A bilateral mechanical lesion of the midbrain and pontine tegmentum was found to abolish completely the tonic components of sound-induced seizures in genetically epilepsy-prone rats (GEPR) that display tonic-clonic seizures. Correlations between varied lesions placements and effects on maximal audiogenic seizures provided evidence that damage to the nucleus reticularis pontis oralis (RPO) of the midbrain and pontine reticular formation (RF) was responsible for the seizure-attenuating effects. Moreover, electrolytic lesions of the pontine RF involving the RPO nucleus were found to abolish the tonic components of the maximal audiogenic seizure. Additionally, bilateral mechanical lesions involving the RPO nucleus were found to attenuate the clonic components of sound-induced seizures in GEPR that display only running seizures and clonus. These findings are consistent with previous studies showing that pontine tegmental lesions attenuate the tonic components of maximal electroshock- and pentylenetetrazol-induced seizures, and lend further support to the hypothesis that all generalized tonic seizures share a common neural substrate. The role of the brainstem RF in tonic versus clonic convulsions is discussed in light of the present findings.

Defuzzification plays an important role in the Bisector methods. The Centroid method determines the output implementation of a fuzzy system since the crisp value generated value by calculating the center of gravity of the possibility best represents the possibility distribution of all possible fuzzy distribution of the outputs. For continuous values, the output control outputs. The focus of this paper is on comparison of Z is calculated using the formula in equation 2 where u(x) several defuzzification strategies in two fuzzy inference systems . r o t s o m * cr designed to analyze questionnaires. Two different questionnaires were analyzed, one having two fuzzy rules and one having three fuzzy rules for the inference component. The output of Centroid,

Multicriteria decision making~MCDM! methods can be powerful aids for evaluating patients' medical information in medical diagnostic systems. Technique ordered preference by similarity to the ideal solution~TOPSIS! is one of the more widely used MCDM methods in decision support systems. For the purpose of this work, the TOPSIS method is modified into a more suitable form and used for the implementation of a web-based medical diagnostic system. In our modified TOPSIS method, we have utilized fuzzy logic so that users can more accurately describe their symptoms. The data given to the modified TOPSIS method are often massive in proportions and may take a considerable amount of time to generate a ranking of alternatives. TOPSIS lends itself to parallel computation because it is virtually a combination of matrix computations. Therefore, computer parallelism is implemented so that a large amount of input data can be handled simultaneously, hence decreasing overall execution time. In addition, to make our MCDM system more accessible, we have designed our system to be web based. The web-based medical diagnosis system includes a dynamically generated web-based user interface, while the parallel implementation of the modified TOPSIS component, in conjunction with the Common Gateway Interface, forms the back end of the system.

Learning objects have long promised dramatic savings of time and money in course and curriculum development, but they have failed to deliver the return on investment that seems a natural extension of their existence -reusability. Because a single hour of online instruction can take up to 300 hours to develop, reusability is the core value message offered by learning object promoters, from the earliest days to the present. Yet, after 12 years of successive evolution, learning objects are still primarily a collection of stand-alone modules that rarely interconnect outside of strictly controlled regimes, such as those imposed by corporate and military training guidelines. Among the contributing factors to this impediment are definition of a learning object, size of a learning object and aesthetics of a learning object.In response to this shortcoming, we propose to introduce a new entity -the learning pod. Engineered for reusability, the learning pod incorporates several modules that bring current technology to create an experientially seamless interconnection between disparate learning objects. These modules communicate with one another to build a consistent unit of instruction that uses several learning objects depending on the requirements. Several technologies including semantic web, XSL/XML and CSS are utilized to achieve presentation cohesiveness.