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Technical category
    • Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • 以智慧型手表的生理徵象監測以建立急診醫護人員的過勞示警

      FutureTech 以智慧型手表的生理徵象監測以建立急診醫護人員的過勞示警

      By collecting the changes of kinds of vital signs(including heart beat / blood pressure / steps), we combined the results with the testers’ fatigue scores which belonged to the matched fatigue typestheir basic profiles. We extracted 780 types of structural features sets by cleaning raw data, then separating into training set, validation settesting set. With machine learning algorithm to auto-optimize regression analysis, finally we come out the correlative features between vital signsfatigue ,the overwork model as our deliverables.