An Evaluation of Density and Stability Tests on Bituminous Mixes

During the past three years the Highway Materials Research Laboratory has experimented with bituminous aggregate mixes in an attempt to determine the densities that can be obtained through certain methods of compaction and the stability of the resulting mixes. The several investigations have resulted in the preparation and testing of approximately six hundred individual samples, most of which were designed to provide information as a guide for specification on particular field projects

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey\u27s 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score \u3c 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret

Binge flying: Behavioural addiction and climate change

Recent popular press suggests that ‘binge flying’ constitutes a new site of behavioural addiction. We theoretically appraise and empirically support this proposition through interviews with consumers in Norway and the United Kingdom conducted in 2009. Consistent findings from across two national contexts evidence a growing negative discourse towards frequent short-haul tourist air travel and illustrate strategies of guilt suppression and denial used to span a cognitive dissonance between the short-term personal benefits of tourism and the air travel’s associated long-term consequences for climate change. Tensions between tourism consumption and changing social norms towards acceptable flying practice exemplify how this social group is beginning to (re)frame what constitutes ‘excessive’ holiday flying, despite concomitantly continuing their own frequent air travels

Juvenile hormone potentiates ecdysone receptor-dependent transcription in a mammalian cell culture system

Juvenile hormone potentiates ecdysone receptor-dependent transcription in a mammalian cell culture system. Insect Biochem. Mol. Biol., 33, 1239-1247. DOI: 10.1016/j.ibmb.2003 Insect development is guided by the combined actions of ecdysteroids and juvenile hormones (JHs). The transcriptional effects of ecdysteroids are mediated by a protein complex consisting of the ecdysone receptor (EcR) and its heterodimeric partner, Ultraspiracle (USP), but a corresponding JH receptor has not been defined conclusively. Given that the EcR ligand binding domain (LBD) is similar to that of the JH-responsive rat farnesoid-X-activated receptor (FXR), we sought to define experimental conditions under which EcR-dependent transcription could be promoted by JH. Chinese hamster ovary (CHO) cells were transfected with a plasmid carrying an ecdysteroid-inducible reporter gene, a second plasmid expressing one of the three amino-terminal variants of Drosophila EcR or an EcR chimera, and a third plasmid expressing either the mouse retinoid X receptor (RXR), or its insect orthologue, USP. Each of the EcR variants responded to the synthetic ecdysteroid, muristerone A (murA), but a maximal response to 20-hydroxyecdysone (20E) was achieved only for specific EcR combinations with its heterodimeric partner. Notably, the Drosophila EcR isoforms were responsive to 20E only when paired with USP, and only EcRB2 activity was further potentiated by JHIII in the presence of 20E. EcR chimeras that fuse the activator domains from VP16 or the glucocorticoid receptor to the Drosophila EcR DNA-binding and ligand-binding domains were responsive to ecdysteroids. Again, the effects of JHIII and 20E were associated with specific partners of the chimeric EcRs. In all experiments, the LBD of EcR proved to be the prerequisite component for potentiation by JHIII, and in this conformation may resemble the FXR LBD. Our results indicate that EcR responsiveness is influenced by the heterodimeric partner and that both the Nterminal domain of EcR and the particular ecdysteroid affect JHIII potentiation