Fuzzy Interactive Approach for a Multi-objective Supplier Selection Problem under Robust Uncertainty

In this paper, the authors proposed a multi-objective Mixed Integer Linear Programming (MILP) model for supplier selection problems. The main aim of the system under the investigation is to plan the companies to supply goods to achieve financial benefit by minimizing the total costs and satisfying the customers with on-time delivery and minimizing rejected items. In this case, some restrictions such as multi-product and multi-period conditions, shortage inventory constraints, and discount circumstances simultaneously are considered. Despite these efforts, due to the uncertainty nature of the problem, some parameters are considering as uncertainty data. For this aim, applying robust counterparts for uncertain parameters plays an essential role in real-world applications of this case. It is concluded that the feasibility and optimality properties of the usual solutions of real-world LPs can be severely affected by small changes of the data and that the robust optimization (RO) methodology can be successfully used to overcome this phenomenon

Sudden Unexpected Natural Death in the Youth; an Iranian Single Center Investigation

Background: Sudden unexpected natural death (SUND) has not been studied in Iran. Herein we investigated its main causes in our country.Methods: Records of 80 cases registered to a single referral center were investigated to determine the distribution of sex, age, and etiology of death.Results: Fifty eight (72.5%), 6 (7.5%), 6 (7.5%) and 4 (5%) of our cases have died due to various types of heart diseases, cerebral events, pulmonary emboli and gastrointestinal bleeding (GIB), respectively. Moreover, men are victims of SUND more that women (83.7% vs.16.3%, respectively).Conclusion: Policies should be planned by the governments to prevent youth mortality in societies. These attempts should especially target ischemic heart disease

Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators

The Study on Relation of Human Papillomavirus High Risk Types with Bladder Transitional Cell Carcinoma

En este programa se hablará sobre las telecomunicaciones, se les contará qué es, como mueve al mundo actualmente y como la Universidad Militar Nueva Granada se prepara para formar a los mejores profesionales en este campo

Molecular investigation of methicillin-resistant staphylococcus aureus isolates from blood: USA600 emerges as the major type

Introduction: The widespread emergence of methicillin-resistant Staphylococcus aureus is turning into a real worry in public health. The goals of the present study were to identify resistance and virulence encoding genes and molecular characteristics of methicillin-resistant S. aureus bloodstream isolates. Methodology: A cross-sectional study was conducted on 84 S. aureus bloodstream isolates during a 10-month period. To evaluate antibiotic susceptibility of the isolates, we used Kirby-Bauer disk diffusion method. In addition, the prevalence of antimicrobial resistance and toxins genes was assessed using polymerase chain reaction. Isolates were typed according to polymorphisms seven housekeeping genes by MLST. Results: All the isolates were resistant to methicillin. The most prevalent resistance gene was mecA gene (100) followed by tetM (57.1), aac (6�)-Ie/aph (2�) (53.6), ant (4�)-Ia (46.4), ermA (45.2), msrA (35.7), msrB (33.3), aph (3�)-IIIa (33.3), ermB (31), ermC (16.7), and mupA (14.3) genes. The presence of toxin encoding genes tst, pvl, eta, and etb were detected in 25, 14.3, 3.6 and 3.6, respectively. The isolates were classified into five different sequence types: ST45 (29.8), ST239 (27.4), ST858 (21.4), ST22 (17.8), and ST59 (3.6). All the high-level mupirocin-resistant (HLMUPR) strains belonged to ST239, while the low-level mupirocin resistant (LLMUPR) strains belonged to ST22 (13) and ST239 (6). Conclusions: To the best of our knowledge, the present study is the first report of ST59 in MRSA bloodstream isolates in Iran. Our data demonstrated the need for thorough epidemiological monitoring to detect emergence and dissemination of MDR-MRSA types in our hospitals. © 2018 Goudarzi et al

Molecular investigation of methicillin-resistant staphylococcus aureus isolates from blood: USA600 emerges as the major type

Introduction: The widespread emergence of methicillin-resistant Staphylococcus aureus is turning into a real worry in public health. The goals of the present study were to identify resistance and virulence encoding genes and molecular characteristics of methicillin-resistant S. aureus bloodstream isolates. Methodology: A cross-sectional study was conducted on 84 S. aureus bloodstream isolates during a 10-month period. To evaluate antibiotic susceptibility of the isolates, we used Kirby-Bauer disk diffusion method. In addition, the prevalence of antimicrobial resistance and toxins genes was assessed using polymerase chain reaction. Isolates were typed according to polymorphisms seven housekeeping genes by MLST. Results: All the isolates were resistant to methicillin. The most prevalent resistance gene was mecA gene (100) followed by tetM (57.1), aac (6�)-Ie/aph (2�) (53.6), ant (4�)-Ia (46.4), ermA (45.2), msrA (35.7), msrB (33.3), aph (3�)-IIIa (33.3), ermB (31), ermC (16.7), and mupA (14.3) genes. The presence of toxin encoding genes tst, pvl, eta, and etb were detected in 25, 14.3, 3.6 and 3.6, respectively. The isolates were classified into five different sequence types: ST45 (29.8), ST239 (27.4), ST858 (21.4), ST22 (17.8), and ST59 (3.6). All the high-level mupirocin-resistant (HLMUPR) strains belonged to ST239, while the low-level mupirocin resistant (LLMUPR) strains belonged to ST22 (13) and ST239 (6). Conclusions: To the best of our knowledge, the present study is the first report of ST59 in MRSA bloodstream isolates in Iran. Our data demonstrated the need for thorough epidemiological monitoring to detect emergence and dissemination of MDR-MRSA types in our hospitals. © 2018 Goudarzi et al