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The physics librarian today faces a complex and fast‐moving discipline and an almost overwhelming array of resources. Beginning selectors in physics are often perplexed. How does research in physics proceed? What kinds of information do physicists seek? Where can this information be found and what is the most effective way of providing it? How are increases in costs and volume of publication affecting collecting in physics? What do new technologies and cooperative arrangements have to offer the physics librarian? This essay, directed especially to the novice selector, seeks first to define physics research and the information needs of physics researchers. It then surveys the trends in technology and in the market‐place that are profoundly altering the way we build research collections in physics.

A theory of knowledge shows that all four systems of nature are recursive combinatorial‐hamiltonian self‐programmed flow‐wave systems that can be deduced from the usual Conservation Law promoted to the Axiom of Science.

The response of a MESFET and an inverted HEMT to the impact of an a particle has been calculated by means of the Monte Carlo Particle Model, a technique for solving Boltzmann's transport and Poisson's field equation self‐consistently in space and time. The calculations show that all the terminals of the MESFET react by generating an initial current pulse followed by another; the timing of the second pulse depends on the angle of incidence of the α particle. The lattice heating rate was found to be largest at the corners of the Ohmic contacts. The HEMT, on the other hand, hardly reacts electrically to the α particle but is more likely to burn out in an a particle radiation environment because of the larger lattice heat generation taking place in the interior of the transistor. The results also support the theory of the hot‐electron induced subsurface catastrophic failure mechanism.

The purpose of this paper is to introduce new non‐classical implementations of neural networks (NNs). The developed implementations are performed in the quantum, nano, and optical domains to perform the required neural computing. The various implementations of the new NNs utilizing the introduced architectures are presented, and their extensions for the utilization in the non‐classical neural‐systolic networks are also introduced.

The introduced neural circuits utilize recent findings in the quantum, nano, and optical fields to implement the functionality of the basic NN. This includes the techniques of many‐valued quantum computing (MVQC), carbon nanotubes (CNT), and linear optics. The extensions of implementations to non‐classical neural‐systolic networks using the introduced neural‐systolic architectures are also presented.

Novel NN implementations are introduced in this paper. NN implementation using the general scheme of MVQC is presented. The proposed method uses the many‐valued quantum orthonormal computational basis states to implement such computations. Physical implementation of quantum computing (QC) is performed by controlling the potential to yield specific wavefunction as a result of solving the Schrödinger equation that governs the dynamics in the quantum domain. The CNT‐based implementation of logic NNs is also introduced. New implementations of logic NNs are also introduced that utilize new linear optical circuits which use coherent light beams to perform the functionality of the basic logic multiplexer by utilizing the properties of frequency, polarization, and incident angle. The implementations of non‐classical neural‐systolic networks using the introduced quantum, nano, and optical neural architectures are also presented.

The introduced NN implementations form new important directions in the NN realizations using the newly emerging technologies. Since the new quantum and optical implementations have the advantages of very high‐speed and low‐power consumption, and the nano implementation exists in very compact space where CNT‐based field effect transistor switches reliably using much less power than a silicon‐based device, the introduced implementations for non‐classical neural computation are new and interesting for the design in future technologies that require the optimal design specifications of super‐high speed, minimum power consumption, and minimum size, such as in low‐power control of autonomous robots, adiabatic low‐power very‐large‐scale integration circuit design for signal processing applications, QC, and nanotechnology.

The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems.

The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation.

A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment.

This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.

The two stages of visual perception according to the models of visual search, the preattentional and the attentional, are presented. The first stage is related to the left cerebral hemisphere, while the second to the left one. The phenomenon of illusory conjunction of features during the object‐integration in the macroscopic world is described. It is suggested that the illusory conjunction is similar to the quantum entanglement of microscopic particles. The imagining of microscopic particles, like electron, is explained by the theory of re‐entrant, namely, as activation of primary visual cortical areas by the higher neural system. It is suggested that both macroscopic object‐integration and the microscopic collapse of the wave function are an exchange of the cerebral hemisphere, which create the ontological model of the perceived phenomenon.

The purpose of this paper is to examine several calibration techniques that have been developed to determine the discrete element method (DEM) parameters for slow and rapid unconfined flow of granular conical pile formation. This paper also aims to discuss some of the methods currently employed to scale particle properties to reduce computational resources and time to solve large DEM models.

DEM models have been calibrated against simple bench‐scale experimental results to examine the validity of selected parameters for the contact, material and mechanical models to simulate the dynamic and static behaviour of cohesionless polyethylene pellets. Methods to determine quantifiable single particle parameters such as static friction and the coefficient of restitution have been highlighted. Numerical and experimental granular pile formation has been investigated using different slumping and pouring techniques to examine the dependency of the type of flow mechanism on the DEM parameters.

The proposed methods can provide cost effective and simple techniques to determine suitable input parameters for DEM models. Rolling friction and particle shape representation has shown to have a significant influence on the bulk flow characteristics via a sensitivity analysis and needs to be accessed based on the environmental conditions.

This paper describes several effective known and novel methodologies to characterise granular materials that are needed to accurately model granular flow using the DEM to provide valuable quantitative data. For the DEM to be a viable predictive tool in industrial applications which often contain huge quantities of particles with random particle shapes and irregular properties, quick and validated techniques to “tune” DEM models are necessary.

Discrete element method (DEM) has been extensively used in the laboratory of particulate and multiphase processing at the University of New South Wales (UNSW) to study the fundamentals of particulate matter at a particle scale. This paper briefly reviews the work in the laboratory, which covers the development of simulation techniques and their application to the study of particle packing and flow, transport properties and constitutive relationships of typical static or dynamic particulate systems. It is concluded, through representative comparison between simulated and measured results under different conditions, that DEM, as a major technique for discrete particle simulation, is an effective method for particle scale research of particulate matter.

The Center for Industrial Research (SI), the University of Oslo (UiO) and a group of Norwegian companies have collaborated between 1990 and 1992 in the research programme ‘Industrial Microelectronics’ with a total cost of 30 MNOK. The programme was sponsored by the Norwegian Scientific and Industrial Research Council (NTNF) as one of the twin programmes constituting a national research initiative in microelectronics. The motivation for the programme is the recognition of microelectronics as a key technology commanding the performance and market success of many of the electronics systems from the Norwegian electronics industry towards the year 2000. The main objective is to stimulate industrial innovation by developing, transferring and exploiting knowledge and methods based upon advanced microelectronics. Focused activities are silicon sensor technology, combined analogue/digital design of application‐specific integrated circuits, large scale instrumentation, sensor packaging and thermal management of electronic systems. SI is focusing on applied research, UiO on education, and collaborating Norwegian companies are using the results in their own R&D projects. It is anticipated that the research results will be fully industrialised within 3–5 years. The programme is co‐ordinated with other Norwegian government‐sponsored research activities as well as European research programmes based on microelectronics. The programme is organised in projects and monitored with a set of milestones strongly indicating the achievement of successful industrial innovation, research results of international standing and high‐quality education of key personnel for the industry. Several successful examples of the research results are highlighted: Design and process methodology for double‐sided microstrip silicon radiation sensors for detection of high energy elementary particles, silicon‐to‐silicon and silicon‐to‐thin film anodic bonding processes for sensor fabrication, combined analogue/digital application‐specific integrated circuits for front‐end instrumentation applications, packaging of radiation sensors and thermal management of electronic systems by evaporation cooling. It is concluded that the programme has successfully achieved results in harmony with the objective.