SUMMARY One of the approaches to improve program understanding is to extract what kinds of design pattern are used in existing objectoriented software. This paper proposes a technique for efficiently and accurately detecting occurrences of design patterns included in source codes. We use both static and dynamic analyses to achieve the detection with high accuracy. Moreover, to reduce computation and maintenance costs, detection conditions are hierarchically specified based on Pree's meta patterns as common structures of design patterns. The usage of Prolog to represent the detection conditions enables us to easily add and modify them. Finally, we have implemented an automated tool as an Eclipse plug-in and conducted experiments with Java programs. The experimental results show the effectiveness of our approach. key words: design patterns, program understanding, meta patterns, dynamic analysis, Prolog
BackgroundTracheal intubation (TI) is a key medical skill used by anesthesiologists and critical care physicians in airway management in operating rooms and critical care units. An objective assessment of dexterity in TI procedures would greatly enhance the quality of medical training. This study aims to investigate whether any biomechanical parameters obtained by 3D-motion analysis of body movements during TI procedures can objectively distinguish expert anesthesiologists from novice residents.MethodsThirteen expert anesthesiologists and thirteen residents attempted TI procedures on an airway mannequin using a Macintosh laryngoscope. Motion capturing technology was utilized to digitally record movements during TI procedures. The skill with which experts and novices measured biomechanical parameters of body motions were comparatively examined.ResultsThe two groups showed similar outcomes (success rates and mean time needed to complete the TI procedures) as well as similar mean absolute velocity values in all 21 body parts examined. However, the experts exhibited significantly lower mean absolute acceleration values at the head and the left hand than the residents. In addition, the mean-absolute-jerk measurement revealed that the experts commanded potentially smoother motions at the head and the left hand. The Receiver Operating Characteristic (ROC) curves analysis demonstrated that mean-absolute-acceleration and -jerk measurements provide excellent measures for discriminating between experts and novices.ConclusionsBiomechanical parameter measurements could be used as a means to objectively assess dexterity in TI procedures. Compared with novice residents, expert anesthesiologists possess a better ability to control their body movements during TI procedures, displaying smoother motions at the selected body parts.
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing labels, including alertness, happiness, energy, health, and stress. We found the similarities and differences between the responses of nurses and doctors. According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants' next day's multidimensional self-reported health and wellbeing status. Our model showed significantly better performances than baseline models and previous state-of-the-art models in the evaluations of binary/3-class classification and regression prediction tasks. We also found features related to heart rate, sleep, and work shift contributed to shift workers' health and wellbeing.
Today, the challenges of an aging society are primarily seen in frailty, sarcopenia, and impaired functionality [...]
We are currently facing a “labor crisis,” particularly in the field of logistics, because of reductions in the labor force. Therefore, industries must make their logistics more efficient by utilizing autonomous mobile robotics technologies. This paper proposes a hierarchized map concept that makes unmanned delivery tasks which use multiple autonomous robots more efficiently. Using our proposed concept, an autonomous mobile robot can move automatically on a more efficient path than using current methods. In addition, the management platform for autonomous robots can be used to prevent accidents such as collisions or deadlocks between autonomous robots.
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