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...     

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 modified particle swarm optimization for disaster relief logistics under uncertain environment

Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.     

Irreducible compositions of polynomials over finite fields of even characteristic

This paper presents some results with the constructive theory of synthesis of irreducible polynomials over a Galois field with even characteristic. We prove a theorem that plays an important role for constructing irreducible polynomials. By this theorem two recurrent methods for constructing families of irreducible polynomials of degree $n2^{k}~(k=1,2,\ldots )$ over $\mathbb F _{2^{s}}$ are proposed. It is shown that in this special case, the sequences of irreducible polynomials are N-polynomial of degree $2^{k}$ .     

Improvement in volume resistivity and morphology of a blend of polyolefin elastomer with linear low-density polyethylene

A silane moisture-cured polyolefin elastomer/linear low-density polyethylene (LLDPE) blend was prepared through a two-step silane-grafting method (Sioplas Process) in an industrial scale twin-screw extruder. The silane-grafted compound was used to make wire and cable coatings. In this work, the effect of some interactive parameters on quality of the products prepared by the above method has been studied, while so far, there have been less experimental investigations. The volume resistivity of cross-linked compound was changed from 2.96 × 10¹⁴ to 7.41 × 10¹⁴ Ω cm with increasing LLDPE component by maximum 10 wt%. Surface morphology of the product was corrected with reduction in benzoyl peroxide (BPO) concentration from 0.2 wt% to 0.13 wt%. BPO at this level acted as an initiator in grafting reaction of vinyl trimethoxysilane. The curing condition and specimen preparation method by injection molding and/or extrusion were factors which influenced the hot-set test results at 200 °C. The results of tensile and elongation studies showed a maximum value of 9 MPa and 397% for the tests, after 6 h curing. With increases in curing time at a specified temperature, the gel content of the cross-linked compound was increased and reached its maximum value. The maximum gel content values were found to be approximately 60%, 80%, and 82% at temperatures of 25, 60, and 85 °C, respectively. The hardness, density, and tear strength of the samples did not vary significantly with the curing temperature.

     

Using AR,MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models.

     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.