Noninvasive 3D Field Mapping of Complex Static Electric Fields

Many upcoming experiments in antimatter research require low-energy antiproton beams. With a kinetic energy in the order of 100 keV, the standard magnetic components to control and focus the beams become less effective. Therefore, electrostatic components are being developed and installed in transfer lines and storage rings. However, there is no equipment available to precisely map and check the electric field generated by these elements. Instead, one has to trust in simulations and, therefore, depend on tight fabrication tolerances. Here we present, for the first time, a noninvasive way to experimentally probe the electrostatic field in a 3D volume with a microsensor. Using the example of an electrostatic quadrupole focusing component, we find excellent agreement between a simulated and real field. Furthermore, it is shown that the spatial resolution of the probe is limited by the electric field curvature which is almost zero for the quadrupole. With a sensor resolution of 61V/m/√Hz, the field deviation due to a noncompliance with the tolerances can be resolved. We anticipate that this compact and practical field strength probe will be relevant also for other scientific and technological disciplines such as atmospheric electricity or safeguarding near power infrastructure

A high-throughput sequencing test for diagnosing inherited bleeding, thrombotic, and platelet disorders.

Inherited bleeding, thrombotic, and platelet disorders (BPDs) are diseases that affect ∼300 individuals per million births. With the exception of hemophilia and von Willebrand disease patients, a molecular analysis for patients with a BPD is often unavailable. Many specialized tests are usually required to reach a putative diagnosis and they are typically performed in a step-wise manner to control costs. This approach causes delays and a conclusive molecular diagnosis is often never reached, which can compromise treatment and impede rapid identification of affected relatives. To address this unmet diagnostic need, we designed a high-throughput sequencing platform targeting 63 genes relevant for BPDs. The platform can call single nucleotide variants, short insertions/deletions, and large copy number variants (though not inversions) which are subjected to automated filtering for diagnostic prioritization, resulting in an average of 5.34 candidate variants per individual. We sequenced 159 and 137 samples, respectively, from cases with and without previously known causal variants. Among the latter group, 61 cases had clinical and laboratory phenotypes indicative of a particular molecular etiology, whereas the remainder had an a priori highly uncertain etiology. All previously detected variants were recapitulated and, when the etiology was suspected but unknown or uncertain, a molecular diagnosis was reached in 56 of 61 and only 8 of 76 cases, respectively. The latter category highlights the need for further research into novel causes of BPDs. The ThromboGenomics platform thus provides an affordable DNA-based test to diagnose patients suspected of having a known inherited BPD.This study, including the enrollment of cases, sequencing, and analysis received support from the National Institute for Health Research (NIHR) BioResource–Rare Diseases. The NIHR BioResource is funded by the NIHR (http://www.nihr.ac.uk). Research in the Ouwehand Laboratory is also supported by grants from Bristol-Myers Squibb, the British Heart Foundation, the British Society of Haematology, the European Commission, the MRC, the NIHR, and the Wellcome Trust; the laboratory also receives funding from National Health Service Blood and Transplant (NHSBT). The clinical fellows received funding from the MRC (C.L. and S.K.W.); the NIHR–Rare Diseases Translational Research Collaboration (S. Sivapalaratnam); and the British Society for Haematology and National Health Service Blood and Transplant (T.K.B.).This is the author accepted manuscript. The final version is available from American Society of Hematology via http://dx.doi.org/10.1182/blood-2015-12-688267

Borosilicate Glass MEMS Lorentz Force Magnetometer

This paper reports on a novel, miniaturized magnetomechanical transducer/sensor made of borosilicate glass with wide dynamic range. The prototype is manufactured with laser micromachining and ablation techniques. Compared to state of the art, borosilicate glass substrate offers the highest thermal shock resistance and is best suited for MEMS magnetometers, for aerospace and space applications or magnetic monitoring systems for diagnostics and plasma stability control of nuclear fusion experiments, where thermal shock resistance is a critical requirement

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Rolling element bearing faults significantly contribute to overall machine failures, which demand different strategies for condition monitoring and failure detection. Recent advancements in machine learning even further expedite the quest to improve accuracy in fault detection for economic purposes by minimizing scheduled maintenance. Challenging tasks, such as the gathering of high quality data to explicitly train an algorithm, still persist and are limited in terms of the availability of historical data. In addition, failure data from measurements are typically valid only for the particular machinery components and their settings. In this study, 3D multi-body simulations of a roller bearing with different faults have been conducted to create a variety of synthetic training data for a deep learning convolutional neural network (CNN) and, hence, to address these challenges. The vibration data from the simulation are superimposed with noise collected from the measurement of a healthy bearing and are subsequently converted into a 2D image via wavelet transformation before being fed into the CNN for training. Measurements of damaged bearings are used to validate the algorithm’s performance

Need Help? Recommending Social Care Institutions

In this paper, we present work-in-progress on a recommender system designed to help people in need find the best suited social care institution for their personal issues. A key requirement in such a domain is to assure and to guarantee the person's privacy and anonymity in order to reduce inhibitions and to establish trust. We present how we aim to tackle this barely studied domain using a hybrid content-based recommendation approach. Our approach leverages three data sources containing textual content, namely (i) metadata from social care institutions, (ii) institution specific FAQs, and (iii) questions that a specific institution has already resolved. Additionally, our approach considers the time context of user questions as well as negative user feedback to previously provided recommendations. Finally, we demonstrate an application scenario of our recommender system in the form of a real-world Web system deployed in Austria.Published at RSBDA Workshop co-located with i-know 201

3D-Printed MEMS Magnetometer Featuring Compliant Mechanism

This paper reports a novel 3D-printed MEMS resonant magnetometer with optical readout which features a mechanical conversion of a vertical oscillation into a horizontal one. This demonstrates the advantages of 3D-printing technology in terms of rapid prototyping, low costs and fast product development cycles. In addition, 3D-printing enables ‘true’ three-dimensional MEMS structures in contrast to the traditional MEMS technology which allows only two dimensional structures. The measurement approach comprises a hybrid implementation of an optical modulator, an LED and a photodetector

Novel 3D-Printed MEMS Magnetometer with Optical Detection

This paper reports a novel 3D printed MEMS magnetometer with optical readout, which demonstrates the advantages of 3D printing technology in terms of rapid prototyping. Low-cost and fast product development cycles favour 3D printing as an effective tool. Sensitivity measurement with such devices indicate high accuracy and good structural performance, considering material and technological uncertainties. This paper is focusing on the novelty of the rapid, 3D-printing prototyping approach and verification of the working principle for printed MEMS magnetometers

Cross-Sensitivity of an Optomechanical MEMS Transducer

This work presents the investigation on a MEMS based optomechanical transducer for displacements or vibration regarding its cross-sensitivities to multidirectional input excitations. The principle of the optomechanical transducer is based on the modulation of the light flux passing through one static and one movable micromechanical aperture. This kind of transducer is of increasing interest for MEMS sensors since it has inherent benefits and can compete with state-ofthe- art readout concepts regarding its resolution. We have experimentally proven that the sensitivities of the device is 3.3 × 107 V/m in x-direction, 8.23 × 106 V/m in y-direction, while it is negligible in z-direction

MOEMS Based Single Chip Lorentz Force Magnetic Gradiometer

The functional principle of an optical gradient magnetic field sensor consisting of two independent laterally oscillating masses on a single chip is reported. These oscillations are caused by the Lorentz forces resulting from an alternating current through the masses interacting with a static magnetic field. Light is modulated by relative in-plane movement of the masses and a fixed frame and subsequently detected by two photodiodes. Evaluation of magnitude and phase of the output signal reveals information about the uniformity of the magnetic field. The sensor is capable of detecting uniaxially strength and direction of magnetic gradient fields, offset gradient fields and homogeneous fields