Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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 Arabian Journal for Science and EngineeringJournal Prestige (SJR): 0.303 Citation Impact (citeScore): 1Number of Followers: 1      Hybrid journal (It can contain Open Access articles) ISSN (Print) 2193-567X - ISSN (Online) 2191-4281 Published by Springer-Verlag  [2469 journals]
• Machine Learning k-Means Cluster Support S-FSCV Algorithm to Estimate
Integrated Network Operating State

Abstract: Abstract Functional operation of integrated network during circumstances like disturbance and improper operation has to be addressed properly to improve the integrated network performance and safety. One of the situations is a island state and requires to secure human beings and device components that are working in integrated network. This paper presents a k-means clustering machine learning (kmc-ML) to obtain upper limit (UL) and lower limit (LL) parameters for continuous change in fuzzy variable. Machine learning (ML) will support to change the position of fuzzy set towards universe of discourse (UoD) either in LL or UL, and hence, mismatch observation can make for better estimation of integrated network state. Identification of state at every abnormal mode (AbM) operation will depend on UoD set to achieve effective outcome. ML-based soft fuzzy set control vector (S-FSCV) decides the integrated network state, i.e. island state or not.
PubDate: 2022-10-04

• ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans
Towards an Automated Clinical Decision Support System

Abstract: Abstract The emergence of deep learning has paved to solve many problems in the real world. COVID-19 pandemic, since the late 2019, has been affecting lives of people across the globe. Chest CT scan images are used to detect it and know its severity in patients. The problem with many existing solutions in COVID-19 detection using CT scan images is that inability to detect the infection when it is in initial stages. As the infection can exist on varied scales, there is need for more comprehensive approach that can ascertain the disease at all scales. Towards this end, we proposed a deep learning-based framework known as Automated Deep Learning-based COVID-19 Detection Framework (ADL-CDF). It does not need a human medical expert in diagnosis as it is capable of detecting automatically. The framework is assisted by two algorithms that involve image processing and deep learning. The first algorithm known as Region of Interest (ROI)-based Image Filtering (ROI-IF) which analyses given input CT scan images of a patient and discards the ones where ROI is missing. This algorithm minimizes time taken for processing besides reducing false positive rate. The second algorithm is known as Multi-Scale Feature Selection algorithm that fits into the deep learning framework’s pipeline to leverage detection performance of the ADL-CDF. The proposed framework is evaluated against ResNet50V2 and Xception. Our empirical study revealed that our model outperforms the state of the art.
PubDate: 2022-10-04

• The Effect of the Basalt Fiber on Mechanical Properties and Corrosion
Durability in Concrete

Abstract: Abstract Cement-based materials have low tensile strength and naturel or synthetic-based fibers can be added to the concrete in order to eliminate this adverse effect. In this study, it was aimed to investigate the effects of basalt fiber on the mechanical properties of concrete and the corrosion performance of reinforcement concrete specimens. Control mix, 1, 2, and 3% basalt fiber additive concrete specimens were produced in the study. Concrete specimens were cured in a standard water curing environment for mechanical, physical, and durability tests. Unit weight, ultrasound transition time, splitting-tensile, compression, flexure, electrical resistivity, and rapid chloride permeability tests were performed on concrete specimens. Furthermore, accelerated corrosion and half-cell potential experiments were made on the reinforcement concrete specimens. The changes occurring under the effect of corrosion and the changes in the mechanical and physical properties of the control and basalt fiber concrete series were examined. A significant increase was observed in bending and splitting tensile strengths compared to the reference concrete by using basalt fibers in the concrete mixture. The best results in corrosion tests were seen in 2% basalt fiber reinforced concrete specimens. It has been observed that concretes by using basalt fiber are more resistant to corrosion than control specimens.
PubDate: 2022-10-04

• EEG Signals to Digit Classification Using Deep Learning-Based
One-Dimensional Convolutional Neural Network

Abstract: Abstract The communication between the human brain and the external devices can be established using Electroencephalograms (EEG)-based Brain–Computer Interface by converting the neural activities of the brain into electric signals. The EEG signals were isolated into an energy–frequency–time spectrum with Hilbert Huang transform that was used by the Deep Learning (DL)-based model to learn discriminative spectro-temporal patterns of the raw EEG signals of ten digits. This paper has two major contributions: first, create a novel dataset known as BrainDigiData of EEG signals of ten digits from (0–9) using a multi-channel EEG device. Second to propose a DL-based one-dimensional Convolutional neural network model BrainDigiCNN to classify the BrainDigiData of EEG signals of digits. The publicly available Mind Big Dataset (MBD) of digits was also used to evaluate the performance of the proposed model. The research done in this paper showed that the band-wise analysis of EEG signals in a complex scenario resulted in improved results as compared to the scenario used in the previously existing work for digit classification using EEG signals. The proposed BrainDigiCNN model achieved the highest average accuracy of 96.99%. The average classification accuracy of 98.27% was achieved for the MBD dataset of 14 channel device EMOTIV EPOC+ and 89.62% on the MBD dataset of 5-channel EMOTIV Insight. The statistical analysis of the proposed model on traditional Machine Learning (ML) classifiers using paired t-test resulted in a p-value less than 0.05 which shows the significant difference between the proposed model and ML classifiers.
PubDate: 2022-10-04

• Stabilization/Solidification of Petroleum Oil-Contaminated Soil using
Different Stabilizers to Deliver a Pavement Subbase Material

Abstract: Abstract The present study aims to explore the possibility of reclaiming petroleum oil-contaminated soil for its use as a pavement subbase material after the stabilization/solidification treatment. The contaminated soil was characterized in terms of its geotechnical and chemical parameters using samples collected from a beach site that was contaminated by petroleum oil spillage. Two industrial solid wastes: cement kiln dust and limestone powder along with ordinary Portland cement were used as alternative stabilizers. The performance of the selected stabilization/solidification treatment strategies was evaluated using the results of mechanical and chemical tests carried out on the specimens of soil after the treatment. Based on the evaluation of test results, it was found that a combination of cement kiln dust and Portland cement (30% and 1.46%) and (30% and 4.12%) can be optimally used as stabilizer for stabilization/solidification treatment of the contaminated soil to deliver a subbase material for rigid and flexible pavements, respectively. The toxicity characteristics leaching procedure analysis revealed that the heavy metals concentrations in all sets of the treated soils were significantly lowered and were found to be within the USEPA permissible limits.
PubDate: 2022-10-03

• Paracetamol and Ibuprofen Removal from Aqueous Phase Using a
Ceramic-Derived Activated Carbon

Abstract: Abstract Emerging pollutants, including pharmaceuticals and personal care products, have been detected in surface and groundwaters. The adsorption of paracetamol and ibuprofen, two widespread drugs, has been studied in aqueous medium, using a ceramic-derived carbon (CeDC) and a commercial activated carbon (CoAC). CeDC yielded a BET surface area of 895 m2 g−1, a bimodal pore size distribution (13.2 and 35 nm) and a total pore volume of 1.99 cm3 g−1. CoAC had an approximate surface area of 1000 m2 g−1, a homogeneous pore size distribution and a total pore volume of 0.42 cm3 g−1. Kinetic and equilibrium tests were carried out in batch systems to study the materials’ sorption performances. The intraparticle diffusion model best fitted the experimental kinetic data. The maximum ibuprofen sorption capacities were 120 mg g−1 and 133 mg g−1 for CoAC and CeDC, respectively, whereas no major differences on the maximum paracetamol sorption capacities (qm) were observed among the sorbents (150–159 mg g−1). Therefore, CeDC, synthesized easily from a ceramic composite, improved time and sorption capacity of paracetamol and ibuprofen compared to the commercial activated carbon, indicating the potential of the developed carbon as an emerging pollutant sorbent material.
PubDate: 2022-10-03

• The EGM Model and the Winner-Takes-All (WTA) Mechanism for a
Memristor-Based Neural Network

Abstract: Abstract Due to the continuous growth of hardware neuromorphic systems, the need for high-speed, low-power, and energy-efficient computer architectures is increasing. Memristors-based neural networks are a promising solution for low-power neuromorphic systems. Spiking neural networks (SNNs) have been considered the optimal hardware implementation of these systems. Previous studies of SNNs rely on complex circuit to implement in situ bio-plausible STDP learning using memristors, which is computationally challenging. In this paper, we propose an SNN that performs both in situ learning and inference using a new efficient programming technique. Our interest lies in applying the winner-takes-all (WTA) mechanism in the SNN architecture used with recurrently connected neurons, allowing real-time processing of patterns. We provide a programming circuit that enables better weight modulation with less power consumption and less space occupation, using a generalized enhanced memristor model (EGM). The proposed programming circuit is connected to leaky integrate-and-fire (LIF) neurons included in a crossbar architecture to perform recognition task. The simulation results not only prove the correctness of the design, but also offer an efficient implementation in terms of area, energy, accuracy, as well as the ability to classify 40,000 images per second.
PubDate: 2022-10-03

• Friction Coefficients Calculation via Surface Roughness Characterization
for Tight Sedimentary Rocks

Abstract: Abstract In the drilling and stimulation operations of deep/ultra-deep oil and gas wells, high in situ stress conditions may increase the occurrence probability of rock shear failures. According to Mole-Coulomb laws, the friction coefficient becomes more significant in evaluating borehole instability, faults/natural fractures activation and hydraulic-natural fractures intersection scenarios. This paper proposes a multiscale model to calculate the rock friction coefficients based on the surface properties at different scales. The key parameters of surface properties are obtained from three-dimensional laser scan of friction planes, and the results are verified by the direct shearing and triaxial compression strength tests. For the flat or new-cutting surfaces, the computed basic friction coefficients range of 0.231–0.509, fitting well with the tested values of 0.303–0.437. It also shows that the basic friction coefficients grow in the order of shale, carbonate and tight sandstone, positively associated with the size of the rock sedimentary particles. For the roughness surfaces, the coefficients are computed based on the former basic friction values and the surface asperity dip angles distribution, and the values located in a wide range of 0.541–1.113, also matched well with the measured friction coefficients via direct shearing tests. When the normal stress increase, the rock friction coefficients generally decline, and the values of some shale and dolomite samples can decrease to 0.2, due to the existence of beddings or fracture fillings. The above outcomes may provide useful insights for wellbore instability assessment and hydraulic fracturing optimization.
PubDate: 2022-10-03

• Study on Compressive Strength and Microstructure of Slag–Calcium Carbide
Residue Solidified Mud Under Wetting–Drying Cycles

Abstract: Abstract The high water content and viscosity make construction mud extremely difficult to manage, causing land occupation and environmental pollution problems. This research aims to investigate the durability of ground granulated blast furnace slag (S) and calcium carbide residue (CCR), two industrial by-products, solidified waste mud under wetting–drying cycles, and assess its serviceability as subgrade material. The tests of unconfined compressive strength (UCS), mass loss, and scanning electron microscope (SEM) were carried out to analyze mechanical and microstructural characterization of solidified mud. The optimal binder content and S:CCR ratio in terms of UCS values were found at 25% and 8:2, respectively. The maximum compressive strength at 7 days reached 3.73 MPa. There was a logarithm function between the development of compressive strength and curing time. The hydration reaction still occurred during the wetting–drying cycles; thus, UCS value first fluctuated and then gradually decreased with the progression of wetting–drying cycles. SEM images showed that the effect of the wetting–drying cycles on the solidified mud was manifested by the increase of pore and the decrease of hydration products. It was found that S-CCR is an environmentally friendly binder for bored pile mud solidification, which facilitates the reduction of a large number of waste by-products and waste mud from landfills.
PubDate: 2022-10-03

• Electroabsorption Modulator-Based Relay for the Transmission of
DPSK-Modulated Signals over the Free Space Optical Link

Abstract: Abstract In this paper, an electroabsorption modulator-based relay is employed for the transmission of on-off keying (OOK) and differential phase shift keying (DPSK) signals over a free space optical (FSO) communication link. The FSO link is based upon the Gamma-Gamma ( $$\varGamma$$ - $$\varGamma$$ ) channel model considering both medium and strong atmospheric turbulence conditions. 10 Gbps modulated OOK and DPSK signals are transmitted over the FSO link by employing all-optical amplify and forward (AOAF) and all-optical regenerate and forward (AORF) relay-assisted techniques. The bit-error-rate results are analyzed with respect to received optical power for the proposed model. It is observed that AORF relay-assisted OOK is performing better than AOAF at high data rates under medium atmospheric turbulence, whereas for DPSK-modulated signals, the performance is almost the same for both AORF and AOAF relay-based FSO links. The AORF-based relay for the transmission of OOK- and DPSK-modulated signals show significant improvement under strong atmospheric turbulence compared to the AOAF-based relay link. Furthermore, under multiple relay scenario for DPSK signal, AOAF results degraded while AORF remains almost the same under strong atmospheric turbulence.
PubDate: 2022-10-03

• Structural Properties and Degradation Efficiency Photocatalyst-based
Composite Titanium Dioxide/Activated Carbon by Charge Trap System for
Groundwater Reach Phenol Treatment

Abstract: Abstract High recombination of the photoexcited electron–hole pairs leading to a drop in the photocatalytic performance. The pore of activated carbon (AC) can be act as a trap of the charge of electron/hole (e/h) which can suppress recombination. The objective in this study maximizing function of the pore of AC for create composite combine with TiO2 that can act in visible light. In the pore, the reaction occurs between the charge and the H2O or O2 produces OH* radicals. These radicals entering the chemical site of pollution to break up the bonding with the final product are harmless. The prepared composite TiO2/activated carbon (TAC) by wet impregnation method were characterized by X-ray diffraction, Fourier-transform infrared, and ultraviolet–visible spectroscopy. Composite TAC with ratio 0.5:1.5, 1:1, 1.5:0.5 shows degradation 94.06%, 94.91%, and 88.98%, respectively, for only 45 min irradiation, indicated that the pore successfully suppressing recombination of the charge. We have performed good efficiency for five adsorption–desorption cycles of TAC. The TAC 1:1 ratio shows the best photocatalyst performance which degraded 94.91% for 45 min irradiation. The composite TAC shows high potential in suppressing recombination of charge, means that, effective and efficient ground wastewater treatment materials in future for improvement human access to the clean water.
PubDate: 2022-10-03

• Impact Behaviour of Nano-Hybrid (Carbon/Glass) Fibre Metal Laminates: An
Experimental Study

Abstract: Abstract Fibre metal laminates (FMLs) can be widely used in structural (automobile, aerospace industries, etc.) applications where performance enhancement (FMLs provide good impact and fatigue resistance, etc.) and cost effectiveness play a vital role. Hybridization (glass/carbon) of FMLs may result in performance enhancement and improved longevity when subjected to various loading conditions. Therefore, in the present study Charpy test was carried out using 300 J Charpy hammer according to ASTM E-23 in flat-wise orientation to understand the influence of 1wt% of alumina (Al2O3), zirconium oxide (ZrO2), titanium oxide (TiO2) nanoparticles reinforcement on impact response of hybrid FMLs. Mean particle size of Al2O3(5.1 nm), ZrO2(25.3 nm), TiO2(63.2 nm) was estimated with the help of X-ray diffraction analysis using Scherer equation. It is observed from the results that hybridization of GLARE (glass laminate aluminium-reinforced epoxy) enhanced the impact strength by 2.4%, but degradation of 0.9%, 1.4%, 1.8% is noticed in Al2O3-, ZrO2-, TiO2-reinforced hybrid FMLs, respectively. Maximum impact strength was identified in Al2O3-reinforced hybrid FMLs when compared to other nano-hybrid FMLs. Nanoparticles dispersion in the matrix, interface region and fractured surface morphology were analysed using scanning electron microscope. However, it is summarized that the impact response of nano-hybrid FMLs strongly depends on the type of fibre and nanoparticles size introduced in the polymer matrix.
PubDate: 2022-10-03

• Least Cost Generation Expansion Planning considering Renewable Energy
Resources Using Sine Cosine Algorithm

Abstract: Abstract Ever-growing development in technology and rising energy demand playing a vital role in seeking the attention of power planners to design such systems which fulfill the electrical demand by minimizing the generation cost through the optimum strategies. Nowadays, Generation Expansion Planning (GEP) is very important to make an adequate energy management system, and it is a highly nonlinear, constrained optimization problem and has been solved in a mathematical programming environment. However, due to the complexity of the GEP problem and the limitations of mathematical programming techniques, the meta-heuristic method has the capability to handle the GEP problem. The Sine Cosine Algorithm (SCA) is a novel population-based algorithm that can handle complex constrained optimization problems. In this research, the GEP problem is mapped into SCA in the presence of Renewable Energy Resources (RER) subject to technical and environmental constraints. The developed SCA-PFA is tested for validation, and case studies are solved using standard test systems. The obtained results are better in terms of cost and computational time when compared with results available in the literature. This shows the promise of the approach.
PubDate: 2022-10-03

• Radiation Shielding Properties of ZnO and Other Glass Modifier Oxides:
BaO, MgO, Na2O, and TiO2, Using EpiXS Software

Abstract: Abstract The study investigates the photon shielding characteristics of ZnO and other glass modifier oxides such as BaO, MgO, Na2O, and TiO2 as a viable additive for anti-radiation glass systems using the EPICS2017 library interpolated by EpiXS software. Their total cross sections and the linear attenuation coefficients are large at low energies but become smaller as photon energy increases. The computed mass attenuation coefficients of ZnO at gamma-ray energies of 356, 662, 1173, and 1332 keV are 0.1017, 0.0739, 0.0554, and 0.0519, respectively. The mean free path of ZnO is longer than that of BaO, but shorter than that of MgO, Na2O, and TiO2. The glass modifiers’ half-value and tenth-value layers are in the sequence BaO < ZnO < TiO2 < MgO < Na2O. For energies of 60, 364, 662, and 1332 keV, the HVL values of ZnO, or the thickness required to reduce the intensity of the incident photon by half, are 0.083, 1.23, 1.67, and 2.38 cm, respectively. Depending on the penetration depth, the highest values for both energy absorption (EABF) and exposure buildup factors were observed in the 400–500 keV energy range. ZnO has the lowest EABF for X-ray energy and the second lowest for 101–120 keV gamma energy. The Zeff of the five oxides at X-ray energy is substantially higher than their Zeff at gamma energy. ZnO is only second to BaO in terms of radiation shielding efficiency, making it a better additive material for various anti-radiation glass systems.
PubDate: 2022-10-03

• Electrocoagulated Batik Sludge Adsorbent for the Adsorption of Acid Red 18
Dye in an Aqueous Solution and its Regeneration Capability

PubDate: 2022-10-03

• Automating Global Threat-Maps Generation via Advancements of News Sensors
and AI

Abstract: Abstract Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countries, and all possible major social media sites. The study revealed 11,668 troubled locations classified successfully with outstanding precision, recall, and F1-score, all evaluated in ubiquitous environment covering mobile, tablet, desktop, and cloud platforms. The system generated interesting global threat maps for robust scenario set of $$3.71 \times {10}^{29}$$ , to be reported as original fully autonomous remote sensing application of this kind. The research discloses attractive news and global threat-maps with trusted overall classification accuracy.
PubDate: 2022-10-02

• The Numerical Simulation of Disturbed Region Corbels Containing
Sustainable Concrete

Abstract: Abstract The concept of sustainable concrete is aimed at using alternative materials instead of natural resources in concrete production. In recent years, many experimental and numerical studies have been carried out to show that sustainable concrete is an alternative to conventional concrete. In this study, a detailed numerical simulation of reinforced concrete corbels produced with sustainable concrete has been conducted comprehensively. According to the author's best knowledge, the novelty of this study: (1) this is the first time that comprehensive numerical simulation results of sustainable concrete corbels are reported in detail, (2) investigation of the applicability of existing numerical models in sustainable concrete corbels, (3) contribution the realistic simulation of the structural behavior of sustainable concrete corbels. It should also be highlighted that the variables included in the study were the recycled concrete aggregate ratio, the shear span-to-effective depth ratio, the tapering ratio, the angle between strut and tie element, and the amount of reinforcement. Furthermore, the numerical simulation results obtained by the finite element and truss analogy methods were extensively compared with the experimental results. In addition, a structural optimization design was executed on a short corbel to check the suitability of the selected truss analogy model. Based on the study, the simulation results obtained from the finite element method agreed fairly well with the experimental results. Furthermore, the truss analogy method provided relatively accurate and conservative results compared with the experimental results depending on the span-to-depth ratio. Besides, some recommendations for designing sustainable concrete corbels were also discussed.
PubDate: 2022-10-01

• Effect of Crude Oil Properties on the Interfacial Tension of Crude Oil/CO2
Under HPHT Conditions

Abstract: Abstract Injection of carbon dioxide (CO2) is an effective method to improve oil recovery from hydrocarbon reservoirs. The interfacial tension (IFT) between CO2 and crude oil is a critical parameter during CO2 flooding. IFT of different crude oils in compressed carbon dioxide was measured at different pressures and temperatures. The minimum miscibility and first-contact miscibility pressures were calculated using vanishing IFT analysis. The minimum miscibility pressure is significantly affected by temperature with less dependency on the crude oil composition. The first-contact miscibility pressure was found to be more sensitive to temperature and crude composition. Measured IFT values at different pressure and temperature conditions were correlated to crude oil density and acid number using a wide range of crude gravity and total acid number. These properties are simple and less costly to measure but they directly reflect the effect of crude oil composition on IFT as a function of pressure. This new approach of correlating IFT between CO2 and crude oil under reservoir conditions will improve the simulation of CO2 EOR projects under reservoir conditions.
PubDate: 2022-10-01

PubDate: 2022-09-30

• Port Isolation and Array Spacing of a $$5 \times 5$$ Rotman Lens Frequency
Decomposer

Abstract: Abstract Dynamic frequency allocation is a critical aspect for the fifth-generation (5G) wireless technology. With strong movement toward millimeter-wave frequencies, analog decomposition of frequency sub-bands is of great importance. In this article, we deeply investigate some of the characteristics of a Rotman lens frequency decomposer in terms of bandwidth, channel isolation, and physical size. This study shows that a compromise between flexibility, optimality, and physical size is needed for Rotman lens frequency decomposer, which in turn is motivated (and weighted) by the required application.
PubDate: 2022-09-30

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