Sequential Microwave-Ultrasound-Assisted Extraction for Isolation of Piperine from Black Pepper (<Emphasis Type="Italic">Piper nigrum</Emphasis> L.)

Piperine is the natural bioactive component of black pepper (Piper nigrum L.) with several astounding therapeutic properties. In this study, sequential microwave-ultrasound-assisted extraction approach was used for isolation of piperine from black pepper. The effect of various factors such as extraction solvent, particle size of pepper, solvent to solid ratio, microwave power and time and ultrasound temperature and time on the extraction yield of piperine was considered. The maximum extraction yield was 46.6 mg piperine/g pepper which was obtained using ethanol as solvent at the particle size of 0.15 mm, solvent to solid ratio of 20:1, microwave power of 100 W for 1 min, and ultrasound temperature of 50 ^° C for 30 min. This extraction yield was higher than those obtained by Soxhlet (39.1 mg/g), microwave-assisted (38.8 mg/g) and ultrasound-assisted (37.0 mg/g) extractions. The purity of the extracted piperine was 81.4% as determined by HPLC analysis. The FTIR and UV-vis analyses confirmed that the structure of piperine remained intact after extraction and purification which is very important for medicinal applications.     

An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi‐objective two‐nested genetic algorithm is presented. The top level algorithm is a multi‐objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two‐tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA‐based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimistic Selection of Cluster Heads Based on Facility Location Problem in Cluster-Based Routing Protocols

Cluster-based routing protocols are one of the most favorable approaches for energy management in wireless sensor networks. The selection of the best cluster heads (CHs), as well as the formation of optimal clusters, is an NP-hard problem. The present study proposes an optimal solution for CHs selection to generate a network topology with optimized network performance. The problem is formulated as facility location problem and a linear programming model is used to solve the optimization problem. Results of analysis o the network simulator (NS2) indicate that applying this method in cluster-based routing protocols prolongs 16 % of the network lifetime, increases 15.5 % of data transmission and improves 5.5 % of throughput, as compared to the results of current heuristic methods such as LEACH, DEEC and EDFCM protocols.     

A review of principle and sun-tracking methods for maximizing solar systems output

Finding energy sources to satisfy the world's growing demand is one of society's foremost challenges for the next half-century. The challenge in converting sunlight to electricity via photovoltaic solar cells is dramatically reducing $/watt of delivered solar electricity. In this context the sun trackers are such devices for efficiency improvement.The diurnal and seasonal movement of earth affects the radiation intensity on the solar systems. Sun-trackers move the solar systems to compensate for these motions, keeping the best orientation relative to the sun. Although using sun-tracker is not essential, its use can boost the collected energy 10–100% in different periods of time and geographical conditions. However, it is not recommended to use tracking system for small solar panels because of high energy losses in the driving systems. It is found that the power consumption by tracking device is 2–3% of the increased energy.In this paper different types of sun-tracking systems are reviewed and their cons and pros are discussed. The most efficient and popular sun-tracking device was found to be in the form of polar-axis and azimuth/elevation types.     

Removal of melanoidin from molasses spent wash using fly ash-clay adsorbents

Removal of melanoidin pigment from molasses spent wash was investigated using a new adsorbent. Solid adsorbents were fabricated from charcoal fly ash and clay. The effect of various molasses concentration (6 to 12 g/l) on removal efficiency was studied. The obtained results revealed that maximum removal efficiency of 82% was achieved at the molasses concentration of 6 g/l and contact time of 7 h. The saturated porous adsorbents were regenerated and reused to conduct similar experiments. The achieved data showed that more than 90% of the capacity of the fresh adsorbent was recovered after regeneration. Various adsorption isotherms of Langmuir, Freundlich, Temkin and Harkins-Jura were applied to interpret the obtained experimental data. The obtained results revealed that the sorption data were well described by the Harkins-Jura model. Also, various kinetic models of pseudo-first order, pseudo-second order, Elovich and intra-particle diffusion were used to predict the characteristic parameters which are useful in process design. It was concluded that the best fit was obtained with pseudo-second order kinetic model at low molasses concentrations.     

Investigation of the effect of magnetic field on mass transfer parameters of CO2 absorption using Fe3O4‐water nanofluid

In this study, the enhancement of physical absorption of carbon dioxide by Fe₃O₄‐water nanofluid under the influence of AC and DC magnetic fields was investigated. Furthermore, a gas‐liquid mass transfer model for single bubble systems was applied to predict mass transfer parameters. The coated Fe₃O₄ nanoparticles were prepared using co‐percipitation method. The results from characterization indicated that the nanoparticles surfaces were covered with hydroxyl groups and nanoparticles diameter were 10–13 nm. The findings showed that the mass transfer rate and solubility of carbon dioxide in magnetic nanofluid increased with an increase in the magnetic field strength. Results indicated that the enhancement of carbon dioxide solubility and average molar flux gas into liquid phase, particularly in the case of AC magnetic field. Moreover, results demonstrated that mass diffusivity of CO₂ in nanofluid and renewal surface factor increased when the intensity of the field increased and consequently diffusion layer thickness decreased. © 2016 American Institute of Chemical Engineers AIChE J, 63: 2176–2186, 2017     

Probabilistic analysis of a two-unit cold standby redundant system subject to failure of controlled weather device

This paper studies stochastic analysis of a two-unit cold standby system taking into account failure of a weather condition device. All failure times and time to repair the weather condition device have a negative exponential distribution, whereas repair rates of the operating unit are general. The regenerative points technique in Markov renewal processes (MRP) is applied to obtain several reliability characteristics of interest to system designers.     

Performance analysis of two‐way wireless‐powered Amplify‐and‐Forward relaying in the presence of co‐channel interference

In this paper, we investigate the performance assessment of a bidirectional relaying system using energy harvesting techniques. We assume independent and nonidentically distributed (i.n.i.d.) Nakagami‐m fading channels where the amplify‐and‐forward relay is subject to co‐channel interference (CCI) due to transmissions of other transmitters. Two different scenarios, namely, scenario I and scenario II are evaluated. In scenario I, both end‐sources provide the required energy for the relay, whereas the relay also harvests energy from the co‐channel interferes. Then, in the first phase of cooperation, both end‐sources send the information to the relay, and after amplifying the received signal, relay transfers information to the appropriate destination in the second time‐slot. In the scenario II, both end‐sources harvest energy from the relay. After that, the information cooperative transmission is done similar to the first scenario. For both considered scenarios, tight closed‐form expressions of outage probability, symbol error probability, ergodic capacity, and throughput are obtained at arbitrary signal‐to‐noise‐ratios (SNRs). To get more insights, simplified high SNR results for both scenarios are also deduced where the diversity orders are obtained. Monte Carlo simulation results are presented to validate the correctness of our proposed analysis. Our results explicitly demonstrate that the first scenario has a better performance than the second one in the medium and high SNR region, whereas the second scenario outperforms the first one in the low SNR regime.     

A dynamic fog service provisioning approach for IoT applications

Internet of Things (IoT) is an ecosystem that can improve the life quality of humans through smart services, thereby facilitating everyday tasks. Connecting to cloud and utilizing its services are now public and common, and the experts seek to find some ways to complete cloud computing to use it in IoT, which in next decades will make everything online. Fog computing, where the cloud computing expands to the edge of the network, is one way to achieve the objectives of delay reduction, immediate processing, and network congestion. Since IoT devices produce variations of workloads over time, IoT application services will experience traffic trace fluctuations. So knowing about the distribution of future workloads required to handle IoT workload while meeting the QoS constraint. As a result, in the context of fog computing, the main objective of resource management is dynamic resource provisioning such that it avoids the excess or dearth of provisioning. In the present work, we first propose a distributed computing framework for autonomic resource management in the context of fog computing. Then, we provide a customized version of a provisioning system for IoT services based on control MAPE‐k loop. The system makes use of a reinforcement learning technique as decision maker in planning phase and support vector regression technique in analysis phase. At the end, we conduct a family of simulation‐based experiments to assess the performance of our introduced system. The average delay, cost, and delay violation are decreased by 1.95%, 11%, and 5.1%, respectively, compared with existing solutions.     

Predictor Feedback Stabilization of Stochastic Linear Delayed Systems with Both Additive and Multiplicative Noises

In this paper we investigate memory control of stochastic linear delayed systems with both additive and multiplicative noises. A new formula is first presented to obtain the prediction vector from the system dynamics and then it is used for feedback to reduce the input delay in the original delayed system. To ensure the stability of closed‐loop system, some matrix inequality conditions are given that in the case of feasibility provide the stabilizing gain of the predictor controller. The proposed method is applied to stochastic quarter‐car model of an active suspension system to show the effectiveness of the approach.