Decentralized Backstepping Control of a Quadrotor with Tilted-rotor under Wind Gusts

International Journal of Control, Automation and Systems - Conventional unmanned aerial vehicles, quadrotor have a plethora of applications for civilian and military purposes. Quadrotors as the...     

Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.     

Graphene oxide‐ellagic acid nanocomposite as effective anticancer and antimicrobial agent

In this study, ellagic acid (ELA), a skin anticancer drug, is capped on the surface(s) of functionalised graphene oxide (GO) nano‐sheets through electrostatic and π–π staking interactions. The prepared ELA‐GO nanocomposite have been thoroughly characterised by using eight techniques: Fourier‐transform infrared spectroscopy (FTIR), zeta potential, X‐ray diffraction (XRD), thermogravimetric analysis (TGA), Raman spectroscopy, atomic force microscopy (AFM) topographic imaging, transmission electron microscopy (TEM), and surface morphology via scanning electron microscopy (SEM). Furthermore, ELA drug loading and release behaviours from ELA‐GO nanocomposite were studied. The ELA‐GO nanocomposite has a uniform size distribution averaging 88 nm and high drug loading capacity of 30 wt.%. The in vitro drug release behaviour of ELA from the nanocomposite was investigated by UV–Vis spectrometry at a wavelength of λ _max 257 nm. The data confirmed prolonged ELA release over 5000 min at physiological pH (7.4). Finally, the IC ₅₀ of this ELA‐GO nanocomposite was found to be 6.16 µg/ml against B16 cell line; ELA and GO did not show any cytotoxic effects up to 50 µg/ml on the same cell lines.     

Optimal Selection of Hybrid Renewable Energy System Using Multi-Criteria Decision-Making Algorithms

Several models of multi-criteria decision-making (MCDM) have identified the optimal alternative electrical energy sources to supply certain load in an isolated region in Al-Minya City, Egypt. The load demand consists of water pumping system with a water desalination unit. Various options containing three different power sources: only DG, PV-B system, and hybrid PV-DG-B, two different sizes of reverse osmosis (RO) units; RO-250 and RO-500, two strategies of energy management; load following (LF) and cycle charging (CC), and two sizes of DG; 5 and 10 kW were taken into account. Eight attributes, including operating cost, renewable fraction, initial cost, the cost of energy, excess energy, unmet load, breakeven grid extension distance, and the amount of CO₂, were used during the evaluation process. To estimate these parameters, HOMER^® software was employed to perform both the simulation and optimization process. Four different weight estimation methods were considered; no priority of criteria, based on a pairwise comparisons matrix of the criteria, CRITIC-method, and entropy-based method. The main findings (output results) confirmed that the optimal option for the case study was hybrid PV-DG-B with the following specification: 5 kW DG, RO-500, and load following control strategy. Under this condition, the annual operating cost and initial costs were $ 5546 and $ 161022, respectively, whereas the cost of energy was 0.077 $/kWh. The excess energy and unmet loads were 40998 and 2371 kWh, respectively. The breakeven grid extension distance and the amount of CO₂ were 3.31 km and 5171 kg per year, respectively. Compared with DG only, the amount of CO₂ has been sharply reduced by 113939 kg per year.     

An efficient short-term energy management system for a microgrid with renewable power generation and electric vehicles

Neural Computing and Applications - According to statistical reports, thermal power plants have long played a critical role in supplying electricity using fossil fuels. However, due to the high...     

Accurate Fault Location Modeling for Parallel Transmission Lines Considering Mutual Effect

A new accurate algorithms based on mathematical modeling of two parallel transmissions lines system (TPTLS) as influenced by the mutual effect to determine the fault location is discussed in this work. The distance relay measures the impedance to the fault location which is the positive-sequence. The principle of summation the positive-, negative-, and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS. Also, the impedance of the transmission line to the fault location is determined. These algorithms are applied to single-line-to-ground (SLG) and double-line-to-ground (DLG) faults. To detect the fault location along the transmission line, its impedance as seen by the distance relay is determined to indicate if the fault is within the relay’s reach area. TPTLS under study are fed from one- and both-ends. A schematic diagrams are obtained for the impedance relays to determine the fault location with high accuracy.     

Preparation and characterisation of ciprofloxacin‐loaded silver nanoparticles for drug delivery

Silver nanoparticles (AgNPs) have shown potential applications in drug delivery. In this study, the AgNPs was prepared from silver nitrate in the presence of alginate as a capping agent. The ciprofloxacin (Cipro) was loaded on the surface of AgNPs to produce Cipro‐AgNPs nanocomposite. The characteristics of the Cipro‐AgNPs nanocomposite were studied by X‐ray diffraction (XRD), UV–Vis, transmission electron microscopy (TEM), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), Fourier‐transform infra‐red analysis (FT‐IR) and zeta potential analyses. The XRD of AgNPs and Cipro‐AgNPs nanocomposite data showed that both have a crystalline structure in nature. The FT‐IR data indicate that the AgNPs have been wrapped by the alginate and loaded with the Cipro drug. The TEM image showed that the Cipro‐AgNPs nanocomposites have an average size of 96 nm with a spherical shape. The SEM image for AgNPs and Cipro‐AgNPs nanocomposites confirmed the needle‐lumpy shape. The zeta potential for Cipro‐AgNPs nanocomposites exhibited a positive charge with a value of 6.5 mV. The TGA for Cipro‐AgNPs nanocomposites showed loss of 79.7% in total mass compared to 57.6% for AgNPs which is due to the Cipro loaded in the AgNPs. The release of Cipro from Cipro‐AgNPs nanocomposites showed slow release properties which reached 98% release within 750 min, and followed the Hixson–Crowell kinetic model. In addition, the toxicity of AgNPs and Cipro‐AgNPs nanocomposites was evaluated using normal (3T3) cell line. The present work suggests that Cipro‐AgNPs are suitable for drug delivery.     

Dual-Port Content Addressable Memory for Cache Memory Applications

Multicore systems oftentimes use multiple levels of cache to bridge the gap between processor and memory speed. This paper presents a new design of a dedicated pipeline cache memory for multicore processors called dual port content addressable memory (DPCAM). In addition, it proposes a new replacement algorithm based on hardware which is called a near-far access replacement algorithm (NFRA) to reduce the cost overhead of the cache controller and improve the cache access latency. The experimental results indicated that the latency for write and read operations are significantly less in comparison with a set-associative cache memory. Moreover, it was shown that a latency of a read operation is nearly constant regardless of the size of DPCAM. However, an estimation of the power dissipation showed that DPCAM consumes about 7% greater than a set-associative cache memory of the same size. These results encourage for embedding DPCAM within the multicore processors as a small shared cache memory.     

Modeling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating

In power plants, flue gases can cause severe corrosion damage in metallic parts such as flue ducts, heat exchangers, and boilers. Coating is an effective technique to prevent this damage. A robust fuzzy model of the surface roughness (R_a and R_z) of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset. The proposed model consists of four nanoparticles (ZnO, ZrO₂, SiO₂, and NiO) with 2%, 4%, 6%, and 8%, respectively. Response surface methodology (RSM) was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct. To prove the superiority of the proposed fuzzy model, the model results were compared with those obtained by ANOVA, with the coefficient of determination and the root-mean-square error (RMSE) used as metrics. For R_a, for the first output response, using ANOVA, the coefficient-of-determination values were 0.9137 and 0.4037, respectively, for training and prediction. Similarly, for R_z, the second output response, the coefficient-of-determination results were 0.9695 and 0.4037, respectively, for training and prediction. In the fuzzy modeling of R_a, for the first output response, the RMSE values were 0.0 and 0.1455, respectively, for training and testing. The values for the coefficient of determination were 1.00 and 0.9807, respectively, for training and testing. The results prove the superiority of fuzzy modeling. For modeling the second output response R_z, the RMSE values were 0.0 and 0.0421, respectively, for training and testing, and the coefficient-of-determination values were 1.00 and 0.9959, respectively, for training and testing.