Abstract: Introduction: The process to build a better safety
culture, methods of error analysis, and preventive measures, starts
with an understanding of the effects when human factors engineering
refer to remote microscopic diagnosis in surgery and specially in
organ transplantation for the remote evaluation of the grafts. It has
been estimated that even in well-organized transplant systems an
average of 8% to 14% of the grafts (G) that arrive at the recipient
hospitals may be considered as diseased, injured, damaged or
improper for transplantation. Digital microscopy adds information on
a microscopic level about the grafts in Organ Transplant (OT), and
may lead to a change in their management. Such a method will
reduce the possibility that a diseased G, will arrive at the recipient
hospital for implantation. Aim: Ergonomics of Digital Microscopy
(DM) based on virtual slides, on Telemedicine Systems (TS) for
Tele-Pathological (TPE) evaluation of the grafts (G) in organ
transplantation (OT). Material and Methods: By experimental
simulation, the ergonomics of DM for microscopic TPE of Renal
Graft (RG), Liver Graft (LG) and Pancreatic Graft (PG) tissues is
analyzed. In fact, this corresponded to the ergonomics of digital
microscopy for TPE in OT by applying Virtual Slide (VS) system for
graft tissue image capture, for remote diagnoses of possible
microscopic inflammatory and/or neoplastic lesions. Experimentation
included: a. Development of an OTE-TS similar Experimental
Telemedicine System (Exp.-TS), b. Simulation of the integration of
TS with the VS based microscopic TPE of RG, LG and PG applying
DM. Simulation of the DM based TPE was performed by 2
specialists on a total of 238 human Renal Graft (RG), 172 Liver Graft
(LG) and 108 Pancreatic Graft (PG) tissues digital microscopic
images for inflammatory and neoplastic lesions on four electronic
spaces of the four used TS. Results: Statistical analysis of specialist‘s
answers about the ability to diagnose accurately the diseased RG, LG
and PG tissues on the electronic space among four TS (A,B,C,D)
showed that DM on TS for TPE in OT is elaborated perfectly on the
ES of a Desktop, followed by the ES of the applied Exp.-TS. Tablet
and Mobile-Phone ES seem significantly risky for the application of
DM in OT (p
Abstract: The aim of the study is to compare behavioral and
EEG reactions in Turkic-speaking inhabitants of Siberia (Tuvinians
and Yakuts) and Russians during the recognition of syntax errors in
native and foreign languages. Sixty-three healthy aboriginals of the
Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and
55 Russians from Novosibirsk participated in the study. EEG were
recorded during execution of error-recognition task in Russian and
English language (in all participants) and in native languages
(Tuvinian or Yakut Turkic-speaking inhabitants). Reaction time (RT)
and quality of task execution were chosen as behavioral measures.
Amplitude and cortical distribution of P300 and P600 peaks of ERP
were used as a measure of speech-related brain activity. In Tuvinians,
there were no differences in the P300 and P600 amplitudes as well as
in cortical topology for Russian and Tuvinian languages, but there
was a difference for English. In Yakuts, the P300 and P600
amplitudes and topology of ERP for Russian language were the same
as Russians had for native language. In Yakuts, brain reactions during
Yakut and English language comprehension had no difference, while
the Russian language comprehension was differed from both Yakut
and English. We found out that the Tuvinians recognized both Russian and
Tuvinian as native languages, and English as a foreign language. The
Yakuts recognized both English and Yakut as foreign languages, but
Russian as a native language. According to the inquirer, both
Tuvinians and Yakuts use the national language as a spoken
language, whereas they do not use it for writing. It can well be a
reason that Yakuts perceive the Yakut writing language as a foreign
language while writing Russian as their native.
Abstract: The UK is leading in online retail and mobile
adoption. However, there is a dearth of information relating to mobile
apparel retail, and developing an understanding about consumer
browsing and purchase behaviour in m-retail channel would provide
apparel marketers, mobile website and app developers with the
necessary understanding of consumers’ needs. Despite the rapid
growth of mobile retail businesses, no published study has examined
shopping behaviour on fashion mobile apps and websites. A mixed method approach helped to understand why fashion
consumers prefer websites on smartphones, when diverse mobile
apps are also available. The following research methods were
employed: survey, eye-tracking experiments, observation, and
interview with retrospective think aloud. The mobile gaze tracking
device by SensoMotoric Instruments was used to understand
frustrations in navigation and other issues facing consumers in
mobile channel. This method helped to validate and compliment
other traditional user-testing approaches in order to optimize user
experience and enhance the development of mobile retail channel.
The study involved eight participants - females aged 18 to 35 years
old, who are existing mobile shoppers. The participants used the
Topshop mobile app and website on a smart phone to complete a task
according to a specified scenario leading to a purchase. The
comparative study was based on: duration and time spent at different
stages of the shopping journey, number of steps involved and product
pages visited, search approaches used, layout and visual clues, as
well as consumer perceptions and expectations. The results from the data analysis show significant differences in
consumer behaviour when using a mobile app or website on a smart
phone. Moreover, two types of problems were identified, namely
technical issues and human errors. Having a mobile app does not
guarantee success in satisfying mobile fashion consumers. The
differences in the layout and visual clues seem to influence the
overall shopping experience on a smart phone. The layout of search
results on the website was different from the mobile app. Therefore,
participants, in most cases, behaved differently on different
platforms. The number of product pages visited on the mobile app
was triple the number visited on the website due to a limited visibility
of products in the search results. Although, the data on traffic trends
held by retailers to date, including retail sector breakdowns for visits
and views, data on device splits and duration, might seem a valuable
source of information, it cannot explain why consumers visit many
product pages, stay longer on the website or mobile app, or abandon
the basket. A comprehensive list of pros and cons was developed by
highlighting issues for website and mobile app, and recommendations
provided. The findings suggest that fashion retailers need to be aware of
actual consumers’ behaviour on the mobile channel and their expectations in order to offer a seamless shopping experience. Added
to which is the challenge of retaining existing and acquiring new
customers. There seem to be differences in the way fashion
consumers search and shop on mobile, which need to be explored in
further studies.
Abstract: This paper describes a sliding mode controller for
autonomous underwater vehicles (AUVs). The dynamic of AUV
model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
To address these difficulties, a nonlinear sliding mode controller is
designed to approximate the nonlinear dynamics of AUV and
improve trajectory tracking. Moreover, the proposed controller can
profoundly attenuate the effects of uncertainties and external
disturbances in the closed-loop system. Using the Lyapunov theory
the boundedness of AUV tracking errors and the stability of the
proposed control system are also guaranteed. Numerical simulation
studies of an AUV are included to illustrate the effectiveness of the
presented approach.
Abstract: The apportionment method is used by many countries, to calculate the distribution of seats in political bodies. For example, this method is used in the United States (U.S.) to distribute house seats proportionally based on the population of the electoral district. Famous apportionment methods include the divisor methods called the Adams Method, Dean Method, Hill Method, Jefferson Method and Webster Method. Sometimes the results from the implementation of these divisor methods are unfair and include errors. Therefore, it is important to examine the optimization of this method by using a bias measurement to figure out precise and fair results. In this research we investigate the bias of divisor methods in the U.S. Houses of Representatives toward large and small states by applying the Stolarsky Mean Method. We compare the bias of the apportionment method by using two famous bias measurements: the Balinski and Young measurement and the Ernst measurement. Both measurements have a formula for large and small states. The Third measurement however, which was created by the researchers, did not factor in the element of large and small states into the formula. All three measurements are compared and the results show that our measurement produces similar results to the other two famous measurements.
Abstract: In this paper the influence of errors of function derivatives in initial time which have been obtained by experiment (uncontrollable inaccuracy) to the results of inverse problem solution was investigated. It was shown that these errors distort the inverse problem solution as a rule near the beginning of interval where the solutions are analyzed. Several methods for removing the influence of uncontrollable inaccuracy have been suggested.
Abstract: The availability to deploy mobile applications for
health care is increasing daily thru different mobile app stores. But
within these capabilities the number of hacking attacks has also
increased, in particular into medical mobile applications. The security
vulnerabilities in medical mobile apps can be triggered by errors in
code, incorrect logic, poor design, among other parameters. This is
usually used by malicious attackers to steal or modify the users’
information. The aim of this research is to analyze the vulnerabilities
detected in mobile medical apps according to risk factor standards
defined by OWASP in 2014.
Abstract: This study presents three different approaches to
estimate bubble point pressures for the binary system of CO2 and
ethyl palmitate fatty acid ethyl ester. The first method involves the
Peng-Robinson (PR) Equation of State (EoS) with the conventional
mixing rule of Van der Waals. The second approach involves the PR
EOS together with the Wong Sandler (WS) mixing rule, coupled with
the UNIQUAC GE model. In order to model the bubble point
pressures with this approach, the volume and area parameter for ethyl
palmitate were estimated by the Hansen group contribution method.
The last method involved the Peng-Robinson, combined with the
Wong-Sandler method, but using NRTL as the GE model. Results
using the Van der Waals mixing rule clearly indicated that this
method has the largest errors among all three methods, with errors in
the range of 3.96-6.22%. The PR-WS-UNIQUAC method exhibited
small errors, with average absolute deviations between 0.95 to 1.97
percent. The PR-WS-NRTL method led to the least errors, where
average absolute deviations ranged between 0.65-1.7%.
Abstract: In this paper, we present a new segmentation approach
for focal liver lesions in contrast enhanced ultrasound imaging. This
approach, based on a two-cluster Fuzzy C-Means methodology,
considers type-II fuzzy sets to handle uncertainty due to the image
modality (presence of speckle noise, low contrast, etc.), and to
calculate the optimum inter-cluster threshold. Fine boundaries are
detected by a local recursive merging of ambiguous pixels. The
method has been tested on a representative database. Compared to
both Otsu and type-I Fuzzy C-Means techniques, the proposed
method significantly reduces the segmentation errors.
Abstract: This paper presents a power control for a Doubly Fed
Induction Generator (DFIG) using in Wind Energy Conversion
System (WECS) connected to the grid. The proposed control strategy
employs two nonlinear controllers, Backstipping (BSC) and slidingmode
controller (SMC) scheme to directly calculate the required
rotor control voltage so as to eliminate the instantaneous errors of
active and reactive powers. In this paper the advantages of BSC and
SMC are presented, the performance and robustness of this two
controller’s strategy are compared between them. First, we present a
model of wind turbine and DFIG machine, then a synthesis of the
controllers and their application in the DFIG power control.
Simulation results on a 1.5MW grid-connected DFIG system are
provided by MATLAB/Simulink.
Abstract: The output error of the globoidal cam mechanism can
be considered as a relevant indicator of mechanism performance,
because it determines kinematic and dynamical behavior of
mechanical transmission. Based on the differential geometry and the
rigid body transformations, the mathematical model of surface
geometry of the globoidal cam is established. Then we present the
analytical expression of the output error (including the transmission
error and the displacement error along the output axis) by considering
different manufacture and assembly errors. The effects of the center
distance error, the perpendicular error between input and output axes
and the rotational angle error of the globoidal cam on the output error
are systematically analyzed. A globoidal cam mechanism which is
widely used in automatic tool changer of CNC machines is applied for
illustration. Our results show that the perpendicular error and the
rotational angle error have little effects on the transmission error but
have great effects on the displacement error along the output axis. This
study plays an important role in the design, manufacture and assembly
of the globoidal cam mechanism.
Abstract: Sound processing is one the subjects that newly
attracts a lot of researchers. It is efficient and usually less expensive
than other methods. In this paper the flow generated sound is used to
estimate the flow speed of free flows. Many sound samples are
gathered. After analyzing the data, a parameter named wave power is
chosen. For all samples the wave power is calculated and averaged
for each flow speed. A curve is fitted to the averaged data and a
correlation between the wave power and flow speed is found. Test
data are used to validate the method and errors for all test data were
under 10 percent. The speed of the flow can be estimated by
calculating the wave power of the flow generated sound and using the
proposed correlation.
Abstract: In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.
Abstract: To construct the lumped spring-mass model
considering the occupants for the offset frontal crash, the SISAME
software and the NHTSA test data were used. The data on 56 kph 40%
offset frontal vehicle to deformable barrier crash test of a MY2007
Mazda 6 4-door sedan were obtained from NHTSA test database. The
overall behaviors of B-pillar and engine of simulation models agreed
very well with the test data. The trends of accelerations at the driver
and passenger head were similar but big differences in peak values.
The differences of peak values caused the large errors of the HIC36
and 3 ms chest g’s. To predict well the behaviors of dummies, the
spring-mass model for the offset frontal crash needs to be improved.
Abstract: This paper addresses a cutting edge method of
business demand forecasting, based on an empirical probability
function when the historical behavior of the data is random.
Additionally, it presents error determination based on the numerical
method technique ‘propagation of errors.’ The methodology was
conducted characterization and process diagnostics demand planning
as part of the production management, then new ways to predict its
value through techniques of probability and to calculate their mistake
investigated, it was tools used numerical methods. All this based on
the behavior of the data. This analysis was determined considering
the specific business circumstances of a company in the sector of
communications, located in the city of Bogota, Colombia. In
conclusion, using this application it was possible to obtain the
adequate stock of the products required by the company to provide its
services, helping the company reduce its service time, increase the
client satisfaction rate, reduce stock which has not been in rotation
for a long time, code its inventory, and plan reorder points for the
replenishment of stock.
Abstract: In more complex systems, such as automotive
gearbox, a rigorous treatment of the data is necessary because there
are several moving parts (gears, bearings, shafts, etc.), and in this
way, there are several possible sources of errors and also noise. The
basic objective of this work is the detection of damage in automotive
gearbox. The detection methods used are the wavelet method, the
bispectrum; advanced filtering techniques (selective filtering) of
vibrational signals and mathematical morphology. Gearbox vibration
tests were performed (gearboxes in good condition and with defects)
of a production line of a large vehicle assembler. The vibration
signals are obtained using five accelerometers in different positions
of the sample. The results obtained using the kurtosis, bispectrum,
wavelet and mathematical morphology showed that it is possible to
identify the existence of defects in automotive gearboxes.
Abstract: In the present study, the kinetics of thermal
degradation of a phenolic and lignin reinforced phenolic foams, and
the lignin used as reinforcement were studied and the activation
energies of their degradation processes were obtained by a DAEM
model. The average values for five heating rates of the mean
activation energies obtained were: 99.1, 128.2, and 144.0 kJ.mol-1 for
the phenolic foam; 109.5, 113.3, and 153.0 kJ.mol-1 for the lignin
reinforcement; and 82.1, 106.9, and 124.4 kJ.mol-1 for the lignin
reinforced phenolic foam. The standard deviation ranges calculated
for each sample were 1.27-8.85, 2.22-12.82, and 3.17-8.11 kJ.mol-1
for the phenolic foam, lignin and the reinforced foam, respectively.
The DAEM model showed low mean square errors (
Abstract: Micro-electromechanical system (MEMS)
accelerometers and gyroscopes are suitable for the inertial navigation
system (INS) of many applications due to low price, small
dimensions and light weight. The main disadvantage in a comparison
with classic sensors is a worse long term stability. The estimation
accuracy is mostly affected by the time-dependent growth of inertial
sensor errors, especially the stochastic errors. In order to eliminate
negative effects of these random errors, they must be accurately
modeled. In this paper, the Allan variance technique will be used in
modeling the stochastic errors of the inertial sensors. By performing
a simple operation on the entire length of data, a characteristic curve
is obtained whose inspection provides a systematic characterization
of various random errors contained in the inertial-sensor output data.
Abstract: An adaptive nonparametric method is proposed for
stable real-time detection of seismoacoustic sources in multichannel
C-OTDR systems with a significant number of channels. This
method guarantees given upper boundaries 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-OTDR-system are presented in this report.
Abstract: The purposes of this study are 1) to study the effects
of participatory error correction process and 2) to find out the
students’ satisfaction of such error correction process. This study is a
Quasi Experimental Research with single group, in which data is
collected 5 times preceding and following 4 experimental studies of
participatory error correction process including providing coded
indirect corrective feedback in the students’ texts with error treatment
activities. Samples include 52 2nd year English Major students,
Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat
University. Tool for experimental study includes the lesson plan of
the course; Reading and Writing English for Academic Purposes II,
and tools for data collection include 5 writing tests of short texts and
a questionnaire. Based on formative evaluation of the students’
writing ability prior to and after each of the 4 experiments, the
research findings disclose the students’ higher scores with statistical
difference at 0.00. Moreover, in terms of the effect size of such
process, it is found that for mean of the students’ scores prior to and
after the 4 experiments; d equals 0.6801, 0.5093, 0.5071, and 0.5296
respectively. It can be concluded that participatory error correction
process enables all of the students to learn equally well and there is
improvement in their ability to write short texts. Finally the students’
overall satisfaction of the participatory error correction process is in
high level (Mean = 4.39, S.D. = 0.76).