This study evaluated the types of daily routines that occurred in child care centers based on four general categorizations: time spent on indoor free choice activities, outdoor activities, group activities and special activities. In addition, resulting child care teacher behaviors were examined based on daily routine types. A total 23 classes' activity times and teacher behaviors were observed. The collected data were analyzed using descriptive statistics, hierarchical cluster, and Mann-Whitney U. Results indicated that there were 2 principle daily routine, 'indoor/outdoor activity time oriented' and 'group activity time oriented'. Analysis showed that teachers who belonged to the 'indoor/outdoor activity time oriented' type showed more positive affect, positive guidance, neural guidance, and less non-involved behavior. Results suggest the importance of time spent on free choice activities in the context of daily routine for quality childcare.
Park, Chan Hwa;Rha, Jong Hay;Kwon, Yeonhee;Choi, Mock Wha
Journal of the Korean Home Economics Association
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v.51
no.1
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pp.45-57
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2013
This study explores the types of daily routines in child care centers based on the time planned for indoor free choice activities, outdoor activities, group activities and nap/rest. The daily routine plans of 63 classes of 0 to 2-year-olds and 51 classes of 3 to 5-year-olds were cluster analyzed. Also, openness and closeness of child care programs as well as the most participated indoor/outdoor activities were examined using questionnaires developed for teachers. The results were as follows: First, teachers planned the most time for free play choice activity and comparatively less time for outdoor play during the day. Time for rest and naps were planned more for 0-2 year-olds whereas group activities were planned more for the 3-5 year-olds. Second, 3 daily routine types were found in the daily plans of child care centers, such as "Low-Activity Oriented," "Free Choice Activity Oriented" and "Group Activity Oriented" in 0 to 2-year-old classes and 3 to 5-year-old classes. Also, "Group Activity Oriented" type in 0 to 2-year-old classes showed more "closed" child care programs than the "Free Choice Activity Oriented" type. However, no difference was shown among the 3 types of daily routines in the openness and closeness of 3 to 5-year-old child care programs. Finally, all children, regardless of the types of daily routine, participated most in block activity and role play indoors and fixed-play equipment, sand play and free play outdoors.
The purpose of this study is to clarify the characteristics between the type of daily routine and spatial configuration of activity areas in classroom and provide alternatives for space planning of activity areas in classroom of child-care centers. This study used the content analysis on daily activity plan and floor plans through field survey. Analysis on floor plans was conducted for 35 classrooms in 9 child-care centers which allowed field survey. The results of this study were as follows; There was no significant difference for classroom size according to the type of daily routine in 3 to 5-year-old classrooms. The average size of classrooms was $61.6m^2$ for care oriented type, $41.4m^2$ for indoor activity oriented type and $48.8m^2$ for group activity oriented type. There was no significant difference in composition of activity areas in classrooms according to the type of daily routine. In case of 0 to 2-year-old classrooms, they were composed of 6 activity areas including gross-motor, role play, block building, language, creative expression, exploration/manipulation. Activity area of the most low frequency was gross-motor area. In case of 3 to 5-year-old classrooms, most classrooms were composed of all the 7 activity areas including art, tone and rhythmic, block building, role play, language, math and science. Most accessible and central areas in 0 to 2-year-old classrooms were gross-motor and role play. Also, most separate areas were exploration/manipulation and block building. Most accessible and central areas in 3 to 5-year-old classroom were art and math. Also, most separate areas were tone and rhythmic and science.
The Internet and all of its possibilities and applications have changed individuals' lifestyles in relation to socializing, working, and how they spend their leisure time. Social networking sites (SNSs), such as Facebook or Instagram, are ideal settings for interacting with others but, unfortunately, they are also ideal settings for motivated offenders to commit cybercrimes. Thus SNS users may be more vulnerable to cybercrime. The purpose of this study is to investigate the occurrence of cybercrime victimization, specifically cyber-harassment, cyber-impersonation, and hacking. Self-report surveys collected from a sample of 147 respondents were examined using the moderated multiple regression analysis and a logistic regression analysis to determine possible relationships between SNS lifestyle exposure activities and cybercrime victimization. The results indicate moderate support for the application of lifestyle-routine activity theory (LRAT) to cybercrime victimization. Possible educational and managerial implications, as well as suggestions for future research, are discussed.
Background: The International Commission on Radiological Protection (ICRP) has recently published report series on the occupational intakes of radionuclides (OIR) for internal dosimetry of radiation workers. In this study, the optimized monitoring program including the monitoring interval and the minimum detectable activity (MDA) of major radionuclides was suggested to perform the routine individual monitoring of internal exposure based on the ICRP OIR. Materials and Methods: The derived recording levels and the critical monitoring quantities were reviewed from international standards or guidelines by the International Atomic Energy Agency (IAEA), the International Organization for Standardization (ISO), and the European Radiation Dosimetry Group (EURADOS). The OIR data viewer provided by ICRP was used to evaluate the monitoring intervals and the MDA, which are derived from the reference bioassay functions and the dose coefficients. Results and Discussion: The optimal monitoring intervals were determined taking account of two requirement conditions on the potential intake underestimation and the MDA values. The MDA requirement values of the selected radionuclides were calculated based on the committed effective dose from 0.1 mSv to 5 mSv. The optimized routine individual monitoring program was suggested including the optimal monitoring intervals and the MDA requirements. The optimal MDA values were evaluated based on the committed effective dose of 0.1 mSv. However, the MDA can be adjusted considering the practical operation of the routine individual monitoring program in the nuclear facilities. Conclusion: The monitoring intervals and the MDA as crucial factors for the routine monitoring were described to suggest the optimized routine individual monitoring program of the occupational intakes. Further study on the alpha/beta-emitting radionuclides as well as short lived gamma-emitting nuclides will be necessary in the future.
KSII Transactions on Internet and Information Systems (TIIS)
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v.12
no.3
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pp.1189-1204
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2018
Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.
KSII Transactions on Internet and Information Systems (TIIS)
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v.15
no.8
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pp.3048-3067
/
2021
This study examines the similarities and differences between the causes of juvenile delinquency in online and offline environments and in personal characteristics. The study utilizes data from the '2014 Survey on Juvenile Victimization in Korea'. The population of this survey is students attending middle and high schools across the country. While this paper is based on the Self-Control theory, opportunity factors based on the Routine Activity theory and the Situational Action theory are also applied. Results show that the causes of offline delinquency are low self-control, routine activity and frequent gaming and SNS use. The causes of online delinquency are high self-control, existence of communication with unknown persons and the spectrum of personal information online. The common element of offline and online delinquency was the presence of delinquent peers. These results show that while online and offline delinquency cannot be explained with the same methodology, at the same time they are not mutually exclusive.
The aim of this study was 1) to confirm the practical efficiency of a routine milk P4 monitoring system for postpartum reproductive management of a dairy herd, and 2) to evaluate the relationship between the blood metabolic profiles, milk quality and body weight of individual cows in the farm records, which may reflect the postpartum nutritional condition, and the time of postpartum resumption of ovarian activity of dairy cows. A total of 116 Holstein cows was used in the present study. First, during the period of Experiment 1, postpartum reproductive management based on weekly measured milk P4 concentration from individual cows was conducted. Compared with the reproductive records of the past two years without P4 monitoring, although the day from calving to first AI did not change, both the number of AI until pregnant (with P4; 1.9 times vs. without P4; 2.9 times) and the days open (with P4; 95.1 days vs. without P4; 135.8 days and 133.8 days) were significantly decreased. In Experiment 2, the measurement of blood constituents such as albumin, blood urea nitrogen, packed cell volume, ammonia, glucose, total cholesterol, non-esterified, AST and $\gamma$-GTP was performed on the blood samples taken once approximately 14 days postpartum, to monitor both health and nutritional conditions. The milk constituent parameters, such as milk protein (MP), milk fat (MF), SNF and lactose, collected from the monthly progeny test of individual cows, were used to monitor the postpartum nutritional status. Furthermore, the data obtained from the routine measurements of body weight were used to calculate the rate of peripartum body weight loss. The resumption day of the postpartum estrous cycle was assumed from the milk P4 profiles of individual cows. There was no clear relationship between each parameter from blood examination and those from resumption time. However, the cows had low values of MP, and SNF, which significantly affected the resumption of the postpartum estrous cycle. Similarly, a higher rate of body weight loss indicated a significant delay (more than 1 month) in the resumption of the postpartum estrous cycle, compared with the groups that had a medium or lower rate of body weight loss. The results of the present study demonstrated that the implementation of routine milk P4 monitoring-based postpartum reproductive management, together with milk quality parameters and routine BW data available in field conditions may be utilized as a practical approach for increasing the postpartum reproductive efficiency of a high yielding dairy herd.
Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.
Purpose This study collects data of the recently activated online black market and analyzes it to present a specific method for preparing for a hacking attack. This study aims to make safe from the cyber attacks, including hacking, from the perspective of individuals and businesses by closely analyzing hacking methods and tools in a situation where they are easily shared. Design/methodology/approach To prepare for the hacking attack through the online black market, this study uses the routine activity theory to identify the opportunity factors of the hacking attack. Based on this, text mining and social network techniques are applied to reveal the most dangerous areas of security. It finds out suitable targets in routine activity theory through text mining techniques and motivated offenders through social network analysis. Lastly, the absence of guardians and the parts required by guardians are extracted using both analysis techniques simultaneously. Findings As a result of text mining, there was a large supply of hacking gift cards, and the demand to attack sites such as Amazon and Netflix was very high. In addition, interest in accounts and combos was in high demand and supply. As a result of social network analysis, users who actively share hacking information and tools can be identified. When these two analyzes were synthesized, it was found that specialized managers are required in the areas of proxy, maker and many managers are required for the buyer network, and skilled managers are required for the seller network.
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