Colonization history of the Swiss Rhine basin by the bullhead (Cottus gobio): inference under a Bayesian spatially explicit framework

The present distribution of freshwater fish in the Alpine region has been strongly affected by colonization events occurring after the last glacial maximum (LGM), some 20,000 years ago. We use here a spatially explicit simulation framework to model and better understand their colonization dynamics in the Swiss Rhine basin. This approach is applied to the European bullhead (Cottus gobio), which is an ideal model organism to study fish past demographic processes since it has not been managed by humans. The molecular diversity of eight sampled populations is simulated and compared to observed data at six microsatellite loci under an approximate Bayesian computation framework to estimate the parameters of the colonization process. Our demographic estimates fit well with current knowledge about the biology of this species, but they suggest that the Swiss Rhine basin was colonized very recently, after the Younger Dryas some 6600 years ago. We discuss the implication of this result, as well as the strengths and limits of the spatially explicit approach coupled to the approximate Bayesian computation framework.

An Active Learning Module for an Introduction to Software Engineering Course

Many schools do not begin to introduce college students to software engineering until they have had at least one semester of programming. Since software engineering is a large, complex, and abstract subject it is difficult to construct active learning exercises that build on the students’ elementary knowledge of programming and still teach basic software engineering principles. It is also the case that beginning students typically know how to construct small programs, but they have little experience with the techniques necessary to produce reliable and long-term maintainable modules. I have addressed these two concerns by defining a local standard (Montana Tech Method (MTM) Software Development Standard for Small Modules Template) that step-by-step directs students toward the construction of highly reliable small modules using well known, best-practices software engineering techniques. “Small module” is here defined as a coherent development task that can be unit tested, and can be car ried out by a single (or a pair of) software engineer(s) in at most a few weeks. The standard describes the process to be used and also provides a template for the top-level documentation. The instructional module’s sequence of mini-lectures and exercises associated with the use of this (and other) local standards are used throughout the course, which perforce covers more abstract software engineering material using traditional reading and writing assignments. The sequence of mini-lectures and hands-on assignments (many of which are done in small groups) constitutes an instructional module that can be used in any similar software engineering course.

Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .

Aufbau hochfunktionaler Intralogistik-Knoten mittels kleinskaliger Module als Cognitive Conveyor

Kleinskalige, multifunktionale Module haben ein hohes Potential bei der wirtschaftlichen und flexiblen Gestaltung intralogistischer Systeme mit hoher Funktionalität. Durch dezentrale Steuerung und eigener Intelligenz der Module ist das System frei skalierbar und der Installationsaufwand wird minimiert. Mittels eines neuartigen Konzeptes der Datenkommunikation für Stetigförderer erfolgt der Informationsaustausch drahtlos mit Hilfe optoelektrischer Elemente. Die Kleinskaligkeit der Transportmodule gegenüber der Transporteinheit im Zusammenhang mit dem Steuerungskonzept erlaubt eine selektive Beschaltung der Module nach Bedarf und damit eine optimierte Energieausnutzung im Betrieb. Prototypen auf Basis von Schwenkrollen mit integrierter Antriebstechnik und Steuerung lassen das Potential des Prinzips erkennen. Das neu entwickelte Konzept der Schrägscheibe hilft bei der anspruchsvollen Integration der Antriebstechnik in das Modul durch das Prinzip der koaxialen Aktoren. Durch omnidirektionalen Funktionsumfang der Module entsteht im Zusammenschluss zu einer Modulmatrix ein hochflexibel einsetzbares Intralogistik-Modul. Die Vernetzung dieser hochfunktionalen Knoten durch einfache Fördertechnik bietet die Möglichkeit einfacher Planung flexibler Logistiksysteme.