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 O₃ and radiation were more conductive. The concentration of SO₂ and NO₂ 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

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^−3 s^−1 and ~7 nm h^−1, respectively. Such high growth and formation rates are typical for polluted areas. Temperature, wind speed, solar radiation, SO₂ and O₃ concentrations were on average higher on nucleation days than on non-event days, whereas relative and absolute humidity and NO₂ 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, Class  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