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    'Floor Material Recognition Method, Control Method, And Storage Medium' in Paten t Application Approval Process (USPTO 20240210960)

    170-175页
    查看更多>>摘要:This patent application has not been assigned to a company or institution.The following quote was obtained by the news editors from the background informa tion supplied bythe inventors: “An autonomous mobile device is a smart device t hat autonomously executes predeterminedtasks in a predetermined zone. Currently available autonomous mobile devices typically include, but arenot limited to, cleaning robots (e.g., smart floor sweeping robots, smart floor mopping robots, windowcleaning robots), companion type mobile robots (e.g., smart electronic pe ts, nanny robots), service typemobile robots (e.g., receptionist robots in rest aurants, hotels, meeting places), industrial inspection smartdevices (e.g., ele ctric power line inspection robots, smart forklift, etc.), security robots (e.g. , home orcommercial use smart bodyguard robots), etc. These service type robots have advantages of time-saving and energy-saving, and are convenient to operate . As a result, people are free from tedious labor, andcan have more time for re st and entertainment, thereby enhancing the comfort of people’s daily lives.

    Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Dia gnostic Ions in O-Glycan Analysis

    175-176页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Structural details of oligosaccharides, or glycans, often carry biological rele vance, which is why theyare typically elucidated using tandem mass spectrometry . Common approaches to distinguish isomersrely on diagnostic glycan fragments f or annotating topologies or linkages. Diagnostic fragments areoften only known informally among practitioners or stem from individual studies, with unclear val idityor generalizability, causing annotation heterogeneity and hampering new an alysts. Drawing on a curatedset of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncoverquantifiably valid and genera lizable diagnostic fragments.