In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.

Provided is an improved apparatus and method for identifying inefficient workers in a workplace. The apparatus receives voice commands from a worker in the workplace and transforms the voice commands into text commands by converting audio signals associated with the voice commands to one or more points in data that are stored in a database. The apparatus further creates a dialogue-stream including the text commands and a time of receipt of each voice command associated with the respective text command. Further, a workflow record may be created from the dialogue-stream and may be used to generate an efficiency metric for the worker. Both the dialogue-stream and workflow record can be used to monitor the efficiency of the worker and implement effective changes to the workflow process.

An exercise machine monitoring and instruction system for the movement of an element of an exercise machine by an exerciser and providing automated feedback to the exerciser to help improve the exercise in real-time. The exercise machine monitoring and instruction system generally includes an exercise machine having a movable element that moves between a first position and a second position in a reciprocating manner, a sensor that detects a real-time position of the movable element, a processor in communication with the sensor to receive the real-time position data from the sensor related to a position of the movable element and a feedback device in communication with the processor that provides real-time instructions to the exerciser on how to adjust their workout to achieve a desired result.

A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.

A contextual lead generation in an automated communication link establishment and management system may store information related to sales calls. The system may identify strengths and weaknesses of a sales representative. The system may provide training content to the sales representative in real time base on the identified strengths and weaknesses.

Various embodiments described herein relate to performance assurance modeling for a portfolio of assets. In this regard, a request to generate one or more performance assurance insights related to one or more assets is received. The request includes a fault descriptor describing one or more faults associated with the one or more assets. In response to the request, a first risk level associated with the one or more faults is determined based on the fault descriptor and asset data associated with the one or more assets. Additionally, in response to the request, a second risk level associated with the one or more faults is generated based on one or more predetermined relationships between faults and asset performance indicator thresholds. The one or more performance assurance insights are then generated based on a comparison between the first risk level and the second risk level.

A work machine, such as a wheel loader, operating in a worksite within a wireless control system includes an operator-specific configuration of machine settings associated with an identification for the operator. As the work machine executes a work function, the operator identification, an initial operator-specific configuration, and sensed performance metrics are transmitted to a control system for the worksite. Linking the operator identification to one or both of the initial operator-specific configuration and the metrics, the control system analyzes performance of the work machine by the operator with increased granularity and flags potential irregularities. A modified operator-specific configuration to change operator performance is returned to the work machine and made to override the initial operator-specific configuration when the operator next takes control of the work machine.

The present application provides a method and system for dynamically predicting a deoxynivalenol content of wheat at harvest, including: on the basis of historical data, screening out by particle swarm optimization algorithm combined factors suitable for establishing a prediction model, and establishing the prediction model by using the combined factors; on the basis of data of a current year, predicting a second flowering date and a second harvest date of wheat in the current year by an agricultural model; then obtaining a weather forecast on the basis of the second flowering date and the second harvest date, and combining the weather forecast and geographic data into correlated factors; and finally predicting the deoxynivalenol content of wheat at harvest by means of the prediction model and the correlated factors. Compared with the prior art, statistical items in the prediction model are more comprehensive, and growth period data of the current year can be dynamically predicted on the basis of growth period indexes model, thus continuously adjusting and establishing the prediction model. In addition, an overhead time for screening multi-dimensional large-batch data by the particle swarm optimization algorithm has more advantages, and the prediction model established by a multiple linear regression algorithm has higher precision.

The present application provides a method and system for dynamically predicting a deoxynivalenol content of wheat at harvest, including: on the basis of historical data, screening out by particle swarm optimization algorithm combined factors suitable for establishing a prediction model, and establishing the prediction model by using the combined factors; on the basis of data of a current year, predicting a second flowering date and a second harvest date of wheat in the current year by an agricultural model; then obtaining a weather forecast on the basis of the second flowering date and the second harvest date, and combining the weather forecast and geographic data into correlated factors; and finally predicting the deoxynivalenol content of wheat at harvest by means of the prediction model and the correlated factors. Compared with the prior art, statistical items in the prediction model are more comprehensive, and growth period data of the current year can be dynamically predicted on the basis of growth period indexes model, thus continuously adjusting and establishing the prediction model. In addition, an overhead time for screening multi-dimensional large-batch data by the particle swarm optimization algorithm has more advantages, and the prediction model established by a multiple linear regression algorithm has higher precision.

A system that allows a contractor to remotely monitor and/or interact with its customers' building control systems, such as heating, ventilating and air conditioning (HVAC) systems, and analyze information obtained from the building control systems over time. Such a system may help the contractor monitor and diagnosis customer building control systems, setup service calls, achieve better customer relations, create more effective marketing opportunities, as well as other functions. In some cases, the disclosed system may include a controller that analyzes data from HVAC systems, determines a thermal model of a space environmentally controlled by an HVAC system, and provides an energy audit of the space that is environmentally controlled by the HVAC system. The controller may output a result of the energy audit to a user.