: models hallucinate plausible but non-existent tool names that reflect naming conventions internalized during pretraining but absent from the provided tool schema. Rather than forcing models to adapt to arbitrary schemas, we propose adapting schemas to align with models' pretrained knowledge. We introduce PA-Tool (Pretraining-Aligned Tool Schema Generation), a training-free method that leverages peakedness-a signal from contamination detection indicating pretraining familiarity-to automatically rename tool components. By generating multiple candidates and selecting those with highest output concentration across samples, PA-Tool identifies pretrain-aligned naming patterns. Experiments on MetaTool and RoTBench show improvements of up to 17% points, with schema misalignment errors reduced by 80%. PA-Tool enables small models to approach state-of-the-art performance while maintaining computational efficiency for adaptation to new tools without retraining. Our work demonstrates that schema-level interventions can unlock the tool-use potential of resource-efficient models by adapting schemas to models rather than models to schemas.

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author: Howell MD, Corrado GS, DeSalvo KB.
original_link: https://pubmed.ncbi.nlm.nih.gov/38227029/
site: pubmed
title: Three Epochs of Artificial Intelligence in Health Care.
created_at: 2025-10-09 17:14:37.087271
summary:
  Importance: Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.

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author: Seunghan Yang;Juntae Lee;Jihwan Bang;Kyuhong Shim;Minsoo Kim;Simyung Chang
date: 23 September, 2025
original_link: https://arxiv.org/abs/2509.19700
pdf_url: https://arxiv.org/pdf/2509.19700
site: arxiv
title: Learning Contextual Retrieval for Robust Conversational Search
created_at: 2025-10-09 17:14:37.090178
summary:
  Abstract:Effective conversational search demands a deep understanding of user intent across multiple dialogue turns. Users frequently use abbreviations and shift topics in the middle of conversations, posing challenges for conventional retrievers. While query rewriting techniques improve clarity, they often incur significant computational cost due to additional autoregressive steps. Moreover, although LLM-based retrievers demonstrate strong performance, they are not explicitly optimized to track user intent in multi-turn settings, often failing under topic drift or contextual ambiguity. To address these limitations, we propose ContextualRetriever, a novel LLM-based retriever that directly incorporates conversational context into the retrieval process. Our approach introduces: (1) a context-aware embedding mechanism that highlights the current query within the dialogue history; (2) intent-guided supervision based on high-quality rewritten queries; and (3) a training strategy that preserves the generative capabilities of the base LLM. Extensive evaluations across multiple conversational search benchmarks demonstrate that ContextualRetriever significantly outperforms existing methods while incurring no additional inference overhead.

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author: Hailin Zhang;Yafei Song;Zhimin Qi
date: 2024
original_link: https://ieeexplore.ieee.org/document/10754108/
site: ieeeXplore
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title: Research on Intent Recognition Method of Air Targets Based on Transformer Model
created_at: 2025-10-09 17:14:37.091522
summary:
  Most existing methods for operational intent recognition of air targets rely on complex models, leading to difficulties in accurately and efficiently identifying the true intent of the target. To overcome this challenge, this paper constructs an air target intent feature set by carefully selecting relevant features and encoding them into time-series data. Furthermore, a novel deep learning approach based on the Transformer model is proposed, aiming to enhance the capability of operational intent recognition for air targets. The Transformer model not only excels in extracting potential feature information from the data but also effectively captures the global relationships within the battlefield situation data information. Additionally, through its self-attention mechanism, Transformer adeptly handles long-term dependencies within sequences. Experimental results demonstrate that the Transformer model achieves an accuracy of 98.86% in air target intent recognition tasks, highlighting its superior performance in this domain.


author: Koen Vellenga;H. Joe Steinhauer;Jonas Andersson;Anders Sjögren
date: 6 October, 2025
original_link: https://arxiv.org/abs/2510.05006
pdf_url: https://arxiv.org/pdf/2510.05006
site: arxiv
title: Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition
created_at: 2025-10-09 17:14:37.092699
summary:
  Abstract:Deep neural networks (DNNs) are increasingly applied to safety-critical tasks in resource-constrained environments, such as video-based driver action and intention recognition. While last layer probabilistic deep learning (LL-PDL) methods can detect out-of-distribution (OOD) instances, their performance varies. As an alternative to last layer approaches, we propose extending pre-trained DNNs with transformation layers to produce multiple latent representations to estimate the uncertainty. We evaluate our latent uncertainty representation (LUR) and repulsively trained LUR (RLUR) approaches against eight PDL methods across four video-based driver action and intention recognition datasets, comparing classification performance, calibration, and uncertainty-based OOD detection. We also contribute 28,000 frame-level action labels and 1,194 video-level intention labels for the NuScenes dataset. Our results show that LUR and RLUR achieve comparable in-distribution classification performance to other LL-PDL approaches. For uncertainty-based OOD detection, LUR matches top-performing PDL methods while being more efficient to train and easier to tune than approaches that require Markov-Chain Monte Carlo sampling or repulsive training procedures.

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author: Yuan R, Wang Q, Xu H, Yu H, Shi P.
original_link: https://pubmed.ncbi.nlm.nih.gov/38943405/
site: pubmed
title: Control strategies for trunk exoskeletons based on motion intent recognition: A review.
created_at: 2025-10-09 17:14:37.093922
keywords:
  Intent recognition; biological signals; control strategy; motion of the relevant body parts; sensor; trunk exoskeletons.

summary:
  Background: Wearable trunk exoskeletons hold immense potential in fields such as healthcare and industry. Previous research has indicated that intention recognition control plays a crucial role in users' daily use of exoskeletons.

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author: 林曦
keywords: 萨特;幻觉;计算精神病学;人工智能;存在主义
source: 社会科学辑刊
site: 维普
title: 人工智能“幻觉”的存在主义阐释
created_at: 2025-10-09 17:14:37.095291
original_link:
  https://www.cqvip.com/doc/journal/3480218689?sign=82724415722e67742f770ddb438cb45b0292ebdde934365c1be51b71ae425099&expireTime=1791537177176&resourceId=3480218689

summary:
  在文本生成和处理领域,人工智能所生成的文本可能是无意义、不连贯或者是循环往复的。人工智能所生成或处理的文本有可能不忠实于文本的来源,这种偏离理想预期的文本成本被称为人工智能“幻觉”。随着人工智能应用的不断拓展,人工智能“幻觉”成为一个亟待探讨和解决的问题。在计算机科学领域中,“幻觉”主要是由输出内容和源内容之间的映射关系的扭曲或者破坏而产生,这其实与萨特的存在主义框架存在同构性。萨特的存在主义在显象和本体之间构建了一种映射关系,而这种映射关系是从存在主义的角度来讨论“幻觉”的产生机制。这一框架为我们探讨人工智能的“幻觉”问题提供了一个阐释学的工具和手段,可以用来检视人工智能“幻觉”的产生机制及其相应的伦理风险。

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author: 黄贺祥;杨震;李嘉胜;罗亚中
keywords: 空间态势感知;意图识别;BiGRU网络;非合作目标
source: 国防科技大学学报
site: 维普
title: 基于BiGRU网络的空间非合作目标受控运动意图识别
created_at: 2025-10-09 17:14:37.096630
original_link:
  https://www.cqvip.com/doc/journal/3506530564?sign=7f075583808a5d34dfb00fb5cd4d53e762f4bc5963ca09c1547e02cdba10bb98&expireTime=1791537244110&resourceId=3506530564

summary:
  针对非合作目标受控运动意图难以识别的问题,提出了一种基于双向门控循环单元(bi-directional gated recurrent unit,BiGRU)网络的意图识别方法。设定非合作目标具有“受控圆形绕飞”“受控水滴绕飞”“定点振荡”“直线逼近”“跳跃逼近”5种受控运动意图,建立非合作目标受控运动意图机动信息数据集。基于非合作目标进入我方航天器观测范围后的机动时序信息,采用BiGRU网络进行训练,以学习时序数据与受控运动意图之间的潜在关联,实现对非合作目标的意图识别。仿真结果表明,基于BiGRU网络的非合作目标受控运动意图识别方法的检测准确率达到98.35%。该方法可以提高对非合作目标的意图识别能力,为我方在轨航天器的安全保障提供技术参考。

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author: 宋雪芬;王琳
date: 2025-08-15
keywords: 柔性电子;;近代物理学;;课程改革;
source: 科教导刊
site: 知网
title: 近代物理学在柔性电子发展中的前沿科学导向作用分析
created_at: 2025-10-09 17:15:58.233173
original_link:
  https://kns.cnki.net/kcms2/article/abstract?v=MsI1aVwi7T8xswHAqKEC5r-2hGj8MUPPbZwTIXtgW0zI6Yn6MKLeluf9ueoi1DI5cIcuaHVFbpHLiLCfzhBcWuO3qkeyDXDcBkmLJGlJDq7mF60AGh1xKjiDYylI1RK2dDotVdxltnWfaaTvMMBOyAUglrmk76uI1Hjt-bwG2mlRia7IMm3n1Q==&uniplatform=NZKPT&language=CHS

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author: 段辛茹;汪青燕;刘彤
date: 2025年7期
keywords: 柔性电子材料;健康监测;互动电子设备;性能评估;测量技术;应用研究
quote: 0
source: 《大众标准化》
site: 万方
title: 可穿戴设备中的柔性电子材料及其应用研究进展
created_at: 2025-10-09 17:15:58.235844
original_link:
  https://d.wanfangdata.com.cn/periodical/ChVQZXJpb2RpY2FsQ0hJMjAyNTA2MjISDmR6YnpoMjAyNTA3MDQ1GghjdGUzaHBkZA%3D%3D

summary:
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author: 彭玉鑫;陈雪垠;章阳坤
keywords: 可穿戴电子器件;传感器智能化;拉伸性;机械鲁棒性;信号灵敏性;材料结构;信号准确性
source: 材料工程
site: 维普
title: 柔性电子材料与器件在可穿戴传感领域的发展现状、挑战与创新策略
created_at: 2025-10-09 17:15:58.237692
original_link:
  https://www.cqvip.com/doc/journal/3465772587?sign=f19ebea2d7d8ba163bca904465ee7b7d4ce9d317cd95e70e9c95a18266653dc2&expireTime=1791537292184&resourceId=3465772587

summary:
[cell repeats an earlier one in this file: 344 characters]

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author: Alessandro Sturniolo
date: 2 October, 2025
original_link: https://arxiv.org/abs/2510.01834
pdf_url: https://arxiv.org/pdf/2510.01834
site: arxiv
title: BabyMOSS stitched sensors: results of characterisation tests for ALICE ITS3 upgrade
created_at: 2025-10-09 17:15:58.240214
summary:
  Abstract:During the Long Shutdown 3 (LS3, scheduled 2026-2030), the innermost 3 layers (Inner Barrel, or IB) of the present ALICE ITS2 will be replaced with large-area, flexible, stitched CMOS 65 nm sensors, arranged in 2 half-barrels of 3 half-layers each, in the framework of the ITS3 upgrade project. For the first time in a High Energy Physics experiment, such large-scale sensors will be bent into truly half-cylindrical shaped half-layers, requiring little mechanical support. This will also help lower the material budget: a reduction down to an average of 0.09% X_{0} per layer is expected, benefitting ITS tracking and vertexing capabilities especially at low momenta.

  In the wafer yield and stitching assessment stage for ITS3 R&D, test devices from the Engineering Run 1 (ER1) submission were developed, including the MOnolithic Stiched Sensors (MOSS) and smaller variants of them (babyMOSS). In particular, babyMOSS is a single Repeated Sensor Unit (RSU) of a MOSS device: the chip is \sim14\times30 mm^{2} in size, and consists of 8 digitally read out pixel matrices (regions) arranged in 2 rows, i.e. half-units (HUs).

  BabyMOSS chip characterisation tests have been performed in laboratory and test beam environments. Laboratory tests include systematic functional scans to study the behaviour of front-end electronics over a range of different settings, whereas test beam measurements, under high-energy charged particle beams, were used to investigate the detection efficiency and spatial resolution.

  In this contribution, we will present the babyMOSS chip characterisation campaign, with a focus on recent test beam results. So far, babyMOSS test beam results have been consistent with full MOSS, and confirmed that babyMOSS devices meet the ITS3 requirements: a detection efficiency >99\%, fake hit rate <10^{-6} hits per pixel and event, and a spatial resolution <6 \mum.

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