Abstract: Managers as the key employees have a very important
role in maintaining the workforce performance which is critical to the
construction companies- success in the future. If motivated
employees start with motivated managers probably it would seem
plausible if the de-motivated ones start with de-motivated managers.
This study aims to analyze the importance of motivated managers to
their successes and construction companies- successes. In this study,
a quantitative method was used and the study area was in Medan,
North Sumatera. Questionnaire survey was distributed directly to
construction companies in Medan which are listed in the
Construction Services Development Board. A total of 60 managers
responded and the completed questionnaires were analyzed using the
descriptive analysis. The results indicated that the respondents
acknowledge the importance of motivation among themselves to the
projects and construction companies- success, implying that it is vital
to maintain the motivation and good performance of the workforce.
Abstract: Tumor classification is a key area of research in the
field of bioinformatics. Microarray technology is commonly used in
the study of disease diagnosis using gene expression levels. The
main drawback of gene expression data is that it contains thousands
of genes and a very few samples. Feature selection methods are used
to select the informative genes from the microarray. These methods
considerably improve the classification accuracy. In the proposed
method, Genetic Algorithm (GA) is used for effective feature
selection. Informative genes are identified based on the T-Statistics,
Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate
solutions of GA are obtained from top-m informative genes. The
classification accuracy of k-Nearest Neighbor (kNN) method is used
as the fitness function for GA. In this work, kNN and Support Vector
Machine (SVM) are used as the classifiers. The experimental results
show that the proposed work is suitable for effective feature
selection. With the help of the selected genes, GA-kNN method
achieves 100% accuracy in 4 datasets and GA-SVM method
achieves in 5 out of 10 datasets. The GA with kNN and SVM
methods are demonstrated to be an accurate method for microarray
based tumor classification.
Abstract: COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.
Abstract: We present a new numerical method for the computation of the steady-state solution of Markov chains. Theoretical analyses show that the proposed method, with a contraction factor α, converges to the one-dimensional null space of singular linear systems of the form Ax = 0. Numerical experiments are used to illustrate the effectiveness of the proposed method, with applications to a class of interesting models in the domain of tandem queueing networks.
Abstract: In this paper, first we introduce the stable distribution, stable process and theirs characteristics. The a -stable distribution family has received great interest in the last decade due to its success in modeling data, which are too impulsive to be accommodated by the Gaussian distribution. In the second part, we propose major applications of alpha stable distribution in telecommunication, computer science such as network delays and signal processing and financial markets. At the end, we focus on using stable distribution to estimate measure of risk in stock markets and show simulated data with statistical softwares.
Abstract: Data mining has been integrated into application systems to enhance the quality of the decision-making process. This study aims to focus on the integration of data mining technology and Knowledge Management System (KMS), due to the ability of data mining technology to create useful knowledge from large volumes of data. Meanwhile, KMS vitally support the creation and use of knowledge. The integration of data mining technology and KMS are popularly used in business for enhancing and sustaining organizational performance. However, there is a lack of studies that applied data mining technology and KMS in the education sector; particularly students- academic performance since this could reflect the IHL performance. Realizing its importance, this study seeks to integrate data mining technology and KMS to promote an effective management of knowledge within IHLs. Several concepts from literature are adapted, for proposing the new integrative data mining technology and KMS framework to an IHL.
Abstract: Database management systems that integrate user preferences promise better solution for personalization, greater flexibility and higher quality of query responses. This paper presents a tentative work that studies and investigates approaches to express user preferences in queries. We sketch an extend capabilities of SQLf language that uses the fuzzy set theory in order to define the user preferences. For that, two essential points are considered: the first concerns the expression of user preferences in SQLf by so-called fuzzy commensurable predicates set. The second concerns the bipolar way in which these user preferences are expressed on mandatory and/or optional preferences.
Abstract: This study describes a micro device integrated with
multi-chamber for polymerase chain reaction (PCR) with different
annealing temperatures. The device consists of the reaction
polydimethylsiloxane (PDMS) chip, a cover glass chip, and is
equipped with cartridge heaters, fans, and thermocouples for
temperature control. In this prototype, commercial software is utilized
to determine the geometric and operational parameters those are
responsible for creating the denaturation, annealing, and extension
temperatures within the chip. Two cartridge heaters are placed at two
sides of the chip and maintained at two different temperatures to
achieve a thermal gradient on the chip during the annealing step. The
temperatures on the chip surface are measured via an infrared imager.
Some thermocouples inserted into the reaction chambers are used to
obtain the transient temperature profiles of the reaction chambers
during several thermal cycles. The experimental temperatures
compared to the simulated results show a similar trend. This work
should be interesting to persons involved in the high-temperature
based reactions and genomics or cell analysis.
Abstract: One astonishing capability of humans is to recognize thousands of different objects visually, and to learn the semantic association between those objects and words referring to them. This work is an attempt to build a computational model of such capacity,simulating the process by which infants learn how to recognize objects and words through exposure to visual stimuli and vocal sounds.One of the main fact shaping the brain of a newborn is that lights and colors come from entities of the world. Gradually the visual system learn which light sensations belong to same entities, despite large changes in appearance. This experience is common between humans and several other mammals, like non-human primates. But humans only can recognize a huge variety of objects, most manufactured by himself, and make use of sounds to identify and categorize them. The aim of this model is to reproduce these processes in a biologically plausible way, by reconstructing the essential hierarchy of cortical circuits on the visual and auditory neural paths.
Abstract: It has become crucial over the years for nations to
improve their credit scoring methods and techniques in light of the
increasing volatility of the global economy. Statistical methods or
tools have been the favoured means for this; however artificial
intelligence or soft computing based techniques are becoming
increasingly preferred due to their proficient and precise nature and
relative simplicity. This work presents a comparison between Support
Vector Machines and Artificial Neural Networks two popular soft
computing models when applied to credit scoring. Amidst the
different criteria-s that can be used for comparisons; accuracy,
computational complexity and processing times are the selected
criteria used to evaluate both models. Furthermore the German credit
scoring dataset which is a real world dataset is used to train and test
both developed models. Experimental results obtained from our study
suggest that although both soft computing models could be used with
a high degree of accuracy, Artificial Neural Networks deliver better
results than Support Vector Machines.
Abstract: Social cognitive theory explains the power to inaugurate change is determined by the mutual influence of personal proclivity and social factors which will shape ones- motivations and expectations. In construction industry, green concept offers an opportunity to leave a lighter footprint on the environment. This opportunity, however, has not been fully grasped by many countries. As such, venturing into green construction for many practitioners would be their maiden experience. Decision to venture into new practice such as green construction will be influenced by certain drivers. This paper explores these drivers which is further expanded into motivational factors and later becomes the platform upon which expectation for green construction stands. This theoretical concept of motivation and expectations, which is adapted from social cognitive theory, focus on developers- view because of their crucial role in green application. This conceptual framework, which serves as the basis for further research, will benefit the industry as it elucidate cognitive angles to attract more new entrants to green business.
Abstract: LSP routing is among the prominent issues in MPLS
networks traffic engineering. The objective of this routing is to
increase number of the accepted requests while guaranteeing the
quality of service (QoS). Requested bandwidth is the most important
QoS criterion that is considered in literatures, and a various number
of heuristic algorithms have been presented with that regards. Many
of these algorithms prevent flows through bottlenecks of the network
in order to perform load balancing, which impedes optimum
operation of the network. Here, a modern routing algorithm is
proposed as MIRAD: having a little information of the network
topology, links residual bandwidth, and any knowledge of the
prospective requests it provides every request with a maximum
bandwidth as well as minimum end-to-end delay via uniform load
distribution across the network. Simulation results of the proposed
algorithm show a better efficiency in comparison with similar
algorithms.
Abstract: For several high speed networks, providing resilience against failures is an essential requirement. The main feature for designing next generation optical networks is protecting and restoring high capacity WDM networks from the failures. Quick detection, identification and restoration make networks more strong and consistent even though the failures cannot be avoided. Hence, it is necessary to develop fast, efficient and dependable fault localization or detection mechanisms. In this paper we propose a new fault localization algorithm for WDM networks which can identify the location of a failure on a failed lightpath. Our algorithm detects the failed connection and then attempts to reroute data stream through an alternate path. In addition to this, we develop an algorithm to analyze the information of the alarms generated by the components of an optical network, in the presence of a fault. It uses the alarm correlation in order to reduce the list of suspected components shown to the network operators. By our simulation results, we show that our proposed algorithms achieve less blocking probability and delay while getting higher throughput.
Abstract: Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.
Abstract: Various formal and informal brand alliances are being formed in professional service firms. Professional service corporate brand is heavily dependent on brands of professional employees who comprise them, and professional employee brands are in turn dependent on the corporate brand. Prior work provides limited scientific evidence of brand alliance effects in professional service area – i.e., how professional service corporate-employee brand allies are affected by an alliance, what are brand attitude effects after alliance formation and how these effects vary with different strengths of an ally. Scientific literature analysis and theoretical modeling are the main methods of the current study. As a result, a theoretical model is constructed for estimating spillover effects of professional service corporate-employee brand alliances and for comparison among different professional service firm expertise practice models – from “brains" to “procedure" model. The resulting theoretical model lays basis for future experimental studies.
Abstract: Within the realm of e-government, the development has moved towards testing new means for democratic decisionmaking, like e-panels, electronic discussion forums, and polls. Although such new developments seem promising, they are not problem-free, and the outcomes are seldom used in the subsequent formal political procedures. Nevertheless, process models offer promising potential when it comes to structuring and supporting transparency of decision processes in order to facilitate the integration of the public into decision-making procedures in a reasonable and manageable way. Based on real-life cases of urban planning processes in Sweden, we present an outline for an integrated framework for public decision making to: a) provide tools for citizens to organize discussion and create opinions; b) enable governments, authorities, and institutions to better analyse these opinions; and c) enable governments to account for this information in planning and societal decision making by employing a process model for structured public decision making.
Abstract: Several combinations of the preprocessing algorithms,
feature selection techniques and classifiers can be applied to the data
classification tasks. This study introduces a new accurate classifier,
the proposed classifier consist from four components: Signal-to-
Noise as a feature selection technique, support vector machine,
Bayesian neural network and AdaBoost as an ensemble algorithm.
To verify the effectiveness of the proposed classifier, seven well
known classifiers are applied to four datasets. The experiments show
that using the suggested classifier enhances the classification rates for
all datasets.
Abstract: A precision CMOS chopping amplifier is adopted in this work to improve a CMOS temperature sensor high sensitive enough for intracranial temperature monitoring. An amplified temperature sensitivity of 18.8 ± 3*0.2 mV/oC is attained over the temperature range from 20 oC to 80 oC from a given 10 samples of the same wafer. The analog frontend design outputs the temperature dependent and the temperature independent signals which can be directly interfaced to a 10 bit ADC to accomplish an accurate temperature instrumentation system.
Abstract: In this work, the condensation fraction and transition
temperature of neutral many bosonic system are studied within the
static fluctuation approximation (SFA). The effect of the potential
parameters such as the strength and range on the condensate fraction
was investigated. A model potential consisting of a repulsive step
potential and an attractive potential well was used. As the potential
strength or the core radius of the repulsive part increases, the
condensation fraction is found to be decreased at the same
temperature. Also, as the potential depth or the range of the attractive
part increases, the condensation fraction is found to be increased. The
transition temperature is decreased as the potential strength or the
core radius of the repulsive part increases, and it increases as the
potential depth or the range of the attractive part increases.
Abstract: The present work was conducted for the synthesis of
nano size zerovalent iron (nZVI) and hexavalent chromium (Cr(VI))
removal as a highly toxic pollutant by using this nanoparticles. Batch
experiments were performed to investigate the effects of Cr(VI),
nZVI concentration, pH of solution and contact time variation on
the removal efficiency of Cr(VI). nZVI was synthesized by
reduction of ferric chloride using sodium borohydrid. SEM and
XRD examinations applied for determination of particle size and
characterization of produced nanoparticles. The results showed that
the removal efficiency decreased with Cr(VI) concentration and pH
of solution and increased with adsorbent dosage and contact time.
The Langmuir and Freundlich isotherm models were used for the
adsorption equilibrium data and the Langmuir isotherm model was
well fitted. Nanoparticle ZVI presented an outstanding ability to
remove Cr(VI) due to high surface area, low particle size and high
inherent activity.