Achieving equity through 'gender autonomy': the challenges for VET policy and practice

This paper is based on research carried out in an EU Fifth Framework project on 'Gender and Qualification'. The research partners from five European countries investigated the impact of gender segregation in European labour markets on vocational education and training, with particular regard to competences and qualifications. The research explored the part played by gender in the vocational education and training experiences of (i) young adults entering specific occupations in child care, electrical engineering and food preparation/service (ii) adults changing occupations

Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

The Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth-atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012-2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 mu gm(-3)) into three sub-categories - low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) - proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ngm 3 h 1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-yearlong time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.Peer reviewe

Constructing a data-driven receptor model for organic and inorganic aerosol : a synthesis analysis of eight mass spectrometric data sets from a boreal forest site

The interactions between organic and inorganic aerosol chemical components are integral to understanding and modelling climate and health-relevant aerosol physicochemical properties, such as volatility, hygroscopicity, light scattering and toxicity. This study presents a synthesis analysis for eight data sets, of non-refractory aerosol composition, measured at a boreal forest site. The measurements, performed with an aerosol mass spectrometer, cover in total around 9 months over the course of 3 years. In our statistical analysis, we use the complete organic and inorganic unit-resolution mass spectra, as opposed to the more common approach of only including the organic fraction. The analysis is based on iterative, combined use of (1) data reduction, (2) classification and (3) scaling tools, producing a data-driven chemical mass balance type of model capable of describing site-specific aerosol composition. The receptor model we constructed was able to explain 83 +/- 8% of variation in data, which increased to 96 +/- 3% when signals from low signal-to-noise variables were not considered. The resulting interpretation of an extensive set of aerosol mass spectrometric data infers seven distinct aerosol chemical components for a rural boreal forest site: ammonium sulfate (35 +/- 7% of mass), low and semi-volatile oxidised organic aerosols (27 +/- 8% and 12 +/- 7 %), biomass burning organic aerosol (11 +/- 7 %), a nitrate-containing organic aerosol type (7 +/- 2 %), ammonium nitrate (5 +/- 2 %), and hydrocarbon-like organic aerosol (3 +/- 1 %). Some of the additionally observed, rare outlier aerosol types likely emerge due to surface ionisation effects and likely represent amine compounds from an unknown source and alkaline metals from emissions of a nearby district heating plant. Compared to traditional, ionbalance-based inorganics apportionment schemes for aerosol mass spectrometer data, our statistics-based method provides an improved, more robust approach, yielding readily useful information for the modelling of submicron atmospheric aerosols physical and chemical properties. The results also shed light on the division between organic and inorganic aerosol types and dynamics of salt formation in aerosol. Equally importantly, the combined methodology exemplifies an iterative analysis, using consequent analysis steps by a combination of statistical methods. Such an approach offers new ways to home in on physicochemically sensible solutions with minimal need for a priori information or analyst interference. We therefore suggest that similar statisticsbased approaches offer significant potential for un- or semi-supervised machine-learning applications in future analyses of aerosol mass spectrometric data.Peer reviewe

Technical Note: Latitude-time variations of atmospheric column-average dry air mole fractions of CO_2, CH_4 and N_2O

We present a comparison of an atmospheric general circulation model (AGCM)-based chemistry-transport model (ACTM) simulation with total column measurements of CO_2, CH_4 and N_2O from the Total Carbon Column Observing Network (TCCON). The model is able to capture observed trends, seasonal cycles and inter hemispheric gradients at most sampled locations for all three species. The model-observation agreements are best for CO_2, because the simulation uses fossil fuel inventories and an inverse model estimate of non-fossil fuel fluxes. The ACTM captures much of the observed seasonal variability in CO_2 and N_2O total columns (~81 % variance, R>0.9 between ACTM and TCCON for 19 out of 22 cases). These results suggest that the transport processes in troposphere and stratosphere are well represented in ACTM. Thus the poor correlation between simulated and observed CH4 total columns, particularly at tropical and extra-tropical sites, have been attributed to the uncertainties in surface emissions and loss by hydroxyl radicals. While the upward-looking total column measurements of CO_2 contains surface flux signals at various spatial and temporal scales, the N_2O measurements are strongly affected by the concentration variations in the upper troposphere and stratosphere

MARV: a tool for genome-wide multi-phenotype analysis of rare variants

Background: Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes. Results: We propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (P TG+FI = 1.8 × 10−9) than observed in single phenotype rare variant analyses (P TG = 6.5 × 10−8 and P FI = 0.27). Conclusions: MARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenotypes, thus paving the way for novel discoveries and insights into biology of complex traits

What do general practitioners know about ADHD? Attitudes and knowledge among first-contact gatekeepers: systematic narrative review

Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood disorder with international prevalence estimates of 5 % in childhood, yet significant evidence exists that far fewer children receive ADHD services. In many countries, ADHD is assessed and diagnosed in specialist mental health or neuro-developmental paediatric clinics, to which referral by General (Family) Practitioners (GPs) is required. In such ‘gatekeeper’ settings, where GPs act as a filter to diagnosis and treatment, GPs may either not recognise potential ADHD cases, or may be reluctant to refer. This study systematically reviews the literature regarding GPs’ views of ADHD in such settings. Methods: A search of nine major databases was conducted, with wide search parameters; 3776 records were initially retrieved. Studies were included if they were from settings where GPs are typically gatekeepers to ADHD services; if they addressed GPs’ ADHD attitudes and knowledge; if methods were clearly described; and if results for GPs were reported separately from those of other health professionals. Results: Few studies specifically addressed GP attitudes to ADHD. Only 11 papers (10 studies), spanning 2000–2010, met inclusion criteria, predominantly from the UK, Europe and Australia. As studies varied methodologically, findings are reported as a thematic narrative, under the following themes: Recognition rate; ADHD controversy (medicalisation, stigma, labelling); Causes of ADHD; GPs and ADHD diagnosis; GPs and ADHD treatment; GP ADHD training and sources of information; and Age, sex differences in knowledge and attitudes. Conclusions: Across times and settings, GPs practising in first-contact gatekeeper settings had mixed and often unhelpful attitudes regarding the validity of ADHD as a construct, the role of medication and how parenting contributed to presentation. A paucity of training was identified, alongside a reluctance of GPs to become involved in shared care practice. If access to services is to be improved for possible ADHD cases, there needs to be a focused and collaborative approach to training

Status report of the JYFL-ECR ion sources

"Ion beam cocktails" are mixtures of ions with near-identical charge-to-mass ratios. In conjunction with the JYFL-ECRIS, the K130-cyclotron acts as a mass analyzer: the switch from one ion to another within the same cocktail is simple and fast. In the case of the first ion beam cocktail, the oxygen and argon gases were mixed into the gas feed line. At the same time the magnesium and iron ion beams were produced using the MIVOC method. Magnesocene and ferrocene compounds were both mixed into the MIVOC chamber. This capability is especially useful in the study of single event effects (SEE) in space electronics. All gaseous elements from H to Xe can be produced. The non-gaseous elements produced so far are C, Mg, Al, Si, S, Ca, Ti, Cr, Fe, Co, Ni, Cu, Zn and Ge. A major technical modification since the construction (in 1990) of the JYFL-ECRIS was made in January 98: a negatively biased disc replaces now the first plasma stage. After a couple of months experience with the modified source the change was found to be towards a correct direction. The source is now much easier to use and the good operating conditions are well repeated. A real advantage is the new magnetic field settings which are practically the same for all kind of beams, gaseous and solids. Due to the requirements of ion beams with higher charges and heavier elements than the present JYFL-ECRIS can produce, JYFL decided to begin a design and construction project of a new ECR ion source, called as ECRIS 2. The project aims to a source that is based mainly on the design of the 14 GHz AECR-U source at the LBNL. Some modifications made into the similar source under construction at the NSCL/MSU will be utilized here. The new source will be installed horizontally in the basement of the ECRIS laboratory. It requires a new beam-line from the source to the cyclotron injection line, since the old vertically located JYFL-ECRIS will be preserved in operation. The new source is planned to be operational during the year 2000

Spatial distributions of XCO2seasonal cycle amplitude and phase over northern high-latitude regions

Satellite-based observations of atmospheric carbon dioxide (CO$_{2}$) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO$_{2}$ (X$_{CO_{2}}$) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X$_{CO_{2}}$ seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X$_{CO_{2}}$ seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5∘ latitude by 20∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X$_{CO_{2}}$ from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X$_{CO_{2}}$ from the GEOS-Chem CO$_{2}$ simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X$_{CO_{2}}$ seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X$_{CO_{2}}$ SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of XCO2 SCA with these land surface contact tracers is stronger than the correlation of X$_{CO_{2}}$ SCA with the SCA of CO$_{2}$ fluxes or the total annual CO$_{2}$ flux within each 5∘ latitude by 20∘ longitude zone. This indicates that accumulation of terrestrial CO$_{2}$ flux during atmospheric transport is a major driver of regional variations in X$_{CO_{2}}$ SCA

Search for Charged Current Coherent Pion Production on Carbon in a Few-GeV Neutrino Beam

The SciBooNE Collaboration has performed a search for charged current coherent pion production from muon neutrinos scattering on carbon, \nu_\mu ^{12}C \to \mu^- ^{12}C \pi^+, with two distinct data samples. No evidence for coherent pion production is observed. We set 90% confidence level upper limits on the cross section ratio of charged current coherent pion production to the total charged current cross section at 0.67\times 10^{-2} at mean neutrino energy 1.1 GeV and 1.36\times 10^{-2} at mean neutrino energy 2.2 GeV.Comment: 18 pages, 16 figures, Minor revisions to match version accepted for publication in Physical Review