【目的】围绕AI场景下科学数据的共享与利用问题,针对现有FAIR原则不足以指导科学数据满足AI就绪的现状,构建面向AI就绪的科学数据共享与利用原则框架。【方法】通过系统梳理传统机器学习、大模型预训练、大模型微调、检索增强生成及智...【目的】围绕AI场景下科学数据的共享与利用问题,针对现有FAIR原则不足以指导科学数据满足AI就绪的现状,构建面向AI就绪的科学数据共享与利用原则框架。【方法】通过系统梳理传统机器学习、大模型预训练、大模型微调、检索增强生成及智能体等5类典型AI任务的数据需求,在传统FAIR“四可”维度的基础上,提出面向AI就绪(即For AI Ready)的科学数据共享与利用原则框架FAIR×FAIR,进而提出与框架相适应的层次化技术栈。【结果】FAIR×FAIR框架明确了13项科学数据满足AI就绪的技术要求,为弥合AI任务与科学数据之间的语义鸿沟提供了系统化方案。【局限】本研究提出的原则框架其实施效果仍需通过后续领域应用案例进一步验证。【结论】FAIR×FAIR框架为AI时代的科学数据共享与高效利用提供了理论依据和实践路径,对推动数据驱动型科研范式的演进具有重要意义。展开更多
In Pakistan, a hierarchical healthcare system is an efficient way of addressing the issueof limited and insufficient healthcare services. Identifying the various degrees of diseasebased on the doctor's diagnosis i...In Pakistan, a hierarchical healthcare system is an efficient way of addressing the issueof limited and insufficient healthcare services. Identifying the various degrees of diseasebased on the doctor's diagnosis is an important step in developing the hierarchical healthcaretreatment structure. This research presents a framework for dealing with the issueof diagnosis values presented as "picture fuzzy numbers (PFNs)". Specifically, the goal ofthis study is to establish some innovative operational laws and "aggregation operators"(AOs) in a picture fuzzy environment. In this regard, we proposed some new neutral orfair operational laws that incorporate the concept of proportional distribution in orderto achieve a neutral or fair remedy to the positive, neutral and negative aspects ofPFNs. Based on the developed operational laws, we proposed the "picture fuzzy fairlyweighted average operator" and the "picture fuzzy fairly ordered weighted averagingoperator". Compared to previous techniques, the proposed AOs provide more generalizedand reliable. Furthermore, using proposed AOs with multiple decision-makers andpartial weight information under PFNs, a "multi-criteria decision-making" algorithmis developed. Finally, we provide an example to show how the novel approach can aidhierarchical treatment systems. This is essential for merging the healthcare capabilitiesof the general public and optimizing the medical care system's service performance.展开更多
Fibre Extrusion Technology Ltd(FET)of Leeds,UK reported another successful exhibition at COMPAMED 2025 in Dusseldorf,following closely on the heels of ITMA ASIA in Singapore.This was the second time that FET had exhib...Fibre Extrusion Technology Ltd(FET)of Leeds,UK reported another successful exhibition at COMPAMED 2025 in Dusseldorf,following closely on the heels of ITMA ASIA in Singapore.This was the second time that FET had exhibited at this leading international trade fair for the medical technology supplier sector,a reflection of the company’s growing role in this sector.More than half of FET’s turnover is currently derived from the burgeoning medical market.COMPAMED is aimed at suppliers of a wide range of high-quality medical technology components,services and production equipment for the medical industry.FET’s expanding role in the medical sector is therefore an ideal fit for this trade show.展开更多
引言深圳,这座以创新为基因、以制造为根基的城市,聚集了超4000家机器人核心企业,形成了覆盖核心零部件、系统集成、场景应用的完整产业链条,成为我国机器人产业的创新高地与出海枢纽。机器人全产业链接会FAIR plus 2026开幕前夕,中国...引言深圳,这座以创新为基因、以制造为根基的城市,聚集了超4000家机器人核心企业,形成了覆盖核心零部件、系统集成、场景应用的完整产业链条,成为我国机器人产业的创新高地与出海枢纽。机器人全产业链接会FAIR plus 2026开幕前夕,中国科学院深圳先进技术研究院产业发展中心主任、深圳市机器人协会常务副理事长兼秘书长毕亚雷接受《机器人产业》杂志专访,分享产业最新洞察与前沿思考,为推动产业发展提供路径参考。展开更多
Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with co...Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.展开更多
Ningbo,a port city located on the coast of the East China Sea,has taken the lead in holding the Central and Eastern European Countries'Products Fair since 2014 by virtue of its long tradition of the Maritime Silk ...Ningbo,a port city located on the coast of the East China Sea,has taken the lead in holding the Central and Eastern European Countries'Products Fair since 2014 by virtue of its long tradition of the Maritime Silk Road and its position as a modern logistics hub.展开更多
文摘【目的】围绕AI场景下科学数据的共享与利用问题,针对现有FAIR原则不足以指导科学数据满足AI就绪的现状,构建面向AI就绪的科学数据共享与利用原则框架。【方法】通过系统梳理传统机器学习、大模型预训练、大模型微调、检索增强生成及智能体等5类典型AI任务的数据需求,在传统FAIR“四可”维度的基础上,提出面向AI就绪(即For AI Ready)的科学数据共享与利用原则框架FAIR×FAIR,进而提出与框架相适应的层次化技术栈。【结果】FAIR×FAIR框架明确了13项科学数据满足AI就绪的技术要求,为弥合AI任务与科学数据之间的语义鸿沟提供了系统化方案。【局限】本研究提出的原则框架其实施效果仍需通过后续领域应用案例进一步验证。【结论】FAIR×FAIR框架为AI时代的科学数据共享与高效利用提供了理论依据和实践路径,对推动数据驱动型科研范式的演进具有重要意义。
文摘In Pakistan, a hierarchical healthcare system is an efficient way of addressing the issueof limited and insufficient healthcare services. Identifying the various degrees of diseasebased on the doctor's diagnosis is an important step in developing the hierarchical healthcaretreatment structure. This research presents a framework for dealing with the issueof diagnosis values presented as "picture fuzzy numbers (PFNs)". Specifically, the goal ofthis study is to establish some innovative operational laws and "aggregation operators"(AOs) in a picture fuzzy environment. In this regard, we proposed some new neutral orfair operational laws that incorporate the concept of proportional distribution in orderto achieve a neutral or fair remedy to the positive, neutral and negative aspects ofPFNs. Based on the developed operational laws, we proposed the "picture fuzzy fairlyweighted average operator" and the "picture fuzzy fairly ordered weighted averagingoperator". Compared to previous techniques, the proposed AOs provide more generalizedand reliable. Furthermore, using proposed AOs with multiple decision-makers andpartial weight information under PFNs, a "multi-criteria decision-making" algorithmis developed. Finally, we provide an example to show how the novel approach can aidhierarchical treatment systems. This is essential for merging the healthcare capabilitiesof the general public and optimizing the medical care system's service performance.
文摘Fibre Extrusion Technology Ltd(FET)of Leeds,UK reported another successful exhibition at COMPAMED 2025 in Dusseldorf,following closely on the heels of ITMA ASIA in Singapore.This was the second time that FET had exhibited at this leading international trade fair for the medical technology supplier sector,a reflection of the company’s growing role in this sector.More than half of FET’s turnover is currently derived from the burgeoning medical market.COMPAMED is aimed at suppliers of a wide range of high-quality medical technology components,services and production equipment for the medical industry.FET’s expanding role in the medical sector is therefore an ideal fit for this trade show.
文摘引言深圳,这座以创新为基因、以制造为根基的城市,聚集了超4000家机器人核心企业,形成了覆盖核心零部件、系统集成、场景应用的完整产业链条,成为我国机器人产业的创新高地与出海枢纽。机器人全产业链接会FAIR plus 2026开幕前夕,中国科学院深圳先进技术研究院产业发展中心主任、深圳市机器人协会常务副理事长兼秘书长毕亚雷接受《机器人产业》杂志专访,分享产业最新洞察与前沿思考,为推动产业发展提供路径参考。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R77)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number NBU-FFR-2026-2248-01.
文摘Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.
文摘Ningbo,a port city located on the coast of the East China Sea,has taken the lead in holding the Central and Eastern European Countries'Products Fair since 2014 by virtue of its long tradition of the Maritime Silk Road and its position as a modern logistics hub.
