Crash reconstruction of pedestrian accidents using optimization techniques

Numerical simulations of vehicle-to-pedestrian crash (VPC) are frequently used to develop a detailed understanding of how pedestrian injuries relate to documented vehicle damage. Given the complexity of the event, modeling the interactions typically involves subjective evaluations of the pre-impact conditions using a limited number of simulations. The goal of this study is to develop a robust methodology for obtaining the pre-impact pedestrian posture and vehicle speed utilizing multi-body simulations and optimization techniques. First, a continuous sequence of the pedestrian gait based on the literature data and simulations was developed for use as a design parameter during the optimization process. Then, the robustness and efficiency of three optimization algorithms were evaluated in a mock (idealized) crash reconstruction. The pre-impact parameters of the pedestrian and the vehicle models were treated as unknown design variables for the purpose of validating the optimization technique. While all algorithms found solutions in close vicinity of the exact solution, a genetic algorithm exhibited the fastest convergence. The response surfaces of the objective function showed higher sensitivities to the pedestrian posture and its relative position with respect to the vehicle than to the vehicle speed for the chosen design space. After validating the methodology with the mock reconstruction, a real-world vehicle-to-pedestrian accident was reconstructed using the data obtained from the field investigation and the optimization methodology. A set of pedestrian and vehicle initial conditions capable of matching all observed contact points was determined. Based on the mock and real-world reconstructions, this study indicates that numerical simulations coupled with optimization algorithms can be used to predict pedestrian and vehicle pre-impact conditions.     

Continuous low-dose infusion of tumor necrosis factor alpha in adipose tissue elevates adipose tissue interleukin 10 abundance and fails to alter metabolism in lactating dairy cows

Repeated bolus doses of tumor necrosis factor-α (TNFα) alters systemic metabolism in lactating cows, but whether chronic release of inflammatory cytokines from adipose tissue has similar effects is unclear. Late-lactation Holstein cows (n = 9–10/treatment) were used to evaluate the effects of continuous adipose tissue TNFα administration on glucose and fatty acid (FA) metabolism. Cows were blocked by feed intake and milk yield and randomly assigned within block to control or TNFα treatments. Treatments (4 mL of saline or 14 µg/kg of TNFα in 4 mL of saline) were infused continuously over 7 d via 2 osmotic pumps implanted in a subcutaneous adipose depot. Plasma, milk samples, milk yield, and feed intake data were collected daily, and plasma glucose turnover rate was measured on d 7. At the end of d 7, pumps were removed and liver and contralateral tail-head adipose biopsies were collected. Results were modeled with the fixed effect of treatment and the random effect of block. Treatment with TNFα increased plasma concentrations of the acute phase protein haptoglobin, but did not alter plasma TNFα, IL-4, IL-6, or IFN-γ concentrations, feed intake, or rectal temperature. Milk yield and composition were unchanged, and treatments did not alter the proportion of short- versus long-chain FA in milk on d 7. Treatments did not alter plasma free FA concentration, liver triglyceride content, or plasma glucose turnover rate. Surprisingly, TNFα infusion tended to decrease liver TNFα and IL-1 receptor 1 mRNA abundance and significantly increased adipose tissue IL-10 protein concentration. Continuous infusion of TNFα did not induce the metabolic responses previously observed following bolus doses delivered at the same rate per day. Metabolic homeostasis may have been protected by an adaptive anti-inflammatory response to control systemic inflammation.     

Landscape development planning

The Eclogues of the Neronian poet Calpurnius Siculus reveal a self‐conscious attitude to the Roman environment, drawing on Greek Pastoral but taking the point of view of wealthy urban Romans. The Pastoral landscape is, ironically, the city‐dweller's inward refuge.     

Self-assembly and alignment of semiconductor nanoparticles on cellulose nanocrystals

The synthesis of cadmium sulfide (CdS), zinc sulfide (ZnS), and lead sulfide (PbS) nanoparticle chains on cellulose nanocrystal (CNC) templates can be accomplished by the reaction of the precursor salts. The use of a cationic surfactant, cetyltrimethylammonium bromide (CTAB), was critical for the synthesis of well-defined semiconductor nanoparticle chains on the surface of the CNCs. The semiconductor nanoparticle particle size and packing density on CNC surface could be controlled by the variation of the precursor concentration and the pH of the salt solution.     

Households' willingness to pay for overhead-to-underground conversion of electricity distribution networks

Underground low-voltage electricity networks have several advantages over overhead networks including reliability of supply, safety and improved visual amenity. The economic viability of replacing existing overhead networks with new underground networks depends on the value of these benefits to households, but no complete value estimates are available in the literature. This paper represents a contribution towards addressing this research gap. A stated choice survey is used to estimate willingness to pay for undergrounding in established residential areas in Canberra, Australia. Average willingness to pay is at least A$6838 per household and there is significant variation in preferences over the population. The results suggest that benefits would be highest in areas with higher household income and older residents where visual amenity, safety, tree trimming, or restrictions on the use of yard space are of concern.     

Effects of interest–major congruence, motivation, and academic performance on timely degree attainment.

[Correction Notice: An erratum for this article was reported in Vol 57(2) of Journal of Counseling Psychology (see record 2010-06436-009). The link to the supplemental material was incorrect. The correct link is provided in the erratum.] Using longitudinal student data from 15 four-year (n = 3,072) and 13 (n = 788) two-year postsecondary institutions, the authors tested the effects of interest–major congruence, motivation, and 1st-year academic performance on timely degree completion. Findings suggest that interest–major congruence has a direct effect on timely degree completion at both institutional settings and that motivation has indirect effects (via 1st-year academic performance). The total effects of both interest–major congruence and motivation on timely degree completion underscore the importance of both constructs in understanding student adjustment and postsecondary success. Implications for theory and counseling practice are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stereotype threat effects on Black and White athletic performance.

Two experiments showed that framing an athletic task as diagnostic of negative racial stereotypes about Black or White athletes can impede their performance in sports. In Experiment 1, Black participants performed significantly worse than did control participants when performance on a golf task was framed as diagnostic of "sports intelligence." In comparison, White participants performed worse than did control participants when the golf task was framed as diagnostic of 'natural athletic ability." Experiment 2 observed the effect of stereotype threat on the athletic performance of White participants for whom performance in sports represented a significant measure of their self-worth. The implications of the findings for the theory of stereotype threat (C. M. Steele, 1997) and for participation in sports are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Scalable Techniques for Mining Causal Structures

Mining for association rules in market basket data has proved a fruitful area of research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form “the existence of item A implies the existence of item B.” However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number of variables. In this paper, we consider the problem of determining casual relationships, instead of mere associations, when mining market basket data. We identify some problems with the direct application of Bayesian learning ideas to mining large databases, concerning both the scalability of algorithms and the appropriateness of the statistical techniques, and introduce some initial ideas for dealing with these problems. We present experimental results from applying our algorithms on several large, real-world data sets. The results indicate that the approach proposed here is both computationally feasible and successful in identifying interesting causal structures. An interesting outcome is that it is perhaps easier to infer the lack of causality than to infer causality, information that is useful in preventing erroneous decision making.     

Microstructure and Phase Formation in a Rapidly Solidified Laser-Deposited Ni-Cr-B-Si-C Hardfacing Alloy

In this study, microstructural evolutions and phase selection phenomena during laser deposition of a hardfacing Ni-Cr-B-Si-C alloy at different processing conditions are experimentally investigated. The results show that even minor variations in the thermal conditions during solidification can modify the type and morphology of the phases. Higher undercoolings obtained at faster cooling rates suppressed the primary borides and encouraged floret-shape mixtures of Ni and Cr₅B₃ via a metastable reaction. Variations in the boride phases are discussed in terms of nucleation- and growth-controlled phase selection mechanisms. These selection processes also influenced the nature and proportion of the Ni-B-Si eutectics by changing the amount of the boron available for the final eutectic reactions. The results of this work emphasize the importance of controlling the cooling rate during deposition of these industrially important alloys using laser beam or other rapid solidification techniques.