Terminologia Anatomica; Considered from the Perspective of Next-Generation Knowledge Sources

This report examines the semantic structure of Terminologia Anatomica, taking one randomly selected page as an example. The focus of analysis is the meaning imparted to an anatomical term by virtue of its location within the structured list. Terminologia’s structure expressed through hierarchies of headings, varied typographical styles, indentations and an alphanumeric code implies specific relationships between the terms embedded in the list. Together, terms and relationships can potentially capture essential elements of anatomical knowledge. The analysis focuses on these knowledge elements and evaluates the consistency and logic in their representation. Most critical of these elements are class inclusion and part-whole relationships, which are implied, rather than explicitly modeled by Terminologia. This limits the use of the term list to those who have some knowledge of anatomy and excludes computer programs from navigating through the terminology. Assuring consistency in the explicit representation of anatomical relationships would facilitate adoption of Terminologia as the anatomical standard by the various controlled medical terminology (CMT) projects. These projects are motivated by the need for computerizing the patient record, and their aim is to generate machineunderstandable representations of biomedical concepts, including anatomy. Because of the lack of a consistent and explicit representation of anatomy, each of these CMTs has generated it own anatomy model. None of these models is compatible with each other, yet each is consistent with textbook descriptions of anatomy. The analysis of the semantic structure of Terminologia Anatomica leads to some suggestions for enhancing the term list in ways that would facilitate its adoption as the standard for anatomical knowledge representation in biomedical informatics

Pathologies of Security Governance: Efforts Against Human Trafficking in Europe

The trafficking of women and girls for the purpose of sexual exploitation has reportedly been booming in Europe since the 1990s. Governments, international organizations, and private actors have addressed the causes and consequences of sex trafficking in various ways. This article shows that the concept of security governance helps to understand efforts against human trafficking and their shortcomings. The anti-trafficking security governance system consists of five approaches: legal measures, prosecution, protection, prevention in countries of origin, and prevention in countries of destination. Although progress has been made, the security governance system is marked by several pathologies, especially a lack of programs that prevent trafficking in countries of origin and destination, insufficient protection for trafficked persons, and deficient networks bringing together the various actors involved in anti-trafficking. To make governance against human trafficking more effective, efficient, and just, the security governance system must be better balanced and networked

What gross weight and range for an advanced HSCT?

A review of studies conducted in 1986 indicates that a 300 passenger, 5500 nautical mile range aircraft should weigh less than 400,000 pounds. Some data from a British Aerospace Society of Automotive Engineers (SAE) paper will be shown that purport to be an advanced Concorde that meets the range payload specifications at a gross weight of 360,000 pounds. Previous studies by Peter Coen of Langley Research Center support these results. The weight of a supersonic transport is important from the point of view of how much effort should be expended in developing lower sonic boom technologies. It is obvious that a 360,000 pound aircraft can be modified to a more acceptable boom level than a 700,000 pound one

How do we approach intrinsic motivation computationally? : a commentary on: What is intrinsic motivation? A typology of computational approaches. by Pierre-Yves Oudeyer and Frederic Kaplan

What is the energy function guiding behavior and learningµ Representationbased approaches like maximum entropy, generative models, sparse coding, or slowness principles can account for unsupervised learning of biologically observed structure in sensory systems from raw sensory data. However, they do not relate to behavior. Behavior-based approaches like reinforcement learning explain animal behavior in well-described situations. However, they rely on high-level representations which they cannot extract from raw sensory data. Combinations of multiple goal functions seems the methodology of choice to understand the complexity of the brain. But what is the set of possible goals. ..