University of Tabriz

About: University of Tabriz is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Population & Nanocomposite. The organization has 12141 authors who have published 20976 publications receiving 313982 citations.

A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults

Abstract: In the case of a compound fault diagnosis of rotating machinery, when two failures with unequal severity occur in distinct parts of the system, the detection of a minor fault is a complicated and challenging task. In this case, the minor fault is overshadowed by the more severe one, and the characteristics of the compound fault are prone to the more severe one. Generally, the proposed methods in the literature consider compound failure as an individual fault type and unrelated to the corresponding single faults, either at the different locations of a sensitive component or in two separate parts, such as the bearing and gear, with approximately the same fault severity. Considering these issues, this study proposes a novel end-to-end fault diagnosis method based on fine-tuned VMD and convolutional neural network (CNN). The main idea is that CNN is trained only on a healthy and single fault dataset, without the use of compound fault data in training. In the test stage of the CNN model, the intelligent method alarms an untrained compound fault state if acquired probabilities of CNN output satisfy a set of probabilistic conditions. The performance of the fine-tuned VMD and the proposed hybrid method is evaluated by the decomposition of a simulated vibration signal and the analysis of a gearbox system with a compound fault scenario in such a way that one fault is minor and the other severe. The results obtained show the high accuracy of the proposed method in compound fault diagnosis and the feature extraction and classification of a minor fault in the presence of a more severe one.

Coupling stir bar sorptive extraction‐dispersive liquid–liquid microextraction for preconcentration of triazole pesticides from aqueous samples followed by GC‐FID and GC‐MS determinations

Abstract: Stir bar sorptive extraction (SBSE) combined with dispersive liquid-liquid microextraction (DLLME) has been developed as a new approach for the extraction of six triazole pesticides (penconazole, hexaconazole, diniconazole, tebuconazole, triticonazole and difenconazole) in aqueous samples prior to GC-flame ionization detection (GC-FID). A series of parameters that affect the performance of both steps were thoroughly investigated. Under optimized conditions, aqueous sample was stirred using a stir bar coated with octadecylsilane (ODS) and then target compounds on the sorbent (stir bar) were desorbed with methanol. The extract was mixed with 25 microL of 1,1,2,2-tetrachloroethane and the mixture was rapidly injected into sodium chloride solution 30% w/v. After centrifugation, an aliquot of the settled organic phase was analyzed by GC-FID. The methodology showed broad linear ranges for the six triazole pesticides studied, with correlation coefficients higher than 0.993, lower LODs and LOQs between 0.53-24.0 and 1.08-80.0 ng/mL, respectively, and suitable precision (RSD < 5.2%). Moreover, the developed methodology was applied for the determination of target analytes in several samples, including tap, river and well waters, wastewater (before and after purification), and grape and apple juices. Also, the presented SBSE-DLLME procedure followed by GC-MS determination was performed on purified wastewater. Penconazole, hexaconazole and diniconazole were detected in the purified wastewater that confirmed the obtained results by GC-FID determination. In short, by coupling SBSE with DLLME, advantages of two methods are combined to enhance the selectivity and sensitivity of the method. This method showed higher enrichment factors (282-1792) when compared with conventional methods of sample preparation to screen pesticides in aqueous samples.

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey

Abstract: The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.

A Review on Potential Role of Silver Nanoparticles and Possible Mechanisms of their Actions on Bacteria.

Abstract: The antimicrobial property of silver is associated to the quantity of silver and the grade of silver released. The ionized silver is extremely sensitive, as it binds to tissue proteins and gets operational alterations in the bacterial cell wall and nuclear membrane leading to cell modification and death.Silver nanoparticles have the talent to anchor to the bacterial cell wall and consequently infiltrate it, so causing physical modifications in the cell membrane like the absorptivity of the cell membrane and death of the cell. There are numerous concepts on the act of silver nanoparticle on bacteria to reason the microbicidal influence.