A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

Development of compositionally modulated multilayer Zn-Ni deposits as replacement for cadmium

Compositionally modulated multilayer (CMM) Zn-Ni deposits were electrodeposited from single acidic bath (pH = 4.7) by using a potentiostatic sequence. The Zn and Ni composition in the alloy was tailored as a function of distance from the steel substrate. X-ray diffraction studies of the deposit showed the presence of γ-phase with a composition of Ni₅Zn₂₁. The corrosion properties of modulated multilayer coatings were studied in 5% NaCl solution using electrochemical corrosion techniques. The polarization resistance of the deposits varied as a function of Ni content between 1700 and 3440 Ω. CMM Zn-Ni with 20 wt% Ni exposed in ASTM B117 salt spray test did not show any red rust formation after 400 h.     

Controllability results for damped second-order impulsive neutral integrodifferential systems with nonlocal conditions

In this paper, we establish a set of sufficient conditions for the controllability of damped second-order impulsive neutral integrodifferential systems with nonlocal initial conditions in Banach spaces. The approach used is the Sadovskii fixed point theorem combined with a noncompact condition on the cosine family of operators. An example is presented to illustrate the result.     

Practical and scalable evolution of digital circuits

This paper addresses the scalability problem prevalent in the evolutionary design of digital circuits and shows that Evolvable Hardware (EHW) can indeed be considered as a viable alternative design methodology for large and complex circuits. Despite the effort by the EHW community to overcome the scalability problems using both direct mapped techniques and developmental approaches, so far only small circuits have been evolved. This paper shows that, by partitioning a digital circuit and making use of a modular developmental approach, namely, the Modular Developmental Cartesian Genetic Programming (MDCGP) technique, it is indeed possible to evolve large circuits. As a proof of concept, a 5 × 5 multiplier is evolved for partition sizes of 32 and 64. It is shown that compared to the direct evolution technique, the MDCGP technique provides five times reduction in terms of evolution times, 6–56% reduction in area and improved fault tolerance. The technique is readily scalable and can be applied to even larger partition sizes, and also to sequential circuits, thus providing a promising path to evolve large and complex circuits.     

Removal of Cr(VI) using co-immobilized activated carbon and <Emphasis Type="Italic">Bacillus subtilis</Emphasis>: fixed-bed column study

Fixed-bed column studies were conducted to evaluate the potential of co-immobilized (activated carbon and Bacillus subtilis) (CI) beads for the removal of Cr(VI) from aqueous solution. The impact of various parameters such as initial Cr(VI) concentrations (100, 150, and 200 mg/L), flow rate (3, 9, and 15 mL/min), and bed depth (10, 15, and 20 cm) on Cr(VI) adsorption onto CI beads were investigated. The breakthrough time increased with a decrease in initial Cr(VI) concentration and flow rate, and an increase in bed height. The breakthrough data obtained for Cr(VI) removal was more adequately described by the Thomas model with high correlation coefficients (R ² = 0.982). The eluent, 0.1 M NaOH, provided high elution efficiencies (~90 %) in all the six cycles. Obtained results pointed out that CI beads could potentially be used for the removal of Cr(VI) from aqueous solution.     

A novel SVM-based handwritten Tamil character recognition system

This paper describes a system for recognizing offline handwritten Tamil characters using support vector machine (SVM). Data samples are collected from different writers on A4 sized documents. They are scanned using a flat bed scanner at a resolution of 300 dpi and stored as gray-scale images. Various preprocessing operations are performed on the digitized image to enhance the quality of the image. Pixel densities are calculated for 64 different zones of the image and these values are used as the features of a character. These features are used to train the SVM. The SVM is tested for the first time to recognize handwritten Tamil characters. The system has achieved a very good recognition accuracy of 82.04% on the handwritten Tamil character database.     

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.     

Development of an image-based model for capillary vasculature of retina

The paper presents a method of development of a detailed network model to represent retinal capillary vasculature. The capillary model is a circular mesh consisting of concentric rings with an increasing diameter. Each of the rings has uniformly distributed bifurcation nodes to represent capillary vessels. The model is customized using the data that has been measured from confocal microscopic images of a mouse retina. The capillary model developed can be connected to networks of larger vessels of the vasculature such as arterial and venous networks to form a complete model of the retinal network. A method to automate such interface connections between capillary and other vascular networks using connecting vessels (i.e., pre-capillary and post-capillary) is also presented in the paper. Such a detailed image-based capillary model together with the arterial and venular networks can be used for various circulation simulations to obtain accurate information on hemodynamic quantities such as the spatial distribution of pressure and flow in the vasculature for both physiological and pathological conditions. The method presented for the development of the capillary model can also be adopted for vasculatures of other organs.     

In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as “Acute Effects”, “NIOSH Toxicity Data”, “Interactions”, “Hepatotoxicity”, “Carcinogenicity”, “Symptoms”, and “Human Toxicity Values” sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.