In Memorium: Herman P. Schwan [1915–2005]

Herman P. Schwan [1915–2005] was a distinguished scientist and engineer, and a founding father of the field of biomedical engineering. A man of integrity, Schwan influenced the lives of many, including his wife and children, and his many students and colleagues. Active in science until nearly the end of his life, he will be very much missed by his family and many colleagues

Annual acknowledgement of manuscript reviewers

CONTRIBUTING REVIEWERS: The Editor of BioMedical Engineering Online would like to thank all the Reviewers who have contributed to the journal in Volume 12 (2013)

State-of-Science Review: SR-E29, Brain-Computer Interfaces and Cognitive Neural Prostheses

This review looks at recently developed technology that allows engineers to record signals from the brain, identify the subject’s intent, and allow the subject to control prosthetic devices or communicate with others. It explores the current status of the technology, focusing on studies aimed at developing assistive devices for human subjects. Lastly, it reviews the impressive accomplishments to date, as well as limitations of the technology that will need to be overcome to enable the development of fully practical assistive technologies

The Precautionary Principle - Common Sense or Environmental Extremism?

The fact that technologies have unintended consequences is self-evident. But risk management - managing the adverse effects of technology - is both uncertain and politically contentious, since it entails making decisions about socially important issues in the face of scientific uncertainty. Originating in European environmental policy in the 1970s, the precautionary principle (PP) is embodied in adages such as better safe than sorry and err on the side of caution . In practice, this means taking steps to avoid possible environmental or health damage, in the face of insufficient scientific evidence. The PP has achieved widespread political support, and - by the Treaty on European Union - it is the foundation of environmental policy in the European Union (EU). The PP is firmly established in international law, and a considerable body of case law is developing about its use in the EU. However, it has also generated immense controversy

DOES RURAL JOB GROWTH LEAD THE ECONOMY OUT OF RECESSION?

This paper explores the dynamics of rural and non-rural job growth to investigate if job growth starts in rural places, making it one of the leading indicators of economic growth. Empirical results provide mixed evidence. The mixed results of the Granger non-causality tests could be sensitive to the non-rural area definition. The relationship between rural job growth and non-rural job growth is not restricted to post-recession periods. Analysis of Bureau of Labor Statistics data suggests the spillover effects of non-rural growth are larger than the spillover effect of rural growth on non-rural areas. But this positive response of rural growth disappears over time and turns sharply negative. In the long run, "backwash" effects outweigh "spread" effects.Community/Rural/Urban Development,

Modeling thermal responses in human subjects following extended exposure to radiofrequency energy

BACKGROUND: This study examines the use of a simple thermoregulatory model for the human body exposed to extended (45 minute) exposures to radiofrequency/microwave (RF/MW) energy at different frequencies (100, 450, 2450 MHz) and under different environmental conditions. The exposure levels were comparable to or above present limits for human exposure to RF energy. METHODS: We adapted a compartmental model for the human thermoregulatory system developed by Hardy and Stolwijk, adding power to the torso skin, fat, and muscle compartments to simulate exposure to RF energy. The model uses values for parameters for "standard man" that were originally determined by Hardy and Stolwijk, with no additional adjustment. The model predicts changes in core and skin temperatures, sweat rate, and changes in skin blood flow as a result of RF energy exposure. RESULTS: The model yielded remarkably good quantitative agreement between predicted and measured changes in skin and core temperatures, and qualitative agreement between predicted and measured changes in skin blood flow. The model considerably underpredicted the measured sweat rates. CONCLUSIONS: The model, with previously determined parameter values, was successful in predicting major aspects of human thermoregulatory response to RF energy exposure over a wide frequency range, and at different environmental temperatures. The model was most successful in predicting changes in skin temperature, and it provides insights into the mechanisms by which the heat added to body by RF energy is dissipated to the environment. Several factors are discussed that may have contributed to the failure to account properly for sweat rate. Some features of the data, in particular heating of the legs and ankles during exposure at 100 MHz, would require a more complex model than that considered here

Regulating Radio-Frequency Fields in Italy

Throughout the world, there has been a move to harmonize exposure limits to radio-frequency (RF) energy from sources such as mobile phones, communications transmitters, radar, industrial equipment, and the like. Harmonization , in practice, means the adoption of a consistent set of exposure limits in different nations around the world. Precautions taken in Italy are discussed, as are the Vatican radio transmitters, public controversy, health concerns, politics and the science involved

Machine learning and medicine: book review and commentary

This article is a review of the book “Master machine learning algorithms, discover how they work and implement them from scratch” (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery. com. An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book