Some picture fuzzy Bonferroni mean operators with their application to multicriteria decision making

In this paper, we extend the Bonferroni mean (BM) operator with the picture fuzzy numbers (PFNs) to propose novel picture fuzzy aggregation operators and demonstrate their application to multicriteria decision making (MCDM). On the basis of the algebraic operational rules of PFNs and BM, we introduce some aggregation operators: the picture fuzzy Bonferroni mean, the picture fuzzy normalized weighted Bonferroni mean, and the picture fuzzy ordered weighted Bonferroni mean. Then, a new picture fuzzy MCDM method is proposed with the help of the proposed operators. Lastly, a practical application of proposed model is given to verify the developed model and related results of the proposed model is compared with the results of the existing models to indicate its applicability.     

Investigating the Interaction Between Oncogene and Tumor Suppressor Protein

It is known that cancer develops when cells in a part of the body begin to grow out of control. Because cancer cells continue to grow and divide with no order, they never differentiate into the specific tissue, and thus, they are functionally different from normal cells. However, there are some genes that help to prevent cells' malignant behavior, and therefore, are referred to as tumor suppressor genes. Here, we have investigated the structural and functional relationships of p53, oncogene and interleukin 2 (IL2) proteins using the resonant recognition model (RRM), a physico-mathematical approach based on digital signal processing methods. In addition, using the RRM concepts, we have designed the peptide analoges that would exhibit tumor-suppression-like activity and be used in anticancer vaccine development.     

Ant colony optimization approach for classification of occupational low back disorder risks

Manual material handling (MMH) tasks, the leading cause of low back disorders (LBDs), are still extensively used in industry in spite of the advanced technology. Classification of industrial jobs related to LBD risks has great significance in preventing injuries and designing workplaces. In this article, industrial jobs have been classified into two categories, low risk and high risk, using ant colony optimization (ACO). ACO classification (ACOCLASS) has obtained better results than studies that used the same experimental data. Ergonomic interventions can be done by means of obtained classification schema for future reductions in low back injuries. © 2008 Wiley Periodicals, Inc.     

Short-term analysis of heart-rate variability of adapted wavelettransforms

The purpose of this study is to characterize the heart rate variability (HRV) signal in the frequency domain by using wavelet and cosine packets. Here, the authors introduce the adapted wavelet transform methods to analyze heart-rate fluctuations. These methods were chosen because the components in the signals can be analyzed and quantified at different scales, e.g., long windows can be used for the low-frequency component while short-time windows can be used for high-frequency components. In this study, adaptive wavelet transform methods are applied to the HRV obtained from normal subjects. A comparison is made on the same data base with results based on the short-term Fourier transform method     

Noninvasive detection of coronary stenoses before and afterangioplasty using eigenvector methods

Previous studies done by our group suggest that partially occluded coronary arteries may generate sounds due to turbulent blood flow. To support these previous findings the frequency spectra of diastolic heart sounds are compared before and after angioplastic surgery. Since the low-level sounds associated with partially occluded coronary arteries are contaminated with considerable background noise, traditional FFT analysis may not produce accurate frequency spectra. Indeed, in a previous study using the same data, no significant differences were found in the diastolic heart sounds before and after angioplastic surgery. In this study, three eigenvector methods (Pisarenko, MUSIC, and Minimum-Norm) have been selected to generate the frequency spectra because of their higher resolution, particularly in the presence of noise. Although the Pisarenko method produced spurious zeros and could not be used, the other two methods produced spectra showing, in most cases, a marked decrease in high-frequency spectral components following angioplasty.     

The Artificial Bee Colony algorithm in layer optimization for the maximum fundamental frequency of symmetrical laminated composite plates

In this study the layer optimization was carried out for maximizing the lowest (first) fundamental frequency of symmetrical laminated composite plates subjected to any combination of the three classical boundary conditions, and the applicability of the Artificial Bee Colony (ABC) algorithm to the layer optimization was investigated. The finite element method was used for calculating the first natural frequencies of the laminated composite plates with various stacking sequences. The ABC algorithm maximizes the first natural frequency of the laminated composite plate defined as an objective function. The optimal stacking sequences were determined for two layer numbers, twenty boundary conditions and two plate length/width ratios. The outer layers of the composite plate had a stiffness increasing effect, and as the number of clamped plate edges was increased both he stiffness and natural frequency of the plate increased. The optimal stacking sequences were in good agreement with those determined by the Ritz-based layerwise optimization method (Narita 2003: J. Sound Vibration 263 (5), 1005–1016) as well as by the genetic algorithm method combined with the finite element method.     

Grey prediction with rolling mechanism for electricity demand forecasting of Turkey

The need for energy supply, especially for electricity, has been increasing in the last two decades in Turkey. In addition, owing to the uncertain economic structure of the country, electricity consumption has a chaotic and nonlinear trend. Hence, electricity configuration planning and estimation has been the most critical issue of active concern for Turkey. The Turkish Ministry of Energy and Natural Resources (MENR) has officially carried out energy planning studies using the Model of Analysis of the Energy Demand (MAED). In this paper, Grey prediction with rolling mechanism (GPRM) approach is proposed to predict the Turkey's total and industrial electricity consumption. GPRM approach is used because of high prediction accuracy, applicability in the case of limited data situations and requirement of little computational effort. Results show that proposed approach estimates more accurate results than the results of MAED, and have explicit advantages over extant studies. Future projections have also been done for total and industrial sector, respectively.     

Minimizing total costs of forest roads with computer-aided design model

Advances in personal computers (PCs) have increased interest in computer-based road-design systems to provide rapid evaluation of alternative alignments. Optimization techniques can provide road managers with a powerful tool that searches for large numbers of alternative alignments in short spans of time. A forest road optimization model, integrated with two optimization techniques, was developed to help a forest road engineer in evaluating alternative alignments in a faster and more systematic manner. The model aims at designing a path with minimum total road costs, while conforming to design specifications, environmental requirements, and driver safety. To monitor the sediment production of the alternative alignments, the average sediment delivered to a stream from a road section was estimated by using a road erosion/delivery model. The results indicated that this model has the potential to initiate a new procedure that will improve the forest road-design process by employing the advanced hardware and software capabilities of PCs and modern optimization techniques.