A computer system and method for intelligent diagnosis are provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises receiving an initial appoint request, receiving a first scanning appointment request, sending a movable practice to patient location, receiving scanning data, and sending the scanning data to remote server for processing.

Systems and methods are provided for dynamically tracking the cleanliness of a vehicle's exterior. In particular, a dynamic strategy for cleaning vehicles is provided. In a system including a fleet of vehicles, the vehicles are regularly cleaned to remove dirt, dust, and any other matter that has stuck to the vehicle.

An indoor pollution prevention system includes a plurality of gas detection modules, one or more intelligent control-driving processing devices, one or more gas-exchange processing devices, and one or more indoor cleaning and filtration devices. The gas-exchange processing device includes one or more flow-guiding component and a cleaning and filtration assembly. The intelligent control-driving processing device controls the operation of the indoor cleaning and filtration device in real-time under a surveillance condition, therefore the air pollution source in the indoor space passes through the indoor cleaning and filtration device, allowing the air pollution source in the indoor space to be filtered and exchanged to become a clean air.

A wireless sensor platform design and a single walled carbon nanotube/ionic liquid-based chemidosimeter system can incorporated into a highly sensitive and selective chemical hazard badge that can dosimetrically detect an analyte down to a sub parts-per-million concentration.

One variation of a method includes, during a calibration period: triggering collection of an initial bioaerosol sample by an air sampler located in an environment; and triggering dispensation of a tracer test load by a dispenser located in the environment; accessing a detected barcode level of a barcode detected in the initial bioaerosol sample; accessing a true barcode level of the barcode contained in the tracer test load; and deriving a calibration factor for the environment based on a difference between the detected barcode level and the true barcode level. The method further includes, during a live period succeeding the calibration period: triggering collection of a first bioaerosol sample by the air sampler; accessing a detected pathogen level of a pathogen detected in the first bioaerosol sample; and interpreting a predicted pathogen level of the pathogen in the environment based on the detected pathogen level and the calibration factor.

A method executed by a control device includes: acquiring odor information indicating an odor intensity of each of a plurality of garbage collection sites, the odor intensity being detected by an odor sensor; determining an order in which garbage is collected from each of the garbage collection sites based on the odor information; and causing a garbage collection device to sequentially collect the garbage from each of the garbage collection sites according to the order.

One variation of a method includes, during a calibration period: triggering collection of an initial bioaerosol sample by an air sampler located in an environment; and triggering dispensation of a tracer test load by a dispenser located in the environment; accessing a detected barcode level of a barcode detected in the initial bioaerosol sample; accessing a true barcode level of the barcode contained in the tracer test load; and deriving a calibration factor for the environment based on a difference between the detected barcode level and the true barcode level. The method further includes, during a live period succeeding the calibration period: triggering collection of a first bioaerosol sample by the air sampler; accessing a detected pathogen level of a pathogen detected in the first bioaerosol sample; and interpreting a predicted pathogen level of the pathogen in the environment based on the detected pathogen level and the calibration factor.

An environmental emission monitoring system may include satellites configured to sense GHG emissions data for an AOI, and a server. The server may be configured to obtain the sensed GHG emissions data from the satellites, obtain geospatial positions of stationary GHG emitting point sources within the AOI, and generate expected stationary GHG emission data for the stationary GHG emitting point sources within the AOI and based upon the geospatial positions. The server may also be configured to obtain geospatial path data for GHG emitting vehicles moving within the AOI, generate expected vehicle GHG emission data for the GHG emitting vehicles moving within the AOI and based on the geospatial path data, and compare a sum of the expected stationary GHG emission data and expected vehicle GHG emission data with the sensed GHG emissions data to identify any stationary GHG emitting point source and any GHG emitting vehicle outside of a respective GHG emission threshold.

One variation of a method for detecting pathogens includes: accessing a timeseries of pathogen data for a pathogen, in a set of pathogens, derived from a series of pathogen samples collected in an environment during a time period; characterizing a pathogen profile, representative of changes in pathogen level of the first pathogen in the environment during the time period, based on the timeseries of pathogen data; accessing a baseline pathogen profile representative of changes in pathogen levels of the set of pathogens in the environment during an initial time period preceding the time period; characterizing a difference between the pathogen profile and the baseline pathogen profile; and, in response to the difference exceeding a threshold difference, selecting a mitigation action configured to reduce pathogen levels of the first pathogen and transmitting a prompt to execute the mitigation action to a user associated with the indoor environment.

Systems and methods for managing post-harvest crop quality and pests. A post-harvest monitoring system receives sensor device measurements from sensors deployed within a commodity storage facility. The system analyzes the sensor measurements and, optionally, other data, and provides a user with a representation of the storage facility that includes air flow, temperature, and/or moisture content readouts, along with stored commodity quality and/or stored commodity business metrics predictions concerning infestation level, visible mold, dry matter loss, germination capacity, gas concentration, and estimates of commodity value and profit margin under a variety of post-harvest monitoring system-recommended or user-specified scenarios. Use of the system thus enhances stored commodity quality, marketability and food safety by providing solutions that combat spoilage manifestations and guide end users to efficient pest management.