3D Restoration of the Sugar Hollow Rift Basin Blue Ridge, Virginia

The tectonic processes of supercontinent breakup create rift structures that are often preserved along the passive margin of continents. The resultant structures and rocks are the foundation from which collisional orogenic structures are eventually created. At Sugar Hollow, 15km northwest of Charlottesville, Virginia, ancient Iapetan rift structures are preserved on the eastern margin of Laurentia in the Virginia Blue Ridge. This location preserves a ~10km2 eastward-thickening graben complex consisting of 8 originally-normal faults that reaches a maximum thickness of ~300m. Some of the 8 faults were reactivated past the null point during the Paleozoic producing apparent thrust geometry. To better understand the original structures that accommodated the opening of the Iapetan Ocean at the close of the Neoproterozoic, the basin was restored to its post-rift state using Midland Valley\u27s Move software. During this process, layers were unfolded and fault blocks were restored to their maximum extensional state, revealing ~12% shortening. However, penetrative ductile deformation was not accounted for during this restoration. Therefore, strain and vorticity analysis were used to better understand the intensity and geometry of ductile deformation across the basin. Using this data, a fully restored 3D model of the Sugar Hollow basin was created. This model revealed additional shortening of ~13%, suggesting that penetrative strain may be at least as important of a restoration consideration as faulting and folding. Consequently, strain and vorticity analysis should be integrated into cross section restoration whenever possible

Statistical Geometry of Packing Defects of Lattice Chain Polymer from Enumeration and Sequential Monte Carlo Method

Voids exist in proteins as packing defects and are often associated with protein functions. We study the statistical geometry of voids in two-dimensional lattice chain polymers. We define voids as topological features and develop a simple algorithm for their detection. For short chains, void geometry is examined by enumerating all conformations. For long chains, the space of void geometry is explored using sequential Monte Carlo importance sampling and resampling techniques. We characterize the relationship of geometric properties of voids with chain length, including probability of void formation, expected number of voids, void size, and wall size of voids. We formalize the concept of packing density for lattice polymers, and further study the relationship between packing density and compactness, two parameters frequently used to describe protein packing. We find that both fully extended and maximally compact polymers have the highest packing density, but polymers with intermediate compactness have low packing density. To study the conformational entropic effects of void formation, we characterize the conformation reduction factor of void formation and found that there are strong end-effect. Voids are more likely to form at the chain end. The critical exponent of end-effect is twice as large as that of self-contacting loop formation when existence of voids is not required. We also briefly discuss the sequential Monte Carlo sampling and resampling techniques used in this study.Comment: 29 pages, including 12 figure

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

Nonclassicality of pure two-qutrit entangled states

We report an exhaustive numerical analysis of violations of local realism by two qutrits in all possible pure entangled states. In Bell type experiments we allow any pairs of local unitary U(3) transformations to define the measurement bases. Surprisingly, Schmidt rank-2 states, resembling pairs of maximally entangled qubits, lead to the most noise-robust violations of local realism. The phenomenon seems to be even more pronounced for four and five dimensional systems, for which we tested a few interesting examples.Comment: 6 pages, journal versio

Writing with non-dominant hand: left-handers perform better with the right hand than right handers with the left

Adult volunteers (7 females, 7 males) aged between 19 and 51 years, 7 right-handers and 7 left-handers, were asked to complete re-training writing tasks by using their non-dominant hand over 10 consecutive days. It is possible for adults to learn quickly to write legibly with their non-dominant hand. Left handers have a higher legibility score initially although right-handers improved with training more than left-handers. Individual’s performance was unrelated to age and sex in the small sample studied.Kristina Laskowski and Maciej Henneber

Differences in Iron Removal from Carbon Nanoonions and Multiwall Carbon Nanotubes for Analytical Purpose

The paper describes the differences between wet iron removal from carbon nanoonions and from multiwall carbon nanotubes for analytical purpose. Nowadays, both carbon nanoonions and multiwall carbon nanotubes are one of the most interesting materials with applicability in electronics, medicine and biotechnology. Medical applications of those nanomaterials require not only recognition of their structure but also measurement of metal impurities concentration. Inductively coupled plasma optical emission spectrometry as a method for Fe-determination requires liquid samples. Hence, we propose various protocols for leaching of iron from studied materials. Our results proved that structure of nanomaterials have an impact on the efficiency of iron removal

Differences in Iron Removal from Carbon Nanoonions and Multiwall Carbon Nanotubes for Analytical Purpose

The paper describes the differences between wet iron removal from carbon nanoonions and from multiwall carbon nanotubes for analytical purpose. Nowadays, both carbon nanoonions and multiwall carbon nanotubes are one of the most interesting materials with applicability in electronics, medicine and biotechnology. Medical applications of those nanomaterials require not only recognition of their structure but also measurement of metal impurities concentration. Inductively coupled plasma optical emission spectrometry as a method for Fe-determination requires liquid samples. Hence, we propose various protocols for leaching of iron from studied materials. Our results proved that structure of nanomaterials have an impact on the efficiency of iron removal

Direct numerical simulations of turbulent flow through a stationary and rotating infinite serpentine passage

Serpentine passages are found in a number of engineering applications including turbine blade cooling passages. The design of effective cooling passages for high-temperature turbine blades depends in part on the ability to predict heat transfer, thus requiring an accurate representation of the turbulent flow field. These passages are subjected to strong curvature and rotational effects, and the resulting turbulent flow field is fairly complex. An understanding of the flow physics for flows with strong curvature and rotation is required in order to improve the design of turbine blade cooling passages. Experimental measurements of certain turbulence quantities for such configurations can be challenging to obtain, especially near solid surfaces, making the serpentine passage an ideal candidate for a direct numerical simulation (DNS). A DNS study has been conducted to investigate the coupled effect of strong curvature and rotation by simulating turbulent flow through a fully developed, smooth wall, round-ended, isothermal serpentine channel subjected to orthogonal mode rotation. The geometry investigated has an average radius of curvature Rc/δ=2.0 in the curved section and dimensions 12πδ×2δ×3πδ in the streamwise, transverse, and spanwise directions. The computational domain consists of periodic inflow/outflow boundaries, two solid wall boundaries, and periodic boundaries in the spanwise direction. The simulations were conducted for Reynolds number, Reb=5600, and rotation numbers, Rob,z=0 and 0.32. Differences observed between the stationary and rotating cases are discussed in terms of the mean velocity, secondary flow, and Reynolds stresses