The tsunami evacuation simulation which combines tsunami inundation simulation and people evacuation simulation was confirmed to be one of the effective disaster education tools from the experiences in Japan. This sim...
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ISBN:
(纸本)9781880843826
The tsunami evacuation simulation which combines tsunami inundation simulation and people evacuation simulation was confirmed to be one of the effective disaster education tools from the experiences in Japan. This simulation technology has been advanced and applied to the west half of the tsunami risk area of Banda Aceh. And, its utilization methodology for tsunami disaster education and for city planning has been studied under the cooperation with the school teachers and the city office staffs. Linking these two simulations onto a common time-domain, the animation is compiled and the number of casualties is counted from the location of evacuee and the spot depth of tsunami. The study originated the road network data and population data of Banda Aceh using a high resolution satellite image and door-to-door visits. Several simulations were developed under different scenario parameters such as the timing of the start of evacuation, the method of moving (car is available or not), and the destination of the evacuees. The simulation handles more than 10 thousand agent models.
Software development is a series of processes activity involving more than one person, especially in medium to large software projects. Each activity of software development is carried out by various combination of di...
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Software development is a series of processes activity involving more than one person, especially in medium to large software projects. Each activity of software development is carried out by various combination of different individual. Collaboration occurs in every phase of the process software development. The right team collaboration method is one of the important things in success software development. This research is focused to develop collaboration model of software development to meet time schedule, budget, scope, and software quality. The collaboration model of software development is generated from survey of collaboration method (about face to face and virtual collaboration) and software development methodology especially agile methodology. The direct collaboration was used in the software specification processes, and combination (with a direct and virtual) collaboration was used during software design, implementation, and validation. Team coordination in every process of software development was done with face to face method. The same artifacts were used in this collaboration model with some collaboration software tools. Combination between face to face and virtual communication was used in this model. The model is implemented in small case software development project. During the software development, some measurement is carried out. The measurement included the measurement of project completion time, cost, software specification, and software quality. The software is developed in time schedule (two months), cost under budged, and meet specification. In addition, the software has built a fairly good quality. Thus, this collaboration model good use in software development to achieved accuracy of schedule, cost, scope, and quality.
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
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