International evidence-based recommendations on ultrasound-guided vascular access

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Developing a Design Framework for UMI Educational Scenarios

Ubiquitous learning (u-learning) is a new paradigm which is based on ubiquitous computing technology. The most significant role of ubiquitous computing technology in u-learning is to construct a ubiquitous learning environment which enables anyone to learn at anytime anyplace. Nonetheless the characteristics of u-learning are still unclear and being debated by the research community. Designing instructional tools that actually promote u-learning experiences is a cumbersome task in the sense of taking into consideration and combining a variety of complex, technological tools and characteristics of u-learning. This study describes the characteristics and design methodology of a UMI-Sci-Ed* Educational Scenario Template as a medium to organize and construct u-learning experiences based in a u-learning environment. It also presents a case study scenario, based on UMI Subject Matter Experts’ interaction with the predefined and designed Educational Scenario Components

The genetic basis of craniofacial and dental abnormalities

The embryonic head development, including the formation of dental structures, is a complex and delicate process guided by specific genetic programs. Genetic changes and environmental factors can disturb the execution of these programs and result in abnormalities in orofacial and dental structures. Orofacial clefts and hypodontia/ oligodontia are examples of such abnormalities frequently seen in dental clinics. An insight into the mechanisms and genes involved in the formation of orofacial and dental structures has been gradually gained by genetic analysis of families and by the use of experimental vertebrate models such as the mouse and chick models. The development of novel clinical therapies for orofacial and dental pathological conditions depends very much on a detailed knowledge of the molecular and cellular processes that are involved in head formation

Knowledge Enriched Short-term Scheduling for Engineer-to-order Products

AbstractContemporary shop-floors are highly affected by the ever-increasing complexity that is caused by the fluctuating customer demands. Therefore, a high degree of flexibility is needed and the scheduling of manufacturing tasks must be agile to changes. For addressing this challenge, this research work proposes a knowledge enriched short-term job-shop scheduling engine. More precisely, it focuses on the short-term scheduling of the resources of the machine shop, through an artificial intelligence algorithm that generates and evaluates alternative assignments of resources to tasks. Based on the requirements of a new order, a similarity mechanism retrieves successfully executed past orders together with a dataset that includes the processing times, the job and task sequence and the suitable resources. Afterwards it adapts these parameters to the requirements of the new order so as to evaluate the alternative schedules and identify a good alternative in a timely manner. The deriving schedule can be presented on mobile devices and it can be manipulated by the planner on-the-fly respecting tasks precedence constraints and machine availability. A case study from the mold making industry is used for validating the proposed framework