A cell survival rate determining device includes a distribution acquisition unit acquiring a particle size distribution in a solution containing particles including cultured cells; a classification unit classifying the particles into a small particle group and a large particle group using a predetermined threshold in the distribution; a ratio calculation unit calculating the value of a ratio between the number of particles belonging to the small particle group and the number of particles belonging to the large particle group; and a survival rate determination unit determining the survival rate of the cultured cells from the value of the ratio using a pre-acquired relationship between the ratio and cell survival rate.

Exemplary systems, methods and computer accessible mediums which can determine quantitative information of a tissue(s) can be provided, which can include receiving optical information regarding the tissue(s), and determining the quantitative information of the tissue(s), in real time and during the receipt of the optical information, using an atlas. The tissue(s) can be part of a mouse, and the quantitative information can be an in-vivo count of cells or bacteria in the tissue(s). The quantitative information can be determined by co-registering the in-vivo count to the atlas. The optical information can be generated using a mirror gantry(s) so as to simultaneously image at least two opposing views of the tissue(s).

An image inspection apparatus includes an illuminating section for irradiating illumination light, a line camera in which a plurality of imaging elements are arrayed to be linearly arranged, the line camera receiving the light irradiated from the illuminating section and reflected on the inspection target object, a display section for displaying an image captured by the line camera, an optical axis adjusting section for adjusting an optical axis of the line camera, a trigger setting section for specifying a trigger that specifies timing when the inspection target object is imaged by the line camera, an aspect ratio adjusting section for adjusting longitudinal and lateral pixel resolutions of the image captured by the line camera, and a display control section for displaying the optical axis adjusting section, the trigger setting section, and the aspect ratio adjusting section on the display section in order.

The present invention is related to the field of bio/chemical sensing, assays and applications. Particularly, the present invention is related to collecting a small amount of a vapor condensate sample (e.g. the exhaled breath condensate (EBC) from a subject of a volume as small as 10 fL (femto-Liter) in a single drop), preventing or significantly reducing an evaporation of the collected vapor condensate sample, analyzing the sample, analyzing the sample by mobile-phone, and performing such collection and analysis by a person without any professionals.

A smoke detector includes: a casing; a first light emitting unit; a second light emitting unit; and a light receiving unit. A second scattering angle that is an angle between the reception axis of the light receiving unit and a second extension extending from an intersection of the second emission axis and the reception axis in a direction away from the second light emitting unit is larger than a first scattering angle that is an angle between the reception axis of the light receiving unit and a first extension extending from an intersection of the first emission axis and the reception axis in a direction away from the first light emitting unit.

Various embodiments of the teachings herein include a method for analyzing an electrode paste and/or an electrode layer for a battery cell. The method may include: measuring a property of the electrode layer paste and/or the electrode layer using a measuring facility; generating measurement data; and determining a quality value of the electrode layer paste and/or the electrode layer using an AI engine based on the measurement data.

A processing device to be used for optical inspection of samples is configured to be communicatively coupled to a camera of an optical inspection device and to a user interface, to receive input data from the user interface and/or from the camera, to provide output data to the user interface, to be communicatively coupled to a storage device, to provide instructions, by accessing instruction data stored at the storage device, for optical quality inspection of samples to be performed by a user via the user interface. The instructions include instructions regarding what kind of optical quality inspection to be performed on which sample. The processing device is further configured to receive quality report data from the optical quality inspection via the user interface, and/or image data of images acquired by the camera, and to store the quality report data and/or the image data in the storage device.

A system including a first container, wherein the first container includes a sample. The system includes a second container, wherein the second container comprises a reagent. Additionally, the system includes a test cell, wherein the test cell is configured to receive the sample and the reagent. Further, the reagent includes at least one of Griess reagent, sulfanilamide, or 1,5-diphenylcarbazide. Moreover, the test cell is situated in a chamber, wherein internal walls of the chamber are white.

The embodiment of the present application relates to the field of substance ingredient detection, for example, relates to a substance ingredient detection method and apparatus, and a detection device. The method includes: obtaining spectral information of a substance to be detected; and matching the spectral information with a pre-obtained prediction model based on a machine learning algorithm to obtain the ingredients of the substance to be detected. In the embodiment of the present application, the spectral information of the substance to be detected is obtained, and then the spectral information is matched with the prediction model based on the machine learning algorithm to obtain the prediction result of the ingredients of the substance to be detected. In the embodiment of the present application, the machine learning algorithm is combined with spectral recognition, the traditional algorithm is abandoned, the recognition speed is improved, and the substance detection efficiency is greatly improved.

Aspects of the present disclosure include reconfigurable integrated circuits for characterizing particles of a sample in a flow stream. Reconfigurable integrated circuits according to certain embodiments are programmed to calculate parameters of a particle in a flow stream from detected light; compare the calculated parameters of the particle with parameters of one or more particle classifications; classify the particle based on the comparison between the parameters of the particle classifications and the calculated parameters of the particle; and adjust one or more parameters of the particle classifications based on the calculated parameters of the particle. Methods for characterizing particles in a flow stream with the subject integrated circuits are also described. Systems and integrated circuit devices programmed for practicing the subject methods, such as on a flow cytometer, are also provided.