Drawing Direction Effect on a Task’s Performance Characteristics among People with Essential Tremor

Essential tremor (ET) is a common movement disorder affecting the performance of various daily tasks, including drawing. While spiral-drawing task characteristics have been described among patients with ET, research about the significance of the drawing direction of both spiral and lines tasks on the performance process is scarce. This study mapped inter-group differences between people with ET and controls related to drawing directions and the intra-effect of the drawing directions on the tremor level among people with ET. Twenty participants with ET and eighteen without ET drew spirals and vertical and horizontal lines on a digitizer with an inking pen. Time-based outcome measures were gathered to address the effect of the drawing directions on tremor by analyzing various spiral sections and comparing vertical and horizontal lines. Significant group differences were found in deviation of the spiral radius from a filtered radius curve and in deviation of the distance curve from a filtered curve for both line types. Significant differences were found between defined horizontal and vertical spiral sections within each group and between both line types within the ET group. A significant correlation was found between spiral and vertical line deviations from filtered curve outcome measures. Achieving objective measures about the significance of drawing directions on actual performance may support the clinical evaluation of people with ET toward developing future intervention methods for improving their functional abilities.

Liquid Force and Rupture Distance between Two Particles

The study of liquid force has a special meaning to industrial manufacturing. By taking the liquid bridges between equal and unequal particles as objects, the liquid force-displacement curves were measured and recorded by using a novel Nano UTM T150 tensile system. The influences of diameter, diameter ratio, liquid volume, and the surface tension on the liquid force-distance curve, the maximum liquid force, and rupture distance were compared and sorted. The results show that the maximum liquid force and rupture distance both increase with the increase in liquid volume, particle diameter, diameter ratio, and surface tension. The diameter plays a decisive role in determining the value of the maximum liquid force compared with surface tension and liquid volume, which only influence the force value in a local range. The rupture distance shows a positive correlation with liquid volume and surface tension and a negative correlation with the diameter or diameter ratio. The maximum liquid force between unequal particles is about half of the sum of the force between the equal spheres of larger and smaller size in that system.

MONG: An extension to Galaxy Clusters

The presence of dark matter, though well established by indirect evidence is yet to be observed directly. Various dark matter detection experiments running for several years have yielded no positive results so far. In view of these negative results, we had earlier proposed alternate models by postulating a minimum gravitational field strength (minimum curvature) and also a minimum acceleration. These postulates led to the modified Newtonian dynamics and modified Newtonian gravity (MONG). The observed flat rotation curves of galaxies had also been accounted for through these postulates. Here we extend these postulates to galaxy clusters and model the dynamical velocity-distance curves for such large-scale structures. The velocity-distance curve of the Virgo cluster, plotted with this model is found to be in accordance with that observed.

Observations on the Instrumental Measurements of Liquid Food Stickiness

While we encounter sticky liquids in our daily life and are able to discriminate between them, instrumental measurements of stickiness are difficult to match to those that relate to our perception. In this paper, we examine some of the factors that influence instrumental measurements of stickiness in liquid foods. The shortcomings of using the maximum peak or the area under the curve are discussed, and a hitherto unused measure, the gradient of the force–distance curve, is suggested as a measure of tension per unit contact area. The zero-perimeter virtual probe, which compensates for the changing meniscus and mass of liquid below it, is introduced. This zero-perimeter approach allows us to extrapolate measures of stickiness such as the gradient of the force–distance curve or the area below that curve. Despite the zero-perimeter correction, there is still a speed dependency on results from instrumentally measured stickiness (for all indexes considered). The speed of the test is responsible for the type of failure (cohesive or adhesive) reported by other authors.

Saturation of Energy Levels of the Hydrogen Atom in Strong Magnetic Field

We demonstrate that the finiteness of the limiting values of the lower energy levels of a hydrogen atom under an unrestricted growth of the magnetic field, into which this atom is embedded, is achieved already when the vacuum polarization (VP) is calculated in the magnetic field within the approximation of the local action of Euler–Heisenberg. We find that the mechanism for this saturation is different from the one acting, when VP is calculated via the Feynman diagram in the Furry picture. We study the effective potential that appears when the adiabatic (diagonal) approximation is exploited for solving the Schrödinger equation for the longitudinal degree of freedom of the electron on the lowest Landau level in the atom. We find that the (effective) potential of a point-like charge remains nonsingular thanks to the growing screening provided by VP. The regularizing length turns out to be α/3π¯λC, where ¯λC is the electron Compton length. The family of effective potentials, labeled by growing values of the magnetic field condenses towards a certain limiting, magnetic-field-independent potential-distance curve. The limiting values of even ground-state energies are determined for four magnetic quantum numbers using the Karnakov–Popov method.

FORWARD MODELLING METODE GAYABERAT DENGAN MODEL INTRUSI DAN PATAHAN MENGGUNAKAN OCTAVE

The gravity method is a geophysical exploration method to measure variations in the acceleration of gravity on the surface of the earth in response to variations in rocks that exist beneath the surface. In gravity exploration requires a preliminary picture as a reference for measurement. This study aims to make forward modeling synthetic OCTAVE based using synthetic data on subsurface rock structures, so as to produce intrusion and fracture models based on differences in the value of the acceleration of gravity from one point to another on the surface of the earth. Synthetic modeling with the geological parameter approach of the study area is based on variations in the price of rock density. The model parameters used in intrusion modeling are the density value of 2.7 g/cm3 and the depth of 850 meters while the fracture modeling uses a density value of 2.7 g/cm3 with a depth of 350 meters and 360 meters and a thickness of 500 meters. From intrusion modeling, the gravity vertical component of attraction force is 0.03 mGal and in the fracture modeling the gravity vertical component of attraction force is 0.0565 mGal. Based on the results of this modeling, distance curve vs. gravity anomaly response is obtained for both cases. In the intrusion rock model obtained by the profile model with an open type down. While the fracture modeling is obtained anomalous profile curve variation which states that in the fracture area with a significant change in the direction of the curve.

Quantification of amyloid fibril polymorphism by nano-morphometry reveals the individuality of filament assembly

Amyloid fibrils are highly polymorphic structures formed by many different proteins. They provide biological function but also abnormally accumulate in numerous human diseases. The physicochemical principles of amyloid polymorphism are not understood due to lack of structural insights at the single-fibril level. To identify and classify different fibril polymorphs and to quantify the level of heterogeneity is essential to decipher the precise links between amyloid structures and their functional and disease associated properties such as toxicity, strains, propagation and spreading. Employing gentle, force-distance curve-based AFM, we produce detailed images, from which the 3D reconstruction of individual filaments in heterogeneous amyloid samples is achieved. Distinctive fibril polymorphs are then classified by hierarchical clustering, and sample heterogeneity is objectively quantified. These data demonstrate the polymorphic nature of fibril populations, provide important information regarding the energy landscape of amyloid self-assembly, and offer quantitative insights into the structural basis of polymorphism in amyloid populations.

Direct Measurement of Adhesion Force of Individual Aerosol Particles by Atomic Force Microscopy

A new method, namely, force–distance curve mapping, was developed to directly measure the adhesion force of individual aerosol particles by atomic force microscopy. The proposed method collects adhesion force from multiple points on a single particle. It also takes into account the spatial distribution of the adhesion force affected by topography (e.g., the variation in the tip angle relative to the surface, as well as the force imposed upon contact), thereby enabling the direct and quantitative measurement of the adhesion force representing each particle. The topographic effect was first evaluated by measuring Polystyrene latex (PSL) standard particles, and the optimized method was then applied on atmospherically relevant model dust particles (quartz, ATD, and CJ-1) and inorganic particles (ammonium sulfate and artificial sea salt) to inter-compare the adhesion forces among different aerosol types. The method was further applied on the actual ambient aerosol particles collected on the western coast of Japan, when the region was under the influence of Asian dust plume. The ambient particles were classified into sea salt (SS), silicate dust, and Ca-rich dust particles based on individual particle analysis (micro-Raman or Scanning Electron Microscope/Energy Dispersive X-ray Spectroscopy (SEM-EDX)). Comparable adhesion forces were obtained from the model and ambient particles for both SS and silicate dust. Although dust particles tended to show smaller adhesion forces, the adhesion force of Ca-rich dust particles was larger than the majority of silicate dust particles and was comparable with the inorganic salt particles. These results highlight that the original chemical composition, as well as the aging process in the atmosphere, can create significant variation in the adhesion force among individual particles. This study demonstrates that force–distance curve mapping can be used as a new tool to quantitatively characterize the physical properties of aerosol particles on an individual basis.

Supervised Machine Learning for Plants Identification Based on Images of Their Leaves

Botanists study in general the characteristics of leaves to give to each plant a scientific name; such as shape, margin...etc. This paper proposes a comparison of supervised plant identification using different approaches. The identification is done according to three different features extracted from images of leaves: a fine-scale margin feature histogram, a Centroid Contour Distance Curve shape signature and an interior texture feature histogram. First represent each leaf by one feature at a time in, then represent leaves by two features, and each leaf was represented by the three features. After that, the authors classified the obtained vectors using different supervised machine learning techniques; the used techniques are Decision tree, Naïve Bayes, K-nearest neighbour, and neural network. Finally, they evaluated the classification using cross validation. The main goal of this work is studying the influence of representation of leaves' images on the identification of plants, and also studying the use of supervised machine learning algorithm for plant leaves classification.