Biodiesel via in situ wet microalgae biotransformation: Zwitter-type ionic liquid supported extraction and transesterification

The production of biodiesel derived from microalgae is among the most forthcoming technologies that provide an ecologic alternative to fossil fuels. Herein, a method was developed that enables the direct extraction and conversion of algal oil to biodiesel without prior isolation. The reaction occurs in aqueous media catalyzed by immobilized Candida antarctica lipase B (Novozyme 435). Zwitter-type ionic liquids were used as cocatalyst to improve the selectivity and reactivity of the enzyme. In a model reaction with sunflower oil, 64% biodiesel was obtained. Applying this method to a slurry of whole-cell Chlorella zof ingiensis in water resulted in 74.8% of lipid extraction, with 27.7% biotransformation products and up to 16% biodiesel. Factors that reduced the lipase activity with whole-cell algae were subsequently probed and discussed. This "in situ" method shows an improvement to existing methods, since it integrates the oil extraction and conversion into an one-pot procedure in aqueous conditions. The extraction is nondisruptive, and is a model for a greener algae to biodiesel process

Git workflow for active learning - a development methodology proposal for data-centric AI projects

As soon as Artificial Intelligence (AI) projects grow from small feasibility studies to mature projects, developers and data scientists face new challenges, such as collaboration with other developers, versioning data, or traceability of model metrics and other resulting artifacts. This paper suggests a data-centric AI project with an Active Learning (AL) loop from a developer perspective and presents ”Git Workflow for AL”: A methodology proposal to guide teams on how to structure a project and solve implementation challenges. We introduce principles for data, code, as well as automation, and present a new branching workflow. The evaluation shows that the proposed method is an enabler for fulfilling established best practices

Efficient two-parameter persistence computation via cohomology

Clearing is a simple but effective optimization for the standard algorithm of persistent homology (PH), which dramatically improves the speed and scalability of PH computations for Vietoris--Rips filtrations. Due to the quick growth of the boundary matrices of a Vietoris--Rips filtration with increasing dimension, clearing is only effective when used in conjunction with a dual (cohomological) variant of the standard algorithm. This approach has not previously been applied successfully to the computation of two-parameter PH. We introduce a cohomological algorithm for computing minimal free resolutions of two-parameter PH that allows for clearing. To derive our algorithm, we extend the duality principles which underlie the one-parameter approach to the two-parameter setting. We provide an implementation and report experimental run times for function-Rips filtrations. Our method is faster than the current state-of-the-art by a factor of up to 20.Comment: This is an extended version of a conference paper that appeared at SoCG 2023, see https://drops.dagstuhl.de/opus/volltexte/2023/1786

symQV: Automated Symbolic Verification of Quantum Programs

We present symQV, a symbolic execution framework for writing and verifying quantum computations in the quantum circuit model. symQV can automatically verify that a quantum program complies with a first-order specification. We formally introduce a symbolic quantum program model. This allows to encode the verification problem in an SMT formula, which can then be checked with a delta-complete decision procedure. We also propose an abstraction technique to speed up the verification process. Experimental results show that the abstraction improves symQV's scalability by an order of magnitude to quantum programs with 24 qubits (a 2^24-dimensional state space).Comment: This is the extended version of a paper with the same title that appeared at FM 2023. Tool available at doi.org/10.5281/zenodo.740032

Optimierung der Energieeffizienz zweibeiniger Roboter durch elastische Kopplungen

In dieser Arbeit wird die Optimierung der Energieeffizienz zweibeiniger Roboter durch den Einsatz elastischer Kopplungen untersucht. Die betrachteten Roboter werden als unteraktuierte Systeme modelliert und mittels Ein-Ausgangs-Linearisierung geregelt. Zur Untersuchung des Einflusses der elastischen Kopplungen auf Energieeffizienz sowie Stabilität und Robustheit werden parallel die Bewegungen der Roboter als auch deren elastische Kopplungen unter Anwendung numerischer Algorithmen optimiert

Prediction of Heat Transfer in a Jet Cooled Aircraft Engine Compressor Cone Based on Statistical Methods

The paper presents the setup and analysis of an experimental study on heat transfer of a jet cooled compressor rear cone with adjacent conical housing. The main goal of the paper is to describe the systematic derivation of empirical correlations for global Nusselt numbers to be used in the design process of a jet engine secondary air system. Based on the relevant similarity parameters obtained from literature, operating points are deduced leading to a full factorial design experiment to identify all effects and interactions. The varied similarity parameters are the circumferential Reynolds number, the non-dimensional mass flow, the non-dimensional spacing between rotor and stator, and the jet incidence angle. The range of the varied similarity parameters covers engine oriented operating conditions and is therefore suitable to predict Nusselt numbers in the actual engine component. In order to estimate measurement uncertainties, a simplified model of the test specimen, consisting of a convectively cooled flat plate, has been derived. Uncertainties of the measured quantities and derived properties are discussed by means of a linear propagation of uncertainties. A sensitivity study shows the effects of the input parameters and their interactions on the global Nusselt number. Subsequently, an empirical correlation for the global Nusselt numbers is derived using a multivariate non-linear regression. The quality of the empirical correlation is assessed by means of statistical hypotheses and by a comparison between measured and predicted data. The predicted values show excellent agreement with experimental data. In a wide range, accuracies of 15% can be reached when predicting global Nusselt numbers. Furthermore, the results of the sensitivity study show that pre-swirled cooling air does not have a positive effect on heat transfer

A Unified View on the Functorial Nerve Theorem and its Variations

The nerve theorem is a basic result of algebraic topology that plays a central role in computational and applied aspects of the subject. In applied topology, one often needs a nerve theorem that is functorial in an appropriate sense, and furthermore one often needs a nerve theorem for closed covers, as well as for open covers. While the techniques for proving such functorial nerve theorems have long been available, there is unfortunately no general-purpose, explicit treatment of this topic in the literature. We address this by proving a variety of functorial nerve theorems. First, we show how one can use relatively elementary techniques to prove nerve theorems for covers by closed convex sets in Euclidean space, and for covers of a simplicial complex by subcomplexes. Then, we prove a more general, "unified" nerve theorem that recovers both of these, using standard techniques from abstract homotopy theory.Comment: 53 pages. Updated exposition and added Appendix D. Comments welcom

Optimization of energy efficiency of walking bipedal robots by use of elastic couplings in the form of mechanical springs

This paper presents a method to optimize the en- ergy efficiency of walking bipedal robots by more than 50 % in a speed range from 0.3 to 2.3 m/s using elastic couplings – mechanical springs with movement speed independent pa- rameters. The considered robot consists of a trunk, two stiff legs and two actuators in the hip joints. It is modeled as un- deractuated system to make use of its natural dynamics and feedback controlled with input-output linearization. A nu- merical optimization of the joint angle trajectories as well as the elastic couplings is performed to minimize the average energy expenditure over the whole speed range. The elastic couplings increase the swing leg motion’s natural frequency thus making smaller steps more efficient which reduce the impact loss at the touchdown of the swing leg. The pro- cess of energy turnover is investigated for the robot with and without elastic couplings. Furthermore, the influence of the elastic couplings’ topology, its degree of nonlinearity, the mass distribution, the joint friction, the coefficient of static friction and the selected actuator is analyzed. It is shown that the optimization of the robot’s motion and elastic coupling towards energy efficiency leads to a slightly slower conver- gence rate of the controller, yet no loss of stability and a lower sensitivity with respect to disturbances. The optimal elastic coupling discovered by the numerical optimization is a linear torsion spring between the legs