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In this paper, a novel omnidirectional wireless power transfer system is proposed. By having a centralized transmitter with an effective charging area where multiple receiver, can be placed limited by a distance of 10 cm. The transmitter utilizes a Class-E amplifier with a frequency of 6.78 MHz. In order to achieve omnidirectional flux with a limited power utilization, a switching matrix with edge-configurable coils has been proposed to achieve both horizontal and vertical flux. This paper deals with simulation, design and preliminary experimental testing of a Class-E amplifier based system with an output power of 15 W for device agnostic consumer electronics.

Electronic spreadsheets are widely used in organizations for various data analytics and decision-making tasks. Even though faults within such spreadsheets are common and can have significant negative consequences, today's tools for creating and handling spreadsheets provide limited support for fault detection, localization, and repair. Being able to predict whether a certain part of a spreadsheet is faulty or not is often central for the implementation of such supporting functionality. In this work, we propose a novel approach to fault prediction in spreadsheet formulas, which combines an extensive catalog of spreadsheet metrics with modern machine learning algorithms. An analysis of the individual metrics from our catalog reveals that they are generally suited to discover a wide range of faults. Their predictive power is, however, limited when considered in isolation. Therefore, in our approach we apply supervised learning algorithms to obtain fault predictors that utilize all data provided by multiple spreadsheet metrics from our catalog. Experiments on different datasets containing faulty spreadsheets show that particularly Random Forests classifiers are often effective. As a result, the proposed method is in many cases able to make highly accurate predictions whether a given formula of a spreadsheet is faulty. 1 1. Results of a preliminary study were published in [1] .

For space exploration, for instance by use of Cube-Sats, there is need for small converters which can both operate isolated and non-isolated with a low EMI profile. Incorporating Electromagnetic Compatibility (EMC) within the design stage is therefore crucial, and can be achieved by applying different EMI mitigation techniques from the very start of the design. This paper investigates inherent reduction of EMI via interleaving, balancing, and symmetrically designing a DC/DC converter. Mass and volume restraints are considered as well during the design. It will be shown that these relatively advanced (but matured) design techniques, already well known and utilized on Earth, can be utilized for a novel design for space based applications with beneficial charateristics.

The Propulsion Test Facility of the TU Braunschweig is capable of investigating future jet engine intakes and fan aerodynamics to a high level of detail. A goal of this facility is the examination of coupled fan-intake-interactions which is not possible in any existing test bench around the world. Before doing research on these interactions, it is important to undergo proper studies of isolated aspirated intakes and fans under varying operating conditions (design and off-design). Therefore comparable result of the well-known LARA nacelle to existing experimental and numerical data has been generated for a first validation purpose. Therefore, comparable studies have been conducted with the LARA nacelle, to that of experimental and numerical investigations performed in the early 1990s at the ONERA F1 wind tunnel (mention reference), in order to generate results for validation. The first results of the validation experiment show differences in peak Mach number between the ONERA F1 and PTF experimental data for identical boundary conditions based on Mach number and crosswind. To investigate this further, a comprehensive numerical study has been carried out. It was inferred that the discrepancy was mainly caused by the Reynolds number effect within the PTF environment and its sensitivity to the inlet flow angle distribution with regard to angle of attack for crosswind. Within the validation test campaign, the experimental investigations showed a separation and reattachment hysteresis, which was identified when crosswind as well as nacelle mass flow had been increased or decreased to set up the different operating points. This phenomenon has still no established theoretical basis for understanding the aerodynamic behaviour. Overall, the applicability of conventional RANS models is shown. Additionally, the sensitivity to the aforementioned boundary conditions and the numerical reproducibility of the hysteresis phenomenon are discussed and compared to new experimental data in detail.

Theoretically, misalignment between the magnetic field and rotational axis in a dense core is considered to be dynamically important in the star formation process, however, extent of this influence remains observationally unclear. For a sample of 32 Class 0 and I protostars in the Perseus Molecular Cloud, we analyzed gas motions using C$^{18}$O data from the SMA MASSES survey and the magnetic field structures using 850 $\mu$m polarimetric data from the JCMT BISTRO-1 survey and archive. We do not find any significant correlation between the velocity gradients in the C$^{18}$O emission in the protostellar envelopes at a 1,000 au scale and the misalignment between the outflows and magnetic field orientations in the dense cores at a 4,000 au scale, and there is also no correlation between the velocity gradients and the angular dispersions of the magnetic fields. However, a significant dependence on the misalignment angles emerges after we normalize the rotational motion by the infalling motion, where the ratios increase from $\lesssim1$ to $\gtrsim1$ with increasing misalignment angles. This suggests that the misalignment could prompt angular momentum transportation to the envelope scale but is not a dominant factor in determining the envelope rotation, and other parameters, like mass accretion in protostellar sources, also play an important role. These results remain valid after taking into account projection effects. The comparison between our estimated angular momentum in the protostellar envelopes and the sizes of the known protostellar disks suggests that significant angular momentum is likely lost between radii of $\sim$1,000-100 au in protostellar envelopes., Comment: Accepted by ApJ

Faults in spreadsheets can represent a major risk for businesses. To minimize such risks, various automated testing and debugging approaches for spreadsheets were proposed. In such approaches, often one main assumption is that the spreadsheet developer is able to indicate if the outcomes of certain calculations correspond to the intended values. This, however, might require that the user performs calculations manually, a process which can easily become tedious and error-prone for more complex spreadsheets. In this work, we propose an interactive spreadsheet algorithmic debugging method, which is based on partitioning the spreadsheet into fragments. Test cases can then be automatically or manually created for each of these smaller fragments, whose correctness or faultiness can be easier assessed by users than test cases that cover the entire spreadsheet. The annotated test cases are then fed into an algorithmic debugging technique, which returns a set of formulas that could have caused any observed failures, i.e., discrepancies between the expected and computed calculation outcomes. Simulation experiments demonstrate that the suggested decomposition approach can speed up the algorithmic debugging process and significantly reduce the number of fault candidates returned by the algorithm. An additional laboratory study shows that fragmenting a spreadsheet with our method furthermore reduces the time needed by users for creating test cases for a spreadsheet.

BACKGROUND Work-related skin diseases (WSD) are caused or worsened by a professional activity. Occupational skin diseases (OSD) need to fulfil additional legal criteria which differ from country to country. OSD range amongst the five most frequently notified occupational diseases (musculoskeletal diseases, neurologic diseases, lung diseases, diseases of the sensory organs, skin diseases) in Europe. OBJECTIVE To retrieve information and compare the current state of national frameworks and pathways to manage patients with occupational skin disease with regard to prevention, diagnosis, treatment and rehabilitation in different European countries. METHODS A questionnaire-based survey of the current situation regarding OSD patient management pathways was carried out with experts on occupational dermatology and/or occupational medicine from 28 European countries contributing to the European Cooperation in Science and Technology (COST) Action TD 1206 (StanDerm) (www.standerm.eu). RESULTS Besides a national health service or a statutory health insurance, most European member states implemented a second insurance scheme specifically geared at occupational diseases [insurance against occupational risks (synonyms: insurance against work accidents and occupational injuries; statutory social accident insurance)]. Legal standards for the assessment of occupationally triggered diseases with a genetic background differ between different countries, however, in most European member states recognition as OSD is possible. In one-third of the countries UV light-induced tumours can be recognized as OSD under specific conditions. CONCLUSION OSD definitions vary between European countries and are not directly comparable, which hampers comparisons between statistics collected in different countries. Awareness of this fact and further efforts for standardization are necessary.

Separation of differentially isotope-labeled bacterial RNA by isopycnic density gradient centrifugation is a critical step in RNA-based stable isotope probing analyses, which help to link the structure and function of complex microbial communities. Using isotope-labeled Escherichia coli RNA, we showed that an 8 mL near-vertical rotor performed better than a 2 mL fixed-angle rotor, thereby corroborating current recommendations. Neither increased concentrations of formamide nor urea in the medium improved the separation results using the fixed-angle rotor.

The development of biomimetic robots has gained research interest in the last years as it may both help understanding processes of motion execution in biological systems as well as developping a novel generation of intelligent and energy efficient robots. However, exact model generation that builds up on observations and robot design is very time intensive. In this paper we present a novel pipeline for co-development of biomimetic hardware and simulation models based on biological Computer Tomography (CT) data. For this purpose we exploit State of the Art rapid prototyping technologies such as 3D Printing and the Neurorobotics Platform for musculoskeletal simulations in virtual environments. The co-development integrates both advantages of virtual and physical experimental models and is expected to increase development speed of controllers that can be tested on the simulated counterpart before application to a printed robot model. We demonstrate the pipeline by generating a one year old infant model as a musculoskeletal simulation model and a print-in-place 3D printed skeleton as a single movable part. Even though we here only introduce the initial body generation and only a first test setup for a modular sensory and control framework, we can clearly spot advantages in terms of rapid model generation and highly biological related models. Engineering costs are reduced and models can be provided to a wide research community for controller testing in an early development phase.

Software product metrics allow practitioners to improve their products and to optimize development processes based on quantifiable characteristics of source code. To facilitate similar benefits for spreadsheet programs, researchers proposed various product metrics for spreadsheets over the last decades. However, to our knowledge, no comprehensive overview of those efforts is currently available. In this paper, we close this gap by conducting a literature review of research works that either inherently or explicitly define product metrics for spreadsheets. We scanned five major digital libraries for scientific papers that define or use spreadsheet product metrics. Based on the identified 37 papers, we created a novel catalog of product metrics for spreadsheets. The catalog can be used by practitioners and researchers as a central reference for spreadsheet product metrics. In the paper, we (i) describe the proposed metrics in detail, (ii) report how often and for what purposes the metrics are used, (iii) identify significant discrepancies in the naming and definition of the metrics, and (iv) investigate how the appropriateness of the metrics was evaluated.

Spreadsheet users are often unaware of the risks imposed by poorly designed spreadsheets. One way to assess spreadsheet quality is to detect smells which attempt to identify parts of spreadsheets that are hard to comprehend or maintain and which are more likely to be the root source of bugs. Unfortunately, current spreadsheet smell detection techniques suffer from a number of drawbacks that lead to incorrect or redundant smell reports. For example, the same quality issue is often reported for every copy of a cell, which may overwhelm users. To deal with these issues, we propose to refine spreadsheet smells by exploiting inferred structural information for smell detection. We therefore first provide a detailed description of our static analysis approach to infer clusters and blocks of related cells. We then elaborate on how to improve existing smells by providing three example refinements of existing smells that incorporate information about cell groups and computation blocks. Furthermore, we propose three novel smell detection techniques that make use of the inferred spreadsheet structures. Empirical evaluation of the proposed techniques suggests that the refinements successfully reduce the number of incorrectly and redundantly reported smells, and novel deficits are revealed by the newly introduced smells.

Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy., 4 pages, 1 figure, to be published in 40th International Conference on Software Engineering: New Ideas and Emerging Results Track

Entering information on a computer keyboard is a ubiquitous mode of expression and communication. We investigate whether typing behavior is connected to two factors: the cognitive demands of a given task and the demographic features of the typist. We utilize features based on keystroke dynamics, stylometry, and "language production", which are novel hybrid features that capture the dynamics of a typists linguistic choices. Our study takes advantage of a large data set (~350 subjects) made up of relatively short samples (~450 characters) of free text. Experiments show that these features can recognize the cognitive demands of task that an unseen typist is engaged in, and can classify his or her demographics with better than chance accuracy. We correctly distinguish High vs. Low cognitively demanding tasks with accuracy up to 72.39%. Detection of non-native speakers of English is achieved with F1=0.462 over a baseline of 0.166, while detection of female typists reaches F1=0.524 over a baseline of 0.442. Recognition of left-handed typists achieves F1=0.223 over a baseline of 0.100. Further analyses reveal that novel relationships exist between language production as manifested through typing behavior, and both cognitive and demographic factors. HighlightsRecognition of cognitive task with linguistic and keystroke features with accuracy of 72.39%.Recognition of gender, handedness, and native-language from short unconstrained text at F1=.462, 0.223, and 0.524, respectively.Developed novel Language Production features hybridizing keystroke dynamics and stylometry.

Graphical abstractDisplay Omitted HighlightsThe Kriging-based EGO techniques performed better than the baseline LHS approach.The use of re-interpolation is crucial to cope with noise.Repeats can be necessary but also decrease the number of possible infill points. Recent research revealed that model-assisted parameter tuning can improve the quality of supervised machine learning (ML) models. The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches. However, the advantages of the tuning often came along with high computation times, meaning a real burden for employing tuning algorithms. While the training with a reduced number of patterns can be a solution to this, it is often connected with decreasing model accuracies and increasing instabilities and noise. Hence, we propose a novel approach defined by a two criteria optimization task, where both the runtime and the quality of ML models are optimized. Because the budgets for this optimization task are usually very restricted in ML, the surrogate-assisted Efficient Global Optimization (EGO) algorithm is adapted. In order to cope with noisy experiments, we apply two hypervolume indicator based EGO algorithms with smoothing and re-interpolation of the surrogate models. The techniques do not need replicates. We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling (LHS), as well as EGO variants with replicates.

Spreadsheets serve as a basis for decision-making processes in many companies and bugs in spreadsheets can therefore represent a considerable risk to businesses. Systematic tests can help to locate such bugs, but providing test cases can be cumbersome and complex for large real-world spreadsheets. To make the specification of test cases easier, we propose to split spreadsheets into smaller logically connected parts (called fragments) which can be individually tested for correctness. We present an algorithmic approach to compute such fragments, which we validated with a laboratory study in the form of a spreadsheet debugging exercise involving 57 subjects. The results show that the fragmentation approach can help to significantly reduce the required efforts to test a spreadsheet.1

Spreadsheets are the most prominent example of end-user programs. Unfortunately, spreadsheets often contain faults. In this paper, we propose a static analysis approach that can be used in a variety of spreadsheet quality assurance techniques. Our approach automatically detects groups of related formula cells and input cells, computation blocks, and the headers of the input and formula cells for a given spreadsheet. In the empirical evaluation, we show that 99% of the blocks of the investigated spreadsheets and more than 76% of the headers can be detected. Furthermore, we explain how the results of the static analysis approach can be used to improve existing spreadsheet smells and we propose four new spreadsheet smells.

Kernel-based methods like Support Vector Machines (SVM) have been established as powerful techniques in machine learning. The idea of SVM is to perform a mapping from the input space to a higher-dimensional feature space using a kernel function, so that a linear learning algorithm can be employed. However, the burden of choosing the appropriate kernel function is usually left to the user. It can easily be shown that the accuracy of the learned model highly depends on the chosen kernel function and its parameters, especially for complex tasks. In order to obtain a good classification or regression model, an appropriate kernel function in combination with optimized pre- and post-processed data must be used. To circumvent these obstacles, we present two solutions for optimizing kernel functions: (a) automated hyperparameter tuning of kernel functions combined with an optimization of pre- and post-processing options by Sequential Parameter Optimization (SPO) and (b) evolving new kernel functions by Genetic Programming (GP). We review modern techniques for both approaches, comparing their different strengths and weaknesses. We apply tuning to SVM kernels for both regression and classification. Automatic hyperparameter tuning of standard kernels and pre- and post-processing options always yielded to systems with excellent prediction accuracy on the considered problems. Especially SPO-tuned kernels lead to much better results than all other tested tuning approaches. Regarding GP-based kernel evolution, our method rediscovered multiple standard kernels, but no significant improvements over standard kernels were obtained.

Typically, the dose-occupancy curves for GABA-A receptor ligands are determined using in vivo binding of [3H]flumazenil. This study describes in vivo binding experiments without the use of tracer ligands. Bound and free fractions were measured directly using a highly sensitive LC/MS/MS detection method after in vivo administration of the GABA-A ligands zolpidem, (RS)-zopiclone, L-838417 and flumazenil, to demonstrate affinity and saturation of the filter-retained, membrane-bound fraction. The in vivo binding of flumazenil and L-838417 both saturated around 200 nM, at a similar level to the specific binding of (S)-zopiclone after doses of the racemic zopiclone, using (R)-zopiclone to estimate non-specific binding. This saturable component represented an estimate of benzodiazepine binding sites available on GABA-A receptors in vivo (200 nM). Dose-occupancy curves were constructed to estimate the dose required to achieve 50% occupancy and matched estimates obtained with tracer methods. In contrast to tracer methods, this method is uniquely suitable to the demonstration of stereoselective binding of the (S)-isomer in vivo after doses of racemic zopiclone. These results demonstrate that the LC/MS/MS measurements of total drug concentrations typically used in early drug development can be adapted to provide information about receptor occupancy in vivo.

Nowadays, constraints play an important role in industry, because most industrial optimization tasks underly several restrictions. Finding good solutions for a particular problem with respect to all constraint functions can be expensive, especially when the dimensionality of the search space is large and many constraint functions are involved. Unfortunately function evaluations in industrial optimization are heavily limited, because often expensive simulations must be conducted. For such high-dimensional optimization tasks, the constraint optimization algorithm COBRA was proposed, making use of surrogate modeling for both the objective and the constraint functions. In this paper we present a new mechanism for COBRA to repair infill solutions with slightly violated constraints. The repair mechanism is based on gradient descent on surrogates of the constraint functions and aims at finding nearby feasible solutions. We test the repair mechanism on a real-world problem from the automotive industry and on other synthetic test cases. It is shown in this paper that with the integration of the repair method, the percentage of infeasible solutions is significantly reduced, leading to faster convergence and better final results.