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The continuous integration and verification of components is essential in distributed design processes. Identifying the optimal integration and verification frequency, however, can be challenging due to the complexity of product development. Especially the effect of human decision-making in partially isolated development scenarios is difficult to consider. Thus, we performed an experimental study based on the following three steps: first, an extension of the existing parameter design framework, which is used to conduct experiments under laboratory conditions, in which human subjects solve quantitative surrogate design tasks. Second, a series of experiments in which 32 subjects divided into groups of two solved 229 parameter design tasks with a varying integration and verification frequency. And, third, a statistical analysis of the results with respect to development time, coupling strength and process costs. According to our results, development time can be reduced by up to 71%, if the integration and verification frequency is doubled. If process costs are also considered, the optimal frequency can be subject to a conflict of goals between reducing development time and minimising process cost.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.
We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.
Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.
We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.
We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.
Based on observations by Bailer-Jones et al. (2018) who propose a close fly-by of the K-type star Gliese 710 in approximately 1.36 Myr we investigate the immediate influence of the stellar passage on trajectories of Oort cloud objects. Using a newly developed GPU-based N-body code (Zimmermann (2021)) we study the motion of 3.6 million testparticles in the outer Solar system where the comets are distributed in three different “layers” around the Sun and the 4 giant planets. We study the immediate influence of Gliese 710 at three passage distances of 12000, 4300, and 1200 au. Additionally, different inclinations of the approaching star are considered. Depending on the passage distance a small number of comets (mainly from the disk and flared disk) is scattered into the observable region (< 5 au) around the Sun. In addition, a huge number of comets (mainly the ones directly in the path of the passing star) shows significant changes of their perihelia. But, they will enter the inner Solar system a long time after the stellar fly-by depending on their dynamical evolution.
The contribution of milk and dairy products to daily iodine intake is high but variable in many industrialised countries. Factors that affect iodine concentrations in milk and dairy products are only poorly understood. Our aim was to: (1) assess the effect of feed iodine concentration on milk iodine by supplementing five groups of five cows each with one of five dosages from 0–2 mg iodine/kg DM; (2) quantify iodine losses during manufacturing of cheese and yogurt from milk with varying iodine concentrations and assess the effect of cellar-ripening; and (3) systematically measure iodine partitioning during heat treatment and skimming of milk. Milk iodine reached a near-steady state after 3 weeks of feeding. Median milk iodine (17–302 μg/l for 0–2 mg iodine/kg DM) increased linearly with feed iodine (R2 0·96; P < 0·001). At curd separation, 75–84 % of iodine was lost in whey. Dairy iodine increased linearly with milk iodine (semi-hard cheese: R2 0·95; P < 0·001; fresh cheese and yogurt: R2 1·00; P < 0·001), and cellar-ripening had no effect. Heat treatment had no significant effect, whereas skimming increased (P < 0·001) milk iodine concentration by only 1–2 μg/l. Mean daily intake of dairy products by Swiss adults is estimated at 213 g, which would contribute 13–52 % of the adults’ RDA for iodine if cow feed is supplemented with 0·5–2 mg iodine/kg DM. Thus, modulation of feed iodine levels can help achieve desirable iodine concentrations in milk and dairy products, and thereby optimise their contribution to human iodine nutrition to avoid both deficiency and excess.
Milk and dairy products are important iodine sources and contribute about 30–40 % of total iodine in the Swiss diet. Information about variation in milk iodine concentration (MIC) in Switzerland is limited. We examined MIC and its potential determinants in milk from organic and conventional farms. We collected bulk milk samples at 3-month intervals over 1 year from thirty-two farms throughout Switzerland and Aosta valley, North-West Italy. We sampled all feed components including tap water, collected information on farm characteristics, feeding and teat disinfection practices by questionnaire and estimated the cows’ winter and summer iodine intake. Iodine in milk and feed components was measured using inductively coupled plasma MS. The overall median MIC was 87 (range 5–371) µg/l. In multivariate analysis, predictors of MIC were as follows: (1) farm type: median MIC from organic and conventional farms was 55 and 93 µg/l (P=0·022); (2) season: 53, 97 and 101 µg/l in September, December and March (P<0·002); and (3) teat dipping: 97 µg/l with v. 56 µg/l without (P=0·028). In conclusion, MIC varied widely between farms because of diverse farming practices that result in large differences in dairy cow exposure to iodine via ingestion or skin application. Standardisation of MIC is potentially achievable by controlling these iodine exposures. In order for milk to be a stable iodine source all year round, dietary iodine could be added at a set level to one feed component whose intake is regular and controllable, such as the mineral supplement, and by limiting the use of iodine-containing teat disinfectants.
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