High precision single-cluster Monte Carlo measurement of the critical exponents of the classical 3D Heisenberg model

We report measurements of the critical exponents of the classical three-dimensional Heisenberg model on simple cubic lattices of size $L^3$ with $L$ = 12, 16, 20, 24, 32, 40, and 48. The data was obtained from a few long single-cluster Monte Carlo simulations near the phase transition. We compute high precision estimates of the critical coupling $K_c$, Binder's parameter $U^* and the critical exponents$\nu,\beta / \nu, \eta$, and$\alpha / \nu$, using extensively histogram reweighting and optimization techniques that allow us to keep control over the statistical errors. Measurements of the autocorrelation time show the expected reduction of critical slowing down at the phase transition as compared to local update algorithms. This allows simulations on significantly larger lattices than in previous studies and consequently a better control over systematic errors in finite-size scaling analyses.Comment: 4 pages, (contribution to the Lattice92 proceedings) 1 postscript file as uufile included. Preprints FUB-HEP 21/92 and HLRZ 89/92. (note: first version arrived incomplete due to mailer problems

Longboard classification using Machine Learning

There are several techniques a rider can choose from that they can perform being distributed along the long-board ride. This research aims to create a machine-learning model that can efficiently classify these techniques at different periods of time using raw acceleration data. This paper presents the complete workflow of the application. This application involves analytical geometry, multidimensional calculus, and linear algebra and can be used to visualize and normalize time-invariant object paths. This model focuses on displacement data calculated from raw acceleration data and gyro sensor data from a smartphone application called Physics Toolbox Sensor Suite . We extracted features from each dynamic window of time in the displacement data and then fed them into machine learning algorithms with various statistical features, including supervised learning classifiers and Long short-term memory. We found that the Decision Tree with post-pruning produces a performance 93.4%, and the Random Forest produces a performance 96.8%. Although Decision Tree works faster than Random Forest, we ultimately used Random Forest classifier in our application, since the application does not perform prediction and classification in real-time

The unexpected importance of mosquito oviposition behaviour for malaria: non-productive larval habitats can be sources for malaria transmission

BACKGROUND: Mosquitoes commute between blood-meal hosts and water. Thus, heterogeneity in human biting reflects underlying spatial heterogeneity in the distribution and suitability of larval habitat as well as inherent differences in the attractiveness, suitability and distribution of blood-meal hosts. One of the possible strategies of malaria control is to identify local vector species and then attack water bodies that contain their larvae. METHODS: Biting and host seeking, not oviposition, have been the focus of most previous studies of mosquitoes and malaria transmission. This study presents a mathematical model that incorporates mosquito oviposition behaviour. RESULTS: The model demonstrates that oviposition is one potential factor explaining heterogeneous biting and vector distribution in a landscape with a heterogeneous distribution of larval habitat. Adult female mosquitoes tend to aggregate around places where they oviposit, thereby increasing the risk of malaria, regardless of the suitability of the habitat for larval development. Thus, a water body may be unsuitable for adult mosquito emergence, but simultaneously, be a source for human malaria. CONCLUSION: Larval density may be a misleading indicator of a habitat's importance for malaria control. Even if mosquitoes could be lured to oviposit in sprayed larval habitats, this would not necessarily mitigate – and might aggravate – the risk of malaria transmission. Forcing mosquitoes to fly away from humans in search of larval habitat may be a more efficient way to reduce the risk of malaria than killing larvae. Thus, draining, fouling, or filling standing water where mosquitoes oviposit can be more effective than applying larvicide

Two Types of Dynamic Cool Coronal Structures Observed with STEREO and HINODE

Solar coronal loops show significant plasma motions during their formation and eruption stages. Dynamic cool coronal structures, on the other hand, are often observed to propagate along coronal loops. In this paper, we report on the discovery of two types of dynamic cool coronal structures, and characterize their fundamental properties. Using the EUV 304 angstrom images from the Extreme UltraViolet Imager (EUVI) telescope on the Solar TErrestrial RElation Observatory (STEREO) and the Ca II filtergrams from the Solar Optical Telescope (SOT) instrument on HINODE, we study the evolution of an EUV arch and the kinematics of cool coronal structures. The EUV 304 angstrom observations show that a missile-like plasmoid moves along an arch-shaped trajectory, with an average velocity of 31 km/s. About three hours later, a plasma arch forms along the trajectory, subsequently the top part of the arch fades away and disappears, meanwhile the plasma belonging to the two legs of the arch flows downward to the arch feet. During the arch formation and disappearance, SOT Ca II images explore dynamic cool coronal structures beneath the arch. By tracking these structures, we classify them into two types. Type I is threadlike in shape and flows downward with a greater average velocity of 72 km/s, finally it combines a loop fibril at chromospheric altitude. Type II is shape-transformable and sometimes rolling as it flows downward with a smaller velocity of 37 km/s, then disappears insularly in the chromosphere. It is suggested that the two types of structures are possibly controlled by different magnetic configurations.Comment: 13 pages, 7 figures, accepted by RA

Finite-Size Scaling Study of the Three-Dimensional Classical Heisenberg Model

We use the single-cluster Monte Carlo update algorithm to simulate the three-dimensional classical Heisenberg model in the critical region on simple cubic lattices of size $L^3$ with $L=12, 16, 20, 24, 32, 40$, and $48$. By means of finite-size scaling analyses we compute high-precision estimates of the critical temperature and the critical exponents, using extensively histogram reweighting and optimization techniques. Measurements of the autocorrelation time show the expected reduction of critical slowing down at the phase transition. This allows simulations on significantly larger lattices than in previous studies and consequently a better control over systematic errors in finite-size scaling analyses.Comment: 9 pages, FUB-HEP 9/92, HLRZ Preprint 56/92, August 199

High-Temperature Series Analyses of the Classical Heisenberg and XY Model

Although there is now a good measure of agreement between Monte Carlo and high-temperature series expansion estimates for Ising ($n=1$) models, published results for the critical temperature from series expansions up to 12{\em th} order for the three-dimensional classical Heisenberg ($n=3$) and XY ($n=2$) model do not agree very well with recent high-precision Monte Carlo estimates. In order to clarify this discrepancy we have analyzed extended high-temperature series expansions of the susceptibility, the second correlation moment, and the second field derivative of the susceptibility, which have been derived a few years ago by L\"uscher and Weisz for general $O(n)$ vector spin models on $D$-dimensional hypercubic lattices up to 14{\em th} order in $K \equiv J/k_B T$. By analyzing these series expansions in three dimensions with two different methods that allow for confluent correction terms, we obtain good agreement with the standard field theory exponent estimates and with the critical temperature estimates from the new high-precision MC simulations. Furthermore, for the Heisenberg model we reanalyze existing series for the susceptibility on the BCC lattice up to 11{\em th} order and on the FCC lattice up to 12{\em th} order.Comment: 15 pages, Latex, 2 PS figures not included. FUB-HEP 18/92 and HLRZ 76/9

A multimodal approach to understanding motor impairment and disability after stroke

Many different measures have been found to be related to behavioral outcome after stroke. Preclinical studies emphasize the importance of brain injury and neural function. However, the measures most important to human outcomes remain uncertain, in part because studies often examine one measure at a time or enroll only mildly impaired patients. The current study addressed this by performing multimodal evaluation in a heterogeneous population. Patients (n = 36) with stable arm paresis 3-6 months post-stroke were assessed across 6 categories of measures related to stroke outcome: demographics/medical history, cognitive/mood status, genetics, neurophysiology, brain injury, and cortical function. Multivariate modeling identified measures independently related to an impairment-based outcome (arm Fugl-Meyer motor score). Analyses were repeated (1) identifying measures related to disability (modified Rankin Scale score), describing independence in daily functions and (2) using only patients with mild deficits. Across patients, greater impairment was related to measures of injury (reduced corticospinal tract integrity) and neurophysiology (absence of motor evoked potential). In contrast, (1) greater disability was related to greater injury and poorer cognitive status (MMSE score) and (2) among patients with mild deficits, greater impairment was related to cortical function (greater contralesional motor/premotor cortex activation). Impairment after stroke is most related to injury and neurophysiology, consistent with preclinical studies. These relationships vary according to the patient subgroup or the behavioral endpoint studied. One potential implication of these results is that choice of biomarker or stratifying variable in a clinical stroke study might vary according to patient characteristics. © 2014 Springer-Verlag Berlin Heidelberg