The Renege Case

Schools of Business, Accounting Programs, the AACSB accreditation standards, the accounting profession and society demand that we require ethical behavior and actions from our students, faculty and administration. Therefore, accounting departments desire to integrate into their programs a serious attempt to help students develop the skills and judgment with which to analyze situations and make ethical decisions

Automatic Detection of Electric Power Troubles (ADEPT)

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base

Automatic Detection of Electric Power Troubles (ADEPT)

ADEPT is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system, and is designed for two modes of operation: real-time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a Laser printer. This system consists of a simulated Space Station power module using direct-current power supplies for Solar arrays on three power busses. For tests of the system's ability to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three busses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modelling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base. A load scheduler and a fault recovery system are currently under development to support both modes of operation

The 2011 February superoutburst of the dwarf nova SDSS J112003.40+663632.4

We report unfiltered photometry of SDSS J112003.40+663632.4 during the 2011 February outburst which revealed the presence of superhumps with peak-to-peak amplitude of up to 0.22 magnitudes showing this to be an SU UMa type dwarf nova. The outburst amplitude was 5.4 magnitudes above mean quiescence and it lasted at least 12 days. The mean superhump period during the plateau phase was Psh = 0.07057(19) d.Comment: Accepted for publication in the Journal of the British Astronomical Association. 12 pages, 5 figure

DETERMINING RELATIONSHIPS BETWEEN KINEMATIC SEQUENCING AND BASEBALL PITCH VELOCITY USING MARKERLESS MOTION CAPTURE

The purpose of this study was to determine how the timings and magnitudes of peak pelvis rotational velocity, peak trunk rotational velocity, peak elbow extension velocity, and peak shoulder internal rotation velocity affect pitch velocity. Eighty pitchers (187.2 ± 8.2cm, 89.3 ± 13.0kg, 20.1 ± 3.3yrs) had a minimum of 3 fastballs recorded and video was processed using pitchAITM. Average pitch velocity was 38.1 ± 2.5 m/s. A multilinear regression generated a significant prediction for pitch velocity (R2 = 0.368 and p \u3c 0.01). Pitcher weight (β = 0.535, p \u3c 0.001), peak pelvis rotational velocity timing (β = -0.157, p = 0.001), peak elbow extension timing (β = 0.122, p = 0.006), and peak shoulder internal rotation timing (β = -0.113, p = 0.018), were significant contributors to the multilinear model. In conclusion, player weight and their kinematic sequence metrics from pitchAITM can be significant predictors of pitch velocity