Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs

Bibliometric analysis of publication metadata is an important tool for investigating emerging fields of technology. However, the application of field definitions to define an emerging technology is complicated by ongoing and at times rapid change in the underlying technology itself. There is limited prior work on adapting the bibliometric definitions of emerging technologies as these technologies change over time. The paper addresses this gap. We draw on the example of the modular keyword nanotechnology search strategy developed at Georgia Institute of Technology in 2006. This search approach has seen extensive use in analyzing emerging trends in nanotechnology research and innovation. Yet with the growth of the nanotechnology field, novel materials, particles, technologies, and tools have appeared. We report on the process and results of reviewing and updating this nanotechnology search strategy. By employing structured text-mining software to profile keyword terms, and by soliciting input from domain experts, we identify new nanotechnology-related keywords. We retroactively apply the revised evolutionary lexical query to 20 years of publication data and analyze the results. Our findings indicate that the updated search approach offers an incremental improvement over the original strategy in terms of recall and precision. Additionally, the updated strategy reveals the importance for nanotechnology of several emerging cited-subject categories, particularly in the biomedical sciences, suggesting a further extension of the nanotechnology knowledge domain. The implications of the work for applying bibliometric definitions to emerging technologies are discussed.     

Workload, control, and social support effects on serum lipids: A longitudinal study among apparently healthy employed adults.

The authors investigated the direct and interactive effects of the job demand– control–support (JDC-S) model’s components on subsequent changes in high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides (TRI) separately for male and female employees. In contrast to all 14 past studies on these relationships, the authors used a longitudinal design. Study participants (N = 1,137, 66% men) were all apparently healthy employees who underwent a routine health check at 2 points in time (Time 1 and Time 2) about 22 months apart. In these analyses, the authors controlled for the Time 1 level of each criterion and for other confounders. Most of the direct and moderating effects found did not support the predictions of the JDC-S model; this finding is in agreement with the majority of past cross-sectional studies. The authors did not find any evidence supporting the existence of a reverse causation for either of the components of the JDC-S model. The authors suggest that serum lipids may not be a physiological mechanism mediating the effects of the JDC-S model on atherosclerotic diseases. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Ammonia synthesis enhanced by magnesium chloride absorption

Conversion to ammonia with Haber–Bosch catalysts can be increased above 95% by selective absorption of ammonia by MgCl₂. The maximum conversion depends on reaction and absorption equilibria. At very short times, the measured conversion rate is the same with and without absorption by the MgCl₂ salt; the overall rate constants are comparable to those in the literature. At larger times, conversion to ammonia can be over seven times greater with MgCl₂ than without. However, the overall rate constants can be over 10 times slower because they are controlled by ammonia diffusion in the solid salt. An approximate, pseudosteady state theory consistent with these results provides a strategy for improving the overall rate while keeping the conversion over 90%. For example, the absorption rates might be increased using smaller particles of absorbent on a porous inert absorbent support. The results provide part of the basis for designing small scale ammonia plants. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1364–1371, 2015     

Generating a multiplicity of policies for agent steering in crowd simulation

Pedestrian steering algorithms range from completely procedural to entirely data‐driven, but the former grossly generalize across possible human behaviors and suffer computationally, whereas the latter are limited by the burden of ever‐increasing data samples. Our approach seeks the balanced middle ground by deriving a collection of machine‐learned policies based on the behavior of a procedural steering algorithm through the decomposition of the space of possible steering scenarios into steering contexts. The resulting algorithm scales well in the number of contexts, the use of new data sets to create new policies, and in the number of controlled agents as the policies become a simple evaluation of the rules asserted by the machine‐learning process. We also explore the use of synthetic data from an “oracle algorithm” that serves as an as‐needed source of samples, which can be stochastically polled for effective coverage. We observe that our approach produces pedestrian steering similar to that of the oracle steering algorithm, but with a significant performance boost. Runtime was reduced from hours under the oracle algorithm with 10 agents to on the order of 10 frames per second (FPS) with 3000 agents. We also analyze the nature of collisions in such a framework with no explicit collision avoidance. Copyright © 2014 John Wiley & Sons, Ltd.     

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.     

Electronic Properties of C60 Thin Films

Abstract

The electronic structure of polycrystalline C₆₀ thin films has been investigated using surface photovoltage spectroscopy (SPS) and conductivity measurements. the films show n-type semiconductivity with an activation energy of ～ 0.8 eV as found from the temperature dependence of the conductivity at high temperatures. the electronic structure emerging from our SPS results comprises a 1.6 eV photo-conduction gap, a mobility gap of about 2.25 eV and two gap states, a donor and an acceptor, at 0.35 eV and (1.0-1.1) eV, respectively, below the photo-conduction edge. the results indicate the possibility of the existence of band tails, extending into the optical gap of these films, as well as other deep gap states.