Abstract: Computing device and method using machine learning to optimize operations of a processing chain of a food factory. The computing device collects data representative of characteristics of a product processed by the processing chain. At least some of the collected data are received from one or more sensor monitoring operations of the processing chain. The computing device determines at least one product characteristic value based on the collected data. The computing device executes the machine learning inference engine, which uses a predictive model for inferring command(s) for controlling processing appliance(s) of the processing chain based on inputs. The inputs comprise the at least one product characteristic value. The computing device transmits the command(s) to the processing appliance(s) of the processing chain. Examples of product characteristic values comprise: a product temperature, a product humidity level, a product geometric characteristic, a product weight, and a product defect measurement.

Abstract: Platform and method for collecting and analyzing transactions performed on digital token pools managed by a blockchain. The platform collects transaction data related to transactions performed on the digital token pools managed by the blockchain. The transaction data related to each transaction are collected from N geographically distributed nodes belonging to the blockchain, the transaction data being stored in data blocks at the N nodes. A validation algorithm is used to determine validated transaction data based on the collected transaction data, comprising comparing for each transaction the transaction data collected from the N nodes and determining the validated transaction data for the transaction based at least on a result of the comparison. The validated transaction data are processed to calculate a value of at least one metric based on the validated transaction data. The transaction data may be related to transactions performed on digital token pools managed by several blockchains.

Abstract: Computing device and method using machine learning to optimize operations of a processing chain of a food factory. The computing device collects data representative of characteristics of a product processed by the processing chain. At least some of the collected data are received from one or more sensor monitoring operations of the processing chain. The computing device determines at least one product characteristic value based on the collected data. The computing device executes the machine learning inference engine, which uses a predictive model for inferring command(s) for controlling processing appliance(s) of the processing chain based on inputs. The inputs comprise the at least one product characteristic value. The computing device transmits the command(s) to the processing appliance(s) of the processing chain. Examples of product characteristic values comprise: a product temperature, a product humidity level, a product geometric characteristic, a product weight, and a product defect measurement.

Abstract: Computing device and method using machine learning to optimize operations of a processing chain of a food factory. The computing device collects data representative of characteristics of a product processed by the processing chain. At least some of the collected data are received from one or more sensor monitoring operations of the processing chain. The computing device determines at least one product characteristic value based on the collected data. The computing device executes the machine learning inference engine, which uses a predictive model for inferring command(s) for controlling processing appliance(s) of the processing chain based on inputs. The inputs comprise the at least one product characteristic value. The computing device transmits the command(s) to the processing appliance(s) of the processing chain. Examples of product characteristic values comprise: a product temperature, a product humidity level, a product geometric characteristic, a product weight, and a product defect measurement.

Abstract: Computing device and method using machine learning to optimize operations of a processing chain of a food factory. The computing device collects data representative of characteristics of a product processed by the processing chain. At least some of the collected data are received from one or more sensor monitoring operations of the processing chain. The computing device determines at least one product characteristic value based on the collected data. The computing device executes the machine learning inference engine, which uses a predictive model for inferring command(s) for controlling processing appliance(s) of the processing chain based on inputs. The inputs comprise the at least one product characteristic value. The computing device transmits the command(s) to the processing appliance(s) of the processing chain. Examples of product characteristic values comprise: a product temperature, a product humidity level, a product geometric characteristic, a product weight, and a product defect measurement.

Abstract: Plural sample container holders, each of which is capable of carrying a plurality of sample containers, are carried in a box-like magazine. The sample handling system, has an incrementably operable first conveyor that conveys the loaded magazines. Intersecting the first conveyor at a given location is a second incrementably operable conveyor which is adapted to access, i.e. withdraw and insert holders, through an access of the magazine and to convey withdrawn holders to and from a sample work station and back to the given location. The second conveyor carries a protruding member which co-acts with a cooperative engageable member carried by each holder. When the holders are being carried by the magazine, their respective cooperative engageable members are in alignment, the magazine being adapted to expose the so aligned cooperative members of the holders.

Abstract: Positioning apparatus is disclosed which positions an object with respect to two predetermined lateral positions. In addition, the object is indexable by the apparatus in a step-by-step manner in an incidental direction with respect to each of the two lateral positions. It includes an endless sprocket chain entrained about two sprocket wheels. The chain carries a first member which is pivotably connected to the chain. A second member is affixed to the first member and is adapted to support the object. The second member is slidably mounted to a movable third member which in turn is guided by guiding means such as rails that guides the third member means in a predetermined aligned relationship with the reaches of the chain. In addition, chain guide means guide the chain along the first and second reaches. Driver means are coupled to one of the sprocket wheels to drive the endless chain.