International Journal of Engineering
Volume:36 Issue: 11, Nov 2023

The primary goal of the engineering design of building is to reduce the weight of the structure and its resistance to fires and earthquakes because fires are inevitable. Hence, the direction of this study was to use lightweight concrete because of its unique advantages in weight loss and fire resistance due to its thermal insulation property. It was also intended to enhance the strength and behavior of this concrete at high temperatures. For this purpose, four mixing designs samples without fibers and nano-SiO2, samples with different proportions of nano-SiO2, samples with different proportions of fibers, and samples with both fibers and nano- SiO2 together were prepared. The results showed damage to samples free of nano-SiO2 and fibers, changing their color, reducing their resistance and reducing their weight. But adding nano-SiO2 fibers or using them together leads to improving the properties of concrete at all temperatures. Due to nano- SiO2, its pozzolanic interactions improve the microstructure, and the fibers prevent cracks in concrete. This study also dealt with the effect of changing the size of the samples on the compressive strength, and the results showed an increase in the resistance of the samples with small sizes, and resulted factors for converting the resistance of non-standard samples into a standard.

Nowadays, various 3D-Printer technologies are commercially available. However, those printers could only be used for a certain material provided by the printer manufacturers. For new material, the commercial printer could not be employed directly and needs to be modified and its printing parameter has to be optimized to fit the property of the new material. This paper aimed to find the optimum parameters (print speed and layer height) based on printability material. The new material that would be developed was a composite of bioceramic powder (hydroxyapatite) and polymer (collagen) in the form of slurry with ratios of 99.84% (w/v) and 0.16% (w/v). While the printer was a commercial 3D-Printer machine with modification on its cartridge container and bracket. The printing parameters were layer height (0.65, 1.0, 1.35 mm) and print speed (14.4, 25, 35.6 mm/min). Optimization of the printing parameter used Response Surface Method (RSM) with 13 sets of specimens. Test specimens for defining printable material were printed in the form of line shape and a rectangular shape for case study. Printability as a responding of the optimum parameter setting was defined on the basis of 5%-maximum dimension error of the printed specimen compared to the 3D-CAD data. Data obtained was analyzed using ANOVA. The results show that the optimum setup printing parameter were 10.009 mm/min for print speed and 0.505 mm for layer height, respectively with the error dimension obtained from the experiment was 0.013 mm2 (0.59%) lower than that of the permitted error of 5% (0.125 mm2).

In this study, the overall performance of a heat exchanger shell-and-double-concentric-tube with simple and perforated helical baffles is investigated in the shell side of the heat exchanger using ANSYS FLUENT 19.2. A comparison between the shell-side with simple helical baffles of the heat exchanger (SHB-SDCTHEX) and the one with perforated helical baffles (PHB-SDCTHEX) using numerous mass flow rates is carried out. For the perforated helical baffles heat transfer rate , thermo-hydraulic performance and effectiveness are around 26.7%, 55.5% and 26.6% higher than the same parameters for the simple helical baffles of the heat exchanger, respectively. It is also observed that the flow and temperature distribution for the perforated helical baffles are more uniform with higher flow turbulence than the simple helical baffles of the heat exchanger. So, the perforated helical baffles could be a better choice for the designers and manufacturers with respect to the simple helical baffles of the heat exchanger.

The dynamic probing test is effective for compaction control in road embankments and pavement layers. However, challenges exist to its use in dense soil types to obtain valid results. The main purpose of this research is to use light weight penetrometer in dense soils and obtain valid results. This study developed and tested three light dynamic penetrometers with different cone geometries in dense soils and compared their results with those of conventional dynamic penetrometers. Over 72 dynamic penetration tests were performed in the field in dense natural soil. The results showed a 50% reduction in the number of blows compared to the dynamic probing light penetrometer (DPL). The coefficients of variation of the results of 8.6% to 15.9% indicate desirable repeatability. To further evaluate the efficiency of these penetrometers, the correlations between their results and the soil characteristics of the dry unit weight in place, compaction percentage and peak shear strength were assessed by statistical residual analysis. This approach showed that these relationships were satisfactory.

In this research, polyvinyl alcohol (PVA)-chitosan composite films were produced using nanocellulose from coconut fibers (Cocos nucifera) in an Indonesian plantation in order to enhance mechanical properties and biodegradability. The process began by separating lignin and hemicellulose by delignification, bleaching, and then cellulose hydrolysis to produce nanocellulose. The PVA was mixed with chitosan with specific compositions and added the nanocellulose in 0%, 1%, 3%, and 5% concentrations, respectively. A tensile test was conducted to obtain tensile strength and elongation break. Biodegradability test was also carried out to determine the level of mass losses. Based on SEM observations, addition of nanocellulose appears to increase the reactivity of the formation of PVA-chitosan composite films, which are characterized by a reduction in film thickness. Addition of 5% nanocellulose resulted in a high quality of nano-composite. The tensile strength, fracture elongation and biodegradability of the composite film were 31.50 MPa, 39.9% and 9.04%, respectively.

Trimming the scrap portion of ultra-high strength steel (UHSS) components poses a significant challenge due to the inherent high strength and hardness characteristics of the material. For UHSS components with a higher geometric complexity such consisting of inclined and curved sections, sharp tilt, and small bend radius, the large trimming load results in poor sheared quality and shape defects, which commonly happen in these areas. This research investigated the effects of applying a small inclination angle to the punch in the trimming of the UHSS parts having an inclined and curved shape. The inclined punch was modified to four sets of different degrees of inclination i.e., 1°, 3°, 5°, and 10°. A comparative analysis of the trimming load, trimming energy, sheared edge quality and shape defects was conducted between these modified punches and the normal punch for their effectiveness in the trimming operation. Results showed that the application of inclination angle significantly decreased the trimming load, reduced the trimming energy, and improved the sheared edge surface quality, as well as prevented the shape defects at the inclined and curved zones as compared to the outcomes produced when trimming using the normal punch. The study suggested that the change to the punch geometry is an effective option to improve the performance of the process as well as the quality of the part, particularly in trimming the high-strength components having complex shapes.

Zinc plant filter cake contains valuable metals that can be reused as a source for obtaining these metals. This study describes an experimental two stage study on the extraction of zinc and nickel from waste zinc filter cake which includes acid leaching of zinc filter cake followed by organic phase aided extraction of metals from the leaching solution. To determine the optimum leaching condition a comprehensive study of the recovery of chemical elements from spent plant residues was experimentally studied at different levels of acid concentrations at different temperatures while measuring chemical elements concentration with respect to time. Experimental results showed that 99% recovery of Ni2+, Zn2+ and 89% recovery of Pb2+ can be achieved at following optimum conditions: 2M nitric acid, T= 358.15 K after 1.5 h of acid leaching at S/L=1/10. Then, the extraction of Zn2+, Ni2+, and Pb2+ was carried out by di-(2-ethylhexyl) phosphoric acid (D2EHPA) that was diluted with kerosene in equal phase ratio and the effect of extractant concentration and pH was studied at T = 298.15 K. Results showed that an increase in pH and extractant concentration can greatly increase zinc and nickel extraction to a maximum achievable amount of 95% and 90 % for Zn2+ and Ni2+, respectively by 25 (v/v%) D2EHPA at pH = 5.5 and organic to aqueous phase ratio (O/A) = 1/1. For modeling of equilibrium concentrations in organic and aqueous phases and activity coefficients calculation, Electrolyte-UNIQUAC-NRF, UNIQUAC-NRF, NRTL and NRTL-based local composition models were used. After that, adjusted parameters were successfully used for calculation of the equilibrium constant of the unknown parameters and the extraction reaction. The obtained results of thermodynamic modeling were in well agreement with the experimental data.

Genomic data is used in various fields of medicine including diagnosis, prediction, and treatment of diseases. Stage detection of cancer progression is crucial for treating patients because the mortality rate of cancer is higher when it is diagnosed in the late stages. Furthermore, the type of treatment varies depending on the cancer stage. This paper presents a Multiple Kernel Learning based algorithm to predict the stage of cancer using genomic data. Because of the high dimension of genomic data, the curse of dimensionality may degrade the stage prediction. To reduce the dimension, features are clustered first in the proposed algorithm. Then, the original data samples are clustered into smaller subsets with reduced dimensions based on the computed feature clusters. Afterward, for each subset, a kernel matrix is calculated. The kernel matrices are weighted and then combined linearly. Finally, a cancer stage prediction model is trained using the combined kernel matrix and Support Vector Machine. The proposed algorithm is compared with the baseline methods. The classification accuracy of the proposed method outperforms the other methods in 13 cancer groups of 15 from the cancer genome atlas program (TCGA) dataset.

The importance of employing appropriate pricing strategies for perishable products within the supply chain cannot be overstated. Pricing is a cross-functional driver of each supply chain, playing an irrefutable role in the success and profitability of the supply chain alongside other factors such as inventory and production policies which has been investigated in this research. The research emphasizes the significant role of pricing in profitability, along with the interplay of production policies and inventory control, highlighting their collective influence on financial outcomes, the subject of dynamic pricing within a multi-product, multi-period problem in a three-level supply chain with perishable products has garnered relatively limited attention. The study focuses on optimizing an integrated production-distribution system with multiple producers and distribution centers serving specific customer groups. Direct shipments between production centers, distribution centers, and retailers are optimized using a vehicle routing problem approach. A mixed-integer programming model is formulated, and a genetic algorithm-based metaheuristic approach is proposed. The BARON solver was initially used to solve two simplified test problems, with results compared to a self-designed genetic algorithm implemented in C#. After confirming the efficiency and effectiveness of our genetic algorithm (GA), the investigation is further extended to encompass five distinct problems, each comprising nine sub-problems. The GA demonstrates its power and adaptability by providing high-quality solutions efficiently within a reasonable computational time.

Today, with the proliferation of complex networks and their large amounts of data, researchers have great concerns about the accurate community detection methods. The difficulty in analyzing these networks stems from their enormous size and the complex relationships among the members of the networks. It is difficult to analyze the deep relationships and mechanisms by just looking at the whole. Traditional methods have some problems and limitations when analyzing these networks such as feature extraction, high reliance on the initial phase settings, computational complexity, neglect of network relationships and content. From the perspective of relationships and interactions between individuals, the environment of complex networks can be compared to a game in which nodes acting as players or agents may join or leave a community based on similar structural or semantic characteristics. Consequently, there is a strong tendency to use cooperative and non-cooperative games to detect communities. Moreover, the amalgamation of deep learning techniques and game theory has recently been proven to be highly effective in extracting communities. Deep learning techniques have demonstrated enhanced capability in feature engineering and automate the process. In this study, the authors make effort to detect rational and accurate communities based on structural and content features with the help of traditional approaches, deep learning, as well as cooperative and non-cooperative games. The efficiency of this study is demonstrated by experimental findings on real datasets, and confirming that it is able enough to identify those communities that are more meaningful.

The continuous growth of the economy and population around the world has led to an increase in transportation demand. Consequently, the number of vehicles keeps increasing to satisfy the continued demand for transportation. Traffic congestion and delay, as a result, is becoming the norm in many big cities. The first step to alleviate the traffic congestion and delay is to gain a better understanding of the traffic operations at the city’s road network. This study tries to investigate and report about the traffic operations at urban roadways in Basra City, Southern of Iraq. With the focus of studying some of the main traffic parameters such as traffic flow, speed, travel time and delay at some selected sites with interrupted traffic flow within Basra City. Field traffic data from 30 roadways sites has been collected by using two techniques, camcorders and floating car technique. Data analysis showed that most of the selected sites are running under their capacity with an average speed close to the posted speed limit. However, the analysis of data also showed that for the majority of times on these selected sites long travel time and traffic delays were experienced, with an average delay of around 3.0 minutes for each 1.0 km road length. This could be attributed to deficiencies in the operation of traffic signals at intersections, presence of illegal on-street curb parking (with double parking sometimes) and the absence of traffic enforcement controls (with associated penalties). 

Cryptocurrencies, with their decentralized nature, are gaining rapid international adoption as a means of payment or a valuable digital asset, independent of the economic policies of governments and without the need for a supervisory institutions such as banks. However, limited research has been conducted on the adoption of cryptocurrencies, most of which employ a general technology acceptance/ adoption model with a positivist approach. The main problem with previous studies is that they have been limited to the structure of general adoption models and only examined a few constructs due to the increasing complexity of the model. On the other hand, due to cryptocurrencies' unique nature and rapid developments, it is necessary to create new comprehensive models that include different dimensions. This paper aims to identify influential factors in the adoption of cryptocurrency technology, understand their interrelationships, and ultimately develop a comprehensive model. With a constructivist approach, this study uses the most important research of the past decade in the field of cryptocurrency adoption and creates a cognitive model of their constructs through a systematic approach. The focal point of our approach is constructivism, accompanied by considering the impact of constructs on each other using fuzzy cognitive maps, which has not been previously done in cryptocurrency adoption. The results of the proposed model indicate that perceived usefulness, attitude, financial value, and perceived ease of use are the most significant constructs that influence the creation of positive intention toward the use and adoption of cryptocurrencies. 

In this article, the equations governing the constant ferromagnetic current are investigated. The Lorentz force restrains this ferrofluid flow in a semi-porous valve. Analyzes were performed on three sub-particle fluids: kerosene and blood, water and magnetite. Modeling in the Cartesian coordinate system using the relevant equations was investigated. A slight thinning should be considered in the lower part of this channel. This research has used two Akbari-Ganji methods (AGM) and finite element method (FEM) to solve the equations. Nonlinear differential equations are solved using the above two methods. In the finite element model, the effect of changing the Hartmann number and the Reynolds number on the flow velocity and the derivatives of the velocity and shear stress of the fluid were investigated. As the Hartmann number increases, the velocity decreases in both directions. The Reynolds number changes in different slip parameters, which shows the opposite behavior for the two directions. Also, the insignificant effect of volume fraction of nanoparticles on velocity and its derivatives and shear stress was investigated. The results of solving the equations with the above two methods were compared with HAM. The results obtained using AGM and FEM and their comparison with previous researches have led to complete agreement, which shows the efficiency of the techniques used in this research.

Facial feature recognition is an important subject in computer vision with numerous applications. The human face plays a significant role in social interaction and personology. Valuable information such as identity, age, gender, and emotions can be revealed via facial features. The purpose of this paper is to present a technique for detecting age, smile, and gender from facial images. A multi-task deep learning (MT-DL) framework was proposed that can simultaneously estimate three important features of the human face with remarkable accuracy. Additionally, the proposed approach aims to reduce the number of trainable network parameters while leveraging the combination of features from different layers to increase the overall accuracy. The conducted tests demonstrate that the proposed method outperforms recent advanced techniques in all three accuracy criteria. Moreover, it was demonstrated that multi-task learning (MTL) is capable of improving the accuracy by 1.55% in the smile task, 2.04% in the gender task, and 3.52% in the age task even with less available data, by utilizing tasks with more available data. Furthermore, the trainable parameters of the network in the MTL mode for estimating three tasks simultaneously increase only by about 40% compared to the single-task mode. The proposed method was evaluated on the IMDB-WIKI and GENKI-4K datasets and produced comparable accuracy to the state-of-the-art methods in terms of smile, age detection, and gender classification.

This paper aims to numerically investigate the structural behavior of reinforced high-strength concrete (HSC) beams retrofitted by Carbon Fiber Reinforced Polymer (CFRP) sheets after cracking. Six pre-cracked HSC beams retrofitted with CFRP sheets having identical reinforcement are numerically tested by four-point loading until failure using Abaqus software, besides two others without CFRP as control beams. CFRP sheets are attached on three beam sides in the shear span after cracking under 60 % of loading. Two shear span distances, two inclinations of CFRP sheets, and the number of sheets are adopted as parameters to compare with the experimental results obtained previously. Test results are matched with the practical findings to calibrate the Abaqus parameters. The results show that retrofitting the cracked beam by CFRP raised its tolerance to the applied load by a range of (13-36) % depending on the shear span to depth ratio and the arrangement of CFRP sheets. When the beam tends to fail in shear, the effect of CFRP is more pronounced than when it tends to fracture in flexure. The inclined sheets are more effective than the vertical ones. Furthermore, two additional parameters are regarded to clarify their effects on the behavior of retrofitted beams: sheet width and concrete compressive strength. Altering the CFRP width does not affect the tolerance, whereas increasing concrete compressive strength raises the beam loading.

In today's digital age, security and safe communication are necessities. Applications frequently transport large amounts of private data as binary images. This research proposes a unique scheme that uses a cover pattern histogram-based decision tree for information concealment and extraction from binary images. This research aims to provide a data-hiding approach with a large capacity for data concealment, possible minor distortion, security, and difficulty discovering hidden data. This method uses high-frequency 3X3 pixel block patterns to obscure data. The two series of pattern's are identified based on sorted block pattern frequency. To construct a decision tree for embedding, these series patterns, key bits, and information bits work together as parameters. Information is encrypted using a secret key to ensure message security before being hidden. A decision tree decides the block suitability and bit embedding with or without flipping at the sender side. A histogram of 3X3 pixel block patterns gets generated for the received image containing concealed data, and two series are recognized similarly to the embedding procedure at the receiver side. A decision tree assesses whether an image block carries an information bit and decides whether the bit is "0" or "1". This decision tree extracts hidden data bits by analyzing series patterns and key bits. The secret key decodes retrieved concealed bits and reveal the original data. According to research, 50-80 % of hidden bits are transmitted without flipping the pixels, automatically reducing visual distortion. This scheme performs better than comparable methods and is applicable in steganography and watermarking.