A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

An Approach to Synthesize Thick α- and γ-Al2O3 Coatings by High-Speed Physical Vapor Deposition

Crystalline α- and γ-Al₂O₃ exhibit in many applications high wear resistance, chemical resistance, and hot hardness, making them interesting materials for production engineering. To synthesize α-Al₂O₃ with high coating thickness of s ≥ 10 μm, chemical vapor deposition at temperatures T > 1000 °C is well established. However, there are almost no studies dealing with the synthesis of thick α-Al₂O₃ by physical vapor deposition (PVD) at high temperatures T > 700 °C. High-temperature deposition of thick coatings can be realized by means of the dense hollow cathode plasma, combined with the transport function of the plasma gas in high-speed (HS) PVD. Herein, crystalline α- and γ-Al₂O₃ films are deposited on cemented carbides at substrate temperatures T _s ≈ 570 °C and T _s ≈ 780 °C by HS-PVD. These coatings exhibit a thickness up to s = 20 μm. Moreover, phase analysis presents α-phases in coatings synthesized at substrate temperature of T _s ≈ 780 °C with significant higher hardness than films by T _s ≈ 570 °C. These release the potential of HS-PVD to synthesize α-Al₂O₃ coatings with high thickness. Thereby, a higher thickness of these coatings is beneficial for the wear protection of turning and die casting tools.     

A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

This paper proposes a novel multi-objective model for an unrelated parallel machine scheduling problem considering inherent uncertainty in processing times and due dates. The problem is characterized by non-zero ready times, sequence and machine-dependent setup times, and secondary resource constraints for jobs. Each job can be processed only if its required machine and secondary resource (if any) are available at the same time. Finding optimal solution for this complex problem in a reasonable time using exact optimization tools is prohibitive. This paper presents an effective multi-objective particle swarm optimization (MOPSO) algorithm to find a good approximation of Pareto frontier where total weighted flow time, total weighted tardiness, and total machine load variation are to be minimized simultaneously. The proposed MOPSO exploits new selection regimes for preserving global as well as personal best solutions. Moreover, a generalized dominance concept in a fuzzy environment is employed to find locally Pareto-optimal frontier. Performance of the proposed MOPSO is compared against a conventional multi-objective particle swarm optimization (CMOPSO) algorithm over a number of randomly generated test problems. Statistical analyses based on the effect of each algorithm on each objective space show that the proposed MOPSO outperforms the CMOPSO in terms of quality, diversity and spacing metrics.     

Thermal buckling analysis of temperature-dependent FG-CNTRC quadrilateral plates

The main objective of the present study is to analyze the thermal buckling of functionally graded carbon nanotube-reinforced composite (FG-CNTRC) quadrilateral plates. Functionally graded patterns are introduced for the distribution of the carbon nanotubes (CNTs) through the thickness direction of the plate. The effective material properties of nanocomposite plate reinforced by CNTs are considered to be temperature-dependent (TD) and estimated using the micromechanical model. By the use of minimum total potential energy principle and based on the first-order shear deformation theory of plates, the stability equations are obtained. In order to use the generalized differential quadrature (GDQ) method and solve the stability equations, the irregular domain of quadrilateral plate is transformed into regular computational domain employing the mapping technique. The efficiency and accuracy of the proposed approach are first validated. Then, a comprehensive parametric study is presented to examine the effects of model parameters on the thermal buckling of FG-CNTRC quadrilateral plates. The results indicate that considering temperature dependency of the material properties plays an important role in the stability of the FG-CNTRC quadrilateral plates subjected to thermal loading.     

A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing

Scheduling means devoting tasks among computational resources, considering specific goals. Cloud computing is facing a dynamic and rapidly evolving situation. Devoting tasks to the computational resources could be done in numerous different ways. As a consequence, scheduling of tasks in cloud computing is considered as a NP-hard problem. Meta-heuristic algorithms are a proper choice for improving scheduling in cloud computing, but they should, of course, be consistent with the dynamic situation in the field of cloud computing. One of the newest bio-inspired meta-heuristic algorithms is the chicken swarm optimization (CSO) algorithm. This algorithm is inspired by the hierarchical behavior of chickens in a swarm for finding food. The diverse movements of the chickens create a balance between the local and the global search for finding the optimal solution. Raven roosting optimization (RRO) algorithm is inspired by the social behavior of raven and the information flow between the members of the population with the goal of finding food. The advantage of this algorithm lies in using the individual perception mechanism in the process of searching the problem space. In the current work, an ICDSF scheduling framework is proposed. It is a hybrid (IRRO-CSO) meta-heuristic approach based on the improved raven roosting optimization algorithm (IRRO) and the CSO algorithm. The CSO algorithm is used for its efficiency in satisfying the balance between the local and the global search, and IRRO algorithm is chosen for solving the problem of premature convergence and its better performance in bigger search spaces. First, the performance of the proposed hybrid IRRO-CSO algorithm is compared with other imitation-based swarm intelligence methods using benchmark functions (CEC 2017). Then, the capabilities of the proposed scheduling hybrid algorithm (IRRO-CSO) are tested using the NASA-iPSC parallel workload and are compared with the other available algorithms. The obtained results from the implementation of the hybrid IRRO-CSO algorithm in MATLAB show an improvement in the average best fitness compared with the following algorithms: IRRO, RRO, CSO, BAT and PSO. Finally, simulation tests performed in cloud computing environment show improvements in terms of reduction of execution time, reduction of response time and the increase in throughput by using the proposed hybrid IRRO-CSO approach for dynamic scheduling.     

Fracture Assessment of U-Notched Graphite Plates Under Tension

The experimentally obtained tensile load-bearing capacity of fifteen U-notched polycrystalline graphite plates reported in literature was theoretically estimated by means of two well-known brittle fracture models, namely the mean stress (MS) and the point stress (PS) criteria. The results showed that while the mean discrepancies between the experimental and the theoretical results for both the models are very good and approximately equal, the discrepancies are significantly different for various notch tip radii. Meanwhile, the results of MS and PS criteria were compared with the results of the strain energy density (SED) criterion reported in literature. Relatively similar value of mean discrepancy was also obtained for the SED model. It was demonstrated in this research that for small values of the notch tip radius, the MS model is the most appropriate failure criterion while the PS and SED criteria are much better models for medium radii. Moreover, for large notch tip radii, the MS and PS criteria are better choices for tensile fracture assessment of U-notched graphite plates than the SED criterion.     

Preparation and study of a thermo-responsive membrane using binary liquid crystal mixtures of cholesteryl cetyl ether and cholesteryl oleyl carbonate

A facile method for the synthesis of thermotropic liquid crystalline cholesteryl cetyl ether (CCE) was carried out from cholesterol and cetyl alcohol using montmorillonite K-10 as an acid catalyst. The aim of this study was to investigate the use of liquid crystalline blends of CCE and cholesteryl oleyl carbonate (COC) with appropriate crystal to smectic phase temperature (T _c?Cs) just above body temperature as a temperature-modulated drug permeation system. Using 30/70?mol ratio of COC/CCE, a mixture of desirable phase transition temperature was obtained. The phase transition behavior of COC/CCE binary liquid crystalline mixture was established by differential scanning calorimetry and polarizing optical microsopy. The COC/CCE-embedded cellulose nitrate membrane was used by an in vitro drug penetration studies. Paracetamol and mesalazine were chosen as hydrophobic and hydrophilic drug models, respectively. Paracetamol permeability through the membrane was higher at temperatures above the phase transition of liquid crystal (LC) blends (39?°C) than its permeability below the phase transition temperature of liquid crystal blends (30?°C). The drug penetration through LC-embedded cellulose membrane was influenced by the pore size of the membrane and therefore the adsorbed amount of LC. There was no penetration of mesalazine through that membrane presumably, due to the differences in hydrophilicity of LC-embedded membrane and permeated drug.     

Thermal Analysis of Non-linear Convective–Radiative Hyperbolic Lumped Systems with Simultaneous Variation of Temperature-Dependent Specific Heat and Surface Emissivity by MsDTM and BPES

With the advent of temperatures near absolute zero, it is often claimed that at very low temperatures the effect of thermal wave propagation must be included by the hyperbolic heat conduction equation (HHCE). In this paper the non-linear convective–radiative HHCE is investigated. Opposite to common numerical analyses, analytical expressions are obtained for the temperature variations by the multi-step differential transformation method. Some conclusions about alteration of the specific heat of the material, temperature steeping, and Vernotte number have been formulated.