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Herein, a novel hydrogel (HG-cl-poly(AA)) was synthesized by grafting acrylic acid (AA) onto Hing gum (HG) using methylene-bis-acrylamide (MBA) as a cross-linker and ammonium persulfate (APS) as an initiator in a hot air oven. The percentage swelling of the hydrogel was examined by optimizing various reaction parameters to ensure its maximum swelling percentage. The formation of crosslinked networks was confirmed using Fourier-Transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and thermogravimetric analysis (TGA) techniques. The surface area and hydrophilicity of the prepared hydrogel were determined using Brunauer–Emmett–Teller analyzer and wettability studies, demonstrating a clear correlation with adsorption. The adsorption of crystal violet (CV) dye on the prepared hydrogel was studied via a batch adsorption system based on the amount of adsorbent, immersion time, pH level, and initial dye concentration. The prepared hydrogel showed a 99% removal rate and an excellent adsorption capacity of 492.61mg/g due to the electrostatic, H-bonding, and dipole–dipole interactions between the adsorbent surface and dye molecules. The results were further analyzed using the Langmuir, Freundlich, and Tempkin isotherm models. The study suggests consistency with the pseudo-second-order kinetic model ($R^2=0.998$), further supported by the best data fit with the Langmuir isotherm model ($R^2=0.991$). The thermodynamic study results indicated that the adsorption process is endothermic and spontaneous. Regeneration (desorption) studies showed that the prepared hydrogel could remove CV dye from an aqueous solution and maintain the highest adsorption capacity even after multiple adsorption and desorption cycles. Therefore, the prepared HG-cl-poly(AA) hydrogel could be a potential adsorbent for dye removal and have an admirable capacity for cleaning the aquatic environment.

Component-based software development (CBSD) is an efficient approach used by software developers to develop new software. The commercial off the shelf (COTS) and open-source software (OSS) are two styles to implement CBSD. The COTS provides the interface and depicts the black-box behavior, but does not support several software quality characteristics. On the other hard, OSS is a more efficient approach compared to COTS due to its source code availability. This research aims to identify the reusability level of OSS components from an online repository of OSS. The OSS components are classified based on Chidamber and Kemerer reusability metrics (CK-metrics). This paper proposed a mathematical model to establish the relationship between the reusability of CK-metrics. Reusability level of OSS component has been measured and most effective CK-metrics obtained by applying the Taguchi design and analysis of variance (ANOVA). The input parameters for the experimental design are evaluated based on the OSS repository. Performance analysis has been carried out based upon the interaction effect between the reusability of CK-metrics. Main effect plots are created to identify the most reusable component of the OSS. The genetic algorithm (GA) is used to predict the optimized value of the different control parameters. The results indicate that the OSS component reusability level is 0.698194. The reusability of software has a significant effect on the quality of software. The quality of software can be improved by increasing the reusability of software components.

This paper reports on the development of the data binding tool and its use in Ada source code reusability and software system design assessment. The tool was built around the metric of data bindings. Data bindings fall in the category of data visibility metrics and are used to measure inter-component interactions. Software system components are defined in the context of the Ada language using a flexible scheme. They are used, along with cluster analysis, to present structural configurations of a software system. The clustering technique as well as the tool design and its problems are discussed. The analysis of dendrograms (trees of components produced by the tool) reveals several classes of systems dendrograms and provides a simple mechanism for Ada source code reusability. Finally, the implications of different design methodologies used to develop the test software are discussed and explanations for the several types of dendrogram formulations are given.

In this study, surface-enhanced Raman spectroscopic (SERS) substrates with reusability were carefully fabricated and investigated. Based on a simple and cost-effective hydrothermal process, zinc sheets were used as a base for growing zinc oxide nanorods (ZnO NRs) with hexagonal structures as templates for the SERS substrates. In the experimentation, the authors explored a variation of the physical NR structures based on precursors of zinc nitrate (Zn(NO${}_3{)}_2)$: hexamethylenetetramine (HMTA) at 1:1 ratio, in aqueous solution with DI water at a concentration of 2.5–20mM. The prepared zinc oxide templates were finally decorated with gold nanoparticles (Au NPs) with the sputtering deposition for 90s in order to promote the SERS-active surface. From physical observations, the scanning electron microscopic (SEM) results showed that the ZnO NRs exhibited an increase in size from 56.4nm to 244.06nm as the solution concentration was increased. Further investigations also demonstrated that the Au-decorated SERS-active samples had the gold nanoparticles covering the top of the ZnO NRs. The prepared SERS substrates were finally measured for the Raman enhancement with methylene blue (MB) as the test molecules. The results showed that the SERS substrates could detect the Raman peaks of the MB at the limit of detection of $1\times 10^{-6}$M. In addition, the SERS substrates were tested for reusability with the UV exposure, up to at least nine cycles. This work therefore reported the progress of the fabrications of the SERS-active materials with the reusable potentials in several SERS applications.

Traditional middleware is usually developed on monolithic and non-evolving entities, resulting in a lack of flexibility and interoperability. Among current architectures, Service Oriented Architectures aim to easily develop more adaptable Information Systems. Most often, Web Service is the fitted technical solution which provides the required loose coupling to achieve such architectures. However, there is still much to be done in order to obtain a genuinely flawless Web Service, and current market implementations still do not provide adaptable Web Service behavior depending on the service contract. In this paper, we present our two last years of work toward a more adaptable SOA. We proposed two approaches that consider Aspect Oriented Programming (AOP) as a new design solution for Web Services. The two approaches enable us to glue new non-functional behaviors to a Web Service without going back to modify, recompile, retest and finally redeploy it.

The Internet has promoted several changes in the world. In this scenario, new theories, paradigms and methodologies have been discussed in the education area. In addition, as the development of learning material is expensive and time-consuming, the use of Learning Objects (LO) has been proposed for improving content reuse. However, the emphasis has been on how to describe LO, but it is not clear how to develop them. In addition, people want to satisfy their learning needs according to their personal characteristics such as knowledge background and learning style. The work presented in this paper proposes the increase of LO semantics. The idea behind our proposal is to develop LO based on a model for structuring knowledge (concepts, demonstrations and interactions) according to their context, representation and composition. This approach enables increased reusability and adaptability while providing better structured content material.

Based on recent advances in modern multifunction myoelectric control devices, a combination of effective feature extraction and classification methods is required to enhance the high classification performance, especially in accuracy viewpoint. However, for realizing practical applications of myoelectric control, the effect of long-term usage or reusability is one of the challenging issues that should be more carefully considered, whereas only a few works have investigated this effect in recent. In this study, the behavior of the state-of-the-art multiple feature extraction methods was investigated with the fluctuating electromyography (EMG) signals recorded during four different days with a large number of trials and subjects. To this end, seven multiple feature sets were compared consisting features based on time domain and time-scale representation. Two major points were emphasized: (1) the optimal robust feature set for continuous (both transient and steady-state signals) EMG pattern classification and (2) the effect of fluctuating EMG signals with feature extraction methods for long-term usage. From the classification results, time domain feature sets yielded better performance than time-scale feature sets. The classification accuracies of the time-domain-feature sets had always achieved above 80% by using linear discriminant analysis (LDA) as a classifier and uncorrelated LDA (ULDA) as a dimensionality reduction, whereas the classification accuracies of the time-scale-feature sets were lower than 70% for the fluctuating EMG signals. The effect of dimensionality reduction for the classification of fluctuating EMG signals was also discussed.

We have synthesized nanocrystalline ZnO by a simple precipitation method. The prepared ZnO was found to be highly phase pure and nanocrystalline hexagonal wurtzite structure. UV–Visible–DRS spectroscopy showed the material to have bandgap energy of 3.22eV. HR-SEM image revealed the material to be made up of distinct hexagonal particles with a highly porous surface. AFM analysis was employed to confirm the high surface roughness and porosity of the material. The photocatalytic activity of the prepared ZnO was evaluated by the degradation of benzene-1,4-diol (hydroquinone), under visible light irradiation. Preliminary experiments showed the catalyst to be effective at neutral pH with an optimum catalyst dosage of 4g/L. Kinetic studies showed the degradation reaction to follow pseudo-first-order kinetics. In the presence of commonly used industrial electrolytes, the catalyst exhibited a decrease in efficiency. Reusability studies showed the catalytic efficiency of ZnO to diminish marginally after the third cycle of reuse.

Silver-doped zinc oxide nanoparticles (NPs) were synthesized employing a facile hydrothermal method supported by the seaweed Turbinaria ornata (TO). TO extract was used as a capping agent. The prepared NPs were accounted for using instrumentational analysis such as FE-SEM, EDS, XRD, FTIR, and UV-Vis. Their characteristic study showed an average size of 26 nm with 81% crystallinity with X-ray Diffraction and the band-gap value as 3.25 eV. The biosynthesized doped particle exhibited rod-shaped morphology for better efficiency in dye degradation of Methylene Blue (MB) dye. They were also tested for adsorption kinetics and photocatalytic degradation of MB using sunlight. The efficiency in degrading the MB dye proclaimed a remarkable change in solar irradiation of about 90% reduction to visible light, which leads to the betterment in solar degradation of dye pollutants at a minimal cost.

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).