A Review on Joint Models in Biometrical Research

In some fields of biometrical research joint modelling of longitudinal measures and event time data has become very popular. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modelled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in an uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research

When one Logic is Not Enough: Integrating First-order Annotations in OWL Ontologies

In ontology development, there is a gap between domain ontologies which mostly use the web ontology language, OWL, and foundational ontologies written in first-order logic, FOL. To bridge this gap, we present Gavel, a tool that supports the development of heterogeneous 'FOWL' ontologies that extend OWL with FOL annotations, and is able to reason over the combined set of axioms. Since FOL annotations are stored in OWL annotations, FOWL ontologies remain compatible with the existing OWL infrastructure. We show that for the OWL domain ontology OBI, the stronger integration with its FOL top-level ontology BFO via our approach enables us to detect several inconsistencies. Furthermore, existing OWL ontologies can benefit from FOL annotations. We illustrate this with FOWL ontologies containing mereotopological axioms that enable new meaningful inferences. Finally, we show that even for large domain ontologies such as ChEBI, automatic reasoning with FOL annotations can be used to detect previously unnoticed errors in the classification

Ontology Pre-training for Poison Prediction

Integrating human knowledge into neural networks has the potential to improve their robustness and interpretability. We have developed a novel approach to integrate knowledge from ontologies into the structure of a Transformer network which we call ontology pre-training: we train the network to predict membership in ontology classes as a way to embed the structure of the ontology into the network, and subsequently fine-tune the network for the particular prediction task. We apply this approach to a case study in predicting the potential toxicity of a small molecule based on its molecular structure, a challenging task for machine learning in life sciences chemistry. Our approach improves on the state of the art, and moreover has several additional benefits. First, we are able to show that the model learns to focus attention on more meaningful chemical groups when making predictions with ontology pre-training than without, paving a path towards greater robustness and interpretability. Second, the training time is reduced after ontology pre-training, indicating that the model is better placed to learn what matters for toxicity prediction with the ontology pre-training than without. This strategy has general applicability as a neuro-symbolic approach to embed meaningful semantics into neural networks

A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - \varphi$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.Comment: Proceedings of the 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Preprint, reviewed version (only minor corrections

Thermomechanical design rules for photovoltaic modules

We present a set of thermomechanical design rules to support and accelerate future (PV) module developments. The design rules are derived from a comprehensive parameter sensitivity study of different PV module layers and material properties by finite element method simulations. We develop a three dimensional finite element method (FEM) model, which models the PV module geometry in detail from busbar and ribbons up to the frame including the adhesive. The FEM simulation covers soldering, lamination, and mechanical load at various temperatures. The FEM model is validated by mechanical load tests on three 60-cell PV modules. Here, for the first time, stress within a solar cell is measured directly using stress sensors integrated in solar cells (SenSoCells®). The results show good accordance with the simulations. The parameter sensitivity study reveals that there are two critical interactions within a PV module: (1) between ribbon and solar cell and (2) between front/back cover and interconnected solar cells. Here, the encapsulant plays a crucial role in how the single layers interact with each other. Therefore, its mechanical properties are essential, and four design rules are derived regarding the encapsulant. Also four design rules concern front and back sides, and three address the solar cells. Finally, two design rules each deal with module size and frame, respectively. Altogether we derive a set of 15 thermomechanical design rules. As a rule of thumb of how well a bill of material will work from a thermomechanical point of view, we introduce the concept of specific thermal expansion stiffness ${\hat{E}}_{\alpha }=E\cdotp \alpha \cdotp {A}_{\mathrm{j}}\cdotp h$ as the product of Young\u27s modulus E, coefficient of thermal expansion $\alpha$, joint area A$_{j}$, and materials height h. The difference between two materials is a measure of how much thermal strain one material can induce in another. A strong difference means that the material with the larger value will induce thermal strain in the other

Populations of Campyloderes sp. (Kinorhyncha, Cyclorhagida): one species with significant morphological variation?

Altogether, 103 adult and 23 juvenile specimens of Campyloderes from 33 locations both in the deep sea and on the continental shelf all over the world were studied by light microscopy (97 specimens) and scanning electron microscopy (28 specimens). Especially from the Faroe Island, the Central American East Pacific Ocean and from the area east and northeast of New Zealand, enough specimens are available to study the regional variation of characters. Specimens both from these regional areas and worldwide reveal a significant morphological variation, especially in the distribution of sensory spots, gland cell outlets, and papillae, whereas characters conventionally used for species identification, such as spine pattern do not vary much. Overlapping character patterns do not allow identification of different species and to discriminate the current populations from previously described species. We conclude that the morphological variation results from ongoing species formation processes. We also report observations that two adult life history stages may exist in Campyloderes. The character set in the ground pattern of Campyloderes is presented

Повышение эффективности деятельности нефтегазового предприятия на основе инноваций

Цель работы – определить экономический эффект от реализации рассмотренного инновационного проекта. Задачи исследования: 1.Выявить особенности инновационной деятельности в нефтегазовом комплексе. 2.Проанализировать деятельность предприятия АО "ТОМСКНЕФТЬ" ВНК. 3.Выявить отраслевые особенности функционирования АО "ТОМСКНЕФТЬ" ВНК. 4.Рассчитать производственную эффективность инновационного проекта по повышению эффективности разработки трудноизвлекаемых запасов. 5.Обосновать решение об использовании инновационного проекта в деятельности предприятия. Объектом исследования является область методического обеспечения принятия эффективных инновационных решений. Предметом исследования являлись принципы, методы, показатели и инструменты оценки экономической эффективности инновационных решений.The purpose of the work is to determine the economic effect of the implementation of the considered innovative project. Research Objectives: 1. To identify the features of innovation in the oil and gas sector. 2. To analyze the activities of the company AO "TOMSKNEFT" VNK. 3. To identify industry-specific features of the functioning of AO "TOMSKNEFT" VNK. 4. To calculate the production efficiency of an innovative project to improve the development of hard-to-recover reserves. 5. Justify the decision to use the innovative project in the activities of the enterprise. The object of research is the field of methodological support for the adoption of effective innovative solutions. The subject of the study was the principles, methods, indicators and tools for assessing the economic efficiency