In this paper, we propose a multicriteria decision making (MCDM) method by using a genetic algorithm (GA). The system consists of three phases. In the first phase, a rough set of Pareto optimal solutions is obtained using Kohonen's self organizing map (SOM). In the second phase, the decision maker (DM) selects his preferred sclutions among the obtained set, where the mechanism of GA is used with the DM's preference assisted by radial basis function network (RBFN). In the third phase, the DM can explore the solution space further for the final decision. 0-7803-2559-1/9 § $4.00 0 1995 IEEE

This study investigated the relationship between types of humor (aggressive humor and affinity humor) of homeroom teachers, as recognized by their students, and five domains of class climate (mutual respect among the students, discipline, willingness, enjoyment, and resistance). The participants included a total of 500 students – 250 primary school students (fourth to sixth grade) and 250 secondary school students (seventh to ninth grade) in Japan. The students answered questions about their homeroom teacher’s type of humor and the climate of their class using a self-report scale. We labeled five variables of class climate as dependent variables, and two types of teachers’ humor as independent variables, and conducted a hierarchal multiple regression analysis with two steps. Findings showed that aggressive humor had a significant negative correlation to all positive class climates, and a significant positive correlation to negative class climates. Affinity humor indicated a significant correlation in the exact opposite manner to the above findings. Furthermore, the interaction effect of the two types of teachers’ humor was insignificant for every variable of class climate. The findings indicated that an ideal class climate could be created if teachers refrained as much as possible from using aggressive humor and used affinity humor.

Background Children with profound intellectual and multiple disabilities (PIMD) or severe motor and intellectual disabilities (SMID) only communicate through movements, vocalizations, body postures, muscle tensions, or facial expressions on a pre- or protosymbolic level. Yet, to the best of our knowledge, there are few systems developed to specifically aid in categorizing and interpreting behaviors of children with PIMD or SMID to facilitate independent communication and mobility. Further, environmental data such as weather variables were found to have associations with human affects and behaviors among typically developing children; however, studies involving children with neurological functioning impairments that affect communication or those who have physical and/or motor disabilities are unexpectedly scarce. Objective This paper describes the design and development of the ChildSIDE app, which collects and transmits data associated with children’s behaviors, and linked location and environment information collected from data sources (GPS, iBeacon device, ALPS Sensor, and OpenWeatherMap application programming interface [API]) to the database. The aims of this study were to measure and compare the server/API performance of the app in detecting and transmitting environment data from the data sources to the database, and to categorize the movements associated with each behavior data as the basis for future development and analyses. Methods This study utilized a cross-sectional observational design by performing multiple single-subject face-to-face and video-recorded sessions among purposively sampled child-caregiver dyads (children diagnosed with PIMD/SMID, or severe or profound intellectual disability and their primary caregivers) from September 2019 to February 2020. To measure the server/API performance of the app in detecting and transmitting data from data sources to the database, frequency distribution and percentages of 31 location and environment data parameters were computed and compared. To categorize which body parts or movements were involved in each behavior, the interrater agreement κ statistic was used. Results The study comprised 150 sessions involving 20 child-caregiver dyads. The app collected 371 individual behavior data, 327 of which had associated location and environment data from data collection sources. The analyses revealed that ChildSIDE had a server/API performance >93% in detecting and transmitting outdoor location (GPS) and environment data (ALPS sensors, OpenWeatherMap API), whereas the performance with iBeacon data was lower (82.3%). Behaviors were manifested mainly through hand (22.8%) and body movements (27.7%), and vocalizations (21.6%). Conclusions The ChildSIDE app is an effective tool in collecting the behavior data of children with PIMD/SMID. The app showed high server/API performance in detecting outdoor location and environment data from sensors and an online API to the database with a performance rate above 93%. The results of the analysis and categorization of behaviors suggest a need for a system that uses motion capture and trajectory analyses for developing machine- or deep-learning algorithms to predict the needs of children with PIMD/SMID in the future.

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