Numerical Analysis and Experimental Validation of Detector Pressure Housing Subject to HPHT

Reservoirs with high pressures and temperatures (HPHT) that were considered to be atypical in the past are now frequent targets for exploration. For downhole oilfield drilling tools and components, the temperature and pressure affect the mechanical strength. To address this issue, a finite element analysis (FEA) for 206.84 MPa (30 ksi) pressure and 165°C has been performed on the pressure housing of the measurement-while-drilling/logging-whiledrilling (MWD/LWD) density tool. The density tool is a MWD/LWD sensor that measures the density of the formation. One of the components of the density tool is the pressure housing that is positioned in the tool. The FEA results are compared with the experimental test performed on the pressure housing of the density tool. Past results show a close match between the numerical results and the experimental test. This FEA model can be used for extreme HPHT and ultra HPHT analyses, and/or optimal design changes.

A Novel Approach of Route Choice in Stochastic Time-varying Networks

Many exist studies always use Markov decision processes (MDPs) in modeling optimal route choice in stochastic, time-varying networks. However, taking many variable traffic data and transforming them into optimal route decision is a computational challenge by employing MDPs in real transportation networks. In this paper we model finite horizon MDPs using directed hypergraphs. It is shown that the problem of route choice in stochastic, time-varying networks can be formulated as a minimum cost hyperpath problem, and it also can be solved in linear time. We finally demonstrate the significant computational advantages of the introduced methods.

Fast Codevector Search Algorithm for 3-D Vector Quantized Codebook

This paper presents a very simple and efficient algorithm for codebook search, which reduces a great deal of computation as compared to the full codebook search. The algorithm is based on sorting and centroid technique for search. The results table shows the effectiveness of the proposed algorithm in terms of computational complexity. In this paper we also introduce a new performance parameter named as Average fractional change in pixel value as we feel that it gives better understanding of the closeness of the image since it is related to the perception. This new performance parameter takes into consideration the average fractional change in each pixel value.

Light Confinement in Low Index Nanometer Areas

In this work we numerically examine structures which could confine light in nanometer areas. A system consisting of two silicon disks with in plane separation of a few tens of nanometers has been studied first. The normalized unitless effective mode volume, Veff, has been calculated for the two lowest whispering gallery mode resonances. The effective mode volume is reduced significantly as the gap between the disks decreases. In addition, the effect of the substrate is also studied. In that case, Veff of approximately the same value as the non-substrate case for a similar two disk system can be obtained by using disks almost twice as thick. We also numerically examine a structure consisting of a circular slot waveguide which is formed into a silicon disk resonator. We show that the proposed structure could have high Q resonances thus raising the belief that it is a very promising candidate for optical interconnects applications. The study includes several numerical calculations for all the geometric parameters of the structure. It also includes numerical simulations of the coupling between a waveguide and the proposed disk resonator leading to a very promising conclusion about its applicability.

Automatic Clustering of Gene Ontology by Genetic Algorithm

Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.

Increasing of Energy Efficiency based on Persian Ancient Architectural Patterns in Desert Regions (Case Study Of Traditional Houses In Kashan)

In general architecture means the art of creating the space. Comprehensive and complete body which is created by a creative and purposeful thought to respond the human needs. Professionally, architecture is the are of designing and comprehensive planning of physical spaces that is created for human-s productivity. The purpose of architectural design is to respond the human needs which is appeared in physical frame. Human in response to his needs is always looking to achieve comfort. Throughout history of human civilization this relative comfort has been inspired by nature and assimilating the facility and natural achievement in the format of artifact patterns base on the nature, so that it is achieved in this comfort level and invention of these factors. All physical factors like regional, social and economical factors are made available to human in order to achieve a specific goal and are made to gain an ideal architecture to respond the functional needs and consider the aesthetics and elemental principles and pay attention to residents- comfort. In this study the Persian architecture with exploiting and transforming the energies into the requisite energies of architecture spaces and importing fuel products, utilities, etc, in order to achieve a relative comfort level will be investigated. In this paper the study of structural and physical specialties of traditional houses in desert regions and Central Plateau of Iran gave us this opportunity to being more familiar with important specialties of energy productivity in architecture body of traditional houses in these regions specially traditional houses of Kashan and in order to use these principles to create modern architectures in these regions.

Improved Plasmonic Demultiplexer Based on Tapered and Rectangular Slot MIM Waveguide

In this paper, we have proposed two novel plasmonic demultiplexing structures based on metal-insulator-metal surfaces which, beside their compact size, have a very good transmission spectrum. The impact of the key internal parameters on the transmission spectrum is numerically analyzed by using the twodimensional (2D) finite difference time domain (FDTD) method. The proposed structures could be used to develop ultra-compact photonic wavelength demultiplexing devices for large-scale photonic integration.

Comparative Study of Decision Trees and Rough Sets Theory as Knowledge ExtractionTools for Design and Control of Industrial Processes

General requirements for knowledge representation in the form of logic rules, applicable to design and control of industrial processes, are formulated. Characteristic behavior of decision trees (DTs) and rough sets theory (RST) in rules extraction from recorded data is discussed and illustrated with simple examples. The significance of the models- drawbacks was evaluated, using simulated and industrial data sets. It is concluded that performance of DTs may be considerably poorer in several important aspects, compared to RST, particularly when not only a characterization of a problem is required, but also detailed and precise rules are needed, according to actual, specific problems to be solved.

Emotional Intelligence and Retention: The Moderating Role of Job Involvement

The main aim of the current study was to examine the effect of emotional intelligence on retention. The study also aimed at analyzing the role of job involvement, as a moderator, in the effect of emotional intelligence on retention. Using data gathered from 241 employees working with hotels and tourism corporations listed in Amman Stock Exchange in Jordan, emotional intelligence, job involvement and retention were measured. Hierarchical regression analyses were used to test the three main hypotheses. Results indicated that retention was related to emotional intelligence. Moreover, the study yielded support for the claim that job involvement had a moderating effect on the relationship between emotional intelligence and retention.

Volatile Organochlorine Compounds Emitted by Temperate Coniferous Forests

Chlorine is one of the most abundant elements in nature, which undergoes a complex biogeochemical cycle. Chlorine bound in some substances is partly responsible for atmospheric ozone depletion and contamination of some ecosystems. As due to international regulations anthropogenic burden of volatile organochlorines (VOCls) in atmosphere decreases, natural sources (plants, soil, abiotic formation) are expected to dominate VOCl production in the near future. Examples of plant VOCl production are methyl chloride, and bromide emission from (sub)tropical ferns, chloroform, 1,1,1-trichloroethane and tetrachloromethane emission from temperate forest fern and moss. Temperate forests are found to emit in addition to the previous compounds tetrachloroethene, and brominated volatile compounds. VOCls can be taken up and further metabolized in plants. The aim of this work is to identify and quantitatively analyze the formed VOCls in temperate forest ecosystems by a cryofocusing/GC-ECD detection method, hence filling a gap of knowledge in the biogeochemical cycle of chlorine.

Location Update Cost Analysis of Mobile IPv6 Protocols

Mobile IP has been developed to provide the continuous information network access to mobile users. In IP-based mobile networks, location management is an important component of mobility management. This management enables the system to track the location of mobile node between consecutive communications. It includes two important tasks- location update and call delivery. Location update is associated with signaling load. Frequent updates lead to degradation in the overall performance of the network and the underutilization of the resources. It is, therefore, required to devise the mechanism to minimize the update rate. Mobile IPv6 (MIPv6) and Hierarchical MIPv6 (HMIPv6) have been the potential candidates for deployments in mobile IP networks for mobility management. HMIPv6 through studies has been shown with better performance as compared to MIPv6. It reduces the signaling overhead traffic by making registration process local. In this paper, we present performance analysis of MIPv6 and HMIPv6 using an analytical model. Location update cost function is formulated based on fluid flow mobility model. The impact of cell residence time, cell residence probability and user-s mobility is investigated. Numerical results are obtained and presented in graphical form. It is shown that HMIPv6 outperforms MIPv6 for high mobility users only and for low mobility users; performance of both the schemes is almost equivalent to each other.

IMDC: An Image-Mapped Data Clustering Technique for Large Datasets

In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.

Autonomously Determining the Parameters for SVDD with RBF Kernel from a One-Class Training Set

The one-class support vector machine “support vector data description” (SVDD) is an ideal approach for anomaly or outlier detection. However, for the applicability of SVDD in real-world applications, the ease of use is crucial. The results of SVDD are massively determined by the choice of the regularisation parameter C and the kernel parameter  of the widely used RBF kernel. While for two-class SVMs the parameters can be tuned using cross-validation based on the confusion matrix, for a one-class SVM this is not possible, because only true positives and false negatives can occur during training. This paper proposes an approach to find the optimal set of parameters for SVDD solely based on a training set from one class and without any user parameterisation. Results on artificial and real data sets are presented, underpinning the usefulness of the approach.

Cross-Industry Innovations – Systematic Identification and Adaption

Due to today-s fierce competition, companies have to be proactive creators of the future by effectively developing innovations. Especially radical innovations allow high profit margins – but they also entail high risks. One possibility to realize radical innovations and reduce the risk of failure is cross-industry innovation (CII). CII brings together problems and solution ideas from different industries. However, there is a lack of systematic ways towards CII. Bridging this gap, the present paper provides a systematic approach towards planned CII. Starting with the analysis of potentials, the definition of promising search strategies is crucial. Subsequently, identified solution ideas need to be assessed. For the most promising ones, the adaption process has to be systematically planned – regarding the risk affinity of a company. The introduced method is explained on a project from the furniture industry.

Analysis of Acoustic Emission Signal for the Detection of Defective Manufactures in Press Process

Small cracks or chips of a product appear very frequently in the course of continuous production of an automatic press process system. These phenomena become the cause of not only defective product but also damage of a press mold. In order to solve this problem AE system was introduced. AE system was expected to be very effective to real time detection of the defective product and to prevention of the damage of the press molds. In this study, for pick and analysis of AE signals generated from the press process, AE sensors/pre-amplifier/analysis and processing board were used as frequently found in the other similar cases. For analysis and processing the AE signals picked in real time from the good or bad products, specialized software called cdm8 was used. As a result of this work it was conformed that intensity and shape of the various AE signals differ depending on the weight and thickness of metal sheet and process type.

Off-Line Hand Written Thai Character Recognition using Ant-Miner Algorithm

Much research into handwritten Thai character recognition have been proposed, such as comparing heads of characters, Fuzzy logic and structure trees, etc. This paper presents a system of handwritten Thai character recognition, which is based on the Ant-minor algorithm (data mining based on Ant colony optimization). Zoning is initially used to determine each character. Then three distinct features (also called attributes) of each character in each zone are extracted. The attributes are Head zone, End point, and Feature code. All attributes are used for construct the classification rules by an Ant-miner algorithm in order to classify 112 Thai characters. For this experiment, the Ant-miner algorithm is adapted, with a small change to increase the recognition rate. The result of this experiment is a 97% recognition rate of the training set (11200 characters) and 82.7% recognition rate of unseen data test (22400 characters).

Using Malolactic Fermentation with Acid- And Ethanol- Adapted Oenococcus Oeni Strain to Improve the Quality of Wine from Champs Bourcin Grape in Sapa - Lao Cai

Champs Bourcin black grape originated from Aquitaine, France and planted in Sapa, Lao cai provice, exhibited high total acidity (11.72 g/L). After 9 days of alcoholic fermentation at 25oC using Saccharomyces cerevisiae UP3OY5 strain, the ethanol concentration of wine was 11.5% v/v, however the sharp sour taste of wine has been found. The malolactic fermentation (MLF) was carried out by Oenococcus oeni ATCCBAA-1163 strain which had been preadapted to acid (pH 3-4) and ethanol (8-12%v/v) conditions. We obtained the highest vivability (83.2%) upon malolactic fermentation after 5 days at 22oC with early stationary phase O. oeni cells preadapted to pH 3.5 and 8% v/v ethanol in MRS medium. The malic acid content in wine was decreased from 5.82 g/L to 0.02 g/L after MLF (21 days at 22oC). The sensory quality of wine was significantly improved.

An Experimental Method for Measuring Clamping Force in Bolted Connections and Effect of Bolt Threads Lubrication on Its Value

In this paper, the details of an experimental method to measure the clamping force value at bolted connections due to application of wrenching torque to tighten the nut have been presented. A simplified bolted joint including a holed plate with a single bolt was considered to carry out the experiments. This method was designed based on Hooke-s law by measuring compressive axial strain of a steel bush placed between the nut and the plate. In the experimental procedure, the values of clamping force were calculated for seven different levels of applied torque, and this process was repeated three times for each level of the torque. Moreover, the effect of lubrication of threads on the clamping value was studied using the same method. In both conditions (dry and lubricated threads), relation between the torque and the clamping force have been displayed in graphs.

Sulfamonomethoxine-Induced Urinary Calculiin Pigs

The authors report a case of swine urolithiasis caused by improper administration of sulfamonomethoxine and which was diagnosed by examination of urinary sediments and analyzing the composition of the uroliths. The chemical composition of urinary calculi obtained from affected pigs with urolithiasis was further confimed as sulfamonomethoxine by fourier transform infrared (FTIR). It is suggested that appearance of typical fanlike or wheat bunchy crystals in urinary sediments under observation of lightmicroscope and determination by FTIR for the crystals are helpful in diagnosing sulfa calculi causced swine urolithiasis.

Knowledge Management in Cross- Organizational Networks as Illustrated by One of the Largest European ICT Associations A Case Study of the “METORA

In networks, mainly small and medium-sized businesses benefit from the knowledge, experiences and solutions offered by experts from industry and science or from the exchange with practitioners. Associations which focus, among other things, on networking, information and knowledge transfer and which are interested in supporting such cooperations are especially well suited to provide such networks and the appropriate web platforms. Using METORA as an example – a project developed and run by the Federal Association for Information Economy, Telecommunications and New Media e.V. (BITKOM) for the Federal Ministry of Economics and Technology (BMWi) – This paper will discuss how associations and other network organizations can achieve this task and what conditions they have to consider.