Abstract: In a world characterized by greed and the lust for
power and its attendant trappings, abuse of legal power is nothing
new to most of us. Legal abuses of power abound in all fields of
human endeavour. Accounts of such abuses dominate the mass media
and for the average individual, no single day goes by without his
getting to hear about at least one such occurrence. This paper briefly
looks at the meaning of legal power, what legal abuse is all about, its
causes, and some of its manifestations in the society. Its
consequences will also be discussed and some suggestions for reform
will be made. In the course of the paper, references will be made to
various jurisdictions around the world.
Abstract: Manufacturing tolerancing is intended to determine
the intermediate geometrical and dimensional states of the part during
its manufacturing process. These manufacturing dimensions also
serve to satisfy not only the functional requirements given in the
definition drawing, but also the manufacturing constraints, for
example geometrical defects of the machine, vibration and the wear
of the cutting tool. The choice of positioning has an important influence on the cost
and quality of manufacture. To avoid this problem, a two-step
approach has been developed. The first step is dedicated to the
determination of the optimum position. As for the second step, a
study was carried out for the tightening effect on the tolerance
interval.
Abstract: An unrecorded experiment of use of the smartphone
as a tool for practical classes of histology is presented in this paper.
Behavior and learning of students of science courses at the University
were analyzed and compared as well as the mode of teaching of this
discipline and the appreciation of the students, using either digital
photographs taken by phone or drawings for record microscopic
observations, analyze and interpret histological sections of human or
animal tissues.
Abstract: In this paper, we describe an application for face
recognition. Many studies have used local descriptors to characterize
a face, the performance of these local descriptors remain low by
global descriptors (working on the entire image). The application of
local descriptors (cutting image into blocks) must be able to store
both the advantages of global and local methods in the Discrete
Cosine Transform (DCT) domain. This system uses neural network
techniques. The letter method provides a good compromise between
the two approaches in terms of simplifying of calculation and
classifying performance. Finally, we compare our results with those
obtained from other local and global conventional approaches.
Abstract: In this paper, we propose two algorithms to optimally
solve makespan and total completion time scheduling problems with
learning effect and job dependent delivery times in a single machine
environment. The delivery time is the extra time to eliminate adverse
effect between the main processing and delivery to the customer. In
this paper, we introduce the job dependent delivery times for some
single machine scheduling problems with position dependent learning
effect, which are makespan are total completion. The results with
respect to two algorithms proposed for solving of the each problem
are compared with LINGO solutions for 50-jobs, 100-jobs and 150-
jobs problems. The proposed algorithms can find the same results in
shorter time.
Abstract: The traditional rhythms of the West African country
of Guinea have played a centuries-long role in defining the different
people groups that make up the country. Throughout their history,
before and since colonization by the French, the different ethnicities
have used their traditional music as a distinct part of their historical
identities. That is starting to change. Guinea is an impoverished
nation created in the early twentieth-century with little regard for the
history and cultures of the people who were included. The traditional
rhythms of the different people groups and their heritages have
remained. Fifteen individual traditional Guinean rhythms were
chosen to represent popular rhythms from the four geographical
regions of Guinea. Each rhythm was traced back to its native village
and video recorded on-site by as many different local performing
groups as could be located. The cyclical patterns rhythms were
transcribed via a circular, spatial design and then copied into a box
notation system where sounds happening at the same time could be
studied. These rhythms were analyzed for their consistency-overperformance
in a Fundamental Rhythm Pattern analysis so rhythms
could be compared for how they are changing through different
performances. The analysis showed that the traditional rhythm
performances of the Middle and Forest Guinea regions were the most
cohesive and showed the least evidence of change between
performances. The role of music in each of these regions is both
limited and focused. The Coastal and High Guinea regions have
much in common historically through their ethnic history and
modern-day trade connections, but the rhythm performances seem to
be less consistent and demonstrate more changes in how they are
performed today. In each of these regions the role and usage of music
is much freer and wide-spread. In spite of advances being made as a
country, different ethnic groups still frequently only respond and
participate (dance and sing) to the music of their native ethnicity.
There is some evidence that this self-imposed musical barrier is
beginning to change and evolve, partially through the development of
better roads, more access to electricity and technology, the nationwide
Ebola health crisis, and a growing self-identification as a
unified nation.
Abstract: The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
Abstract: Cryosorption pumps are considered safe, quiet, and
ultra-high vacuum production pumps which have their application
from Semiconductor industries to ITER [International Thermonuclear
Experimental Reactor] units. The principle of physisorption of gases
over highly porous materials like activated charcoal at cryogenic
temperatures (below -1500°C) is involved in determining the
pumping speed of gases like Helium, Hydrogen, Argon, and
Nitrogen. This paper aims at providing detailed overview of
development of Cryosorption pump and characterization of different
activated charcoal materials that optimizes the performance of the
pump. Different grades of charcoal were tested in order to determine
the pumping speed of the pump and were compared with
commercially available Varian cryopanel. The results for bare panel,
bare panel with adhesive, cryopanel with pellets, and cryopanel with
granules were obtained and compared. The comparison showed that
cryopanel adhered with small granules gave better pumping speeds
than large sized pellets.
Abstract: Digital cameras to reduce cost, use an image sensor to
capture color images. Color Filter Array (CFA) in digital cameras
permits only one of the three primary (red-green-blue) colors to be
sensed in a pixel and interpolates the two missing components
through a method named demosaicking. Captured data is interpolated
into a full color image and compressed in applications. Color
interpolation before compression leads to data redundancy. This
paper proposes a new Vector Quantization (VQ) technique to
construct a VQ codebook with Differential Evolution (DE)
Algorithm. The new technique is compared to conventional Linde-
Buzo-Gray (LBG) method.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: Maintaining factory default battery endurance rate
over time in supporting huge amount of running applications on
energy-restricted mobile devices has created a new challenge for
mobile applications developer. While delivering customers’
unlimited expectations, developers are barely aware of efficient use
of energy from the application itself. Thus, developers need a set of
valid energy consumption indicators in assisting them to develop
energy saving applications. In this paper, we present a few software
product metrics that can be used as an indicator to measure energy
consumption of Android-based mobile applications in the early of
design stage. In particular, Trepn Profiler (Power profiling tool for
Qualcomm processor) has used to collect the data of mobile
application power consumption, and then analyzed for the 23
software metrics in this preliminary study. The results show that
McCabe cyclomatic complexity, number of parameters, nested block
depth, number of methods, weighted methods per class, number of
classes, total lines of code and method lines have direct relationship
with power consumption of mobile application.
Abstract: The use of wireless technology in industrial networks
has gained vast attraction in recent years. In this paper, we have
thoroughly analyzed the effect of contention window (CW) size on
the performance of IEEE 802.11-based industrial wireless networks
(IWN), from delay and reliability perspective. Results show that the
default values of CWmin, CWmax, and retry limit (RL) are far from
the optimum performance due to the industrial application
characteristics, including short packet and noisy environment. In this
paper, an adaptive CW algorithm (payload-dependent) has been
proposed to minimize the average delay. Finally a simple, but
effective CW and RL setting has been proposed for industrial
applications which outperforms the minimum-average-delay solution
from maximum delay and jitter perspective, at the cost of a little
higher average delay. Simulation results show an improvement of up
to 20%, 25%, and 30% in average delay, maximum delay and jitter
respectively.
Abstract: This contribution is focused on the methodology for
identifying levels of quality and improving quality through new
logistics model in railway transport. It is oriented on the application
of dynamic quality models, which represent an innovative method of
evaluation quality services. Through this conception, time factor,
expected, and perceived quality in each moment of the transportation
process within logistics chain can be taken into account. Various
models describe the improvement of the quality which emphases the
time factor throughout the whole transportation logistics chain.
Quality of services in railway transport can be determined by the
existing level of service quality, by detecting the causes of
dissatisfaction employees but also customers, to uncover strengths
and weaknesses. This new logistics model is able to recognize critical
processes in logistic chain. It includes service quality rating that must
respect its specific properties, which are unrepeatability,
impalpability, their use right at the time they are provided and
particularly changeability, which is significant factor in the
conditions of rail transport as well. These peculiarities influence the
quality of service regarding the constantly increasing requirements
and that result in new ways of finding progressive attitudes towards
the service quality rating.
Abstract: This article describes the results of research focused
on quality of railway freight transport services. Improvement of these
services has a crucial importance in customer considering on the
future use of railway transport. Processes filling the customer
demands and output quality assessment were defined as a part of the
research. In this contribution is introduced the map of quality
planning and the algorithm of applied methodology. It characterizes a
model which takes into account characters of transportation with
linking a perception services quality in ordinary and extraordinary
operation. Despite the fact that rail freight transport has its solid
position in the transport market, lots of carriers worldwide have been
experiencing a stagnation for a couple of years. Therefore, specific
results of the research have a significant importance and belong to
numerous initiatives aimed to develop and support railway transport
not only by creating a single railway area or reducing noise but also
by promoting railway services. This contribution is focused also on
the application of dynamic quality models which represent an
innovative method of evaluation quality services. Through this
conception, time factor, expected, and perceived quality in each
moment of the transportation process can be taken into account.
Abstract: From the start, the importance of having a plan to
sustain tourism was acknowledged. The correct methods to monitor
that type of tourism have been researched. Thus, we propose in this
work to analyze the applicability of a monitoring and assistance
method on the understanding of the tourism sustainability in a small
size destiny or getaway. In this study, the subject is Lagoa da
Confusão, in the state of Tocantins and the analysis was carried out
through the efficiency of the local indicators, according to the WOT
approach. We concluded that the sustainable tourism key points that
were analyzed demonstrated to be important evaluation and
quantification tools for the proposed tasks to be developed in the
mentioned destiny. This is a study of an interdisciplinary character
and the deductive method was chosen as the guiding line.
Abstract: Abstract—[Tris (1,10-phenanthroline) lanthanum(III)]
trithiocyanate is a new compound that has shown high ability for
stopping the synthesis of DNA and also acting as a photosensitizer.
Nowadays, the radiation dose assessment resource (RADAR) method
is known as the most common method for absorbed dose calculation.
177Lu was produced by (n, gamma) reaction in a research reactor.
177Lu-PL3 was prepared in the optimized condition. The
radiochemical yield was checked by ITLC method. The
biodistribution of the complex was investigated by intravenously
injection to wild-type rats via their tail veins. In this study, the
absorbed dose of 177Lu-PL3 to human organs was estimated by
RADAR method. 177Lu was prepared with a specific activity of 2.6-3
GBq.mg-1 and radionuclide purity of 99.98 %. Final preparation of
the radiolabelled complex showed high radiochemical purity of >
99%. The results show that liver and spleen have received the highest
absorbed dose of 1.051 and 0.441 mSv/MBq, respectively. The
absorbed dose values for these two dose-limiting tissues suggest
more biological studies special in tumor-bearing animals.
Abstract: CuO thin films were deposited by spray ultrasonic
pyrolysis with different precursor solution. Two staring solution slats
were used namely: copper acetate and copper chloride. The influence
of these solutions on CuO thin films proprieties of is instigated. The
X rays diffraction (XDR) analysis indicated that the films deposed
with copper acetate are amorphous however the films elaborated with
copper chloride have monoclinic structure. UV- Visible transmission
spectra showed a strong absorbance of the deposited CuO thin films
in the visible region. Electrical characterization has shown that CuO
thin films prepared with copper acetate have a higher electrical
conductivity.
Abstract: Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.
Abstract: People, throughout the history, have made estimates
and inferences about the future by using their past experiences.
Developing information technologies and the improvements in the
database management systems make it possible to extract useful
information from knowledge in hand for the strategic decisions.
Therefore, different methods have been developed. Data mining by
association rules learning is one of such methods. Apriori algorithm,
one of the well-known association rules learning algorithms, is not
commonly used in spatio-temporal data sets. However, it is possible
to embed time and space features into the data sets and make Apriori
algorithm a suitable data mining technique for learning spatiotemporal
association rules. Lake Van, the largest lake of Turkey, is a
closed basin. This feature causes the volume of the lake to increase or
decrease as a result of change in water amount it holds. In this study,
evaporation, humidity, lake altitude, amount of rainfall and
temperature parameters recorded in Lake Van region throughout the
years are used by the Apriori algorithm and a spatio-temporal data
mining application is developed to identify overflows and newlyformed
soil regions (underflows) occurring in the coastal parts of
Lake Van. Identifying possible reasons of overflows and underflows
may be used to alert the experts to take precautions and make the
necessary investments.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.