Robustness of radial basis functions

Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment. Furthermore, upper bounds on the mean square error under noise contaminated parameters and inputs are determined if the network parameters are constrained. To achieve robuster neural network architectures fundamental methods are introduced to identify sensitive parameters and neurons.     

Does age make a difference? Predicting physical activity of South Koreans.

Social cognition models of health behavior are commonly understood as being universal, which implies that they are applicable to groups varying in age or cultural background, for example. Cultural uniqueness and characteristics of life-span development, however, necessitate the study of differential effects. Accordingly, the health action process approach (HAPA) was examined in younger and middle-aged/older adults from South Korea (N = 697) who participated in a longitudinal health screening study with a 6-month time lag. The HAPA model had a good fit within the middle-aged/older adult sample. Physical activity was predicted by planning, coping self-efficacy, and intention, which were, in turn, predicted by action self-efficacy, outcome expectancies, and risk perceptions. Conversely, the results indicated a poor model fit in the younger adult sample. The results suggest a different motivation for the involvement in physical activity as a function of age. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Material forces for inelastic models at large strains: application to fracture mechanics

Elastomeric materials show a wide range of different elastic and inelastic properties. Additionally, this class of materials is subjected to large deformations. Considering all these effects, fracture mechanical investigations are very challenging tasks and cannot be performed with standard approaches. Effects of inhomogeneities and discontinuities such as cracks can be investigated with the so-called material force approach in an efficient and elegant way. For comprehensive investigations of inelastic materials, the complete balance of the material motion problem has to be formulated. In this case, the material volume forces depend on the internal history variables which are required for the inelastic constitutive model. This paper derives a general formulation for rate-dependent and rate-independent inelastic materials based on a multiplicative split of the deformation gradient to cover viscoelastic and elastoplastic materials at finite deformations.     

Monitoring post-tsunami vegetation recovery in Phang-Nga province,Thailand, based on IKONOS imagery and field investigations – a contribution to the analysis of tsunami vulnerability of coastal ecosystems

A major tsunami in December 2004 devastated the coastal ecosystems along the Andaman Sea coast of Thailand. Since intact coastal ecosystems provide many important services for local communities at the Andaman Sea, it is crucial to investigate to what extent (in terms of percentage area and speed) the affected ecosystems were capable of recovering after the tsunami. Field measurements and multi-date IKONOS imagery were used to estimate the recovery and succession patterns of coastal vegetation types in the Phang-Nga province of Thailand, three years after the tsunami. Thus, this study contributes to a holistic understanding of the ecological vulnerability of the coastal area to tsunamis. A zone-based change detection approach is applied by comparing two change detection techniques: the first method involves the calculation of a recovery rate based on multi-temporal TNDVI (transformed normalized difference vegetation index) images (TNDVI approach), whereas the second approach is a combined approach of the change vector analysis (CVA). Although these two methods provide different types of information (quantitative for the TNDVI approach, qualitative for the CVA), they are comparable in terms of results and accuracies. The results reveal that recovery processes vary based on the type of the ecosystem and, furthermore, are strongly influenced by human activities. Grasslands, coconut plantations and the mixed vegetation cover could recover faster than the mangroves and casuarina forests. Among the forest ecosystems, recovery rates of casuarina forests were higher than for mangroves, but the recovery area was smaller. This study also discusses the potential and some limitations and inaccuracies of applying high-resolution optical imagery for assessing vegetation recovery at a local scale.     

Deforestation in the Miombo woodlands: a pixel-based semi-automated change detection method

Most methods of change detection require a considerable amount of effort and expertise. The procedures of change detection are visual-, classification-, object- or vector-based. The target of this research was to develop an automated and generally unsupervised combination of methods to quantify deforestation on a per pixel basis. The study area was the Gutu district in Zimbabwe. In the first step, Landsat Thematic Mapper (TM) scenes were spectrally unmixed by the Spectral Mixture Analysis (SMA). The calculation of the necessary endmembers was performed by means of the N-FINDR algorithm. After the unmixing process, the data were analysed with change vector analysis (CVA) utilizing spherical statistics. Thereafter, a combination of constraints, including a Bayesian threshold and spherical angles, was applied to identify deforestation. The combination of these methods provided an accurate idea of the state of deforestation and enabled attribution to ‘fire-induced’ and ‘non fire-induced’ classes.     

MI-Pack: Increased confidence of metabolite identification in mass spectra by integrating accurate masses and metabolic pathways

Metabolite identification is of central importance to metabolomics as it provides the route to new knowledge. Automated identification of the thousands of peaks detected by high resolution mass spectrometry is currently not possible, largely due to the finite mass accuracy of the spectrometer and the complexity that one peak can be assigned to one or more empirical formula(e) and each formula maps to one or more metabolites. Biological samples are not, however, composed of random metabolite mixtures, but instead comprise of thousands of compounds related through specific chemical transformations. Here we evaluate if prior biological knowledge of these transformations can improve metabolite identification accuracy.Our identification algorithm - which uses metabolite interconnectivity from the KEGG database to putatively identify metabolites by name - is based on mapping an experimentally-derived empirical formula difference for a pair of peaks to a known empirical formula difference between substrate-product pairs derived from KEGG, termed transformation mapping (TM). To maximize identification accuracy, we also developed a novel semi-automated method to calculate a mass error surface associated with experimental peak-pair differences. The TM algorithm with mass error surface has been extensively validated using simulated and experimental datasets by calculating false positive and false negative rates of metabolite identification. Compared to the traditional identification method of database searching accurate masses on a single-peak-by-peak basis, the TM algorithm reduces the false positive rate of identification by > 4-fold, while maintaining a minimal false negative rate. The mass error surface, putative identification of metabolite names, and calculation of false positive and false negative rates collectively advance and improve upon related previous research on this topic [1, 2]. We conclude that inclusion of prior biological knowledge in the form of metabolic pathways provides one route to more accurate metabolite identification.     

Clinorotation Increases the Growth of Utricular Otoliths of Developing Cichlid Fish

It has been shown earlier that hypergravity slows down inner ear otolith growth in developing fish as an adaptation towards increased environmental gravity. Suggesting that otolith growth is regulated by the central nervous system, thus adjusting otolithic weight to produce a test mass, applying functional weightlessness should yield an opposite effect, i.e. larger than normal otoliths. Therefore, larval siblings of cichlid fish (Oreochromis mossambicus) were housed for 7 days in a submersed, two-dimensional clinostat, which provided a residual gravity of approximately 0.007g. After the experiment, otoliths were dissected and their size (area grown during the experiment) was determined. Maintenance in the clinostat resulted in significantly larger utricular otoliths (lapilli, involved in graviperception). There were no statistical significant differences regarding saccular otoliths obtained (sagittae, involved in transmitting linear acceleration and, especially, in the hearing process). These results indicated, that the animals had in fact received functional weightlessness. In line and contrasting results on the otoliths of other teleost species kept at actual microgravity (spaceflight) or within rotating wall vessels are discussed.     

Augmentation

A semantic and pragmatic interpreter that combines automatic and interactive disambiguation is described. This augmentor has an interactive disambiguation component that is called upon to aid automatic disambiguation when automated strategies prove inadequate. In addition to interactive disambiguation, the augmentor also provides the user interface for the KBMT-89 project.