Binders for jute-reinforced unsaturated polyester resin

Reinforcement of polyester resin with jute cloth under constant load is studied, The pretreatment of jute cloth with tetrahydrofuran and the effect of binding properties of poly(vinyl acetate) and acrylic acid and their effect on the impact, tensile, and water absorptions of jute cloth polyester composites are reported and their chemical reactions are discussed.     

Analytical Modeling of Short-Channel Fully-Depleted Triple Work Function Metal Gate (TWFMG) SOI MESFET

Silicon - In this paper, a new structure: triple work function metal gate SOI MESFET, intended for integration into the deep-submicron CMOS technology, is proposed. The gate of the device consists...     

An Engineered sgsh Mutant Zebrafish Recapitulates Molecular and Behavioural Pathobiology of Sanfilippo Syndrome A/MPS IIIA

Mucopolysaccharidosis IIIA (MPS IIIA, Sanfilippo syndrome type A), a paediatric neurological lysosomal storage disease, is caused by impaired function of the enzyme N-sulfoglucosamine sulfohydrolase (SGSH) resulting in impaired catabolism of heparan sulfate glycosaminoglycan (HS GAG) and its accumulation in tissues. MPS IIIA represents a significant proportion of childhood dementias. This condition generally leads to patient death in the teenage years, yet no effective therapy exists for MPS IIIA and a complete understanding of the mechanisms of MPS IIIA pathogenesis is lacking. Here, we employ targeted CRISPR/Cas9 mutagenesis to generate a model of MPS IIIA in the zebrafish, a model organism with strong genetic tractability and amenity for high-throughput screening. The sgsh^Δex5−6 zebrafish mutant exhibits a complete absence of Sgsh enzymatic activity, leading to progressive accumulation of HS degradation products with age. sgsh^Δex5−6 zebrafish faithfully recapitulate diverse CNS-specific features of MPS IIIA, including neuronal lysosomal overabundance, complex behavioural phenotypes, and profound, lifelong neuroinflammation. We further demonstrate that neuroinflammation in sgsh^Δex5−6 zebrafish is largely dependent on interleukin-1β and can be attenuated via the pharmacological inhibition of Caspase-1, which partially rescues behavioural abnormalities in sgsh^Δex5−6 mutant larvae in a context-dependent manner. We expect the sgsh^Δex5−6 zebrafish mutant to be a valuable resource in gaining a better understanding of MPS IIIA pathobiology towards the development of timely and effective therapeutic interventions.     

A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints

In the present paper, the dynamic facilities layout problem is studied in presence of ambiguity of information flow. Product demand (and consequently material flow) is defined as fuzzy numbers with different membership functions. The problem is modeled in fuzzy programming. Three models of expected value, chance-constrained programming and dependent-chance programming and two hybrid intelligent algorithms are then presented. At the end, efficiency of algorithms for solving fuzzy models of dynamic facilities layout is shown through some numerical examples.     

Global stabilization of a class of delay discrete-time nonlinear systems via state and output feedback

This paper deals with stabilization of a class of delay discrete-time nonlinear systems through state and output feedback. We provide an explicit bounded state feedback law as an extension of the Jurdjevic-Quinn method, from nonlinear theory, to this class of systems. Next, we present a useful and systematic approach to design an observer for the same class of systems. Then, we show how the global stabilization problem via dynamic output feedback can be solved by using the two previous results. Finally, numerical examples are given to illustrate the effectiveness of the proposed design method.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

Nadir compromise programming: A model for optimization of multi-objective portfolio problem

In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importance of investment in financial portfolios, we propose a new method, called Nadir Compromising Programming (NCP) by expanding a CP-based method for optimization of multi-objective problems. In order to illustrate NCP performance and operational capability, we implement a case study by selecting a portfolio with 35 stock indices of Iran stock market. Results of comparing the CP method and proposed method under the same conditions indicate that NCP method results are more consistent with DM purposes.     

Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

A hybrid multi-criteria decision-making model for firms competence evaluation

In this paper, we present a hybrid multi-criteria decision-making (MCDM) model to evaluate the competence of the firms. According to the competence-based theory reveals that firm competencies are recognized from exclusive and unique capabilities that each firm enjoy in marketplace and are tightly intertwined within different business functions throughout the company. Therefore, competence in the firm is a composite of various attributes. Among them many intangible and tangible attributes are difficult to measure. In order to overcome the issue, we invite fuzzy set theory into the measurement of performance. In this paper first we calculate the weight of each criterion through adaptive analytic hierarchy process (AHP) approach (A³) method, and then we appraise the performance of firms via linguistic variables which are expressed as trapezoidal fuzzy numbers. In the next step we transform these fuzzy numbers into interval data by means of α-cut. Then considering different values for α we rank the firms through TOPSIS method with interval data. Since there are different ranks for different α values, we apply linear assignment method to obtain final rank for alternatives.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.