Synthesis and Characterization of Poly(ether-block-amide) Membranes

Poly(ether-block-amide) membranes were made via casting a solution on a nonsolvent (water) surface. In this research, effects of different parameters such as ratio of solvent mixture (n-butanol/isopropanol), temperature, composition of coagulation bath (water) and polymer concentration, on quality of the thin film membranes were studied. The mechanism of membrane formation involves solution spreading, solvent–nonsolvent exchange, and partial evaporation of the solvent steps. Solvent- nonsolvent exchange is the main step in membrane formation and determines membrane morphology. However, at higher temperature of polymeric solution greater portion of solvent evaporates. The results showed that type of demixing process (mutual affinity between solvent and nonsolvent) has important role in film formation. Also, addition of solvent to the nonsolvent bath is effective on membrane morphology. The film quality enhances with increasing isopropanol ratio in the solvent mixture. This behavior can be related to increasing of solution surface tension, reduction of interfacial tension between solution and nonsolvent and delayed solvent-nonsolvent demixing. Uniform films were made at a temperature rang of 60–80 °C and a polymer concentration of 4–7 wt%. Morphology of the membranes was investigated with scanning electron micrograph (SEM). Pervaporation of ethyl butyrate/water mixtures was studied using these membranes and high separation performance was achieved. For ethyl butyrate/water mixtures, It was observed that both permeation flux and separation factor increase with increasing ethyl butyrate content in the feed. Increasing temperature in limited range studied resulted in decreasing separation factor and increasing permeation flux.     

Computing the throughput of Concatenation State Machines

Concatenation State Machine (CSM) is a labeled directed And–Or graph representing a deterministic push-down transducer. In the high-performance version of CSM, labels associated to edges are words (rather than letters) over the input alphabet. The throughput of a path p is defined as the sum of the lengths of the labels of the path, divided by the number of edges of p. The throughput of a CSM M is defined as the infimum of the throughput of all accepting paths of M. In this paper we give an $\mathrm{O}(nm\log (\frac{\mathit{max}-\mathit{min}}{\epsilon }))$ algorithm, computing an ε-approximation of the throughput of a CSM M, where n is the number of nodes, m is the number of edges, and max (min) is the maximum (respectively, minimum) of the lengths of the edge labels of M. While we have been interested in a particular case of an And–Or graph representing a transducer, we have actually solved the following problem: if a real weight function is defined on the edges of an And–Or graph G, we compute an ε-approximation of the infimum of the complete hyper-path mean weights of G. This problem, if restricted to digraphs, is strongly connected to the problem of finding the minimum cycle mean.     

Highly efficient nonlinear regression for big data with lexicographical splitting

This paper considers the problem of online piecewise linear regression for big data applications. We introduce an algorithm, which sequentially achieves the performance of the best piecewise linear (affine) model with optimal partition of the space of the regressor vectors in an individual sequence manner. To this end, our algorithm constructs a class of \(2^D\) sequential piecewise linear models over a set of partitions of the regressor space and efficiently combines them in the mixture-of-experts setting. We show that the algorithm is highly efficient with computational complexity of only \(O(mD^2)\), where m is the dimension of the regressor vectors. This efficient computational complexity is achieved by efficiently representing all of the \(2^D\) models using a “lexicographical splitting graph.” We analyze the performance of our algorithm without any statistical assumptions, i.e., our results are guaranteed to hold. Furthermore, we demonstrate the effectiveness of our algorithm over the well-known data sets in the machine learning literature with computational complexity fraction of the state of the art.     

Modulated large-pore mesoporous silica as an efficient base catalyst for the Henry reaction

In this study, mesoporous silica materials with tuned pores and surface areas were successfully synthesized by adjusting the amount of applied hexane and controlling the hydrothermal temperature. The synthesized silica materials were then functionalized by an amine group to produce solid base catalysts and be applicable as efficient heterogeneous base catalysts for the Henry reaction. The mesoporous silica catalysts possessing large-pores and surface area expose their active catalytic sites and thereby improve contacts with reactants fulfilling the reactions expeditiously in comparison with solid base catalysts possessing small-pores and surface area. The results indicated that the yield of the products is significantly dependent on the structure of the applied solid base catalysts. The modulated large-pore solid base catalysts presented high catalytic activity in Henry reactions and could be reused for five consecutive cycles.     

Sleep scoring using polysomnography data features

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments.     

Fast CU size and prediction mode decision method for HEVC encoder based on spatial features

In the high-efficiency video coding (HEVC) standard, intra prediction has higher computational complexity compared with H.264/AVC (advanced video coding) because of increasing the number of intra prediction modes and also higher number of coding unit (CU) sizes. The HEVC encoder evaluates 35 prediction modes on five possible prediction unit (PU) sizes to find the one with the minimum rate–distortion cost. Although this approach improves coding efficiency, it is very time-consuming. In this paper, we propose a fast intra prediction method to reduce the complexity of I-frame coding. The proposed method consists of three stages which is based on smoothness spatial feature. In the first stage, a measure is introduced to estimate CU smoothness by using sum of absolute differences (SAD) among CU pixels in four directions. By considering that a smooth region can be predicted with larger CUs, when the measured smoothness parameter is lower than a predefined threshold, only the prediction modes in the current CU are evaluated. In the second stage, the number of intra prediction modes is reduced based on the calculated SADs in the previous stage. In the last stage, if the first three candidate modes resulted from rough mode decision stage in the previous PU and the current PU are similar, then the best mode prediction of the previous PU is selected as the best candidate mode. Experimental results indicate that the proposed method can reduce the coding time on average to 43 % and maintain coding video quality, whereas bitrate increases negligibly (0.5 %).     

Variational rationality,variational principles and the existence of traps in a changing environment

Journal of Global Optimization - This paper has two aspects. Mathematically, in the context of global optimization, it provides the existence of an optimum of a perturbed optimization problem that...