In the mathematical modelling of compactional flow in porous media, the constitutive relation is typically modelled in terms of a nonlinear relationship between effective pressure and porosity, and compaction is essentially poroelastic. However, at depths deeper than 1 km where pressure is high, compaction becomes more akin to a viscous one. Two mathematical models of compaction in porous media are formulated and the noninear equations are then solved numerically. The essential features of numerical profiles of poroelastic and viscous compaction are thus compared with asymptotic solutions. Two distinguished styles of density-driven compaction in fast and slow compacting sediments are analysed and shown in this paper.
Engineering optimization problems usually contain multifaceted performance requirements that can be riddled with unquantifiable specifications and incompatible performance objectives. Such problems typically possess competing design requirements which are very difficult – if not impossible – to quantify and capture at the time of model formulation. There are invariably unmodelled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, when solving many “real life” mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objective(s), but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the nature-inspired, Firefly Algorithm can be used to concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new co-evolutionary approach is very computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures.
This chapter introduces the fundamentals of algorithms in the context of data mining, optimization, and machine learning, including the feasibility, constraints, optimality, Lagrange multipliers, KKT conditions, and gradient-based techniques.
The bat algorithm (BA) is a population-based algorithm and it typically has good convergence for solving many optimization problems. This chapter first introduces the original bat algorithm with detailed descriptions of the main steps, followed by demo codes in both Matlab and C++. Discussions of main parameters and their settings are also carried out.
The “firefly algorithm” (FFA) is a modern metaheuristic algorithm, inspired by the behavior of fireflies. This algorithm and its variants have been successfully applied to many continuous optimization problems. This work analyzes the performance of the FFA when solving combinatorial optimization problems. In order to improve the results, the original FFA is extended and improved for self-adaptation of control parameters, and thus more directly balancing between exploration and exploitation in the search process of fireflies. We use a new population model to increase the selection pressure, and the next generation selects only the fittest between a parent and an offspring population. As a result, the proposed memetic self-adaptive FFA (MSA-FFA) is compared with other well-known graph coloring algorithms such as Tabucol, the hybrid evolutionary algorithm, and an evolutionary algorithm with stepwise adaptation of weights. Various experiments have been conducted on a huge set of randomly generated graphs. The results of these experiments show that the results of the MSA-FFA are comparable with other tested algorithms.
We examined the influence of process uniformity on the device performance for the spin-orbit torque (SOT) magnetic random access memory (MRAM) devices. By optimizing the lithography process through changing the multi-energies exposure compensation and pretreatment in developing, we demonstrated 1.6 times sigma improvement of the critical dimensions (CD), leading to 49.5%, 54.2%, and 63.2% sigma% reduction for SOT channel resistance (R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> ), magnetic tunnel junction (MTJ) resistance (R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> ) and switching current (I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</inf> ) respectively. These promising results will help to deliver SOT-MRAM to the mass manufacturing in the semiconductor industry.
A supply chain is a system which moves products from a supplier to customers. The supply chains are ubiquitous. They play a key role in all economic activities. Inspired by biological principles of nutrients' distribution in protoplasmic networks of slime mould Physarum polycephalum we propose a novel algorithm for a supply chain design. The algorithm handles the supply networks where capacity investments and product flows are variables. The networks are constrained by a need to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of a flux during the iterative process, which is used in real-time update of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Practicality and flexibility of our algorithm is illustrated on numerical examples.
No abstract is provided for this article.
No abstract is provided for this article.
The variations of gravity were measured with a high precision LaCoste-Romberg D gravimeter during a total solar eclipse to investigate the effect of a solar eclipse on the gravitational field. The observed anomaly $(7.0\ifmmode\pm\else\textpm\fi{}2.7)$$\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}8}$ ${\mathrm{m}/\mathrm{s}}^{2}$during the eclipse implies that there may be a shielding property of gravitation.
No abstract is provided for this article.
With the extensive application of numerical simulations in engineering fields such as mining, tunnelling, and petroleum, enhancing the accuracy of simulation tools to ensure their reliability has become a widely discussed issue. Using basic rock mechanics experiments to validate numerical models often encounters problems when these models are applied to more complex tests, resulting in distorted simulation results. To address this issue, this study uses single impact tests indicative of percussive drilling as an example, focusing on two crucial aspects during the bit-rock interaction: bit energy loss and the breakage morphology, including metrics for the extent of rock cracking. Seven criteria related to these two aspects were selected to validate the accuracy of the FDEM model. The simulation results were validated against two types of hard sedimentary rocks: St Anne limestone and Rhune sandstone. The numerical simulation results at different impact energies agreed well with experimental results across these seven criteria. The FDEM simulation results accurately reproduced the experimental observations, capturing the different crack morphologies and their evolution as observed in experimental CT scans and high-speed video recordings. The presence of median cracks in limestone and absence in sandstone, as observed in experiments, was reproduced by the simulations. Notably, the simulation results reveal that the generation of side cracks is not necessarily caused by the closure of median cracks because median cracks do not always initiate, as postulated in the literature. Instead, FDEM simulation results indicate that the propagation of side cracks is primarily caused by the tearing induced by the elastic recovery of the rock.
Welcome to NPSec 2009, the fifth Workshop on Secure Network Protocols, co-located with ICNP. This year, the workshop is being held in Princeton, NJ - a wonderful opportunity to observe rich fall colors at the East Coast while listening to stimulating papers at the workshop!
No abstract is provided for this article.
The bat algorithm is a bio-inspired algorithm that uses frequency tuning and echolocation characteristics. This chapter introduces the fundamental idea of the bat algorithm and its variants. A demo implementation is also given, followed by the discussions of convergence and stability analysis.
