Abstract: The paper shows that on transferring sense from the
SL to the TL, the translator’s reading against the grain determines the
creation of a faulty pattern of rendering the original meaning in the
receiving culture which reflects the use of misleading transformative
codes. In this case, the translator is a writer per se who decides what
goes in and out of the book, how the style is to be ciphered and what
elements of ideology are to be highlighted. The paper also proves that
figurative language must not be flattened for the sake of clarity or
naturalness. The missing figurative elements make the translated text
less interesting, less challenging and less vivid which reflects poorly
on the writer. There is a close connection between style and the
writer’s person. If the writer’s style is very much altered in a
translation, the translation is useless as the original writer and his /
her imaginative world can no longer be discovered. The purpose of the paper is to prove that adaptation is a dangerous
tool which leads to variants that sometimes reflect the original less
than the reader would wish to. It contradicts the very essence of the
process of translation which is that of making an original work
available in a foreign language. If the adaptive transformative codes
are so flexible that they encourage the translator to repeatedly leave
out parts of the original work, then a subversive pattern emerges
which changes the entire book. In conclusion, as a result of using adaptation, manipulative or
subversive effects are created in the translated work. This is generally
achieved by adding new words or connotations, creating new figures
of speech or using explicitations. The additional meanings of the
original work are neglected and the translator creates new meanings,
implications, emphases and contexts. Again s/he turns into a new
author who enjoys the freedom of expressing his / her own ideas
without the constraints of the original text. Reading against the grain
is unadvisable during the process of translation and consequently,
following personal common sense becomes essential in the field of
translation as well as everywhere else, so that translation should not
become a source of fantasy.
Abstract: This paper evaluates the accrual based scheduling for
cloud in single and multi-resource system. Numerous organizations
benefit from Cloud computing by hosting their applications. The
cloud model provides needed access to computing with potentially
unlimited resources. Scheduling is tasks and resources mapping to a
certain optimal goal principle. Scheduling, schedules tasks to virtual
machines in accordance with adaptable time, in sequence under
transaction logic constraints. A good scheduling algorithm improves
CPU use, turnaround time, and throughput. In this paper, three realtime
cloud services scheduling algorithm for single resources and
multiple resources are investigated. Experimental results show
Resource matching algorithm performance to be superior for both
single and multi-resource scheduling when compared to benefit first
scheduling, Migration, Checkpoint algorithms.
Abstract: This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.
Abstract: This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm.
Abstract: This study presents a hybrid metaheuristic algorithm
to obtain optimum designs for steel space buildings. The optimum
design problem of three-dimensional steel frames is mathematically
formulated according to provisions of LRFD-AISC (Load and
Resistance factor design of American Institute of Steel Construction).
Design constraints such as the strength requirements of structural
members, the displacement limitations, the inter-story drift and the
other structural constraints are derived from LRFD-AISC
specification. In this study, a hybrid algorithm by using teachinglearning
based optimization (TLBO) and harmony search (HS)
algorithms is employed to solve the stated optimum design problem.
These algorithms are two of the recent additions to metaheuristic
techniques of numerical optimization and have been an efficient tool
for solving discrete programming problems. Using these two
algorithms in collaboration creates a more powerful tool and
mitigates each other’s weaknesses. To demonstrate the powerful
performance of presented hybrid algorithm, the optimum design of a
large scale steel building is presented and the results are compared to
the previously obtained results available in the literature.
Abstract: In recent decades, probabilistic constrained optimal
control problems have attracted much attention in many research
fields. Although probabilistic constraints are generally intractable
in an optimization problem, several tractable methods haven been
proposed to handle probabilistic constraints. In most methods,
probabilistic constraints are reduced to deterministic constraints
that are tractable in an optimization problem. However, there is a
gap between the transformed deterministic constraints in case of
known and unknown probability distribution. This paper examines
the conservativeness of probabilistic constrained optimization method
for unknown probability distribution. The objective of this paper is
to provide a quantitative assessment of the conservatism for tractable
constraints in probabilistic constrained optimization with unknown
probability distribution.
Abstract: In the present paper the design of plate heat exchangers
is formulated as an optimization problem considering two
mathematical modelling. The number of plates is the objective
function to be minimized, considering implicitly some parameters
configuration. Screening is the optimization method used to solve the
problem. Thermal and hydraulic constraints are verified, not viable
solutions are discarded and the method searches for the convergence to
the optimum, case it exists. A case study is presented to test the
applicability of the developed algorithm. Results show coherency with
the literature.
Abstract: Land reallocation is one of the most important steps in
land consolidation projects. Many different models were proposed for
land reallocation in the literature such as Fuzzy Logic, block priority
based land reallocation and Spatial Decision Support Systems. A
model including four parts is considered for automatic block
reallocation with genetic algorithm method in land consolidation
projects. These stages are preparing data tables for a project land,
determining conditions and constraints of land reallocation, designing
command steps and logical flow chart of reallocation algorithm and
finally writing program codes of Genetic Algorithm respectively. In
this study, we designed the first three steps of the considered model
comprising four steps.
Abstract: Batch production plants provide a wide range of
scheduling problems. In pharmaceutical industries a batch process
is usually described by a recipe, consisting of an ordering of tasks
to produce the desired product. In this research work we focused
on pharmaceutical production processes requiring the culture of
a microorganism population (i.e. bacteria, yeasts or antibiotics).
Several sources of uncertainty may influence the yield of the culture
processes, including (i) low performance and quality of the cultured
microorganism population or (ii) microbial contamination. For
these reasons, robustness is a valuable property for the considered
application context. In particular, a robust schedule will not collapse
immediately when a cell of microorganisms has to be thrown away
due to a microbial contamination. Indeed, a robust schedule should
change locally in small proportions and the overall performance
measure (i.e. makespan, lateness) should change a little if at all.
In this research work we formulated a constraint programming
optimization (COP) model for the robust planning of antibiotics
production. We developed a discrete-time model with a multi-criteria
objective, ordering the different criteria and performing a
lexicographic optimization. A feasible solution of the proposed
COP model is a schedule of a given set of tasks onto available
resources. The schedule has to satisfy tasks precedence constraints,
resource capacity constraints and time constraints. In particular
time constraints model tasks duedates and resource availability
time windows constraints. To improve the schedule robustness, we
modeled the concept of (a, b) super-solutions, where (a, b) are input
parameters of the COP model. An (a, b) super-solution is one in
which if a variables (i.e. the completion times of a culture tasks)
lose their values (i.e. cultures are contaminated), the solution can be
repaired by assigning these variables values with a new values (i.e.
the completion times of a backup culture tasks) and at most b other
variables (i.e. delaying the completion of at most b other tasks).
The efficiency and applicability of the proposed model is
demonstrated by solving instances taken from a real-life
pharmaceutical company. Computational results showed that
the determined super-solutions are near-optimal.
Abstract: Work presented is interested in the characterization of
the quasistatic mechanical properties and in fatigue of a composite
laminated in jute/epoxy. The natural fibers offer promising prospects
thanks to their interesting specific properties, because of their low
density, but also with their bio-deterioration. Several scientific
studies highlighted the good mechanical resistance of the vegetable
fiber composites reinforced, even after several recycling. Because of
the environmental standards that become increasingly severe, one
attends the emergence of eco-materials at the base of natural fibers
such as flax, bamboo, hemp, sisal, jute. The fatigue tests on
elementary vegetable fibers show an increase of about 60% of the
rigidity of elementary fibers of hemp subjected to cyclic loadings. In
this study, the test-tubes manufactured by the method infusion have
sequences of stacking of 0/90° and ± 45° for the shearing and tensile
tests. The quasistatic tests reveal a variability of the mechanical
properties of about 8%. The tensile fatigue tests were carried out for
levels of constraints equivalent to half of the ultimate values of the
composite. Once the fatigue tests carried out for well-defined values
of cycles, a series of static tests of traction type highlights the
influence of the number of cycles on the quasi-static mechanical
behavior of the laminate jute/epoxy.
Abstract: In this article, we deal with a variant of the classical
course timetabling problem that has a practical application in many
areas of education. In particular, in this paper we are interested in
high schools remedial courses. The purpose of such courses is to
provide under-prepared students with the skills necessary to succeed
in their studies. In particular, a student might be under prepared in
an entire course, or only in a part of it. The limited availability
of funds, as well as the limited amount of time and teachers at
disposal, often requires schools to choose which courses and/or which
teaching units to activate. Thus, schools need to model the training
offer and the related timetabling, with the goal of ensuring the
highest possible teaching quality, by meeting the above-mentioned
financial, time and resources constraints. Moreover, there are some
prerequisites between the teaching units that must be satisfied. We
first present a Mixed-Integer Programming (MIP) model to solve
this problem to optimality. However, the presence of many peculiar
constraints contributes inevitably in increasing the complexity of
the mathematical model. Thus, solving it through a general-purpose
solver may be performed for small instances only, while solving
real-life-sized instances of such model requires specific techniques
or heuristic approaches. For this purpose, we also propose a heuristic
approach, in which we make use of a fast constructive procedure
to obtain a feasible solution. To assess our exact and heuristic
approaches we perform extensive computational results on both
real-life instances (obtained from a high school in Lecce, Italy) and
randomly generated instances. Our tests show that the MIP model is
never solved to optimality, with an average optimality gap of 57%.
On the other hand, the heuristic algorithm is much faster (in about the
50% of the considered instances it converges in approximately half of
the time limit) and in many cases allows achieving an improvement
on the objective function value obtained by the MIP model. Such an
improvement ranges between 18% and 66%.
Abstract: Wireless Sensor Networks (WSNs) enable new
applications and need non-conventional paradigms for the protocol
because of energy and bandwidth constraints, In WSN, sensor node’s
life is a critical parameter. Research on life extension is based on
Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme,
which rotates Cluster Head (CH) among sensor nodes to distribute
energy consumption over all network nodes. CH selection in WSN
affects network energy efficiency greatly. This study proposes an
improved CH selection for efficient data aggregation in sensor
networks. This new algorithm is based on Bacterial Foraging
Optimization (BFO) incorporated in LEACH.
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: In this paper, we consider a cognitive relay network
(CRN) in which the primary receiver (PR) is protected by peak
transmit power ¯PST and/or peak interference power Q constraints.
In addition, the interference effect from the primary transmitter (PT)
is considered to show its impact on the performance of the CRN. We
investigate the outage probability (OP) and outage capacity (OC) of
the CRN by deriving closed-form expressions over Rayleigh fading
channel. Results show that both the OP and OC improve by increasing
the cooperative relay nodes as well as when the PT is far away from
the SR.
Abstract: Based on Business and Consumer Survey (BCS) data,
the European Commission (EC) regularly publishes the monthly
Economic Sentiment Indicator (ESI) for each EU member state. ESI
is conceptualized as a leading indicator, aimed ad tracking the overall
economic activity. In calculating ESI, the EC employs arbitrarily
chosen weights on 15 BCS response balances. This paper raises the
predictive quality of ESI by applying nonlinear programming to find
such weights that maximize the correlation coefficient of ESI and
year-on-year GDP growth. The obtained results show that the highest
weights are assigned to the response balances of industrial sector
questions, followed by questions from the retail trade sector. This
comes as no surprise since the existing literature shows that the
industrial production is a plausible proxy for the overall Croatian
economic activity and since Croatian GDP is largely influenced by
the aggregate personal consumption.
Abstract: The elastic properties and fracture of two-dimensional
graphene were calculated purely from the atomic bonding (stretching
and bending) based on molecular mechanics method. Considering the
representative unit cell of graphene under various loading conditions,
the deformations of carbon bonds and the variations of the interlayer
distance could be realized numerically under the geometry constraints
and minimum energy assumption. In elastic region, it was found that
graphene was in-plane isotropic. Meanwhile, the in-plane deformation
of the representative unit cell is not uniform along armchair direction
due to the discrete and non-uniform distributions of the atoms. The
fracture of graphene could be predicted using fracture criteria based on
the critical bond length, over which the bond would break. It was
noticed that the fracture behavior were directional dependent, which
was consistent with molecular dynamics simulation results.
Abstract: This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.
Abstract: In this study, we demonstrate the production of natural gas hydrates from permeable marine sediments with simultaneous mechanisms for methane recovery and methane-air or methane-air/carbon dioxide replacement. The simultaneous melting happens until the chemical potentials become equal in both phases as natural gas hydrate depletion continues and self-regulated methane-air replacement occurs over an arbitrary point. We observed certain point between dissociation and replacement mechanisms in the natural gas hydrate reservoir, and we call this boundary as critical methane concentration. By the way, when carbon dioxide was added, the process of chemical exchange of methane by air/carbon dioxide was observed in the natural gas hydrate. The suggested process will operate well for most global natural gas hydrate reservoirs, regardless of the operating conditions or geometrical constraints.
Abstract: In a practical power system, the power plants are not
located at the same distance from the center of loads and their fuel
costs are different. Also, under normal operating conditions, the
generation capacity is more than the total load demand and losses.
Thus, there are many options for scheduling generation. In an
interconnected power system, the objective is to find the real and
reactive power scheduling of each power plant in such a way as to
minimize the operating cost. This means that the generator’s real and
reactive powers are allowed to vary within certain limits so as to meet
a particular load demand with minimum fuel cost. This is called
optimal power flow problem. In this paper, Economic Load Dispatch
(ELD) of real power generation is considered. Economic Load
Dispatch (ELD) is the scheduling of generators to minimize total
operating cost of generator units subjected to equality constraint of
power balance within the minimum and maximum operating limits of
the generating units. In this paper, genetic algorithms are considered.
ELD solutions are found by solving the conventional load flow
equations while at the same time minimizing the fuel costs.
Abstract: Cooperative spectrum sensing is a crucial challenge in
cognitive radio networks. Cooperative sensing can increase the
reliability of spectrum hole detection, optimize sensing time and
reduce delay in cooperative networks. In this paper, an efficient
central capacity optimization algorithm is proposed to minimize
cooperative sensing time in a homogenous sensor network using OR
decision rule subject to the detection and false alarm probabilities
constraints. The evaluation results reveal significant improvement in
the sensing time and normalized capacity of the cognitive sensors.