Solutions In Short Time Research Articles

An accurate application of the integral method applied to the diffusion of oxygen in absorbing tissue

Accurate integral methods are applied to a one dimensional moving boundary problem describing the diffusion of oxygen in absorbing tissue. These methods have been well studied for classic Stefan problems but this situation is unusual because there is no condition which contains the velocity of the moving boundary explicitly. This paper begins by giving a short time solution and then discusses some of the previous integral methods found in the literature. The main drawbacks of these solutions are that they cannot be solved from t=0 and also cannot determine the end behaviour. This is due to the non-uniform initial profile which integral methods typically fail to capture. The use of a novel transformation removes this non-uniformity and, on applying optimal integral methods to the resulting system, leads to simple and yet very accurate approximate solutions that overcome the deficiencies of previous methods.

An Accurate Mesh-Based Equivalent Circuit Approach to Thermal Modeling

This paper presents a method of automatically constructing and parameterizing accurate lumped parameter thermal equivalent circuits (TECs) with nodes arranged in a regular mesh pattern. The approach exhibits a number of advantages over conventional lumped parameter TECs including superior thermal field resolution, reduced model construction and setup times and more accurate identification of hot-spot temperatures and their location. The method serves as a desirable compromise between the fine detail and high computational cost of a full finite element analysis and the coarse detail and short solution times afforded by lumped parameter TECs. The benefits of the method are demonstrated by the analysis and experimental test of a demonstration linear actuator.

Improved particle swarm optimization algorithm for circuit partitioning problems

Partitioning is the vital part of VLSI Physical design stage. Particle Swarm Optimization algorithm is proposed for minimization of cut size, as it is the major objective of partitioning. PSO is inspired by the social behavior of animals. The proposed algorithm is based on inertia weight results in high efficiency and converge to the global optimal solution in short time. The algorithm is tested with various benchmark functions and an example circuit. Experimental results shows that PSO algorithm with inertia weight and boundary conditions minimize the interconnect between the partitions.

Practical Approach to Water System Optimal Operation

Optimal pump scheduling is a major consideration when dealing with minimizing operational costs of a water distribution system. Pump operation must balance between three factors. Water balance constraints, including consumer demand and water tank volumes. Hydraulic constraints determining water pump operating point. Electrical tariff rate effecting energy cost.Optimization models may assume linear or discrete pump operation, depending on type and accuracy of the model in use. Linear operation assumes the pump may operate during part of the time step while discrete operation requires the pump to be either on or off during the entire time step. Linear optimization models commonly have short solution times, but cannot contain non-linear constraints such as hydraulic headloss. By such, linear model results may be difficult to implement in a real water system as the hydraulic behavior of the system may render the optimal solution impractical. Likewise, if the pump operation partially uses the time step the pump may be forced to come in and out of duty often causing mechanical ware and tare.Discrete operation provides smooth pump operation and may contain non-linear hydraulic constraint to calculate a more realistic working point for the pump. Discrete models have long solution times due the vast amount of pump operating combinations, which must be explored. Heuristic techniques may be used to shorten solution times but these do not assure global minimization of the solution.The goal of the research is to create a minimum cost optimal operation water distribution system model that utilizes the short solution time of a linear model but also includes non-linear hydraulic constraints effecting pump energy consumption and discrete pump operation. The motivation is to use the model for real-time pump scheduling and for water system design.

Integrated project scheduling and staff assignment with controllable processing times.

This paper addresses a decision problem related to simultaneously scheduling the tasks in a project and assigning the staff to these tasks, taking into account that a task can be performed only by employees with certain skills, and that the length of each task depends on the number of employees assigned. This type of problems usually appears in service companies, where both tasks scheduling and staff assignment are closely related. An integer programming model for the problem is proposed, together with some extensions to cope with different situations. Additionally, the advantages of the controllable processing times approach are compared with the fixed processing times. Due to the complexity of the integrated model, a simple GRASP algorithm is implemented in order to obtain good, approximate solutions in short computation times.

Comparison of Two Integer Programming Formulations for a Single Machine Family Scheduling Problem to Minimize Total Tardiness

This paper studies the single machine family scheduling problem in which the goal is to minimize total tardiness. We analyze two alternative mixed-integer programming (MIP) formulations with respect to the time required to solve the problem using a state-of-the-art commercial MIP solver. The two formulations differ in the number of binary variables: the first formulation has O(n2) binary variables whereas the second formulation has O(n3) binary variables, where n denotes the number of jobs to be scheduled. Our findings indicate that despite the significant higher number of binary variables, the second formulation leads to significantly shorter solution times for problem instances of moderate size.

Effects of Short Time Solution Treatment at High Temperature Using Induction Heating Apparatus on Mechanical Properties in JIS AC4CH Alloys

Effects of Short Time Solution Treatment at High Temperature Using Induction Heating Apparatus on Mechanical Properties in JIS AC4CH Alloys

Drying-toasting kinetics of presoaked soybean in fluidised bed. Experimental study and mathematical modelling with analytical solutions

The aim of this work was to study the mass transfer kinetics during fluidised-bed drying-toasting of soaked soybean to provide design information for production of whole soybean snack. The experimental work consisted of determinations of thin layer drying-toasting curves of soaked soybeans in a fluidised-bed dryer at air temperatures between 100 and 160°C, for 60min, using an air velocity of 2.5m/s. The analytical solution for short times was not suitable for this high temperature process. In contrast, calculations by complete analytical solution for constant diffusion coefficient (Deff) and constant radius (R0), an infinite convergent series, were markedly accurate. However, previous studies on particle density and volume revealed non-negligible shrinkage during drying-toasting, possibly caused by the fact that the ratio of Deff to R2 remains substantially unaltered. Values of the diffusion coefficient (around 10−9m2/s) and activation energy (31kJ/mol) are within the ranges expected for food drying at elevated temperatures.

The solution of direct and inverse transient heat conduction problems with layered materials using exponential basis functions

In this paper we present an extension of the formulation given in (EABE, 37 (2013) 868–883) to direct and inverse heat conduction problems in layered materials. Expressing the numerical solution as a series of exponential basis functions (EBFs) defined in space and time, is the main idea of the presented method. We shall show that the use of the EBFs provides a versatile tool for the solution of a variety of problems such as the classical heat diffusion with internal heat sources and the inverse problems in layered media with perfect and imperfect contacts. To this end, we choose a proper set of EBFs satisfying the governing differential equation and interface conditions. Then a collocation technique is employed to satisfy both initial and boundary conditions. The proposed formulation can also be easily extended to the solution of non-Fourier heat conduction problems or the analysis of thermal wave propagation. The capability of the presented method is investigated in the solution of some direct and inverse heat conduction problems including a boundary identification problem and backward heat conduction problems. Issues pertaining to short time solution and penetration time, in inverse heat conduction problems, are addressed in the problems solved.

Hyperbolic mean curvature flow with a forcing term: Evolution of plane curves

In this paper we study the evolution of closed strictly convex plane curves moving by the hyperbolic mean curvature flow with a forcing term. It is shown that the flow admits a unique short-time smooth solution and the convexity of the curves is preserved during the evolution. When the forcing term is a negative constant, we prove the curves either converge to a point or a C0 curve. For a positive constant forcing term, the flow has a unique smooth solution in any finite time and expands to infinity as t tends to infinity if the initial curvature is smaller than M, the flow will blow up in a finite time if the initial curvature is larger than M.

Mass transfer around oblate spheroidal drops in a biaxial stretching motion

AbstractMass transfer around a small eccentricity oblate spheroidal drop in a biaxial stretching and creeping flow, was investigated theoretically. The results at small capillary (Ca ≪ 1) and large Peclet numbers (Pe ≫ 1), obtained via regular perturbations in Ca, suggest that: at very short times, the mass transfer rate to or from the drop represents, at O(Ca), mass transfer by diffusion only around a sphere. At long times or at steady‐state, the mass transfer rate is, at O(Ca), slightly larger than that of a spherical drop, it increases with increasing the capillary number and decreases with increasing the viscosity ratio. Steady‐state is established faster, as the viscosity ratio decreases and as the capillary number increases. Exact and approximate analytical solutions, valid at all times, which converge to the asymptotic solutions for short and long times, are also presented.

A dissipation-rate reserving DG method for wave catching-up phenomena in a nonlinearly elastic composite bar

A tensile wave can be destructive, as it can generate microcracks and cause the interface detachments in a composite structure. Recently Huang, Dai, Chen and Kong [14] proposed to use material nonlinearity to generate wave catching-up phenomena such that a tensile wave can catch the first transmitted compressive wave, leading to the reduction of the former. They gave the existence and asymptotic solutions in short time. In this paper, we develop a dissipation-rate reserving discontinuous Galerkin (DG) method to solve numerically the system of balance laws with discontinuous flux and inflow boundary condition for waves in a two-material nonlinearly elastic composite bar. Physically, the aim is to examine the reduction ratio of the tensile wave due to wave catching-up phenomena in long time. The stability analysis for the DG method is also presented. And, a contribution is that the numerical fluxes are chosen to satisfy the local dissipation rate across a shock. Numerical results are compared with the available asymptotic solutions in short time and good agreements are found. In particular, the numerical solutions give a high resolution for various interactions between the tensile shock, compressive shock and rarefaction wave. The results confirm that when the stress-strain relation of the second nonlinearly elastic material is convex, indeed the tensile wave can catch the previously transmitted compressive wave. The simulation results by the DG method show that the catching-up phenomena can reduce the magnitude of the tensile wave by more than 400%. We also examine how material parameters influence the reduction ratio. As a verification, the solutions by a weighted essentially non-oscillatory (WENO) finite volume method are also presented, which are in agreement with those by the DG method.

GRASP with ejection chains for the dynamic memory allocation in embedded systems

In the design of electronic embedded systems, the allocation of data structures to memory banks is a main challenge faced by designers. Indeed, if this optimization problem is solved correctly, a great improvement in terms of efficiency can be obtained. In this paper, we consider the dynamic memory allocation problem, where data structures have to be assigned to memory banks in different time periods during the execution of the application. We propose a GRASP to obtain high quality solutions in short computational time, as required in this type of problem. Moreover, we also explore the adaptation of the ejection chain methodology, originally proposed in the context of tabu search, for improved outcomes. Our experiments with real and randomly generated instances show the superiority of the proposed methods compared to the state-of-the-art method.

Optimization of power production through coordinated use of hydroelectric and conventional power units

In the present work a methodology to tackle the problem of simultaneous utilization of hydroelectric and conventional power units with the goal of optimizing power production operations over the short term is presented. Most problem formulations found in the literature result in the development of nonlinear optimization programs, which are solved with stochastic methods. The methodology presented in this paper leads to the development of a convex mixed integer quadratic programming (MIQP) model, which is a special type of nonlinear model that enables reaching the global optimum solution in short computational time. The efficiency of the proposed approach is demonstrated by its application to a realistic power production system.

A hybrid metaheuristic for the cyclic antibandwidth problem

In this paper, we propose a hybrid metaheuristic algorithm to solve the cyclic antibandwidth problem. This hard optimization problem consists of embedding an n-vertex graph into the cycle Cn, such that the minimum distance (measured in the cycle) of adjacent vertices is maximized. It constitutes a natural extension of the well-known antibandwidth problem, and can be viewed as the dual problem of the cyclic bandwidth problem.Our method hybridizes the artificial bee colony methodology with tabu search to obtain high-quality solutions in short computational times. Artificial bee colony is a recent swarm intelligence technique based on the intelligent foraging behavior of honeybees. The performance of this algorithm is basically determined by two search strategies, an initialization scheme that is employed to construct initial solutions and a method for generating neighboring solutions. On the other hand, tabu search is an adaptive memory programming methodology introduced in the eighties to solve hard combinatorial optimization problems. Our hybrid approach adapts some elements of both methodologies, artificial bee colony and tabu search, to the cyclic antibandwidth problem. In addition, it incorporates a fast local search procedure to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown with respect to the current state-of-the-art algorithm for this problem.

Intelligent variable neighbourhood search for the minimum labelling spanning tree problem

Given a connected, undirected graph whose edges are labelled (or coloured), the minimum labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest number of distinct labels (or colours). In recent work, the MLST problem has been shown to be NP-hard and some effective heuristics have been proposed and analyzed. In a currently ongoing project, we investigate an intelligent optimization algorithm to solve the problem. It is obtained by the basic Variable Neighbourhood Search heuristic with the integration of other complements from machine learning, statistics and experimental algorithmics, in order to produce high-quality performance and to completely automate the resulting optimization strategy. Computational experiments show that the proposed metaheuristic has high-quality performance for the MLST problem and it is able to obtain optimal or near-optimal solutions in short computational running time.

A memetic approach to construct transductive discrete support vector machines

Transductive learning involves the construction and application of prediction models to classify a fixed set of decision objects into discrete groups. It is a special case of classification analysis with important applications in web-mining, corporate planning and other areas. This paper proposes a novel transductive classifier that is based on the philosophy of discrete support vector machines. We formalize the task to estimate the class labels of decision objects as a mixed integer program. A memetic algorithm is developed to solve the mathematical program and to construct a transductive support vector machine classifier, respectively. Empirical experiments on synthetic and real-world data evidence the effectiveness of the new approach and demonstrate that it identifies high quality solutions in short time. Furthermore, the results suggest that the class predictions following from the memetic algorithm are significantly more accurate than the predictions of a CPLEX-based reference classifier. Comparisons to other transductive and inductive classifiers provide further support for our approach and suggest that it performs competitive with respect to several benchmarks.

A New Progressive Algorithm for a Multiple Longest Common Subsequences Problem and Its Efficient Parallelization

The multiple longest common subsequence (MLCS) problem, which is related to the measurement of sequence similarity, is one of the fundamental problems in many fields. As an NP-hard problem, finding a good approximate solution within a reasonable time is important for solving large-size problems in practice. In this paper, we present a new progressive algorithm, Pro-MLCS, based on the dominant point approach. Pro-MLCS can find an approximate solution quickly and then progressively generate better solutions until obtaining the optimal one. Pro-MLCS employs three new techniques: 1) a new heuristic function for prioritizing candidate points; 2) a novel d-index-tree data structure for efficient computation of dominant points; and 3) a new pruning method using an upper bound function and approximate solutions. Experimental results show that Pro-MLCS can obtain the first approximate solution almost instantly and needs only a very small fraction, e.g., 3 percent, of the entire running time to get the optimal solution. Compared to existing state-of-the-art algorithms, Pro-MLCS can find better solutions in much shorter time, one to two orders of magnitude faster. In addition, two parallel versions of Pro-MLCS are developed: DPro-MLCS for distributed memory architecture and DSDPro-MLCS for hierarchical distributed shared memory architecture. Both parallel algorithms can efficiently utilize parallel computing resources and achieve nearly linear speedups. They also have a desirable progressiveness property-finding better solutions in shorter time when given more hardware resources.

Controlling growth rate anisotropy for formation of continuous ZnO thin films from seeded substrates

Solution-processed zinc oxide (ZnO) thin films are promising candidates for low-temperature-processable active layers in transparent thin film electronics. In this study, control of growth rate anisotropy using ZnO nanoparticle seeds, capping ions, and pH adjustment leads to a low-temperature (90 ° C) hydrothermal process for transparent and high-density ZnO thin films. The common 1D ZnO nanorod array was grown into a 2D continuous polycrystalline film using a short-time pure solution method. Growth rate anisotropy of ZnO crystals and the film morphology were tuned by varying the chloride (Cl−) ion concentration and the initial pH of solutions of zinc nitrate and hexamethylenetetramine (HMTA), and the competitive adsorption effects of Cl− ions and HMTA ligands on the anisotropic growth behavior of ZnO crystals were proposed. The lateral growth of nanorods constituting the film was promoted by lowering the solution pH to accelerate the hydrolysis of HMTA, thereby allowing the adsorption effects from Cl− to dominate. By optimizing the growth conditions, a dense ∼100 nm thickness film was fabricated in 15 min from a solution of [Cl−]/[Zn2+] = 1.5 and pH= 4.8 ± 0.1. This film shows >80% optical transmittance and a field-effect mobility of 2.730 cm2 V−1 s−1 at zero back-gate bias.

Diffusion-controlled emissions of volatile organic compounds (VOCs): Short-, mid-, and long-term emission profiles

This paper indicates that diffusion-controlled organic emissions can be identified as processes of three stages, i.e., short-, mid-, and long-term emissions. Based on two generalized mass-diffusion models, exact series and short-time solutions are derived and validated by using two reported environmental-chamber tests. Long-term emissions can be predicted by a new exponential-decay model, a simplified version of a series solution. Short-term emissions can be modeled efficiently by small-time solutions. Besides having simple forms, small-time solutions require no roots of transcendental equations and converge very fast for short times. They serve necessary complements to the exact series solutions. Especially interesting is the mid-stage emission between the very early and the late emission stages, which indicates that there is a straight-line relation between air-phase concentrations and 1/t (t is time). This simplest mathematical relation is the most desirable for engineering applications. Since all derivation has a solid mathematical–physics basis, the models presented in this paper may contribute to casting new light on the underlying mechanisms of diffusion-limited organic emissions.