Rice cDNA Encoding PROLM is Capable of Rescuing Salt Sensitive Yeast Phenotypes G19 and Axt3K from Salt Stress

Rice seed expression (cDNA) library in the Lambda Zap 11® phage constructed from the developing grain 10-20 days after flowering was transformed into yeast for functional complementation assays in three salt sensitive yeast mutants S. cerevisiae strain CY162, G19 and Axt3K. Transformed cells of G19 and Axt3K with pYES vector with cDNA inserts showed enhance tolerance than those with empty pYes vector. Sequencing of the cDNA inserts revealed that they encode for the putative proteins with the sequence homologous to rice putative protein PROLM24 (Os06g31070), a prolamin precursor. Expression of this cDNA did not affect yeast growth in absence of salt. Axt3k and G19 strains expressing the PROLM24 were able to grow upto 400 mM and 600 mM of NaCl respectively. Similarly, Axt3k mutant with PROLM24 expression showed comparatively higher growth rate in the medium with excess LiCl (50 mM). The observation that expression of PROLM24 rescued the salt sensitive phenotypes of G19 and Axt3k indicates the existence of a regulatory system that ameliorates the effect of salt stress in the transformed yeast mutants. However, the exact function of the cDNA sequence, which shows partial sequence homology to yeast UTR1 is not clear. Although UTR1 involved in ferrous uptake and iron homeostasis in yeast cells, there is no evidence to prove its role in Na+ homeostasis in yeast cells. Absence of transmembrane regions in Os06g31070 protein indicates that salt tolerance is achieved not through the direct functional complementation of the mutant genes but through an alternative mechanism.

Optimal Allocation of DG Units for Power Loss Reduction and Voltage Profile Improvement of Distribution Networks using PSO Algorithm

This paper proposes a Particle Swarm Optimization (PSO) based technique for the optimal allocation of Distributed Generation (DG) units in the power systems. In this paper our aim is to decide optimal number, type, size and location of DG units for voltage profile improvement and power loss reduction in distribution network. Two types of DGs are considered and the distribution load flow is used to calculate exact loss. Load flow algorithm is combined appropriately with PSO till access to acceptable results of this operation. The suggested method is programmed under MATLAB software. Test results indicate that PSO method can obtain better results than the simple heuristic search method on the 30-bus and 33- bus radial distribution systems. It can obtain maximum loss reduction for each of two types of optimally placed multi-DGs. Moreover, voltage profile improvement is achieved.

Agreement Options in Multi-person Decision on Optimizing High-Rise Building Columns

This paper presents a conceptual model of agreement options for negotiation support in multi-person decision on optimizing high-rise building columns. The decision is complicated since many parties involved in choosing a single alternative from a set of solutions. There are different concern caused by differing preferences, experiences, and background. Such building columns as alternatives are referred to as agreement options which are determined by identifying the possible decision maker group, followed by determining the optimal solution for each group. The group in this paper is based on three-decision makers preferences that are designer, programmer, and construction manager. Decision techniques applied to determine the relative value of the alternative solutions for performing the function. Analytical Hierarchy Process (AHP) was applied for decision process and game theory based agent system for coalition formation. An n-person cooperative game is represented by the set of all players. The proposed coalition formation model enables each agent to select individually its allies or coalition. It further emphasizes the importance of performance evaluation in the design process and value-based decision.

A National Idea in Conditions of the Islamic Revival

Discussion and development of principles of the uniform nation formation within the limits of the Kazakhstan state obviously became one of the most pressing questions of the day. The fact that this question has not been solved "from above" as many other questions has caused really brisk discussion, shows us increase of civil consciousness in Kazakhstan society, and also the actuality of this theme which can be carried in the category of fatal questions. In any sense, nation building has raised civil society to a much higher level. It would be better to begin with certain definitions. First is the word "nation". The second is the "state". Both of these terms are very closely connected with each other, so that in English language they are in general synonyms. In Russian more shades of these terms exist. For example in Kazakhstan the citizens of the country irrespective of nationality (but mainly with reference to non-kazakhs) are called «kazakhstanians», while the name of the title nation is \"Kazakhs\". The same we can see in Russia, where, for example, the Chechen or the Yakut –are \"Rossiyane\" which means “the citizens of Russian Federation, but not \"Russians\". The paper was written under the research project “Islam in modern Kazakhstan: the nature and outcome of the religious revival”.

A New Heuristic Statistical Methodology for Optimizing Queuing Networks Using Discreet Event Simulation

Most of the real queuing systems include special properties and constraints, which can not be analyzed directly by using the results of solved classical queuing models. Lack of Markov chains features, unexponential patterns and service constraints, are the mentioned conditions. This paper represents an applied general algorithm for analysis and optimizing the queuing systems. The algorithm stages are described through a real case study. It is consisted of an almost completed non-Markov system with limited number of customers and capacities as well as lots of common exception of real queuing networks. Simulation is used for optimizing this system. So introduced stages over the following article include primary modeling, determining queuing system kinds, index defining, statistical analysis and goodness of fit test, validation of model and optimizing methods of system with simulation.

Implementation of Technology Concept for the Reduction of Cyanobacteria in Laboratory

Following the research in the Department of environmental engineering in Faculty of mechanical engineering on Technical University of Kosice and experiences with electrocoagulation style of disposal waste water, there were designed and partly examining the equipment of two stage revitalization on the standing and little fusible water of tenet electrolysis on the little tarns. With the cooperation with vet experts was that manners prove and it is innocuous for animals, during which time cyanobacteria are totally paralyzed. For the implementation of science and research results have been obtained by means EU funds for structural development.

Deactivation of Cu - Cr/γ-alumina Catalysts for Combustion of Exhaust Gases

The paper relates to a catalyst, comprising copperchromium spinel, coated on carrier γ-Al2O3. The effect of preparation conditions on the active component composition and activity behavior of the catalysts is discussed. It was found that the activity of carbon monoxide, DME, formaldehyde and methanol oxidation reaches a maximum at an active component content of 20 – 30 wt. %. Temperature calcination at 500oC seems to be optimal for the γ– alumina supported CuO-Cr2O3 catalysts for CO, DME, formaldehyde and methanol oxidation. A three months industrial experiment was carried out to elucidate the changes in the catalyst composition during industrial exploitation of the catalyst and the main reasons for catalyst deactivation. It was concluded that the CuO–Cr2O3/γ–alumina supported catalysts have enhanced activity toward CO, DME, formaldehyde and methanol oxidation and that these catalysts are suitable for industrial application. The main reason for catalyst deactivation seems to be the deposition of iron and molybdenum, coming from the main reactor, on the active component surface.

e-Collaborative Learning Circles

In this paper, we introduce an e-collaborative learning circles methodology which utilizes the information and communication technologies (ICTs) in e-educational processes. In e-collaborative learning circles methodology, the teachers and students announce their research projects on various mailing lists and discussion boards using available ICTs. The teachers & moderators and students who are already members of the e-forums, discuss the project proposals in their classrooms sent out by the potential global partner schools and return the requested feed back to the proposing school(s) about their level of the participation and contribution in the research. In general, an e-collaborative learning circle project is implemented with a small and diverse group (usually 8-10 participants) from around the world. The students meet regularly over a period of weeks/months through the ICTs during the ecollaborative learning process. When the project is completed, a project product (e-book / DVD) is prepared and sent to the circle members. In this research, when taking into account the interests and motivation of the participating students with the facilitating role of the teacher(s), the students in each circle do research to obtain new data and information, thus enabling them to have the opportunity to meet both different cultures and international understandings across the globe. However, while the participants communicate along with the members in the circle they also practice and develop their communication language skills. Finally, teachers and students find the possibility to develop their skills in using the ICTs as well.

A Continuous Time Sigma Delta Modulators Using CMOS Current Conveyors

In this paper, a alternative structure method for continuous time sigma delta modulator is presented. In this modulator for implementation of integrators in loop filter second generation current conveyors are employed. The modulator is designed in CMOS technology and features low power consumption (65db), and with 180khZ bandwidth. Simulation results confirm that this design is suitable for data converters.

ANN based Multi Classifier System for Prediction of High Energy Shower Primary Energy and Core Location

Cosmic showers, during the transit through space, produce sub - products as a result of interactions with the intergalactic or interstellar medium which after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools such as Artificial Neural Network (ANN)s can be trained as classifiers to adapt and learn the surrounding variations. But single classifiers fail to reach optimality of decision making in many situations for which Multiple Classifier System (MCS) are preferred to enhance the ability of the system to make decisions adjusting to finer variations. This work describes the formation of an MCS using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) with data inputs from correlation mapping Self Organizing Map (SOM) blocks and the output optimized by another SOM. The results show that the setup can be adopted for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

A Novel VLSI Architecture for Image Compression Model Using Low power Discrete Cosine Transform

In Image processing the Image compression can improve the performance of the digital systems by reducing the cost and time in image storage and transmission without significant reduction of the Image quality. This paper describes hardware architecture of low complexity Discrete Cosine Transform (DCT) architecture for image compression[6]. In this DCT architecture, common computations are identified and shared to remove redundant computations in DCT matrix operation. Vector processing is a method used for implementation of DCT. This reduction in computational complexity of 2D DCT reduces power consumption. The 2D DCT is performed on 8x8 matrix using two 1-Dimensional Discrete cosine transform blocks and a transposition memory [7]. Inverse discrete cosine transform (IDCT) is performed to obtain the image matrix and reconstruct the original image. The proposed image compression algorithm is comprehended using MATLAB code. The VLSI design of the architecture is implemented Using Verilog HDL. The proposed hardware architecture for image compression employing DCT was synthesized using RTL complier and it was mapped using 180nm standard cells. . The Simulation is done using Modelsim. The simulation results from MATLAB and Verilog HDL are compared. Detailed analysis for power and area was done using RTL compiler from CADENCE. Power consumption of DCT core is reduced to 1.027mW with minimum area[1].

Experimental Investigation of the Maximum Axial Force in the Folding Process of Aluminum Square Columns

In this paper, a semi empirical formula is presented based on the experimental results to predict the first pick (maximum force) value in the instantaneous folding force- axial distance diagram of a square column. To achieve this purpose, the maximum value of the folding force was assumed to be a function of the average folding force. Using the experimental results, the maximum value of the force necessary to initiate the first fold in a square column was obtained with respect to the geometrical quantities and material properties. Finally, the results obtained from the semi empirical relation in this paper, were compared to the experimental results which showed a good correlation.

The Tag Authentication Scheme using Self-Shrinking Generator on RFID System

Since communications between tag and reader in RFID system are by radio, anyone can access the tag and obtain its any information. And a tag always replies with the same ID so that it is hard to distinguish between a real and a fake tag. Thus, there are many security problems in today-s RFID System. Firstly, unauthorized reader can easily read the ID information of any Tag. Secondly, Adversary can easily cheat the legitimate reader using the collected Tag ID information, such as the any legitimate Tag. These security problems can be typically solved by encryption of messages transmitted between Tag and Reader and by authentication for Tag. In this paper, to solve these security problems on RFID system, we propose the Tag Authentication Scheme based on self shrinking generator (SSG). SSG Algorithm using in our scheme is proposed by W.Meier and O.Staffelbach in EUROCRYPT-94. This Algorithm is organized that only one LFSR and selection logic in order to generate random stream. Thus it is optimized to implement the hardware logic on devices with extremely limited resource, and the output generating from SSG at each time do role as random stream so that it is allow our to design the light-weight authentication scheme with security against some network attacks. Therefore, we propose the novel tag authentication scheme which use SSG to encrypt the Tag-ID transmitted from tag to reader and achieve authentication of tag.

Interconnect Analysis of a Novel Multiplexer Based Full-Adder Cell for Power and Propagation Delay Optimizations

The proposed multiplexer-based novel 1-bit full adder cell is schematized by using DSCH2 and its layout is generated by using microwind VLSI CAD tool. The adder cell layout interconnect analysis is performed by using BSIM4 layout analyzer. The adder circuit is compared with other six existing adder circuits for parametric analysis. The proposed adder cell gives better performance than the other existing six adder circuits in terms of power, propagation delay and PDP. The proposed adder circuit is further analyzed for interconnect analysis, which gives better performance than other adder circuits in terms of layout thickness, width and height.

The Game of Synchronized Triomineering and Synchronized Tridomineering

In synchronized games players make their moves simultaneously rather than alternately. Synchronized Triomineering and Synchronized Tridomineering are respectively the synchronized versions of Triomineering and Tridomineering, two variants of a classic two-player combinatorial game called Domineering. Experimental results for small m × n boards (with m + n ≤ 12 for Synchronized Triomineering and m + n ≤ 10 for Synchronized Tridomineering) and some theoretical results for general k×n boards (with k = 3, 4, 5 for Synchronized Triomineering and k = 3 for Synchronized Tridomineering) are presented. Future research is indicated.

Real-time Laser Monitoring based on Pipe Detective Operation

The pipe inspection operation is the difficult detective performance. Almost applications are mainly relies on a manual recognition of defective areas that have carried out detection by an engineer. Therefore, an automation process task becomes a necessary in order to avoid the cost incurred in such a manual process. An automated monitoring method to obtain a complete picture of the sewer condition is proposed in this work. The focus of the research is the automated identification and classification of discontinuities in the internal surface of the pipe. The methodology consists of several processing stages including image segmentation into the potential defect regions and geometrical characteristic features. Automatic recognition and classification of pipe defects are carried out by means of using an artificial neural network technique (ANN) based on Radial Basic Function (RBF). Experiments in a realistic environment have been conducted and results are presented.

Soccer Video Edition Using a Multimodal Annotation

In this paper, we present an approach for soccer video edition using a multimodal annotation. We propose to associate with each video sequence of a soccer match a textual document to be used for further exploitation like search, browsing and abstract edition. The textual document contains video meta data, match meta data, and match data. This document, generated automatically while the video is analyzed, segmented and classified, can be enriched semi automatically according to the user type and/or a specialized recommendation system.

Multivariate School Travel Demand Regression Based on Trip Attraction

Since primary school trips usually start from home, attention by many scholars have been focused on the home end for data gathering. Thereafter category analysis has often been relied upon when predicting school travel demands. In this paper, school end was relied on for data gathering and multivariate regression for future travel demand prediction. 9859 pupils were surveyed by way of questionnaires at 21 primary schools. The town was divided into 5 zones. The study was carried out in Skudai Town, Malaysia. Based on the hypothesis that the number of primary school trip ends are expected to be the same because school trips are fixed, the choice of trip end would have inconsequential effect on the outcome. The study compared empirical data for home and school trip end productions and attractions. Variance from both data results was insignificant, although some claims from home based family survey were found to be grossly exaggerated. Data from the school trip ends was relied on for travel demand prediction because of its completeness. Accessibility, trip attraction and trip production were then related to school trip rates under daylight and dry weather conditions. The paper concluded that, accessibility is an important parameter when predicting demand for future school trip rates.

Feature Reduction of Nearest Neighbor Classifiers using Genetic Algorithm

The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum subset of weakly correlated features that better discriminate the pattern classes. This is usually a difficult task in practice, normally requiring the application of heuristic knowledge about the specific problem domain. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy. Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector. In this paper a new approach is presented to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm. In this approach each feature value is first normalized by a linear equation, then scaled by the associated weight prior to training, testing, and classification. A knn classifier is used to evaluate each set of feature weights. The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. By this approach, the number of features used in classifying can be finely reduced.

Statistics over Lyapunov Exponents for Feature Extraction: Electroencephalographic Changes Detection Case

A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.