There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The term photonic crystals appears because of the analogy between electron waves in crystals and the light waves in artificial periodic dielectric structures. During the recent years the investigation of one-, two-and three-dimensional periodic structures has attracted a widespread attention of the world optics community because of great potentiality of such structures in advanced applied optical fields. The interest in periodic structures has been stimulated by the fast development of semiconductor technology that now allows the fabrication of artificial structures, whose period is comparable with the wavelength of light in the visible and infrared ranges.

http://ieeexplore.ieee.org/iel5/6354/16969/00781867.pdf

Photonic crystals

The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

The dynamic crack propagation in composites with inclusions is analyzed by a time-domain boundary element method (BEM) in conjunction with the sub-region technique. The crack-growth direction and the crack-tip instantaneous velocity are determined by the maximum circumferential stress criterion. The instantaneous velocity is well smoothed by a bisection technique. New crack-tip elements of inconstant length are added to the active crack-tip to simulate the fast crack growth. Square root shape functions are adopted as to describe the proper asymptotic behavior in the vicinity of the crack-tips. The computation time for the dynamic fracture problems in composites with multiple inclusions is reduced by a numerical method. The influences of the inclusions on the shielding ratio, the crack growing path and the crack-tip instantaneous speed are well investigated.

Dynamic crack propagation in matrix involving inclusions by a time-domain BEM

A tunable and multi-functional one-dimensional metasurface, which is formed by engraving periodic semi-ellipse grooves on the surface of an aluminum half-space, is proposed in this paper. One characteristic of the metasurface is the manipulation of multi-physical fields, i.e. it could be utilized to manipulate surface elastic and acoustic waves simultaneously. The dispersion curves of the elastic and acoustic waves can be effectively tuned by adding liquids into the grooves. Based on the tunability different applications can be realized by adding different volumes of different liquids into the grooves. As an example, simultaneous rainbow trapping of the surface elastic and acoustic waves is demonstrated in the metasurface. Moreover, a resonant cavity where the elastic and acoustic waves are highly confined is reported. The proposed metasurface paves the way to the design of multi-functional devices for simultaneous control of elastic and acoustic waves.A tunable and multi-functional one-dimensional metasurface, which is formed by engraving periodic semi-ellipse grooves on the surface of an aluminum half-space, is proposed in this paper. One characteristic of the metasurface is the manipulation of multi-physical fields, i.e. it could be utilized to manipulate surface elastic and acoustic waves simultaneously. The dispersion curves of the elastic and acoustic waves can be effectively tuned by adding liquids into the grooves. Based on the tunability different applications can be realized by adding different volumes of different liquids into the grooves. As an example, simultaneous rainbow trapping of the surface elastic and acoustic waves is demonstrated in the metasurface. Moreover, a resonant cavity where the elastic and acoustic waves are highly confined is reported. The proposed metasurface paves the way to the design of multi-functional devices for simultaneous control of elastic and acoustic waves.

/pdf/liquid-assisted-tunable-metasurface-for-simultaneous-drw8gnk74q.pdf

Liquid-assisted tunable metasurface for simultaneous manipulation of surface elastic and acoustic waves

In this paper, the surface effect on the static bending behavior of functionally graded porous (FGP) nanobeams subjected to a concentrated transverse load is studied by using Reddy’s higher-order b...

Surface Effect on Static Bending of Functionally Graded Porous Nanobeams Based on Reddy’s Beam Theory

Re-polarization of elastic waves at a frictional contact interface—II. Incidence of a P or SV wave

In this paper, the elastic wave propagation in periodic cylinder magnetoelectroelastic composite structures is studied using the plane wave expansion method. The band structure characteristics of magnetoelectroelastic rods embedded in polymer matrix and the reverse case are investigated taking the electric, magnetic and mechanical coupling effects into account. The generalised eigenvalue equation is derived to analyse the in-plane and out-of-plane modes, respectively. The numerical calculations for both the cases with Kagome lattices are performed. The relation between the gap widths and filling fractions are discussed in detail. The effects of the magnetoelectricity on the band structures and widths of band gaps are analysed. The band gap characteristics are illustrated further and the results will be helpful to design such kind of composite structures.

Yue-Sheng Wang

Papers

Dynamic crack propagation in matrix involving inclusions by a time-domain BEM

Liquid-assisted tunable metasurface for simultaneous manipulation of surface elastic and acoustic waves

Surface Effect on Static Bending of Functionally Graded Porous Nanobeams Based on Reddy’s Beam Theory

Re-polarization of elastic waves at a frictional contact interface—II. Incidence of a P or SV wave

Band gap behaviours of periodic magnetoelectroelastic composite structures with Kagome lattices