A Monte Carlo simulation study of the mechanical and conformational properties of networks of helical polymers. I. General concepts

We study the mechanical and conformational properties of networks of helical polymers with a combination of Monte Carlo simulations based on the Wang-Landau algorithm and the three-chain model. We find that the stress-strain behavior of these networks has novel features not observed in typical networks made of synthetic polymers. In particular, we find that as these networks are stretched they first strengthen, then soften and finally strengthen again. This non-monotonic behavior of the stress correlates with the one of the helical content and is rationalized by the elongation-induced formation and melting of the helical structure of the polymer. We complement these results with a study of other conformational properties of the polymer strands that clarify the molecular mechanisms behind the mechanical behavior of these networks. Finally, we present a qualitative comparison of some of our results with the theoretical ones recently reported by Kutter and Terentjev.     

Soft handoff algorithms for CDMA cellular networks

This paper discusses the design of soft handoff algorithms for cellular communication systems. The handoff process is modeled as a hybrid system and handoff design is cast as an optimization problem based on such a model. Performance is evaluated in terms of call quality, average number of active base stations, average number of active set updates, and average amount of interference. A soft handoff algorithm, which achieves a tradeoff between these performance criteria, is obtained using principles of dynamic programming. One key feature of the algorithm is that it incorporates the effects of mobility and shadow fading in the handoff decision. Different diversity combining schemes are considered including selective combining, equal gain combining (EGC), and various optimized combining (OC) methods in the soft handoff mode. For EGC and OC, Wilkinson's and Schwartz and Yeh's methods are used to compute the statistics for the power sum of the signals. Simulation results indicate that the performance of the handoff algorithm is a function of the different combining schemes and of the different methods used to compute the statistics of the power sum. Moreover, it is observed that interference cancellation is important in order for the algorithm to be viable for cellular systems which experience interference due to using nonorthogonal multiple access.     

Colour and COD removal from textile effluent by coagulation and advanced oxidation processes

The aim of this study was to compare the performance of coagulation, Fenton's oxidation (Fe²⁺/H₂O₂) and ozonation for the removal of chemical oxygen demand (COD) and colour from biologically pretreated textile wastewater. FeSO₄ and FeCl₃ were used as coagulants at varying doses and varying colour removal efficiency was measured. For the Fenton process, COD and colour removal efficiencies were found to be 78% and 95% for the Fenton process, and to be 64% and 71% for the Fenton-like process (Fe³⁺/H₂O₂), respectively. Ozonation experiments were conducted at different initial pH values and fixed ozone doses. Ozonation resulted in 43% COD and 97% colour removal whereas these rates increased to 54% and 99% when 5 mg/l hydrogen peroxide was added to the wastewater before ozonation at the same dose. The operating costs of all proposed treatment systems were also evaluated in this study.     

Evolutionary design of neural network architectures: a review of three decades of research

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Cost-conscious comparison of supervised learning algorithms over multiple data sets

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi²Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from “best” to “worst” where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi²Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.     

Film Blowing of Layered Silicate Nanocomposites

Bubble formation and stability in the film blowing processing of in situ polymerized and melt-compounded polyamide 6-based layered silicate nanocomposites (LSNs) are correlated to their underlying rheology, structure, and crystallization behavior. The layered silicates enhance melt elasticity, induce γ -form crystallinity, and increase crystallization rates without having any significant effect on the extent of crystallinity. A bubble stability quantification method employed to assess the level of instability during the film blowing process finds the in situ polymerized LSNs to be more stable than PA6, while melt-compounded LSNs do not display such an improved processability. All of the LSN films produced by film blowing possess superior mechanical properties compared to neat nylon 6, despite their relatively rougher film surfaces.     

Soluble and conductive polypyrrole copolymers containing silicone tegomers

Soluble and processable conductive copolymers of silicone tegomers and pyrrole were developed. This was easily accomplished by the oxidative polymerization of pyrrole monomer by Ce(IV) salt in the presence of silicone tegomers with hydroxyl chain ends. The resulting copolymers were soluble in dimethylformamide. The products were characterized by Fourier transform infrared, ¹H‐NMR, and four‐point probe conductivity, and their surface properties were investigated with contact‐angle measurements. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2896–2901, 2003     

Redox initiation system of ceric salt and α,ω‐dihydroxy poly(dimethylsiloxane)s for vinyl polymerization

The redox system of ceric salt and α,ω‐dihydroxy poly(dimethylsiloxane) is used to polymerize vinyl monomers such as acrylonitrile and styrene to produce block copolymers. The concentration and type of α,ω‐dihydroxy poly(dimethylsiloxane) affects the yield and the molecular weight of the copolymers. The copolymers have about 20°C lower glass‐transition temperatures and much higher contact angle values than of the corresponding homopolymer of vinyl monomers, although the weight percent of α,ω‐dihydroxy poly(dimethylsiloxane) of the copolymers is in the range of 1–2%. © 2006 Wiley Periodicals Inc. J Appl Polym Sci 102: 2112–2116, 2006     

Machining strategy development and parameter selection in 5-axis milling based on process simulations

Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closed-loop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a non-intrusive diagnostic and prognostic tool for FDM machine maintenance and process control.