Journal of Engineering and Sustainable Development (JEASD)

The main objective of this work is to investigate numerically double-diffusive mixed convection flow in a channel filled with a hybrid nanofluid of Cu-Al2O3 in a porous media. It is considered that the cavity's left wall is continuously heated. The fluid flow enters the channel at low concentration and temperature. This work has been looked into two cases: Case 1, where it is supposed that the left wall cavity has high concentrations, and Case 2, where it is expected that the right wall cavity has high concentrations. The cavity's remaining walls are impermeable and thermally insulated. Non-dimension governing equations are solved with the finite element method. Parameters effects of Reynolds number(10≤Re≤100), Richardson number (2≤Ri≤10), Darcy number (Da=10-2,10-4), Lewis number (1≤Le≤5), buoyancy ratio, N=1, the solid volume fraction (ϕ=0.02), and Prandtl number (Pr=6.2). The results show that increasing Reynolds, Richardson, and Darcy numbers increases the average Nusselt while it decreases by increasing Lewis number. In addition, Sherwood numbers increase with increasing Reynolds, Lewis, and Darcy numbers while they decrease with Richardson numbers for both cases. When the Reynolds number increases from 10 to 50 at (Da=10-4) and (Ri =10), the Nusselt number increases by 102%, while the Sherwood number decreases to 72%.

Fibers are widely used in construction because they have mechanical and thermal properties that improve the properties of structures added to them. This paper addresses the improvements made to the compressed earth block (CEB) when palm fibers and glass are added. This block is made up of soil and cement. The fiber ratio varies between 0% and 0.4%, and a compressive force of 2.5 MPa is applied to the samples. The physical, mechanical, and thermal properties of the CEB are studied according to systematic criteria using a numerical simulation program to find out how heat is transferred inside the samples. Preliminary results showed that the addition of fibers reduces the bulk density value between 1.88% and 11%, increases the compressive strength between 14.28% and 28.85%, decreases the thermal conductivity value between 1.38% and 22.22%, and decreases specific heat observed by 19.34% and 25.77%. These results give a clear view of the changes in the CEB when fibers are added and how to improve some properties.

The Bekasi River in Jakarta, Indonesia, faces significant flood risks due to high rainfall intensity, extensive urban development, and climate change. This paper aims to reduce the impact of floods by implementing efficient flood management measures, such as constructing retaining walls along the river. The appropriate height and stability of retaining walls were determined by conducting hydrological and hydraulic analyses using HEC-HMS and HEC-RAS, respectively, accounting for maximum rainfall and flood discharge levels. The results showed that the rainfall intensity in the Bekasi watershed with a return period of 2 years obtained a designed rainfall of 132 mm/hour using the Log Pearson III distribution, the maximum flood discharge for the return period Q50 is 1155 m3/second, the retaining wall design dimensions will be obtained 3-meter height with cross-section slope 0.002. Results indicated that almost all river channels overflow except near the Bekasi Dam, necessitating riverbank restoration and increased channel capacity. The paper highlights the need for reinforced retaining walls to protect the riverbank and improve flood resilience

Significant changes have been made in the harvesting operations implemented within the oil palm industry. Mechanisation has replaced conventional manual harvesting methods, which are physically intensive work, leading to increased effectiveness. However, despite the known association of manual labour with musculoskeletal problems among workers, it remains crucial in harvesting operations. Electromyography (EMG) has emerged as an essential tool for monitoring muscle activities. This paper aimed to develop an Arduino-based EMG system for muscle activity monitoring in oil palm harvesters as a cost-effective and user-friendly method. The results showed that there was a direct correlation between recorded values and lifted loads, whereby 8kg had the highest reading while 2kg had the lowest reading. It reflects some positive trends in mean values in the weight categories found in the dataset, specifically 0.4238 for 2kg, 0.5078 for 4kg, and 0.7937 for 8kg. Similarly, standard deviations showed an increasing pattern with an increase in weight, indicating a trend for more variation at higher weights. The prototype demonstrated promising results regarding capturing muscle activities under various scenarios, which indicate its potential to assist in designing and developing ergonomically improved machines. Further research needs to be conducted to test the accuracy.

This research focuses on reducing noise in 3D laser printers, commonly used in businesses and home offices. By fine-tuning printer components and analyzing noise levels, the primary goal is to minimize the noise produced by a 3D laser printer. The study involves modifying various printer parts and using a Raspberry Pi 4 as an AI control system. Noise reduction is tested by adjusting the speed and acceleration of the machine while maintaining precision. A sound level meter and analysis program are employed to measure and analyze the noise levels. Results indicate that altering components, such as stepper motor drivers, significantly reduces noise. Findings suggest that further noise reduction can be achieved through additional component tuning and noise insulation strategies. This approach improves the user experience by providing a quieter operation and enhances the overall performance and efficiency of 3D laser printers. By focusing on minimizing noise, this research provides practical solutions for creating quieter and more efficient 3D laser printers, benefiting both businesses and home offices. Insights from this study can be applied to develop advanced 3d laser printers with optimized noise levels.

This paper presents an integrated approach to breast cancer diagnosis that combines unsupervised and supervised learning techniques. The method involves using a pre-trained VGG19 model to process sub-images from the BreaKHis dataset, divided into nine parts for comprehensive analysis. This will be followed by a complete description of the architecture and workings of the variational Autoencoder (VAE) used for unsupervised Learning. The encoder network maps the input features to lower dimensions, capturing the most essential information. VAE learns a compressed representation of sub-images, facilitating a more profound understanding of underlying patterns and structures. For this reason, we then employ k-means clustering on the encoded representation to find naturally occurring clusters in our data set comprising a histopathological image. Every single sub-image is later fed into the VGG19-SVM model for classification purposes. During magnification at 100x, this model has attained a fantastic accuracy rate of 98.56%. Combining unsupervised analysis with VAE/k-means clustering and supervised classification with VGG19/SVM can integrate information from both methods, thereby improving the accuracy and robustness of such a task as sub-image classification in breast cancer histopathology.

The development of a compact and straightforward circularly polarized hexagon-microstrip patch antenna with two layers and a single feed is presented. Circular polarization is achieved using two strategies. The first technique incorporates a narrow slot along one vertex of a hexagonal patch's diagonal, opposite the feed line. The second one is based on embedding a complementary split ring resonator on the hexagonal patch. The proposed antenna is excited by an aperture coupling feed, and it is implemented on two substrate materials; the patch substrate is Roger droid/RT-5880, and the second feed substrate is RO 4350. This work compares the performance of the suggested antennas, including their radiation characteristics, axial ratio, and return loss, in two different scenarios. The proposed antenna has overall dimensions of (0.8λ0 - 0.8λ0 - 0.037λ0) at 3.4 GHz. The slot patch antenna has 2.65% bandwidth for its 3-dB axial ratio and 5.71% impedance bandwidth from 3.4 to 3.6 GHz at 3.5 GHz of the center frequency. The proposed patch antenna also demonstrates a maximum gain of 1.2 dBi. High-frequency structure simulation software was used to design and simulate the proposed hexagon configurations.

In recent years, the growing interest in automatic speech recognition (ASR) has been driven by its wide-ranging applications across various domains. Integrating speech recognition technologies into smart systems highlights the pivotal role of human-machine interaction. This study introduces a robust ASR system that leverages convolutional neural networks (CNNs) in conjunction with Mel-frequency cepstral coefficients (MFCCs). The model's architecture was improved by extensively examining hyperparameters, effectively recognizing ten different spoken commands. The model conducted training and evaluation using the Google Speech dataset, comprising 65,000 audio clips collected from a wide range of speakers across the globe. This dataset accurately represents the natural variations in speech found in real-world scenarios. The design comprises eight storage layers, encompassing convolutional and fully connected layers. It consists of a total of 183,345 weights and utilizes ReLU activation. It is worth mentioning that the average F1-score obtained during the training, validation, and testing stages is 99.06 %, 94.68%, and 95.27%, respectively. Furthermore, the proposed model exhibits about 1.3% improvement in experimental test accuracy over existing methods, confirming its effectiveness in real-world applications.

Vanadium is considered one of the heavy metals that can be found in crude oil. Since vanadium is toxic to specific refining catalysts, the amount of vanadium in crude oil can affect its selling price. At the same time, it is responsible for equipment corrosion when temperatures reach high enough. This paper investigates vanadium adsorption by two adsorbents named nano-zeolite and alum sludge (residual from a sedimentary tank in a water treatment plant). A batch adsorption unit was selected to determine the effect of different operating factors such as adsorbent dose, agitation speed, temperature, and contact time in vanadium removal. The highest removal efficiency was 89% and 67%, performed at 2 g of nano-zeolite and 4.5 g of alum sludge, respectively, at 500 rpm agitation speed and 80˚C temperature with a contact time of 480 min for nano-zeolite and 420 min for alum sludge. Adsorption isotherm data was fitting to Langmuir and Freundlich models with R2 of 0.9665, 0.9727 for nano-zeolite and 0.9907, 0.9964 for alum sludge, respectively. Pseudo-first-order and pseudo-second-order models show excellent conformity to adsorption kinetic data with R2 of 0.9617, 0.96624 for nano-zeolite, and 0.97494, 0.96515 for alum sludge, respectively

Video steganography is a method for concealing information within a video without substantially changing its visual content. The utilization of high-definition videos has garnered considerable interest from industries. H.265/HEVC, the recent video coding technology, is a promising field for video steganography. In this paper, a high concealment capacity based on three chaotic maps for the HEVC video standard is proposed, where the confidential data will be encrypted using two chaotic maps and then concealed in randomly selected Discrete Cosine Transform (DCT) coefficients of Transform Blocks (TBs), which are also randomly chosen using one chaotic map. The technique used in the DCT domain to achieve superior embedding capacity at the same visual quality compared with the state-of-the-art schemes in the compressed domain, and the use of three novel chaotic maps to protect the secret information and get uncrackable security level are the significant contributions of this paper. The simulation findings proved that the proposed approach has an average concealing capacity reaching 41.3 Kbits/frame. This payload exceeds what recent cutting-edge techniques could achieve in 1280 x 720 video frame dimensions with a ) of -0.009 dB and a Bit Rate Increase (BRI) of 0.0747 at a Quantization Parameter (QP) value of 32. Furthermore, the critical space size of the suggested scheme is , which makes it very secure against all types of brute-force attacks.

Iraq currently faces an absence of water, worsened by population growth. As a result, if new water sources are not supplied, the country's yearly per capita water supply will decrease. This study developed a simple, promising, and economical method for on-site greywater treatment, employing agricultural waste as a biofilter medium and irrigation water in rural Iraqi areas. Experiments were carried out in this study to evaluate the multimedia filter device's efficacy in treating greywater. Three bioreactor columns were filled to the necessary height with various substrates—wood chipsBTF1, rice huskBTF2, and date palm fiberBTF3 at different operation conditions. The pollutant removal efficiency (Chemical Oxygen Demand, Total suspended solids, nitrate, and Phosphorous) for BTF1 was 61.6, 70.3, 45 and 42.45, for BTF2, it was 65.7, 43, 50.21%, and 55%. And for BTF3, it was 63.3, 75.6, 55, and 52.52, respectively. Rice husk is the most effective medium for eliminating pollutants, and using agricultural wastes as biofilter media could be a promising option for greywater treatment, especially in rural areas that lack sanitation services and produce a high amount of this waste annually.

Recognizing underwater objects based on radiated noise information is one of the most crucial issues in underwater acoustics. Underwater acoustic target signals are altered by elements such as the undersea environment and the ship's operational circumstances; hence, generalizing the recognition model is crucial. Most conventional Machine Learning (ML) algorithms often encounter difficulties when dealing with the costly recognition model for massive data analysis. However, Convolutional Neural Networks (CNNs) can automatically extract features for precise categorization. DenseNet is a powerful CNN network, but it has a data duplication problem, so in this paper, an approach using multi-resolution with a dense CNN model for underwater acoustic radar signal detection is proposed to overcome the DensNet problem. At first, the wavelet decomposition with different levels is applied to the input signal to represent the suitable data. The decomposed signals are inputs to the dense CNN. Our detection approach beats other CNN models and achieves an overall accuracy of 99.5% at 0 dB SNR based on experimental findings evaluated on a real-world passive sonar data set.

The present work aims to utilize a mixture of paraffin wax and grinded cane material as composite materials in building interior insulation. Two grinded cane material particle sizes (2 and 4 mm) were adopted. In addition, the effect of the mixing ratio of the grinded cane material (25, 50, and 75%) on enhancing mechanical, acoustic, and thermal performances was investigated. Moreover, two building ceiling models commonly seen in Baghdad were used to calculate the cooling load reduction rate. The outcomes of the experiments showed that filling the work specimens with the composite material of large particle sizes of grinded cane contributed to reducing the thermal cooling load of buildings, where the reduction rate in cooling load for the first ceiling model reached 1.17% when mixing 75% of grinded cane large particles, while, for the second ceiling model, the reduction rate reached 1.02% for the same mixing ratio. Mechanical performance significantly improved in a range of 285 to 433%, 168 to 521%, and 57 to 81.5%, respectively, in both tensile, impact and flexural tests. Moreover, increasing the mixing ratio and the particle size of the grinded cane material offers superior mechanical properties and better acoustic performance, where the noise levels were reduced by a range of 4 to 7.5%.

Enhancement of roadway embankment utilizing several techniques, such as cement columns and applying EPS (expanded polystyrene) Geofoam blocks as subbase materials, helps develop the efficiency of embankment performance under the applied traffic loading for local materials. A PLAXIS 3D (ver.20) finite element program is used for the simulation models of roadway embankments. The results showed that applying the cement column reduced the vertical deformation of the roadway embankment to (50mm) corresponding to approximately (58%) of the reference model. The EPS Geofoam blocks highly lowered the vertical deformation to (20mm) about (83%) of the reference model to those in the cement column. The EPS Geofoam blocks provide a convenient solution for improving the roadway embankment with an (83%) reduction in maximum vertical displacement and sustaining traffic loading on the pavement. The reduction in displacement is well done by utilizing the EPS Geofoam blocks in a granular layer of pavement embankment. The EPS Geofoam blocks acted better in reducing vertical stresses (about 22%) higher than the cement column. This benefits the use of EPS Geofoam blocks in the base layer to enhance roadway embankment against failure.