Aerospace medicine and biology: A continuing bibliography with indexes (supplement 292)

This bibliography lists 192 reports, articles and other documents introduced into the NASA scientific and technical information system in December, 1986

CHARACTERIZATION AND COMPARISON OF STRESS HISTORY IN VARIOUS SIZED TWIN- SCREW EXTRUDERS USING RESIDENCE-STRESS DISTRIBUTIONS

Extrusion is used in production across a broad spectrum of industries, including piping and tubing, food, plastics and pharmaceutical. Because some applications involve stress-sensitive ingredients, it is important to be able to predict the amount of stress exerted on the material. Unfortunately, characterization of the stress magnitudes within a twin-screw extruder is extremely difficult due to its complexity. This thesis presents an approach to characterizing the stress history through the use of residence-stress distributions. Stress beads are used to determine the percentage of polymer that is exposed to a particular magnitude of stress at each location along the residence distribution. A comparison of various mixing geometries on three different sized extruders is performed for a wide range of operating conditions. An extensive DOE analysis of the results yields characteristic equations that are capable of predicting the amount of stress bead breakup for any given operating parameters

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

System of Systems conceptual design methodology for space exploration

The scope of the research is to identify and develop a design methodology for System-of-System (a set of elements and sub-elements able to interact and cooperate in order to complete a mission), based on models, methods and tools, to support the decision makers during the space exploration scenarios design and evaluation activity in line with the concurrent design philosophy. Considering all combinations of system parameters (such as crew size, orbits, launchers, spacecraft, ground and space infrastructures), a large number of mission concept options are possible, even though not all of them are optimal or even feasible. The design methodology is particularly useful in the first phases of the design process (Phase 0 and A) to choose rationally and objectively the best mission concepts that ensure the higher probability of mission success in compliance with the high level requirements deriving from the “user needs”. The first phases of the project are particularly critical for the success of the entire mission because the results of this activity are the starting point of the more costly detailed design phases. Thus, any criticality in the baseline design will involve inevitably into undesirable and costly radical system redesigns during the advanced design phases. For this reason, it is important to develop reliable mathematical models that allow prediction of the system performances notwithstanding the poorly defined environment of very high complexity. In conjunction with the development of the design methodology for system-of-systems and in support of it, a software tool has been developed. The tool has been developed into Matlab environment and provides users with a useful graphical interface. The tool integrates the model of the mission concept, the models of the space elements at system and subsystem level, the cost-effectiveness model or value, the sensitivity and multi-objective optimization analysis. The tool supports users to find a system design solution in compliance with requirements and constraints, such as mass budgets and costs, and provides them with information about cost-effectiveness of the mission. The developed methodology has been applied for the design of several space elements (Man Tended Free Flyer, Cargo Logistic Vehicle, Rover Locomotion System) and several mission scenarios (Moon surface infrastructure support, Cis-Lunar infrastructure delivering, Cis-Lunar infrastructure logistic support), in order to assess advantages and disadvantages of the proposed method. The results of the design activity have been discussed and accepted by the European Space Agency (ESA) and have also been compared and presented to the scientific community. Finally, in a particular case, the study of the locomotion system of a lunar rover, the results of the methodology have been verified through the production and testing of the same system

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

Visual Attention Relates to Operator Performance in Spacecraft Docking Training

BACKGROUND: Manually controlled docking of a spacecraft to a space station is an operational task that poses high demands on cognitive and perceptual functioning. Effective processing of visual information is crucial for success. Eye tracking can reveal the operator’s attentional focus unobtrusively and objectively. Therefore, our aim was to test the feasibility of eye tracking during a simulation of manual docking and to identify links between visual information processing and performance. METHODS: We hypothesized that duration and number of gazes to specific regions of interest of the simulation (total dwell time and number of dwells) would be associated with docking accuracy. Eye movements were recorded in 10 subjects (30% women, M = 33.4 yr old) during the 6° head-down tilt bed rest study AGBRESA during 20 training sessions with the 6df learning program for spacecraft docking. RESULTS: Subjects’ gaze was directed most frequently and longest to the vizor (185 dwells and 22,355 ms per task) followed by the two instrument displays (together 75 dwells and 4048 ms per task). We observed a significant positive relationship between number and duration of visual checks of speed and distance to the docking point and the accuracy of the docking maneuver. DISCUSSION: In conclusion, eye tracking provides valuable information related to docking accuracy that might prospectively offer the opportunity to improve docking training effectiveness

Automated Characterisation and Classification of Liver Lesions From CT Scans

Cancer is a general term for a wide range of diseases that can affect any part of the body due to the rapid creation of abnormal cells that grow outside their normal boundaries. Liver cancer is one of the common diseases that cause the death of more than 600,000 each year. Early detection is important to diagnose and reduce the incidence of death. Examination of liver lesions is performed with various medical imaging modalities such as Ultrasound (US), Computer tomography (CT), and Magnetic resonance imaging (MRI). The improvements in medical imaging and image processing techniques have significantly enhanced the interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. Moreover, CAD systems can help physician, as a second opinion, in characterising lesions and making the diagnostic decision. Thus, CAD systems have become an important research area. Particularly, these systems can provide diagnostic assistance to doctors to improve overall diagnostic accuracy. The traditional methods to characterise liver lesions and differentiate normal liver tissues from abnormal ones are largely dependent on the radiologists experience. Thus, CAD systems based on the image processing and artificial intelligence techniques gained a lot of attention, since they could provide constructive diagnosis suggestions to clinicians for decision making. The liver lesions are characterised through two ways: (1) Using a content-based image retrieval (CBIR) approach to assist the radiologist in liver lesions characterisation. (2) Calculating the high-level features that describe/ characterise the liver lesion in a way that is interpreted by humans, particularly Radiologists/Clinicians, based on the hand-crafted/engineered computational features (low-level features) and learning process. However, the research gap is related to the high-level understanding and interpretation of the medical image contents from the low-level pixel analysis, based on mathematical processing and artificial intelligence methods. In our work, the research gap is bridged if a relation of image contents to medical meaning in analogy to radiologist understanding is established. This thesis explores an automated system for the classification and characterisation of liver lesions in CT scans. Firstly, the liver is segmented automatically by using anatomic medical knowledge, histogram-based adaptive threshold and morphological operations. The lesions and vessels are then extracted from the segmented liver by applying AFCM and Gaussian mixture model through a region growing process respectively. Secondly, the proposed framework categorises the high-level features into two groups; the first group is the high-level features that are extracted from the image contents such as (Lesion location, Lesion focality, Calcified, Scar, ...); the second group is the high-level features that are inferred from the low-level features through machine learning process to characterise the lesion such as (Lesion density, Lesion rim, Lesion composition, Lesion shape,...). The novel Multiple ROIs selection approach is proposed, in which regions are derived from generating abnormality level map based on intensity difference and the proximity distance for each voxel with respect to the normal liver tissue. Then, the association between low-level, high-level features and the appropriate ROI are derived by assigning the ability of each ROI to represents a set of lesion characteristics. Finally, a novel feature vector is built, based on high-level features, and fed into SVM for lesion classification. In contrast with most existing research, which uses low-level features only, the use of high-level features and characterisation helps in interpreting and explaining the diagnostic decision. The methods are evaluated on a dataset containing 174 CT scans. The experimental results demonstrated that the efficacy of the proposed framework in the successful characterisation and classification of the liver lesions in CT scans. The achieved average accuracy was 95:56% for liver lesion characterisation. While the lesion’s classification accuracy was 97:1% for the entire dataset. The proposed framework is developed to provide a more robust and efficient lesion characterisation framework through comprehensions of the low-level features to generate semantic features. The use of high-level features (characterisation) helps in better interpretation of CT liver images. In addition, the difference-of-features using multiple ROIs were developed for robust capturing of lesion characteristics in a reliable way. This is in contrast to the current research trend of extracting the features from the lesion only and not paying much attention to the relation between lesion and surrounding area. The design of the liver lesion characterisation framework is based on the prior knowledge of the medical background to get a better and clear understanding of the liver lesion characteristics in medical CT images

Design methodologies for space systems in a System of Systems (SoS) architecture

L'abstract è presente nell'allegato / the abstract is in the attachmen