Promotion of healthy nutrition of seafarers

Nutrition disorders arise from various interacting factors: cultural, environmental, genetic, physiological, and psychological. Excessive consumption of highly processed food, sugar, salt, alcohol, and saturated fats is a problem nowadays, and consumption of fish, vegetables, and fruit is insufficient. Overeating and an unbalanced diet are often accompanied by stress and a lack of physical activity. This is intensified by easy access to “comfort food”, “fast food”, and “junk food”. The number of people suffering from overweight and obesity, so-called diseases of civilization, is increasing. Not only is being overweight a risk factor for the development many other metabolic diseases, but it also significantly worsens the quality of life. This also concerns people working at sea. Obesity is favoured by emotional eating disorders (EED), uncontrolled/compulsive eating - binge eating disorders (BED), and night eating disorders (NED). Most frequently, eating is a reaction to stress or boredom. It alleviates tension and improves the mood, also of seafarers

Neutrino interaction event filtering at liquid argon time projection chambers using neural networks with minimal input model bias

In current and future neutrino oscillation experiments using liquid argon time projection chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using 3D Convolutional Neural Networks (CNNs) trained on low-level timing information from only the scintillation light signal of interactions inside LAr-TPCs. We further present a means of mitigating biases from imperfect simulations by applying Domain Adversarial Neural Networks (DANNs). These techniques are applied to example simulations from the ICARUS detector, the far detector of the Short Baseline Neutrino experiment at Fermilab. The results show that cosmic background is reduced by up to 74% whilst neutrino interaction selection efficiency remains over 94%, even in cases where the simulation poorly describes the data

Adversarial methods to reduce simulation bias in neutrino interaction event filtering at Liquid Argon Time Projection Chambers

For current and future neutrino oscillation experiments using large liquid argon time projection chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D submanifold sparse convolutional network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the short baseline neutrino program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying domain adversarial neural networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adversarial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.For current and future neutrino oscillation experiments using large Liquid Argon Time Projection Chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D Submanifold Sparse Convolutional Network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the Short Baseline Neutrino (SBN) program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying Domain Adversarial Neural Networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adverserial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training

Adversarial methods to reduce simulation bias in neutrino interaction event filtering at liquid argon time projection chambers

For current and future neutrino oscillation experiments using large liquid argon time projection chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D submanifold sparse convolutional network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the short baseline neutrino program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying domain adversarial neural networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adversarial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.ISSN:1550-7998ISSN:0556-2821ISSN:1550-236

Characterization of SiPM arrays in different series and parallel configurations

A number of innovative experiments dedicated to neutrino and rare-event physics use liquefied noble-gases both as a target and as a detector. These media have the remarkable property to efficiently produce scintillation photons after the passage of ionizing particles. Scintillation light, which is used for triggering and timing purposes, is traditionally detected by large area Photo-Multiplier Tubes (PMTs) working at cryogenic temperature. Silicon Photo-Multiplier (SiPM) arrays are gradually substituting PMTs in many applications, especially where low voltages are required and magnetic field is present. One of the problems of this devices is the small active area. For this reason we built several prototype arrays made by different SiPM models with a common readout: the basic unit is a device with an active area of (1.2×1.2)cm2 . A fast signal leading edge is crucial to realize devices to be used for triggering and timing. To this purpose we studied different series/parallel electrical configurations to obtain the best timing performance, by operating our custom arrays both at room and cryogenic temperatures

Associations between physical activity patterns and dietary patterns in a representative sample of Polish girls aged 13-21 years: a cross-sectional study (GEBaHealth Project)

Abstract Background Similar to other countries, trends of decreasing levels of physical activity (PA) and an increasing prevalence of unhealthy dietary patterns are observed among girls in Poland. Better understanding of potentially inter-related behaviours within this population can help to design tailored interventions. The purpose of this study was to determine associations between PA patterns and dietary patterns in a representative sample of Polish girls. Methods Girls aged 13-21 years (n = 1107) were randomly selected for the study. PA was assessed using International Physical Activity Questionnaire – Long (IPAQ-L). Dietary data were collected with food frequency questionnaires. PA patterns and dietary patterns were drawn separately by Principal Component Analysis (PCA). Logistic regression was used to find the associations between PA patterns and dietary patterns. Results Four major PA patterns (‘School/work activity’, ‘Active recreation’, ‘Yard activity’ and ‘Walking and domestic activity’) and four dietary patterns (‘Traditional Polish’, ‘Fruit & vegetables’, ‘Fast food & sweets’ and ‘Dairy & fats’) were identified. Level of PA was the highest in the upper tertile of ‘School/work activity’ pattern (mean 1372.2 MET-minutes/week, 95 % Confidence Intervals [CI]: 1285.9–1458.5). Girls in upper tertiles of ‘Yard activity’, ‘Active recreation’ and ‘School/work activity’ patterns had significantly higher chances of being in the upper tertile of the ‘Fruit and vegetables’ dietary pattern (odds ratio [OR] 2.17, 95 % CI: 1.50–3.14, p < 0.0001; OR 2.02, 95 % CI: 1.41–2.91; p < 0.001 and OR 1.76, 95 % CI: 1.24–2.51, p < 0.01 respectively; all adjusted for confounders) in comparison to bottom tertiles. Weak, but significant inverse associations were found between upper tertiles of ‘Active recreation’ and ‘Yard activity’ patterns and unhealthy dietary patterns. Conclusions We found associations between PA patterns and dietary patterns in the population of Polish girls. Girls with the highest adherence to the ‘School/work activity’ pattern had the highest levels of PA and presented pro-healthy dietary behaviours. School should be recognised as potentially efficient and important setting to maximise girls' PA potential. The after-school time is the area that should also be targeted to increase daily PA or to at least sustain the level of PA after completing education

Health- and Taste-Related Attitudes Associated with Dietary Patterns in a Representative Sample of Polish Girls and Young Women: A Cross-Sectional Study (GEBaHealth Project)

Attitudes can be predictors of certain health-related behaviours. The attitudes of young females towards health and taste have not been yet fully examined and their associations with dietary behaviours remain unclear. The aim of the study was to investigate if attitudes are associated with dietary patterns in a representative sample of Polish girls. The study population consisted of 1107 girls, aged 13–21 and living in Poland. Attitudes were assessed using the Health and Taste Attitudes Scale (HTAS) and categorised as negative, neutral or positive. Dietary data was obtained using a Food Frequency Questionnaire. Dietary patterns (DPs), derived previously with a Principal Component Analysis (PCA), were ‘Traditional Polish’, ‘Fruit and vegetables’, ‘Fast food and sweets’ and ‘Dairy and fats’. The associations between attitudes and DPs were assessed using Spearman’s correlation coefficients and logistic regression. The reference group were girls with neutral attitudes. Odds ratios (ORs) were adjusted for age, socioeconomic status (SES), and body mass index (BMI). The correlations between attitudes and DPs ranged from −0.28 for attitudes towards health and ‘Fast food and sweets’ and ‘Traditional Polish’ DPs to 0.33 for attitudes towards health and the ‘Fruit and vegetables’ DP (p &lt; 0.05). In the logistic regression analysis, the strongest associations within health-related HTAS subscales were observed between negative attitudes towards natural products and the ‘Fast food and sweets’ DP (OR: 10.93; 95% CI: 3.32–36.01) and between positive attitudes towards health and the ‘Fruit and vegetables’ DP (OR: 5.10; 3.11–8.37). The strongest associations within taste-related HTAS subscales were observed between positive attitudes towards craving for sweet foods and the ‘Traditional Polish’ DP (OR: 1.93; 1.43–2.61) and between positive attitudes towards using food as a reward and the ‘Dairy and fats’ DP (OR: 2.08; 1.22–3.55) as well as the ‘Fast food and sweets’ DP (OR: 2.07; 1.14–3.74). Positive attitudes towards health were associated with a pro-healthy dietary pattern characterised by the consumption of fruit and vegetables, while negative attitudes towards natural products as well as a strong craving for sweets and using food as a reward were associated with less healthy dietary patterns. To improve the dietary habits of girls and young women, positive attitudes towards health should be strengthened and supported by emphasizing the sensory values of pro-healthy foods

Characterization of SiPM arrays with common bias and common readout for applications in liquid argon

We have built a number of test arrays made of 16 SiPMs of 3 × 3 mm$^2$ with a mixed series–parallel configuration and common bias and readout. With this technique it is possible to increase the total active area keeping low the bias voltage and readout channels. To further increase the total area we connected few arrays together. Tests were performed in terms of pulse amplitude, charge and timing features

Perceived Health and Nutrition Concerns as Predictors of Dietary Patterns among Polish Females Aged 13–21 Years (GEBaHealth Project)

Health-related concerns can often be factors influencing health-related behaviours. It remains unclear whether a high level of concerns is associated with pro-healthy or unhealthy dietary behaviours and whether any associations between nutrition-related concerns and dietary behaviours exist in a population of girls and young women. The aim of the study was to investigate the associations between perceived health and nutrition concerns and dietary patterns in a representative sample of Polish young females. Data was collected in 2012 through a cross-sectional quantitative survey within the GEBaHealth (Girls Eating Behaviours and Health) project in a group of 1107 Polish girls aged 13–21 years old. Dietary patterns were identified by Principal Component Analysis (PCA) based on dietary data collected with Food Frequency Questionnaires (FFQs). Nutrition and health concerns were assessed separately by two indices: Health Concern Index (HCI) and Nutrition Concern Index (NCI); both based on the Health Concern Scale (HCS). The associations between perceived health and nutrition concerns and each dietary pattern were investigated using logistic regression analysis. Displaying a higher level of health concerns increased the chances of adherence to the upper tertile of ‘Fruit &amp; vegetables’ pattern (adjusted odds ratio [adj. ORs]: 1.46, 95% Confidence Interval [95% CI]: 1.02–2.10). Displaying a lower level of health concerns increased the chances of the adherence to the upper tertiles of ‘Traditional Polish’, ‘Dairy &amp; fats’, ‘Fruit and vegetables’ and ‘Fast food &amp; sweets’ patterns (adj. ORs: 1.87, 95% CI: 1.31–2.67; 1.66, 95% CI: 1.18–2.34; 1.57, 95% CI: 1.11–2.22; 1.52, 95% CI: 1.08–2.13; respectively). No significant associations were found between levels of nutrition concerns and dietary patterns in the adjusted model. We found associations between self-perceived health concerns and dietary patterns in our study sample, suggesting health concerns can be an important predictor of dietary behaviours in girls and young women. To increase the effectiveness of healthy eating, an emphasis should be laid on health, reinforced with awareness of nutrition, when advising on food-related decisions