Abstract: Nanotechnology is the new cyber, according to several major leaders in this field. Just as cyber is entrenched across global society now, nano is poised to be major capabilities enabler of the next decades. Expert members from the National Nanotechnology Initiative (in U.S.) representing government and science disciplines say nano has great significance for the military and the general public. It is predicted that after next 15 years nanotechnology will replace information technology as the most economic technology platform. Nanotechnology has even wider applications than information technology.
Abstract: This paper deals with the issue of biomass and sorted
municipal waste gasification and cogeneration using hot-air turbo-set.
It brings description of designed pilot plant with electrical output 80
kWe. The generated gas is burned in secondary combustion chamber
located beyond the gas generator. Flue gas flows through the heat
exchanger where the compressed air is heated and consequently
brought to a micro turbine. Except description, this paper brings our
basic experiences from operating of pilot plant (operating parameters,
contributions, problems during operating, etc.). The principal
advantage of the given cycle is the fact that there is no contact
between the generated gas and the turbine. So there is no need for
costly and complicated gas cleaning which is the main source of
operating problems in direct use in combustion engines because the
content of impurities in the gas causes operation problems to the units
due to clogging and tarring of working surfaces of engines and
turbines, which may lead as far as serious damage to the equipment
under operation. Another merit is the compact container package
making installation of the facility easier or making it relatively more
mobile. We imagine, this solution of cogeneration from biomass or
waste can be suitable for small industrial or communal applications,
for low output cogeneration.
Abstract: The use of permanent magnets (PM) is increasing in
permanent magnet synchronous machines (PMSM) to fulfill the
requirements of high efficiency machines in modern industry. PMSM
are widely used in industrial applications, wind power plants and the
automotive industry. Since PMSM are used in different
environmental conditions, the long-term effect of NdFeB-based
magnets at high temperatures and their corrosion behavior have to be
studied due to the irreversible loss of magnetic properties.
In this paper, the effect of magnetic properties due to corrosion
and increasing temperature in a climatic chamber has been presented.
The magnetic moment and magnetic field of the magnets were
studied experimentally.
Abstract: Over the past era, there have been a lot of efforts and
studies are carried out in growing proficient tools for performing
various tasks in big data. Recently big data have gotten a lot of
publicity for their good reasons. Due to the large and complex
collection of datasets it is difficult to process on traditional data
processing applications. This concern turns to be further mandatory
for producing various tools in big data. Moreover, the main aim of
big data analytics is to utilize the advanced analytic techniques
besides very huge, different datasets which contain diverse sizes from
terabytes to zettabytes and diverse types such as structured or
unstructured and batch or streaming. Big data is useful for data sets
where their size or type is away from the capability of traditional
relational databases for capturing, managing and processing the data
with low-latency. Thus the out coming challenges tend to the
occurrence of powerful big data tools. In this survey, a various
collection of big data tools are illustrated and also compared with the
salient features.
Abstract: In this study, a three dimensional numerical heat
transfer model has been used to simulate the laser structuring of
polymer substrate material in the Three-Dimensional Molded
Interconnect Device (3D MID) which is used in the advanced multifunctional
applications. A finite element method (FEM) transient
thermal analysis is performed using APDL (ANSYS Parametric
Design Language) provided by ANSYS. In this model, the effect of
surface heat source was modeled with Gaussian distribution, also the
effect of the mixed boundary conditions which consist of convection
and radiation heat transfers have been considered in this analysis. The
model provides a full description of the temperature distribution, as
well as calculates the depth and the width of the groove upon material
removal at different set of laser parameters such as laser power and
laser speed. This study also includes the experimental procedure to
study the effect of laser parameters on the depth and width of the
removal groove metal as verification to the modeled results. Good
agreement between the experimental and the model results is
achieved for a wide range of laser powers. It is found that the quality
of the laser structure process is affected by the laser scan speed and
laser power. For a high laser structured quality, it is suggested to use
laser with high speed and moderate to high laser power.
Abstract: Estimation of a proportion has many applications in
economics and social studies. A common application is the estimation
of the low income proportion, which gives the proportion of people
classified as poor into a population. In this paper, we present this
poverty indicator and propose to use the logistic regression estimator
for the problem of estimating the low income proportion. Various
sampling designs are presented. Assuming a real data set obtained
from the European Survey on Income and Living Conditions, Monte
Carlo simulation studies are carried out to analyze the empirical
performance of the logistic regression estimator under the various
sampling designs considered in this paper. Results derived from
Monte Carlo simulation studies indicate that the logistic regression
estimator can be more accurate than the customary estimator under
the various sampling designs considered in this paper. The stratified
sampling design can also provide more accurate results.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: A method is proposed for stable detection of
seismoacoustic sources in C-OTDR systems that guarantee given
upper bounds for probabilities of type I and type II errors. Properties
of the proposed method are rigorously proved. The results of
practical applications of the proposed method in a real C-OTDRsystem
are presented.
Abstract: In this paper the issue of dimensionality reduction is
investigated in finger vein recognition systems using kernel Principal
Component Analysis (KPCA). One aspect of KPCA is to find the
most appropriate kernel function on finger vein recognition as there
are several kernel functions which can be used within PCA-based
algorithms. In this paper, however, another side of PCA-based
algorithms -particularly KPCA- is investigated. The aspect of
dimension of feature vector in PCA-based algorithms is of
importance especially when it comes to the real-world applications
and usage of such algorithms. It means that a fixed dimension of
feature vector has to be set to reduce the dimension of the input and
output data and extract the features from them. Then a classifier is
performed to classify the data and make the final decision. We
analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in
this paper and investigate the optimal feature extraction dimension in
finger vein recognition using KPCA.
Abstract: This paper analyzes innovation trends in South Korea
by means of the number of patent applications filed by residents and
nonresidents during the period 1965 to 2012. Making use of patent
data released by the World Intellectual Property Organization
(WIPO), we search for the presence of multiple structural changes in
patent application series in this country. These changes may suggest
that firms’ innovative activity has been modified as a result of
implementing some science, technology and innovation (STI)
policies. Accordingly, the new regulations implemented in this
country in the last decades have influenced its innovative activity.
The question conducting this research is thus how STI policies in
South Korea have influenced its innovation activity. The results
confirm the existence of multiple structural changes in the series of
patent applications resulting from alternative STI policies
implemented during these years.
Abstract: In remote sensing, shadow causes problems in many
applications such as change detection and classification. It is caused
by objects which are elevated, thus can directly affect the accuracy of
information. For these reasons, it is very important to detect shadows
particularly in urban high spatial resolution imagery which created a
significant problem. This paper focuses on automatic shadow
detection based on a new spectral index for multispectral imagery
known as Shadow Detection Index (SDI). The new spectral index
was tested on different areas of WorldView-2 images and the results
demonstrated that the new spectral index has a massive potential to
extract shadows with accuracy of 94% effectively and automatically.
Furthermore, the new shadow detection index improved road
extraction from 82% to 93%.
Abstract: We present our approach on using continuous delivery
pattern for release management. One of the key practices of agile and
lean teams is the continuous delivery of new features to stakeholders.
The main benefits of this approach lie in the ability to release new
applications rapidly which has real strategic impact on the
competitive advantage of an organization. Organizations that
successfully implement Continuous Delivery have the ability to
evolve rapidly to support innovation, provide stable and reliable
software in more efficient ways, decrease the amount of resources
need for maintenance, and lower the software delivery time and costs.
One of the objectives of this paper is to elaborate a case study where
IT division of Central Securities Depository Institution (MKK) of
Turkey apply Continuous Delivery pattern to improve release
management process.
Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: The purpose of this work is to optimize a Switched Reluctance Motor (SRM) for an automotive application, specifically for a fully electric car. A new optimization approach is proposed. This unique approach transforms automotive customer requirements into an optimization problem, based on sound knowledge of a SRM theory. The approach combines an analytical and a finite element analysis of the motor to quantify static nonlinear and dynamic performance parameters, as phase currents and motor torque maps, an output power and power losses in order to find the optimal motor as close to the reality as possible, within reasonable time. The new approach yields the optimal motor which is competitive with other types of already proposed motors for automotive applications. This distinctive approach can also be used to optimize other types of electrical motors, when parts specifically related to the SRM are adjusted accordingly.
Abstract: Turbulent flow in complex geometries receives considerable attention due to its importance in many engineering applications. It has been the subject of interest for many researchers. Some of these interests include the design of storm water channels. The design of these channels requires testing through physical models. The main practical limitation of physical models is the so called “scale effect”, that is, the fact that in many cases only primary physical mechanisms can be correctly represented, while secondary mechanisms are often distorted. These observations form the basis of our study, which centered on problems associated with the design of storm water channels near the Dead Sea, in Israel. To help reach a final design decision we used different physical models. Our research showed good coincidence with the results of laboratory tests and theoretical calculations, and allowed us to study different effects of fluid flow in an open channel. We determined that problems of this nature cannot be solved only by means of theoretical calculation and computer simulation. This study demonstrates the use of physical models to help resolve very complicated problems of fluid flow through baffles and similar structures. The study applies these models and observations to different construction and multiphase water flows, among them, those that include sand and stone particles, a significant attempt to bring to the testing laboratory a closer association with reality.
Abstract: The present environmental issues have made aircraft jet noise reduction a crucial problem in aero-acoustics research. Acoustic studies reveal that addition of chevrons to the nozzle reduces the sound pressure level reasonably with acceptable reduction in performance. In this paper comprehensive numerical studies on acoustic characteristics of different types of chevron nozzles have been carried out with non-reacting flows for the shape optimization of chevrons in supersonic nozzles for aerospace applications. The numerical studies have been carried out using a validated steady 3D density based, k-ε turbulence model. In this paper chevron with sharp edge, flat edge, round edge and U-type edge are selected for the jet acoustic characterization of supersonic nozzles. We observed that compared to the base model a case with round-shaped chevron nozzle could reduce 4.13% acoustic level with 0.6% thrust loss. We concluded that the prudent selection of the chevron shape will enable an appreciable reduction of the aircraft jet noise without compromising its overall performance. It is evident from the present numerical simulations that k-ε model can predict reasonably well the acoustic level of chevron supersonic nozzles for its shape optimization.
Abstract: This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.
Abstract: Partial shadowing is one of the problems that are always faced in terrestrial applications of solar photovoltaic (PV). The effects of partial shadow on the energy yield of conventional mono-crystalline and multi-crystalline PV modules have been researched for a long time. With deployment of new thin-film solar PV modules in the market, it is important to understand the performance of new PV modules operating under the partial shadow in the tropical zone. This paper addresses the impacts of different partial shadowing on the operating characteristics of four different types of solar PV modules that include multi-crystalline, amorphous thin-film, CdTe thin-film and CIGS thin-film PV modules.
Abstract: The article is proposing a base plan for the future Patient Care Information Systems in a changing health care environment where it is necessary to assure quality patient care services and reducing cost and where new technology trends give the opportunities to develop clinical applications and services patient focused according to new business objectives.
Abstract: Biometallic materials are the most important materials for use in biomedical applications especially in manufacturing a variety of biological artificial replacements in a modern worlds, e.g. hip, knee or shoulder joints, due to their advanced characteristics. Titanium (Ti) and its alloys are used extensively in biomedical applications based on their high specific strength and excellent corrosion resistance. Beta-Ti alloys containing completely biocompatible elements are exceptionally prospective materials for manufacturing of bioimplants. They have superior mechanical, chemical and electrochemical properties for use as biomaterials. These biomaterials have the ability to introduce the most important property of biochemical compatibility which is low elastic modulus. This review examines current information on the recent developments in alloying elements leading to improvements of beta Ti alloys for use as biomaterials. Moreover, this paper focuses mainly on the evolution, evaluation and development of the modulus of elasticity as an effective factor on the performance of beta alloys.