It is the habit not the handle that affects tooth brushing - a randomised counterbalanced cross over study with young and healthy adults.

OBJECTIVE: To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness. METHODS: This is a randomized counterbalanced cross-over study. N = 50 university students and employees brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants also rated their self-perceived oral cleanliness and directly compared the two brushes regarding their handling and compared them to the brushed they used at home. RESULTS: The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042). DISCUSSION: The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.

Transport Efficiency of Biofunctionalized Magnetic Particles Tailored by Surfactant Concentration.

Controlled transport of surface-functionalized magnetic beads in a liquid medium is a central requirement for the handling of captured biomolecular targets in microfluidic lab-on-chip biosensors. Here, the influence of the physiological liquid medium on the transport characteristics of functionalized magnetic particles and on the functionality of the coupled protein is studied. These aspects are theoretically modeled and experimentally investigated for prototype superparamagnetic beads, surface-functionalized with green fluorescent protein immersed in buffer solution with different concentrations of a surfactant. The model reports on the tunability of the steady-state particle substrate separation distance to prevent their surface sticking via the choice of surfactant concentration. Experimental and theoretical average velocities are discussed for a ratchet-like particle motion induced by a dynamic external field superposed on a static locally varying magnetic field landscape. The developed model and experiment may serve as a basis for quantitative forecasts on the functionality of magnetic particle transport-based lab-on-chip devices.

Casimir Effect in MEMS: Materials, Geometries, and Metrologies-A Review.

Casimir force densities, i.e., force per area, become very large if two solid material surfaces come closer together to each other than 10 nm. In most cases, the forces are attractive. In some cases, they can be repulsive depending on the solid materials and the fluid medium in between. This review provides an overview of experimental and theoretical studies that have been performed and focuses on four main aspects: (i) the combinations of different materials, (ii) the considered geometries, (iii) the applied experimental measurement methodologies and (iv) a novel self-assembly methodology based on Casimir forces. Briefly reviewed is also the influence of additional parameters such as temperature, conductivity, and surface roughness. The Casimir effect opens many application possibilities in microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS), where an overview is also provided. The knowledge generation in this fascinating field requires interdisciplinary approaches to generate synergetic effects between technological fabrication metrology, theoretical simulations, the establishment of adequate models, artificial intelligence, and machine learning. Finally, multiple applications are addressed as a research roadmap.

Dataset of a parameterized U-bend flow for deep learning applications.

This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations, five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation, as well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container used to generate the data are published as part of this work.

Reconstruction of incomplete X-ray diffraction pole figures of oligocrystalline materials using deep learning.

X-ray diffraction crystallography allows non-destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X-ray diffraction has been highly time-consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep-learning-based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU-based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and material.

Three-dimensional close-to-substrate trajectories of magnetic microparticles in dynamically changing magnetic field landscapes.

The transport of magnetic particles (MPs) by dynamic magnetic field landscapes (MFLs) using magnetically patterned substrates is promising for the development of Lab-on-a-chip (LOC) systems. The inherent close-to-substrate MP motion is sensitive to changing particle-substrate interactions. Thus, the detection of a modified particle-substrate separation distance caused by surface binding of an analyte is expected to be a promising probe in analytics and diagnostics. Here, we present an essential prerequisite for such an application, namely the label-free quantitative experimental determination of the three-dimensional trajectories of superparamagnetic particles (SPPs) transported by a dynamically changing MFL. The evaluation of defocused SPP images from optical bright-field microscopy revealed a "hopping"-like motion of the magnetic particles, previously predicted by theory, additionally allowing a quantification of maximum jump heights. As our findings pave the way towards precise determination of particle-substrate separations, they bear deep implications for future LOC detection schemes using only optical microscopy.

Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulses.

X-ray free-electron lasers (XFELs) as the world's brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached and entered the attosecond regime, which holds new promises for single-molecule imaging and studying nonlinear and ultrafast phenomena such as localized electron dynamics. The technological evolution of XFELs toward well-controllable light sources for precise metrology of ultrafast processes has been, however, hampered by the diagnostic capabilities for characterizing X-ray pulses at the attosecond frontier. In this regard, the spectroscopic technique of photoelectron angular streaking has successfully proven how to non-destructively retrieve the exact time-energy structure of XFEL pulses on a single-shot basis. By using artificial intelligence techniques, in particular convolutional neural networks, we here show how this technique can be leveraged from its proof-of-principle stage toward routine diagnostics even at high-repetition-rate XFELs, thus enhancing and refining their scientific accessibility in all related disciplines.

Translatory and rotatory motion of exchange-bias capped Janus particles controlled by dynamic magnetic field landscapes.

Magnetic Janus particles (MJPs), fabricated by covering a non-magnetic spherical particle with a hemispherical magnetic in-plane exchange-bias layer system cap, display an onion magnetization state for comparably large diameters of a few microns. In this work, the motion characteristics of these MJPs will be investigated when they are steered by a magnetic field landscape over prototypical parallel-stripe domains, dynamically varied by superposed external magnetic field pulse sequences, in an aqueous medium. We demonstrate, that due to the engineered magnetization state in the hemispherical cap, a comparably fast, directed particle transport and particle rotation can be induced. Additionally, by modifying the frequency of the applied pulse sequence and the strengths of the individual field components, we observe a possible separation between a combined or an individual occurrence of these two types of motion. Our findings bear importance for lab-on-a-chip systems, where particle immobilization on a surface via analyte bridges shall be used for low concentration analyte detection and a particle rotation over a defined position of a substrate may dramatically increase the immobilization (and therefore analyte detection) probability.

On Methodological and Technological Challenges for Proactive Health Management in Smart Homes.

Health management in smart homes has advanced during the last years. With proactive health management in such environments further progress for health prevention and care is to be expected. Challenges for proactive health management in three areas are summarized and briefly discussed: pattern recognition and machine learning, information privacy and user-oriented design, and sensor-enhanced health information systems architectures.

Evolutionary optimization of radial basis function classifiers for data mining applications.

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Online segmentation of time series based on polynomial least-squares approximations.

The paper presents SwiftSeg, a novel technique for online time series segmentation and piecewise polynomial representation. The segmentation approach is based on a least-squares approximation of time series in sliding and/or growing time windows utilizing a basis of orthogonal polynomials. This allows the definition of fast update steps for the approximating polynomial, where the computational effort depends only on the degree of the approximating polynomial and not on the length of the time window. The coefficients of the orthogonal expansion of the approximating polynomial-obtained by means of the update steps-can be interpreted as optimal (in the least-squares sense) estimators for average, slope, curvature, change of curvature, etc., of the signal in the time window considered. These coefficients, as well as the approximation error, may be used in a very intuitive way to define segmentation criteria. The properties of SwiftSeg are evaluated by means of some artificial and real benchmark time series. It is compared to three different offline and online techniques to assess its accuracy and runtime. It is shown that SwiftSeg-which is suitable for many data streaming applications-offers high accuracy at very low computational costs.

Online signature verification with support vector machines based on LCSS kernel functions.

In this paper, a new technique for online signature verification or identification is proposed. The technique integrates a longest common subsequences (LCSS) detection algorithm which measures the similarity of signature time series into a kernel function for support vector machines (SVM). LCSS offers the possibility to consider the local variability of signals such as the time series of pen-tip coordinates on a graphic tablet, forces on a pen, or inclination angles of a pen measured during a signing process. Consequently, the similarity of two signature time series can be determined in a more reliable way than with other measures. A proprietary database with signatures of 153 test persons and the SVC 2004 benchmark database are used to show the properties of the new SVM-LCSS. We investigate its parameterization and compare it to SVM with other kernel functions such as dynamic time warping (DTW). Our experiments show that SVM with the LCSS kernel authenticate persons very reliably and with a performance which is significantly better than that of the best comparing technique, SVM with DTW kernel.

Processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks.

Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning.