Inclusive design: developing students' knowledge and attitude through empathic modelling

Cataloged from PDF version of article.To enhance the function and quality of built environments, designers should consider all possible users in their design projects. Therefore, it is essential to incorporate inclusive design in the education of the design student. This study focuses on the educational objectives of and related learning activities in a course where inclusive design is one of the main subjects. Through empathic modelling, students' engagement with the course was enhanced. Within the course, students simulated disabled users while they experienced the campus environment using wheelchairs, crutches or blindfolds. Their experiences were reflected through descriptive texts and poster designs. Descriptive texts were analysed through developing themes and codes whereas posters were analysed through a content analysis method. Our findings showed that students developed their knowledge of inclusive design concerning the physical environment, the self and the social environment. They also developed immediate emotional responses and a positive attitude towards diversity and inclusion. Thus, empathic modelling supported the development of cognitive and affective learning domains of the novice designer, supporting inclusive design education

A Retrospective Evaluation of Crown-fractured Permanent Teeth Treated in a Pediatric Dentistry Clinic

A retrospective study was carried out on the dental trauma records of 93 patients (55 boys, 38 girls) with 129 crown-fractured teeth. The patients’ average age was 9.57 years (SD 1.57), ranging between 7 and 15 years. Uncomplicated crown facture (comprising enamel–dentin) was the most observed type of injury (n = 107, 83%). Only 15 patients (16.13%) sought treatment in less than 24 h following the injury. Of 41 injured teeth (31.79%) the apices were open at the time of presentation at the clinic. The initial treatment of these injured teeth were interim restoration with acid-etch and composite (69%), Cvek amputation (2.33%), fragment reattachment (1.55%), apexification (APX, 10.07%), and root-canal treatment (RCT, 17.05%). Out of 94 teeth, which were diagnosed as vital on admittance, 23 (24.46%) later developed pulp necrosis and required APX or RCT depending on their apical status. In 66 teeth (51.16%) definitive treatment was provided with only esthetic restoration (ER), while in 15.50% and 26.68% of injured teeth ER was carried out following APX and RCT, and RCT, respectively. Definitive treatment was provided in 3–6 months for 29.45% of the injured teeth, while 27.13% and 20.16% of teeth received definitive treatment within 1–3 months and 6 months to 1 year, respectively. Type of crown-fracture, elapsed time following injury, and vitality of the tooth on admittance and pulp necrosis observed were significantly related to the total time spent for definitive treatment (P \u3c 0.05)

Pulpal Tissue in Bilateral Talon Cusps of Primary Central Incisors: Report of a Case

Talon cusp is a tooth anomaly that affects both the primary and the permanent dentitions. However, the occurrence of this anomalous cusp is rather infrequent in the primary dentition. Only 7 cases of bilateral talon cusps affecting the primary teeth have been reported in the dental literature. This is a case report of bilateral talon cusps on primary maxillary central incisors whose histologic evaluation revealed the existence of pulpal tissue in the anomalous cusps

Adhesive Fragment Reattachment after Orthodontic Extrusion: A Case Report

In the treatment of crown fractures, adhesive fragment reattachment provides a good alternative to other restorative techniques, offering several advantages. The present paper reports a case in which the treatment of a cervical crown fracture was accomplished by reattaching the tooth fragment with a flowable resin composite. Orthodontic root extrusion was performed with a modified Hawley appliance prior to fragment reattachment. The clinical and radiographic results after 2.5 years were successful

Voting Features based Classifier with Feature Construction and its Application to Predicting Financial Distress

Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms

Problem representation for refinement

In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples

Voting Features based Classifier with Feature Construction and its Application to Predicting Financial Distress

Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms

Classification by voting feature intervals

A new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive Bayesian Classifier, which also considers each feature separately. Experiments on real-world datasets show that VFI achieves comparably and even better than NBC in terms of classification accuracy. Moreover, VFI is faster than NBC on all datasets. © Springer-Verlag Berlin Heidelberg 1997

Concept representation with overlapping feature intervals

This article presents a new form of exemplar-based learning method, based on overlapping feature intervals. In this model, a concept is represented by a collection of overlappling intervals for each feature and class. Classification with Overlapping Feature Intervals (COFI) is a particular implementation of this technique. In this incremental, inductive, and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each feature dimension for each class. Initially, an interval is a point on a feature-class dimension; then it can be expanded through generalization. No specialization of intervals is done on feature-class dimensions by this algorithm. Classification in the COFI algorithm is based on a majority voting among the local predictions that are made individually by each feature. An evaluation of COFI and its comparison with similar other classification techniques is given