Constrained Motif Discovery in Time Series

The goal of motif discovery algorithms is to efficiently find unknown recurring patterns. In this paper, we focus on motif discovery in time series. Most available algorithms cannot utilize domain knowledge in any way which results in quadratic or at least super-linear time and space complexity. In this paper we define the Constrained Motif Discovery problem which enables utilization of domain knowledge into the motif discovery process. The paper then provides two algorithms called MCFull and MCInc for efficiently solving the constrained motif discovery problem. We also show that most unconstrained motif discovery problems be converted into constrained ones using a change-point detection algorithm. A novel change-point detection algorithm called the Robust Singular Spectrum Transform (RSST) is then introduced and compared to traditional Singular Spectrum Transform using synthetic and real-world data sets. The results show that RSST achieves higher specificity and is more adequate for finding constraints to convert unconstrained motif discovery problems to constrained ones that can be solved using MCFull and MCInc. We then compare the combination of RSST and MCFull or MCInc with two state-of-the-art motif discovery algorithms on a large set of synthetic time series. The results show that the proposed algorithms provided four to ten folds increase in speed compared the unconstrained motif discovery algorithms studied without any loss of accuracy. RSST+MCFull is then used in a real world human-robot interaction experiment to enable the robot to learn free hand gestures, actions, and their associations by watching humans and other robots interacting.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

一种基于复数域网络编码的双层卫星通信系统

针对卫星通信网络吞吐量不足、可靠性不高的问题,提出一种基于复数域网络编码(Complex Field Network Coding,CFNC)的卫星通信方案。该方案在信号发送前对源信息作预编码处理,即在复数域上选取一个大小合适的参数化空时码与源信号相乘,编码后的信号与源信号在复数域上有着一一映射关系。对该方案的吞吐量和成对差错概率(Pairwise Error Probability,PEP)做了详尽的理论分析,结果表明,采用该编码方案的卫星通信系统在终端发射功率不变的情况下,吞吐量比路由模式提高了100%以上,比传统的CFNC方式至少可提高75%。该方案还可以扩展至更多的地面源节点,从而支持多用户网络通信。最后,仿真实验表明,在较高的信噪比下,PEP仿真值逼近于渐近值,验证了理论分析的正确性。     

Efficient Hybrid Genetic Algorithm for Resource Leveling via Activity Splitting

Resource leveling problem is an attractive field of research in project management. Traditionally, a basic assumption of this problem is that network activities could not be split. However, in real-world projects, some activities can be interrupted and resumed in different time intervals but activity splitting involves some cost. The main contribution of this paper lies in developing a practical algorithm for resource leveling in large-scale projects. A novel hybrid genetic algorithm is proposed to tackle multiple resource-leveling problems allowing activity splitting. The proposed genetic algorithm is equipped with a novel local search heuristic and a repair mechanism. To evaluate the performance of the algorithm, we have generated and solved a new set of network instances containing up to 5,000 activities with multiple resources. For small instances, we have extended and solved an existing mixed integer programming model to provide a basis for comparison. Computational results demonstrate that, for large networks, the proposed algorithm improves the leveling criterion at least by 76% over the early schedule solutions. A case study on a tunnel construction project has also been examined.     

Study of rigidity of semiconducting vanadate glasses and its importance in use of coatings

The elastic moduli of some multicomponent vanadate based glasses were analysed in terms of the bond compression model by some physical parameters such as, the density, average stretching force constant and average atomic ring size. These parameters were calculated for all the glass series and for all the glass composition to estimate the rigidity of these glasses. The results showed that the average force constant and the elastic moduli of these glasses are sensitive to the decrease in PbO content. This behaviour was attributed to the increase in the molar volume and the role of different modifiers. These parameters along with the coordination number of the glasses affect the glass transition temperature. The correlation between the elastic moduli and thermal properties of these samples showed that 0·25MoO₃–0.25PbO–0·5V₂O₅ glass is the most rigid and has an applicable glass transition temperature for coating.     

A Multi-objective Optimization for Sizing and Placement of Voltage-controlled Distributed Generation Using Supervised Big Bang–Big Crunch Method

Abstract—This article presents an efficient multi-objective optimization approach based on the supervised big bang–big crunch method for optimal planning of dispatchable distributed generator. The proposed approach aims to enhance the system performance indices by optimal sizing and placement of distributed generators connected to balanced/unbalanced distribution networks. The distributed generation units in the proposed algorithms are modeled as a voltage-controlled node with the flexibility to be converted to a constant power node in the case of reactive power limit violation. The proposed algorithm is implemented in the MATLAB (The MathWorks, Natick, Massachusetts, USA) environment, and the simulation studies are performed on IEEE 69-bus and IEEE 123-node distribution test systems. Validation of the proposed method is done by comparing the results with published results obtained from other competing methods, and the consequent discussions prove the effectiveness of the proposed approach.     

The role of advertisements in the marketing of gated communities as a new Western suburban lifestyle: a case study of the Greater Cairo Region, Egypt

This paper examines the advertising themes and rhetoric that have been assembled in the marketing of the Greater Cairo Region’s (GCR) newly built gated communities. We demonstrate how place-marketing strategies, in this case, selling the Egyptian dream home, draws upon specific landscape offerings and values. It shows how aspects of globalization interact with processes of urbanization in the GCR to create new landscapes of housing consumption. The globalization of mass media has influenced consumption preferences and brought new consumption choices to the GCR’s residents. This study concludes that the demand for gated communities in the GCR, in large part, has been created by developers who foster an image of these areas as symbols of ‘modernism’ and Western lifestyles. Underlying these sales efforts is the common assumption held by developers, potential buyers and segments of the larger society that the lifestyles of Western urbanization should naturally emerge as the result of economic development.     

Biosorption potential of the mediterranean plant (Posidonia oceanica) for the removal of Cu2+ ions from aqueous media : Equilibrium,kinetic, thermodynamic and mechanism analysis

The biosorption characteristics of copper(II) ions using Posidonia oceanica biomass were investigated. Experimental parameters affecting the biosorption process such as pH level, contact time, biosorbent dosage and temperature were studied. The equilibrium data were applied to the Langmuir, Freundlich and Dubinin-Radushkevich (D-R) isotherm models. The Langmuir model fitted very well the equilibrium data, and the maximum uptake of Cu(II) by Posidonia oceanica was found to be 76.92 mg/g. The mean free energy E (10.78 kJ/mol) from the D-R isotherm indicated a chemical ion-exchange mechanism. Kinetic results showed that the pseudo-second-order kinetic model was well fitted to the experimental data. Thermodynamic parameters depicted the exothermic nature of biosorption and the process was feasible and spontaneous. The results of FTIR (Fourier-transform infrared spectroscopy) revealed that carboxyl, amine, and hydroxyl groups on the biomass surface were involved in the biosorption of Cu(II) ions.     

Influence of Chemical Reaction on Mass Transport in Yield Stress Exhibiting Flow Regime

Theoretical Foundations of Chemical Engineering - In this study, the simulations for first-order chemical reactions (constructive and destructive) in the flow of the Casson fluid with...     

Processing of High‐Density Magnesia Spinel Electro‐Conducting Ceramic Composite and its Oxidation at 1400°C

Dense conductive ceramic composites of MgAl₂O₄ and MoSi₂ were processed using combustion synthesis under‐load methodology. The starting reactants were blends of MoO₃, SiO₂, MgO, and Al powders. The study revealed that to obtain dense composite with homogeneous microstructure, 30 wt. % of MoSi₂, 18.5 μm Al average particle size, and 175 MPa load are required. The produced dense composite was found to have a low apparent porosity (<1.0 vol. %), moderate density 4.61 g/cm³, and low electrical resistivity 0.3 Ωcm. The dense composite exhibited excellent thermodynamic stability between its phases at 1400°C in open atmosphere.