A telework effect prediction apparatus (100) includes a storage unit (110) for storing a telework effect prediction model (111) for predicting a degree of effectiveness of teleworking based on a congestion degree of a commuting route and a cooperative work amount of a user, an acquisition unit (120) for acquiring a predicted value of the congestion degree of the commuting route of a specific user on a designated date, a calculation unit (130) for calculating a cooperative work amount from a work schedule of the specific user on the designated date, a prediction unit (140) for predicting the degree of effectiveness of the teleworking from the acquired predicted value and the calculated cooperative work amount by using the telework effect prediction model; and an output unit (150) for outputting information based on the predicted degree of effectiveness.

New and improved methods and systems for modeling the performance of selected company metrics. Multiple, non-traditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and/or the performance of a selected industry or industry sector may be analyzed.

A modeling method and an apparatus are disclosed. The method includes: obtaining a first data set of a first indicator, and determining, based on the first data set, a second indicator similar to the first indicator; and determining a first model based on one or more second models associated with the second indicator. The first model is used to detect a status of the first indicator, and the status of the first indicator includes an abnormal state or a normal state. The second models are used to detect a status of the second indicator, and the status of the second indicator includes an abnormal state or a normal state.

A system and method for detecting changes in an agricultural field uses a time series of target images of the agricultural field in which a vegetation index value is calculated for each target image. A target trend line is calculated from the time series of the vegetation index values. A time series of candidate images of one or more candidate fields having one or more attributes that correspond to one or more attributes of the agricultural field is also acquired in which an expected trend line can be determined from calculated vegetation index values representative of respective candidate images. An alert is generated in response to a deviation of the target trend line from the expected trend line that meets alert criteria.

A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Automated fertigation systems and methods determine crop N status from a vegetation index calculated from acquired image data of indicator blocks having at least two plots, one with a reduced N application rate (canary) and one with an increased N application rate (reference) versus a bulk area N application rate. In a preferred method, sub-regions are defined in a field being managed. In each sub-region, N (nitrogen) is applied to create adjacent canary and reference plots, wherein a canary plot is given less than a designated N amount and a reference plot. The sub-regions are subsequently imaged. A fertigation decision is made for each sub-region based upon automatic analysis of the vegetation indices of the canary and reference plots in each sub-region.

A system and method for employee retention includes determining an employee's happiness level by analysis of their attire from a captured image. Image capture and employee identification is commenced when an employee uses their employer ID to enter a work premises. Captured images are analyzed for attire content. Attire content is categorized and analyzed relative to other fashion images to gage an employee's likes or dislikes, along with their happiness level. Remedial suggestions are generated when a happiness level falls below a set threshold level. Remedial suggestions may include gifts personal to an employee as determined from their attire. Captured images and web data are subject to artificial intelligence and machine learning to continuously improve analysis and recommendations.

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: estimating, using a machine learning model, a respective budget for expenditures based on respective parameters of each of one or more project initiatives associated with physical stores; determining, using a mixed integer linear programming formulation, a time window to execute the each of the one or more project initiatives; generating one or more respective recommendations for the each of the one or more project initiatives for a predetermined time period; and sending instructions to display the one or more respective recommendations on a graphical user interface, wherein the graphical user interface displays a respective status of each of the one or more respective recommendations. Other embodiments are disclosed.

Systems and methods are provided for generating engagement recommendations suggesting ways that one or more creators of content may maximize subscribership and/or subscription-based revenue, as well as support recommendations suggesting ways that the one or more creators of content may realize successful support of their content creation. Engagement recommendations can involve suggestions regarding when and/or how to engage one or more subscribers that results in a positive impact to subscribership and/or subscription-based revenue. Support recommendations can involve suggestions regarding when and/or how to elicit support in line with preferred indicia of success set forth by the one or more creators.

The present disclosure provides a consistency evaluation method for a traditional extraction process and a modern extraction process of a traditional Chinese medicine (TCM) compound preparation. In the present disclosure, the reference correlation degree is calculated based on the relative deviation. The reference correlation degree is within a threshold interval of [−∞, 1]. If the relative deviation is 0, the reference correlation degree is 100%, and at the moment, the quality of a modern extraction sample is completely consistent with that of a reference sample. Thus, compared with the relative deviation, the reference correlation degree can represent a quality difference between the modern extraction sample and the reference sample more intuitively and clearly. Comprehensive evaluation conducted in combination of a subjective weighting method and an objective weighting method reflects the subjective will of a decision maker, but does not depart from actual data, making a final weighting result more persuasive.