Abstract: We present a method for the selection of students
in interdisciplinary studies based on the hybrid averaging
operator. We assume that the available information given in
the problem is uncertain so it is necessary to use interval
numbers. Therefore, we suggest a new type of hybrid
aggregation called uncertain induced generalized hybrid
averaging (UIGHA) operator. It is an aggregation operator
that considers the weighted average (WA) and the ordered
weighted averaging (OWA) operator in the same formulation.
Therefore, we are able to consider the degree of optimism of
the decision maker and grades of importance in the same
approach. By using interval numbers, we are able to represent
the information considering the best and worst possible results
so the decision maker gets a more complete view of the
decision problem. We develop an illustrative example of the
proposed scheme in the selection of students in
interdisciplinary studies. We see that with the use of the
UIGHA operator we get a more complete representation of the
selection problem. Then, the decision maker is able to
consider a wide range of alternatives depending on his
interests. We also show other potential applications that could
be used by using the UIGHA operator in educational problems
about selection of different types of resources such as
students, professors, etc.
Abstract: Hospital staff and managers are under pressure and
concerned for effective use and management of scarce resources. The
hospital admissions require many decisions that have complex and
uncertain consequences for hospital resource utilization and patient
flow. It is challenging to predict risk of admissions and length of stay
of a patient due to their vague nature. There is no method to capture
the vague definition of admission of a patient. Also, current methods
and tools used to predict patients at risk of admission fail to deal with
uncertainty in unplanned admission, LOS, patients- characteristics.
The main objective of this paper is to deal with uncertainty in
health system variables, and handles uncertain relationship among
variables. An introduction of machine learning techniques along with
statistical methods like Regression methods can be a proposed
solution approach to handle uncertainty in health system variables. A
model that adapts fuzzy methods to handle uncertain data and
uncertain relationships can be an efficient solution to capture the
vague definition of admission of a patient.
Abstract: Students with high level skills are in demand, especially in scare skill environments. If universities wish to be successful and competitive, its students need to be adequately equipped with the necessary tools. Work Integrated Learning (WIL) is an essential component of the education of a student. The relevance of higher education should be assessed in terms of how it meets the needs of society and the world of work in a global economy. This paper demonstrates how to use Habermas's theory of communicative action to reflect on students- perceptions on their integration in the work environment to achieve social integration and financial justification. Interpretive questionnaires are used to determine the students- view of how they are integrated into society, and contributing to the economy. This paper explores the use of Habermas-s theory of communicative action to give theoretical and methodological guidance for the practice of social findings obtained in this inquiry.
Abstract: Pomegranate and pomegranate juices (PJs) have taken
great attention for their health benefits in the last years. As there is an
increasing concern about potential health benefits of ellagic acid, it is
of great interest to evaluate alterations in ellagic acid concentration of
commercial PJs. The purpose of this study is to analyze total
phenolic, free and total ellagic acid content of six commercial PJs
sold in Turkish markets using HPLC.
The results showed that some commercial PJs had markedly high
total phenolic and ellagic acid content. Total phenolic substances of
commercial PJs range from 796.71 to 4608.91 mg GAE/l. Free
amount of ellagic acid in commercial PJs range from 27.64 to 111.78
mg/l. Samples are hydrolyzed with concentrated HCl at 93oC for 2
and 24 hour and influences of temperature and time parameters on
hydrolization were investigated. Thermal processing for
pasteurization increased ellagic acid via ellagitannins hydrolysis.
Abstract: Technological newness and innovativeness are
important aspects of small firm development, growth and wealth
creation. The contribution of the study to entrepreneurship
personality research and to technology-related research in
entrepreneurship is that the model of the general personality driven
technological development was developed and empirically tested.
Hypotheses relating the big five personality factors (OCEAN:
openness, conscientiousness, extraversion, agreeableness, and
neuroticism) and technological developments were tested by using
multiple regression analysis on survey data from a sample of 160
entrepreneurs from Slovenia. The model reveals two personality
factors, which are predictive of technological developments:
openness (positive impact) and neuroticism (negative impact). In
addition, a positive impact of firm age on technological
developments was found. Other personality factors
(conscientiousness, extraversion and agreeableness) of entrepreneurs
may not be considered important for their firm technological
developments.
Abstract: A bond graph model of a two degrees of freedom
PUMA is described. System inversion gives the system input
required to generate a given system output. In order to get the system
inversion of the PUMA manipulator, a linearization of the nonlinear
bond graph is obtained. Hence, the bicausality of the linearized bond
graph of the PUMA manipulator is applied. Thus, the bicausal bond
graph provides a systematic way of generating the equations of the
system inversion. Simulation results to verify the calculated input for
a given output are shown.
Abstract: The waves of eGovernment are rising very fast
through almost all public administration, or at least most of the
public administrations around the world, and not only the public
administration, but also the entire government and all of their
organization as a whole. The government uses information
technology, and above all the internet or web network, to facilitate
the exchange of services between government agencies and citizens,
businesses, employees and other non-governmental agencies. With
efficient and transparent information exchange, the information
becomes accessible to the society (citizens, business, employees etc.),
and as a result of these processes the society itself becomes the
information society or knowledge society. This paper discusses the
knowledge management for eGovernment development in
significance and role. Also, the paper reviews the role of virtual
communities as a knowledge management mechanism to support
eGovernment in Montenegro. It explores the need for knowledge
management in eGovernment, identifies knowledge management
technologies, and highlights the challenges for developing countries,
such as Montenegro in the implementation of eGovernment. The
paper suggests that knowledge management is needed to facilitate
information exchange and transaction processing with citizens, as
well as to enable creation of knowledge society.
Abstract: In the current context of globalization, accountability has become a key subject of real interest for both, national and international business areas, due to the need for comparability and transparency of the economic situation, so we can speak about the harmonization and convergence of international accounting. The paper presents a qualitative research through content analysis of several reports concerning the roadmap for convergence. First, we develop a conceptual framework for the evolution of standards’ convergence and further we discuss the degree of standards harmonization and convergence between US GAAP and IAS/IFRS as to October 2012. We find that most topics did not follow the expected progress. Furthermore there are still some differences in the long-term project that are in process to be completed and other that were reassessed as a lower priority project.
Abstract: This paper presents the 20-GHz fractional PLL (Phase
Locked Loop) circuit for the next generation Wi-Fi by using 90 nm
TSMC process. The newly suggested millimeter wave 16/17
pre-scalar is designed and verified by measurement to make the
fractional PLL having a low quantization noise. The operational
bandwidth of the 60 GHz system is 15 % of the carrier frequency
which requires large value of Kv (VCO control gain) resulting in
degradation of phase noise. To solve this problem, this paper adopts
AFC (Automatic Frequency Controller) controlled 4-bit millimeter
wave VCO with small value of Kv. Also constant Kv is implemented
using 4-bit varactor bank. The measured operational bandwidth is 18.2
~ 23.2 GHz which is 25 % of the carrier frequency. The phase noise of
-58 and -96.2 dBc/Hz at 100 KHz and 1 MHz offset is measured
respectively. The total power consumption of the PLL is only 30 mW.
Abstract: Wireless mesh networks based on IEEE 802.11
technology are a scalable and efficient solution for next generation
wireless networking to provide wide-area wideband internet access to
a significant number of users. The deployment of these wireless mesh
networks may be within different authorities and without any
planning, they are potentially overlapped partially or completely in
the same service area. The aim of the proposed model is design a new
model to Enhancement Throughput of Unplanned Wireless Mesh
Networks Deployment Using Partitioning Hierarchical Cluster
(PHC), the unplanned deployment of WMNs are determinates there
performance. We use throughput optimization approach to model the
unplanned WMNs deployment problem based on partitioning
hierarchical cluster (PHC) based architecture, in this paper the
researcher used bridge node by allowing interworking traffic between
these WMNs as solution for performance degradation.
Abstract: Materials used on exterior spacecraft surfaces are subjected to many environmental threats which can cause degradation, atomic oxygen is one of the most threats. We prepared organic silicon atomic-oxygen-protection film using method of polymerization. This paper presented the effects on the film structure and its durability of the preparation processing, and analyzed the polymerization theory, the film structure and composition of the film. At last, we tested the film in our ground based atomic oxygen simulator, and indicated that the film worked well.
Abstract: Image compression can improve the performance of
the digital systems by reducing time and cost in image storage
and transmission without significant reduction of the image quality.
Furthermore, the discrete cosine transform has emerged as the new
state-of-the art standard for image compression. In this paper, a
hybrid image compression technique based on reversible blockade
transform coding is proposed. The technique, implemented over
regions of interest (ROIs), is based on selection of the coefficients
that belong to different transforms, depending on the coefficients is
proposed. This method allows: (1) codification of multiple kernals
at various degrees of interest, (2) arbitrary shaped spectrum,and (3)
flexible adjustment of the compression quality of the image and the
background. No standard modification for JPEG2000 decoder was
required. The method was applied over different types of images.
Results show a better performance for the selected regions, when
image coding methods were employed for the whole set of images.
We believe that this method is an excellent tool for future image
compression research, mainly on images where image coding can
be of interest, such as the medical imaging modalities and several
multimedia applications. Finally VLSI implementation of proposed
method is shown. It is also shown that the kernal of Hartley and
Cosine transform gives the better performance than any other model.
Abstract: In this study, a fuzzy similarity approach for Arabic
web pages classification is presented. The approach uses a fuzzy
term-category relation by manipulating membership degree for the
training data and the degree value for a test web page. Six measures
are used and compared in this study. These measures include:
Einstein, Algebraic, Hamacher, MinMax, Special case fuzzy and
Bounded Difference approaches. These measures are applied and
compared using 50 different Arabic web pages. Einstein measure was
gave best performance among the other measures. An analysis of
these measures and concluding remarks are drawn in this study.
Abstract: The objective of this research was to investigate biodegradation of water hyacinth (Eichhornia crassipes) to produce bioethanol using dilute-acid pretreatment (1% sulfuric acid) results in high hemicellulose decomposition and using yeast (Pachysolen tannophilus) as bioethanol producing strain. A maximum ethanol yield of 1.14g/L with coefficient, 0.24g g-1; productivity, 0.015g l-1h-1 was comparable to predicted value 32.05g/L obtained by Central Composite Design (CCD). Maximum ethanol yield coefficient was comparable to those obtained through enzymatic saccharification and fermentation of acid hydrolysate using fully equipped fermentor. Although maximum ethanol concentration was low in lab scale, the improvement of lignocellulosic ethanol yield is necessary for large scale production.
Abstract: Based on general proportional integral (GPI) observers and sliding mode control technique, a robust control method is proposed for the master-slave synchronization of chaotic systems in the presence of parameter uncertainty and with partially measurable output signal. By using GPI observer, the master dynamics are reconstructed by the observations from a measurable output under the differential algebraic framework. Driven by the signals provided by GPI observer, a sliding mode control technique is used for the tracking control and synchronization of the master-slave dynamics. The convincing numerical results reveal the proposed method is effective, and successfully accommodate the system uncertainties, disturbances, and noisy corruptions.
Abstract: Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
Abstract: This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.
Abstract: The public sector holds large amounts of data of
various areas such as social affairs, economy, or tourism. Various
initiatives such as Open Government Data or the EU Directive on
public sector information aim to make these data available for public
and private service providers. Requirements for the provision of
public sector data are defined by legal and organizational
frameworks. Surprisingly, the defined requirements hardly cover
security aspects such as integrity or authenticity.
In this paper we discuss the importance of these missing
requirements and present a concept to assure the integrity and
authenticity of provided data based on electronic signatures. We
show that our concept is perfectly suitable for the provisioning of
unaltered data. We also show that our concept can also be extended
to data that needs to be anonymized before provisioning by
incorporating redactable signatures. Our proposed concept enhances
trust and reliability of provided public sector data.
Abstract: This paper introduces a novel design for boring bar with enhanced damping capability. The principle followed in the design phase was to enhance the damping capability minimizing the loss in static stiffness through implementation of composite material interfaces. The newly designed tool has been compared to a conventional tool. The evaluation criteria were the dynamic characteristics, frequency and damping ratio, of the machining system, as well as the surface roughness of the machined workpieces. The use of composite material in the design of damped tool has been demonstrated effective. Furthermore, the autoregressive moving average (ARMA) models presented in this paper take into consideration the interaction between the elastic structure of the machine tool and the cutting process and can therefore be used to characterize the machining system in operational conditions.
Abstract: Microstrip lines, widely used for good reason, are
broadband in frequency and provide circuits that are compact and
light in weight. They are generally economical to produce since they
are readily adaptable to hybrid and monolithic integrated circuit (IC)
fabrication technologies at RF and microwave frequencies. Although,
the existing EM simulation models used for the synthesis and
analysis of microstrip lines are reasonably accurate, they are
computationally intensive and time consuming. Neural networks
recently gained attention as fast and flexible vehicles to microwave
modeling, simulation and optimization. After learning and
abstracting from microwave data, through a process called training,
neural network models are used during microwave design to provide
instant answers to the task learned.This paper presents simple and
accurate ANN models for the synthesis and analysis of Microstrip
lines to more accurately compute the characteristic parameters and
the physical dimensions respectively for the required design
specifications.