The increased demand for medical diagnosis procedures has been recognized as one of the contributors to the rise of health care costs in the U.S. in the last few years. Nuclear medicine is a subspecialty of radiology that uses advanced technology and radiopharmaceuticals for the diagnosis and treatment of medical conditions. Procedures in nuclear medicine require the use of radiopharmaceuticals, are multi-step, and have to be performed under strict time window constraints. These characteristics make the scheduling of patients and resources in nuclear medicine challenging. In this work, we derive a stochastic online scheduling algorithm for patient and resource scheduling in nuclear medicine departments which take into account the time constraints imposed by the decay of the radiopharmaceuticals and the stochastic nature of the system when scheduling patients. We report on a computational study of the new methodology applied to a real clinic. We use both patient and clinic performance measures in our study. The results show that the new method schedules about 600 more patients per year on average than a scheduling policy that was used in practice by improving the way limited resources are managed at the clinic. The new methodology finds the best start time and resources to be used for each appointment. Furthermore, the new method decreases patient waiting time for an appointment by about two days on average.

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A neural network based on a competitive learning rule, when trained with the part machine incidence matrix of a large number of parts, classifies the parts and machines into part families and machine cells, respectively. This classification compares well with the classical clustering techniques. The steady state values of the activations and interconnecting strengths enable easier identification of the part families, machine cells, overlapping parts and bottleneck machines. Neural networks are mostly applied by treating them as a blackbox, i.e. the interaction with the environment and the information acquisition and retrieval occurs at the input and the output level of the network. This paper presents an approach where knowledge is extracted from the external and internal structure of the neural network.

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For the lastfive years, Texas A&M has been a member of the National Science Foundation's Foundation Coalition whose goal is to improve engineering education by stressing the connections among engineering, the sciences, and the arts.This was accomplished by integrating the concepts from calculus, physics, chemistry, English, and engineering beginning at the freshman level. One of the major impediments to expanding the program to all engineering students was how to handle non-standard students (i.e. those who had credit is some of their courses, or those who were not ready to take certain classes). A t Texas A&M, these "non-standard" students comprise about 40% of the incoming freshman class, with the majority to these being deficient in mathematics, thus not being ready to enroll in college calculus.To address these students, in the spring of 1996, Texas A h M began to develop a modified curriculum whose goals were still the integration of material across the freshman classes. This program was implemented in the fall of 1996, refined during that year and is currently being used for some of the freshman who enrolled in the College of Engineering this year and who were deficient in calculus. Beginning in the fall of 1998, all incoming freshmen engineering students who are not ready to enroll in calculus will be enrolled in this program. This paper will address the design and implementation of the "pre-calculus " track program at Texas A&M.University of Alabama) focusing on the creation of an enduringfoundation for student development and life longlearning. The Foundation Coalition (FC) has four major thrusts for educational transformation:To integrate course material across disciplines in order to motivate engineering problem solving and design;To develop the student's ability to work as a productive member of a "technical" team;To change the pedagogy of the classroom from a passive lecture to that of an active/collaborative leaming experience; andTo use technology in the classroom in order to provide the students with enhanced design and problem solving tools. 0 Evolution of the Pre-Calculus Freshman Integrated CurriculumThe College of Engineering at Texas A&M has a common curriculum for all engineering programs in the first year. At the beginning of the FC some departments were not satisfied with this curriculum. This concern has been heightened by the declining financial support of the instruction of ENGR 109 (Engineering Problem Solving & Computing) from the college level, and perception of the actual content of the courses in the common curriculum (both the engineering and non-engineering courses) taken during the fi-eshman year. The original required curriculum is shown in Figure 1. Some of the specific concerns with the curriculum were: ENGL 104 does not provide enough seats for all entering first year students; most engineering students must take CHEM 101 before CHEM 102; some engineering faculty members are not certain of the value of ENGR 109; several engineering programs are inquiring about the need for E...

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In general terms, traditional strategic planning may be described as a mechanism for generating a set of targets and approaches for achieving the established purpose of an organization. For it to be effective, a strategic plan must serve as the basis for creating a set of well-defined operations that align with the organizational goals and strategies. Because the task of generating an effective plan requires knowledge, time, patience, and persistence, not all organizations are prepared to devote the time and energy that is required to produce a meaningful plan. It is the intent of this paper to describe an approach to quality planning which was used to make major curricular changes in the first-year engineering education program at Texas A&M University. The planners' intention in this instance was to apply quality function deployment matrices, systems engineering concepts, quality management principles, and multi-attribute utility analysis to the evaluation of curriculum alternatives in an effort to make the planning process less complex and more systematic.

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