Abstract: Physics Education Research (PER) results have shown
that students do not achieve the expected level of competency in
understanding the concepts of different domains of Physics learning
when taught by the traditional teaching methods, the concepts of
Electricity and Magnetism (E&M) being one among them.
Simulation being one of the valuable instructional tools renders an
opportunity to visualize varied experiences with such concepts.
Considering the electric force concept which requires extensive use
of vector representations, we report here the outcome of the research
results pertaining to the student understanding of this concept and the
role of simulation in using vector representation. The simulation
platform provides a positive impact on the use of vector
representation.
The first stage of this study involves eliciting and analyzing
student responses to questions that probe their understanding of the
concept of electrostatic force and this is followed by four stages of
student interviews as they use the interactive simulations of electric
force in one dimension. Student responses to the questions are
recorded in real time using electronic pad. A validation test interview
is conducted to evaluate students' understanding of the electric force
concept after using interactive simulation. Results indicate lack of
procedural knowledge of the vector representation. The study
emphasizes the need for the choice of appropriate simulation and
mode of induction for learning.
Abstract: The spring-driven ball-type check valve is one of the
most important components of hydraulic systems: it controls the
position of the ball and prevents backward flow. To simplify the
structure, the spring must be eliminated, and to accomplish this, the
flow pattern and the behavior of the check ball in L-shaped pipe must
be determined. In this paper, we present a full-scale model of a check
ball made of acrylic resin, and we determine the relationship between
the initial position of the ball, the position and diameter of the inflow
port. The check flow rate increases in a standard center inflow model,
and it is possible to greatly decrease the check-flow rate by shifting the
inflow from the center.
Abstract: Nowadays, the successful implementation of ICTs is
vital for almost any kind of organization. Good governance and ICT
management are essential for delivering value, managing
technological risks, managing resources and performance
measurement. In addition, outsourcing is a strategic IT service
solution which complements IT services provided internally in
organizations. This paper proposes the measurement tools of a new
holistic maturity model based on standards ISO/IEC 20000 and
ISO/IEC 38500, and the frameworks and best practices of ITIL and
COBIT, with a specific focus on IT outsourcing. These measurement
tools allow independent validation and practical application in the
field of higher education, using a questionnaire, metrics tables, and
continuous improvement plan tables as part of the measurement
process. Guidelines and standards are proposed in the model for
facilitating adaptation to universities and achieving excellence in the
outsourcing of IT services.
Abstract: Cross flow water tube heat exchanger can be designed
and made operational using methods of model building and
simulation of the system. This paper projects the design and
development of a model of cross flow water tube heat-exchanger
system, simulation and validation of control analysis of different
tuning methods. Feedback and override control system is developed
using inputs acquired with the help of sensory system. A
mathematical model is formulated for analysis of system behaviour.
The temperature is regulated at the desired set point automatically.
Abstract: Nowadays, Photovoltaic-PV Farms/ Parks and large
PV-Smart Grid Interface Schemes are emerging and commonly
utilized in Renewable Energy distributed generation. However, PVhybrid-
Dc-Ac Schemes using interface power electronic converters
usually has negative impact on power quality and stabilization of
modern electrical network under load excursions and network fault
conditions in smart grid. Consequently, robust FACTS based
interface schemes are required to ensure efficient energy utilization
and stabilization of bus voltages as well as limiting switching/fault
onrush current condition. FACTS devices are also used in smart grid-
Battery Interface and Storage Schemes with PV-Battery Storage
hybrid systems as an elegant alternative to renewable energy
utilization with backup battery storage for electric utility energy and
demand side management to provide needed energy and power
capacity under heavy load conditions. The paper presents a robust
interface PV-Li-Ion Battery Storage Interface Scheme for
Distribution/Utilization Low Voltage Interface using FACTS
stabilization enhancement and dynamic maximum PV power tracking
controllers.
Digital simulation and validation of the proposed scheme is done
using MATLAB/Simulink software environment for Low Voltage-
Distribution/Utilization system feeding a hybrid Linear-Motorized
inrush and nonlinear type loads from a DC-AC Interface VSC-6-
pulse Inverter Fed from the PV Park/Farm with a back-up Li-Ion
Storage Battery.
Abstract: The study is devoted to define the optimal conditions
for the nitriding of pure iron at atmospheric pressure by using NH3-
Ar-C3H8 gas mixtures. After studying the mechanisms of phase
formation and mass transfer at the gas-solid interface, a mathematical
model is developed in order to predict the nitrogen transfer rate in the
solid, the ε-carbonitride layer growth rate and the nitrogen and
carbon concentration profiles. In order to validate the model and to
show its possibilities, it is compared with thermogravimetric
experiments, analyses and metallurgical observations (X-ray
diffraction, optical microscopy and electron microprobe analysis).
Results obtained allow us to demonstrate the sound correlation
between the experimental results and the theoretical predictions.
Abstract: This paper presents the hardware implemented and
validation for a special system to assist the unprofessional users of
car with back trailers. The system consists of two platforms; the front
car platform (C) and the trailer platform (T). The main objective is to
control the Trailer platform using the actuators found in the front
platform (c). The mobility of the platform (C) is investigated and
inverse and forward kinematics model is obtained for both platforms
(C) and (T).The system is simulated using Matlab M-file and the
simulation examples results illustrated the system performance. The
system is constructed with a hardware setup for the front and trailer
platform. The hardware experimental results and the simulated
examples outputs showed the validation of the hardware setup.
Abstract: Maturity models, used descriptively to explain
changes in reality or normatively to guide managers to make
interventions to make organizations more effective and efficient, are
based on the principles of statistical quality control and PDCA
continuous improvement (Plan, Do, Check, Act). Some frameworks
developed over the concept of maturity models include COBIT,
CMM, and ITIL.
This paper presents some limitations of traditional maturity
models, most of them related to the mechanistic and reductionist
principles over which those models are built. As systems theory helps
the understanding of the dynamics of organizations and
organizational change, the development of a systemic maturity model
can help to overcome some of those limitations.
This document proposes a systemic maturity model, based on a
systemic conceptualization of organizations, focused on the study of
the functioning of the parties, the relationships among them, and their
behavior as a whole. The concept of maturity from the system theory
perspective is conceptually defined as an emergent property of the
organization, which arises as a result of the degree of alignment and
integration of their processes. This concept is operationalized through
a systemic function that measures the maturity of organizations, and
finally validated by the measuring of maturity in some organizations.
For its operationalization and validation, the model was applied to
measure the maturity of organizational Governance, Risk and
Compliance (GRC) processes.
Abstract: The purpose of this work is examining the multiproduct
multi-stage in a battery production line. To improve the
performances of an assembly production line by determine the
efficiency of each workstation. Data collected from every
workstation. The data are throughput rate, number of operator, and
number of parts that arrive and leaves during part processing. Data
for the number of parts that arrives and leaves are collected at least at
the amount of ten samples to make the data is possible to be analyzed
by Chi-Squared Goodness Test and queuing theory. Measures of this
model served as the comparison with the standard data available in
the company. Validation of the task time value resulted by comparing
it with the task time value based on the company database. Some
performance factors for the multi-product multi-stage in a battery
production line in this work are shown.
The efficiency in each workstation was also shown. Total
production time to produce each part can be determined by adding
the total task time in each workstation. To reduce the queuing time
and increase the efficiency based on the analysis any probably
improvement should be done. One probably action is by increasing
the number of operators how manually operate this workstation.
Abstract: A novel simulation method to determine the
displacements of machine tools due to thermal factors is presented.
The specific characteristic of this method is the employment of
original CAD data from the design process chain, which is
interpreted by an algorithm in terms of geometry-based allocation of
convection and radiation parameters. Furthermore analogous models
relating to the thermal behaviour of machine elements are
automatically implemented, which were gained by extensive
experimental testing with thermography imaging. With this a
transient simulation of the thermal field and in series of the
displacement of the machine tool is possible simultaneously during
the design phase. This method was implemented and is already used
industrially in the design of machining centres in order to improve
the quality of herewith manufactured workpieces.
Abstract: The fundamental issues in ICT Governance (ICTG)
implementation for Malaysian Public Sector (MPS) is how ICT be
applied to support improvements in productivity, management
effectiveness and the quality of services offered to its citizens. Our
main concern is to develop and adopt a common definition and
framework to illustrate how ICTG can be used to better align ICT
with government’s operations and strategic focus. In particular, we
want to identify and categorize factors that drive a successful ICTG
process. This paper presents the results of an exploratory study to
identify, validate and refine such Critical Success Factors (CSFs) and
confirmed seven CSFs and nineteen sub-factors as influential factors
that fit MPS after further validated and refined. The Delphi method
applied in validation and refining process before being endorsed as
appropriate for MPS. The identified CSFs reflect the focus areas that
need to be considered strategically to strengthen ICT Governance
implementation and ensure business success.
Abstract: A simple, accurate and precise direct spectrophotometric method has been developed for the determination of cefixime in tablets and capsules. The method is based on the reaction of cefixime with a mixture of potassium iodide and potassium iodate to form yellow coloured product in ethanol-distilled water medium at room temperature which absorbed maximally at 352 nm. The factors affecting the reaction product were carefully studied and optimized. The validation parameters based on International Conference on Harmonisation (ICH, USA) guidelines were followed. The effect of common excipients used as additives has been tested and the tolerance limit was calculated for the determination of cefixime. Beer’s law is obeyed in the concentration range of 4 – 24 ug mL-1 with apparent molar absorptivity of 1.52 × 104 L mol-1cm-1 and Sandell’s sensitivity of 0.033 ug/cm2/ 0.001 absorbance unit. The limits of detection and quantitation for the proposed method are 0.32 and 1.06 ug mL-1, respectively. The proposed method has been successfully applied for the determination of cefixime in pharmaceutical formulations. The results obtained by the proposed method were statistically compared with the reference method using t- and F- values and found no significant difference between the two methods. The proposed method can be used as an alternate method for routine quality control analysis of cefixime in pharmaceutical formulations.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: The most important problem occurs on oil spills in sea
water is to reduce the oil spills size. This study deals with the
development of high pressurized nozzle using dispersion method for
oil leakage in offshore. 3D numerical simulation results were
obtained using ANSYS Fluent 13.0 code and correlate with the
experimental data for validation. This paper studies the contribution
of the process on flow speed and pressure of the flow from two
different geometrical designs of nozzles and to generate a spray
pattern suitable for dispersant application. Factor of size distribution
of droplets generated by the nozzle is calculated using pressures
ranging from 2 to 6 bars. Results obtain from both analyses shows a
significant spray pattern and flow distribution as well as distance.
Results also show a significant contribution on the effect of oil
leakage in terms of the diameter of the oil spills break up.
Abstract: This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.
Abstract: This paper presents a comparative study between two
neural network models namely General Regression Neural Network
(GRNN) and Back Propagation Neural Network (BPNN) are used
to estimate radial overcut produced during Electrical Discharge
Machining (EDM). Four input parameters have been employed:
discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and
discharge voltage (V). Recently, artificial intelligence techniques, as
it is emerged as an effective tool that could be used to replace
time consuming procedures in various scientific or engineering
applications, explicitly in prediction and estimation of the complex
and nonlinear process. The both networks are trained, and the
prediction results are tested with the unseen validation set of the
experiment and analysed. It is found that the performance of both the
networks are found to be in good agreement with average percentage
error less than 11% and the correlation coefficient obtained for the
validation data set for GRNN and BPNN is more than 91%. However,
it is much faster to train GRNN network than a BPNN and GRNN is
often more accurate than BPNN. GRNN requires more memory space
to store the model, GRNN features fast learning that does not require
an iterative procedure, and highly parallel structure. GRNN networks
are slower than multilayer perceptron networks at classifying new
cases.
Abstract: Biopharmaceuticals manufacturing is one of the major economic activities worldwide. Ninety-three percent of the workforce in a biomanufacturing environment concentrates in production-related areas. As a result, strategic collaborations between industry and academia are crucial to ensure the availability of knowledgeable workforce needed in an economic region to become competitive in biomanufacturing. In the past decade, our institution has been a key strategic partner with multinational biotechnology companies in supplying science and engineering graduates in the field of industrial biotechnology. Initiatives addressing all levels of the educational pipeline, from K-12 to college to continued education for company employees have been established along a ten-year span. The Amgen BioTalents Program was designed to provide undergraduate science and engineering students with training in biomanufacturing. The areas targeted by this educational program enhance their academic development, since these topics are not part of their traditional science and engineering curricula. The educational curriculum involved the process of producing a biomolecule from the genetic engineering of cells to the production of an especially targeted polypeptide, protein expression and purification, to quality control, and validation. This paper will report and describe the implementation details and outcomes of the first sessions of the program.
Abstract: Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.
Abstract: Recent changes in supply chains, especially globalization and collaboration, have created new risks for enterprises of all sizes. A variety of complex frameworks, often based on enterprise risk management strategies have been presented under the heading of Supply Chain Risk Management (SCRM). The literature on promotes the benefits of a robust SCRM strategy; however, implementing SCRM is difficult and resource demanding for Large Enterprises (LEs), and essentially out of reach for Small & Medium Enterprises (SMEs). This research debunks the idea that SCRM is necessary for all enterprises and instead proposes a simple and effective Vendor Selection Template (VST). Empirical testing and a survey of supply chain practitioners provide a measure of validation to the VST. The resulting VSTis a valuable contribution because is easy to use, provides practical results, and is sufficiently flexible to be universally applied to SMEs.
Abstract: Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.