Abstract: Singular value decomposition based optimisation of
geometric design parameters of a 5-speed gearbox is studied. During
the optimisation, a four-degree-of freedom torsional vibration model
of the pinion gear-wheel gear system is obtained and the minimum
singular value of the transfer matrix is considered as the objective
functions. The computational cost of the associated singular value
problems is quite low for the objective function, because it is only
necessary to compute the largest and smallest singular values (μmax
and μmin) that can be achieved by using selective eigenvalue solvers;
the other singular values are not needed. The design parameters are
optimised under several constraints that include bending stress,
contact stress and constant distance between gear centres. Thus, by
optimising the geometric parameters of the gearbox such as, the
module, number of teeth and face width it is possible to obtain a
light-weight-gearbox structure. It is concluded that the all optimised
geometric design parameters also satisfy all constraints.
Abstract: Particle size distribution, the most important
characteristics of aerosols, is obtained through electrical
characterization techniques. The dynamics of charged nanoparticles
under the influence of electric field in Electrical Mobility
Spectrometer (EMS) reveals the size distribution of these particles.
The accuracy of this measurement is influenced by flow conditions,
geometry, electric field and particle charging process, therefore by
the transfer function (transfer matrix) of the instrument. In this work,
a wire-cylinder corona charger was designed and the combined fielddiffusion
charging process of injected poly-disperse aerosol particles
was numerically simulated as a prerequisite for the study of a
multichannel EMS. The result, a cloud of particles with no uniform
charge distribution, was introduced to the EMS. The flow pattern and
electric field in the EMS were simulated using Computational Fluid
Dynamics (CFD) to obtain particle trajectories in the device and
therefore to calculate the reported signal by each electrometer.
According to the output signals (resulted from bombardment of
particles and transferring their charges as currents), we proposed a
modification to the size of detecting rings (which are connected to
electrometers) in order to evaluate particle size distributions more
accurately. Based on the capability of the system to transfer
information contents about size distribution of the injected particles,
we proposed a benchmark for the assessment of optimality of the
design. This method applies the concept of Von Neumann entropy
and borrows the definition of entropy from information theory
(Shannon entropy) to measure optimality. Entropy, according to the
Shannon entropy, is the ''average amount of information contained in
an event, sample or character extracted from a data stream''.
Evaluating the responses (signals) which were obtained via various
configurations of detecting rings, the best configuration which gave
the best predictions about the size distributions of injected particles,
was the modified configuration. It was also the one that had the
maximum amount of entropy. A reasonable consistency was also
observed between the accuracy of the predictions and the entropy
content of each configuration. In this method, entropy is extracted
from the transfer matrix of the instrument for each configuration.
Ultimately, various clouds of particles were introduced to the
simulations and predicted size distributions were compared to the
exact size distributions.
Abstract: Grains, including oats (Avena sativa L.), have been
recognized functional foods, because provide beneficial effect on the
health of the consumer and decrease the risk of various diseases. Oats
are good source of soluble fibre, essential amino acids, unsaturated
fatty acids, vitamins and minerals. Oat breeders have developed oat
varieties and improved yielding ability potential of oat varieties.
Therefore, the aim of investigation was to analyze the composition of
perspective oat varieties and breeding lines grains grown in different
conditions and evaluate functional properties. In the studied samples
content of protein, starch, β-glucans, total dietetic fibre, composition
of amino acids and vitamin E were determined. The results of
analysis showed that protein content depending of varieties ranged
9.70% to 17.30% total dietary fibre 13.66 g100g-1 to 30.17 g100g-1,
content of β-glucans 2.7 g100g-1 to 3.5 g100g-1, amount of
vitamin E (α-tocopherol) determined from 4 mgkg-1 to 9.9 mgkg-1.
The sums of essential amino acids in oat grain samples were
determined from 31.63 gkg-1 to 54.90 gkg-1. It is concluded that
amino acids composition of husked and naked oats grown in organic
or conventional conditions is close to optimal for human health.
Abstract: In this paper, we provided a literature survey on the
artificial stock problem (ASM). The paper began by exploring the
complexity of the stock market and the needs for ASM. ASM
aims to investigate the link between individual behaviors (micro
level) and financial market dynamics (macro level). The variety of
patterns at the macro level is a function of the AFM complexity. The
financial market system is a complex system where the relationship
between the micro and macro level cannot be captured analytically.
Computational approaches, such as simulation, are expected to
comprehend this connection. Agent-based simulation is a simulation
technique commonly used to build AFMs. The paper proceeds by
discussing the components of the ASM. We consider the roles
of behavioral finance (BF) alongside the traditionally risk-averse
assumption in the construction of agent’s attributes. Also, the
influence of social networks in the developing of agents interactions is
addressed. Network topologies such as a small world, distance-based,
and scale-free networks may be utilized to outline economic
collaborations. In addition, the primary methods for developing
agents learning and adaptive abilities have been summarized.
These incorporated approach such as Genetic Algorithm, Genetic
Programming, Artificial neural network and Reinforcement Learning.
In addition, the most common statistical properties (the stylized facts)
of stock that are used for calibration and validation of ASM are
discussed. Besides, we have reviewed the major related previous
studies and categorize the utilized approaches as a part of these
studies. Finally, research directions and potential research questions
are argued. The research directions of ASM may focus on the macro
level by analyzing the market dynamic or on the micro level by
investigating the wealth distributions of the agents.
Abstract: Bloom’s Taxonomy has been changed during the
years. The idea of this writing is about the revision that has happened
in both facts and terms. It also contains case studies of using
cognitive Bloom’s taxonomy in teaching geometric solids to the
secondary school students, affective objectives in a creative
workshop for adults and psychomotor objectives in fixing a
malfunctioned refrigerator lamp. There is also pointed to the
important role of classification objectives in adult education as a way
to prevent memory loss.
Abstract: Residential block construction of big cities in China
began in the 1950s, and four models had far-reaching influence on
modern residential block in its development process, including unit
compound and residential district in 1950s to 1980s, and gated
community and open community in 1990s to now. Based on analysis
of the four models’ fabric, the article takes residential blocks in
Hangzhou west area as an example and carries on the studies from
urban structure level and block spacial level, mainly including urban
road network, land use, community function, road organization, public
space and building fabric. At last, the article puts forward “Semi-open
Sub-community” strategy to improve the current fabric.
Abstract: A robust sequential nonparametric method is proposed
for adaptation to background noise parameters for real-time. The
distribution of background noise was modelled like to Huber
contamination mixture. The method is designed to operate as an
adaptation-unit, which is included inside a detection subsystem of an
integrated multichannel monitoring system. The proposed method
guarantees the given size of a nonasymptotic confidence set for noise
parameters. Properties of the suggested method are rigorously
proved. The proposed algorithm has been successfully tested in real
conditions of a functioning C-OTDR monitoring system, which was
designed to monitor railways.
Abstract: In developing countries, one of the most important
restrictions about the economic growth is the lack of national savings
which are supposed to finance the investments. In order to overcome
this restriction and achieve the higher rate of economic growth by
increasing the level of output, countries choose the external
borrowing. However, there is a dispute in the literature over the
correlation between external debt and economic growth. The aim of
this study is to examine the effects of external debt on Turkish
economic growth by using VAR analysis with the quarterly data over
the period of 2002:01-2014:04. In this respect, Johansen
Cointegration Test, Impulse- Response Function and Variance
Decomposition Tests will be used for analyses. Empirical findings
show that there is no cointegration in the long run.
Abstract: Psychopathic disorders are taking an important part in
judge sentencing, especially in Canada. First, we will see how this
phenomenon can be illustrated by the high proportion of psychopath
offenders incarcerated in North American prisons. Many decisions in
Canadians courtrooms seem to point out that psychopathy is often
used as a strong argument by the judges to preserve public safety.
The fact that psychopathy is often associated with violence,
recklessness and recidivism, could explain why many judges consider
psychopathic disorders as an aggravating factor. Generally, the judge
reasoning is based on Article 753 of Canadian Criminal Code related
to dangerous offenders, which is used for individuals who show a
pattern of repetitive and persistent aggressive behaviour. Then we
will show how, with cognitive neurosciences, the psychopath’s
situation in courtrooms would probably change. Cerebral imaging
and news data provided by the neurosciences show that emotional
and volitional functions in psychopath’s brains are impaired.
Understanding these new issues could enable some judges to
recognize psychopathic disorders as a mitigating factor. Finally, two
important questions ought to be raised in this article: can exploring
psychopaths ‘brains really change the judge sentencing in Canadian
courtrooms? If yes, can judges consider psychopathy more as a
mitigating factor than an aggravating factor?
Abstract: Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
Abstract: The lifetime of a wireless sensor network can be
effectively increased by using scheduling operations. Once the
sensors are randomly deployed, the task at hand is to find the largest
number of disjoint sets of sensors such that every sensor set provides
complete coverage of the target area. At any instant, only one of these
disjoint sets is switched on, while all other are switched off. This
paper proposes a heuristic search method to find the maximum
number of disjoint sets that completely cover the region. A
population of randomly initialized members is made to explore the
solution space. A set of heuristics has been applied to guide the
members to a possible solution in their neighborhood. The heuristics
escalate the convergence of the algorithm. The best solution explored
by the population is recorded and is continuously updated. The
proposed algorithm has been tested for applications which require
sensing of multiple target points, referred to as point coverage
applications. Results show that the proposed algorithm outclasses the
existing algorithms. It always finds the optimum solution, and that
too by making fewer number of fitness function evaluations than the
existing approaches.
Abstract: New design of three dimensional (3D) flywheel system
based on gimbal and gyro mechanics is proposed. The 3D flywheel
device utilizes the rotational motion of three spherical shells and the
conservation of angular momentum to achieve planar locomotion.
Actuators mounted to the ring-shape frames are installed within the
system to drive the spherical shells to rotate, for the purpose of steering
and stabilization. Similar to the design of 2D flywheel system, it is
expected that the spherical shells may function like a “flyball” to store
and supply mechanical energy; additionally, in comparison with
typical single-wheel and spherical robots, the 3D flywheel can be used
for developing omnidirectional robotic systems with better mobility.
The Lagrangian method is applied to derive the equation of motion of
the 3D flywheel system, and simulation studies are presented to verify
the proposed design.
Abstract: The purpose of the paper is to address the strategic
risk issues surrounding Hindi film distribution in Mumbai for a film
distributor, who acts as an entrepreneur when launching a product
(movie) in the market (film territory).The paper undertakes a
fundamental review of films and risk in the Hindi film industry and
applies Grounded Theory technique to understand the complex
phenomena of risk taking behavior of the film distributors (both
independent and studios) in Mumbai. Rich in-depth interviews with
distributors are coded to develop core categories through constant
comparison leading to conceptualization of the phenomena of
interest. This paper is a first-of-its-kind-attempt to understand risk
behavior of a distributor, which is akin to entrepreneurial risk
behavior under conditions of uncertainty.
Abstract: Superabsorbent polymers received much attention and
are used in many fields because of their superior characters to
traditional absorbents, e.g., sponge and cotton. So, it is very
important but challenging to prepare highly and fast-swelling
superabsorbents. A reliable, efficient and low-cost technique for
removing heavy metal ions from wastewater is the adsorption using
bio-adsorbents obtained from biological materials, such as
polysaccharides-based hydrogels superabsorbents. In this study, novel multi-functional superabsorbent composites
type semi-interpenetrating polymer networks (Semi-IPNs) were
prepared via graft polymerization of acrylamide onto chitosan
backbone in presence of gelatin, CTS-g-PAAm/Ge, using potassium
persulfate and N,N’-methylene bisacrylamide as initiator and
crosslinker, respectively. These hydrogels were also partially
hydrolyzed to achieve superabsorbents with ampholytic properties
and uppermost swelling capacity. The formation of the grafted
network was evidenced by Fourier Transform Infrared Spectroscopy
(ATR-FTIR) and Thermogravimetric Analysis (TGA). The porous
structures were observed by Scanning Electron Microscope (SEM).
From TGA analysis, it was concluded that the incorporation of the Ge
in the CTS-g-PAAm network has marginally affected its thermal
stability. The effect of gelatin content on the swelling capacities of
these superabsorbent composites was examined in various media
(distilled water, saline and pH-solutions). The water absorbency was
enhanced by adding Ge in the network, where the optimum value was
reached at 2 wt. % of Ge. Their hydrolysis has not only greatly
optimized their absorption capacity but also improved the swelling
kinetic.These materials have also showed reswelling ability. We
believe that these super-absorbing materials would be very effective
for the adsorption of harmful metal ions from wastewater.
Abstract: In this study, nuclear magnetic resonance
spectroscopy and nuclear quadrupole resonance spectroscopy
parameters of 14N (Nitrogen in imidazole ring) in N–H…O hydrogen
bonding for Histidine hydrochloride monohydrate were calculated via
density functional theory. We considered a five-molecule model
system of Histidine hydrochloride monohydrate. Also we examined
the trends of environmental effect on hydrogen bonds as well as
cooperativity. The functional used in this research is M06-2X which
is a good functional and the obtained results has shown good
agreement with experimental data. This functional was applied to
calculate the NMR and NQR parameters. Some correlations among
NBO parameters, NMR and NQR parameters have been studied
which have shown the existence of strong correlations among them.
Furthermore, the geometry optimization has been performed using
M062X/6-31++G(d,p) method. In addition, in order to study
cooperativity and changes in structural parameters, along with
increase in cluster size, natural bond orbitals have been employed.
Abstract: Steepest descent method is a simple gradient method
for optimization. This method has a slow convergence in heading to
the optimal solution, which occurs because of the zigzag form of the
steps. Barzilai and Borwein modified this algorithm so that it
performs well for problems with large dimensions. Barzilai and
Borwein method results have sparked a lot of research on the method
of steepest descent, including alternate minimization gradient method
and Yuan method. Inspired by previous works, we modified the step
size of the steepest descent method. We then compare the
modification results against the Barzilai and Borwein method,
alternate minimization gradient method and Yuan method for
quadratic function cases in terms of the iterations number and the
running time. The average results indicate that the steepest descent
method with the new step sizes provide good results for small
dimensions and able to compete with the results of Barzilai and
Borwein method and the alternate minimization gradient method for
large dimensions. The new step sizes have faster convergence
compared to the other methods, especially for cases with large
dimensions.
Abstract: Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
classification.
Abstract: Optic disk segmentation plays a key role in the mass
screening of individuals with diabetic retinopathy and glaucoma
ailments. An efficient hardware-based algorithm for optic disk
localization and segmentation would aid for developing an automated
retinal image analysis system for real time applications. Herein,
TMS320C6416DSK DSP board pixel intensity based fractal analysis
algorithm for an automatic localization and segmentation of the optic
disk is reported. The experiment has been performed on color and
fluorescent angiography retinal fundus images. Initially, the images
were pre-processed to reduce the noise and enhance the quality. The
retinal vascular tree of the image was then extracted using canny
edge detection technique. Finally, a pixel intensity based fractal
analysis is performed to segment the optic disk by tracing the origin
of the vascular tree. The proposed method is examined on three
publicly available data sets of the retinal image and also with the data
set obtained from an eye clinic. The average accuracy achieved is
96.2%. To the best of the knowledge, this is the first work reporting
the use of TMS320C6416DSK DSP board and pixel intensity based
fractal analysis algorithm for an automatic localization and
segmentation of the optic disk. This will pave the way for developing
devices for detection of retinal diseases in the future.
Abstract: Single angle connections, which are bolted to the beam
web and the column flange, are studied to investigate their
moment-rotation behavior. Elastic–perfectly plastic material behavior
is assumed. ABAQUS software is used to analyze the nonlinear
behavior of a single angle connection. The identical geometric and
material conditions with Lipson’s test are used for verifying finite
element models. Since Kishi and Chen’s Power model and Lee and
Moon’s Log model are accurate only for a limited range of mechanism,
simpler and more accurate hyperbolic function models are proposed.
Abstract: Neural activity in the human brain starts from the
early stages of prenatal development. This activity or signals
generated by the brain are electrical in nature and represent not only
the brain function but also the status of the whole body. At the
present moment, three methods can record functional and
physiological changes within the brain with high temporal resolution
of neuronal interactions at the network level: the
electroencephalogram (EEG), the magnet oencephalogram (MEG),
and functional magnetic resonance imaging (fMRI); each of these has
advantages and shortcomings. EEG recording with a large number of
electrodes is now feasible in clinical practice. Multichannel EEG
recorded from the scalp surface provides very valuable but indirect
information about the source distribution. However, deep electrode
measurements yield more reliable information about the source
locations intracranial recordings and scalp EEG are used with the
source imaging techniques to determine the locations and strengths of
the epileptic activity. As a source localization method, Low
Resolution Electro-Magnetic Tomography (LORETA) is solved for
the realistic geometry based on both forward methods, the Boundary
Element Method (BEM) and the Finite Difference Method (FDM). In
this paper, we review the findings EEG- LORETA about epilepsy.