326 research outputs found
Determination of physical emulsion stabilization mechanisms of wood hemicelluloses via rheological and interfacial characterization
Using discriminant analysis as a nucleation event classification method
More than three years of measurements of aerosol size-distribution and different gas and meteorological parameters made in Po Valley, Italy were analysed for this study to examine which of the meteorological and trace gas variables effect on the emergence of nucleation events. As the analysis method, we used discriminant analysis with non-parametric Epanechnikov kernel, included in non-parametric density estimation method. The best classification result in our data was reached with the combination of relative humidity, ozone concentration and a third degree polynomial of radiation. RH appeared to have a preventing effect on the new particle formation whereas the effects of O<sub>3</sub> and radiation were more conductive. The concentration of SO<sub>2</sub> and NO<sub>2</sub> also appeared to have significant effect on the emergence of nucleation events but because of the great amount of missing observations, we had to exclude them from the final analysis
Composite Films of Arabinoxylan and Fibrous Sepiolite: Morphological, Mechanical, and Barrier Properties
Nucleation and growth of new particles in Po Valley, Italy
Aerosol number distribution measurements are reported at San Pietro Capofiume (SPC) station (44°39' N, 11°37' E) for the time period 2002–2005. The station is located in Po Valley, the largest industrial, trading and agricultural area in Italy with a high population density. New particle formation was studied based on observations of the particle size distribution, meteorological and gas phase parameters. The nucleation events were classified according to the event clarity based on the particle number concentrations, and the particle formation and growth rates. Out of a total of 769 operational days from 2002 to 2005 clear events were detected on 36% of the days whilst 33% are clearly non-event days. The event frequency was high during spring and summer months with maximum values in May and July, whereas lower frequency was observed in winter and autumn months. The average particle formation and growth rates were estimated as ~6 cm<sup>−3</sup> s<sup>−1</sup> and ~7 nm h<sup>−1</sup>, respectively. Such high growth and formation rates are typical for polluted areas. Temperature, wind speed, solar radiation, SO<sub>2</sub> and O<sub>3</sub> concentrations were on average higher on nucleation days than on non-event days, whereas relative and absolute humidity and NO<sub>2</sub> concentration were lower; however, seasonal differences were observed. Backtrajectory analysis suggests that during majority of nucleation event days, the air masses originate from northern to eastern directions. We also study previously developed nucleation event correlations with environmental variables and show that they predict Po Valley nucleation events with variable success
Culturally and linguistically diverse nursing students' experiences of integration into the working environment: A qualitative study
Background
Understanding how culturally and linguistically diverse students experience clinical practice and their competence development is important to retaining registered nurses. This study aimed to describe culturally and linguistically diverse students' experiences of clinical practice, perceptions of their career path, and intentions to stay in the nursing profession.
Methods
A descriptive qualitative study was conducted. The participants were culturally and linguistically diverse nursing students (n = 22) from six Finnish higher education institutions. Nine focus-group interviews, with up to six students per group, were conducted during the spring and summer of 2021. Data were analysed using inductive content analysis.
Results
The factors which affected culturally and linguistically diverse students' intentions to stay in nursing profession in Finland consisted of support during university studies and clinical practice, perceived equality, nursing competence development, successful integration into the workplace and social life, and clinical practice experiences.
Conclusions
The results support the development of a model for culturally and linguistically diverse nurses' integration into the Finnish health care settings by identifying the key factors for an effective transition to the profession
Successful aerobic bioremediation of groundwater contaminated with higher chlorinated phenols by indigenous degrader bacteria
© 2018 Elsevier Ltd The xenobiotic priority pollutant pentachlorophenol has been used as a timber preservative in a polychlorophenol bulk synthesis product containing also tetrachlorophenol and trichlorophenol. Highly soluble chlorophenol salts have leaked into groundwater, causing severe contamination of large aquifers. Natural attenuation of higher-chlorinated phenols (HCPs: pentachlorophenol + tetrachlorophenol) at historically polluted sites has been inefficient, but a 4-year full scale in situ biostimulation of a chlorophenol-contaminated aquifer by circulation and re-infiltration of aerated groundwater was remarkably successful: pentachlorophenol decreased from 400 μg L−1 to <1 μg L−1 and tetrachlorophenols from 4000 μg L−1 to <10 μg L−1. The pcpB gene, the gene encoding pentachlorophenol hydroxylase - the first and rate-limiting enzyme in the only fully characterised aerobic HCP degradation pathway - was present in up to 10% of the indigenous bacteria already 4 months after the start of aeration. The novel quantitative PCR assay detected the pcpB gene in situ also in the chlorophenol plume of another historically polluted aquifer with no remediation history. Hotspot groundwater HCPs from this site were degraded efficiently during a 3-week microcosm incubation with one-time aeration but no other additives: from 5400 μg L−1 to 1200 μg L−1 and to 200 μg L−1 in lightly and fully aerated microcosms, respectively, coupled with up to 2400% enrichment of the pcpB gene. Accumulation of lower-chlorinated metabolites was observed in neither in situ remediation nor microcosms, supporting the assumption that HCP removal was due to the aerobic degradation pathway where the first step limits the mineralisation rate. Our results demonstrate that bacteria capable of aerobic mineralisation of xenobiotic pentachlorophenol and tetrachlorophenol can be present at long-term polluted groundwater sites, making bioremediation by simple aeration a viable and economically attractive alternative
Self-Reported Restrictive Eating, Eating Disorders, Menstrual Dysfunction, and Injuries in Athletes Competing at Different Levels and Sports
The purpose of this study was to investigate the prevalence of self-reported restrictive eating, current or past eating disorder, and menstrual dysfunction and their relationships with injuries. Furthermore, we aimed to compare these prevalences and associations between younger (aged 15-24) and older (aged 25-45) athletes, between elite and non-elite athletes, and between athletes competing in lean and non-lean sports. Data were collected using a web-based questionnaire. Participants were 846 female athletes representing 67 different sports. Results showed that 25%, 18%, and 32% of the athletes reported restrictive eating, eating disorders, and menstrual dysfunction, respectively. Higher rates of lean sport athletes compared with non-lean sport athletes reported these symptoms, while no differences were found between elite and non-elite athletes. Younger athletes reported higher rates of menstrual dysfunction and lower lifetime prevalence of eating disorders. Both restrictive eating (OR 1.41, 95% CI 1.02-1.94) and eating disorders (OR 1.89, 95% CI 1.31-2.73) were associated with injuries, while menstrual dysfunction was associated with more missed participation days compared with a regular menstrual cycle (OR 1.79, 95% CI 1.05-3.07). Our findings indicate that eating disorder symptoms and menstrual dysfunction are common problems in athletes that should be managed properly as they are linked to injuries and missed training/competition days
Identification of new particle formation events with deep learning
New particle formation (NPF) in the atmosphere is globally an
important source of climate relevant aerosol particles. Occurrence of NPF
events is typically analyzed by researchers manually from particle size
distribution data day by day, which is time consuming and the classification
of event types may be inconsistent. To get more reliable and consistent
results, the NPF event analysis should be automatized. We have developed an
automatic analysis method based on deep learning, a subarea of machine
learning, for NPF event identification. To our knowledge, this is the first
time that a deep learning method, i.e., transfer learning of a convolutional
neural network (CNN), has successfully been used to automatically classify
NPF events into different classes directly from particle size distribution
images, similarly to how the researchers carry out the manual classification. The
developed method is based on image analysis of particle size distributions
using a pretrained deep CNN, named AlexNet, which was transfer learned to
recognize NPF event classes (six different types). In transfer learning, a
partial set of particle size distribution images was used in the training
stage of the CNN and the rest of the images for testing the success of the
training. The method was utilized for a 15-year-long dataset measured at San
Pietro Capofiume (SPC) in Italy. We studied the performance of the training
with different training and testing of image number ratios as well as with
different regions of interest in the images. The results show that clear
event (i.e., classes 1 and 2) and nonevent days can be identified with an
accuracy of ca. 80 %, when the CNN classification is compared with that
of an expert, which is a good first result for automatic NPF event analysis.
In the event classification, the choice between different event classes is
not an easy task even for trained researchers, and thus overlapping or confusion
between different classes occurs. Hence, we cross-validated the learning
results of CNN with the expert-made classification. The results show that the
overlapping occurs, typically between the adjacent or similar type of classes,
e.g., a manually classified Class 1 is categorized mainly into classes 1 and
2 by CNN, indicating that the manual and CNN classifications are very
consistent
for most of the days. The classification would be more consistent, by
both human and CNN, if only two different classes are used for event days
instead of three classes. Thus, we recommend that in the future analysis,
event days should be categorized into classes of quantifiable (i.e., clear
events, classes 1 and 2) and nonquantifiable (i.e., weak events, ClassÂ
3). This would better describe the difference of those classes: both
formation and growth rates can be determined for quantifiable days but not
both for nonquantifiable days. Furthermore, we investigated more deeply the
days that are classified as clear events by experts and recognized as
nonevents by the CNN and vice versa. Clear misclassifications seem to occur
more commonly in manual analysis than in the CNN categorization, which is
mostly due to the inconsistency in the human-made classification or errors in
the booking of the event class. In general, the automatic CNN classifier has
a better reliability and repeatability in NPF event classification than
human-made classification and, thus, the transfer-learned pretrained CNNs
are powerful tools to analyze long-term datasets. The developed NPF event
classifier can be easily utilized to analyze any long-term datasets more
accurately and consistently, which helps us to understand in detail
aerosol–climate interactions and the long-term effects of climate change on
NPF in the atmosphere. We encourage researchers to use the model in other
sites. However, we suggest that the CNN should be transfer learned again for
new site data with a minimum of ca. 150 figures per class to obtain good
enough classification results, especially if the size distribution evolution
differs from training data. In the future, we will utilize the method for
data from other sites, develop it to analyze more parameters and evaluate how
successfully CNN could be trained with synthetic NPF event data.</p
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