Direct laser printing of thin-film polyaniline devices

We report the fabrication of electrically functional polyaniline thin-film microdevices. Polyaniline films were printed in the solid phase by Laser Induced Forward Transfer directly between Au electrodes on a Si/SiO2 substrate. To apply solid-phase deposition, aniline was in situ polymerized on quartz substrates. Laser deposition preserves the morphology of the films and delivers sharp features with controllable dimensions. The electrical characteristics of printed polyaniline present ohmic behavior, allowing for electroactive applications. Results on gas sensing of ammonia are presented.Comment: In Pres

Asynchronous Graph Pattern Matching on Multiprocessor Systems

Pattern matching on large graphs is the foundation for a variety of application domains. Strict latency requirements and continuously increasing graph sizes demand the usage of highly parallel in-memory graph processing engines that need to consider non-uniform memory access (NUMA) and concurrency issues to scale up on modern multiprocessor systems. To tackle these aspects, graph partitioning becomes increasingly important. Hence, we present a technique to process graph pattern matching on NUMA systems in this paper. As a scalable pattern matching processing infrastructure, we leverage a data-oriented architecture that preserves data locality and minimizes concurrency-related bottlenecks on NUMA systems. We show in detail, how graph pattern matching can be asynchronously processed on a multiprocessor system.Comment: 14 Pages, Extended version for ADBIS 201

Contribution of intermediate-volatility organic compounds from on-road transport to secondary organic aerosol levels in Europe

Atmospheric organic compounds with an effective saturation concentration (C∗) at 298 K between 103 and 106 µg m−3 are called intermediate-volatility organic compounds (IVOCs), and they have been identified as important secondary organic aerosol (SOA) precursors. In this work, we simulate IVOCs emitted from on-road diesel and gasoline vehicles over Europe with a chemical transport model (CTM), utilizing a new approach in which IVOCs are treated as lumped species that preserve their chemical characteristics. This approach allows us to assess both the overall contribution of IVOCs to SOA formation and the role of specific compounds. For the simulated early-summer period, the highest concentrations of SOA formed from the oxidation of on-road IVOCs (SOA-iv) are predicted for major European cities, like Paris, Athens, and Madrid. In these urban environments, on-road SOA-iv can account for up to a quarter of the predicted total SOA. Over Europe, unspeciated cyclic alkanes in the IVOC range are estimated to account for up to 72 % of the total on-road SOA-iv mass, with compounds with 15 to 20 carbons being the most prominent precursors. The sensitivity of the predicted SOA-iv concentrations to the selected parameters of the new lumping scheme is also investigated. Active multigenerational aging of the secondary aerosol products has the most significant effect as it increases the predicted SOA-iv concentrations by 67 %.</p

Changes in PM2.5 concentrations and their sources in the US from 1990 to 2010

Significant reductions in emissions of SO2, NOx, volatile organic compounds (VOCs), and primary particulate matter (PM) took place in the US from 1990 to 2010. We evaluate here our understanding of the links between these emissions changes and corresponding changes in concentrations and health outcomes using a chemical transport model, the Particulate Matter Comprehensive Air Quality Model with Extensions (PMCAMx), for 1990, 2001, and 2010. The use of the Particle Source Apportionment Algorithm (PSAT) allows us to link the concentration reductions to the sources of the corresponding primary and secondary PM. The reductions in SO2 emissions (64 %, mainly from electric-generating units) during these 20 years have dominated the reductions in PM2.5, leading to a 45 % reduction in sulfate levels. The predicted sulfate reductions are in excellent agreement with the available measurements. Also, the reductions in elemental carbon (EC) emissions (mainly from transportation) have led to a 30 % reduction in EC concentrations. The most important source of organic aerosol (OA) through the years according to PMCAMx is biomass burning, followed by biogenic secondary organic aerosol (SOA). OA from on-road transport has been reduced by more than a factor of 3. On the other hand, changes in biomass burning OA and biogenic SOA have been modest. In 1990, about half of the US population was exposed to annual average PM2.5 concentrations above 20 µg m−3, but by 2010 this fraction had dropped to practically zero. The predicted changes in concentrations are evaluated against the observed changes for 1990, 2001, and 2010 in order to understand whether the model represents reasonably well the corresponding processes caused by the changes in emissions.This work was supported by the Center for Air, Climate, and Energy Solutions (CACES), which was supported under assistance agreement no. R835873 awarded by the U.S. Environmental Protection Agency and the Horizon-2020 Project REMEDIA of the European Union under grant agreement no. 874753.Peer ReviewedPostprint (published version

The effect of orthodontic treatment on smile attractiveness: a systematic review.

BACKGROUND Smile attractiveness is a primary factor for patients to seek orthodontic treatment, however, there is yet no systematic evaluation of this topic in the literature. OBJECTIVES To assess the current evidence on the effect of orthodontic treatment on smile attractiveness. SEARCH METHODS Seven electronic databases (MEDLINE, Cochrane Library, Virtual Health Library, SCOPUS, Web of Science, Google Scholar and Embase) were searched on 14 September 2022. SELECTION CRITERIA Studies evaluating smile attractiveness before and after orthodontic treatment or only after completion of orthodontic treatment. DATA COLLECTION AND ANALYSIS Extracted data included study design and setting, sample size and demographics, malocclusion type, treatment modality and method for outcome assessment. Risk of bias was assessed with the ROBINS-I tool for non-randomised studies. Random-effects meta-analyses of mean differences and their 95% confidence intervals (CIs) were planned a priori. METHODS After elimination of duplicate studies, data extraction and risk of bias assessment according to the Cochrane guidelines, an evaluation of the overall evidence was performed. The included studies were evaluated based on the characteristics of their study and control groups and based on their main research question. Also, all outcome measures were standardized into a common assessment scale (0-100), in order to obtain more easily interpretable results. RESULTS Ten studies were included in this review, nine of which were assessed as being at serious risk of bias and one at moderate risk of bias. The large heterogeneity between the included studies did not allow for a meta-analysis. Orthodontic treatment has a moderately positive effect on smile attractiveness. When compared to no treatment, orthodontic treatment with premolar extractions improves smile attractiveness by 22%. Also, surgical correction of Class III cases increases smile attractiveness by 7.5% more than camouflage treatment. No other significant differences were shown between different types of treatment. CONCLUSION Based on the available data, orthodontic treatment seems to moderately improve the attractiveness of the smile. There is significant bias in the current literature assessing the effect of orthodontics on smile attractiveness; therefore, the results cannot be accepted with certainty

Stability and relapse after orthodontic treatment of deep bite cases—a long-term follow-up study

The purpose of this long-term follow-up study was twofold—firstly, to assess prevalence of relapse after treatment of deep bite malocclusion and secondly, to identify risk factors that predispose patients with deep bite malocclusion to relapse. Sixty-one former patients with overbite more than 50% incisor overlap before treatment were successfully recalled. Clinical data, morphometrical measurements on plaster casts before treatment, after treatment and at long-term follow-up, as well as cephalometric measurements before and after treatment were collected. The median follow-up period was 11.9 years. Patients were treated by various treatment modalities, and the majority of patients received at least a lower fixed retainer and an upper removable bite plate during retention. Relapse was defined as increase in incisor overlap from below 50% after treatment to equal or more than 50% incisor overlap at long-term follow-up. Ten per cent of the patients showed relapse to equal or larger than 50% incisor overlap, and their amount of overbite increase was low. Among all cases with deep bite at follow-up, gingival contact and palatal impingement were more prevalent in partially corrected noncompliant cases than in relapse cases. In this sample, prevalence and amount of relapse were too low to identify risk factors of relaps

High dimensional biological data retrieval optimization with NoSQL technology.

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data

ATMOSPHERIC AEROSOL SOURCE-RECEPTOR RELATIONSHIPS: THE ROLE OF COAL-FIRED POWER PLANTS

This report describes the technical progress made on the Pittsburgh Air Quality Study (PAQS) during the period of August 2001 through January of 2002. The major activity during this project period was the continuation of the ambient monitoring effort. Work also progressed on organizing the upcoming source characterization effort, and there was continued development of several three-dimensional air quality models. The first PAQS data analysis workshop for the project was held at Carnegie Mellon in December 2001. Two new instruments were added to site during this project period: a single particle mass spectrometer and an in situ VOC instrument. The single particle mass spectrometer has been deployed since the middle of September and has collected more than 150 days of data. The VOC instrument was only deployed during the intensive sampling period. Several instruments experienced operational issues during this project period. The overall data recovery rate for the project has been high

Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0

Accurately predicting urban PM2.5 concentrations and composition has proved challenging in the past, partially due to the resolution limitations of computationally intensive chemical transport models (CTMs). Increasing the resolution of PM2.5 predictions is desired to support emissions control policy development and address issues related to environmental justice. A nested grid approach using the CTM PMCAMx-v2.0 was used to predict PM2.5 at increasing resolutions of 36 km × 36 km, 12 km × 12 km, 4 km × 4 km, and 1 km × 1 km for a domain largely consisting of Allegheny County and the city of Pittsburgh in southwestern Pennsylvania, US, during February and July 2017. Performance of the model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Novel surrogates were developed to allocate emissions from cooking and on-road traffic sources to the 1 km × 1 km resolution grid. Total PM2.5 mass is reproduced well by the model during the winter period with low fractional error (0.3) and fractional bias (+0.05) when compared to regulatory measurements. Comparison with speciated measurements during this period identified small underpredictions of PM2.5 sulfate, elemental carbon (EC), and organic aerosol (OA) offset by a larger overprediction of PM2.5 nitrate. In the summer period, total PM2.5 mass is underpredicted due to a large underprediction of OA (bias = −1.9 µg m−3, fractional bias = −0.41). In the winter period, the model performs well in reproducing the variability between urban measurements and rural measurements of local pollutants such as EC and OA. This effect is less consistent in the summer period due to a larger fraction of long-range-transported OA. Comparison with total PM2.5 concentration measurements from low-cost sensors showed improvements in performance with increasing resolution. Inconsistencies in PM2.5 nitrate predictions in both periods are believed to be due to errors in partitioning between PM2.5 and PM10 modes and motivate improvements to the treatment of dust particles within the model. The underprediction of summer OA would likely be improved by updates to biogenic secondary organic aerosol (SOA) chemistry within the model, which would result in an increase of long-range transport SOA seen in the inner modeling domain. These improvements are obvious topics for future work towards model improvement. Comparison with regulatory monitors showed that increasing resolution from 36 to 1 km improved both fractional error and fractional bias in both modeling periods. Improvements at all types of measurement locations indicated an improved ability of the model to reproduce urban–rural PM2.5 gradients at higher resolutions.</p