Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed from data collected from ISO 8178‐4 (1996) test cycle B‐type tests (ISO) and an expanded set of tests (EXP) to predict hydrocarbon (HC) emissions from a diesel engine. LMR using the ISO training data (R2 = 0.94) resulted in overfitting of the model as applied to the evaluation data (R2 = 0.49). LMR based on the expanded data (R2 = 0.68) was a better LMR model when applied to the evaluation data (R2 = 0.64). NPN using the expanded training data (R2 = 0.99) resulted in the best model when applied to the evaluation data (R2 = 0.98) and is preferred for predicting HC when the larger set of test mode data are available. NPN using the ISO training data (R2 = 0.99) resulted in a satisfactory fit for the evaluation data (R2 = 0.91), although with a higher average absolute error (0.52 vs. 0.42 g/kWh) than NPN using the EXP training data. This model was also considered suitable for predicting HC. Results of this initial study suggest that data could be collected during ISO 8178‐4 emission tests and modeled with NPN to predict HC emissions for a diesel engine operating at various constant speeds and loads

Modeling NO Emissions of an Off-road Diesel Engine Based on Emission Tests

Emissions values determined by the ISO 8178 emission certification tests do not necessarily represent emissions of a tractor in operation (Hansson et al., 2001). Rather than using ISO 8178 tests solely for certification, data collected during the tests may be suitable for predicting nitrogen oxide (NOx) emissions of an engine operating at constant loads and speeds. Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed with data collected from ISO 8178-4 (1996) test cycle B-type tests (ISO) and an expanded set of tests (EXP) to predict NOx emissions from a diesel engine. LMR using the ISO training data (R2 = 0.94) resulted in over-training of the model, as applied to the evaluation data (R2 = 0.51). LMR based on the expanded data (R2 = 0.60) was a better LMR model, when applied to the evaluation data (R2 = 0.73). NPN using the ISO training data (R2 = 0.99) resulted in a considerable improvement over the LMR models for the evaluation data (R2 = 0.81). NPN using the EXP training data (R2 = 0.96) resulted in the best model when applied to the evaluation data (R2 = 0.95). When applied to the evaluation data, the mean absolute error of the NPN EXP based model was significantly less than from the NPN ISO based model. The NPN model based on EXP data is recommended for predicting NOx. Data could be collected during ISO 8178-4 emission tests that included additional test modes and modeled with NPN to predict NOx emissions for a diesel engine operating at various constant speeds and loads

Access to Opportunity Project: Final Report

This project’s goal is to lift up promising approaches, suggest new strategies and encourage honest conversations that result in public policy solutions to income and racial segregation and poverty. The overarching question that motivates this work is: What are effective policies and strategies that promote access to high-opportunity amenities for low-income families? As a first step, the researchers surveyed efforts on the ground in the metropolitan areas encompassing Seattle, Washington; Portland, Oregon; and San Diego, California, to determine whether there were any candidates for deeper study. We selected these three metropolitan areas for several reasons. First, prior interaction revealed that attention had been given to this question and that parties in each had embarked on purposeful efforts to make progress. Second, they represent a diverse array of communities that vary in significant ways, including along key economic, demographic, and social dimensions, and in some regards are bellwethers for changes beginning to take place in many parts of the country. As a consequence, experiences and successes in these places could potentially be applied to a diverse set of other urban areas across the United States. The three regions are among the largest in the United States, with Seattle and Portland being the largest in their respective states and San Diego third in California (behind Los Angeles and the Bay Area). Despite their size, they differ in important ways that result in different social and political dynamics prevailing in each location. In considering access to opportunity, one must understand the opportunities that are available in order to tailor skill-building efforts and investments in “connective infrastructure,” such as mass transit and suburban affordable housing, so that they are maximally effective. From an economic perspective, the three regions are quite different, which means that the approaches observed across the regions will potentially vary in measurable ways. In each metropolitan area, we sought the counsel of key governmental, practitioner, academic, and philanthropic players. During the course of our initial visits to each region, we met with and interviewed almost 80 people—28 in Seattle, 26 in Portland, and 24 in San Diego. Through these conversations, we identified 27 projects—nine in each metropolitan area—as being promising examples of cases where lower-income families may have achieved increased access to high-opportunity amenities. Given time, available funding, and the presence of partners willing to support our research effort by providing access to program data and program participants, we chose three projects for examination: • The San Diego Housing Commission’s Achievement Academy • Seattle/King County’s A Regional Coalition for Housing (ARCH) • Humboldt Gardens in Northeast Portlan

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

Significance Will different researchers converge on similar findings when analyzing the same data? Seventy-three independent research teams used identical cross-country survey data to test a prominent social science hypothesis: that more immigration will reduce public support for government provision of social policies. Instead of convergence, teams’ results varied greatly, ranging from large negative to large positive effects of immigration on social policy support. The choices made by the research teams in designing their statistical tests explain very little of this variation; a hidden universe of uncertainty remains. Considering this variation, scientists, especially those working with the complexities of human societies and behavior, should exercise humility and strive to better account for the uncertainty in their work. Abstract This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

Measuring loss aversion under ambiguity: a method to make prospect theory completely observable

We propose a simple, parameter-free method that, for the first time, makes it possible to completely observe Tversky and Kahneman’s (1992) prospect theory. While methods exist to measure event weighting and the utility for gains and losses separately, there was no method to measure loss aversion under ambiguity. Our method allows this and thereby it can measure prospect theory’s entire utility function. Consequently, we can properly identify properties of utility and perform new tests of prospect theory. We implemented our method in an experiment and obtained support for prospect theory. Utility was concave for gains and convex for losses and there was substantial loss aversion. Both utility and loss aversion were the same for risk and ambiguity, as assumed by prospect theory, and sign-comonotonic trade-off consistency, the central condition of prospect theory, held

Use of Genomic DNA as an Indirect Reference for Identifying Gender-Associated Transcripts in Morphologically Identical, but Chromosomally Distinct, Schistosoma mansoni Cercariae

BACKGROUND: The use of DNA microarray technology to study global Schistosoma gene expression has led to the rapid identification of novel biological processes, pathways or associations. Implementation of standardized DNA microarray protocols across laboratories would assist maximal interpretation of generated datasets and extend productive application of this technology. METHODOLOGY/PRINCIPAL FINDINGS: Utilizing a new Schistosoma mansoni oligonucleotide DNA microarray composed of 37,632 elements, we show that schistosome genomic DNA (gDNA) hybridizes with less variation compared to complex mixed pools of S. mansoni cDNA material (R = 0.993 for gDNA compared to R = 0.956 for cDNA during ‘self versus self’ hybridizations). Furthermore, these effects are species-specific, with S. japonicum or Mus musculus gDNA failing to bind significantly to S. mansoni oligonucleotide DNA microarrays (e.g R = 0.350 when S. mansoni gDNA is co-hybridized with S. japonicum gDNA). Increased median fluorescent intensities (209.9) were also observed for DNA microarray elements hybridized with S. mansoni gDNA compared to complex mixed pools of S. mansoni cDNA (112.2). Exploiting these valuable characteristics, S. mansoni gDNA was used in two-channel DNA microarray hybridization experiments as a common reference for indirect identification of gender-associated transcripts in cercariae, a schistosome life-stage in which there is no overt sexual dimorphism. This led to the identification of 2,648 gender-associated transcripts. When compared to the 780 gender-associated transcripts identified by hybridization experiments utilizing a two-channel direct method (co-hybridization of male and female cercariae cDNA), indirect methods using gDNA were far superior in identifying greater quantities of differentially expressed transcripts. Interestingly, both methods identified a concordant subset of 188 male-associated and 156 female-associated cercarial transcripts, respectively. Gene ontology classification of these differentially expressed transcripts revealed a greater diversity of categories in male cercariae. Quantitative real-time PCR analysis confirmed the DNA microarray results and supported the reliability of this platform for identifying gender-associated transcripts. CONCLUSIONS/SIGNIFICANCE: Schistosome gDNA displays characteristics highly suitable for the comparison of two-channel DNA microarray results obtained from experiments conducted independently across laboratories. The schistosome transcripts identified here demonstrate, for the first time, that gender-associated patterns of expression are already well established in the morphologically identical, but chromosomally distinct, cercariae stage

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings