International Journal of Engineering
Volume:37 Issue: 4, Apr 2024

Composite materials are the most important materials in materials science and engineering, which contain two or more materials. In materials engineering, the scanning electron microscopy (SEM) technique is an approach to measure the material''s particle size. A new procedure was used instead of SEM is called Artificial Intelligence (AI). Artificial Intelligence (AI) is an interdisciplinary science and branch of computer science that involves solving problems that require human intelligence and capabilities. The computer vision is a subfield of AI, which uses some algorithms to detect the details of images by using computer called image processing. Detecting the particles and measuring the size of materials scanned by SEM is an essential task that helps to describe their feature, traditionally, the size is calculated manually by adding mesh to an SEM image or by drawing a diagonal line in an arbitrary particle. In this paper, a new model based on Artificial Intelligence (AI) is proposed using computer vision to analyze the size of all particles. This model is used to detect the particle size of additives in composite materials like graphene flakes and measure the size of them depending on the reference size fixed on the scanning electron microscope (SEM). The model was used based on the Open-source Computer Vision (OpenCV) library, utilizing multi-layers of canny edge detection, Sobel filter, Brightness and contrast algorithms, using Python 3. The results have achieved very satisfied indication with a very low process time = 0.2 mili-seconds.

In this work, a Symmetrical Dual Gate Tunnel Field Effect Transistor (SDGTFET) is proposed with gate dielectric materials in 10nm technology. The electrical performance parameters of this proposed device are investigated using technology computer aided design (TCAD) simulator. The new SDGTFET employing with high-k dielectric material such as hafnium oxide (HfO2) and interfacial layer (IL). The 2nm HfO2 with 30 dielectric constant is used between the interfacial layer and the metal gate on both sides of the device. The variation of the drain current with the varying of gate length, effective gate materials and effective oxide layer thickness of the device is evaluated in this work. By optimizing the proposed device with gate dielectric material the on current gets ∼4.2 times enhanced and the averaged subthreshold swing (SSavg) becomes reduced from 90.2 mV/dec to 53.8 mV/dec. Therefore, the SDGTFET structure has better performance than single material and double material TFET and shows a lower ambipolar current and a better on current to off current ratio.

The need for practical and economical solutions to increase the stability of embankments and reduce their settlement is a significant issues in geotechnical engineering. An effective approach to improve the overall performance of embankment systems is using structural elements such as piles. In recent years the use of helical piles has gained consideration due to their proper performance under compressive and tensile loads, quick and easy installation, and elimination of concreting problems. This study investigates the performance of embankments supported by helical piles through a 3D numerical study using the Abaqus software. The validation was performed according to the experimental and field data provided by other researchers. Then, 3D numerical models were developed to investigate the effects of pile caps, the ratio of helix diameter to shaft diameter, the number of helices, and the optimum spacing of helices. The finite element modeling results indicated that increasing the number of helices and changing their spacing had no significant impact on controlling the settlement. It was also found that the load transfer mechanism parameters had a direct relationship with the helix dimensions and shaft diameter. Adding helices to piles increased their bearing capacity, improving parameters of the load transfer mechanism, such that the arching in a pile with three helices decreased by 40% compared to the one with one helix. The results also revealed that on average, 80% of the load imposed on a pile was sustained by the shaft, and helices had a smaller effect on the settlement of pile-supported embankments.

Today, focusing on gaining a competitive edge in the global business market lies in enhancing supply chain performance. This study endeavors to examine the attainment of Returns To Scale (RTS) within a four-level green supply chain framework through the application of Data Envelopment Analysis (DEA). To achieve this objective, the Banker, Charnes, and Cooper (BCC) multiplicative model are employed to determine the return to scale at each level within the complete supply chain, ultimately culminating in estimating the overall return to scale for the entire supply chain. The statistical population for this applied research, aligned with its objectives, comprises 42 cement companies. The assessment of returns to scale in these companies, featuring a four-level chain encompassing suppliers, manufacturers, distributors, and customers, is measured. The outcomes of the model reveal that return to scale remains constant in 28 companies, exhibits a decreasing trend in 14 companies, and conversely demonstrates an increasing trajectory in 2 companies within the supplying sector, one company in manufacturing, and 14 companies in distribution. The findings underscore that an increasing return to scale renders the expansion of Decision-Making Units (DMUs) economically viable. Conversely, a diminishing return to scale suggests a rational limitation of DMUs.

This study focuses on evaluating the effectiveness of various cell concentrations of bacillus bacteria in mending cracks within recycled concrete containing coarse aggregates. In this investigation, the introduction of bacillus bacterial sustainable concrete as a solution for addressing crack repairs. This innovative concrete formulation not only provides environmentally friendly alternatives but also offers economic benefits. This research involves the incorporation of coarse aggregates into the concrete mix, along with the partial substitution of cement by Fly ash and Ground Granulated Blast Furnace Slag (GGBS), each accounting for 20% of the mix. The coarse aggregates consist of Recycled Concrete Aggregate (RCA) in varying proportions: 0%, 50%, and 100%. Additionally, Bacillus licheniformis was used at concentrations of 103, 105, and 107 cells/mL, respectively. The findings indicate a positive correlation between the healing percentage of cracks, as measured by Ultrasonic Pulse Velocity (UPV), and the concentration of bacteria. Furthermore, it is observed that recycled aggregates possess inherent pores that allow for water absorption through these pores. Therefore, RCA is subjected to a 24-hour water immersion process before its incorporation into the concrete mix. While the compressive strength of the concrete remains consistent between RCA 0% and RCA 50%, it decreases significantly at RCA 100%. However, the performance of the bacteria exhibits proportionality to cell concentration. Notably, the effectiveness of the bacteria remains consistent regardless of changes in RCA proportions. This study underscores the promising potential of Bacillus bacteria in enhancing the durability and sustainability of concrete structures, with particular relevance to RCC applications.

SQL injection (SQLi) is one of the most common attacks against database servers and has the potential to threaten server services by utilizing SQL commands to change, delete, or falsify data. In this study, researchers tested SQLi attacks against websites using a number of tools, including Whois, SSL Scan, Nmap, Open Web Application Security Project (OWASP) Zap, and SQL Map. Then, researchers identified SQLi vulnerabilities on the tested web server. Next, researchers developed and implemented mitigation measures to protect the website from SQLi attacks. Test results using OWASP Zap identified 14 vulnerabilities, with five of them at a medium level of 35%, seven at a low level of 50%, and two at an informational level of 14%. Meanwhile, testing using SQL Map succeeded in gaining access to the database and username on the web server. The next step in this research is to provide recommendations for installing a firewall on the website as a mitigation measure to reduce the risk of SQLi attacks. The main contribution of this research is the development of a structured methodology to identify and address SQLi vulnerabilities in web servers, which play an important role in maintaining data security and integrity in a rapidly evolving online environment.

Recently, the difference in the most effective competencies is considered the main competitive factor in organizations. To this end, organizations seek to improve a number of their functional capabilities, expertise, and capacities to enhance their operational area. Therefore, when an organization focuses on the quality of its services or products, it is trying to improve maintainability to gain a competitive advantage. In this study, a closed-loop, multi-objective, multi-level, multi-commodity, and multi-period mathematical model for a supply chain with producer, and distributor components is presented to locate and allocate items. The presented model can control environmental, economic, and social factors along the chain. One of the most important and unique aspects of the current study is considering different scenarios in the closed-loop supply chain (CLSC) so that the quality of the produced and transported products is paid attention to according to perishability. In addition, to control environmental effects, the model can minimize total CO2 emissions. The problem is solved on small, medium, and large scales using Epsilon Constraint and NSGA-II methods. According to the obtained results, the flow according to the boom scenario is more than the stagnation scenario. Finally, according to the sensitivity analysis, the number of centers established increases with an increase in demand. The results show that the non-dominated sorting genetic algorithm (NSGA-II) model can predict the behavior of the model well in the long term. For this purpose, Mean ideal distance (MID) index, has been used for evaluation of calculation. the value of standard MID is equal to 6.56 that shows the model accuracy is adequate.

Computer vision has extensive applications in various sports domains, and cricket, a complex game with different event types, is no exception. Recognizing umpire signals during cricket matches is essential for fair and accurate decision-making in gameplay. This paper presents the Cricket Umpire Action Video dataset (CUAVd), a novel dataset designed for detecting umpire postures in cricket matches. As the umpire possesses the power to make crucial judgments concerning incidents that occur on the field, this dataset aims to contribute to the advancement of automated systems for umpire recognition in cricket. The proposed Attention-based Deep Convolutional GRU Network accurately detects and classifies various umpire signal actions in video sequences. The method achieved remarkable results on our prepared CUAVd dataset and publicly available datasets, namely HMDB51, Youtube Actions, and UCF101. The DC-GRU Attention model demonstrated its effectiveness in capturing temporal dependencies and accurately recognizing umpire signal actions. Compared to other advanced models like traditional CNN architectures, CNN-LSTM with Attention, and the 3DCNN+GRU model, the proposed model consistently outperformed them in recognizing umpire signal actions. It achieved a high validation accuracy of 94.38% in classifying umpire signal videos correctly. The paper also evaluated the models using performance metrics like F1-Measure and Confusion Matrix, confirming their effectiveness in recognizing umpire signal actions. The suggested model has practical applications in real-life situations such as sports analysis, referee training, and automated referee assistance systems where precise identification of umpire signals in videos is vital.

In this research, an innovative method has been applied to obtain the shape of initial sheet in the forming of different rectangular geometries. So that we can define rules that automatically determine the optimal shape of the initial sheet at different heights. Sensitivity analysis method was used for optimization, and Python software was used to link with Abaqus software. By examining the geometrical parameters and forming pressure, a flowchart was presented to determine the appropriate pressure. By examining the height changes in different geometries, it was found that the optimization reduces the maximum pressure and the maximum radius of the punch, which were obtained by 4 and 8%, respectively. It was found that the difference in the size of the longitudinal and transverse sides of the section has a direct effect on the changes in the node coordinates to plot the target curve and reduces the number of optimization steps. According to the results, it was shown that by automation and determining the pressure flowchart, the shape of the initial sheet can be determined without repeating the optimization stages and experimental tests. It also prevents material waste.

One of the efforts to reduce water losses in irrigation channels is to provide lined materials in the earth''s irrigation channels. Construction of these lined materials in Indonesia requires raw materials (such as sand, gravel, and split stone) mined from nature, and in Indonesia, known as class C excavated materials. Excessive exploitation of class C excavated materials will impact environmental damage. To overcome these problems, therefore, research is needed to find alternative lined materials, and in this research, the alternative lined material is Glass Fiber Reinforced Polymer (GFRP). The study''s primary objective was to determine the value of the Manning roughness coefficient for lined channels made of GFRP material. The research involved experimental testing using an open channel model (flume) lined with GFRP material. The flow tests were conducted with three variations of the pump flow discharge and nine variations of the channel bottom slope. The test results from the physical model were compared with the results of the mathematical model simulation using the HEC-RAS software. The model''s performance was evaluated using the graphical technique and quantitative statistics specifically the Nash-Sutcliffe Efficiency (NSE) method. Model evaluation with the NSE method shows that the performance of the physical model is “very good”. The Manning roughness coefficient value for rigid-lined GFRP material range from 0.0071 to 0.0102. The recommended Manning roughness coefficient value for practical application in the design of irrigation channels in Indonesia is 0.0081.

Equipping renewable energy resources generation units in the distribution network to reduce economical and emission concerns are the examples of active distribution network(ADN). The other advantages of utilizing distributed generators (DGs) are improving technical constraints of ADN. In this paper multi-benefit functions are defined as main functions. Each of functions illustrates the positive impacts of utilizing wind turbines in the improving technical constraints of the ADN. Voltage stability (VS) is one of the main technical indices of the ADN. Several VSIs are defined to evaluate voltage stability of the ADN. The previous indices could not give the proper results about allocating DGs and accurate evaluating of voltage stability of ADN. This work proposes the new VSI. To this aim active power loss (APL), reactive power loss (RPL) and voltage stability index (VSI) are considered as technical constraints. In order to evaluate the presence of WT on improving APL and RPL, WTs are considered in two operational modes; unified power factor (UPF) and (APF). The main benefit function is solved by implementing genetic algorithm (GA). Multiplying weights to the APL, RPL and VSI (which are improved by attendance WTs) in benefit function formulation, make the multi-criteria decision formation to the proposed optimization problem. By employing analytical hierarchy process (AHP) technique and considering each technical constraints as main criteria, the obtained solutions are arranged. To verify the positive effectiveness of the proposed VSI, its results are compared with the results of other VSIs in the 33, 67 and 118 bus IEEE radial DN.

The development of green product processes is a strategic approach to minimize the impact of organizational supply chain on the environment while simultaneously expanding its economic performance. To achieve this task, it is crucial to emphasize aspects related to performance in optimizing resource utilization and implementing sustainability principles within an organizational domain. To this end, a multi-objective mixed-integer linear programming model is presented in this study with the objective of minimizing the production time of textiles, transportation costs, and inventory of the products, as well as minimizing the environmental effects of the processes of developing green products. In this model, the constraints and problem parameters are deterministic and solved using weighted sum methods, utilizing real data obtained from the "Oyaz" industrial group. By solving the model, an optimal combination for the values of the objective functions is obtained both collectively and separately. Furthermore, the capability of the proposed model is evaluated for solving large-scale instances using the NSGA-II algorithm. This metaheuristic method has demonstrated satisfactory capabilities compared to the mathematical model because of the slight difference in modeling errors while confirming the accuracy of the developed mathematical model, proving the accuracy and efficiency of the NSGA-II algorithm. Consequently, the sensitivity analysis examines the influence of changing key parameters, such as the maximum storage capacity of production centers, on the decisions of the proposed model. This parameter change is determined through consultation with experts in the textile field. Based on the results obtained, changing the maximum storage capacity has a considerable impact on fibers and cotton. Additionally, if the capacity is changed to the maximum possible value, it has the greatest impact on the purifiers.

One of the major coal mines in the eastern part of Iran is Agh-Darband mine which is located in Sarakhs. Agh-Darband plant works with a content of less than 37% ash at inputs and about 12% ash in the product. Due to the equipment being depreciated and the lack of optimal conditions for the processing system, a large amount of the coal in feed is entered to the tailing part so that ash content in the tailing is about 75%. The purpose of this research is to investigate whether the flotation method can process the mine tailing with 75% ash content and reduce it to return to the processing circuit. For this purpose, various flotation tests were performed on 70 kg of tailing of Agh-Darband mine. The optimum conditions was obtained at neutral pH with 2000 g/t of oil collector, 30 g/t of pine oil as a frother, and a solid content of 18%, which the ash content of coal and recovery were 34% and 45%, respectively. In addition, using the experimental design software (DX7), the mechanical parameters of the cell such as mixer circulating speed, the distance of the mixer from the bottom, the aeration rate, skimmer circulating speed, and cell volume were optimized. Optimal values of parameters were mixer circulating speed= 1227.2 rpm, the distance of the mixer from the bottom= 0.13H, the aeration rate= 1 (completely open), skimmer circulating speed= 8.49 seconds, and the cell volume= 1 L, which resulted in the percentage of ash= 35.34% and the recovery= 52%.

Concrete dams are anisotropic due to lift joints that affect their performance. Lift joints are usually ignored in numerical analyses of concrete dams and the dam body is assumed to be homogeneous and isotropic.  In this study, the seismic behavior of gravity dams was evaluated considering the anisotropy caused by lift joints, and the orthotropic and isotropic state responses were compared. Moreover, in the seismic loading range, a more detailed evaluation was done by applying the real effects of strain rate. Koyna concrete gravity dam was selected for the case study. The results showed that concrete anisotropy leads to larger dynamic displacements and greater damage to the dam body. By considering the orthotropic properties of concrete can lead to more realistic results. The maximum compressive and tensile stresses also increased in the anisotropic model compared to the homogeneous and isotropic model, indicating the usefulness of incorporating the orthotropic behavior of concrete in seismic analysis. In addition, considering the strain rate in the seismic loading range had an insignificant effect on the results. Therefore, considering the large dynamic increase factor in numerical analyses causes the error.

Cement mortar is used as a bonding agent between building materials in the construction of stone masonry and brick masonry. The focus on reducing the environmental burden caused by the high emission of carbon with the consumption of cement has gained interest. In this study, experimental investigations are conducted using two slag-based materials, i.e., Portland Slag Cement (PSC) and Processed Granulated Blast Furnace Slag Sand (PGBFS, iron slag), as a replacement for Ordinary Portland Cement (OPC) and River Sand (RS). The paper aims to investigate the influence of PSC with slag sand on the strength, durability, and microstructure of cement mortar. The present work specifically investigates the strength improvement of cement mortar with slag cement and slag sand by varying the curing period, comparing the results at 7, 14, and 28 days of curing. OPC is replaced fully with PSC, and River sand is replaced partially or fully with slag sand in different percentages, i.e., 0%, 20%, 40%, 60%, 80% and 100% for different types of mixes. Results showed the highest increase in compressive strength and high resistance to acid attack in cement mortar with 100% PSC and 60% Slag Sand replacement. The consumption of proposed materials will benefit the construction industry to achieve the net zero target.

Propagating micro-cracks in a structure decreases its load-bearing capacity and leads to the collapse of the entire structure. Addition of various additives in all kinds of concrete or concrete ingredients, as several studies have shown, could significantly make concrete reclaim from the specifications and attributes point of view. A possible manner to the common ruin and expensive preservation of concrete infrastructure, utilizing encapsulating healing factors is helpful for the self-healing of concrete. The self-healing concrete with microencapsulated Preyssler, and calcium nitrate was studied in this paper. Microcapsules were synthesized by in-situ polymerization of urea-formaldehyde as a shell around the core materials inclusive of Preyssler, calcium nitrate. Physicochemical characterization of microcapsules was conducted by Fourier transformation infrared spectroscopy, field emission scanning electron microscopy, and Transmission Electron Microscope. The mechanical assessment of cementitious specimens with different dosages of microcapsules (0%,0.5%, 1%, 1.5%, and 2%) was performed by compressive tests. Also, by measurement before and after damage after 10 days, the self-healing potential was tested. After the concrete was damaged by exerting 30% of its final load, all samples were incubated by immersion in water. According to the results, the sample containing 0.5% UFN, the sample containing 1.5% UFP, and the sample containing 1.5% UFNP have higher repair rates than others. This scope of research because of its interdisciplinary nature would own several possibilities to be pioneering with making an opening gate to link sciences and engineering such as material, chemistry, science, nanotechnology, and the field of engineering to persuade a wide spectrum of contribution in engineering sciences and usages.

This study employs fine glass powder as a partial replacement for ordinary Portland cement in order to better understand the complex effects of high temperature on the microstructure and compressive strength of cement mortar. The study aims to identify the optimal mix that maximizes the benefits of glass powder incorporation by experimenting with different glass powder sizes (38 µm, 53 µm, and 75 µm) and replacement rates (10%, 20%, and 30%). At 25oC, 200oC, 400oC, 600oC, and 800oC, mortars containing glass powder were subjected to compressive strength tests. Mortar samples were examined using X-ray diffraction (XRD), scanning electron microscopy (SEM), and Thermo Gravimetric analysis (TGA) to gain insight into their behavior at high temperatures. Mortar mixes containing 10% glass powder (38 µm size) performed best at temperatures below 400oC, with an average residual strength index value of 90.7% compared to 87.8% in the reference sample. The glass powder mortars with 10% and 20% replacement rates functioned better at higher temperatures of 800oC, losing just 52-57% of their strength as opposed to 72% in the control sample. The increased pozzolanic activity attributable to the addition of glass powder is shown by XRD and SEM analyses to account for the increased strength of mortar by consuming more portlandite (Ca(OH)2). TGA study has shown that at temperatures exceeding 400oC, to bermorite and other hydrated products become dehydrated, which may account for the strength reduction.

Cold spray (CS) with Metal matrix composite (MMC) is an alternative process for improving surface properties, which is crucial in plastic manufacturing. Understanding of particle behavior during impact is required for CS. This study focused on developing a simplified computational framework using the single-shot particle impact model to predict the adherence of matrix particles in the low-velocity impact. In this work, the hardened SKD11 coated with Al matrix/TiN reinforcement composite was selected, aiming to verify the proposed framework. Al particle impact at different temperatures (300K, 623K, and 723K) under the low-velocity range of 350–600 m/s were simulated, revealing the particle temperature affects the cohesive area. As the particle temperature increases, the areas also increase under similar velocity. The flattening ratio was calculated from the simulation and found to be influenced by the particle velocity. The CS of pure Al and Al/TiN (75:25 wt.%) on the hardened SKD11 under 623K and 723K was carried out under the experiment with the estimated pressure based on the flattening ratio and particle behavior. The results suggest the coatings could be developed using estimated pressure. Al/TiN coating was deposited at different initial particle temperatures. Results reveal that low coating porosity (<0.01%) could be obtained for both cases, and the higher particle temperature reveals higher thickness and %porosity, which agree well with the computational results. The developed framework shows high potential for designing CS for MMC coating, provided the reinforcement particles do not significantly affect the matrix particle flow or impact conditions.