Actor-based slicing techniques for efficient reduction of Rebeca models

Slicing is a program analysis technique which can be used for reducing the size of the model and avoid state space explosion in model checking. In this work a static slicing technique is proposed for reducing Rebeca models with respect to a property. For applying the actor-based slicing techniques, the Rebeca control flow graph (RCFG) and the Rebeca dependence graph (RDG) are introduced. We propose two different approaches for constructing the RDG, where each approach can be more effective under certain conditions. As the static slicing usually produces large slices, two other slicing-based reduction techniques, step-wise slicing and bounded slicing, are proposed as simple novel ideas. Step-wise slicing first generates slices that overapproximate the behavior of the original model and then refines it, and bounded slicing is based on the semantics of nondeterministic assignments in Rebeca. We also propose a static slicing algorithm for deadlock detection (in absence of any particular property). The efficiency of these techniques is checked by applying them to several case studies which are included in this paper. Similar techniques can be applied on the other actor-based languages.     

Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites (entities) mining systems by using fuzzy linear programming (LP) was studied.LP is the most commonly used operations research methods in mining engineering.After the introductory review of properties and limitations of applying LP,short reviews of the general settings of deterministic and fuzzy LP models are presented.With the purpose of comparative analysis,the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines.After the assessment,LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering.After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models,the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.     

Preparation and evaluation of functionalized goethite nanorods coated by molecularly imprinted polymer for selective extraction of bisphenol A in aqueous medium

Molecular imprinting method is widely used to recognize various templates. Recognition of bisphenol A (BPA) as a harmful template is important. Herein a novel BPA molecular imprinted polymer nanoparticles (BMIPN) is reported by core-shell surface molecular imprinting technology. In this process, BPA was imprinted by polymerizing methacrylic acid (MAA) as functional monomer, in presence of ethylene glycol dimethacrylate (EGDMA) as a cross-linker and goethite nanorods (GNR) functionalized with fumaric acid. The formation of different materials in different steps was confirmed by Fourier transform infrared spectroscopy (FT-IR), the size and surface morphological of material was observed by field emission scanning electron microscopy (FESEM) and Transmission electron microscopy (TEM). The BMIPN structure was characterized by X-ray diffraction (XRD) and thermal gravimetric analysis (TGA). The important effective factors on adsorption process such as pH, contact time, amount of GNR, initial concentration of BPA, release and reusability and selectivity of BMIPN were studied. The excellent selectivity behavior of BMIPN was evaluated in the presence of phenol (Ph), 4, 4′-biphenol (BPh), hydroquinone (HQ) and 2, 2′-Bis(4-hydroxyphenyl)butane (BPB), as interferences. Based on the isotherm and kinetic studies, the experimental data obey Langmuir isotherm and the pseudo-second-order kinetic models. The results showed that BMIPN had excellent properties for adsorbing BPA in a short time even in the presence of analogous.     

Impedance and AC Conductivity of GdCr1−xCoxO3 (x = 0, 0.33, 0.5, 0.67 and 1) Perovskites

Perovskite series GdCr_1?xCo_xO₃ (x = 0, 0.33, 0.5, 0.67 and 1) was obtained using a solution combustion method. The powder XRD was used for identification and structural characterization of the obtained perovskites. All compounds crystallize within the space group Pnma. The morphology of samples was studied using SEM. The impedance and AC conductivity of GdCr_1?xCo_xO₃ were studied using impedance spectroscopy in a frequency range from 10 Hz to 10 MHz and in temperature interval 297–337 K. Changes in electric modulus and DC conductivity, with increasing of the value of x in the structures, were observed. The AC conductivity obeyed the universal power law, σ(ω) = σ(0) + Aωⁿ and revealed semiconductor behavior. The calculated activation energies of existing processes varied with the cobalt content and applied frequency. The impedance spectra showed non‐Debye behavior with a distribution of relaxation times for relaxation and conductive processes. The conduction mechanism for pure orthochromite and orthocobaltite was defined and two types of conduction were observed in the investigated temperature range for the complex perovskites. In order to explain the results, an equivalent circuit with fitted values of circuit components was proposed.     

Determination of flow stress properties of machinable materials with help of simple compression and orthogonal machining test

This paper describes a possible method for predicting values of orthogonal metal cutting properties such as shear angle, cutting force etc., on a basis of the well known Hollomon equation, using a simple compression test in order to avoid any cutting experiments. There are two possibilities: the flow stress properties can be obtained from an independent material test; or by measuring the active and passive cutting forces from the orthogonal machining test itself. This paper is concerned with a material flow stress equation, including the effects of strain (ε), strain rate ( ) and temperature (T), which is one of the five equations that have to be solved in simulation analysis with the finite element method. In finding a solution for those five equations, it is necessary to dispose of flow stress properties by rearrangement of the Hollomon equation and so making it usable for cutting process investigation. The rearrangement is described in this paper.     

AN UNSUPERVISED INCREMENTAL LEARNING ALGORITHM FOR DOMAIN-SPECIFIC LANGUAGE DEVELOPMENT

While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, and speech recognition, its application to problems in programming language and software engineering domains has been limited. We have found a new application area for grammar inference which intends to make domain-specific language development easier for domain experts not well versed in programming language design, and finds a second application in construction of renovation tools for legacy software systems. As a continuation of our previous efforts to infer context-free grammars (CFGs) for domain-specific languages which previously involved a genetic-programming based CFG inference system, we discuss extensions to the inference capabilities of GenInc, an incremental learning algorithm for inferring CFGs. We show that these extensions enable GenInc to infer more comprehensive grammars, discuss the results of applying GenInc to various domain-specific languages and evaluate the results using a comprehensive suite of grammar metrics.     

Minimizing the number of late jobs in a stochastic setting using a chance constraint

We consider the single-machine scheduling problem of minimizing the number of late jobs. We omit here one of the standard assumptions in scheduling theory, which is that the processing times are deterministic. In this scheduling environment, the completion times will be stochastic variables as well. Instead of looking at the expected number of on time jobs, we present a new model to deal with the stochastic completion times, which is based on using a chance constraint to define whether a job is on time or late: a job is on time if the probability that it is completed by the deterministic due date is at least equal to a certain given minimum success probability. We have studied this problem for four classes of stochastic processing times. The jobs in the first three classes have processing times that follow: (i) A gamma distribution with shape parameter p _j and scale parameter β, where β is common to all jobs; (ii) A negative binomial distribution with parameters p _j and r, where r is the same for each job; (iii) A normal distribution with parameters p _j and σ _j ². The jobs in the fourth class have equally disturbed processing times, that is, the processing times consist of a deterministic part and a random component that is independently, identically distributed for each job. We show that the first two cases have a common characteristic that makes it possible to solve these problems in O(nlog n) time through the algorithm by Moore and Hodgson. To analyze the third and fourth problem we need the additional assumption that the due dates and the minimum success probabilities are agreeable. We show that under this assumption the third problem is -hard in the ordinary sense, whereas the fourth problem is solvable by Moore and Hodgson’s algorithm. We further indicate how the problem of maximizing the expected number of on time jobs (with respect to the standard definition) can be tackled if we add the constraint that the on time jobs are sequenced in a given order and when we require that the probability that a job is on time amounts to at least some given lower bound. Supported by EC Contract IST-1999-14186 (Project alcom-FT).     

A non-parametric CAE approach to office rents: Identification of Helsinki metropolitan area submarkets

The study attempts to identify and estimate the office rents of submarkets in the Helsinki metropolitan area. We applied a non-parametric empirical approach called the CAE method to identify six parameters: highway APD (access point distance), car traffic density, light rail APD, main retail distance, office building density and effective age. Our results suggest that car traffic density is the single most influential parameter. Office rent decreases with effective age and increases with the density of office buildings. Longer distances to highway access points and to the main retail centres decrease office rents, while shorter distances to the light rail access points increase office rents in general and particularly for locations close to highway access points. We identified local peaks by inspecting multiple graphs. The local peaks were considered evidence for the existence of commercial office submarkets within the Helsinki metropolitan area. We identified seven submarkets at different rent levels. Interpreting submarkets from the CAE graphs allowed us to recognise particular business districts in the Helsinki metropolitan area. In addition, it is of great significance that the roles of the given and estimated variables can be exchanged. The method is directly applicable in real estate studies using adapted database and prescribed smoothing parameters.     

Model-driven engineering of process control software – beyond device-centric abstractions

This paper presents a new, two-level, model-driven engineering approach to industrial process control software. The first level (infrastructure engineering) is concerned with the following: the definition of the development process and guidelines, the definition of a domain-specific modeling language, the specification of the model transformations, and the development of a tool suite. This tool suite enables modeling of the process control software and the automatic code generation for programmable logic controllers. In the second level (application engineering), the process control software is engineered using the results of the infrastructure level. The approach is demonstrated on excerpts from an industrial project.