Abstract: To date, theoretical studies concerning the Carbon
Fiber Reinforced Polymer (CFRP) strengthening of RC beams with
openings have been rather limited. In addition, various numerical
analyses presented so far have effectively simulated the behaviour of
solid beam strengthened by FRP material. In this paper, a two
dimensional nonlinear finite element analysis is presented to validate
against the laboratory test results of six RC beams. All beams had the
same rectangular cross-section geometry and were loaded under four
point bending. The crack pattern results of the finite element model
show good agreement with the crack pattern of the experimental
beams. The load midspan deflection curves of the finite element
models exhibited a stiffer result compared to the experimental beams.
The possible reason may be due to the perfect bond assumption used
between the concrete and steel reinforcement.
Abstract: The need to evaluate and understand the natural
drainage pattern in a flood prone, and fast developing environment is
of paramount importance. This information will go a long way to
help the town planners to determine the drainage pattern, road
networks and areas where prominent structures are to be located. This
research work was carried out with the aim of studying the Bayelsa
landscape topography using digitized topographic information, and to
model the natural drainage flow pattern that will aid the
understanding and constructions of workable drainages. To achieve
this, digitize information of elevation and coordinate points were
extracted from a global imagery map. The extracted information was
modeled into 3D surfaces. The result revealed that the average
elevation for Bayelsa State is 12 m above sea level. The highest
elevation is 28 m, and the lowest elevation 0 m, along the coastline.
In Yenagoa the capital city of Bayelsa were a detail survey was
carried out showed that average elevation is 15 m, the highest
elevation is 25 m and lowest is 3 m above the mean sea level. The
regional elevation in Bayelsa, showed a gradation decrease from the
North Eastern zone to the South Western Zone. Yenagoa showed an
observed elevation lineament, were low depression is flanked by high
elevation that runs from the North East to the South west. Hence,
future drainages in Yenagoa should be directed from the high
elevation, from South East toward the North West and from the
North West toward South East, to the point of convergence which is
at the center that flows from South East toward the North West.
Bayelsa when considered on a regional Scale, the flow pattern is from
the North East to the South West, and also North South. It is
recommended that in the event of any large drainage construction at
municipal scale, it should be directed from North East to the South
West or from North to South. Secondly, detail survey should be
carried out to ascertain the local topography and the drainage pattern
before the design and construction of any drainage system in any part
of Bayelsa.
Abstract: The rapid urbanization of cities has a bane in the form
road accidents that cause extensive damage to life and limbs. A
number of location based factors are enablers of road accidents in the
city. The speed of travel of vehicles is non-uniform among locations
within a city. In this study, the perception of vehicle users is captured
on a 10-point rating scale regarding the degree of variation in speed
of travel at chosen locations in the city. The average rating is used to
cluster locations using fuzzy c-means clustering and classify them as
low, moderate and high speed of travel locations. The high speed of
travel locations can be classified proactively to ensure that accidents
do not occur due to the speeding of vehicles at such locations. The
advantage of fuzzy c-means clustering is that a location may be a
part of more than one cluster to a varying degree and this gives a
better picture about the location with respect to the characteristic
(speed of travel) being studied.
Abstract: The use of the oncologic index ISTER allows for a more effective planning of the radiotherapic facilities in the hospitals. Any change in the radiotherapy treatment, due to unexpected stops, may be adapted by recalculating the doses to the new treatment duration while keeping the optimal prognosis. The results obtained in a simulation model on millions of patients allow the definition of optimal success probability algorithms.
Abstract: The turbulent mixing of coolant streams of different
temperature and density can cause severe temperature fluctuations in
piping systems in nuclear reactors. In certain periodic contraction
cycles these conditions lead to thermal fatigue. The resulting aging
effect prompts investigation in how the mixing of flows over a sharp
temperature/density interface evolves. To study the fundamental
turbulent mixing phenomena in the presence of density gradients,
isokinetic (shear-free) mixing experiments are performed in a square
channel with Reynolds numbers ranging from 2-500 to 60-000.
Sucrose is used to create the density difference. A Wire Mesh Sensor
(WMS) is used to determine the concentration map of the flow in the
cross section. The mean interface width as a function of velocity,
density difference and distance from the mixing point are analyzed
based on traditional methods chosen for the purposes of
atmospheric/oceanic stratification analyses. A definition of the
mixing layer thickness more appropriate to thermal fatigue and based
on mixedness is devised. This definition shows that the thermal
fatigue risk assessed using simple mixing layer growth can be
misleading and why an approach that separates the effects of large
scale (turbulent) and small scale (molecular) mixing is necessary.
Abstract: In the literature of fuzzy measures, there exist many
well known parametric and non-parametric measures, each with its
own merits and limitations. But our main emphasis is on
applications of these measures to a variety of disciplines. To extend
the scope of applications of these fuzzy measures to geometry, we
need some special fuzzy measures. In this communication, we have
introduced two new fuzzy measures involving trigonometric
functions and simultaneously provided their applications to obtain
the basic results already existing in the literature of geometry.
Abstract: In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively
Abstract: Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.
Abstract: Reduction of Single Input Single Output (SISO) discrete systems into lower order model, using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method the original discrete system is, first, converted into equivalent continuous system by applying bilinear transformation. The denominator of the equivalent continuous system and its reciprocal are differentiated successively, the reduced denominator of the desired order is obtained by combining the differentiated polynomials. The numerator is obtained by matching the quotients of MCF. The reduced continuous system is converted back into discrete system using inverse bilinear transformation. In the evolutionary technique method, Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example.
Abstract: In this work we study the reflection of circularly
polarised light from a nano-structured biological material found in
the exocuticle of scarabus beetles. This material is made of a stack
of ultra-thin (~5 nm) uniaxial layers arranged in a left-handed
helicoidal stack, which resonantly reflects circularly polarized light.
A chirp in the layer thickness combined with a finite absorption
coefficient produce a broad smooth reflectance spectrum. By
comparing model calculations and electron microscopy with
measured spectra we can explain our observations and quantify most
relevant structural parameters.
Abstract: Migration in breast cancer cell wound healing assay
had been studied using image fractal dimension analysis. The
migration of MDA-MB-231 cells (highly motile) in a wound healing
assay was captured using time-lapse phase contrast video microscopy
and compared to MDA-MB-468 cell migration (moderately motile).
The Higuchi fractal method was used to compute the fractal
dimension of the image intensity fluctuation along a single pixel
width region parallel to the wound. The near-wound region fractal
dimension was found to decrease three times faster in the MDA-MB-
231 cells initially as compared to the less cancerous MDA-MB-468
cells. The inner region fractal dimension was found to be fairly
constant for both cell types in time and suggests a wound influence
range of about 15 cell layer. The box-counting fractal dimension
method was also used to study region of interest (ROI). The MDAMB-
468 ROI area fractal dimension was found to decrease
continuously up to 7 hours. The MDA-MB-231 ROI area fractal
dimension was found to increase and is consistent with the behavior
of a HGF-treated MDA-MB-231 wound healing assay posted in the
public domain. A fractal dimension based capacity index has been
formulated to quantify the invasiveness of the MDA-MB-231 cells in
the perpendicular-to-wound direction. Our results suggest that image
intensity fluctuation fractal dimension analysis can be used as a tool
to quantify cell migration in terms of cancer severity and treatment
responses.
Abstract: This paper presents a complete procedure for tool path
planning and blade machining in 5-axis manufacturing. The actual
cutting contact and cutter locations can be determined by lead and tilt
angles. The tool path generation is implemented by piecewise curved
approximation and chordal deviation detection. An application about
drive surface method promotes flexibility of tool control and stability
of machine motion. A real manufacturing process is proposed to
separate the operation into three regions with five stages and to modify
the local tool orientation with an interactive algorithm.
Abstract: Cooktop burners are widely used nowadays. In
cooktop burner design, nozzle efficiency and greenhouse
gas(GHG) emissions mainly depend on heat transfer from the
premixed flame to the impinging surface. This is a complicated
issue depending on the individual and combined effects of various
input combustion variables. Optimal operating conditions for
sustainable burner design were rarely addressed, especially in the
case of multiple slot-jet burners. Through evaluating the optimal
combination of combustion conditions for a premixed slot-jet
array, this paper develops a practical approach for the sustainable
design of gas cooktop burners. Efficiency, CO and NOx emissions
in respect of an array of slot jets using premixed flames were
analysed. Response surface experimental design were applied to
three controllable factors of the combustion process, viz.
Reynolds number, equivalence ratio and jet-to-vessel distance.
Desirability Function Approach(DFA) is the analytic technique
used for the simultaneous optimization of the efficiency and
emission responses.
Abstract: Iron ore and coal are the two major important raw
materials being used in Iron making industries. Usually ore fines
containing around 5% Alumina are rejected due to higher proportion
of alumina. Therefore, a technology or process which may reduce
the alumina content by 2% by beneficiation process will be highly
attractive . In addition fine coals with ash content is used nearly 12%
is directly injected in blast furnace. Fast fluidization is a technology
by using dry beneficiation of coal and iron ore can be done. During
the fluidization process the iron ore band coal is fluidized at high
velocity in the riser of a fast fluidized bed, the heavier and coarse
particles is generally settled at the bottom in a dense zone of the riser
while the finer and lighter particle are entrained to the top dilute zone
and then via a cyclone is fed back to the bottom of the riser column.
Most of the alumina and low ash fine size coals being lighter are
expected to move up to the riser and by a natural beneficiation of
ores is expected to take place in the riser. Therefore in this study an
attempt has been made for dry beneficiation of iron ore and coal in a
fluidized bed and its hydrodynamic characterization.
Abstract: This paper deals with the design, development & implementation of a temperature sensor using zigbee. The main aim of the work undertaken in this paper is to sense the temperature and to display the result on the LCD using the zigbee technology. ZigBee operates in the industrial, scientific and medical (ISM) radio bands; 868 MHz in Europe, 915 MHz in the USA and 2.4 GHz in most jurisdictions worldwide. The technology is intended to be simpler and cheaper than other WPANs such as Bluetooth. The most capable ZigBee node type is said to require only about 10 % of the software of a typical Bluetooth or Wireless Internet node, while the simplest nodes are about 2 %. However, actual code sizes are much higher, more like 50 % of the Bluetooth code size. ZigBee chip vendors have announced 128-kilobyte devices. In this work undertaken in the design & development of the temperature sensor, it senses the temperature and after amplification is then fed to the micro controller, this is then connected to the zigbee module, which transmits the data and at the other end the zigbee reads the data and displays on to the LCD. The software developed is highly accurate and works at a very high speed. The method developed shows the effectiveness of the scheme employed.
Abstract: The authors present a mixed method for reducing the order of the large-scale dynamic systems. In this method, the denominator polynomial of the reduced order model is obtained by using the modified pole clustering technique while the coefficients of the numerator are obtained by Pade approximations. This method is conceptually simple and always generates stable reduced models if the original high-order system is stable. The proposed method is illustrated with the help of the numerical examples taken from the literature.
Abstract: In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.
Abstract: We present analysis of spatial patterns of generic
disease spread simulated by a stochastic long-range correlation SIR
model, where individuals can be infected at long distance in a power
law distribution. We integrated various tools, namely perimeter,
circularity, fractal dimension, and aggregation index to characterize
and investigate spatial pattern formations. Our primary goal was to
understand for a given model of interest which tool has an advantage
over the other and to what extent. We found that perimeter and
circularity give information only for a case of strong correlation–
while the fractal dimension and aggregation index exhibit the growth
rule of pattern formation, depending on the degree of the correlation
exponent (β). The aggregation index method used as an alternative
method to describe the degree of pathogenic ratio (α). This study may
provide a useful approach to characterize and analyze the pattern
formation of epidemic spreading
Abstract: In this paper, real-coded genetic algorithm (RCGA) optimization technique has been applied for large-scale linear dynamic multi-input-multi-output (MIMO) system. The method is based on error minimization technique where the integral square error between the transient responses of original and reduced order models has been minimized by RCGA. The reduction procedure is simple computer oriented and the approach is comparable in quality with the other well-known reduction techniques. Also, the proposed method guarantees stability of the reduced model if the original high-order MIMO system is stable. The proposed approach of MIMO system order reduction is illustrated with the help of an example and the results are compared with the recently published other well-known reduction techniques to show its superiority.
Abstract: Due to the non- intuitive nature of Quantum
algorithms, it becomes difficult for a classically trained person to
efficiently construct new ones. So rather than designing new
algorithms manually, lately, Genetic algorithms (GA) are being
implemented for this purpose. GA is a technique to automatically
solve a problem using principles of Darwinian evolution. This has
been implemented to explore the possibility of evolving an n-qubit
circuit when the circuit matrix has been provided using a set of
single, two and three qubit gates. Using a variable length population
and universal stochastic selection procedure, a number of possible
solution circuits, with different number of gates can be obtained for
the same input matrix during different runs of GA. The given
algorithm has also been successfully implemented to obtain two and
three qubit Boolean circuits using Quantum gates. The results
demonstrate the effectiveness of the GA procedure even when the
search spaces are large.