International Journal of Engineering
Volume:36 Issue: 6, Jun 2023

In this article, the synthesis of magnetite nanostructures was successfully carried out by the sonochemical process. In this method, stoichiometric amount of iron chlorides (FeCl3.6H2O and FeCl2.4H2O), ammonia (NH3) and polyvinylpyrolidone (PVP) were used to synthesize pure Fe3O4 nanoparticles. The effect of initial sonication power of the ultrasonic device on the size and morphology of the final products as one of the effective parameters was investigated. For this, the initial power of the sonicator was evaluated at 90, 70, 50 and 30 W at 40°C. Characterization of Fe3O4 nanoparticles was done by transmission electron microscope (TEM) and X-ray powder diffraction (XRD) and its magnetic properties were investigated by vibrating sample magnetometer (VSM). Investigation of the XRD pattern after annealing showed that pure Fe3O4 phase was successfully formed during the sonochemical process. TEM images determined the size of Fe3O4 nanoparticles to be 10-50 nm. The results showed that increasing the initial power of the system reduced the particle size and improved the magnetic properties of nanoparticles.

Emotions are the accelerators of human intellect and innovation and creativity, so the ability to recognize emotions is in high demand. Real-time hardware has hurdles of Noise and hardware factors as compared to simulations. An electrocardiogram (ECG) sensor (AD8232), a temperature sensor (LM35), and a signal processing circuit is  hardware of the proposed real-time emotion identification. The RR intervals are calculated from the ECG data. Emotions prediction using machine learning makes use of RR intervals and body temperature as features. One of the four emotions (namely 1. Happy 2. Stressed 3. Neutral 4. Sad.) is visualized at the serial port of the processor by using WESAD benchmark dataset and the HRV, serial, and pickle libraries.This article's innovation factors are (1) Use of ECG for emotion detection rather than disease detection with Emotion induction method, RR interval capturing and design of RR interval GUI for real time capture of temperature and ECG (2) Display of current emotion on Arduino serial port. (3) Measurement of Class performance using F1 score, macro average, and weighted average instead of general term accuracy. (4) Use of the probability based  Navies Bayes as compared to traditional KNN, SVM, Random Forest nethods  (5) Class wise performance for example Navies Bayes' specificity or accuracy is lower than SVM's (0.96), but its recall or sensitivity is higher (0.97) vs. (0.94) for stress.In this article, we presented performance parameters in terms of interactive computations, tabular form and graphical display.

Stance detection is a recent research topic that has become an emerging paradigm  of the importance of opinion-mining. It is intended to determine the author’s views toward a specific topic or claim. Stance detection has become an important module in numerous applications such as fake news detection, argument search, claim validation, and author profiling. Despite considerable progress made in this regard in languages like English, unfortunately, we have not made good progress in some languages such as Persian, where we are confronted with a lack of datasets in this area. In this paper, two solutions are used to address this issue: 1) the use of data augmentation and 2) the application of different learning approaches (machine learning, deep learning, and transfer learning) and a meaningful combination of their outcomes. The results show that each of these solutions can not only enhance stance detection performance, but when both are combined, a very significant improvement in the results is achieved.

In this study, the effects of aging times and temperatures on the microstructure, hardness and compression strength of Al0.7CoCrFeNi high entropy alloy have been investigated. The alloy was cast in a vacuum induction melting furnace, homogenized at 1250 ˚C for 6 h; then aged at 700 to 1000˚C for 2-8 h. The as-cast structure is dendritic and includes FCC(A1) and BCC(A2, B2) phases with the hardness of 497 HV. During ageing, B2 precipitates at the grain boundaries at 700˚C and the hardness increases about 7%. The ratio of BCC to FCC phases on the basis of XRD in as-cast alloy is approximately equal which is increased by ageing at 700˚C. At 800˚C, the formation of a destructive and hard phase of σ cause to increases the hardness to 543 HV and the ratio of (A2+B2)/A1 has decreased. At 1000 ˚C, the ratio of (A2+ B2)/A1 increases, and the peak intensity of σ decreases, so that the hardness value decreases to 385 HV. The results of hot deformation test showed that the alloy at the strain rate of 10-3 s-1 and temperatures of 800, 900, 1000 and 1100˚C has a yield strength of 306, 179, 91 and 50 MPa, respectively.

Coral reefs are exposed to extinction due to the sediment blocking through coral colonies. In this condition, there is no practical solution that originates from nature. Among all aquatic animals, marine tubular sponges have marvelous mechanisms. These natural creatures can inspire the design of a device for managing sediment-flow hydrodynamics. They suck flow from body perforation and pump water and undigested materials from the top outlet. Therefore, coinciding with receiving nutrients, the flow becomes circulated. This may help the momentum transfer through the coral colonies. In the current study, a synthetic sponge by motivating the tubular sponges was designed. Synthetic sponges’ suction/pumping discharge was constant at 150 L/h. They have a body diameter of 8 and 15 cm and a height of 20 cm. The perforation area distribution changes to understand how it may influence sediment-flow hydrodynamics. The numerical modeling based on Reynolds Averaged Navier Stokes (RANS) equations and image processing technique (surface LIC) were deployed to determine the vortical flow patterns. Results confirmed that choosing the best body perforation configuration and area distribution can generate the dipole vortex. In this condition, a tornado combines with dipole and erodes the sediments to ≈ 30% near the bed. Moreover, the sediment concentration reduces to ≈ 20%  in the water column at X/D =1. In this condition, it can be observed that the emergence of specific vorticities and re-circulations develops the suspension of particles. Therefore, the synthetic sponge with precise design can be practical for enhancing the momentum transferring and preventing pollutant blockage among coral colonies.

Two centrifuge tests on a quay wall and a cantilevered retaining wall with saturated granular backfills were simulated using Finn-Byrne model. Capabilities of Finn-Byrne model in liquefaction analysis of the quay wall and the cantilevered retaining wall were evaluated. The quay wall model subjected to a horizontal acceleration time history and the cantilevered retaining wall model subjected to a horizontal and a vertical time history. The constitutive model is a linear elastic – perfectly plastic model. Hooke’s elasticity and Mohr-coulomb criterion for the yield surface were assumed for the backfill material behavior. The excess porewater pressure generation, acceleration, wall lateral displacement, lateral earth pressures, deformation pattern, and backfill settlements were monitored and compared with centrifuge tests’ results. The results showed that the adopted model is suitable for stability and displacement analyses of the quay walls and cantilevered retaining walls. However, a care should be taken when assessing the backfill settlements and dynamic earth pressure behind the wall stem. The rest of the results showed a good agreement with the centrifuge tests’ results.

Disease prediction of a human means predicting the probability of a patient’s disease after examining the combinations of the patient’s symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN), and RUSboost for the detection of diseases with the symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy model, by resolving the issue of the earlier researcher’s models. The proposed model is using the medical dataset from Kaggle and transforms the data by assigning the weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms: Random Forest, Long Short-Term Memory (LSTM), and SVM. Parallel to this, the history of the patient can be analyzed using LSTM Algorithm. SVM is then used to conclude, the possible disease. The proposed model has achieved better accuracy and reliability as compared to state-of-the-art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries.

In this paper, a new method, based on the estimation of irradiation and temperature values, was proposed for Maximum Power Point Tracking (MPPT) in photovoltaic systems. The proposed estimation method is based on a new Extended Kalman Particle Filter (EKPF). Given that the basis of the proposed method is a particle filter, firstly, the estimation is performed with high accuracy, although the target system has severe nonlinearity; secondly, there is no limitation for the probability density functions of the measurement and process noise. This method works for Gaussian and non-Gaussian noises. To show the estimation accuracy, the proposed method will be compared with the common method based on extended Kalman filter (EKF) and both methods will be evaluated due to the root means square error criterion. Due to the accurate estimation, MPPT is performed with good performance. For validation, the proposed MPPT method was compared with the EKF method and the conventional incremental conductance (InC) method. The simulations show that the efficiency is improved from 0.1% to 1% compared to the EKF, and from 0.8% to 8.65% compared to the InC method, which shows the performance of the proposed MPPT method in noisy environments.

The prices of aggregate are increasing in India due to the massive demand for natural aggregate for infrastructure development. An attempt has been made to check the feasibility of the past developed technique for developing angular-shaped light-weight fly ash coarse aggregate from three different types of fly ashes. In this study, the effects of binder content, water content and hot water bath curing temperature on the compressive strength of blocks, as well as the impact value of prepared aggregate for fly ash-binder mixes were investigated. A relationship between impact value and compressive strength has also been suggested to predict the impact value of fly ash aggregate based on the compressive strength of block. For making angular-shaped fly ash aggregate, it was found that the fly ash with CaO content of 0.71%-3.85% requires higher binder content and curing temperature than that required for fly ash with CaO content of 10.45%. The resulting lightweight aggregates from three fly ashes have a compacted structure and angular shape for good interlocking. The results of mechanical properties test showed that the aggregate also meets the criteria of Indian code specifications for structural concrete aggregate.

In many steel reinforced concrete members, steel bars are not avoidable during concrete core drilling and the presence of these steel bars have a direct impact on the results of this test. This study aims to examine the effect of steel bars presence on the test results of recycled aggregate lightweight concrete (LWC) cores. For the purpose, one lightweight concrete mix was made with a total number of 48 concrete cores were taken from a slab having the dimensions of 1 m width, 1.5 m length and 0.15m thickness. Each core has the dimensions of 90 mm in diameter and 150 mm in height. Three different sizes of steel bars (12, 16 and 20 mm) were used in six different locations (25, 45 and 65 mm) from the base of the core and (15 and 30 mm) from the center line of the core. A recycled crashed clay brick (CCB) was used as an alternative to the coarse aggregate. Compare to the density of the normal concrete (2400 kg/m3), the LWC was able to achieve nearly 20% reduction of the total weight by fully replacing of normal aggregate with CCB. It has been found that the presence of the steel increases the compressive strength of the LWC cores. This effect is more noticeable when the location of the steel bar is near to the mid-height or the centerline of the concrete core. Also, the influence of the steel bar diameter has increased by increasing the size of the steel bar.

A Hetero Dielectric Tunnel field effect transistor with the spacer on both sides of the gate is proposed in this paper. The performance and characteristics of Hetero Dielectric Tunnel field effect transistor using the ATLAS Technology Computer-Aided Design in 5nm regime were analyzed. The band-to-band tunneling leakage current will be reduced by introducing heterojunction and hetero dielectric spacer material in the proposed structure. In Hetero Dielectric Tunnel field effect transistor, double metal gate and high-k dielectric spacer improves high on the current and subthreshold swing. The high-k dielectric Hafnium oxide spacer is placed on both sides of the source and drains to import the tunneling mechanism. The proposed device in the 5nm node has improved DC characteristics such as a High ON-state current of 1.68 x 10-5 Amp & OFF-state Current reduced from 7. 83x 10-11 Amp to 5.13 x 10-12 Amp and ION / IOFF ratio has increased from 3.22 x 105 to 3.27 x 10  compared to conventional dual gate Tunnel field effect transistor. Therefore, this device is suitable for low power applications

Although quartz crystal resonators (QCR) have been used for airborne detection of particles and viruses, they suffer from various limitations, such as low sensitivity compared to other devices. Therefore, it is necessary to develop a new device capable of achieving high sensitivity, which can be used for practical airborne detections. The current study reports a comprehensive parametric theoretical model for analyzing the response of ultra-sensitive pillar-enhanced QCR (QCR-P) for airborne detection of nanoparticles. The electromechanical model comprised an equivalent circuit integrated with pillars containing nanoparticles. It was shown that pillar height and particle radius play a critical role in the response of QCR-P devices. The study revealed that selecting the optimal pillar height can lead to a significant frequency shift depending on the nanoparticle radius and pillar height, while it is independent of particle mass density. These results underscore the potential of utilizing pillars to substantially enhance the sensitivity of conventional QCR up to 140 times in the airborne detection of nanoparticles. These findings can be utilized to design optimum pillar heights to achieve maximum sensitivity in the airborne detection of nanoparticles and proteins, thereby enabling the adoption of ultra-sensitive pillar-enhanced quartz crystal resonators for practical airborne applications.

Piezoelectric beams are widely used in micro-electromechanical systems. At the microscale, the influence of the size effect on a piezoelectric beam cannot be ignored. In this paper, higher-order elasticity theories are considered to predict the behaviors of piezoelectric micro-structures and a size-dependent dynamic model of a laminated piezoelectric microbeam is established. The governing equations for the laminated piezoelectric microbeam are derived using the variational principle. The natural frequencies of piezoelectric microbeams are obtained by size-dependent dynamic models. The results reveal that the size effect can enhance the structural stiffness at the microscale. The natural frequency obtained by using the classical model is smaller than that obtained using the size-dependent model. Compared with the modified couple stress model, the modified couple stress model underestimates the size-dependent response. Thus, the modified couple stress model is a simplification of the modified strain gradient model. The influence of beam thickness on the natural frequency is also discussed. With increasing the thickness, the natural frequency of the size-dependent models gradually approaches the result of the classical model. If the value of h/l is greater than 15, the influence of the size effect can be neglected. Additionally, the relative thickness can influence the natural frequency, and if the relative thickness is greater than 5 or less than −5, the bilayer beam can be simplified to a single-layer beam.

In this article, equipment overhaul is considered in a multi-stage flow shop scheduling problem. In this problem, the equipments are disassembled in the first stage, overhaul and repairs are done on the equipment in parallel workshops in the second stage, and the assembly operation is done in parallel workshops in the third stage. Considering a three-stage overhaul with parallel machines in the second and third stages is new in the overhaul industry. The sequence of equipment processing is determined in the first stage, as well as the allocation and sequence of equipment in the second and third stages should be done in such a way that the total completion time of jobs is minimized. Unlike most articles, the sequence of processing jobs is not the same in all stages and changes with the use of decoding. For the next innovation: in order to solve the problem, a new mathematical model is presented. Two new improved algorithms, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are presented to solve the problem in large dimensions. By using the shortest processing time (SPT) heuristic, these two algorithm have been improved and Hybrid GA (HGA) and Hybrid PSO (HPSO) algorithms have been presented. In order to achieve better results with the current conditions, the parameters setting is done by one-way analysis of variance (ANOVA). Finally, it is possible to improve the performance of the equipment by applying the discussed issues.

Globalization and increased virtual communication have posed many challenges to high-tech companies; hence, such companies are sparing efforts to detect the best technologies in this field to solve new and emerging challenges addressing traffic load, communication system security, and infrastructure optimization. Telecommunications companies deal with a highly dynamic and uncertain environment, where their relevant technologies are changing and developing at an increasing speed. Regarding such an environment in telecommunications companies, the present study aimed to present an efficient model for formulating technology strategies for these companies. The proposed model is a hybrid method of attractiveness-capability matrix and, multi-criteria decision-making approaches in an uncertain and dynamic environment. The model provides the attractiveness-capability evaluation factors and criteria regarding the requirements of dynamic and uncertain environments in these companies. This approach provides a more accurate picture of the rapidly changing technologies in formulating technology strategy. The model also used the fuzzy TOPSIS to control the uncertainty aroused by widespread emerging technologies in such organizations. The proposed model is implemented concerning the requirements of the Mobile Communications Company of Iran (MCI), and its results are discussed in detail.

With the emergence of virtual social networks, predicting social events such as elections using social network data has attracted the attention of researchers. In this paper, three indicators for election prediction have been proposed. First, the tweets are grouped based on a specific time window. Next, the indicator values for each candidate in each time window are calculated based on the sentiment scores and re-tweet numbers. In fact, the indicators are calculated based on the ratio of features related to positive to negative sentiments. Finally, using the aging estimation method, the indicator values for each party on the election date are predicted. The party with larger predicted indicator values will be considered as the winner. Investigations into Twitter data related to 2016 and 2020 US presidential elections on a four-month time span indicate that the indicator values and elections can be predicted with a high accuracy.

Electrohydraulic actuation systems offer definative position control and an energy-efficient solution. Such systems are widely used in mobile machinery, robotics, and various stationary systems. Achieving good control of actuator position of the variable displacement electrohydraulic actuation system by an open loop control is the objective of this study. For square position (reference position) control, amplitude is taken as 0.1 m, at 0.05, 0.15 and 0.25 Hertz of frequency. Square position control is accomplished with LabVIEW algorithm through the application of compact RIO controller having input and output module. Appropriate control of voltage supply is obtained, when response position and reference position show appropiate accuracy. A higher Pearson’s correlation coefficient near to 1 and lower the Mean absolute error, Mean deviation of error and standard deviation of error represent the best response position. It is observed that highest value of correlation coefficient achieved at 0.05 Hertz of frequency for response R3.  At a lower frequency, square position control is better with higher correlation coefficient and lowest values of errors.

The hardness and corrosion resistance of nickel (Ni) deposit on a substrate could be reached by controlling electrolyte temperature during deposition. In this research, the electrodeposition of Ni at various temperatures of electrolytes was performed. Electrodeposited Ni films using an optical digital camera, X-ray diffraction (XRD), scanning electron microscope with energy-dispersive x-ray spectroscopy (SEM-EDS), microhardness test, and potentiostat were investigated. The bright deposit occurred at 25 °C; an increase in the temperature to 40 °C leads to a change of color into semi-bright. Shifting to a higher temperature would increase the deposition rate, cathodic current efficiency, grain size, and oxygen content. The X-ray reflections in the planes (111), (200), and (220) correspond to as the Ni phase with a face center cubic (FCC) crystal structure. Decreasing crystallite size and micro-strain promoted to reach high hardness. Increasing the corrosion current density implies decreasing polarization resistance. The sample at the lowest electrolyte temperature has a better hardness, and the sample formed at 25 °C sulfate solution had less corrosion rate.