Abstract: The illustrative embodiments described herein provide a computer implemented method, apparatus, and computer program product for managing a batch production system. In an illustrative embodiment, an application forecast is generated for a set of batch jobs in a critical path of a batch application. The application forecast comprises a sum of execution times for the batch jobs in the critical path. In response to detecting an abend from production data generated during the execution of the batch application, the abend is mapped against known abends in an abend database to identify a set of abend solutions. Thereafter, a notification is generated which comprises at least one of a status of the batch application and the set of abend solutions, wherein the status is generated based on the application forecast.

Abstract: An HVAC system includes a grid of intersecting ducts having one or more inlets, outlets, and intersections. Air may be received into the inlets and directed through the outlets into one or more zones of a building. One or more HVAC units may be connected to the inlets and mechanical valves may be located at the intersections to control the air flow through the grid. A control system may be provided to control the temperature of each zone by adjusting the mechanical valves (and/or turning selected HVAC units “on” or “off”). In certain embodiments, the HVAC system includes at least one reading device to read temperature preference information associated with an occupant of a zone. The control system may then align the temperature of the zone with the temperature preference information when the occupant is inside the zone. A corresponding method and apparatus are also disclosed herein.

Abstract: The present invention can increase the odds of choosing the right people for a team by considering their “rate of interest/knowledge” in multiple topics. Given a known contacts network, represented by nodes interconnected by links, several different sub-networks are identified within it, corresponding to different topics or areas of expertise required to a specific project. For each sub-network, there will be nodes with an associated grade, based on that person's knowledge/interest for the topic related to that sub-network. As such, each node/person receives a grade for each topic. Using these grades, a weight of each link between the nodes is calculated. This process is performed for every node for each topic and associated grade. After that, a superposition of all sub-networks is made and a multiple interest network is yielded.

Abstract: A system and method of optimizing a manufacturing process so as to reduce toxic waste in the process. In an embodiment, the method includes: describing the manufacturing process using a plurality of nodes, each node representing a component or a process; connecting the plurality of nodes with directed edges to form a directed graph, the directed graph representing possible manufacturing process flows from a begin node to an end node; assigning to each edge a value representative of a cost resulting from toxic waste generated by a component or process; and determining a shortest path from the begin node to the end node in the directed graph to identify manufacturing process flow having a minimum cost.

Abstract: A system and method for optimizing manufacturing processes to increase usage of recycling in a manufacturing process. In an embodiment, the method comprises: describing a manufacturing process using a plurality of nodes, each node representing a component or a process; connecting the plurality of nodes with directed edges to form a directed graph, the directed graph representing possible manufacturing process flows from a begin node to an end node; assigning to each edge a value representative of the benefit resulting from usage of recycling in a component or process; and determining a longest path from the begin node to the end node in the directed graph to identify a manufacturing process flow maximizing usage of recycling in the manufacturing process.

Abstract: A system and method for automated stock market investment. In an embodiment, the method includes: i) inputting M previous time period values for the stock into a M-order finite impulse response (FIR) filter, the M-order finite impulse filter having a filter order M, a least mean square (LMS) prediction algorithm with step-size mu, and M adjustable filter coefficients; ii) obtaining an output from the M-order FIR filter, the output from the M-order FIR filter being a predicted next time period value for the stock; iii) comparing the predicted next time period value for the stock with an actual next time period value for the stock to calculate a prediction error; iv) inputting the calculated prediction error into an adaptive algorithm to obtain an adjustment for the at least one adjustable filter coefficient; and v) applying the adjustment for the at least one adjustable filter coefficient and repeating all steps until halted.

Abstract: A periodical expense planning system, method and data model considers income, predicted and unpredicted expenses, in order to maximize final balance and profit for the analysis period. It applies multi-period linear optimization techniques, analyzing the expenses of a house cell over N periods for every period within N. The model analyzes the predicted expenses (known costs, but not necessarily the same amount on every period) of the k future periods to come, and also takes into account the actual costs incurred in the r previous periods (having that k+r=N). By doing that, the model calculates the optimal amount that can be taken away from the model in each period (i.e., money to be invested and thus give the user earnings) is achieved, and a planning for the expenses' payment is given. The variables under analysis for each period are how much to save for the next period, how much can be taken away from the model (i.e., the money to be invested), how much should be put into the system (i.e.