A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Synthesis,Structural and Biomedical Characterization of Hydroxyapatite/Borosilicate Bioactive Glass Nanocomposites

Journal of Inorganic and Organometallic Polymers and Materials - In this work, a borosilicate glass sample (5SiO2–45B2O3–20Na2O–25CaO–5Ag2O) was added to nano-sized...     

Islam, Jihad, and Terrorism in Post-9/11 Arabic Discussion Boards

This study analyzed the contents of three of the most popular Arabic-language online message boards regarding the attacks of September 11, 2001 on the United States. Although terrorists claimed that the attacks were committed in the name of Islam, those who posted messages on all three forums rejected this claim. More than 43% of the messages condemned the attacks as a criminal act of terrorism that contradicts the core teachings of Islam. Some 30% saw some justification behind the attacks, even if they felt sorry for the victims and their families. However, those participants viewed the attacks as a political, rather than a religious, issue.     

An efficient IoT based smart farming system using machine learning algorithms

Multimedia Tools and Applications - This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop...     

Raycasting of Light Field Galleries from Volumetric Data

The paper describes a technique to generate high‐quality light field representations from volumetric data. We show how light field galleries can be created to give unexperienced audiences access to interactive high‐quality volume renditions. The proposed light field representation is lightweight with respect to storage and bandwidth capacity and is thus ideal as exchange format for visualization results, especially for web galleries. The approach expands an existing sphere‐hemisphere parameterization for the light field with per‐pixel depth. High‐quality paraboloid maps from volumetric data are generated using GPU‐based ray‐casting or slicing approaches. Different layers, such as isosurfaces, but not restricted to, can be generated independently and composited in real time. This allows the user to interactively explore the model and to change visibility parameters at run‐time.     

QuePED: Revisiting queueing networks for the performance evaluation of database designs

Databases form the common component of many software systems. However, performance models specifically targeted at the database design have not been extensively studied. In this paper, we propose QuePED – a queueing network performance evaluation model for database designs. QuePED provides for the performance modelling of database design constructs, including active database rules; thus, deviating from current methods that consider database designs as processing demands on the hardware architecture. We present a formal specification of QuePED and describe its application to database designs. Experimental evaluation has shown the ability of QuePED to capture the steady state performance of an implementation of the TPC-C benchmark.     

High-level user interfaces for transfer function design with semantics

Many sophisticated techniques for the visualization of volumetric data such as medical data have been published. While existing techniques are mature from a technical point of view, managing the complexity of visual parameters is still difficult for non-expert users. To this end, this paper presents new ideas to facilitate the specification of optical properties for direct volume rendering. We introduce an additional level of abstraction for parametric models of transfer functions. The proposed framework allows visualization experts to design high-level transfer function models which can intuitively be used by non-expert users. The results are user interfaces which provide semantic information for specialized visualization problems. The proposed method is based on principal component analysis as well as on concepts borrowed from computer animation.     

Ear recognition with feed-forward artificial neural networks

Biometrics has become one of the most important techniques in recognizing a person’s identity. A person’s face, iris and fingerprint are mostly used in biometrics today. It has been established that there are no two ears exactly alike, even in the cases of identical twins. In this paper, we define a 7-element ear feature set and design and train a feed-forward artificial neural network to recognize a human ear. We train and test the network with 51 ear pictures from 51 different persons. Simulation experiments with various networks with various number of layers and number of neurons per layer and with and without noise are conducted. Results indicate that a 95 % ear recognition accuracy is achieved with a simple 3-layer feed-forward neural network with only a total of 18 neurons even in the presence of some noise. This design outstands previous work in simplicity and implementation cost.     

Integrated standalone hybrid solar PV,fuel cell and diesel generator power system for battery or supercapacitor storage systems in Khorfakkan,United Arab Emirates

Renewable energy resources play a very important rule these days to assist the conventional energy systems for doing its function in the UAE due to high greenhouse gas (GHG) emissions and energy demand. In this paper, the analysis and performance of integrated standalone hybrid solar PV, fuel cell and diesel generator power system with battery energy storage system (BESS) or supercapacitor energy storage system (SCESS) in Khorfakkan city, Sharjah were presented. HOMER Pro software was used to model and simulate the hybrid energy system (HES) based on the daily energy consumption for Khorfakkan city. The simulation results show that using SCESS as an energy storage system will help the performance of HES based on the Levelized cost of energy (LCOE) and greenhouse gas (GHG) emissions. The HES with SCESS has renewable fraction (68.1%) and 0.346 $/kWh LCOE. The HES meets the annual AC primary load of the city (13.6 GWh) with negligible electricity excess and with an unmet electrical load of 1.38%. The reduction in GHG emissions for HES with SCESS was 83.2%, equivalent to saving 814,428 gallons of diesel.