Nonlinear state feedback design for continuous polynomial systems

The objective of this paper is to simplify the complexity of practical implementation of the input-state feedback linearization technique for the control of input-affine systems. A polynomial approach which makes use of the Taylor series expansion and the Kronecker product is developed. Our work aims to address the problem of synthesizing a polynomial control via a nonlinear analytical coordinates transformation. To check the effectiveness of the investigated approach, we consider the control problem of a series DC motor. A comparative study with the input-state feedback linearization control is developed.     

Evaluating the impact of mass housings' in-between spaces' spatial configuration on users' social interaction

This research examines the impact of in-between space configuration on users' social practices. The effects of different degrees of permeability of these spaces on the social interactions of the inhabitants are also analyzed. This research is based on a comparative approach using space syntax analysis and snapshot observations on four mass housing neighborhoods in Setif, Algeria. The neighborhoods are selected according to their degree of permeability, location in the city, realization period, and syntax measures from integrated to segregated. The activity patterns of two-week days for each study case are observed and recorded. Correlation results of observed social activities and syntactic measures demonstrate that spatial configuration affects people's use of in-between spaces. Moreover, connectivity and clustering coefficients are revealed to be important indicators of social activities. Previous urban studies argued that integrated spaces are mostly used by people. However, social interaction in the studied cases increases in the least connected areas, adjoining spaces to the buildings, and most closed spaces. This research finds that social activities are dependent on the degree of permeability, which when low, gives rise to increased social interaction and activities.     

Prevention of 7-ketocholesterol-induced side effects by natural compounds

Abstract

Cholesterol oxidation products, also named oxysterols, can be formed either by cholesterol auto-oxidation, enzymatically or both. Among these oxysterols, 7-ketocholesterol (7KC) is mainly formed during radical attacks that take place on the carbon 7 of cholesterol. As increased levels of 7KC have been found in the tissues, plasma and/or cerebrospinal fluid of patients with major diseases, especially age-related diseases (cardiovascular diseases, eye diseases, neurodegenerative diseases), some cancers, and chronic inflammatory diseases, it is suspected that 7KC, could contribute to their development. Since 7KC, provided by the diet or endogenously formed, is not or little efficiently metabolized, except in hepatic cells, its cellular accumulation can trigger numerous side effects including oxidative stress, inflammation and cell death. To counteract 7KC-induced side effects, it is necessary to characterize the metabolic pathways activated by this oxysterol to identify potential targets for cytoprotection and geroprotection. Currently, several natural compounds (tocopherols, fatty acids, polyphenols, etc) or mixtures of compounds (oils) used in traditional medicine are able to inhibit the deleterious effects of 7KC. The different molecules identified could be valued in different ways (functional foods, recombinant molecules, theranostic) to prevent or treat diseases associated with 7KC.     

Ag‐nanocomposite based on carboxymethylcellulose for humidity detection: Green synthesis and sensing performances

A novel highly sensitive Ag‐nanocomposite for humidity detection has been successfully prepared. Initially, cellulose isolated from Tunisian palm date petiole was converted to carboxymethyl cellulose (CMC) as biomatrix under heterogeneous conditions. The synthesized product was thoroughly characterized by means of FT‐IR spectroscopy, viscosity analysis, and high performance size exclusion chromatography multiangle laser light scattering. CMC was used as reducing and stabilizing agent to prepare CMC‐stabilized silver nanoparticles via a rapid green method. The bioreduction of silver ions under different experimental conditions, including Ag⁺ concentration and pH, was investigated. Optimal experimental conditions provided a long‐term stable colloidal suspension and well‐dispersed spherical shape Ag NPs with a size ranging from 13 to 28 nm. Ag‐nanocomposite coated quartz microbalance crystal was used as sensitive layer for humidity detection. A comparative study showed that the immobilized metallic nanostructures greatly reduced changes in visco‐elastic properties, increased surface area as well as surface local charge density of the CMC. Consequently, sensor performances were greatly enhanced: better stability even at higher relative humidity (RH), good reproducibility and linearity (11–98% RH), low hysteresis characteristics, and rapid response and recovery times (14 and 6 s, respectively) were obtained. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43686.     

On the use of textual feature extraction techniques to support the automated detection of refactoring documentation

Innovations in Systems and Software Engineering - Refactoring is the art of improving the internal structure of a program without altering its external behavior, and it is an important task when it...     

Towards a new possibilistic query translation tool for cross-language information retrieval

Multimedia Tools and Applications - Approaches of query translation in Cross-Language Information Retrieval (CLIR) have frequently used dictionaries which suffer from translation ambiguity....     

Training a neural network to select dispatching rules in real time

Dispatching rules are often suggested to schedule manufacturing systems in real-time. Numerous dispatching rules exist. Unfortunately no dispatching rule (DR) is known to be globally better than any other. Their efficiency depends on the characteristics of the system, operating condition parameters and the production objectives. Several authors have demonstrated the benefits of changing dynamically these rules, so as to take into account the changes that can occur in the system state. A new approach based on neural networks (NN) is proposed here to select in real time, each time a resource becomes available, the most suited DR. The selection is made in accordance with the current system state and the workshop operating condition parameters. Contrarily to the few learning approaches presented in the literature to select scheduling heuristics, no training set is needed. The NN parameters are determined through simulation optimization. The benefits of the proposed approach are illustrated through the example of a simplified flow-shop already published. It is shown that the NN can automatically select efficient DRs dynamically: the knowledge is only generated from simulation experiments, which are driven by the optimization method. Once trained offline, the resulting NN can be used online, in connection with the monitoring system of a flexible manufacturing system.     

On the use of many quality attributes for software refactoring: a many-objective search-based software engineering approach

Search-based software engineering (SBSE) solutions are still not scalable enough to handle high-dimensional objectives space. The majority of existing work treats software engineering problems from a single or bi-objective point of view, where the main goal is to maximize or minimize one or two objectives. However, most software engineering problems are naturally complex in which many conflicting objectives need to be optimized. Software refactoring is one of these problems involving finding a compromise between several quality attributes to improve the quality of the system while preserving the behavior. To this end, we propose a novel representation of the refactoring problem as a many-objective one where every quality attribute to improve is considered as an independent objective to be optimized. In our approach based on the recent NSGA-III algorithm, the refactoring solutions are evaluated using a set of 8 distinct objectives. We evaluated this approach on one industrial project and seven open source systems. We compared our findings to: several other many-objective techniques (IBEA, MOEA/D, GrEA, and DBEA-Eps), an existing multi-objective approach a mono-objective technique and an existing refactoring technique not based on heuristic search. Statistical analysis of our experiments over 31 runs shows the efficiency of our approach.     

A robust multi-objective approach to balance severity and importance of refactoring opportunities

Refactoring large systems involves several sources of uncertainty related to the severity levels of code smells to be corrected and the importance of the classes in which the smells are located. Both severity and importance of identified refactoring opportunities (e.g. code smells) are difficult to estimate. In fact, due to the dynamic nature of software development, these values cannot be accurately determined in practice, leading to refactoring sequences that lack robustness. In addition, some code fragments can contain severe quality issues but they are not playing an important role in the system. To address this problem, we introduced a multi-objective robust model, based on NSGA-II, for the software refactoring problem that tries to find the best trade-off between three objectives to maximize: quality improvements, severity and importance of refactoring opportunities to be fixed. We evaluated our approach using 8 open source systems and one industrial project, and demonstrated that it is significantly better than state-of-the-art refactoring approaches in terms of robustness in all the experiments based on a variety of real-world scenarios. Our suggested refactoring solutions were found to be comparable in terms of quality to those suggested by existing approaches, better prioritization of refactoring opportunities and to carry an acceptable robustness price.