Abstract: There is provided a fan assembly comprising a motor-driven impeller arranged to generate an airflow, an air outlet arranged to emit the airflow, at least one filter assembly that is arranged to purify the airflow, a plurality of sensors arranged to measure a value for each of a plurality of air quality characteristic, and a processor. The processor is configured to receive measured values for each of the plurality of air quality characteristics, identify one of a corresponding set of intervals within which the measured value falls, select an air quality index value associated with the identified interval, and generate a corresponding speed value using the selected air quality index value. The processor is then configured to set a speed of the motor-driven impeller in dependence upon the highest of the speed values.

Abstract: Various reverse osmosis (RO) filter systems are disclosed herein. The RO filter systems can include one or more RO filters connected to and in fluid communication with an end cap filter component and/or a fluid turbine component. Some RO filter systems can further include a filter component connected to and in fluid communication with the one or more RO filters and/or a fluid turbine component. Additional RO filter systems can further include a pre-filter component connected to the filter component.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.

Abstract: Autonomous carriers or totes that include vacuum units are provided. As the totes move or are moved through a warehouse carrying products, they collect debris. The debris can be analyzed at the tote, and actions can be performed based upon the analysis.

Abstract: Features are applied to a mathematical model to produce a cutting pattern for opening a container. The cutting pattern specifies which of one or more cutting tools is to be used and the location of where cuts are to be made on the container. The cutting pattern is sent to a container opening machine. The container opening machine is operated and the container cut and opened by the container opening machine according to the cutting pattern.

Abstract: An automated container cutting system includes a cutting tool configured to cut a container and a force feedback sensor operatively connected to the cutting tool. The force feedback sensor is configured to measure resistive force exerted on the cutting tool. The automated container cutting system includes a processor communicatively coupled to the force feedback sensor. The processor is configured to receive resistive force data from the force feedback sensor and determine whether the cutting tool has penetrated through the wall of the container using the received resistive force data.

Abstract: Features are applied to a mathematical model to produce a cutting pattern for opening a container. The cutting pattern specifies which of one or more cutting tools is to be used and the location of where cuts are to be made on the container. The cutting pattern is sent to a container opening machine. The container opening machine is operated and the container cut and opened by the container opening machine according to the cutting pattern.

Abstract: A parent tote container is segmented into smaller, child tote containers. Each of the child tote containers includes products that are commonly sold together and are removed from the tote containers at picker stations with the same type of picker mechanism. Items are moved from the child tote containers to a customer order tote container at a picker station.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning and digital embedding processes to generate digital maps of biology and user interfaces for evaluating map efficacy. In particular, in one or more embodiments, the disclosed systems receive perturbation data for a plurality of perturbation experiment units corresponding to a plurality of perturbation classes. Further, the systems generate, utilizing a machine learning model, a plurality of perturbation experiment unit embeddings from the perturbation data. Additionally, the systems align, utilizing an alignment model, the plurality of perturbation experiment unit embeddings to generate aligned perturbation unit embeddings. Moreover, the systems aggregate the aligned perturbation unit embeddings to generate aggregated embeddings. Furthermore, the systems generate perturbation comparisons utilizing the perturbation-level embeddings.

Abstract: First scanned images of the first container are received from a scanning device that show the contents of the interior of the first container before the first container is cut and opened. Second scanned images that are of the contents of the first container after the first container is cut and opened are also received. The images are analyzed and, based upon the analysis, selective modifications to the operating parameters of the container opening machine are determined and made.

Abstract: A system and method for detecting hazardous items in a recycling facility, including: receiving, from a first non-visible spectrum image capturing device, a first non-visible spectrum image of items on a conveyor belt. The system and method may include providing, to an AI module, the first non-visible spectrum image of the items on the conveyor belt, where the AI module includes functionality to identify features of the items captured in the first non-visible spectrum image; compare the identified features of the items on the conveyor belt to features of hazardous items of the AI module; and identify a hazardous item in the first non-visible spectrum image based on a similarity between the identified features to the features of hazardous items of the AI module. The system and method may include providing, in response to identifying the hazardous item, an indication of the presence of the hazardous item.

Abstract: Systems, methods, and apparatuses are described herein for predicting resource shortages using machine learning.

Abstract: The system of the present disclosure reduces the temperature of any air conditioning condenser/radiator, either through retrofit or by original manufacturing. The system is connected to a water supply for pre-cooling ambient air drawn into the air conditioning unit by providing a misting spray of water that evaporates rapidly to cool the air conditioning condenser. The system has a water supply connection connected to the water supply, a water treatment assembly for treating aspects of the water supplied, and a water delivery assembly that receives clean water from the water treatment assembly and delivers the misting spray proximate the air conditioning unit. The system may be controlled by using a wireless technology such as a Z-wave or other type of wireless controller and one or more sensors.

Abstract: The contents of totes and the amount a tote container is filled are monitored. A central database stores product dimensions and tote dimensions as well as a percentage of which a tote is to be filled. Image scanners that obtain three-dimensional images are used to determine if the actual dimensions of the tote, the product, and the fill amount are consistent with the expected or optimal values of these characteristics. When there is a discrepancy between measured and expected values, then one or more actions can be taken.

Abstract: Examples provide a system and method for autonomously placing items into non-rigid containers. An image analysis component analyzes image data generated by one or more cameras associated with picked items ready for bagging and/or a non-rigid container, such as, but not limited to, a bag. The image analysis component generates dynamic placement data identifying how much space is available inside the bag, bag tension, and/or contents of the bag. A dynamic placement component generates a per-item assigned placement for a selected item ready for bagging based on a per-bag placement sequence and the dynamic placement data. Instructions, including the per-item assigned placement designating a location within the interior of the non-rigid container to the selected item and an orientation for the selected item after bagging, is sent to at least one robotic device. The robotic device places the selected item into the non-rigid container in accordance with the instructions.

Abstract: The contents of totes and the amount a tote container is filled are monitored. A central database stores product dimensions and tote dimensions as well as a percentage of which a tote is to be filled. Image scanners that obtain three-dimensional images are used to determine if the actual dimensions of the tote, the product, and the fill amount are consistent with the expected or optimal values of these characteristics. When there is a discrepancy between measured and expected values, then one or more actions can be taken.

Abstract: There is provided a fan assembly comprising a motor-driven impeller arranged to generate an airflow, an air outlet arranged to emit the airflow, a plurality of sensors arranged to measure a value for each of a plurality of air quality characteristics, a display and a processor. The processor is configured to receive measured values for each of the plurality of air quality characteristics, identify one of a corresponding set of intervals within which the measured value falls and select an air quality index value associated with the identified interval. The processor is then configured to identify the highest of the selected air quality index values as a current overall air quality index value and to cause the display to display a time series plot of the current air quality index value and a number of preceding air quality index values.

Abstract: A solution for automated food selection includes: an order processing component operable to receive an order identifying an item; a selection component comprising: a first hyperspectral sensor operable to sense a reflection from the item and produce a sensor output based at least on the reflection; and a picking mechanism; a control component operable to: based at least on identification of the item, select a hyperspectral profile from a set of hyperspectral profiles; compare the sensor output with the selected hyperspectral profile; and based at least on the comparison, determine whether to select the item for fulfillment of the order, wherein the picking mechanism is operable to divert the item to a selection output based at least on a determination to select the item for fulfillment of the order; and a transport component operable to transport the item to an order storage zone.