Laser separation of isotopes

Laser separation of isotopes was first shown to be scientifically practicable at the start of the 1970s, when the isotopes of nitrogen, chlorine, hydrogen, and many other elements were successfully separated. Its most important applications are for uranium and plutonium enrichment. Enriched uranium is used as fuel for nuclear power stations, while enriched plutonium can be used to design more effective nuclear weapons. Ninety-eight per cent of all enriched uranium is now produced in gaseous diffusion installations, the remaining 2 per cent being produced by gaseous centrifugation. Research into laser separation is being conducted in numerous countries and will lead to the production of enriched plutonium on an industrial scale by the end of this decade.     

On the interplay between normalisation,bias, and performance of paper impact metrics

We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics’ ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers’ semantic information).In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts.In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years.Lastly, we also find that personalising PageRank with papers’ citation counts reduces time bias but increases field bias. Similarly, using papers’ associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers’ citation counts and time-rescaled for citation windows smaller than 7 to 10 years.     

Cross‐Validated Prediction of Academic Performance of First‐Year University Students: Identifying Risk Factors in a Nonselective Environment

Early prediction of academic performance is important for student support. The authors explored, in a multivariate approach, whether pre‐entry data (e.g., high school study results, preparative activities, expectations, capabilities, motivation, and attitude) could predict university students’ first‐year academic performance. Preregistered applicants for a bachelor's program filled out an intake questionnaire before study entry. Outcome data (first‐year grade point average, course credits, and attrition) were obtained 1 year later. Prediction accuracy was assessed by cross‐validation. Students who performed better in preparatory education, followed a conventional educational path before entering, and expected to spend more time on a program‐related organization performed better during their first year at university. Concrete preuniversity behaviors were more predictive than psychological attributions such as self‐efficacy. Students with a “love of learning” performed better than leisure‐oriented students. The intake questionnaire may be used for identifying up front who may need additional support, but is not suitable for student selection.     

Echoes of Early Life: Recent Insights From Mathematical Modeling

In the last decades, developmental origins of health and disease (DOHaD) has emerged as a central framework for studying early‐life effects, that is, the impact of fetal and early postnatal experience on adult functioning. Apace with empirical progress, theoreticians have built mathematical models that provide novel insights for DOHaD. This article focuses on three of these insights, which show the power of environmental noise (i.e., imperfect indicators of current and future conditions) in shaping development. Such noise can produce: (a) detrimental outcomes even in ontogenetically stable environments, (b) individual differences in sensitive periods, and (c) early‐life effects tailored to predicted future somatic states. We argue that these insights extend DOHaD and offer new research directions.     

巨大的挑战

"Keep staring:I might do a trick!"That's the message on a T-shirt of mine. People stare at me all the time, so I thought this shirt would be funny.See, I'm a dwarf(矮子).     

Selection to the Master’s Phase at the Binary Divide,a Dutch Case Study

Tertiary Education and Management - This article presents a case study of selection for graduate study at the Vrije Universiteit (VU) in the Netherlands. The VU developed and implemented an...     

Globalised vs averaged: Bias and ranking performance on the author level

We analyse the difference between the averaged (average of ratios) and globalised (ratio of averages) author-level aggregation approaches based on various paper-level metrics. We evaluate the aggregation variants in terms of (1) their field bias on the author-level and (2) their ranking performance based on test data that comprises researchers that have received fellowship status or won prestigious awards for their long-lasting and high-impact research contributions to their fields. We consider various direct and indirect paper-level metrics with different normalisation approaches (mean-based, percentile-based, co-citation-based) and focus on the bias and performance differences between the two aggregation variants of each metric. We execute all experiments on two publication databases which use different field categorisation schemes. The first uses author-chosen concept categories and covers the computer science literature. The second covers all disciplines and categorises papers by keywords based on their contents. In terms of bias, we find relatively little difference between the averaged and globalised variants. For mean-normalised citation counts we find no significant difference between the two approaches. However, the percentile-based metric shows less bias with the globalised approach, except for citation windows smaller than four years. On the multi-disciplinary database, PageRank has the overall least bias but shows no significant difference between the two aggregation variants. The averaged variants of most metrics have less bias for small citation windows. For larger citation windows the differences are smaller and are mostly insignificant.In terms of ranking the well-established researchers who have received accolades for their high-impact contributions, we find that the globalised variant of the percentile-based metric performs better. Again we find no significant differences between the globalised and averaged variants based on citation counts and PageRank scores.