Reduced Sampling for Construction of Quadratic Response Surface Approximations Using Adaptive Experimental Design

The purpose of this paper is to reduce the computational complexity per step from O(n^2) to O(n) for optimization based on quadratic surrogates, where n is the number of design variables. Applying nonlinear optimization strategies directly to complex multidisciplinary systems can be prohibitively expensive when the complexity of the simulation codes is large. Increasingly, response surface approximations, and specifically quadratic approximations, are being integrated with nonlinear optimizers in order to reduce the CPU time required for the optimization of complex multidisciplinary systems. For evaluation by the optimizer, response surface approximations provide a computationally inexpensive lower fidelity representation of the system performance. The curse of dimensionality is a major drawback in the implementation of these approximations as the amount of required data grows quadratically with the number n of design variables in the problem. In this paper a novel technique to reduce the magnitude of the sampling from O(n^2) to O(n) is presented. The technique uses prior information to approximate the eigenvectors of the Hessian matrix of the response surface approximation and only requires the eigenvalues to be computed by response surface techniques. The technique is implemented in a sequential approximate optimization algorithm and applied to engineering problems of variable size and characteristics. Results demonstrate that a reduction in the data required per step from O(n^2) to O(n) points can be accomplished without significantly compromising the performance of the optimization algorithm. A reduction in the time (number of system analyses) required per step from O(n^2) to O(n) is significant, even more so as n increases. The novelty lies in how only O(n) system analyses can be used to approximate a Hessian matrix whose estimation normally requires O(n^2) system analyses

KKT conditions satisfied using adaptive neighboring in hybrid cellular automata for topology optimization

The hybrid cellular automaton (HCA) method is a biologically inspired algorithm capable of topology synthesis that was developed to simulate the behavior of the bone functional adaptation process. In this algorithm, the design domain is divided into cells with some communication property among neighbors. Local evolutionary rules, obtained from classical control theory, iteratively establish the value of the design variables in order to minimize the local error between a field variable and a corresponding target value. Karush-Kuhn-Tucker (KKT) optimality conditions have been derived to determine the expression for the field variable and its target. While averaging techniques mimicking intercellular communication have been used to mitigate numerical instabilities such as checkerboard patterns and mesh dependency, some questions have been raised whether KKT conditions are fully satisfied in the final topologies. Furthermore, the averaging procedure might result in cancellation or attenuation of the error between the field variable and its target. Several examples are presented showing that HCA converges to different final designs for different neighborhood configurations or averaging schemes. Although it has been claimed that these final designs are optimal, this might not be true in a precise mathematical sense—the use of the averaging procedure induces a mathematical incorrectness that has to be addressed. In this work, a new adaptive neighboring scheme will be employed that utilizes a weighting function for the influence of a cell’s neighbors that decreases to zero over time. When the weighting function reaches zero, the algorithm satisfies the aforementioned optimality criterion. Thus, the HCA algorithm will retain the benefits that result from utilizing neighborhood information, as well as obtain an optimal solution

Homotopy methods for constraint relaxation in unilevel reliability based design optimization

Reliability based design optimization is a methodology for finding optimized designs that are characterized with a low probability of failure. The main ob jective in reliability based design optimization is to minimize a merit function while satisfying the reliability constraints. The reliability constraints are constraints on the probability of failure corre- sponding to each of the failure modes of the system or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a relia- bility analysis. During the last few years, a variety of different techniques have been devel- oped for reliability based design optimization. Traditionally, these have been formulated as a double-loop (nested) optimization problem. The upper level optimization loop gen- erally involves optimizing a merit function sub ject to reliability constraints and the lower level optimization loop(s) compute the probabilities of failure corresponding to the failure mode(s) that govern the system failure. This formulation is, by nature, computationally intensive. A new efficient unilevel formulation for reliability based design optimization was developed by the authors in earlier studies. In this formulation, the lower level optimiza- tion (evaluation of reliability constraints in the double loop formulation) was replaced by its corresponding first order Karush-Kuhn-Tucker (KKT) necessary optimality conditions at the upper level optimization. It was shown that the unilevel formulation is computation- ally equivalent to solving the original nested optimization if the lower level optimization is solved by numerically satisfying the KKT conditions (which is typically the case), and the two formulations are mathematically equivalent under constraint qualification and general- ized convexity assumptions. In the unilevel formulation, the KKT conditions of the inner optimization for each probabilistic constraint evaluation are imposed at the system level as equality constraints. Most commercial optimizers are usually numerically unreliable when applied to problems accompanied by many equality constraints. In this investigation an optimization framework for reliability based design using the unilevel formulation is de- veloped. Homotopy methods are used for constraint relaxation and to obtain a relaxed feasible design. A series of optimization problems are solved as the relaxed optimization problem is transformed via a homotopy to the original problem. A heuristic scheme is employed in this paper to update the homotopy parameter. The proposed algorithm is illustrated with example problems

Anti-HTLV antibody profiling reveals an antibody signature for HTLV-I-Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP)

<p>Abstract</p> <p>Background</p> <p>HTLV-I is the causal agent of adult T cell leukemia (ATLL) and HTLV-I-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Biomarkers are needed to diagnose and/or predict patients who are at risk for HAM/TSP or ATLL. Therefore, we investigated using luciferase immunoprecipitation technology (LIPS) antibody responses to seven HTLV-I proteins in non-infected controls, asymptomatic HTLV-I-carriers, ATLL and HAM/TSP sera samples. Antibody profiles were correlated with viral load and examined in longitudinal samples.</p> <p>Results</p> <p>Anti-GAG antibody titers detected by LIPS differentiated HTLV-infected subjects from uninfected controls with 100% sensitivity and 100% specificity, but did not differ between HTLV-I infected subgroups. However, anti-Env antibody titers were over 4-fold higher in HAM/TSP compared to both asymptomatic HTLV-I (<it>P </it>< 0.0001) and ATLL patients (<it>P </it>< 0.0005). Anti-Env antibody titers above 100,000 LU had 75% positive predictive value and 79% negative predictive value for identifying the HAM/TSP sub-type. Anti-Tax antibody titers were also higher (<it>P </it>< 0.0005) in the HAM/TSP compared to the asymptomatic HTLV-I carriers. Proviral load correlated with anti-Env antibodies in asymptomatic carriers (<it>R </it>= 0.76), but not in HAM/TSP.</p> <p>Conclusion</p> <p>These studies indicate that anti-HTLV-I antibody responses detected by LIPS are useful for diagnosis and suggest that elevated anti-Env antibodies are a common feature found in HAM/TSP patients.</p

Early Pleistocene large mammals from Maka’amitalu, Hadar, lower Awash Valley, Ethiopia

The Early Pleistocene was a critical time period in the evolution of eastern African mammal faunas, but fossil assemblages sampling this interval are poorly known from Ethiopia's Afar Depression. Field work by the Hadar Research Project in the Busidima Formation exposures (similar to 2.7-0.8 Ma) of Hadar in the lower Awash Valley, resulted in the recovery of an early Homo maxilla (A.L. 666-1) with associated stone tools and fauna from the Maka'amitalu basin in the 1990s. These assemblages are dated to similar to 2.35 Ma by the Bouroukie Tuff 3 (BKT-3). Continued work by the Hadar Research Project over the last two decades has greatly expanded the faunal collection. Here, we provide a comprehensive account of the Maka'amitalu large mammals (Artiodactyla, Carnivora, Perissodactyla, Primates, and Proboscidea) and discuss their paleoecological and biochronological significance. The size of the Maka'amitalu assemblage is small compared to those from the Hadar Formation (3.45-2.95 Ma) and Ledi-Geraru (2.8-2.6 Ma) but includes at least 20 taxa. Bovids, suids, and Theropithecus are common in terms of both species richness and abundance, whereas carnivorans, equids, and megaherbivores are rare. While the taxonomic composition of the Maka'amitalu fauna indicates significant species turnover from the Hadar Formation and Ledi-Geraru deposits, turnover seems to have occurred at a constant rate through time as taxonomic dissimilarity between adjacent fossil assemblages is strongly predicted by their age difference. A similar pattern characterizes functional ecological turnover, with only subtle changes in dietary proportions, body size proportions, and bovid abundances across the composite lower Awash sequence. Biochronological comparisons with other sites in eastern Africa suggest that the taxa recovered from the Maka'amitalu are broadly consistent with the reported age of the BKT-3 tuff. Considering the age of BKT-3 and biochronology, a range of 2.4-1.9 Ma is most likely for the faunal assemblage.info:eu-repo/semantics/publishedVersio

Proceedings of the ANDROID Doctoral School

The Doctoral School initiative which was set up by the ANDROID network is a core element of the overall project that aims to strengthen the link between research and teaching in the area of disaster resilience. The mixed teaching space that we have developed as part of this ongoing project has attempted to encourage and promote the work of doctoral students in this field. The ANDROID disaster resilience network doctoral school consists of two programmes: 1. Online Doctoral School (ODS) and 2. Residential Doctoral School (RDS) The interlinked programmes work together to deliver on a varied number of teaching and research driven objectives. The online doctoral school which was conducted in Spring 2013 provided an innovative platform to transfer and develop the knowledge base of doctoral candidates. This was achieved through the conduct of a series of domain expert presentations along with thematic sessions aimed at engaging the doctoral researchers in knowledge discovery through detailed discussion. The online doctoral school will be rolled out again in Spring 2014

Smell and taste changes are early indicators of the COVID-19 pandemic and political decision effectiveness

In response to the COVID-19 pandemic, many governments have taken drastic measures to avoid an overflow of intensive care units. Accurate metrics of disease spread are critical for the reopening strategies. Here, we show that self-reports of smell/taste changes are more closely associated with hospital overload and are earlier markers of the spread of infection of SARS-CoV-2 than current governmental indicators. We also report a decrease in self-reports of new onset smell/taste changes as early as 5 days after lockdown enforcement. Cross-country comparisons demonstrate that countries that adopted the most stringent lockdown measures had faster declines in new reports of smell/taste changes following lockdown than a country that adopted less stringent lockdown measures. We propose that an increase in the incidence of sudden smell and taste change in the general population may be used as an indicator of COVID-19 spread in the population