Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 274)

This bibliography lists 128 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1985

Complex interaction of sensory and motor signs and symptoms in chronic CRPS.

Spontaneous pain, hyperalgesia as well as sensory abnormalities, autonomic, trophic, and motor disturbances are key features of Complex Regional Pain Syndrome (CRPS). This study was conceived to comprehensively characterize the interaction of these symptoms in 118 patients with chronic upper limb CRPS (duration of disease: 43±23 months). Disease-related stress, depression, and the degree of accompanying motor disability were likewise assessed. Stress and depression were measured by Posttraumatic Stress Symptoms Score and Center for Epidemiological Studies Depression Test. Motor disability of the affected hand was determined by Sequential Occupational Dexterity Assessment and Michigan Hand Questionnaire. Sensory changes were assessed by Quantitative Sensory Testing according to the standards of the German Research Network on Neuropathic Pain. Almost two-thirds of all patients exhibited spontaneous pain at rest. Hand force as well as hand motor function were found to be substantially impaired. Results of Quantitative Sensory Testing revealed a distinct pattern of generalized bilateral sensory loss and hyperalgesia, most prominently to blunt pressure. Patients reported substantial motor complaints confirmed by the objective motor disability testings. Interestingly, patients displayed clinically relevant levels of stress and depression. We conclude that chronic CRPS is characterized by a combination of ongoing pain, pain-related disability, stress and depression, potentially triggered by peripheral nerve/tissue damage and ensuing sensory loss. In order to consolidate the different dimensions of disturbances in chronic CRPS, we developed a model based on interaction analysis suggesting a complex hierarchical interaction of peripheral (injury/sensory loss) and central factors (pain/disability/stress/depression) predicting motor dysfunction and hyperalgesia

Microwave imaging techniques for biomedical applications

Microwaves have been considered for medical applications involving the detection of organ movements and changes in tissue water content. More particularly cardiopulmonary interrogation via microwaves has resulted in various sensors monitoring ventricular volume change or movement, arterial wall motion, respiratory movements, pulmonary oedema, etc. In all these applications, microwave sensors perform local measurements and need to be displaced for obtaining an image reproducing the spatial variations of a given quantity. Recently, advances in the area of inverse scattering theory and microwave technology have made possible the development of microwave imaging and tomographic instruments. This paper provides a review of such equipment developed at Suplec and UPC Barcelona, within the frame of successive French-Spanish PICASSO cooperation programs. It reports the most significant results and gives some perspectives for future developments. Firstly, a brief historical survey is given. Then, both technological and numerical aspects are considered. The results of preliminary pre-clinical assessments and in-lab experiments allow to illustrate the capabilities of the existing equipment, as well as its difficulty in dealing with clinical situations. Finally, some remarks on the expected development of microwave imaging techniques for biomedical applications are given.Peer ReviewedPostprint (published version

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 164

This bibliography lists 275 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1977

Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 180 reports, articles and other documents introduced into the NASA scientific and technical information system in February 1985

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program Final report, 1 Feb. 1969 - 24 Aug. 1970

Research progress in technology transfer by NASA Biomedical Application Tea

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 144

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975