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This paper describes osmotically-driven pressure generation in a membrane-bound compartment while taking into account volume expansion, solute dilution, surface area to volume ratio, membrane hydraulic permeability, and changes in osmotic gradient, bulk modulus, and degree of membrane fouling. The emphasis lies on the dynamics of pressure generation; these dynamics have not previously been described in detail. Experimental results are compared to and supported by numerical simulations, which we make accessible as an open source tool. This approach reveals unintuitive results about the quantitative dependence of the speed of pressure generation on the relevant and interdependent parameters that will be encountered in most osmotically-driven pressure generators. For instance, restricting the volume expansion of a compartment allows it to generate its first 5 kPa of pressure seven times faster than without a restraint. In addition, this dynamics study shows that plants are near-ideal osmotic pressure generators, as they are composed of many small compartments with large surface area to volume ratios and strong cell wall reinforcements. Finally, we demonstrate two applications of an osmosis-based pressure generator: actuation of a soft robot and continuous volume delivery over long periods of time. Both applications do not need an external power source but rather take advantage of the energy released upon watering the pressure generators. [ABSTRACT FROM AUTHOR]

We present novel results that relate energy and information transfer with sensitivity to initial conditions in chaotic multi-dimensional Hamiltonian systems. We show the relation among Kolmogorov-Sinai entropy, Lyapunov exponents, and upper bounds for the Mutual Information Rate calculated in the Hamiltonian phase space and on bi-dimensional subspaces. Our main result is that the net amount of transfer from kinetic to potential energy per unit of time is a power-law of the upper bound for the Mutual Information Rate between kinetic and potential energies, and also a power-law of the Kolmogorov-Sinai entropy. Therefore, transfer of energy is related with both transfer and production of information. However, the power-law nature of this relation means that a small increment of energy transferred leads to a relatively much larger increase of the information exchanged. Then, we propose an “experimental” implementation of a 1-dimensional communication channel based on a Hamiltonian system, and calculate the actual rate with which information is exchanged between the first and last particle of the channel. Finally, a relation between our results and important quantities of thermodynamics is presented. [ABSTRACT FROM AUTHOR]

We develop a method for systematically constructing Lagrangian functions for dissipative mechanical, electrical, and electromechanical systems. We derive the equations of motion for some typical electromechanical systems using deterministic principles that are strictly variational. We do not use any ad hoc features that are added on after the analysis has been completed, such as the Rayleigh dissipation function. We generalise the concept of potential, and define generalised potentials for dissipative lumped system elements. Our innovation offers a unified approach to the analysis of electromechanical systems where there are energy and power terms in both the mechanical and electrical parts of the system. Using our novel technique, we can take advantage of the analytic approach from mechanics, and we can apply these powerful analytical methods to electrical and to electromechanical systems. We can analyse systems that include non-conservative forces. Our methodology is deterministic, and does does require any special intuition, and is thus suitable for automation via a computer-based algebra package. [ABSTRACT FROM AUTHOR]

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems. [ABSTRACT FROM AUTHOR]

The structure-dynamics-function has become one of central problems in modern sciences, and it is a great challenge to unveil the organization rules for different dynamical processes on networks. In this work, we study the vibration spectra of the classical mass spring model with different masses on complex networks, and pay our attention to how the mass spatial configuration influences the second-smallest vibrational frequency () and the largest one (). For random networks, we find that becomes maximal and becomes minimal if the node degrees are point-to-point-positively correlated with the masses. In these cases, we call it point-to-point matching. Moreover, becomes minimal under the condition that the heaviest mass is placed on the lowest-degree vertex, and is maximal as long as the lightest mass is placed on the highest-degree vertex, and in both cases all other masses can be arbitrarily settled. Correspondingly, we call it single-point matching. These findings indicate that the matchings between the node dynamics (parameter) and the node position rule the global systems dynamics, and sometimes only one node is enough to control the collective behaviors of the whole system. Therefore, the matching rules might be the common organization rules for collective behaviors on networks. [ABSTRACT FROM AUTHOR]

A two-dimensional steady forced convective flow of a Newtonian fluid past a convectively heated permeable vertically moving plate in the presence of a variable magnetic field and radiation effect has been investigated numerically. The plate moves either in assisting or opposing direction to the free stream. The plate and free stream velocities are considered to be proportional to whilst the magnetic field and mass transfer velocity are taken to be proportional to where is the distance along the plate from the leading edge of the plate. Instead of using existing similarity transformations, we use a linear group of transformations to transform the governing equations into similarity equations with relevant boundary conditions. Numerical solutions of the similarity equations are presented to show the effects of the controlling parameters on the dimensionless velocity, temperature and concentration profiles as well as on the friction factor, rate of heat and mass transfer. It is found that the rate of heat transfer elevates with the mass transfer velocity, convective heat transfer, Prandtl number, velocity ratio and the magnetic field parameters. It is also found that the rate of mass transfer enhances with the mass transfer velocity, velocity ratio, power law index and the Schmidt number, whilst it suppresses with the magnetic field parameter. Our results are compared with the results existing in the open literature. The comparisons are satisfactory. [ABSTRACT FROM AUTHOR]

Repair success for injuries to the flexor tendon in the hand is often limited by the in vivo behaviour of the suture used for repair. Common problems associated with the choice of suture material include increased risk of infection, foreign body reactions, and inappropriate mechanical responses, particularly decreases in mechanical properties over time. Improved suture materials are therefore needed. As high-performance materials with excellent tensile strength, spider silk fibres are an extremely promising candidate for use in surgical sutures. However, the mechanical behaviour of sutures comprised of individual silk fibres braided together has not been thoroughly investigated. In the present study, we characterise the maximum tensile strength, stress, strain, elastic modulus, and fatigue response of silk sutures produced using different braiding methods to investigate the influence of braiding on the tensile properties of the sutures. The mechanical properties of conventional surgical sutures are also characterised to assess whether silk offers any advantages over conventional suture materials. The results demonstrate that braiding single spider silk fibres together produces strong sutures with excellent fatigue behaviour; the braided silk sutures exhibited tensile strengths comparable to those of conventional sutures and no loss of strength over 1000 fatigue cycles. In addition, the braiding technique had a significant influence on the tensile properties of the braided silk sutures. These results suggest that braided spider silk could be suitable for use as sutures in flexor tendon repair, providing similar tensile behaviour and improved fatigue properties compared with conventional suture materials. [ABSTRACT FROM AUTHOR]

We report an experimental study of mouse sperm motility that shows chief aspects characteristic of neurons: the anesthetic (produced by tetracaine) and excitatory (produced by either caffeine or calcium) effects and their antagonic action. While tetracaine inhibits sperm motility and caffeine has an excitatory action, the combination of these two substances balance the effects, producing a motility quite similar to that of control cells. We also study the effects of these agents (anesthetic and excitatory) on the melting points of pure lipid liposomes constituted by 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and dipalmitoyl phosphatidic acid (DPPA). Tetracaine induces a large fluidization of the membrane, shifting the liposomes melting transition temperature to much lower values. The effect of caffeine is null, but its addition to tetracaine-doped liposomes greatly screen the fluidization effect. A high calcium concentration stiffens pure lipid membranes and strongly reduces the effect of tetracaine. Molecular Dynamics Simulations are performed to further understand our experimental findings at the molecular level. We find a strong correlation between the effect of antagonic molecules that could explain how the mechanical properties suitable for normal cell functioning are affected and recovered. [ABSTRACT FROM AUTHOR]

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.