International Journal of Integrated Engineering
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Design and Fabricate of 3D Printed X-Band Sample Size Based Polyethylene Terephthalate Glycol (PETG) Rectangular Waveguide
Full text linkThis work discusses the design and fabrication of 3D-printed dielectric materials for rectangular waveguides, with specific attention given to the effects of infill density and pattern changes on dielectric attributes. Microwave CST Studio is used to design the waveguide structures, which were produced through additive manufacturing and later tested with a two-port rectangular waveguide measurement system. The S-parameters were measured using a Keysight PNA-L Network Analyzer N5234B, whereby the reflection coefficient (S11) and the transmission coefficient (S21) were measured to determine how the material interacts with electromagnetic waves. It was found that greater infill densities correlated with increased values of real permittivity, whereas infill patterns have an essential influence on the material\u27s dielectric behavior. Several patterns were tested: concentric, cross, cubic, and zigzag; the zigzag pattern gave the best results concerning the effective permittivity, while the concentric pattern presented the least because of the greater air gaps. The strong dependence of permittivity on infill density illustrates the possibility of controlling dielectric properties through additive manufacturing using PETG material. It has been demonstrated that 3D printing methods can fabricate waveguides and RF circuits that enable control over dielectric properties
Synthesis of Surfactant-Supported Algerian Clay for the Adsorption of Ni (II) in Water Media
Full text linkThe purpose of this research is to prepare HDTMA-modified Algerian clay (HDTMA-Ac) and examine its ability to adsorb ions of nickel in water media. Characterization techniques such as X-ray diffraction, FTIR spectroscopy, thermogravimetric analysis (TGA), and the point of zero charges (pHpzc) were used to analyse the sample properties. The adsorption capacity of HDTMA-Ac towards Ni (II) ions was investigated under several operating conditions, such as pH and initial concentration of Ni (II). Results showed that the raw clay was successfully modified by intercalation of the surfactant ions in the interlayer space of clay. The maximum removal efficiency of Ni(II) on HDTMA-Ac was found to be 95.71, 84.47, 72.82 and 79.29 % (at pH≈7), respectively, for initial concentrations of 10, 100, 200, and 300 mg/L. The pseudo-second-order model well described the adsorption kinetics of Ni(II). The Langmuir isotherm provided the best fit to the experimental data with a maximum monolayer adsorption capacity of 59.6 mg/g. The findings of this work suggest the potential of the produced HDTMA-Ac material that can be used to remove toxic metals from wastewater
Study of the Durability Against Acid Sulfuric Attack and Accelerated Carbonation of Eco-Efficient Self-Compacting Concrete Blended with Marble Powder
Full text linkReinforced concrete structures can suffer from numerous problems such as the aggressivity of the environment and the carbonation phenomena. The introduction of fine industrial by-products (FIB) can reduce the permeability and hence improve the concrete durability. Marble industry generates high quantity of fine residue (marble powder) that can be used as FIB to produce an eco-cement. In this concern, five self-compacting concrete (SCC) mixtures were prepared using a constant water to binder ratio of 0.40, one as reference mix and four containing marble powder as a partial replacement of ordinary Portland cement (OPC). The durability of SCC subjected to sulfuric acid attack and accelerated carbonation were assessed. X-Ray diffraction analysis (XRD) were carried out to identify the mineralogical composition before and after durability tests. The results have shown that the use of marble powder is beneficial to improve the resistance of SCC to acid sulfuric attack. However, the incorporation of marble powder seems to accelerate the carbonation of SCC specimens.
Wearable Mask for Real-Time Tracking of Physiological Parameters During Athletic Training
Full text linkThis research aims to develop a wearable mask capable of measuring oxygen intake during sports activities. The mask incorporates various sensors to monitor heart rate, oxygen intake levels, blood oxygen levels, and body temperature. The MAX30100 sensor was used for heart rate and blood oxygen (SpO2) monitoring, while the combination of the MQ-135 gas sensor and the MPX5010DP pressure sensor was utilized for detecting oxygen intake levels. An NTC thermistor was employed for body temperature measurement. The Arduino Mega 2560 microcontroller was utilized for data processing and sensor interaction. The mask was tested in different activity states such as idle, walking, exercising, and running to collect real-time data. Results indicate that the mask accurately measured oxygen intake and other vital parameters. The collected data was processed and displayed on a 16x2 LCD with an I2C module, facilitating easy tracking of metrics over time. This innovation holds significant potential for athletic training by offering personalized insights into respiratory performance, enhancing training efficacy, and reducing the risk of overexertion. Overall, it improves the performance of athletes
Enhanced Solar Energy Conversion Through Optimized Multijunction Photovoltaic Cells
Full text linkAs technology advances, the demand for energy sources continues to grow. In response to this demand, renewable energy has emerged as a key solution, leveraging environmentally friendly methods such as photovoltaic technology. However, existing solar cells exhibit low efficiency and power conversion rates. This study aims to identify the optimal materials and number of junctions necessary to enhance solar cell efficiency through the multijunction concept, which has been modelled using Matlab Simulink. The research employed various equations to design solar cells, including calculations for energy bandgap, reverse leakage, short circuit current, open circuit voltage, and power output. The findings revealed that a quadruple junction utilizing specific semiconductor materials achieved an impressive efficiency of 58.84%, a notable improvement compared to the 11.03% efficiency of single-junction solar cells
Corrosion Analysis Tool Using Pencil Graphite Electrode Sensor with Machine Learning Algorithm
Full text linkCorrosion is an electrochemical reaction that leads to the deterioration of metallic materials, posing significant challenges across various industries. Traditional corrosion analysis methods require manual data collection using electrode sensors and laboratory-based analysis, limiting automation, mobility, and predictive capabilities. To address these issues, a Corrosion Analysis Tool was developed using a Pencil Graphite Electrode Sensor in combination with machine learning algorithms. The tool integrates regression analysis to enhance data integrity, automate predictions, and minimize human errors. Cloud computing is employed to replace traditional physical servers, facilitating remote access and real-time analysis. A mobile application is also developed to provide users with a convenient and efficient corrosion analysis platform. The system was evaluated by comparing its corrosion rate analysis results with traditional laboratory experiments conducted by chemical science students. Results demonstrated high accuracy, with minimal deviations between the corrosion rate values obtained from the Corrosion Analysis Tool and manually computed rates. The differences observed were 0.236 × 10⁻⁸ for a 7-day immersion, 0.049 × 10⁻⁸ for a 14-day immersion, 0.071 × 10⁻⁸ for a 21-day immersion, and 0.014 × 10⁻⁸ for a 28-day immersion, confirming the system\u27s reliability. The precision test further verified that the tool effectively reduces human errors and enhances data integrity. Furthermore, the tool streamlines project management by centralizing data storage and organization, preventing data redundancy and loss. In conclusion, the Corrosion Analysis Tool successfully automates corrosion analysis, improves mobility, and enhances data-driven decision-making for researchers. The system meets all user requirements, offering a robust solution to traditional corrosion analysis challenges. Its predictive capabilities, powered by machine learning, provide valuable insights for future corrosion prevention strategies. By incorporating cloud-based storage and mobile accessibility, the tool modernizes corrosion analysis and contributes to advancements in materials science and engineering
Improvement of Preprocessing for Spiral and Wave Handwriting Image Classification Using DenseNet-169
Full text linkParkinson\u27s disease (PD) is the second most common neurodegenerative disorder, impacting over 10 million people. Key symptoms include slowed limb movements, difficulty writing, and involuntary tremors. Tremor is the first motor symptom of Parkinson\u27s disease, seen in about 75% of patients. Neurologists assess tremors through various non-invasive tests. This may involve assessing handwriting and spiral drawing. The analysis is still performed manually by neurologists, which can introduce subjectivity. Applications using computer vision techniques should be developed to classify handwriting as healthy or tremor-affected, aiding neurologists in making more objective decisions. DenseNet-169 can classify spiral and wave images in tremor and non-tremor classes with the addition of preprocessing obtained a training accuracy of 100% while the system test accuracy is 93% while without preprocessing, the system accuracy is 81%
Advanced Heat Sink Integration Strategies for Monocrystalline Solar Panel Performance Enhancement
Full text linkRenewable energy is an alternative solution to solve the problem of global warming. One of the ways to help these problems is to take advantage of solar energy. Solar panels are tools that can transform solar energy into electrical energy. However, solar panels have efficiency sensitivity. In several previous studies, heat sinks can increase the efficiency of solar panels. Therefore, this research is conducted to determine the increase in the efficiency of monocrystalline solar panels using a heat sink. This study uses solar panels of monocrystalline type with the MS50M-18 series. Data collection is carried out with a data logger system using a digital multimeter and Arduino as the direct way, taking at most 140 data every 15 seconds. This study concludes that there is an increased efficiency of solar panels using a heat sink from 8.8% to 11.89% on radiation 700 W/m2 where the maximum temperature is from 77 ◦C to 72 ◦C when using a heat sink. These findings demonstrate the effectiveness of passive cooling using heat sinks, contributing to optimizing solar panel performance in moderate irradiance conditions. The proposed system offers a low-cost and scalable solution for improving solar energy generation in various environmental settings
IoT-Integrated Machine Learning for Precision Watering in Bamboo Mushroom Farming
Full text linkThis study offers a method for enhancing bamboo mushroom farming. We aim to increase productivity by combining machine learning methods for device-level computing with the Internet of Things (IoT). The first step is to record the ideal environmental conditions in the bamboo mushroom greenhouse. The IoT devices collect data on temperature, humidity, soil moisture, and water usage, storing it in the cloud. The regression model is then formulated for irrigation control and predicting water consumption in the bamboo mushroom farm. Later, the microcontroller is programmed to control the water pump in a systematic manner to release water. The study found that temperature, soil moisture, and relative humidity are the primary factors affecting water content. The proposed method increased mushroom volume by 46.67% and saved 22% of water usage, demonstrating the successful integration of machine learning into smart farming at the device level
Design of High Efficiency Class E Power Amplifier Utilizing 0.18-µm RF CMOS Technology for 5G Network
Full text linkThe design of high-efficiency class E power amplifiers faces challenges due to low transistor breakdown voltages, high parasitic capacitances, and limited quality of on-chip passive components, which reduce power efficiency and linearity. Existing solutions offer moderate efficiencies but often require complex trade-offs that are not ideal for high-frequency applications. This research aims to optimize a class E power amplifier design to achieve higher efficiency and output power for 5G newtork, addressing these limitations while maintaining performance suitable for modern wireless communication systems. This paper proposed a high efficiency class E PA for 5G network. The proposed PA is implemented using the 0.18-µm RF CMOS process technology, and the circuit is designed and simulated using Cadence software. The proposed PA consists of a power stage and a driver stage. The power stage and the driver share a single source. The simulation results show that at input power of 0 dBm and supply voltage of 1.8 V, the proposed PA demonstrates a maximum peak power added efficiency (PAE) of 55 %. Meanwhile, a maximum output power (Pout) of 13.1 dBm is delivered by the proposed PA. Since the PA exhibits a stability factor (K value > 1), it is unconditionally stable. In addition, the PA achieves s-parameter of S11, S22 and S21 performances of -13.8 dB, -29.3 dB and 19.7dB, respectively. Furthermore, the layout of the proposed PA is 1.82 mm2 including the pads.