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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.