Review: the latest advances in biomedical applications of chitosan hydrogel as a powerful natural structure with eye-catching biological properties

Journal of Materials Science - Chitosan is one of the natural cationic polymers with unique properties such as non-toxicity, biodegradability, biocompatibility, environmentally friendly that has...     

Effect of Extraordinary Large Floods on at-site Flood Frequency

The peak flow of extraordinary large floods that occur during a period of systematic record is a controversial problem for flood frequency analysis (FFA) using traditional methods. The present study suggests that such floods be treated as historic flood data even though their historical period is unknown. In this paper, the extraordinary large flood peak was first identified using statistical outlier tests and normal probability plots. FFA was then applied with and without the extraordinary large floods. In this step, two goodness-of-fit tests including mean absolute relative deviation and mean squared relative deviation were used to identify the best-fit probability distributions. Next, the generalized extreme value (GEV), three-parameter lognormal (LN3), log-Pearson type III (LP3), and Wakeby (WAK) probability distributions were used to incorporate and adjust the extraordinary large floods with other systematic data. Finally, procedures with and without historical adjustment were compared for the extraordinary large floods in terms of goodness-of-fit and flood return-period quantiles. The results of this comparison indicate that historical adjustment from an operational perspective was more viable than without adjustment procedure. Furthermore, the results without adjustment were unreasonable (subject to over- and under-estimation) and produced physically unrealistic estimates that were not compatible with the study area. The proposed approach substantially improved the probability estimation of rare floods for efficient design of hydraulic structures, risk analysis, and floodplain management.     

Proactive task migration with a self-adjusting migration threshold for dynamic thermal management of multi-core processors

Request for more computation power steadily forces designers to provide more powerful processors using more number of cores on a single chip. The increasing complexity of processors leads to higher integration density, power density, and temperature. For avoiding thermal emergencies, various dynamic thermal management techniques have been presented. In this paper, we present a novel online self-adjusting temperature threshold schema for dynamic thermal management to minimize both average and peak temperature with very low performance overhead. Our proposed algorithm adjusts migration threshold according to workload and hardware platforms. The experimental results indicate that our technique can significantly decrease the average and peak temperature compared to Linux standard scheduler, and two well-known thermal management techniques: PDTM and TAS.     

Predictive model for shear strength estimation in reinforced concrete beams with recycled aggregates using Gaussian process regression

Neural Computing and Applications - In order to attain sustainable development, recycled concrete aggregates (RCAs) are increasingly utilized in civil engineering projects. Therefore, it is vital...     

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

In this paper, we consider a Hopfield like Chaotic Neural Networks which have both self-coupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron’s outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron’s outputs with each other is guaranteed, when all neuron’s have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.     

Numerical solution of fuzzy relational equations based on smooth fuzzy norms

In this paper, we study and formulate a BP learning algorithm for fuzzy relational neural networks based on smooth fuzzy norms for functions approximation. To elaborate the model behavior more, we have used different fuzzy norms led to a new pair of fuzzy norms. An important practical case in fuzzy relational equations (FREs) is the identification problem which is studied in this work. In this work we employ a neuro-based approach to numerically solve the set of FREs and focus on generalized neurons that use smooth s-norms and t-norms as fuzzy compositional operators.     

A non-reference image fusion metric based on mutual information of image features

The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria.     

A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

Efficient biodegradation of naphthalene by a newly characterized indigenous Achromobacter sp. FBHYA2 isolated from Tehran Oil Refinery Complex

A bacterial strain, FBHYA2, capable of degrading naphthalene, was isolated from the American Petroleum Institute (API) separator of the Tehran Oil Refinery Complex (TORC). Strain FBHYA2 was identified as Achromobacter sp. based on physiological and biochemical characteristics and also phylogenetic similarity of 16S rRNA gene sequence. The optimal growth conditions for strain FBHYA2 were pH 6.0, 30 °C and 1.0% NaCl. Strain FBHYA2 can utilize naphthalene as the sole source of carbon and energy and was able to degrade naphthalene aerobically very fast, 48 h for 96% removal at 500 mg/L concentration. The physiological response of Achromobacter sp., FBHYA2 to several hydrophobic chemicals (aliphatic and aromatic hydrocarbons) was also investigated. No biosurfactant was detected during bacterial growth on any aliphatic/aromatic hydrocarbons. The results of hydrophobicity measurements showed no significant difference between naphthalene- and LB-grown cells. The capability of the strain FBHYA2 to degrade naphthalene completely and rapidly without the need to secrete biosurfactant may make it an ideal candidate to remediate polycyclic aromatic hydrocarbon (PAH)-contaminated sites.     

Fermentation of table cream by Lactobacillus plantarum strains: effect on fungal growth,aflatoxin M1 and ochratoxin A

Aflatoxin M1 (AFM₁) and ochratoxin A (OTA) are two main mycotoxins in milk and dairy products. In the present work, the ability of four Lactobacillus strains (L. plantarum PTCC 1058, L. plantarum LP3, L. plantarum AF1 and L. plantarum LU5) to remove AFM1 and OTA in fermented cream was studied during 24 h fermentation. The antifungal activity of the mentioned lactobacilli against the defined fungi (Aspergillus flavus PTCC 5004, Aspergillus parasiticus PTCC 5018, Aspergillus nidulans PTCC 5014, Aspergillus ochraceus PTCC 5060) was also evaluated. The results showed that the cell counts of all strains were increased by 64–70% during fermentation. All Lactobacillus strains decreased the amount of AFM₁ significantly (P ≤ 0.05) in the range of 26–52%, which the highest AFM₁-reducing effect was related to L. plantarum LU5 (from 0.5 to 0.24 μg kg⁻¹). The mean OTA removal by Lactobacillus strains in fermented cream also ranged from 32 to 58%. Amongst Lactobacillus strains, the cell-free culture supernatants of L. plantarum LU5 showed the highest (inhibition zone of 26.7 ± 1.2 mm) and L. plantarum LP3 and L. plantarum PTCC 1058 the lowest antifungal activities. The fermented creams contained Lactobacillus strains exhibited the highest and lowest antifungal activities against A. ochraceus and A. parasiticus, respectively. L. plantarum LU5, with the inhibition zone of 27.6 ± 0.9 mm, was the most effective fungal inhibitor, while L. plantarum PTCC 1058 had the lowest antifungal activity.