Activity and Diversity of Collembola (Insecta) and Mites (Acari) in Litter of a Degraded Midwestern Oak Woodland

Litter-inhabiting Collembola and mites were sampled using pitfall traps over a twelve-month period from four sub-communities within a 100-acre (40-ha) oak-woodland complex in northern Cook County, Illinois. Sampled locations included four areas where future ecological restoration was planned (mesic woodland, dry-mesic woodland, mesic upland forest, and buckthorn-dominated savanna) and a mesic woodland control that would not be restored. Fifty-eight mite and 30 Collembola taxa were identified out of 5,308 and 190,402 individuals trapped, respectively. There was a significant positive relationship between litter mass and both mite diversity and the ratio of Oribatida to Prostigmata and a significant negative relationship between Collembola diversity and litter. Based on multivariate analysis, Collembola and mite composition differed by sub-community and season interaction

Bachelor-MARSYS education cruise in the Baltic Sea Cruise No. AL551

06.03 – 13.03.2021, Kiel (Germany) – Kiel (Germany) BALTEACH -

2. Wochenbericht AL568b

Fahrtabschnitt 31.01. – 01.02.202

Support Vektor Regression für Anwendungen im Bereich der Elasto-Plastizität

In der vorliegenden Arbeit werden Untersuchungen zur Modellreduktion mechanischer Systeme mit elasto-plastischem Materialverhalten durchgeführt. Das untersuchte Verfahren zur Modellreduktion ist in dieser Arbeit die Support Vektor Regression (SVR). Nach einem Überblick zum Stand der Technik im Rahmen der Modellreduktion mechanischer Systeme, wird die Theorie der Kontinuumsmechanik und der Finiten Elemente Methode (FEM) bereitgestellt. Im Anschluss an eine ausführliche Darlegung des mathematischen Hintergrunds der Support Vektor Regression wird die Methode auf den eindimensionalen, rein phänomenologischen elasto-plastischen Fall angewendet. Ein wesentliches Kapitel widmet sich der Anwendung auf den drei- und zweidimensionalen elasto-plastischen Berechnungsfall unter vorangegangener Finite-Elemente Berechnung. In den Studien wird gezeigt, dass die Matérn 5/2 Kernel Funktion sehr gut für Anwendungen im Bereich der Elasto-Plastizität geeignet und mit entsprechend gewählten Parametern anderen Kernel Funktionen überlegen ist. Mit verschiedenen, in dieser Dissertation vorgestellten adaptiven Sampling-Methoden kann eine teilweise Verbesserung im Gegensatz zu der homogenen, kartesischen Verfeinerung erzielt werden. Im Rahmen der FE-Anwendung zeigt sich die eigentliche Stärke der Modellreduktion mittels SVR. Die ungleichmäßigen und nicht stetigen Antwortflächen der gewählten Größe von Interesse können mit der Support Vektor Regression hinreichend präzise angenähert werden. Die Sensitivität in den verschiedenen Parameter-Richtungen lässt eine zusätzliche Reduktion der nötigen Trainingsdaten mittels anisotropen Gitterstrukturen zu.In this thesis, investigations on model reduction of mechanical systems with elasto-plastic material behaviour are carried out. The investigated method for model reduction in this work is the Support Vector Regression (SVR). After a state of the art in model reduction of mechanical systems, the theory of continuum mechanics and the nite element method (FEM) is provided. Following a detailed presentation of the mathematical background of Support Vector Regression, the method is applied to the one-dimensional, purely phenomenological elasto-plastic case. A essential chapter is addressed towards the application of the method to the three- and twodimensional elasto-plastic case, preceded by a nite element calculation. In the numerical studies, it is shown that the Mat ern 5/2 kernel function is very well suited for applications in the eld of elasto-plasticity, and with appropriately chosen parameters is superior to other kernel functions. With di erent adaptive sampling methods presented in this dissertation, a partial improvement can be achieved compared with the homogeneous cartesian re nement. In the context of the FE application, the real strength of model reduction using SVR becomes apparent.The non-uniform and non-smooth response surfaces of the selected quantity of interest can be approximated with su cient precision using Support Vector Regression. The sensitivity in the di erent parameter directions allows an additional reduction of the necessary training data by means of anisotropic grid structures

1. Wochenbericht AL568b

Fahrtabschnitt 24.01. – 30.01.202

Globally supported surrogate model based on support vector regression for nonlinear structural engineering applications

This work presents a global surrogate modelling of mechanical systems with elasto-plastic material behaviour based on support vector regression (SVR). In general, the main challenge in surrogate modelling is to construct an approximation model with the ability to capture the non-smooth behaviour of the system under interest. This paper investigates the ability of the SVR to deal with discontinuous and high non-smooth outputs. Two different kernel functions, namely the Gaussian and Matèrn 5/2 kernel functions, are examined and compared through one-dimensional, purely phenomenological elasto-plastic case. Thereafter, an essential part of this paper is addressed towards the application of the SVR for the two-dimensional elasto-plastic case preceded by a finite element method. In this study, the SVR computational cost is reduced by using anisotropic training grid where the number of points are only increased in the direction of the most important input parameters. Finally, the SVR accuracy is improved by smoothing the response surface based on the linear regression. The SVR is constructed using an in-house MATLAB code, while Abaqus is used as a finite element solver

MalaQuick™ versus ParaSight F® as a diagnostic aid in travellers' malaria

In this study we assessed whether travellers can perform malaria rapid tests, following the provided information leaflet, and correctly interpret performed and pre-prepared test strips. Two Plasmodium falciparum testing systems, namely MalaQuick™ (ICT) and ParaSight F® were used. Test performance and test interpretation of pre-prepared tests were compared. There was no significant difference in test performance between the 2 tests. Interpretation of prepared test strips in both test systems was very reliable in blood parasite densities between 0·1% and 2%, but major problems were encountered at low parasitaemia (2% blood parasites). Low parasitaemia ParaSight F test strips were correctly interpreted by 52·1% compared with 10·8% correct interpretations with MalaQuick (P 2% blood parasites) pre-prepared test strips was higher with MalaQuick (96·8%) than with ParaSight F (33·8%), P < 0·0001. Both tests were associated with high levels of false-negative interpretations which render them unsuitable as self-diagnostic kits. Efforts must be made to assist lay individuals in test performance by technical test improvement, by equiping the test strips with an additional reading aid for interpretation, and by providing instruction by a skilled perso

Monitoring winter spawning activity of Western Baltic cod (Gadus morhua) (2021-25) Cruise No. AL568b

January 24th – February 1st 2022 Kiel (Germany) – Kiel (Germany) Winter cod 2021-2

A new Lagrangian in-time particle simulation module (Itpas v1) for atmospheric particle dispersion

Trajectory models are intuitive tools for airflow studies. But in general, they are limited to non-turbulent, i.e. laminar flow, conditions. Therefore, trajectory models are not particularly suitable for investigating airflow within the turbulent atmospheric boundary layer. To overcome this, a common approach is handling the turbulent uncertainty as a random deviation from a mean path in order to create a statistic of possible solutions which envelops the mean path. This is well known as the Lagrangian particle dispersion model (LPDM). However, the decisive factor is the representation of turbulence in the model, for which widely used models such as FLEXPART and HYSPLIT use an approximation. A conceivable improvement could be the use of a turbulence parameterisation approach based on the turbulent kinetic energy (TKE) at high temporal resolution. Here, we elaborated this approach and developed the LPDM Itpas, which is coupled online to the German Weather Service's mesoscale weather forecast model COSMO. It benefits from the prognostically calculated TKE as well as from the high-frequency wind information. We demonstrate the model's applicability for a case study on agricultural particle emission in eastern Germany. The results obtained are discussed with regard to the model's ability to describe particle transport within a turbulent boundary layer. Ultimately, the simulations performed suggest that the newly introduced method based on prognostic TKE sufficiently represents the particle transport