Attributed concept maps: fuzzy integration and fuzzy matching

A concept map, typically depicted as a connected graph, is composed of a collection of propositions. Each proposition forming a semantic unit consists of a small set of concept nodes interconnected to one another with relation links. Concept maps possess a number of appealing features which make them a promising tool for teaching, learning, evaluation, and curriculum planning. We extend concept maps by associating their concept nodes and relation links with attribute values which indicate the relative significance of concepts and relationships in knowledge representation. The resulting maps are called attributed concept maps (ACM). Assessing students will be conducted by matching their ACMs with those prebuilt by experts. The associated techniques are referred to as map matching techniques. The building of an expert ACM has in the past been done by only one specialist. We integrate a number of maps developed by separate experts into a single map, called the master map (MM), which will serve as a prototypical map in map matching. Both map integration and map matching are conceptualized in terms of fuzzy set discipline. Experimental results have shown that the proposed ideas of ACM, MM, fuzzy map integration, and fuzzy map matching are well suited for students with high performances and difficult subject materials.

Reconstruction of pharyngeal defects using AlloDerm and sternocleidomastoid muscle flap

OBJECTIVE

To report our experience in reconstructing pharyngeal defects that cannot be closed primarily, using acellular dermal matrix (AlloDerm, LifeCell Corp., Branchburg, NJ) and sternocleidomastoid (SCM) muscle flap.

STUDY DESIGN

Prospective, nonrandomized, nonblinded study in the setting of an academic tertiary care medical center.

METHODS

Fourteen patients underwent reconstruction of through-and-through defects of partial pharyngectomy for squamous cell carcinoma using AlloDerm graft. Primary closure of the defects was not possible because of substantial loss of pharyngeal tissue. The graft was reinforced with superiorly based SCM muscle flap in 10 patients. The remaining four patients did not receive any flap. Eleven lesions involved the lateral pharyngeal wall, and three were piriform sinus lesions. Patients were followed for a period ranging from 3 to 20 months. Outcome measures were determined on several parameters including graft take rate, evidence of graft contracture, postoperative complications, resumption of diet, intelligibility of speech, and decannulation. All patients were evaluated by a speech pathologist by means of a bedside swallowing examination. Five patients had videofluoroscopic studies. The three patients with piriform sinus lesions underwent videostroboscopic examination to assess vocal cord function.

RESULTS

There was a high success rate for graft take. Two patients developed postoperative fistulas that resolved with conservative management. One of the 10 patients with SCM flap and one of the 4 patients without SCM flap developed fistulas. Clinically significant graft contracture or pharyngeal stenosis was not observed in any patients. All patients resumed oral intake. Ten patients resumed a normal diet, two tolerated a soft diet, and two could take purees. Decannulation was successful in all patients. Two of the three patients with piriform sinus lesions had vocal cord palsies after surgery resulting in breathy dysphonia. They underwent type I thyroplasty for vocal rehabilitation. All patients had intelligible speech.

CONCLUSIONS

Thick AlloDerm, reinforced with SCM muscle or cervical soft tissue, provides a useful alternative option for reconstruction of pharyngeal defects that cannot be closed primarily. It is safe and effective and provides excellent functional outcomes.

SO dynamic deformation for building of 3-D models

Three-dimensional (3D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3D modeling is to construct a representation of a 3D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, where the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems.