Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

We define the torsion element in effect algebras and use it to characterize MV-effect algebra and 0-homogeneous effect algebras
in chain-complete effect algebras. As an application, we prove that every element of an orthocomplete homogeneous atomic effect
algebra has a unique basic decomposition into a sum of a sharp element and unsharp multiples of atoms. Further, we characterize
homogeneity by the set of all sharp elements in orthocomplete atomic effect algebras.

The purpose of this study was to study the effects of 12 weeks of circuit weight training and aerobic exercise on body composition,
physical fitness, and pulse wave velocity in obese collegiate women. Twelve obese collegiate women were randomly assigned
either to an exercise training group (TG) or control group (CG). The main exercise program was composed of an approximately
40–65 min session of circuit weight training (resistance training and aerobic exercise) as well as jogging at an intensity
of 50–70% of the age-predicted heart rate reserve. The circuit weight training program was made by Korean Institute of Sport
Science and was modified as needed for obese collegiate women. All analyses were performed using SPSS and all data was reported
as mean ± standard deviation (SD). Significant differences between groups were determined using a two-way repeated measures
analysis of variance (ANOVA) with a post hoc test (Tukey method). Statistical significance was accepted for all tests at a
value of p < 0.05. The results indicated that after the 12-week intervention, there were no significant changes in body weight, % body
fat, or WC in either group. There was a significant interaction of time by group with respect to body weight (p < 0.05), % body fat (p < 0.01), and WC (p < 0.01) and there was a significant change in back strength between the TG before beginning the program and the TG after
having completed the program (p < 0.01). There was also a significant interaction of time by group with respect to back strength (p < 0.01), grip strength (p < 0.05), sit and reach (p < 0.01), sargent-jump (p < 0.01), and the one leg balance with eyes closed (p < 0.01); however, these differences were not statistically significant between groups. Further, there was a significant interaction
of time by group with respect to the 1,200 m run for cardiopulmonary endurance (p < 0.01); however, this difference was not statistically significant between the TG pre and TG post. In addition, there was
a significant in sit-ups (p < 0.01) and the 1,200 m run (p < 0.01) between the TG and CG. There was no significant difference in side-steps between the TG and CG. Further, there were
no significant differences in the pulse wave velocity, RPP, SBP, DBP, and MAP between the TG and CG. In conclusion, circuit
weight training and aerobic exercise had favorable effects on the occurrence of obesity and physical fitness in obese collegiate
women.

Process capability indices have been introduced for measuring process reproduction capability of a manufacturing industry.
The loss-based capability index

Cpm

takes into account the degree of process targeting (centering), which essentially measures the process performance on the
basis of average process loss. Generally, the underlying manufacturing process data are obtained from the output responses
of continuous quantities, which are always assumed to be real numbers. However, in a practical situation, the data collected
by output process measurements are often imprecise (fuzzy data). We propose a constructive methodology for obtaining the fuzzy
estimate of

Cpm

using fuzzy data, which is based on “resolution identity” in fuzzy sets theory. We propose four decision rules to judge the
process condition by simultaneously introducing randomness and fuzziness. Finally, we present a sequence of testing steps
for assessing manufacturing process performance using the critical value of

Cpm

with fuzzy data and can be easily implemented in real situations.

EASEA is a framework designed to help non-expert programmers to optimize their problems by evolutionary computation. It allows
to generate code targeted for standard CPU architectures, GPGPU-equipped machines as well as distributed memory clusters.
In this paper, EASEA is presented by its underlying algorithms and by some example problems. Achievable speedups are also
shown onto different NVIDIA GPGPUs cards for different optimization algorithm families.

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly
reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding
(FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two
novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and
nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected
to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused
by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments
by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results
show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.

Let

\Upomega

be a complete residuated lattice. Let

SetR(\Upomega)

be the category of sets with similarity relations with values in

\Upomega

(called

\Upomega

-sets), which is an analogy of the category of classical sets with relations as morphisms. A fuzzy set in an

\Upomega

-set in the category

SetR(\Upomega)

is a morphism from

\Upomega

-set to a special

\Upomega

-set

(\Upomega,«),

where

«

is the biresiduation operation in

\Upomega.

In the paper, we prove that fuzzy sets in

\Upomega

-sets in the category

SetR(\Upomega)

can be expressed equivalently as special cut systems

(Ca)a Î \Upomega.

The use of mechanical trading systems allows managing a huge amount of data related to the factors affecting investment performance
(macroeconomic variables, company information, industrial indicators, market variables, etc.) while avoiding the psychological
reactions of traders when they invest in financial markets. When trading is executed in an intra-daily frequency instead a
daily frequency, mechanical trading systems needs to be supported by very powerful engines since the amount of data to deal
with grow while the response time required to support trades gets shorter. Numerous studies document the use of genetic algorithms
(GAs) as the engine driving mechanical trading systems. The empirical insights provided in this paper demonstrate that the
combine use of GA together with a GPU-CPU architecture speeds up enormously the power and search capacity of the GA for this
kind of financial applications. Moreover, the parallelization allows us to implement and test previous GA approximations.
Regarding the investment results, we can report 870% of profit for the S&P 500 companies in a 10-year time period (1996–2006),
when the average profit of the S&P 500 in the same period was 273%.

In Classical Bayesian approach, estimation of lifetime data usually is dealing with precise information. However, in real
world, some informations about an underlying system might be imprecise and represented in the form of vague quantities. In
these situations, we need to generalize classical methods to vague environment for studying and analyzing the systems of interest.
In this paper, we propose the Bayesian estimation of failure rate and mean time to failure based on vague set theory in the
case of complete and censored data sets. To employ the Bayesian approach, model parameters are assumed to be vague random
variables with vague prior distributions. This approach will be used to induce the vague Bayes estimate of failure rate and
mean time to failure by introducing and applying a theorem called “Resolution Identity” for vague sets. In order to evaluate
the membership degrees of vague Bayesian estimate for these quantities, a computational procedure is investigated. In the
proposed method, the original problem is transformed into a nonlinear programming problem which is then divided into eight
subproblems to simplifying computations.

The C4.5 decision tree (DT) can be applied in various fields and discovers knowledge for human understanding. However, different
problems typically require different parameter settings. Rule of thumb or trial-and-error methods are generally utilized to
determine parameter settings. However, these methods may result in poor parameter settings and unsatisfactory results. On
the other hand, although a dataset can contain numerous features, not all features are beneficial for classification in C4.5
algorithm. Therefore, a novel scatter search-based approach (SS + DT) is proposed to acquire optimal parameter settings and
to select the beneficial subset of features that result in better classification results. To evaluate the efficiency of the
proposed SS + DT approach, datasets in the UCI (University of California, Irvine) Machine Learning Repository are utilized
to assess the performance of the proposed approach. Experimental results demonstrate that the parameter settings for the C4.5
algorithm obtained by the SS + DT approach are better than those obtained by other approaches. When feature selection is considered,
classification accuracy rates on most datasets are increased. Therefore, the proposed approach can be utilized to identify
effectively the best parameter settings for C4.5 algorithm and useful features.

Protein xylosyltransferases are the group of enzymes which are involved in transferring xylose from UDP-d-xylose to serine residue in a protein. These enzymes are commonly found in multicellular organisms and in some unicellular
organisms. Previously we had identified the xylosyltransferase (XT) genes in EST sequence of a unicellular organism Trichomonas vaginalis through in silico approach based on the sequence homology. To corroborate if these genes are putative XT genes, we designed a workflow based
on the sequence characteristics of xylosyltransferase, to verify if any of the putative XT gene sequences have sequence motifs.
The XT genes in T. vaginalis predicted by Hidden Markov Model (HMM) were further analyzed with PfamHMM to identify if each putative sequence belongs to
a known protein family, with TMHMM to examine whether the predicted XTs are Golgi xylosyltransferases and with MEME to find
out the conserved motifs. The results confirmed our earlier study that these XTs are related to N-linked XTs in plants. To
confirm the in silico results further, we analyzed the N-linked glycans of T. vaginalis and the empirical data also confirmed the computational analysis.