Quantized wrinkles and fracture of stiff membranes on soft films

Stiff membranes on soft substrates may wrinkle and fold during compression1-11, but the strong post-buckling nonlinearity3,12 and the propensity of overall bending of these systems4,9,11 under large compression make the intriguing morphological evolution ill-controlled and less understood. Here, we present a simple peeling strategy that controllably makes stiff nanomembranes on soft microfilms wrinkled, then folded with a preset period, and ultimately fractured into regular ribbons. The fold and fracture periods exhibit a quantized, stepped dependence on the microfilm thickness, with the period doubled per step. The controlled wrinkle-to-fold-to-fractures transitions can be quantified by both computations and a scaling law, showing generality to different forms of compressive loading. This quantized wrinkle evolution deepens our understanding of complex behaviors of such natural and artificial systems as cerebral cortexes, skins, and coating materials, and opens a way to advanced manufacturing by fracturing large-area nanomembranes into uniformly shaped microflakes.

Assembly and Disassembly Mechanics of a Cylindrical Snap Fit

Snap fit is a common mechanical mechanism. It uses the physical asymmetry that is easy to assemble but difficult to disassemble to provide a simple and fast link between objects. The ingenious combination of geometric shape, bending elasticity and friction of the snap fit is the mechanism behind the easy to assemble but difficult to disassemble disassemble. Yoshida and Wada (2020) has done a groundbreaking work in the analysis of the elastic snap fit. During our study of their paper, while we really enjoyed their research, unfortunately we detected several questioning formulations. After careful checking, we found that those formulations are not typographical, therefore it is necessary to make corrections. This paper reformulates the linear elasticity of a cylindrical snap fit, obtains an exact solution and proposes an accurate relation between the opening angle and bending tangent angle. Under the first order approximation, our formulations can reduced to the results of Yoshida and Wada and hence confirms the scientific correctness of Yoshida and Wada's work. Furthermore, this paper also derives a correct vertical displacement expression, and propose a new way of disassembly by bending for the first time and formulate a scaling law by data fitting. All formulations are validated by finite element simulation and experiment. The research here is helpful to the design of elastic snap fit or adjustable mechanical mechanism and metamaterial cell.

Closed Form Solution of Plane-Parallel Turbulent Flow Along an Unbounded Plane Surface

In this paper, a century-old problem is solved; namely, to find a unified analytic description of the non-uniform distribution of mean velocity across the entire domain of turbulent flow for all Reynolds numbers within the framework of the Prandtl mixing length theory. This study obtains a closed form solution of the mean velocity profile of plane turbulent flow for the Prandtl theory, and as well an approximate analytical solution for the van Driest mixing length theory. The profiles of several useful quantities are given based the closed form solution, such as turbulent viscosity, Reynolds turbulent stress, Kolmogorov's scaling law, and energy dissipation density. The investigation shows that the energy dissipation density at the surface is finite, whereas Landau's energy dissipation density is infinite. Strictly speaking, the closed form solution reveals that the universality of the turbulent velocity logarithmic profile no longer holds, but the von K\'arm\'an constant is still universal. Furthermore, a new formulation of the resistance coefficient of turbulent flow in pipes is formulated in implicit form.

Improved scaling law for the prediction of deuterium retention in beryllium co-deposits

Hydrogen isotope co-deposition with Be eroded from the first wall is expected to be the main fusion fuel retention mechanism in ITER. Since good fuel accounting is crucial for economic and safety reasons, reliable predictions of hydrogen isotope retention are needed. This study builds upon the well-established empirical De Temmerman scaling law [1] that predicts D/Be ratios in co-deposited layers based on deposition temperature, deposition rate, and deuterium particle energy. Expanding the data used in the original development of the scaling law with an additional dataset obtained with more recent measurements using a different technique to the original De Temmerman approach, allows us to obtain new values for free parameters and improve the prediction capabilities of the new scaling law. In an effort to improve the model even further, scaling with D2 background pressure was included and a new two-term model derived, describing D retention in low- and high-energy traps separately.

A Review of the Fractal Market Hypothesis for Trading and Market Price Prediction

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.

A theory of synaptic transmission

Rapid and precise neuronal communication is enabled through a highly synchronous release of signaling molecules neurotransmitters within just milliseconds of the action potential. Yet neurotransmitter release lacks a theoretical framework that is both phenomenologically accurate and mechanistically realistic. Here, we present an analytic theory of the action-potential-triggered neurotransmitter release at the chemical synapse. The theory is demonstrated to be in detailed quantitative agreement with existing data on a wide variety of synapses from electrophysiological recordings in vivo and fluorescence experiments in vitro. Despite up to ten orders of magnitude of variation in the release rates among the synapses, the theory reveals that synaptic transmission obeys a simple, universal scaling law, which we confirm through a collapse of the data from strikingly diverse synapses onto a single master curve. This universality is complemented by the ability of the theory to readily extract, through a fit to the data, the kinetic and energetic parameters that uniquely identify each synapse. The theory provides a means to detect cooperativity among the SNARE complexes that mediate vesicle fusion and reveals such cooperativity in several existing data sets. The theory is further applied to establish connections between molecular constituents of synapses and synaptic function. The theory allows competing hypotheses of short-term plasticity to be tested and identifies the regimes where particular mechanisms of synaptic facilitation dominate or, conversely, fail to account for the existing data for the paired-pulse ratio. The derived trade-off relation between the transmission rate and fidelity shows how transmission failure can be controlled by changing the microscopic properties of the vesicle pool and SNARE complexes. The established condition for the maximal synaptic efficacy reveals that no fine tuning is needed for certain synapses to maintain near-optimal transmission. We discuss the limitations of the theory and propose possible routes to extend it. These results provide a quantitative basis for the notion that the molecular-level properties of synapses are crucial determinants of the computational and information-processing functions in synaptic transmission.

Mammalian splicing divergence is shaped by drift, buffering in trans, and a scaling law

Alternative splicing is ubiquitous, but the mechanisms underlying its pattern of evolutionary divergence across mammalian tissues are still underexplored. Here, we investigated the cis-regulatory divergences and their relationship with tissue-dependent trans-regulation in multiple tissues of an F1 hybrid between two mouse species. Large splicing changes between tissues are highly conserved and likely reflect functional tissue-dependent regulation. In particular, micro-exons frequently exhibit this pattern with high inclusion levels in the brain. Cis-divergence of splicing appears to be largely non-adaptive. Although divergence is in general associated with higher densities of sequence variants in regulatory regions, events with high usage of the dominant isoform apparently tolerate more mutations, explaining why their exon sequences are highly conserved but their intronic splicing site flanking regions are not. Moreover, we demonstrate that non-adaptive mutations are often masked in tissues where accurate splicing likely is more important, and experimentally attribute such buffering effect to trans-regulatory splicing efficiency.