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Treatment of the first natural frequency of a rotating nanocomposite beam reinforced with graphene platelet is discussed here. In regard of the Timoshenko beam theory hypothesis, the motion equations are acquired. The effective elasticity modulus of the rotating nanocomposite beam is specified resorting to the Halpin–Tsai micro mechanical model. The Ritz technique is utilized for the sake of discretization of the nonlinear equations of motion. The first natural frequency of the rotating nanocomposite beam prior to the buckling instability and the associated post-critical natural frequency is computed by means of a powerful iteration scheme in reliance on the Newton–Raphson method alongside the iteration strategy. The impact of adding the graphene platelet to a rotating isotropic beam in thermal ambient is discussed in detail. The impression of support conditions, and the weight fraction and the dispersion type of the graphene platelet on the acquired outcomes are studied. It is elucidated that when a beam has not undergone a temperature increment, by reinforcing the beam with graphene platelet, the natural frequency is enhanced. However, when the beam is in a thermal environment, at low-to-medium range of rotational velocity, adding the graphene platelet diminishes the first natural frequency of a rotating O-GPL nanocomposite beam. Depending on the temperature, the post-critical natural frequency of a rotating X-GPL nanocomposite beam may be enhanced or reduced by the growth of the graphene platelet weight fraction.

Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities ( k ). Although it is difficult to attain these ideal conditions without complex material engineering, the low k implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model.

This work was carried out to explore the compounds of bismuth with carbon using density functional theory (DFT)-based computations. The structures of the compounds BiC, BiC2, BiC3, Bi2C3, BiC5, and Bi2C5 were predicted at a generalized gradient approximation (GGA-PBE) level of theory. The calculations were carried out on the structures in unit cell and supercell geometries in slab and bulk periodicities. The structural and electronic properties of the mentioned compounds were investigated in detail. The calculations of the structures revealed lattice constants of the compounds for cubic unit cell as 212.2 pm for BiC, 176.9 pm for BiC2, 240.5 pm for BiC3, 232.4 pm for Bi2C3, and 354.5 pm for Bi2C5. The compounds BiC, BiC2, BiC3, BiC5, and Bi2C5 were found to be metallic, whereas Bi2C3 exhibited semiconducting character with a band gap of 0.305 eV. This work provides an initial framework for preparing new 2D materials from BixCy.

The excellent optical properties of gold nanoparticles (AuNPs) make them promising for numerous applications. Herein, we present a facile synthesis of both surfactant-free (SF−AuNPs) and non-toxic D-fructose (DF)-coated gold nanoparticles (DF−AuNPs) via the plasma–liquid interactions (PLIs) method. Moreover, we demonstrate that both SF−AuNPs and DF−AuNPs are potential candidates for trace detection via surface-enhanced Raman scattering (SERS) and catalytic degradation of toxic dyes. However, SF−AuNPs have superior SERS and catalytic performance compared to the DF−AuNPs due to their surfactant-free nature. Moreover, SF−AuNPs have also been shown to quench the fluorescence of analyte molecules, making their SERS-based trace detection more efficient. In particular, SERS enhancement of rhodamine 6G (R6G) and catalytic reduction of a toxic dye methylene blue (MB) have been explored.

Two-dimensional (2D) SnO is a p-type semiconductor that has received research and industrial attention for device-grade applications due to its bipolar conductivity and transparent semiconductor nature. The first-principles investigations based on the generalized gradient approximation (GGA) level of theory often failed to accurately model its structure due to interlayer Van der Waals interactions. This study is carried out to calculate structural and electronic properties of bulk and layered structures of SnO using dispersion correction scheme DFT+D3 with GGA-PBE to deal with the interactions which revealed good agreement of the results with reported data. The material in three-dimensional bulk happened to be an indirect gap semiconductor with a band gap of 0.6 eV which is increased to 2.85 eV for a two-dimensional monolayer structure. The detailed analysis of the properties demonstrated that the SnO monolayer is a promising candidate for future optoelectronics and spintronics devices, especially thin film transistors.

Due to the need for structures with refined properties to bear against different loading conditions, recently, carbon nanotubes (CNTs) have been used widely to reinforce them. The extremely high stiffness of CNTs makes them significant as one of the best reinforcements to improve the mechanical behaviors of structures. This work focuses on microbeam buckling response with an initial curvature that includes three layers. The mid-layer that is known as the core is constituted of functionally graded porous (FGP) materials and two CNT-reinforced composite skins are bonded to the core to integrate it. The whole structure is affected by the hygrothermal environment and springs and shear layers are put below it. For the first time, for such a structure, a refined shear deformation theory (RSDT) as a higher-order theory that considers thickness stretching effect in polar coordinates is used that presents more accurate results, especially for deeply curved beams. Modified couple stress theory (MCST) in combination with the virtual displacement principle is utilized to establish the governing equations. The obtained results demonstrate the significance of porosity percentage and CNTs’ addition to the skins on the critical nanotubes buckling load. Also, the different behaviors of the microstructure at various temperatures are analyzed and discussed in detail.

Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.

This is the first investigation of yttrium (Y) and erbium (Er) co-doped hydroxyapatite (HAp) structures, conducted using theoretical and experimental procedures. By using a wet chemical method, the materials were synthesized by varying the concentration of Y amounts of 0.13, 0.26, 0.39, 0.52, 0.65, and 0.78 at.% every virtual 10 atoms of calcium, whereas Er was kept fixed at 0.39 at.%. Spectroscopic, thermal, and in vitro biocompatibility testing were performed on the generated samples. Theoretical calculations were carried out to compute the energy bandgap, density of states, and linear absorption coefficient. The effects of Y concentration on thermal, morphological, and structural parameters were investigated in detail. Raman and Infrared (FTIR) spectroscopies confirmed the formation of the HAp structure in the samples. Theoretical investigations indicated that the increasing amount of Y increased the density from 3.1724 g cm−3 to 3.1824 g cm−3 and decreased the bandgap energy from 4.196 eV to 4.156 eV, except for the sample containing 0.39 at. % of the dopant, which exhibited a decrease in the bandgap. The values of linear absorption appeared reduced with an increase in photon energy. The samples exhibited cell viability higher than 110%, which revealed excellent biocompatibility for biological applications of the prepared samples.

Polytetrafluoroethylene (PTFE) ions were deposited on titanium substrate by using a 1.5 kJ Mather plasma focus device in argon, equipped with a PTFE source. The PTFE and argon ions generated during different number of shots of dense plasma focus (DPF) resulted in deposition of PTFE on the Ti surface. Prepared samples were characterized for structural properties, elemental composition, surface morphology, wear resistance and frictional behavior by X-ray diffraction, energy dispersive X-ray, scanning electron microscope and pin on disc test, respectively. The area of the coherent X-ray scattering region of PTFE coated on Ti estimated by XRD is 9 nm. Both XRD and SEM show that the area of the coherent X-ray scattering region increases with the increase in the number of focus shots. The EDX results confirmed that the concentration of carbon and fluorine on the Ti substrate increases with the increase in energy of ion flux. Finally, the pin on disc test confirms that PTFE ion plasma coating on the Ti surface reduces the friction up to 35% and enhances wear resistance of the Ti surface up to 89%. The above analysis reflects that PTFE coating shows remarkable tribological behavior with low value of friction coefficient and enhanced value of wear resistance. Moreover, this study provides an intuition for organizing the design of self-lubricating and effective wear-resistant coatings.

Numerical simulation and modelling of lithium-ion batteries are fundamentally crucial for thermal management systems, and are progressively becoming compelling in its rudimentary understanding on electrochemical performances as well as thermal attributes. This research observes the relationship between various cell units and battery cells using a three-dimensional model through coupling of mass, charge, and energy conservation equations, as well as, the phenomenon of charging-discharging cycles that lead lithium-ion batteries towards an aging process limiting its energy storage as well as power output capabilities. This phenomenon of battery aging along with its repercussions on battery degradation must be contemplated for development of battery design and management. This research utilizes Numerical Simulation using COMSOL for simulation of the battery at various design conditions. A constant charging and discharging of the battery must escalate the temperature inside the lithium-ion battery. Discharging temperatures are higher than charging temperatures; however, the temperature difference between the discharging and charging of the battery decreases with increasing C-rate. Lithium-ion batteries are modelled in COMSOL and are varied across C-rates ranging from 0.5C, 1C, 2C or higher. Aging criteria of battery is fulfilled through a series of 1000 cycles, 5000 cycles, and 10000 cycles for broader range of data. Retrieved results include plots of working voltage and discharge time, voltage and battery capacity as well as temperature plots and contours for better visualization. A complete visualization of temperature and capacity points of the battery will allow in-depth understanding of the aging process and its complications along with a potential solution for battery longevity.

The current paper is aimed to investigate the effects of waviness, random orientation, and agglomeration factor of nanoreinforcements on wave propagation in fluid-conveying multi-walled carbon nanotubes (MWCNTs)-reinforced nanocomposite cylindrical shell based on first-order shear deformable theory (FSDT). The effective mechanical properties of the nanocomposite cylindrical shell are estimated employing a combination of a novel form of Halpin-Tsai homogenization model and rule of mixture. Utilized fluid flow obeys Newtonian fluid law and it is axially symmetric and laminar flow and it is considered to be fully developed. The effect of flow velocity is explored by implementing Navier-Stokes equation. The kinetic relations of nanocomposite shell are calculated via FSDT. Moreover, the governing equations are derived using the Hamiltonian approach. Afterward, a method which solves problems analytically is applied to solve the obtained governing equations. Effects of a wide range of variants such as volume fraction of MWCNTs, radius to thickness ratio, flow velocity, waviness factor, random orientation factor, and agglomeration factor on the phase velocity and wave frequency of a fluid conveying MWCNTs-reinforced nanocomposite cylindrical shell were comparatively illustrated and the results were discussed in detail.

The existence of shadows in natural scenes cause challenges in computer vision applications such as object detection. In this paper, a proposed approach is introduced for shadow detection in single images. Unlike other approaches, our algorithm uses the variation in the RGB components in order to locate the drop in intensity and analyze it. The input image would be subjected to noise reduction using a Gaussian filter then vertical scanning is applied where the pixels at every column of the image are collected, grouped using a threshold to obtain smooth knee points through the variation of the intensity levels and analyzed. Shadow removal is done and then horizontal scanning is carried out following the same procedure. The analysis and optimization process was done on 300 images and the final testing was done on 4000 images from the SBU shadow images dataset. The results have shown the success of our algorithm to detect high accuracy of shadows than other approaches.

Social distancing is a necessary precaution measure taken in order to have more control over the outbreak of infectious diseases such as COVID-19. Most of Social distancing monitoring approaches are based on Bluetooth and mobile phones that require an app to be downloaded on all phones. This paper proposes a different approach to monitor social distancing, using cameras, and combining different computer vision algorithms. The approach utilizes the concept of inverse perspective mapping (IPM) together with the camera’s intrinsic information to produce a bird’s eye view with real-world coordinates of the frame being processed from a video source. The process starts with image enhancement, foreground detection using Gaussian Mixture Model (GMM) background subtraction, tracking using Kalman filter, computing real-world distance measurements between individuals, and detecting those who have been in less than 2 meters apart as they are considered to be in contact. This tool could assist the efforts of the governments to contain the virus. It can be implemented in closed areas or institutions, monitor the extent of people’s commitment, and provide analysis and a faster approach to detect possibly corona suspicion cases. The approach is tested on the task decomposition data set, which included frames of closed areas and the camera’s intrinsic parameters. Another data set was created with different scenarios to increase the confidence level of our algorithm. The results showed the success of our approach in detecting the violation in social distancing with accurate measures of the real-world coordinates.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

In this paper, a discrimination system, using a neural network for electromyogram (EMG) externally controlled Arm is proposed. In this system, the Artificial Neural Network (ANN) is used to learn the relation between the power spectrum of EMG signal analysed by Fast Fourier Transform (FFT) and the performance desired by handicapped people. The Neural Network can discriminate 4 performances of the EMG signals simultaneously. The digital signal processing was realized using MATLAB and LabVIEW software.

The changes in properties of materials upon introduction of impurities is well documented but less is known about the location of foreign atoms in different hosts. This study is carried out with the motivation to explore dopant location in hexagonal GaN using density functional theory based calculations. The dopant site location of the individual dopants Ti, Ce, and Ti-Ce codoped wurtzite GaN was investigated by placing the dopants at cationic lattice sites as well as off-cationic sites along the c-axis. The geometry optimization relaxed individual dopants on cationic Ga sites but in the case of codoping Ce settled at site 7.8% away along [0001 ¯] and Ti adjusted itself at site 14% away along [0001] from regular cationic sites. The analysis of the results indicates that optimized geometry is sensitive to the starting position of the dopants. The magnetic exchange interactions between Ti and Ce ions are responsible for their structural relaxation in the matrix.

The electrochemical deposition of the composites polyaniline (PANI):polypyrrole (PPy)/activated carbon (AC) and polyaniline (PANI): 3, 4-polyethylenedioxythiophene (PEDOT)/AC films is carried out in this work. The electrochemical character of the fabricated samples is investigated via cyclic voltammetry (CV), galvanostatic charge–discharge (GCD) and electrochemical impedance spectroscopy (EIS) using a three-electrode setup. The values of the specific capacitance of the composites PANI:PPy/AC and PANI:PEDOT/AC at a current density of 1 Ag−1 are evaluated as 586 Fg−1 and 611 Fg−1, respectively. The values of energy density are 40 Whkg−1 and 2094 Wkg−1, whereas power density is recorded as 44 Whkg−1 and 2160 Wkg−1 for respective composites PANI:PPy/AC and PANI:PEDOT/AC. Moreover, the respective composites appeared to retain cyclic stabilities of 92% and 90%. This study points to the potential of the prepared composites for application as electrodes in supercapacitors.

There have been unprecedented research efforts to produce efficient and sustainable electrode materials for lithium-ion batteries (LIB). The research efforts to increase energy density, life cycle, safety, and rate-capability are still in progress. This work is carried out to explore the prospects of a new anode material CrGeTe3 for use in LIB studied via first-principles predictions. The investigation of Li: CrGeTe3 system and lithiation mechanism in the proposed material pointed out its suitability for lithium intercalation compound. The energy-profiling at highly symmetry points in the host structure revealed that BGe-Cr is most favorable energy site for lithiation in the material. The maximum value of the lithium insertion voltage is 3.41 V and the storage capacity is 620.60 mAh g−1. The diffusion barrier faced by lithium atom is 0.27 eV which is the minimum value, while moving along the x-axis within the top and middle hexagonal rings. The steric interaction of Li with Ge and Te atoms pointed structural reliability and hence stable operation of the anode to address capacity fading problems in LIB.

In rotating machinery, rolling element bearings are one of the most critical components and a large majority of system failures arise from faulty bearings. Hence, there is an increasing demand to find an effective and reliable condition monitoring technique. In this paper, a procedure for detecting various types of bearing faults using thermal imaging is presented and assessed. Five different fault cases are tested: No Fault (NF), Line Fault (LF), Small Circle Fault (SCF), Double Line Fault (DLF), and Large Circle Fault (LCF). Experiments were conducted on the BENTLY NEVADA RK4 Rotor Kit. A novel signal processing algorithm is proposed to detect the faults which involves utilizing the Discrete Wavelet Transform (DWT). The HAAR wavelet is used as the mother wavelet and a decomposition level of 7 is used. The inverse of HAAR is applied on the decomposed signal and the envelop spectrum is plotted. A classifier is then created to identify the fault that utilizes the Support Vector Machine (SVM) classifier to extract the features and a confusion matrix is developed to detect the prediction accuracy.

Turbulent bed contactors (TBC) employed extensively in process industry and received much attention due to their high efficiency, and low capital and operating costs. In this paper, bed hydrodynamics including pressure drop is studied consecutively with the increase of superficial gas velocity in a TBC with various static bed heights, particle sizes, liquid flow rates and rheological liquid properties. A computational model capable of simulating hydrodynamic characteristics in a TBC at different operating conditions is constructed using ANSYS Fluent Software. The simulation results are validated against experimental data obtained. As a first stage, the results of simulations for two phases (gas-solid) for the TBC are presented. Computed pressure drop is validated against experimental data. Good agreement between the two is found for different gas velocities. This study presents a new insight into design and operational parameters that may impact TBC efficiencies.

Six Degree of Freedom (6 DOF) parallel manipulators have been utilized for many applications. In this paper, a 6 DOF manipulator is used as a robust vehicle stabilizer in order to fix the orientation of the upper platform. The paper starts with kinematic analysis of a Stewart platform. This analysis is formulated to cope with the design of vehicle stabilizer. The design and selection of mechanical components including primary joints based on comprehensive dynamic simulation. After validating simulation results of proposed design, they were implemented in to build a physical model. To improve system accuracy and performance, and to eliminate associated vibrations, a linear regression model of ground rise is embedded in the system to estimate and predict upcoming elevations. This has lowered the percentage error of platform orientation, made the system more stable.

The increasing demand for construction materials and the need to improve the performance of asphalt mixes have led to the depletion of available resources of aggregates. Lots of aggregate alternatives had been studied to partially or fully replace aggregate in asphalt concrete mixes. Indeed, date seeds are found easily in Kuwait. Their properties; such as light weight, very low crushing strength, and low impact value; represent a potential domain to enhance asphalt mix performance. The main objective of this paper is investigating the possibility of using activated date seeds additives in Hot Mix Asphalt (HMA) as a partial replacement of Fine Aggregates (sand). A total of 3 asphalt concrete mixes with different percentages by weight were prepared. Physical and mechanical properties of date seeds and Fine Aggregates (sand) were determined and compared in the asphalt concrete mix design of ratios 7, 10, and 15%. Compressive Strength and Marshall Stability tests were performed to investigate the effect of activated date seeds additives on HMA. Eventually, the findings indicated that the ratio of 10% gave the highest Marshall Stability. On the other hand, the ratio of 7% found to give the highest retained strength index. a deeper statistical analysis is performed to evaluate the robustness and significance of studied factors on response variables represented in Marshal Stability and Flow. The first part of the analysis included recognizing data outliers in data sets and eliminating them to compare basic statistical values with raw data. The minor differences in these values implies minor variability in the measurement and indicates minimal experimental error. T-test analysis was performed to study the influence of date seed percentages on different performed measurements. ANOVA tests were also represented to demonstrate the statistical significance factors on considered response variables. The results supported the significant influence of the included measurements on Marshal stability and flow values.

Nawash, J. M., & Alkhedher, M. A. (2017, March). Crystal growth of PMN-PT for energy harvesting applications. In Renewable Energy Congress (IREC), 2017 8th International (pp. 1-6). IEEE., The harvesting of mechanical energy has been investigated extensively over the last decade. This type of harvesting is widely utilized in the applications of vibration-based energy harvester. Energy harvesting using piezoelectric materials is an efficient system due to high voltage output and power intensity in addition to its suitability in autonomous electric devices. One of the most promising piezoelectric materials is the relaxor ferroelectric PMN-PT: Pb(Mg1/3Nb2/3)O3-PbTiO3 single crystal. The exceptionally high values of the electromechanical coupling factor (K33), piezoelectric constant (d33), thickness coupling coefficient (Kt), and dielectric constant with low loss have made it a unique candidate to harvest energy. However, the growth of PMN-PT single crystals presents many challenges. Among those challenges is the decomposition of PbO into Pb and O2, which in turn may eventually ruin the crucible, even during the first growth. In this paper, an exhibition of several PMN-PT growth attempts in different crucible types and shapes are demonstrated. Based on the experimental results, the analyses of XRD, Kt, dielectric constant, and Glow Discharge Mass Spectroscopy are discussed.

Al Sabbah, S. A., Al-Khedher, M. A., & Younes, T. M. (2012). Evaluation of Multiregional Fuzzy-Based pH Cascade Control with ANFIS-Based pH Observer. Research Journal of Applied Sciences, 7(2), 60-65., In pH reactors, determination and control of pH is a common problem concerning chemical-based industrial processes due to the non-linearity observed in the titration curve. The researchers introduced a modified multiregional Fuzzy-Based Control System to overcome the complexity of precise control of pH. In order to compensate for the experimental inaccuracies in measurements of pH in-situ values; an observer for pH is implemented using Adaptive Neuro-Fuzzy Inference System (ANFIS). The pH control approach and ANFIS-based observer are integrated in a nonlinear cascade structure to ensure the dynamic modifications and stability enhancement. The cascade structure is designed using a multiregional fuzzy PI controller in the master loop and a Wiener Model-based fuzzy proportional controller as a slave one. The Multiregional Fuzzy Cascade Control (MFCC) structure is developed to implicate the three main regions of the titration curve.

An efficient automotive security system is implemented for anti-theft using an embedded system occupied with a Global Positioning System (GPS) and a Global System of Mobile (GSM). The client interacts through this system with vehicles and determines their current locations and status using Google Earth. The user can track the position of targeted vehicles on Google Earth. Using GPS locator, the target current location is determined and sent, along with various parameters received by vehicle's data port, via Short Message Service (SMS) through GSM networks to a GSM modem that is connected to PC or laptop. The GPS coordinates are corrected using a discrete Kalman filter. To secure the vehicle, the user of a group of users can turn off any vehicle of the fleet if any intruders try to run it by blocking the gas feeding line. This system is very safe and efficient to report emergency situations as crash reporting or engine failure.

Al-Khedher, M., Pezeshki, C., McHale, J., & Knorr, F. (2011). Adaptive Neuro-Fuzzy Modeling of Mechanical Behavior for Vertically Aligned Carbon Nanotube Turfs. Journal of Materials Science & Technology, 27(4), 301-308., Several characterization methods were developed to study the mechanical and structural properties of vertically aligned carbon nanotubes (VACNTs). Establishing analytical models at nanoscale to interpret these properties is complicated due to the nonuniformity and irregularity in quality of as-grown samples. In this paper, we propose a new methodology to investigate the correlation between indentation resistance of multi-wall carbon nanotube (MWCNT) turfs, Raman spectra and the geometrical properties of the turf structure using adaptive neuro-fuzzy phenomenological modeling. This methodology yields a novel approach for modeling at the nanoscale by evaluating the effect of structural morphologies on nanomaterial properties using Raman Spectroscopy., https://s3.amazonaws.com/academia.edu.documents/30328685/Energy_conservation_and_mangment2010.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1521961567&Signature=qtR8MTRje%2B8ivdQrYrNwLtONRUg%3D&response-content-disposition=inline%3B%20filename%3DAn_adaptive_model_for_predicting_of_glob.pdf

Al-Khedher, M. A., Yassar, R. S., Pezeshki, C., & Field, D. P. (2006). A novel structural-based approach to model the age hardening behaviour of aluminium alloys. Modelling and Simulation in Materials Science and Engineering, 14(6), 905., A new approach based on an artificial neural networks (ANN) model was used to model the ageing behaviour of an Al–Mg–Si alloy. A systematic combination of hardness measurements, transmission electron microscopy (TEM), image analysis and the ANN method was used to correlate the key precipitate parameters with the age-hardening response. The ageing behaviour of AA6022 during isothermal heating was characterized by hardness measurements and the structural evolution was studied by TEM. To distinguish the precipitate morphology at each stage of ageing, an image analysis algorithm capable of capturing orientation gradient, nearest neighbour distances, number density, shapes and size of precipitates was developed. A parametric study was performed to identify the significance of each precipitate parameter, and then the most important parameters were used to train the ANN model. The model combines the most important precipitate parameters including volume fraction, shape, size and distance between precipitates. It was found that the model is able to successfully predict the age hardening behaviour of AA6022 in both deformed and undeformed conditions.

Al Khawaldah, M., Al-Khedher, M., Al-Adwan, I., & Al Rawashdeh, A. (2014). An autonomous exploration strategy for cooperative mobile robots. Journal of Software Engineering and Applications, 7(03), 142., Frontier-based exploration methods are efficient for multi-robot exploration systems. In this pa- per, enhanced frontier-based techniques are used with team of cooperating mobile robots to ex- plore unknown environment. The aim of the exploration algorithm is to minimize the exploration time by coordinating the robots to maximize overall utility by minimizing the potential of over- lapping in information gained amongst the robots. The proposed frontier-based exploration algo- rithm is based on a new bidding function to decrease the overlap between the robots in addition to the utility and cost parameters. A special parameter depends on the future positions of the robots is also considered. The proposed algorithm has been tested with different environments. We also compared it to A* algorithm. Comparisons demonstrated the efficiency of proposed algorithm.

Al-Khedher, M. A., & Alshamasin, M. S. (2012, June). SCARA robot control using neural networks. In Intelligent and Advanced Systems (ICIAS), 2012 4th International Conference on (Vol. 1, pp. 126-130). IEEE., A SCARA industrial robot model is identified based on a 4-axis structure using Lagrangian mechanics, also the dynamic model for the electromechanical actuator and motion transmission systems is identified. A conventional PD controller is implemented and compared to neural networks control system to achieve precise position control of SCARA manipulator. The performance of the modeled system is simulated using several desired tracking motion for each joint. Neural networks control method has shown a remarkable improvement of tracking capabilities for the SCARA robot over conventional PD controller. The proposed neural network controller has the potential to accurately control real-time manipulator applications.

Al-Khedher, M. A. (2012). Hybrid GPS-GSM localization of automobile tracking system. arXiv preprint arXiv:1201.2630., An integrated GPS-GSM system is proposed to track vehicles using Google Earth application. The remote module has a GPS mounted on the moving vehicle to identify its current position, and to be transferred by GSM with other parameters acquired by the automobile's data port as an SMS to a recipient station. The received GPS coordinates are filtered using a Kalman filter to enhance the accuracy of measured position. After data processing, Google Earth application is used to view the current location and status of each vehicle. This goal of this system is to manage fleet, police automobiles distribution and car theft cautions.

Al-Khedher, M. A., Pezeshki, C., McHale, J. L., & Knorr, F. J. (2012). Empirical modeling of nanoindentation of vertically aligned carbon nanotube turfs using intelligent systems. Fullerenes, Nanotubes and Carbon Nanostructures, 20(3), 200-215., Establishing analytical models at the nanoscale to interpret the mechanical and structural properties of vertically aligned carbon nanotubes (VACNTs) is complicated due to the nonuniformity in quality of as-grown samples and the lack of an accurate procedure to evaluate structural properties of nanotubes in these samples. In this paper, we present a comparative study of empirical methodologies to investigate the correlation between indentation resistance of multi-wall carbon nanotube (MWCNT) turfs, Raman features and the morphological properties of the turf structure using adaptive neuro-fuzzy system and probabilistic neural networks. Both methodologies provide comprehensive and innovative approaches for phenomenological modeling of VACNTs morphologies, mechanical properties and Raman Spectra using intelligent-based systems.

Bisher, A. M., & Al-Khedher, M. A. (2008, May). Intelligent mapping and locomotion system for blind guidance robot. In Mechatronics and Its Applications, 2008. ISMA 2008. 5th International Symposium on (pp. 1-6). IEEE., Robot movement in its working environment requires tracking the correct path between the start point and the end point. This paper employs artificial intelligence methodologies in mapping and path search procedures for robot navigation. Robot localization was determined using dead reckoning method with a new error correction algorithm. A topology and odometry unit along with a motion control unit is designed using a PIC microcontroller that controls the motors using PWM to drive the robot.