An environmental pertinence interface generated by an example apparatus, method, and computer program product is provided. The apparatus receives an interface request from a mobile device and location data relating to the mobile device. The apparatus queries a database to identify environmental objects that satisfy a proximity threshold. The apparatus identifies environmental pertinence digital content items when the proximity threshold is satisfied. The apparatus applies user permissions rules to determine a user-permitted environmental pertinence digital content item set and generates an environmental pertinence interface for display by the mobile device.

Behavior data associated with a user is obtained. The behavior data is generated when the user uses an Internet service and includes a user identification and identification information indicating the Internet service. At least one predefined carbon-saving quantity quantization algorithm is determined based on the identification information related to the Internet service. A carbon-saving quantity associated with the user is calculated based on the obtained behavior data and the determined at least one predefined carbon-saving quantity quantization algorithm. Based on the calculated carbon-saving quantity associated with the user and the user identification, user data is processed. The user data is related to the carbon-saving quantity associated with the user.

An ecological flow determination method for considering a lifting amount a belongs to a technical field of environmental engineering and includes the following steps: collecting, by a collecting device, data needed to calculate an ecological flow; determining, by a calculating device, an ecological base flow; selecting an upper limit and a lower limit of the ecological base flow so as to determine a range of the ecological base flow; verifying the lower limit of the ecological base flow; calculating water demands of landscape wetland, sediment discharge and dilution self purification of three service objects; comparing the water demands of the three service objects so as to determine the lifting amount, and finding out a minimum value and a maximum value to determine a lower limit and an upper limit of the lifting amount in the range; combining the ecological base flow and the lifting amount to determine the ecological flow.

Hierarchical structure data (200) includes: a first hierarchical structure in which first nodes are hierarchized, the first hierarchical structure corresponding to a first analysis axis which is an analysis axis of a GHG emission amount; a second hierarchical structure in which second nodes are hierarchized, the second hierarchical structure corresponding to a second analysis axis which is different from the first analysis axis; and a plurality of emission amount nodes which are nodes of the GHG emission amount. The plurality of first nodes include two or more first connection nodes that connect to an emission amount node, and the plurality of second nodes include two or more second connection nodes that connect to an emission amount node. When one of the first nodes is selected as a first selection node and one of the second nodes is selected as a second selection node, an extraction unit (104) extracts a chain of nodes that leads to the first selection node via the first connection node from the emission amount node to which the first connection node connects, and extracts a chain of nodes that leads to the second selection node via the second connection node from the emission amount node to which the second connection node connects.

A multi-hardware energy-consumption-oriented channel pruning method and a related product. The method includes: ranking importance of a filter in a to-be-pruned convolutional neural network (CNN) model by using a feature distribution discrepancy (FDD) evaluation model based on a feature distribution of an original network model, and deleting a filter with a lowest importance ranking to generate a candidate first pruning model; determining an energy consumption of the candidate first pruning model by using an energy consumption estimation model based on actual measured data; performing trade-off processing on importance of a filter in the candidate first pruning model and the energy consumption of the candidate first pruning model by using a multi-objective evolutionary solving model, and obtaining a pruning scheme corresponding to each hardware device; and pruning the to-be-pruned CNN model by using the pruning scheme, and obtaining a second pruning model corresponding to each hardware device.

A computer-implemented method for producing a product by selection of one or more chemical precursors, the method comprising: (a) receiving, from an interface, at least two data sets comprising (i) material data related to chemical or physical properties of the one or more chemical precursors and (ii) environmental impact metrics data related to environmental impact metrics for the one or more chemical precursors necessary for manufacturing the product; (b) providing an environmental impact calculation model describing a functional relationship between the material data and the environmental impact metrics data; (c) optionally retrieving, from a database, historical environmental impact metrics data for the one or more chemical precursors, the historical environmental impact metrics data comprising historic environmental impact metrics corresponding to the one or more chemical precursors; (d) ranking the at least two data sets by a distance from predefined minimum values in multiple dimensions of the environmental impact calculation model and generating thereon based ranking results; and (e) optionally obtaining a degree of matching between the ranking results of the environmental impact calculation model and the historical environmental impact metrics data and generating thereon based matching results; and (f) selecting one or more chemical precursors out of a plurality of chemical precursors based on the ranking results and/or the matching results and adding the selected one or more chemical precursors to a production process of the product.