Experimental Evaluation of the Influence of Human-Structure Interaction for Vibration Serviceability

The effects of human-structure interaction on the dynamic performance of occupied structures have long been observed. The inclusion of the effects of human-structure interaction is important to ensure that the dynamic response of a structure is not overestimated. Previous observations, both in service and in the laboratory, have yielded results indicating that the effects are dependent on the natural frequency of the structure, the posture of the occupants, and the mass ratio of the occupants to the structure. These results are noteworthy, but are limited in their application,because the data are sparse and are only pertinent to a specific set of characteristics identified in a given study. To examine these characteristics simultaneously and consistently, an experimental test structure was designed with variable properties to replicate a variety of configurations within a controlled setting focusing on the effects of passive occupants. Experimental modal analysis techniques were employed to both the empty and occupied conditions of the structure and the dynamic properties associated with each condition were compared. Results similar to previous investigations were observed, including both an increase and a decrease in natural frequency of the occupied structure with respect to the empty structure, as well as the identification of a second mode of vibration. The damping of the combined system was higher for all configurations. Overall, this study provides a broad data set representing a wide array of configurations. The experimental results of this study were used to assess current recommendations for the dynamic properties of a crowd to analytically predict the effects of human-structure interaction. The experimental results were used to select a set of properties for passive, standing occupants and develop a new model that can more accurately represent the behavior of the human-structure system as experimentally measured in this study.

NADPH P450 oxidoreductase: structure, function, and pathology of diseases

Cytochrome P450 oxidoreductase (POR) is an enzyme that is essential for multiple metabolic processes, chiefly among them are reactions catalyzed by cytochrome P450 proteins for metabolism of steroid hormones, drugs and xenobiotics. Mutations in POR cause a complex set of disorders that often resemble defects in steroid metabolizing enzymes 17α-hydroxylase, 21-hydroxylase and aromatase. Since our initial reports of POR mutations in 2004, more than 200 different mutations and polymorphisms in POR gene have been identified. Several missense variations in POR have been tested for their effect on activities of multiple steroid and drug metabolizing P450 proteins. Mutations in POR may have variable effects on different P450 partner proteins depending on the location of the mutation. The POR mutations that disrupt the binding of co-factors have negative impact on all partner proteins, while mutations causing subtle structural changes may lead to altered interaction with specific partner proteins and the overall effect may be different for each partner. This review summarizes the recent discoveries related to mutations and polymorphisms in POR and discusses these mutations in the context of historical developments in the discovery and characterization of POR as an electron transfer protein. The review is focused on the structural, enzymatic and clinical implications of the mutations linked to newly identified disorders in humans, now categorized as POR deficiency.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.

COMPUTATIONAL ANALYSIS OF THE AEROELASTIC DYNAMICS OF A WIND TURBINE BLADE WITH VARIABLE-SPEED STALL CONTROL

The accuracy of simulating the aerodynamics and structural properties of the blades is crucial in the wind-turbine technology. Hence the models used to implement these features need to be very precise and their level of detailing needs to be high. With the variety of blade designs being developed the models should be versatile enough to adapt to the changes required by every design. We are going to implement a combination of numerical models which are associated with the structural and the aerodynamic part of the simulation using the computational power of a parallel HPC cluster. The structural part models the heterogeneous internal structure of the beam based on a novel implementation of the Generalized Timoshenko Beam Model Technique.. Using this technique the 3-D structure of the blade is reduced into a 1-D beam which is asymptotically equivalent. This reduces the computational cost of the model without compromising its accuracy. This structural model interacts with the Flow model which is a modified version of the Blade Element Momentum Theory. The modified version of the BEM accounts for the large deflections of the blade and also considers the pre-defined structure of the blade. The coning, sweeping of the blade, tilt of the nacelle and the twist of the sections along the blade length are all computed by the model which aren’t considered in the classical BEM theory. Each of these two models provides feedback to the other and the interactive computations lead to more accurate outputs. We successfully implemented the computational models to analyze and simulate the structural and aerodynamic aspects of the blades. The interactive nature of these models and their ability to recompute data using the feedback from each other makes this code more efficient than the commercial codes available. In this thesis we start off with the verification of these models by testing it on the well-known benchmark blade for the NREL-5MW Reference Wind Turbine, an alternative fixed-speed stall-controlled blade design proposed by Delft University, and a novel alternative design that we proposed for a variable-speed stall-controlled turbine, which offers the potential for more uniform power control and improved annual energy production.. To optimize the power output of the stall-controlled blade we modify the existing designs and study their behavior using the aforementioned aero elastic model.

Crystal chemistry and hydrogen bonding of rustumite Ca10(Si2O7)2(SiO4)(OH)2Cl2 with variable OH, Cl, F

Three samples of the skarn mineral rustumite Ca10(Si2O7)2(SiO4)(OH)2Cl2, space group C2/c, a ≈7.6, b ≈ 18.5, c ≈ 15.5 Å, β ≈ 104°, with variable OH, Cl, F content were investigated by electron microprobe, single-crystal X-ray structure refinements, and Raman spectroscopy. “Rust1LCl” is a low chlorine rustumite Ca10(Si2O7)2(SiO4)(OH1.88F0.12)(Cl1.28,OH0.72) from skarns associated with the Rize batholith near Ikizedere, Turkey. “Rust2F” is a F-bearing rustumite Ca10(Si2O7)2(SiO4)(OH1.13F0.87) (Cl1 96OH0.04) from xenoliths in ignimbrites of the Upper Chegem Caldera, Northern Caucasus, Russia. “Rust3LClF” represents a low-Cl, F-bearing rustumite Ca10(Si2O7)2(SiO4)0.87(H4O4)0.13(OH1.01F0.99) (Cl1.00 OH1.00) from altered merwinite skarns of the Birkhin massif, Baikal Lake area, Eastern Siberia, Russia. Rustumite from Birkhin massif is characterized by a significant hydrogarnet-like or fluorine substitution at the apices of the orthosilicate group, leading to specific atomic displacements. The crystal structures including hydrogen positions have been refined from single-crystal X-ray data to R1 = 0.0205 (Rust1_LCl), R1 = 0.0295 (Rust2_F), and R1 = 0.0243 (Rust3_LCl_F), respectively. Depletion in Cl and replacement by OH is associated with smaller unit-cell dimensions. The substitution of OH by F leads to shorter hydrogen bonds O-H⋯F instead of O-H⋯OH. Raman spectra for all samples have been measured and confirm slight strengthening of the hydrogen bonds with uptake of F.This study discusses the complex crystal chemistry of the skarn mineral rustumite and may provide a wider understanding of the chemical reactions related to contact metamorphism of limestones.

Molecular structure of antibodies against small ligands

Antibodies which bind bioactive ligands can serve as a template for the generation of a second antibody which may react with the physiological receptor. This phenomenon of molecular mimicry by antibodies has been described in a variety of systems. In order to understand the chemical and molecular mechanisms involved in these interactions, monoclonal antibodies directed against two pharmacologically active alkaloids, morphine and nicotine, were carefully studied using experimental and theoretical molecular modeling techniques. The molecular characterization of these antibodies involved binding studies with ligand analogs and determination of the variable region amino acid sequence. A three-dimensional model of the anti-morphine binding site was constructed using computational and graphics display techniques. The antibody response in BALB/c mice to morphine appears relatively restricted, in that all of the antibodies examined in this study contained a $\lambda$ light chain, which is normally found in only 5% of mouse immunoglobulins. This study represents the first use of theoretical and experimental modeling techniques to describe the antigen binding site of a mouse Fv region containing a $\lambda$ light chain. The binding site model indicates that a charged glutamic acid residue and aromatic side chains are key features in ionic and hydrophobic interactions with the ligand morphine. A glutamic acid residue is found in the identical position in the anti-nicotine antibody and may play a role in binding nicotine. ^

Plasticity to wind is modular and genetically variable in Arabidopsis thaliana

Thigmomorphogenesis, the characteristic phenotypic changes by which plants react to mechanical stress, is a widespread and probably adaptive type of phenotypic plasticity. However, little is known about its genetic basis and population variation. Here, we examine genetic variation for thigmomorphogenesis within and among natural populations of the model system Arabidopsis thaliana. Offspring from 17 field-collected European populations was subjected to three levels of mechanical stress exerted by wind. Overall, plants were remarkably tolerant to mechanical stress. Even high wind speed did not significantly alter the correlation structure among phenotypic traits. However, wind significantly affected plant growth and phenology, and there was genetic variation for some aspects of plasticity to wind among A. thaliana populations. Our most interesting finding was that phenotypic traits were organized into three distinct and to a large degree statistically independent covariance modules associated with plant size, phenology, and growth form, respectively. These phenotypic modules differed in their responsiveness to wind, in the degree of genetic variability for plasticity, and in the extent to which plasticity affected fitness. It is likely, therefore, that thigmomorphogenesis in this species evolves quasi-independently in different phenotypic modules.

Effects of forest management on ground-dwelling beetles (Coleoptera; Carabidae, Staphylinidae) in Central Europe are mainly mediated by changes in forest structure

Forest management is known to influence species diversity of various taxa but inconsistent or even contrasting effects are reported for arthropods. Regional differences in management as well as differences in regional species pools might be responsible for these inconsistencies, but, inter-regional replicated studies that account for regional variability are rare. We investigated the effect of forest type on the abundance, diversity, community structure and composition of two important ground-dwelling beetle families, Carabidae and Staphylinidae, in 149 forest stands distributed over three regions in Germany. In particular we focused on recent forestry history, stand age and dominant tree species, in addition to a number of environmental descriptors. Overall management effects on beetle communities were small and mainly mediated by structural habitat parameters such as the cover of forest canopy or the plant diversity on forest stands. The general response of both beetle taxa to forest management was similar in all regions: abundance and species richness of beetles was higher in older than in younger stands and species richness was lower in unmanaged than in managed stands. The abundance ratio of forest species-to-open habitat species differed between regions, but generally increased from young to old stands, from coniferous to deciduous stands and from managed to unmanaged stands. The response of both beetle families to dominant tree species was variable among regions and staphylinid richness varied in the response to recent forestry history. Our results suggest that current forest management practices change the composition of ground-dwelling beetle communities mainly by favoring generalists and open habitat species. To protect important forest beetle communities and thus the ecosystem functions and services provided by them, we suggest to shelter remaining ancient forests and to develop near-to-nature management strategies by prolonging rotation periods and increasing structural diversity of managed forests. Possible geographic variations in the response of beetle communities need to be considered in conservation-orientated forest management strategies.

Elucidating the internal structure of psychophysical timing performance in the sub-second and second range by utilizing confirmatory factor analysis

The most influential theoretical account in time psychophysics assumes the existence of a unitary internal clock based on neural counting. The distinct timing hypothesis, on the other hand, suggests an automatic timing mechanism for processing of durations in the sub-second range and a cognitively controlled timing mechanism for processing of durations in the range of seconds. Although several psychophysical approaches can be applied for identifying the internal structure of interval timing in the second and sub-second range, the existing data provide a puzzling picture of rather inconsistent results. In the present chapter, we introduce confirmatory factor analysis (CFA) to further elucidate the internal structure of interval timing performance in the sub-second and second range. More specifically, we investigated whether CFA would rather support the notion of a unitary timing mechanism or of distinct timing mechanisms underlying interval timing in the sub-second and second range, respectively. The assumption of two distinct timing mechanisms which are completely independent of each other was not supported by our data. The model assuming a unitary timing mechanism underlying interval timing in both the sub-second and second range fitted the empirical data much better. Eventually, we also tested a third model assuming two distinct, but functionally related mechanisms. The correlation between the two latent variables representing the hypothesized timing mechanisms was rather high and comparison of fit indices indicated that the assumption of two associated timing mechanisms described the observed data better than only one latent variable. Models are discussed in the light of the existing psychophysical and neurophysiological data.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.

KLIKK proteases of Tannerella forsythia: putative virulence factors with a unique domain structure.

Comparative genomics of virulent Tannerella forsythia ATCC 43037 and a close health-associated relative, Tannerella BU063, revealed, in the latter, the absence of an entire array of genes encoding putative secretory proteases that possess a nearly identical C-terminal domain (CTD) that ends with a -Lys-Leu-Ile-Lys-Lys motif. This observation suggests that these proteins, referred to as KLIKK proteases, may function as virulence factors. Re-sequencing of the loci of the KLIKK proteases found only six genes grouped in two clusters. All six genes were expressed by T. forsythia in routine culture conditions, although at different levels. More importantly, a transcript of each gene was detected in gingival crevicular fluid (GCF) from periodontitis sites infected with T. forsythia indicating that the proteases are expressed in vivo. In each protein, a protease domain was flanked by a unique N-terminal profragment and a C-terminal extension ending with the CTD. Partially purified recombinant proteases showed variable levels of proteolytic activity in zymography gels and toward protein substrates, including collagen, gelatin, elastin, and casein. Taken together, these results indicate that the pathogenic strain of T. forsythia secretes active proteases capable of degrading an array of host proteins, which likely represents an important pathogenic feature of this bacterium.

The impact of the position effect on the factorial structure of the Culture Fair Test (CFT)

The Culture Fair Test (CFT) is a psychometric test of fluid intelligence consisting of four subtests; Series, Classification, Matrices, and Topographies. The four subtests are only moderately intercorrelated, doubting the notion that they assess the same construct (i.e., fluid intelligence). As an explanation of these low correlations, we investigated the position effect. This effect is assumed to reflect implicit learning during testing. By applying fixed-links modeling to analyze the CFT data of 206 participants, we identified position effects as latent variables in the subtests; Classification, Matrices, and Topographies. These position effects were disentangled from a second set of latent variables representing fluid intelligence inherent in the four subtests. After this separation of position effect and basic fluid intelligence, the latent variables representing basic fluid intelligence in the subtests Series, Matrices, and Topographies could be combined to one common latent variable which was highly correlated with fluid intelligence derived from the subtest Classification (r=.72). Correlations between the three latent variables representing the position effects in the Classification, Matrices, and Topographies subtests ranged from r=.38 to r=.59. The results indicate that all four CFT subtests measure the same construct (i.e., fluid intelligence) but that the position effect confounds the factorial structure

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

HEAD OF HOUSEHOLD STRUCTURE: ITS INFLUENCE ON THE DIETARY QUALITY OF U.S. HOUSEHOLDS, 1977-78 (UNITED STATES, NATIONWIDE FOOD CONSUMPTION SURVEY)

Using analysis of variance, household data collected in the Spring portion of the 1977-78 Nationwide Food Consumption Survey conducted by the United States Department of Agriculture were analyzed to examine the relationship between household characteristics and dietary quality of the household food supply. Results indicated that head of household structure was a statistically significant variable, with female headed households having higher dietary quality.^ Further analysis indicated that neither race, degree of urbanization, regional location, the education level of the female head, nor her employment status were significant variables in influencing dietary quality. The influence of head of household structure remained significant when these variables were controlled. However, income, household size, and family life cycle stage had statistically significant effects on dietary quality, and when individually controlled, the influence of head of household structure disappeared. ^

Response of a natural phytoplankton community from the Qingdao coast (Yellow Sea, China) to variable CO2 levels over a short-term incubation experiment

Since marine phytoplankton play a vital role in stabilizing earth's climate by removing significant amount of atmospheric CO2, their responses to increasing CO2 levels are indeed vital to address. The responses of a natural phytoplankton community from the Qingdao coast (NW Yellow Sea, China) was studied under different CO2 levels in microcosms. HPLC pigment analysis revealed the presence of diatoms as a dominant microalgal group; however, members of chlorophytes, prasinophytes, cryptophytes and cyanophytes were also present. delta 13CPOM values indicated that the phytoplankton community probably utilized bicarbonate ions as dissolved inorganic carbon source through a carbon concentration mechanism (CCM) under low CO2 levels, and diffusive CO2 uptake increased upon the increase of external CO2 levels. Although, considerable increase in phytoplankton biomass was noticed in all CO2 treatments, CO2-induced effects were absent. Higher net nitrogen uptake under low CO2 levels could be related to the synthesis of CCM components. Flow cytometry analysis showed slight reduction in the abundance of Synechococcus and pico-eukaryotes under the high CO2 treatments. Diatoms did not show any negative impact in response to increasing CO2 levels; however, chlorophytes revealed a reverse tend. Heterotrophic bacterial count enhanced with increasing CO2 levels and indicated higher abundance of labile organic carbon. Thus, the present study indicates that any change in dissolved CO2 concentrations in this area may affect phytoplankton physiology and community structure and needs further long-term study.