scholarly journals Parameterization of ion-induced nucleation rates based on ambient observations

2010 ◽  
Vol 10 (9) ◽  
pp. 21697-21720 ◽  
Author(s):  
T. Nieminen ◽  
P. Paasonen ◽  
H. E. Manninen ◽  
V.-M. Kerminen ◽  
M. Kulmala
Keyword(s):  
Large Scale ◽  
Formation Rate ◽  
Global Radiation ◽  
Atmospheric Models ◽  
Organic Vapor ◽  
Depth Analysis ◽  
Using Data ◽  
One Year ◽  
Project Data ◽  
Semi Empirical

Abstract. Atmospheric ions participate in the formation of new atmospheric aerosol particles, yet their exact role in this process has remained unclear. Here we derive a new simple parameterization for ion-induced nucleation or, more precisely, for the formation rate of charged 2-nm particles. The parameterization is semi-empirical in the sense that it is based on comprehensive results of one-year-long atmospheric cluster and particle measurements in the size range ∼1–42 nm within the EUCAARI (European Integrated project on Aerosol Cloud Climate and Air Quality interactions) project. Data from 12 field sites across Europe measured with different types of air ion and cluster mobility spectrometers were used in our analysis, with more in-depth analysis made using data from four stations with concomitant sulphuric acid measurements. The parameterization was given in two slightly different forms: a more accurate one that requires information on sulfuric acid and nucleating organic vapor concentrations, and a simpler one in which this information is replaced with the global radiation intensity. In principle, these new parameterizations are applicable to all large-scale atmospheric models containing size-resolved aerosol microphysics.

scholarly journals Parameterization of ion-induced nucleation rates based on ambient observations

2011 ◽  
Vol 11 (7) ◽  
pp. 3393-3402 ◽  
Author(s):  
T. Nieminen ◽  
P. Paasonen ◽  
H. E. Manninen ◽  
K. Sellegri ◽  
V.-M. Kerminen ◽  
...  
Keyword(s):  
Sulphuric Acid ◽  
Large Scale ◽  
Formation Rate ◽  
Global Radiation ◽  
Cluster Ions ◽  
Depth Analysis ◽  
Using Data ◽  
One Year ◽  
Project Data ◽  
Semi Empirical

Abstract. Atmospheric ions participate in the formation of new atmospheric aerosol particles, yet their exact role in this process has remained unclear. Here we derive a new simple parameterization for ion-induced nucleation or, more precisely, for the formation rate of charged 2-nm particles. The parameterization is semi-empirical in the sense that it is based on comprehensive results of one-year-long atmospheric cluster and particle measurements in the size range ~1–42 nm within the EUCAARI (European Integrated project on Aerosol Cloud Climate and Air Quality interactions) project. Data from 12 field sites across Europe measured with different types of air ion and cluster mobility spectrometers were used in our analysis, with more in-depth analysis made using data from four stations with concomitant sulphuric acid measurements. The parameterization is given in two slightly different forms: a more accurate one that requires information on sulfuric acid and nucleating organic vapor concentrations, and a simpler one in which this information is replaced with the global radiation intensity. These new parameterizations are applicable to all large-scale atmospheric models containing size-resolved aerosol microphysics, and a scheme to calculate concentrations of sulphuric acid, condensing organic vapours and cluster ions.

scholarly journals Annual changes in a bird assembly on artificial perches: Implications for ecological restoration in a subtropical agroecosystem

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Huilquer Francisco Vogel ◽  
Erica Spotswood ◽  
João Batista Campos ◽  
Fernando Campanhã Bechara
Keyword(s):  
Forest Restoration ◽  
Large Scale ◽  
Fruit Production ◽  
Frugivorous Birds ◽  
Abundance Estimates ◽  
Bird Assemblage ◽  
Abundance And Diversity ◽  
Using Data ◽  
Tropical Forest Restoration ◽  
One Year

Artificial perches are used in tropical forest restoration projects to increase the dispersal of seeds into restored areas. The ability of perches to enhance seed deposition depends on their ability to attract seed dispersing birds, as well as the correlation between the season of bird visits to perches and the phenology of fruit production in adjacent forests. Using data collected from a large-scale restoration project, we characterized the community of birds that utilize artificial perches over the course of one year. We hypothesized that the structure of a bird assemblage that uses artificial perches is affected by seasonal variation. We aimed to describe the richness, abundance and diversity of a bird assemblage on artificial perches in a subtropical Atlantic forest restoration experiment in Southern Brazil. Richness and abundance estimates of the avian fauna were obtained from eight artificial perches placed in four experimental plots (∼2 y-old). Parameters of richness and abundance were compared using ANOVA. The bird assemblage was described using SHE analysis [richness (S), diversity (H') and evenness (E)], with additional estimates of occurrence and dominance. In total, 451 records of 32 ± 3.16 SD species were obtained. Thraupidae was the most numerous family (nine species, 28.12% of the total). Richness and abundance varied seasonally and were highest during spring and summer. Five migratory species of flycatchers were recorded between spring and early autumn. Perches were ineffective in attracting specialized frugivorous birds, emphasizing that seed dispersal tends to be carried out primarily by generalist omnivores in the initial phase of forest regeneration.

scholarly journals Semi-empirical parameterization of size-dependent atmospheric nanoparticle growth in continental environments

2013 ◽  
Vol 13 (15) ◽  
pp. 7665-7682 ◽  
Author(s):  
S. A. K. Häkkinen ◽  
H. E. Manninen ◽  
T. Yli-Juuti ◽  
J. Merikanto ◽  
M. K. Kajos ◽  
...  
Keyword(s):  
Large Scale ◽  
Cloud Condensation Nuclei ◽  
Anthropogenic Pollution ◽  
Particle Growth ◽  
Oxidation Products ◽  
Forest Site ◽  
Nanoparticle Growth ◽  
One Year ◽  
20 Nm ◽  
Semi Empirical

Abstract. The capability to accurately yet efficiently represent atmospheric nanoparticle growth by biogenic and anthropogenic secondary organics is a challenge for current atmospheric large-scale models. It is, however, crucial to predict nanoparticle growth accurately in order to reliably estimate the atmospheric cloud condensation nuclei (CCN) concentrations. In this work we introduce a simple semi-empirical parameterization for sub-20 nm particle growth that distributes secondary organics to the nanoparticles according to their size and is therefore able to reproduce particle growth observed in the atmosphere. The parameterization includes particle growth by sulfuric acid, secondary organics from monoterpene oxidation (SORGMT) and an additional condensable vapor of non-monoterpene organics ("background"). The performance of the proposed parameterization was investigated using ambient data on particle growth rates in three diameter ranges (1.5–3 nm, 3–7 nm and 7–20 nm). The growth rate data were acquired from particle/air ion number size distribution measurements at six continental sites over Europe. The longest time series of 7 yr (2003–2009) was obtained from a boreal forest site in Hyytiälä, Finland, while about one year of data (2008–2009) was used for the other stations. The extensive ambient measurements made it possible to test how well the parameterization captures the seasonal cycle observed in sub-20 nm particle growth and to determine the weighing factors for distributing the SORGMT for different sized particles as well as the background mass flux (concentration). Besides the monoterpene oxidation products, background organics with a concentration comparable to SORGMT, around 6 × 107 cm−3 (consistent with an additional global SOA yield of 100 Tg yr−1) was needed to reproduce the observed nanoparticle growth. Simulations with global models suggest that the "background" could be linked to secondary biogenic organics that are formed in the presence of anthropogenic pollution.

scholarly journals Semi-empirical parameterization of size-dependent atmospheric nanoparticle growth in continental environments

2013 ◽  
Vol 13 (3) ◽  
pp. 8489-8535 ◽  
Author(s):  
S. A. K. Häkkinen ◽  
H. E. Manninen ◽  
T. Yli-Juuti ◽  
J. Merikanto ◽  
M. K. Kajos ◽  
...  
Keyword(s):  
Large Scale ◽  
Cloud Condensation Nuclei ◽  
Anthropogenic Pollution ◽  
Particle Growth ◽  
Oxidation Products ◽  
Forest Site ◽  
Nanoparticle Growth ◽  
One Year ◽  
20 Nm ◽  
Semi Empirical

Abstract. The capability to accurately yet efficiently represent atmospheric nanoparticle growth by biogenic and anthropogenic secondary organics is a challenge for current atmospheric large-scale models. It is, however, crucial to predict nanoparticle growth accurately in order to reliably estimate the atmospheric cloud condensation nuclei (CCN) concentrations. In this work we introduce a~simple semi-empirical parameterization for sub-20 nm particle growth that distributes secondary organics to the nanoparticles according to their size and is therefore able to reproduce particle growth observed in the atmosphere. The parameterization includes particle growth by sulfuric acid, secondary organics from monoterpene oxidation (SORGMT) and an additional condensable non-monoterpene organics ("background"). The performance of the proposed parameterization was investigated using ambient data on particle growth rates in three size ranges (1.5–3 nm, 3–7 nm and 7–20 nm). The growth rate data was acquired from particle/air ion number size distribution measurements at six continental sites over Europe. The longest time series of 7 yr (2003 to 2009) was obtained from a boreal forest site in Hyytiälä, Finland, while about one year of data (2008–2009) was used for the other stations. The extensive ambient measurements made it possible to test how well the parameterization captures the seasonal cycle observed in sub-20 nm particle growth and to determine the weighing factors for distributing the SORGMT for different sized particles as well as the background mass flux (/concentration). Besides the monoterpene oxidation products, background organics with a concentration comparable to SORGMT, around 6 × 107 cm−3 (consistent with an additional global SOA yield of 100 Tg yr−1) was needed to reproduce the observed nanoparticle growth. Simulations with global models suggest that the "background" could be linked to secondary biogenic organics that are formed in the presence of anthropogenic pollution.

Carpooling: travelers’ perceptions from a big data analysis

2018 ◽  
Vol 30 (5) ◽  
pp. 554-571 ◽  
Author(s):  
Maria Vincenza Ciasullo ◽  
Orlando Troisi ◽  
Francesca Loia ◽  
Gennaro Maione
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Web Crawler ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Depth Analysis ◽  
Research Findings ◽  
One Year

Purpose The purpose of this paper is to provide a better understanding of the reasons why people use or do not use carpooling. A further aim is to collect and analyze empirical evidence concerning the advantages and disadvantages of carpooling. Design/methodology/approach A large-scale text analytics study has been conducted: the collection of the peoples’ opinions have been realized on Twitter by means of a dedicated web crawler, named “Twitter4J.” After their mining, the collected data have been treated through a sentiment analysis realized by means of “SentiWordNet.” Findings The big data analysis identified the 12 most frequently used concepts about carpooling by Twitter’s users: seven advantages (economic efficiency, environmental efficiency, comfort, traffic, socialization, reliability, curiosity) and five disadvantages (lack of effectiveness, lack of flexibility, lack of privacy, danger, lack of trust). Research limitations/implications Although the sample is particularly large (10 percent of the data flow published on Twitter from all over the world in about one year), the automated collection of people’s comments has prevented a more in-depth analysis of users’ thoughts and opinions. Practical implications The research findings may direct entrepreneurs, managers and policy makers to understand the variables to be leveraged and the actions to be taken to take advantage of the potential benefits that carpooling offers. Originality/value The work has utilized skills from three different areas, i.e., business management, computing science and statistics, which have been synergistically integrated for customizing, implementing and using two IT tools capable of automatically identifying, selecting, collecting, categorizing and analyzing people’s tweets about carpooling.

Mentoring to Promote Professional Well-being

Relay Journal ◽  
2019 ◽  
pp. 306-318
Author(s):  
Hatice Karaaslan
Keyword(s):  
Positive Emotions ◽  
Well Being ◽  
Research Experience ◽  
Reflective Dialogue ◽  
Frequent Contact ◽  
Depth Analysis ◽  
Junior Colleague ◽  
One Year ◽  
Personal Reflections

This article elaborates on a follow-up mentoring session conducted with a junior colleague who had frequent contact with me over a period of one year during her coursework as she considered me a senior instructor with substantial research experience. The purpose was to exploit the strategies of advising in a mentoring context utilizing intentional reflective dialogue (IRD) to encourage reflection on professional well-being. To facilitate the process and achieve an in-depth analysis of her level of professional well-being, I employed Seligman’s (2011) PERMA model, explaining professional well-being with reference to its components of positive emotions, engagement, relationships, meaning, and accomplishment. In the article, I briefly give information on the context and background, the purpose, and the professional well-being model used. I then outline the flow of the session, and point out and discuss how the strategies of advising have been exploited through a series of IRD exchanges in an effort to stimulate an in-depth discussion. Finally, I present my personal reflections as well as the potential implications to be considered while conducting mentor-mentee sessions and improving professional well-being in educational settings.

Student Perceptions of Community: A Different Perspective

NASPA Journal ◽  
1998 ◽  
Vol 35 (4) ◽  
Author(s):  
Jackie Clark ◽  
Joan Hirt
Keyword(s):  
Student Perceptions ◽  
Large Scale ◽  
Research University ◽  
Small Scale ◽  
Social Environments ◽  
Common Assumption ◽  
Small Communities ◽  
Public Research University ◽  
The Common ◽  
Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

scholarly journals Insectivorous songbirds as early indicators of future defoliation by spruce budworm

2021 ◽  
Author(s):  
Marion Germain ◽  
Daniel Kneeshaw ◽  
Louis De Grandpré ◽  
Mélanie Desrochers ◽  
Patrick M. A. James ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Spruce Budworm ◽  
Detection Methods ◽  
Maximum Probability ◽  
Point Count ◽  
Probability Of Presence ◽  
Time Lagged ◽  
One Year ◽  
Local Sampling

Abstract Context Although the spatiotemporal dynamics of spruce budworm outbreaks have been intensively studied, forecasting outbreaks remains challenging. During outbreaks, budworm-linked warblers (Tennessee, Cape May, and bay-breasted warbler) show a strong positive response to increases in spruce budworm, but little is known about the relative timing of these responses. Objectives We hypothesized that these warblers could be used as sentinels of future defoliation of budworm host trees. We examined the timing and magnitude of the relationships between defoliation by spruce budworm and changes in the probability of presence of warblers to determine whether they responded to budworm infestation before local defoliation being observed by standard detection methods. Methods We modelled this relationship using large-scale point count surveys of songbirds and maps of cumulative time-lagged defoliation over multiple spatial scales (2–30 km radius around sampling points) in Quebec, Canada. Results All three warbler species responded positively to defoliation at each spatial scale considered, but the timing of their response differed. Maximum probability of presence of Tennessee and Cape May warbler coincided with observations of local defoliation, or provided a one year warning, making them of little use to guide early interventions. In contrast, the probability of presence of bay-breasted warbler consistently increased 3–4 years before defoliation was detectable. Conclusions Early detection is a critical step in the management of spruce budworm outbreaks and rapid increases in the probability of presence of bay-breasted warbler could be used to identify future epicenters and target ground-based local sampling of spruce budworm.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

Sign in / Sign up

Export Citation Format

Share Document