Properties enhancement using oil palm shell nanoparticles of fibers reinforced polyester hybrid composites

Oil palm shell (OPS) nanoparticles were utilized as filler in fibers reinforced polyester hybrid composites. The OPS nanoparticles were successfully produced from the raw OPS using high-energy ball milling process. Fundamental properties including morphology, crystalline size, and particle size of the OPS nanoparticles were determined. Tri-layer natural fiber reinforcement (kenaf–coconut–kenaf fiber mat) polyester hybrid composites were prepared by hand lay-up techniques. The influences of the OPS nanoparticles loading in the natural fibers reinforced polyester hybrid composites were determined by analyzing physical, mechanical, morphological, and thermal properties of the composites. Results showed that the incorporation of the OPS nanoparticles into the hybrid composites enhanced the composite properties. Further, the natural fibers reinforced polyester hybrid composite had the highest physical, mechanical, morphological, and thermal characteristics at 3 wt.% OPS nanoparticles loading.     

Artificial neural network approach for solving fuzzy differential equations

The current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed-forward neural networks. First, the fuzzy differential equation is replaced by a system of ordinary differential equations. A trial solution of this system is written as a sum of two parts. The first part satisfies the initial condition and contains no adjustable parameters. The second part involves a feed-forward neural network containing adjustable parameters (the weights). Hence by construction, the initial condition is satisfied and the network is trained to satisfy the differential equations. This method, in comparison with existing numerical methods, shows that the use of neural networks provides solutions with good generalization and high accuracy. The proposed method is illustrated by several examples.     

An application of a merit function for solving convex programming problems

This paper presents a gradient neural network model for solving convex nonlinear programming (CNP) problems. The main idea is to convert the CNP problem into an equivalent unconstrained minimization problem with objective energy function. A gradient model is then defined directly using the derivatives of the energy function. It is also shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. It is also found that a larger scaling factor leads to a better convergence rate of the trajectory. The validity and transient behavior of the neural network are demonstrated by using various examples.     

Dynamic modeling of continuous evaporative cooling KCl-NaCl crystallizers

Previous work on the modeling of potash crystallizers has been mainly limited to the estimation of crystal size distribution (CSD) in the presence of only one solid component (KCl). In the present study, an attempt has been made to develop a model that incorporates NaCl as a second component that may co-precipitate along with KCl under certain operating conditions. Addition of water to prevent co-saturation or as a means of internal fines dissolution is also taken into consideration. External fines dissolution using a heat exchanger is incorporated in the model. In addition to the CSD, the model is able to predict crystal impurity resulting from co-saturation with NaCl. The predictive capability of the model is tested using limited dynamic experimental data obtained from a 1 m³ pilot plant continuous evaporative crystallizer and the steady-state experimental data from a two-stage evaporative Swenson DTB industrial potash crystallizer circuit. In both cases, a good agreement between the model predictions and the experimental data was noticed.     

Biosynthesis of zinc oxide nanoparticles using anjbar (root of Persicaria bistorta) extract and their cytotoxic effects on human breast cancer cell line (MCF‐7)

Biosynthesis of nanoparticles (NPs) using biomass is now one of the best methods for synthesising NPs due to their nontoxic and biocompatibility. Plants are the best choice among all biomass to synthesise large‐scale NPs. The objectives of this study were to synthesise zinc oxide nanoparticles (ZnO‐NPs) using Anjbar (root of Persicaria bistorta) [An/ZnO‐NPs] and investigate the cytotoxic and anti‐oxidant effects. For this purpose, the An/ZnO‐NPs were synthesised by using Bistort extract and characterised using UV–Visible spectroscopy, transmission electron microscope, field emission scanning electron microscope, x‐ray diffraction and Fourier‐transform infrared spectroscopy. The cytotoxic effects of the An/ZnO‐NPs on MCF‐7 cells were followed by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide assays at 24, 48, and 72 h. Nuclear morphology changed and apoptosis in cells was investigated using acridine orange/propodium iodide (AO/PI) staining and flow cytometry analysis. The pure biosynthesised ZnO‐NPs were spherical in shape and particles sizes ranged from 1 to 50 nm. Treated MCF‐7 cells with different concentrations of ZnO‐NPs inhibited cell viability in a time‐ and dose‐dependent manner with IC50 about 32 μg/ml after 48 h of incubation. In flow cytometry analysis the sub‐G1 population, which indicated apoptotic cells, increased from 12.6% at 0 μg/ml (control) to 92.8% at 60 μg/ml, 48 h after exposure. AO/PI staining showed that the treated cells displayed morphologic evidence of apoptosis, compared to untreated groups. Inspec keywords: cancer, cellular biophysics, toxicology, particle size, nanofabrication, X‐ray diffraction, nanomedicine, nanoparticles, ultraviolet spectra, scanning electron microscopy, visible spectra, transmission electron microscopy, patient treatment, field emission electron microscopy, Fourier transform infrared spectra, drug delivery systemsOther keywords: anjbar, cytotoxic effects, human breast cancer cell line, biomass, transmission electron microscope, field emission scanning electron microscope, Fourier‐transform infrared spectroscopy, flow cytometry analysis, ZnO‐NPs inhibited cell viability, antioxidant effects, MCF‐7 cells, biosynthesised ZnO‐NP, biosynthesised ZnO‐NP, acridine orange‐propodium iodide staining, An‐ZnO‐NP, Persicaria bistorta, zinc oxide nanoparticle biosynthesis, 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide     

Predetection quality diversity scheme for DECT outdoor applications

A simple and effective metric for antenna diversity in DECT is introduced. Using a segment of only 2 to 4 bit periods, this metric provides a reliable estimation of the received signal quality. To emulate ideal selection diversity in a single receiver, this metric is calculated by performing two successive measurements on each antenna during the DECT preamble. When using a correlative clock timing, this diversity provides a performance improvement similar to an ideal CRC-driven selection diversity employing two parallel receivers. This offers a cheap and simple solution to DECT outdoor applications such as PCS     

Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms

Combined simulation-optimization models have been widely used to address the management of water resources issues. This paper presents a simulation-optimization model for conjunctive use of surface water and groundwater at a basin-wide scale, the Zayandehrood river basin in west central Iran. In the Zayandehrood basin, in the past 10 years, a historical low rainfall in the head of the basin, combined with growing demand for water, has triggered great changes in water management at basin and irrigation system level. The conjunctive use model that coupled numerical simulation with nonlinear optimization is used to minimize shortages of water in meeting irrigation demands for four irrigation systems. Constraints guarantee the maximum/minimum cumulative groundwater drawdown and maximum capacity of irrigation systems. A support vector machines (SVMs) model is developed as a simulator of surface water and groundwater interaction model while a genetic algorithm (GA) is used as the optimization model. Conjunctive use model runs for three scenarios. Results show that the accuracy of SVMs as a simulator for surface water and groundwater interaction model is good and that it is possible to decrease the water shortage for irrigation systems with application of proposed SVMs-GA model.     

Fumonisin B(1) in maize harvested in Iran during 1999.

The fumonisin B(1) (FB(1)) contamination of maize collected in two areas of Iran during 1999 was determined. The 20 maize samples from Mazandaran Province, situated on the Caspian littoral of Iran, consisted of random samples of farmers' lots and were all contaminated with FB(1) at a mean level of 3.18 mg kg(-1) (range 0.68-7.66 mg kg(-1)). The 10 samples (of the same maize cultivar) from Isfahan Province in central Iran were purchased as maize cobs in local retail markets and had mean FB levels of 0.22 mg kg(-1) (mean of all samples, 6/10 samples positive, range <0.01-0.88 mg kg(-1)). The FB levels in Mazandaran, an area of high oesophageal cancer, were significantly (p < 0.0001) higher than the FB levels found in maize from Isfahan, an area of low oesophageal cancer in Iran.     

Comparison of mean time to first failure and mean up time

Two unit standby redundant repairable system, parallel systems and series systems are studied. The time between failures and the repair time are assumed exponentially distributed. Expressions for mean time to first failure (MTTFF) and mean up time (MUT) have been obtained. Comparisons have been made between MTTFF and MUT and the difference illustrated by numerical examples for different systems.     

Fuzzy cost support vector regression on the fuzzy samples

This paper presents a new version of support vector regression (SVR) named Fuzzy Cost SVR (FCSVR) with a unique property of operating on fuzzy data where fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown to be superior in the experimental results as a property compared with conventional SVR.