Critical evaluation of the computational methods used in the forced polymer translocation

In forced polymer translocation, the average translocation time, $\tau$, scales with respect to pore force, $f$, and polymer length, $N$, as $\tau \sim f^{-1} N^{\beta}$. We demonstrate that an artifact in Metropolis Monte Carlo method resulting in breakage of the force scaling with large $f$ may be responsible for some of the controversies between different computationally obtained results and also between computational and experimental results. Using Langevin dynamics simulations we show that the scaling exponent $\beta \le 1 + \nu$ is not universal, but depends on $f$. Moreover, we show that forced translocation can be described by a relatively simple force balance argument and $\beta$ to arise solely from the initial polymer configuration

Polymer scaling and dynamics in steady-state sedimentation at infinite Péclet number

We consider the static and dynamical behavior of a flexible polymer chain under steady-state sedimentation using analytic arguments and computer simulations. The model system comprises a single coarse-grained polymer chain of N segments, which resides in a Newtonian fluid as described by the Navier-Stokes equations. The chain is driven into nonequilibrium steady state by gravity acting on each segment. The equations of motion for the segments and the Navier-Stokes equations are solved simultaneously using an immersed boundary method, where thermal fluctuations are neglected. To characterize the chain conformation, we consider its radius of gyration RG(N). We find that the presence of gravity explicitly breaks the spatial symmetry leading to anisotropic scaling of the components of RG with N along the direction of gravity RG,∥ and perpendicular to it RG,⊥, respectively. We numerically estimate the corresponding anisotropic scaling exponents ν∥≈0.79 and ν⊥≈0.45, which differ significantly from the equilibrium scaling exponent νe=0.588 in three dimensions. This indicates that on the average, the chain becomes elongated along the sedimentation direction for large enough N. We present a generalization of the Flory scaling argument, which is in good agreement with the numerical results. It also reveals an explicit dependence of the scaling exponents on the Reynolds number. To study the dynamics of the chain, we compute its effective diffusion coefficient D(N), which does not contain Brownian motion. For the range of values of N used here, we find that both the parallel and perpendicular components of D increase with the chain length N, in contrast to the case of thermal diffusion in equilibrium. This is caused by the fluid-driven fluctuations in the internal configuration of the polymer that are magnified as polymer size becomes larger.Peer reviewe

Measurements of muon flux in the Pyh\"asalmi underground laboratory

The cosmic-ray induced muon flux was measured at several depths in the Pyh\"asalmi mine (Finland) using a plastic scintillator telescope mounted on a trailer. The flux was determined at four different depths underground at 400 m (980 m.w.e), at 660 m (1900 m.w.e), at 990 m (2810 m.w.e) and at 1390 m (3960 m.w.e) with the trailer, and also at the ground surface. In addition, previously measured fluxes from depths of 90 m (210 m.w.e) and 210 m (420 m.w.e) are shown. A relation was obtained for the underground muon flux as a function of the depth. The measured flux follows well the general behaviour and is consistent with results determined in other underground laboratories.Comment: 8 pages, 2 figures. Submitted to Nuclear Instrum. Methods

Dynamics of forced biopolymer translocation

We present results from our simulations of biopolymer translocation in a solvent which explain the main experimental findings. The forced translocation can be described by simple force balance arguments for the relevant range of pore potentials in experiments and biological systems. Scaling of translocation time with polymer length varies with pore force and friction. Hydrodynamics affects this scaling and significantly reduces translocation times.Comment: Published in: http://www.iop.org/EJ/article/0295-5075/85/5/58006/epl_85_5_58006.htm

NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING

Asset management systems are beneficial for maintaining building infrastructure and can be used to keep up-to-date records of relevant safety assets, such as smoke detectors, exit signs, and fire extinguishers. Existing methods for locating and identifying these assets in buildings primarily rely on surveys and images, which only provide 2D locations and can be tedious for large-scale structures. Indoor point clouds, which can be captured quickly for buildings using mobile scanning techniques, can provide us with 3D asset locations. In this paper, we study the feasibility of using 3D point clouds of buildings combined with deep learning techniques to identify safety-related assets, particularly small-sized assets like fire switches and exit signs. We adopt the state-of-the-art Deep Learning network, Kernel Point-Fully Convolutional Network (KP-FCNN), to identify these assets through semantic segmentation. Using the obtained results, we create a 3D-geometry model of the building with assets pinpointed, providing scene semantics and delivering more value. Our method is tested using three different point cloud datasets obtained from a depth camera, a mobile laser scanner, and an iPhone lidar sensor