ReConNet: A Tool for Modeling and Simulating with Reconfigurable Place/Transition Nets

In this contribution we present a tool for modeling and simulation with reconfigurable Petri nets. Taking the idea of algebraic graph transformations to marked Petri nets we obtain Petri nets whose net structure can be changed dynamically. The rule-based change of the net structure enables the adequate modeling of complex, dynamic structures as for example of  the scenarios of the Living Place Hamburg. The tool \reconnet \ uses decorated  place/transition nets that are extended by various annotations. Especially, they  have transition labels that may change when the transition fires. The  transformation approach is based on the well-known algebraic transformation approach, but here we use a variant, namely the cospan approach, that inverts the relation between  left- and right-hand sides and interface in the  rules

The inverted singlet–triplet gap: a vanishing myth?

Molecules with an inverted singlet–triplet gap (STG) between the first excited singlet and triplet states, for example, heptazine, have recently been reported and gained substantial attention since they violate the famous Hund’s rule. Utilizing state-of-the-art high-level ab initio methods, the singlet–triplet gap vanishes and approaches zero from below whatever is improved in the theoretical description of the molecules: the basis set or the level of electron correlation. Seemingly, the phenomenon of inverted singlet–triplet gaps tends to vanish the closer we observe

Ceramic-based thermoelectric generator processed via spray-coating and laser structuring

Processing technology to improve the manufacturing of thermoelectric generators (TEGs) is a growing field of research. In this paper, an adaptable and scalable process comprising spray-coating and laser structuring for fast and easy TEG manufacturing is presented. The developed process combines additive and subtractive processing technology towards an adaptable ceramic-based TEG, which is applicable at high temperatures and shows a high optimization potential. As a prototype, a TEG based on Ca3Co4O9 (CCO) and Ag on a ceramic substrate was prepared. Microstructural and thermoelectric characterization is shown, reaching up to 1.65 ​μW ​cm−2 at 673 ​K and a ΔT of 100 ​K. The high controllability of the developed process also enables adaptation for different kinds of thermoelectric materials

Developing a Mobile Game Environment to Support Disadvantaged Learners

Schmitz, B., Hoffmann, M., Klamma, R., Klemke, R., & Specht, M. (2012). Developing a Mobile Game Environment to Support Disadvantaged Learners. Proceedings of 12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012) (pp. 223-227). July, 4-6, 2012, Rome, Italy: IEEE Computer Society CPS.This paper reports on the development of WeBuild, a mobile learning game designed to engage learners difficult to reach with IT learning. The development is based on a mobile game engine for the Android smart phone that was devised to support the required multiplayer and location based services. We played and tested the mobile learning game in a training facility of the building industry. The results indicate that the learners accepted the game for the low entry barriers and were motivated to use the game in an educational context. This paper describes the WeBuild prototype and the underlying game engine. Eventually, it presents results from the game session that was carried to assess interface and gameplay usability, technical functionality and motivational aspects of the game design

Geometry dependence of excitonic couplings and the consequences for configuration-space sampling

Excitonic coupling plays a key role for the understanding of excitonic energy transport (EET) in, for example, organic photovoltaics. However, the calculation of realistic systems is often beyond the applicability range of accurate wavefunction methods so that lower-scaling semi-empirical methods are used to model EET events. In the present work, the distance and angle dependence of excitonic couplings of dimers of selected organic molecules are evaluated for the semi-empirical long-range corrected density functional based tight binding (LC-DFTB) method and spin opposite scaled second order approximate coupled cluster singles and doubles (SOS-CC2). While semi-empirically scaled methods can lead to slightly increased deviations for excitation energies, the excitonic couplings and their dependence on the dimer geometry are reproduced. LC-DFTB yields a similar accuracy range as density-functional theory (DFT) employing the ωB97X functional while the computation time is reduced by several orders of magnitude. The dependence of the exchange contributions to the excitonic couplings on the dimer geometry is analyzed assessing the calculation of Coulombic excitonic couplings from monomer local excited states only, which reduces the computational effort significantly. The present work is a necessary first step toward the simulation of excitonic energy transport using semi-empirical methods

Dynamic carbon flux network of a diverse marine microbial community

The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton—heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.DFG, 248198858, GRK 2032: Grenzzonen in urbanen WassersystemenTU Berlin, Open-Access-Mittel – 202

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

Socioeconomic position and self-rated health among female and male adolescents: The role of familial determinants in explaining health inequalities. Results of the German KiGGS study

Objective: Although health inequalities in adolescence are well documented, the underlying mechanisms remain unclear. Few studies have examined the role of the family in explaining the association between the family’s socioeconomic position and adolescents’ self-rated health. The current study aimed to explore whether the association between socioeconomic position and self-rated health was mediated by familial determinants. Methods: Using data from wave 2 of the”German Health Interview and Examination Survey for Children and Adolescents” (KiGGS) (1,838 female and 1,718 male 11- to 17-year-olds), linear regression analyses were conducted to decompose the total effects of income, education, occupational status, socioeconomic position index and adolescents’ subjective social status on self-rated health into direct effects and indirect effects through familial determinants (family cohesion, parental well-being, parental stress, parenting styles, parental obesity, smoking and sporting activity). Results: A significant total effect of all socioeconomic position indicators on self-rated health was found, except for income in male adolescents. In female adolescents, more than 70% of the total effects of each socioeconomic position indicator were explained by familial mediators, whereas no significant direct effects remained. The most important mediator was parental well-being, followed by family cohesion, parental smoking and sporting activity. In male adolescents, the associations between income, parental education, the socioeconomic position index and subjective social status were also mediated by familial determinants (family cohesion, parental smoking, obesity and living in a single-mother family). However, a significant direct effect of subjective social status remained. Conclusion: The analysis revealed how a family’s position of socioeconomic disadvantage can lead to poorer health in adolescents through different family practices. The family appears to play an important role in explaining health inequalities, particularly in female adolescents. Reducing health inequalities in adolescence requires policy interventions (macro-level), community-based strategies (meso-level) and programs to improve parenting and family functioning (micro-level).Peer Reviewe