Use of membrane separation in enzymatic hydrolysis of waste paper

A three-stage process containing phosphoric acid pretreatment, enzymatic hydrolysis, and membrane filtration was performed on waste paper as a lignocellulosic material. In the first two stages, the effect of phosphoric acid concentration, enzyme loading, hydrolysis time, and substrate concentration on the amount of products was investigated. At the third stage using a proper membrane, the effect of substrate concentration and transmembrane pressure (TMP) on yield of the reducing sugars was studied. The novelty of the present study was to demonstrate the application of ultrafiltration membrane on the enzymatic hydrolysis process of waste paper. The reducing sugars concentration was determined by using the 3,5-dinitrosalicylic acid (DNS) reagent method. According to the results, a value of 0.5% was determined as the optimum concentration for phosphoric acid in the pretreatment stage. The reducing sugars yield was obtained as 67.4% in this concentration. Moreover, for the enzymatic hydrolysis of waste paper, the suitable amounts of cellulase enzyme loading and hydrolysis time were determined as 50 mg/g substrate and 48 h, respectively. In the filtration stage, increase of substrate concentration and decrease of TMP resulted in higher rejection of the reducing sugars. The experimental results revealed that the highest rejection was 19.2% at TMP of 3 bar and substrate concentration of 100 g/L.     

SOI LDMOSFET with Up and Down Extended Stepped Drift Region

To increase the breakdown voltage and decrease the ON resistance, a silicon-on-insulator (SOI) lateral double-diffused metal–oxide–semiconductor field-effect transistor (LDMOSFET) in which the drift region extends to the up and down oxides in a step shape is proposed. This up and down extended stepped drift SOI (UDESD-SOI) structure demonstrates a modified lateral electric field distribution with additional peaks as well as a decrease of the usual peaks near the drain and gate. Two-dimensional (2D) simulations were used to compare the characteristics of the proposed UDESD-SOI structure with those of other structures, viz. down extended stepped drift SOI (DESD-SOI), up extended stepped drift SOI (UESD-SOI), and conventional SOI (C-SOI). Under the same conditions, the breakdown voltage of the UDESD-SOI structure was nearly 35%, 117%, and 318% higher compared with the DESD-SOI, UESD-SOI, and C-SOI structure, respectively. To determine the optimum parameters for the UDESD-SOI structure leading to the highest breakdown voltage, a comparative study was performed to investigate the effect of the doping concentration in the drift region, buried oxide (BOX) thickness, and thickness of up and down extended steps (T ₁ and T ₂, respectively). In addition, the drain current (ON resistance) of the UDESD-SOI structure was found to be 13%, 43%, and 229% higher (16%, 65%, and 257% lower) than the values for the DESD-SOI, UESD-SOI, and C-SOI structure, respectively.     

Adaptive Data Hiding,Using Pixel-Value-Differencing and LSB Substitution

With the development of Internet communications, the security of message sending on the Internet has become very important. This article proposes a new adaptive data hiding method with a large data-embedding capacity for gray-scale images to raise the security of sending a message between sender and receiver in networks. At first, the image is divided into some blocks consisting of two consecutive pixels. If the values of both pixels are small, fewer secret bits will be embedded within the two pixels, otherwise, the difference value of two pixels is calculated, and according to the obtained difference value, the method will estimate the number of embedding bits into LSBs of two pixels. This number is adaptive and depends on the range to which the difference value belongs. A readjusting phase is presented to keep the difference of value pixels in the same range before and after embedding. Experimental results show that our method has increased the capacity of embedding bits in comparison with the several other methods.     

Exopolysaccharide produced by Vibrio neocaledonicus sp. as a green corrosion inhibitor: Production and structural characterization

An exopolysaccharide substances produced by Vibrio neocaledonicus sp. was introduced as a novel green inhibitor against the corrosion of carbon steel in artificial seawater and acidic media. The produced extracellular polymeric substance (EPS) is heterogeneous with composition of polysaccharides, nucleic acids and protein and average molecular weight of 29,572 Da. Adsorption of EPS on the metal surfaces and formation of Fe-EPS complexes acted as a barrier to prevent the oxygen penetration and hindered anodic and cathodic reactions. The inhibitory effect increases with increasing EPS concentration and exposure time. The highest corrosion inhibitory effect (95.1%) was observed for 10 g/L of EPS after 5 days of exposure in seawater. This is the highest inhibitory effect ever been reported by EPSs. While, the optimum concentration of EPS with the highest inhibition efficiency in 1 N H₂SO₄ was 1000 ppm. The influence of different parameters, such as initial pH, growth phase, various nitrogen and carbon sources on the production of EPS and its corrosion inhibitory effect were also investigated. According to results, the optimum culture medium for EPS production is contained artificial seawater including 5% mannitol as carbon source and 0.1% (NH₄)₂SO₄ as nitrogen source at pH = 8. This medium could produce 22.24 g/L EPS during 3 days’ incubation at 30 °C. The corrosion inhibitory efficiency of obtained EPS was 95.97%.     

Design of proportional-derivative-type state feedback controllers for congestion control of transmission control protocol networks

A new proportional-derivative-type state feedback controller is proposed for congestion control of transmission control protocol (TCP) networks. An analytical TCP model is adopted. In the proposed control scheme, it is possible to efficiently control the TCP traffic using only the queue length at the router without the need to know the TCP window size which is not available locally. The results are presented in terms of delay-dependent linear matrix inequality. The proposed method is verified by simulation examples using NS software, and the effectiveness and superiority of our method over other control schemes, such as the proportional-integral, random early detection and generalised minimum variancemethods, are also shown.     

OPTIMIZED QUERY FORWARDING FOR RESOURCE DISCOVERY IN UNSTRUCTURED PEER-TO-PEER GRIDS

One of the challenges of resource discovery in unstructured peer-to-peer grid systems is minimizing network traffic. The network traffic arises by query messages that are broadcasted to other peers in order to find the appropriate resources. Blind search methods that are employed in such systems do not work well because every specific query generates high query traffic, which quickly overwhelms the network. Informed search methods usually use recorded history of previous queries to decide where the new queries should be sent. Such methods can reduce network traffic but do not consider the path length. In this study, a method was proposed in which both the path length and network traffic are considered. This approach reduces the hop numbers and prevents massive flooding of query messages. To do this, it selects optimum neighbor peer(s) in order to optimize query forwarding. The proposed approach uses statistical tables that are obtained from recorded history of previous queries. Then a genetic algorithm is applied to these statistical tables to find the optimum neighbor peer(s). The proposed approach showed that query forwarding through the optimum neighbor peer(s) has a greater probability of finding a requested resource with lower hop numbers. This method was compared with random walk and flooding approaches. It was observed that the network traffic remarkably decreased in comparison to a flooding approach, whereas it was similar to the results obtained by a random walk method. Moreover, this method provided a higher success rate in comparison to the random walk approach, whereas it was similar to the results obtained by a flooding method.     

Overlap concentration of an aqueous poly(N-vinylcaprolactam) solution in the dynamic and static states

The overlap concentration of poly(N-vinylcaprolactam)s (PVCLs) of low molecular weights was studied with dynamic and static methods (with viscosity and refractive-index measurements) at three temperatures (25, 30, and 35°C). This concentration, determined by viscometry, was lower than the overlap concentration determined by refractometry. Also, the influence of the temperature and molecular weight on the viscosity and refractive index was investigated, and the opposite effect of the temperature on the viscosity and refractive index of PVCL solutions was observed. The experimental overlap concentration values were compared with those calculated with the Rao, Huggins, and Fuoss equations. The results showed that the Huggins equation is the best equation to describe the dilute solution properties of this polymer. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions

The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning scenario by creating a contrast vector for each customer based on its historical behaviors. The performance evaluation of proposed model is made possible by a real credit card data-set provided by FICO, and it is found that the proposed model has significant performance than other state-of-the-art classifiers.     

Gold nanostructures integrated on hollow carbon N-doped nanocapsules as a novel high-performance aptasensing platform for Helicobacter pylori detection

Journal of Materials Science - Nowadays, there is a lot of interest in the design of advanced nanomaterials in the field of electrochemical aptasensors, which can improve analytical performance. In...     

Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method

A multicomponent analysis method based on principal component analysis-artificial neural network models (PC-ANN) is proposed for the determination of phenolic compounds. The method relies on the oxidative coupling of phenols (phenol, 2 chlorophenol, 3-chlorophenol and 4-chlorophenol) to N,N-diethyl-p-phenylenediamine in the presence of hexacyanoferrate(III). The reaction monitored at analytical wavelength 680 nm of the dye formed. Phenols can be determined individually over the concentration range 0.1-7.0 microg ml(-1). Differences in the kinetic behavior of the four species were exploited by using PC-ANN, to resolve mixtures of phenol. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous quantitation of four analytes in mixtures with relative standard errors of prediction in the region of 5% for four species. The results show that PC-ANN is an efficient method for prediction of the four analytes.