Modelica - A Language for Physical System Modeling, Visualization and Interaction

Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, for example for modeling of mechatronics including cars, aircrafts and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena, No particular variable needs to be solved for manually. A Modelica tool will have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented

Uncovering entrenched gender norms in sustainable livelihood schemes in Gujarat

Billie Elmqvist Thurén recently spent two months in Gujarat as part of the Tata Social Intern scheme, an internship programme administered by LSE Careers and the India Observatory. In this article, she discusses her project, which explored the effectiveness of Tata programmes promoting sustainable livelihoods. Focussing specifically on gender issues, she finds that while the intiatives reach out to women and have helped them generate an income of their own, gender norms continue to limit the scope of what they are able to do

Практикоориентированное обучение на примере дисциплины "патологическая анатомия"

МЕДИЦИНСКИЕ УЧЕБНЫЕ ЗАВЕДЕНИЯОБРАЗОВАНИЕ МЕДИЦИНСКОЕСТУДЕНТЫ МЕДИЦИНСКИХ УЧЕБНЫХ ЗАВЕДЕНИЙПАТОЛОГИЧЕСКАЯ АНАТОМИЯ (ДИСЦИПЛИНА)ПРАКТИКО-ОРИЕНТИРОВАННОЕ ОБУЧЕНИ

Comparing Overlapping Data Distributions Using Visualization

We present results from a preregistered and crowdsourced user study where we asked members of the general population to determine whether two samples represented using different forms of data visualizations are drawn from the same or different populations. Such a task reduces to assessing whether the overlap between the two visualized samples is large enough to suggest similar or different origins. When using idealized normal curves fitted on the samples, it is essentially a graphical formulation of the classic Student's t-test. However, we speculate that using more sophisticated visual representations, such as bar histograms, Wilkinson dot plots, strip plots, or Tukey boxplots will both allow people to be more accurate at this task as well as better understand its meaning. In other words, the purpose of our study is to explore which visualization best scaffolds novices in making graphical inferences about data. However, our results indicate that the more abstracted idealized bell curve representation of the task yields more accuracy.Comment: 16 pages, 8 figure

Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential Statistics

Statisticians are not only one of the earliest professional adopters of data visualization, but also some of its most prolific users. Understanding how these professionals utilize visual representations in their analytic process may shed light on best practices for visual sensemaking. We present results from an interview study involving 18 professional statisticians (19.7 years average in the profession) on three aspects: (1) their use of visualization in their daily analytic work; (2) their mental models of inferential statistical processes; and (3) their design recommendations for how to best represent statistical inferences. Interview sessions consisted of discussing inferential statistics, eliciting participant sketches of suitable visual designs, and finally, a design intervention with our proposed visual designs. We analyzed interview transcripts using thematic analysis and open coding, deriving thematic codes on statistical mindset, analytic process, and analytic toolkit. The key findings for each aspect are as follows: (1) statisticians make extensive use of visualization during all phases of their work (and not just when reporting results); (2) their mental models of inferential methods tend to be mostly visually based; and (3) many statisticians abhor dichotomous thinking. The latter suggests that a multi-faceted visual display of inferential statistics that includes a visual indicator of analytically important effect sizes may help to balance the attributed epistemic power of traditional statistical testing with an awareness of the uncertainty of sensemaking.Comment: 16 pages, 8 tables, 3 figure

Opportunities for Increasing Resilience and Sustainability of Urban Social–Ecological Systems: Insights from the URBES and the Cities and Biodiversity Outlook Projects

Urban futures that are more resilient and sustainable require an integrated social–ecological system approach to urban policymaking, planning, management, and governance. In this article, we introduce the Urban Biodiversity and Ecosystem Services (URBES) and the Cities and Biodiversity Outlook (CBO) Projects as new social–ecological contributions to research and practice on emerging urban resilience and ecosystem services. We provide an overview of the projects and present global urbanization trends and their effects on ecosystems and biodiversity, as a context for new knowledge generated in the URBES case-study cities, including Berlin, New York, Rotterdam, Barcelona, and Stockholm. The cities represent contrasting urbanization trends and examples of emerging science–policy linkages for improving urban landscapes for human health and well-being. In addition, we highlight 10 key messages of the global CBO assessment as a knowledge platform for urban leaders to incorporate state-of-the-art science on URBES into decision-making for sustainable and resilient urban development