Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing,”

Abstract. Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine

Prevalence and antimicrobial resistance of shigella species isolated from diarrheal patients in Ahvaz, Southwest Iran

Introduction: Shigellosis is a significant global human health problem, and Shigella is in charge of almost 165 million cases of this disease annually, of whom 163 million cases are in developing countries and 1.5 million cases are in developed countries. The main aims of the current survey were to identify Shigella spp. isolated from diarrheal patients by conventional biochemical tests, determine the antimicrobial susceptibility profiles by disk diffusion method, and detect the ipaH gene using the PCR assay. Methods: The bacterial isolates were identified as Shigella spp. by microbiological tests and were serogrouped by the slide agglutination test. Antimicrobial susceptibility testing was performed using the disk diffusion method. PCR was performed to detect the ipaH gene. Results: The Shigella strains were isolated from 522 patients with various diarrhea, including bloody diarrhea (3), mucoid plus bloody diarrhea (1.9), mucoid diarrhea (3.2), and watery diarrhea (3.2). Overall, 69 (13.2) isolates were positive for Shigella spp., of which 34 (49.3) serotypes were identified as Shigella flexneri, 22 (31.9) serotypes were identified as Shigella sonnei, 9 (13) serotypes were identified as Shigella boydii, and 4 (5.8) serotypes were identified as Shigella dysenteriae. Antibiotic susceptibility tests revealed that the highest resistance percentage was related to ampicillin (82) and trimethoprim-sulfamethoxazole (77), and ciprofloxacin and ceftriaxone were the best antibiotics against Shigella isolates. Conclusion: We concluded that Shigella spp. can be considered as an etiological agent of diarrhea in southwest Iran. Since the drug resistance pattern of Shigella differs geographically and over time within a country, continuous and regular surveillance program is necessary. © 2019 Sheikh et al

Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel

This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nano-structure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly

The first report of emerging mobilized colistin-resistance (mcr) genes and ERIC-PCR typing in Escherichia coli and Klebsiella pneumoniae clinical isolates in southwest Iran

Mojtaba Moosavian,1,2 Nasrin Emam21Infectious and Tropical Diseases Research Center, Health Research Institue, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 2Department of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranBackground: The emergence of the plasmid-mediated mcr colistin-resistance gene in bacteria poses a potential threat for treatment of patients, especially when hospitalized. The aims of this study were to search for the presence of mcr-1 and mcr-2 genes among colistin-resistant Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) isolates from clinical specimens and to determine the fingerprint of strains by enterobacterial repetitive intergenic consensus sequences PCR (ERIC-PCR) method.Methods: In this study, 712 nonduplicate Enterobacteriaceae isolates from clinical specimens were examined. All of the isolates were subcultured on suitable media, and the isolated colonies were identified by standard biochemical tests. Antimicrobial susceptibility test on 7 antibiotics was performed by disk diffusion method, and minimal inhibitory concentration (MIC) of isolates to colistin was determined by the E-test method. These isolates were typed by ERIC-PCR method, and the presence of mcr-1 and mcr-2 genes was investigated by PCR method.Results: Out of 712 nonduplicate Enterobacteriaceae, 470 isolates, including 351 (74.7%) E. coli and 119 (25.3%) K. pneumoniae, were detected. The results of antibiogram tests showed that most of the isolates (81.3%) were resistant to ceftazidime; however, the most susceptibility among of E. coli and K. pneumoniae isolates was observed (81.5%) to colistin. The typing results by ERIC-PCR method showed 36 and 23 fingerprint patterns for colistin-resistant E. coli and K. pneumoniae strains, respectively. Among 64 (13.6%) colistin-phenotypically-resistant Enterobacteriaceae, 8 isolates (1.7%) had mcr-1 gene. These 8 isolates were attributed to E. coli and K. pneumoniae with 6 and 2 isolates, respectively. Whereas no isolates carrying the mcr-2 gene was found. These colistin-resistant isolates displayed colistin MIC values >2 μg/ml in the antibiotic concentration by E-test method.Conclusion: Spreading of Enterobacteriaceae strains harboring plasmid-mediated mcr could fail the colistin-included therapy regimen as the last line of treatment against multidrug-resistant bacterial infections.Keywords: Enterobacteriaceae; colistin, mcr-1, mcr-

Typing of staphylococcal cassette chromosome mec encoding methicillin resistance in Staphylococcus aureus isolates in Ahvaz, Iran

Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial pathogen. We sought to determine the frequency of the different types of SCCmec in MRSA isolates by performing a cross-sectional study. A total of 72 S. aureus isolates were collected from Imam Khomeini and Golestan hospitals and analysed for MRSA and SCCmec typing by multiplex PCR. The pattern of antibiotic resistance among S. aureus isolates was determined by disc diffusion analysis. Of the 72 S. aureus isolates, 29 (40.27%) were recognized as MRSA. SCCmec type III was the most common type, with 55.17% (16/29), followed by type II with 27.58% (8/29); type IV with 10.34% (3/29); and type I with 6.89% (2/29). All 29 MRSA isolates were resistant to chloramphenicol and erythromycin. In addition, resistance to cephalothin, gentamicin, clindamycin, ciprofloxacin, tetracycline and rifampicin was seen in 24 (75%), 26 (63.4%), 17 (94.4%), 27 (71.05%), 10 (71.42%) and 13 (68.42%) MRSA isolates, respectively. A decreased sensitivity of MRSA to the antibiotics used was observed, with type III SCCmec being the predominant isolate. Keywords: mecA, methicillin, MRSA, SCCmec typin

Primary composite lymphoma of the lung: A case report

Herein, we report a rare case of primary lung lymphoma in a 61 year-old woman with a history of 6-month nonspecific symptoms like dry cough, fever, chills and weight loss. She was admitted to a hospital and received broadspectrum antibiotics but discharged without full recovery. In her second hospital admission, a bronchoscopic evaluation and transbronchial biopsy were performed, which were not diagnostic. Finally, an open lung biopsy was done. Immunohistochemical (IHC) staining of the specimen suggested pulmonary Hodgkin lymphoma. Because of disease recurrence, a second bronchoscopy was performed and endobronchial biopsy revealed transformation to anaplastic lymphoma. In the second recurrence, we decided to reevaluate the last biopsy specimens in greater details. Finally, after conduction of several staining patterns, the diagnosis of primary composite lymphoma of the lung was made. © 2014 NRITLD