Brownian molecular motors driven by rotation-translation coupling

We investigated three models of Brownian motors which convert rotational diffusion into directed translational motion by switching on and off a potential. In the first model a spatially asymmetric potential generates directed translational motion by rectifying rotational diffusion. It behaves much like a conventional flashing ratchet. The second model utilizes both rotational diffusion and drift to generate translational motion without spatial asymmetry in the potential. This second model can be driven by a combination of a Brownian motor mechanism (diffusion driven) or by powerstroke (drift driven) depending on the chosen parameters. In the third model, elements of both the Brownian motor and powerstroke mechanisms are combined by switching between three distinct states. Relevance of the model to biological motor proteins is discussed.Comment: 11 pages, 8 figure

The halo mass function through the cosmic ages

In this paper we investigate how the halo mass function evolves with redshift, based on a suite of very large (with N_p = 3072^3 - 6000^3 particles) cosmological N-body simulations. Our halo catalogue data spans a redshift range of z = 0-30, allowing us to probe the mass function from the dark ages to the present. We utilise both the Friends-of-Friends (FOF) and Spherical Overdensity (SO) halofinding methods to directly compare the mass function derived using these commonly used halo definitions. The mass function from SO haloes exhibits a clear evolution with redshift, especially during the recent era of dark energy dominance (z < 1). We provide a redshift-parameterised fit for the SO mass function valid for the entire redshift range to within ~20% as well as a scheme to calculate the mass function for haloes with arbitrary overdensities. The FOF mass function displays a weaker evolution with redshift. We provide a `universal' fit for the FOF mass function, fitted to data across the entire redshift range simultaneously, and observe redshift evolution in our data versus this fit. The relative evolution of the mass functions derived via the two methods is compared and we find that the mass functions most closely match at z=0. The disparity at z=0 between the FOF and SO mass functions resides in their high mass tails where the collapsed fraction of mass in SO haloes is ~80% of that in FOF haloes. This difference grows with redshift so that, by z>20, the SO algorithm finds a ~50-80% lower collapsed fraction in high mass haloes than does the FOF algorithm, due in part to the significant over-linking effects known to affect the FOF method.Comment: v4, 16 pages, 16 colour figures. Changed to match MNRAS print version. NOTE: v1 of this paper has a typo in the fitting function. Please ensure you use the latest versio

Implementation of a Simultaneous Localization and Mapping Algorithm in an Autonomous Robot

A robot was built and programmed to implement a Simultaneous Localization and Mapping (SLAM) Algorithm. Traditional robotic mapping suffers from compounding sensor error, thus resulting in maps that become highly erroneous over time. SLAM combats this problem by taking a probabilistic approach to mapping. By combining odometry data with sensor measurements of surrounding landmarks through a Kalman Filter, the robot was able to accurately map its surrounding environment, and localize itself within that environment

Weird weather in Bristol during the Grindelwald Fluctuation (1560-1630 CE)

The Grindelwald Fluctuation (1560–1630) was a cooling phase during the ‘Little Ice Age’ (c.1300–1850). Poor weather during the Fluctuation contributed to harvest failures, mass starvation and political crises across the globe. This paper examines information taken from Bristol chronicles that discuss some of the extreme weather events of the period. The entries support the notion that the Grindelwald Fluctuation featured some extraordinarily poor weather, such as great frosts, floods, severe storms, unseasonal snowfalls and droughts

crs: A package for nonparametric spline estimation in R

crs is a library for R written by Jeffrey S. Racine (Maintainer) and Zhenghua Nie. This add-on package provides a collection of functions for spline-based nonparametric estimation of regression functions with both continuous and categorical regressors. Currently, the crs package integrates data-driven methods for selecting the spline degree, the number of knots and the necessary bandwidths for nonparametric conditional mean, IV and quantile regression. A function for multivariate density spline estimation with mixed data is also currently in the works. As a bonus, the authors have also provided the first simple R interface to the NOMAD (‘nonsmooth mesh adaptive direct search’) optimization solver which can be applied to solve other mixed integer optimization problems that future users might find useful in other settings. Although the crs package shares some of the same functionalities as its kernel-based counterpart—the np package by the same author—it currently lacks some of the features the np package provides, such as hypothesis testing and semiparametric estimation. However, what it lacks in breadth, crs makes up in speed. A Monte Carlo experiment in this review uncovers sizable speed gains compared to its np counterpart, with a marginal loss in terms of goodness of fit. Therefore, the package will be extremely useful for applied econometricians interested in employing nonparametric techniques using large amounts of data with a small number of discrete covariates

Data Science in Stata 16: Frames, Lasso, and Python Integration

Stata is one of the most widely used software for data analysis, statistics, and model fitting by economists, public policy researchers, epidemiologists, among others. Stata's recent release of version 16 in June 2019 includes an up-to-date methodological library and a user-friendly version of various cutting edge techniques. In the newest release, Stata has implemented several changes and additions that include:• Lasso• Multiple data sets in memory• Meta-analysis• Choice models• Python integration• Bayes-multiple chains• Panel-data ERMs• Sample-size analysis for CIs• Panel-data mixed logit• Nonlinear DSGE models• Numerical integrationThis review covers the most salient innovations in Stata 16. It is the first release that brings along an implementation of machine-learning tools. The three innovations we considered are: (1) Multiple data sets in Memory, (2) Lasso for causal inference, and (3) Python integration

Maintenance Task Classification: Towards Automated Robotic Maintenance for Industry

AbstractThe business model of high-value capital assets is shifting from purchasing a physical product to acquiring a result or a function supported by the product combined with a number of related services. One such service, maintenance, is perhaps the most efficient way to keep the function available during the product lifecycle. Automation has played a vital role in industry throughout history, particularly within the production line. With the movement towards providing product service systems the need for services such as maintenance are increasingly important for a manufactured product, and the pull towards automation may drive down costs and improve performance time. Although currently robotic applications to maintenance beyond monitoring and inspection tasks are not common, this research aims at exploring the feasibility of future maintenance robots that can perform a variety of maintenance tasks. As its first step, this work looks first at investigation, cataloging and classification of a number of maintenance tasks using standard industrial engineering techniques such as time motion, method or workflow analysis. This involves decomposing the maintenance work into a number of ‘unit tasks’ required to be performed in order to accomplish the specified maintenance

The effect of reionization on direct measurements of the mean free path

Recent measurements of the ionizing photon mean free path (MFP) based on composite quasar spectra may point to a late end to reionization at $z<6$. These measurements are challenging, however, because they rely on assumptions about the proximity zones of the quasars in the analysis. For example, some of the $z\sim 6$ quasars in the composite might have been close to large-scale regions where reionization was still ongoing ("neutral islands"), and it is unclear how this would affect the measurements. We address the question here with mock MFP measurements from radiative transfer simulations. We find that, even in the presence of neutral islands, the inferred MFP tracks to within $30 \%$ the true attenuation scale of the spatially averaged IGM, which includes opacity from both the ionized medium and the islands. During reionization, this scale is always shorter than the MFP in the ionized medium. The inferred MFP is sensitive at the $< 50\%$ level to assumptions about the quasar environments and lifetimes for realistic models. We demonstrate that future analyses with improved data may require explicitly modeling the effects of neutral islands on the composite spectra, and we outline a method for doing this. Lastly, we quantify the effects of neutral islands on Lyman-series transmission, which has been modeled with optically thin simulations in previous MFP analyses. Neutral islands can suppress transmission at $\lambda_{\rm rest} < 912$ \r{A} significantly, up to a factor of 2 for $z_{\rm qso} = 6$ in a plausible reionization scenario, owing to absorption by many closely spaced lines as quasar light redshifts into resonance. However, the suppression is almost entirely degenerate with the spectrum normalization, thus does not significantly bias the inferred MFP.Comment: 11 pages, 8 figures, submitted to MNRA

Inhibition versus switching deficits in different forms of rumination

ABSTRACT-Individuals who depressively ruminate about their current dysphoria tend to perseverate more than nonruminators. The goal of the current study was to determine whether such perseverative tendencies are associated with an inability to switch attention away from old to new information or with an inability to effectively inhibit the processing of previously relevant information. We used a task-switching paradigm that can distinguish between these two processes. Two experiments showed that depressive rumination is associated with a deficit in inhibiting prior mental sets. The second experiment also demonstrated that, in contrast to depressive rumination, angry and intellectual rumination are associated with difficulties in switching to a new task set, but not with inhibition of a prior task set. This study suggests that different forms of rumination are associated with different cognitive mechanisms and that both deficits may contribute to the perseveration that is associated with ruminative tendencies