EATMR: an energy-aware trust algorithm based the AODV protocol and multi-path routing approach in wireless sensor networks

Telecommunication Systems - Queueing models play a significant role in analysing the performance of power management systems in various electronic devices and communication systems. This paper...     

Effect of hexagonal structure nanoparticles on the morphological performance of the ceramic scaffold using analytical oscillation response

Porous bony scaffolds are utilized to manage the growth and migration of cells from adjacent tissues to a defective position. In the current investigation, the effect of titanium oxide (TiO₂) nanoparticles on mechanical and physical properties of porous bony implants made of polymeric polycaprolactone (PCL) is studied. The bio-nanocomposite scaffolds are prepared with composition of nanocrystalline hydroxyapatite (HA) and TiO₂ powder using the freeze-drying technique for different weight fractions of TiO₂ (0 wt%, 5 wt%, 10 wt%, and 15 wt%). In order to identify the microstructure and morphology of the fabricated porous bio-nanocomposites, the X-ray diffraction (XRD), atomic force microscope (AFM) and scanning electron microscopy (SEM) are employed. Also, the biocompatibility and biodegradability of the manufactured scaffolds are examined by placing them in a simulated body fluid (SBF) for 21 days, their weight and pH changes are measured. The rate of degradation of the PCL-HA scaffold can be controlled by varying the percentage of its constituent components. Due to an increasing growth and activity of bone cells and the apatite formation on the free surface of the fabricated bio-nanocomposite implants as well as their reasonable mechanical properties, they have the potential to be used as a bone substitute. Additionally, with the aid of the experimentally extracted mechanical properties of the scaffolds, the vibrational characteristics of a beam-type implant made of the proposed porous bio-nanocomposites are explored. The results obtained from SEM image indicate that the scaffolds produced by the employed method have high total porosity (70%–85%) and effective porosity. The pore size is obtained between 60 and 200 μm, which is desirable for the growth and propagation of bone cells. Also, it is revealed that the addition of TiO₂ nanoparticles leads to reduce the rate of dissolution of the fabricated bio-nanocomposite scaffolds.     

A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Water Resources Management - In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have...     

Prolonged drug release using PCL–TMZ nanofibers induce the apoptotic behavior of U87 glioma cells

In situ prolonged delivery of drugs at the site of tumor can be satisfactorily accelerated patient recovery. We compared the effect of temozolomide while incorporated by polycaprolactone nanofibers on the apoptotic behavior of U87 glioma cells. After biocompatibility evaluation of nanofibers by scanning electron microscope and 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide analysis, the apoptosis of U87 cells was evaluated using p53, Bcl2 and Bax genes expression. It was found that nanofiber-temozolomide group showed a greater ability to induce apoptosis as well as have a significantly diminished initial burst release of drug compared with other groups and have promising potential in treating cancer.     

A hybrid algorithm for scheduling scientific workflows in IaaS cloud with deadline constraint

The Journal of Supercomputing - Scientific workflows are used to process large amounts of data and perform complex analyses; thus, they require powerful computing resources to produce the desired...     

A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems

Engineering with Computers - Optimizing the high computational real-world problems is a challenging task that has taken a great deal of efforts in the last decade. The meta-heuristic algorithms...     

A computationally efficient multi-domain active learning method for crop mapping using satellite image time-series

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide precious information for several agricultural applications, such as crop monitoring, yield forecasting, and crop inventory. However, several issues affect the classification performance of SITS data. As one of the most challenging problems, constituent images of time-series provide different levels of information about crops. These differences are the result of dynamic spectral responses of crops and also the variable atmospheric and sensor conditions. The second issue is the unavailability of adequate high-quality samples for training the classifier. In this study, we proposed a novel computationally efficient Multi-Domain Active Learning (MDAL) method which takes advantage of Multiple Kernel Learning (MKL) and Active Learning (AL) algorithms to address these two issues. The proposed method uses MKL algorithms to address the issues associated with different information level of the data, which generally cannot be modelled using the well-known classification algorithms. AL algorithms were also used for semi-automatic selection of training samples. However, most of the MKL algorithms are very computationally demanding. Consequently, using them in the MDAL method can dramatically increase the computational costs. Thus, in this paper, we presented the similarity-based MKL algorithms. Thanks to their low computational complexities, these algorithms are the most suitable MKL algorithms that can be used in the MDAL method. We evaluated the proposed method using two multispectral SITS datasets, acquired by RapidEye and SPOT sensors. The obtained results of the MDAL method for these datasets respectively showed 8.2% and 5.87% increase in the overall accuracy of classification as compared to the accuracy of the standard AL algorithm. The results also showed that in the case of adopting the SimpleMKL algorithm (a common MKL algorithm in the literature) the computational time of the MDAL method is 577 and 474 seconds for RapidEye and SPOT datasets, respectively. However, in the case of adopting the similarity-based MKL algorithms, these computational times respectively decreases to 4 and 2 seconds.     

Neo-fuzzy neuron learning using backfitting algorithm

Neural Computing and Applications - This paper proposes an automatic and simple approach to design a neo-fuzzy neuron for identification purposes. The proposed approach uses the backfitting...     

New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R²). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.