Training on the Ceasing Intention of Betelnut Addiction

According to the governmental data, the cases of oral cancers doubled in the past 10 years. This had brought heavy burden to the patients- family, the society, and the country. The literature generally evidenced the betel nut contained particular chemicals that can cause oral cancers. Research in Taiwan had also proofed that 90 percent of oral cancer patients had experience of betel nut chewing. It is thus important to educate the betel-nut hobbyists to cease such a hazardous behavior. A program was then organized to establish several training classes across different areas specific to help ceasing this particular habit. Purpose of this research was to explore the attitude and intention toward ceasing betel-nut chewing before and after attending the training classes. 50 samples were taken from a ceasing class with average age at 45 years old with high school education (54%). 74% of the respondents were male in service or agricultural industries. Experiences in betel-nut chewing were 5-20 years with a dose of 1-20 pieces per day. The data had shown that 60% of the respondents had cigarette smoking habit, and 30% of the respondents were concurrently alcoholic dependent. Research results indicated that the attitude, intentions, and the knowledge on oral cancers were found significant different between before and after attendance. This provided evidence for the effectiveness of the training class. However, we do not perform follow-up after the class. Noteworthy is the test result also shown that participants who were drivers as occupation, or habitual smokers or alcoholic dependents would be less willing to quit the betel-nut chewing. The test results indicated as well that the educational levels and the type of occupation may have significant impacts on an individual-s decisions in taking betel-nut or substance abuse.

Towards Benchmarking English Residential Gas Consumption

The UK Government has emphasized the role of Local Authorities as a key player in its flagship residential energy efficiency strategies, by identifying and targeting areas for energy efficiency improvements. Residential energy consumption in England is characterized by significant geographical variation in energy demand, which makes centralized targeting of areas for energy efficiency intervention difficult. This paper draws on research which aims to understand how demographic, social, economic, urban form and climatic factors influence the geographical variations in English residential gas consumption. The paper reports the findings of a multiple regression model that shows how 64% of the geographical variation in residential gas consumption is accounted for by variations in these factors. Results from this study, after further refinement and validation, can be used by Local Authorities to identify areas within their boundaries that have higher than expected gas consumption, these may be prime targets for energy efficiency initiatives.

Electrical Resistivity of Subsurface: Field and Laboratory Assessment

The objective of this paper is to study the electrical resistivity complexity between field and laboratory measurement, in order to improve the effectiveness of data interpretation for geophysical ground resistivity survey. The geological outcrop in Penang, Malaysia with an obvious layering contact was chosen as the study site. Two dimensional geoelectrical resistivity imaging were used in this study to maps the resistivity distribution of subsurface, whereas few subsurface sample were obtained for laboratory advance. In this study, resistivity of samples in original conditions is measured in laboratory by using time domain low-voltage technique, particularly for granite core sample and soil resistivity measuring set for soil sample. The experimentation results from both schemes are studied, analyzed, calibrated and verified, including basis and correlation, degree of tolerance and characteristics of substance. Consequently, the significant different between both schemes is explained comprehensively within this paper.

A New Design of Mobile Thermoelectric Power Generation System

This paper presents a compact thermoelectric power generator system based on temperature difference across the element. The system can transfer the burning heat energy to electric energy directly. The proposed system has a thermoelectric generator and a power control box. In the generator, there are 4 thermoelectric modules (TEMs), each of which uses 2 thermoelectric chips (TEs) and 2 cold sinks, 1 thermal absorber, and 1 thermal conduction flat board. In the power control box, there are 1 storing energy device, 1 converter, and 1 inverter. The total net generating power is about 11W. This system uses commercial portable gas stoves or burns timber or the coal as the heat source, which is easily obtained. It adopts solid-state thermoelectric chips as heat inverter parts. The system has the advantages of being light-weight, quite, and mobile, requiring no maintenance, and havng easily-supplied heat source. The system can be used a as long as burning is allowed. This system works well for highly-mobilized outdoors situations by providing a power for illumination, entertainment equipment or the wireless equipment at refuge. Under heavy storms such as typhoon, when the solar panels become ineffective and the wind-powered machines malfunction, the thermoelectric power generator can continue providing the vital power.

Design of Auto Exposure Unit Based On 2-Way Histogram Equalization

Histogram equalization is often used in image enhancement, but it can be also used in auto exposure. However, conventional histogram equalization does not work well when many pixels are concentrated in a narrow luminance range.This paper proposes an auto exposure method based on 2-way histogram equalization. Two cumulative distribution functions are used, where one is from dark to bright and the other is from bright to dark. In this paper, the proposed auto exposure method is also designed and implemented for image signal processors with full-HD images.

Comparative Analysis of the Public Funding for Greek Universities: An Ordinal DEA/MCDM Approach

This study performs a comparative analysis of the 21 Greek Universities in terms of their public funding, awarded for covering their operating expenditure. First it introduces a DEA/MCDM model that allocates the fund into four expenditure factors in the most favorable way for each university. Then, it presents a common, consensual assessment model to reallocate the amounts, remaining in the same level of total public budget. From the analysis it derives that a number of universities cannot justify the public funding in terms of their size and operational workload. For them, the sufficient reduction of their public funding amount is estimated as a future target. Due to the lack of precise data for a number of expenditure criteria, the analysis is based on a mixed crisp-ordinal data set.

Meta-Classification using SVM Classifiers for Text Documents

Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated three approaches to build a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a metaclassifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers can significantly improve the accuracy of classification and that our meta-classification strategy gives better results than each individual classifier. For 7083 Reuters text documents we obtained a classification accuracies up to 92.04%.

An Overview of Islanding Detection Methods in Photovoltaic Systems

The issue of unintentional islanding in PV grid interconnection still remains as a challenge in grid-connected photovoltaic (PV) systems. This paper discusses the overview of popularly used anti-islanding detection methods, practically applied in PV grid-connected systems. Anti-islanding methods generally can be classified into four major groups, which include passive methods, active methods, hybrid methods and communication base methods. Active methods have been the preferred detection technique over the years due to very small non-detected zone (NDZ) in small scale distribution generation. Passive method is comparatively simpler than active method in terms of circuitry and operations. However, it suffers from large NDZ that significantly reduces its performance. Communication base methods inherit the advantages of active and passive methods with reduced drawbacks. Hybrid method which evolved from the combination of both active and passive methods has been proven to achieve accurate anti-islanding detection by many researchers. For each of the studied anti-islanding methods, the operation analysis is described while the advantages and disadvantages are compared and discussed. It is difficult to pinpoint a generic method for a specific application, because most of the methods discussed are governed by the nature of application and system dependent elements. This study concludes that the setup and operation cost is the vital factor for anti-islanding method selection in order to achieve minimal compromising between cost and system quality.

European Radical Right Parties as Actors in Securitization of Migration

This study reveals that anti-immigrant policies in Europe result from a process of securitization, and that, within this process, radical right parties have been formulating discourses and approaches through a construction process by using some common security themes. These security themes can be classified as national security, economic security, cultural security and internal security. The frequency with which radical right parties use these themes may vary according to the specific historical, social and cultural characteristics of a particular country.

A Novel Arabic Text Steganography Method Using Letter Points and Extensions

This paper presents a new steganography approach suitable for Arabic texts. It can be classified under steganography feature coding methods. The approach hides secret information bits within the letters benefiting from their inherited points. To note the specific letters holding secret bits, the scheme considers the two features, the existence of the points in the letters and the redundant Arabic extension character. We use the pointed letters with extension to hold the secret bit 'one' and the un-pointed letters with extension to hold 'zero'. This steganography technique is found attractive to other languages having similar texts to Arabic such as Persian and Urdu.

Artificial Intelligence Model to Predict Surface Roughness of Ti-15-3 Alloy in EDM Process

Conventionally the selection of parameters depends intensely on the operator-s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The proposed models use peak current, pulse on time, pulse off time and servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be exercised as precious tools for the process planning for EDM.

A Comparison of Adaline and MLP Neural Network based Predictors in SIR Estimation in Mobile DS/CDMA Systems

In this paper we compare the response of linear and nonlinear neural network-based prediction schemes in prediction of received Signal-to-Interference Power Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The nonlinear predictor is Multilayer Perceptron MLP and the linear predictor is an Adaptive Linear (Adaline) predictor. We solve the problem of complexity by using the Minimum Mean Squared Error (MMSE) principle to select the optimal predictors. The optimized Adaline predictor is compared to optimized MLP by employing noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an urban environment. The results show that the Adaline predictor can estimates SIR with the same error as MLP when the user has the velocity of 5 km/h and 60 km/h but by increasing the velocity up-to 120 km/h the mean squared error of MLP is two times more than Adaline predictor. This makes the Adaline predictor (with lower complexity) more suitable than MLP for closed-loop power control where efficient and accurate identification of the time-varying inverse dynamics of the multi path fading channel is required.

Routing Capability and Blocking Analysis of Dynamic ROADM Optical Networks (Category - II) for Dynamic Traffic

Reconfigurable optical add/drop multiplexers (ROADMs) can be classified into three categories based on their underlying switching technologies. Category I consists of a single large optical switch; category II is composed of a number of small optical switches aligned in parallel; and category III has a single optical switch and only one wavelength being added/dropped. In this paper, to evaluate the wavelength-routing capability of ROADMs of category-II in dynamic optical networks,the dynamic traffic models are designed based on Bernoulli, Poisson distributions for smooth and regular types of traffic. Through Analytical and Simulation results, the routing power of cat-II of ROADM networks for two traffic models are determined.

Prediction the Deformation in Upsetting Process by Neural Network and Finite Element

In this paper back-propagation artificial neural network (BPANN) is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature and coefficient of friction). The output data are the coefficient of polynomial that fitted on barreling curves. Neural network was trained using barreling curves generated by finite element simulations of the upsetting and the corresponding material parameters. This technique was tested for three different specimens and can be successfully employed to predict the deformation of the upsetting process

Automatic Design Algorithm for the Tower Crane Foundations

Foundation of tower crane serves to ensure stability against vertical and horizontal forces. If foundation stress is not sufficient, tower crane may be subject to overturning, shearing or foundation settlement. Therefore, engineering review of stable support is a highly critical part of foundation design. However, there are not many professionals who can conduct engineering review of tower crane foundation and, if any, they have information only on a small number of cranes in which they have hands-on experience. It is also customary to rely on empirical knowledge and tower crane renter-s recommendations rather than designing foundation on the basis of engineering knowledge. Therefore, a foundation design automation system considering not only lifting conditions but also overturning risk, shearing and vertical force may facilitate production of foolproof foundation design for experts and enable even non-experts to utilize professional knowledge that only experts can access now. This study proposes Automatic Design Algorithm for the Tower Crane Foundations considering load and horizontal force.

Optimization of Diverter Box Configuration in a V94.2 Gas Turbine Exhaust System using Numerical Simulation

The bypass exhaust system of a 160 MW combined cycle has been modeled and analyzed using numerical simulation in 2D prospective. Analysis was carried out using the commercial numerical simulation software, FLUENT 6.2. All inputs were based on the technical data gathered from working conditions of a Siemens V94.2 gas turbine, installed in the Yazd power plant. This paper deals with reduction of pressure drop in bypass exhaust system using turning vanes mounted in diverter box in order to alleviate turbulent energy dissipation rate above diverter box. The geometry of such turning vanes has been optimized based on the flow pattern at diverter box inlet. The results show that the use of optimized turning vanes in diverter box can improve the flow pattern and eliminate vortices around sharp edges just before the silencer. Furthermore, this optimization could decrease the pressure drop in bypass exhaust system and leads to higher plant efficiency.

From Mother Tongue Education to Multilingual Higher Education

Through the time, the higher education has changed the learning system since mother tongue to bilingual, and in this new century has been coming develop a multilingual education. All as part of globalization process of the countries and the education. Nevertheless, this change only has been effectively in countries of the first world, the rest have been lagging. Therefore, these countries require strengthen their higher education systems through models that give way to multilingual and bilingual education. In this way, shows a new model adapted from a systemic form to allow a higher bilingual and multilingual education in Latin America. This systematization aims to increase the skills and competencies student’s, decrease the time learning of a second tongue, add to multilingualism in the American Latin Universities, also, contribute to position the region´s countries in a better global status, and stimulate the development of new research in this area.

Judges System for Classifiers Specialization

In this paper we designed and implemented a new ensemble of classifiers based on a sequence of classifiers which were specialized in regions of the training dataset where errors of its trained homologous are concentrated. In order to separate this regions, and to determine the aptitude of each classifier to properly respond to a new case, it was used another set of classifiers built hierarchically. We explored a selection based variant to combine the base classifiers. We validated this model with different base classifiers using 37 training datasets. It was carried out a statistical comparison of these models with the well known Bagging and Boosting, obtaining significantly superior results with the hierarchical ensemble using Multilayer Perceptron as base classifier. Therefore, we demonstrated the efficacy of the proposed ensemble, as well as its applicability to general problems.

Managing Iterations in Product Design and Development

The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.

Effect of Spray Stand-off on Hardness of Thermally Sprayed Coatings

The mechanical and tribological properties in WC-Co coatings are strongly affected by hardness and elasticity specifications. The results revealed the effect of spraying distance on microhardness and elasticity modulus of coatings. The metallurgical studies have been made on coated samples using optical microscopy, scanning electron microscopy (SEM).