On the Operation Mechanism and Device Modeling of AlGaN/GaN High Electron Mobility Transistors (HEMTs)

In this work, the physical based device model of AlGaN/GaN high electron mobility transistors (HEMTs) has been established and the corresponding device operation behavior has been investigated also by using Sentaurus TCAD from Synopsys. Advanced AlGaN/GaN hetero-structures with GaN cap layer and AlN spacer have been considered and the GaN cap layer and AlN spacer are found taking important roles on the gate leakage blocking and off-state breakdown voltage enhancement.

Stability Optimization of Functionally Graded Pipes Conveying Fluid

This paper presents an exact analytical model for optimizing stability of thin-walled, composite, functionally graded pipes conveying fluid. The critical flow velocity at which divergence occurs is maximized for a specified total structural mass in order to ensure the economic feasibility of the attained optimum designs. The composition of the material of construction is optimized by defining the spatial distribution of volume fractions of the material constituents using piecewise variations along the pipe length. The major aim is to tailor the material distribution in the axial direction so as to avoid the occurrence of divergence instability without the penalty of increasing structural mass. Three types of boundary conditions have been examined; namely, Hinged-Hinged, Clamped- Hinged and Clamped-Clamped pipelines. The resulting optimization problem has been formulated as a nonlinear mathematical programming problem solved by invoking the MatLab optimization toolbox routines, which implement constrained function minimization routine named “fmincon" interacting with the associated eigenvalue problem routines. In fact, the proposed mathematical models have succeeded in maximizing the critical flow velocity without mass penalty and producing efficient and economic designs having enhanced stability characteristics as compared with the baseline designs.

Molecular Epidemiology and Genotyping of Bovine Viral Diarrhea Virus in Xinjiang Uygur Autonomous Region of China

As part of national epidemiological survey on bovine viral diarrhea virus (BVDV), a total of 274 dejecta samples were collected from 14 cattle farms in 8 areas of Xinjiang Uygur Autonomous Region in northwestern China. Total RNA was extracted from each sample, and 5--untranslated region (UTR) of BVDV genome was amplified by using two-step reverse transcriptase-polymerase chain reaction (RT-PCR). The PCR products were subsequently sequenced to study the genetic variations of BVDV in these areas. Among the 274 samples, 33 samples were found virus-positive. According to sequence analysis of the PCR products, the 33 samples could be arranged into 16 groups. All the sequences, however, were highly conserved with BVDV Osloss strains. The virus possessed theses sequences belonged to BVDV-1b subtype by phylogenetic analysis. Based on these data, we established a typing tree for BVDV in these areas. Our results suggested that BVDV-1b was a predominant subgenotype in northwestern China and no correlation between the genetic and geographical distances could be observed above the farm level.

Constructing a Fuzzy Net Present Value Method to Evaluating the BOT Sport Facilities

This paper is to develop a fuzzy net present value (FNPV) method by taking vague cash flow and imprecise required rate of return into account for evaluating the value of the Build-Operate-Transfer (BOT) sport facilities. In order to clearly manifest a more realistic capital budgeting model based on the classical net present value (NPV) method, some uncertain financial elements in NPV formula will be fuzzified as triangular fuzzy numbers. Through the conscientious manipulation of fuzzy set theory, we will find that the proposed FNPV model is a more explicit extension of classical (crisp) model and could be more practicable for the financial managers to capture the essence of capital budgeting of sport facilities than non-fuzzy model.

Rapid Urbanization and the Challenge of SustainableUrban Development in Palestinian Cities

Palestinian cities face the challenges of land scarcity, high population growth rates, rapid urbanization, uneven development and territorial fragmentation. Due to geopolitical constrains and the absence of an effective Palestinian planning institution, urban development in Palestinian cities has not followed any discernable planning scheme. This has led to a number of internal contradictions in the structure of cities, and adversely affected land use, the provision of urban services, and the quality of the living environment. This paper explores these challenges, and the potential that exists for introducing a more sustainable urban development pattern in Palestinian cities. It assesses alternative development approaches with a particular focus on sustainable development, promoting ecodevelopment imperatives, limiting random urbanization, and meeting present and future challenges, including fulfilling the needs of the people and conserving the scarce land and limited natural resources. This paper concludes by offering conceptual proposals and guidelines for promoting sustainable physical development in Palestinian cities.

Application of GM (1, 1) Model Group Based on Recursive Solution in China's Energy Demand Forecasting

To learn about China-s future energy demand, this paper first proposed GM(1,1) model group based on recursive solutions of parameters estimation, setting up a general solving-algorithm of the model group. This method avoided the problems occurred on the past researches that remodeling, loss of information and large amount of calculation. This paper established respectively all-data-GM(1,1), metabolic GM(1,1) and new information GM (1,1)model according to the historical data of energy consumption in China in the year 2005-2010 and the added data of 2011, then modeling, simulating and comparison of accuracies we got the optimal models and to predict. Results showed that the total energy demand of China will be 37.2221 billion tons of equivalent coal in 2012 and 39.7973 billion tons of equivalent coal in 2013, which are as the same as the overall planning of energy demand in The 12th Five-Year Plan.

A New Distribution Network Reconfiguration Approach using a Tree Model

Power loss reduction is one of the main targets in power industry and so in this paper, the problem of finding the optimal configuration of a radial distribution system for loss reduction is considered. Optimal reconfiguration involves the selection of the best set of branches to be opened ,one each from each loop, for reducing resistive line losses , and reliving overloads on feeders by shifting the load to adjacent feeders. However ,since there are many candidate switching combinations in the system ,the feeder reconfiguration is a complicated problem. In this paper a new approach is proposed based on a simple optimum loss calculation by determining optimal trees of the given network. From graph theory a distribution network can be represented with a graph that consists a set of nodes and branches. In fact this problem can be viewed as a problem of determining an optimal tree of the graph which simultaneously ensure radial structure of each candidate topology .In this method the refined genetic algorithm is also set up and some improvements of algorithm are made on chromosome coding. In this paper an implementation of the algorithm presented by [7] is applied by modifying in load flow program and a comparison of this method with the proposed method is employed. In [7] an algorithm is proposed that the choice of the switches to be opened is based on simple heuristic rules. This algorithm reduce the number of load flow runs and also reduce the switching combinations to a fewer number and gives the optimum solution. To demonstrate the validity of these methods computer simulations with PSAT and MATLAB programs are carried out on 33-bus test system. The results show that the performance of the proposed method is better than [7] method and also other methods.

Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm

Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.

Application of Biogas Technology in Turkey

The potential, opportunities and drawbacks of biogas technology use in Turkey are evaluated in this paper. Turkey is dependent on foreign sources of energy. Therefore, use of biogas technology would provide a safe way of waste disposal and recovery of renewable energy, particularly from a sustainable domestic source, which is less unlikely to be influenced by international price or political fluctuations. Use of biogas technology would especially meet the cooking, heating and electricity demand in rural areas and protect the environment, additionally creating new job opportunities and improving social-economical conditions.

Discovering Complex Regularities by Adaptive Self Organizing Classification

Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optmize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is also able to automatically suggest a strategy for number of classes optimization.The tool is used to classify macroeconomic data that report the most developed countries? import and export. It is possible to classify the countries based on their economic behaviour and use an ad hoc tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation.

Mineral Chemistry and Petrography of Lava Successions From Kepsut-Dursunbey Volcanic Field, NW Turkey: Implications For Magmatic Processes and Crystallization Conditions

Kepsut-Dursunbey volcanic field (KDVF) is located in NW Turkey and contains various products of the post-collisional Neogene magmatic activity. Two distinct volcanic suites have been recognized; the Kepsut volcanic suite (KVS) and the Dursunbey volcanic suite (DVS). The KVS includes basaltic trachyandesitebasaltic andesite-andesite lavas and associated pyroclastic rocks. The DVS consists of dacite-rhyodacite lavas and extensive pumice-ash fall and flow deposits. Petrographical features (i.e. existence of xenocrysts, glomerocrysts, and mixing-compatible textures) and mineral chemistry of phenocryst assemblages of both suites provide evidence for magma mixing/AFC. Calculated crystallization pressures and temperatures give values of 5.7–7.0 kbar and 927–982 °C for the KVS and 3.7–5.3 kbar and 783-787°C for the DVS, indicating separate magma reservoirs and crystallization in magma chambers at deep and mid crustal levels, respectively. These observations support the establishment and evolution of KDVF magma system promoted by episodic basaltic inputs which may generate and mix with crustal melts.

Modeling Converters during the Warm-up Period for Hydrocarbon Oxidation

Catalytic converters are used for minimizing the release of pollutants to the atmosphere. It is during the warm-up period that hydrocarbons are seen to be released in appreciable quantities from these converters. In this paper the conversion of a fast oxidizing hydrocarbon propylene is analysed using two numerical methods. The quasi steady state method assumes the accumulation terms to be negligible in the gas phase mass and energy balance equations, however this term is present in the solid phase energy balance. The unsteady state model accounts for the accumulation term to be present in the gas phase mass and energy balance and in the solid phase energy balance. The results derived from the two models for gas concentration, gas temperature and solid temperature are compared.

Association of the p53 Codon 72 Polymorphism with Colorectal Cancer in South West of Iran

The p53 tumor suppressor gene plays two important roles in genomic stability: blocking cell proliferation after DNA damage until it has been repaired, and starting apoptosis if the damage is too critical. Codon 72 exon4 polymorphism (Arg72Pro) of the P53 gene has been implicated in cancer risk. Various studies have been done to investigate the status of p53 at codon 72 for arginine (Arg) and proline (Pro) alleles in different populations and also the association of this codon 72 polymorphism with various tumors. Our objective was to investigate the possible association between P53 Arg72Pro polymorphism and susceptibility to colorectal cancer among Isfahan and Chaharmahal Va Bakhtiari (a part of south west of Iran) population. We investigated the status of p53 at codon 72 for Arg/Arg, Arg/Pro and Pro/Pro allele polymorphisms in blood samples from 145 colorectal cancer patients and 140 controls by Nested-PCR of p53 exon 4 and digestion with BstUI restriction enzyme and the DNA fragments were then resolved by electrophoresis in 2% agarose gel. The Pro allele was 279 bp, while the Arg allele was restricted into two fragments of 160 and 119 bp. Among the 145 colorectal cancer cases 49 cases (33.79%) were homozygous for the Arg72 allele (Arg/Arg), 18 cases (12.41%) were homozygous for the Pro72 allele (Pro/Pro) and 78 cases (53.8%) found in heterozygous (Arg/Pro). In conclusion, it can be said that p53Arg/Arg genotype may be correlated with possible increased risk of this kind of cancers in south west of Iran.

Combining Gene and Chemo Therapy using Multifunctional Polymeric Micelles

Non-viral gene carriers composed of biodegradable polymers or lipids have been considered as a safer alternative for gene carriers over viral vectors. We have developed multi-functional nano-micelles for both drug and gene delivery application. Polyethyleneimine (PEI) was modified by grafting stearic acid (SA) and formulated to polymeric micelles (PEI-SA) with positive surface charge for gene and drug delivery. Our results showed that PEI-SA micelles provided high siRNA binding efficiency. In addition, siRNA delivered by PEI-SA carriers also demonstrated significantly high cellular uptake even in the presence of serum proteins. The post-transcriptional gene silencing efficiency was greatly improved by the polyplex formulated by 10k PEI-SA/siRNA. The amphiphilic structure of PEI-SA micelles provided advantages for multifunctional tasks; where the hydrophilic shell modified with cationic charges can electrostatically interact with DNA or siRNA, and the hydrophobic core can serve as payloads for hydrophobic drugs, making it a promising multifunctional vehicle for both genetic and chemotherapy application.

Adaptive Fuzzy Control on EDF Scheduling

EDF (Early Deadline First) algorithm is a very important scheduling algorithm for real- time systems . The EDF algorithm assigns priorities to each job according to their absolute deadlines and has good performance when the real-time system is not overloaded. When the real-time system is overloaded, many misdeadlines will be produced. But these misdeadlines are not uniformly distributed, which usually focus on some tasks. In this paper, we present an adaptive fuzzy control scheduling based on EDF algorithm. The improved algorithm can have a rectangular distribution of misdeadline ratios among all real-time tasks when the system is overloaded. To evaluate the effectiveness of the improved algorithm, we have done extensive simulation studies. The simulation results show that the new algorithm is superior to the old algorithm.

Learning Classifier Systems Approach for Automated Discovery of Censored Production Rules

In the recent past Learning Classifier Systems have been successfully used for data mining. Learning Classifier System (LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: IF P THEN D UNLESS C, where Censor C is an exception to the rule. Such rules are employed in situations, in which conditional statement IF P THEN D holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the IF P THEN D part of CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function is proposed that incorporates basic constraints on CPR. Experimental results are presented to demonstrate the performance of the proposed learning classifier system.

Error Correction Codes in Wireless Sensor Network: An Energy Aware Approach

Link reliability and transmitted power are two important design constraints in wireless network design. Error control coding (ECC) is a classic approach used to increase link reliability and to lower the required transmitted power. It provides coding gain, resulting in transmitter energy savings at the cost of added decoder power consumption. But the choice of ECC is very critical in the case of wireless sensor network (WSN). Since the WSNs are energy constraint in nature, both the BER and power consumption has to be taken into count. This paper develops a step by step approach in finding suitable error control codes for WSNs. Several simulations are taken considering different error control codes and the result shows that the RS(31,21) fits both in BER and power consumption criteria.

Performance Analysis of a Free-Space Optical Code Division Multiple Access through Atmospheric Turbulence Channel

In this paper, the effect of atmospheric turbulence on bit error probability in free-space optical CDMA scheme with Sequence Inverse Keyed (SIK) optical correlator receiver is analyzed. Here Intensity Modulation scheme is considered for transmission. The turbulence induced fading is described by the newly introduced gamma-gamma pdf[1] as a tractable mathematical model for atmospheric turbulence. Results are evaluated with Gold and Kasami code & it is shown that Gold sequence can be used for more efficient transmission than Kasami sequence in an atmospheric turbulence channel.

A Combined Practical Approach to Condition Monitoring of Reciprocating Compressors using IAS and Dynamic Pressure

A Comparison and evaluation of the different condition monitoring (CM) techniques was applied experimentally on RC e.g. Dynamic cylinder pressure and crankshaft Instantaneous Angular Speed (IAS), for the detection and diagnosis of valve faults in a two - stage reciprocating compressor for a programme of condition monitoring which can successfully detect and diagnose a fault in machine. Leakage in the valve plate was introduced experimentally into a two-stage reciprocating compressor. The effect of the faults on compressor performance was monitored and the differences with the normal, healthy performance noted as a fault signature been used for the detection and diagnosis of faults. The paper concludes with what is considered to be a unique approach to condition monitoring. First, each of the two most useful techniques is used to produce a Truth Table which details the circumstances in which each method can be used to detect and diagnose a fault. The two Truth Tables are then combined into a single Decision Table to provide a unique and reliable method of detection and diagnosis of each of the individual faults introduced into the compressor. This gives accurate diagnosis of compressor faults.

Analyzing Methods of the Relation between Concepts based on a Concept Hierarchy

Data objects are usually organized hierarchically, and the relations between them are analyzed based on a corresponding concept hierarchy. The relation between data objects, for example how similar they are, are usually analyzed based on the conceptual distance in the hierarchy. If a node is an ancestor of another node, it is enough to analyze how close they are by calculating the distance vertically. However, if there is not such relation between two nodes, the vertical distance cannot express their relation explicitly. This paper tries to fill this gap by improving the analysis method for data objects based on hierarchy. The contributions of this paper include: (1) proposing an improved method to evaluate the vertical distance between concepts; (2) defining the concept horizontal distance and a method to calculate the horizontal distance; and (3) discussing the methods to confine a range by the horizontal distance and the vertical distance, and evaluating the relation between concepts.