RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells
RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour +2 more
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Recent Developments and Applications of Drone Swarm: Techniques, Strategies, and Challenges. [PDF]
Raj R, Kos A.
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A neural network-based framework for enterprise financial error correction using AI and big data. [PDF]
Bndyan Q +4 more
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Thoracic electrical impedance tomography from technological promise to clinical reality: the Chinese experience. [PDF]
Zhao Z.
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Strategic value driven by artificial intelligence in global businesses: a bibliometric and qualitative analysis of the most influential literature. [PDF]
Zambonino-Torres MJ +2 more
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Health Resorts as an integrated community healthcare model for fibromyalgia syndrome: a strategic SWOT analysis. [PDF]
Regazzo G +3 more
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Twelve Practical Tips for Integrating AI Into Medical Education: Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains. [PDF]
Jalali A, Harbi Houssein K, Fotsing S.
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The Application of the Thought of Strategic Options in Enterprise Strategic Change
2008 International Seminar on Business and Information Management, 2008In dynamic and competitive times, retaining strategic flexibility through strategic changing is an effective method for enterprise to cope with changes. Strategy change, like ldquooptionrdquo in the financial economics, is to gain the highest possible risk investment return.
Di Zhao, Ruiwen Zhang
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Distributed strategic learning with application to network security
Proceedings of the 2011 American Control Conference, 2011We consider in this paper a class of two-player nonzero-sum stochastic games with incomplete information. We develop fully distributed reinforcement learning algorithms, which require for each player a minimal amount of information regarding the other player. At each time, each player can be in an active mode or in a sleep mode.
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