Microstructure and Mechanical Characteristics of Welded AISI 1020 Low Carbon Steel Based on the Influence of Weld Joint Design and Shielded Metal Arc Welding Process

This study examines the impact of joint design on the microstructure and mechanical properties of welded AISI 1020 low carbon steel using Shielded Metal Arc Welding (SMAW) with E6013 electrodes. Bevel, butt, and half-lap joints were welded under identical conditions and assessed for mechanical and microstructural performance. The bevel joint exhibited the best overall performance, with improved tensile strength (188.39 MPa), yield strength (113.98 MPa), and impact strength (34.54 J/mm²) compared to butt and half-lap joints due to better weld penetration and load distribution. Microstructural analysis using optical microscope confirmed the presence of distinct ferrite morphologies, including ferrite, Widmanstätten ferrite, and acicular ferrite in the weld metal. The uniform distribution of phases and minimal welding defects in the weld metal zones of the bevel joint further support its mechanical superiority. These results highlight the importance of joint design in optimizing welded steel structures, with the bevel joint proving most suitable for high-strength applications. Hence, the research contributes to the understanding of the effects of joint geometry on welded steel properties and provides practical insights for industrial welding applications

Potential Utilization of Oil Palm Mesocarp and Oil Palm Empty Fruit Bunch Fiber Powder as Natural coagulant-flocculant for POME Treatment

The palm oil industry is one of the agro-based industries that has a high contribution to the global economy, including Malaysia. However, there is a negative impact on the environment caused by palm oil production, which results in high waste pollution known as palm oil mill effluent (POME). A common practice for the palm oil industry regarding the POME treatment is using conventional coagulant and flocculant agents due to their effectiveness and affordable cost. However, high usage of agents in wastewater treatment can threaten human and environmental health, such as air and soil pollution, water pollution, and disease transmission. The palm oil industry also produces other waste such as oil palm mesocarp (OPM) and oil palm empty fruit bunch (OPEFB) which have the potential to be utilized due to their existence of a hydroxyl group in cellulose and lignin. Therefore, this study provides a novel approach by utilizing naturally occurring functional groups in OPM-OPEFB to facilitate pollutant removal in POME as sustainable natural coagulants-flocculants. The effective treatment of POME is critical for reducing its environmental footprint, given the high organic content and large quantities generated by the palm oil industry. This study demonstrates the ability of these biopolymers to achieve significant reductions in turbidity and suspended solids, aligning with the principles of green chemistry. The effectiveness of lignocellulose biomass in enhancing coagulation-flocculation, offering a sustainability alternative to conventional chemical coagulants. For the coagulation-flocculation treatment of POME, jar tests were performed to evaluate the effectiveness of the process. The parameters measured for the untreated and treated POME are pH, dissolved oxygen (DO), turbidity (TUR), biochemical oxygen demand (BOD), total suspended solids (TSS), and ammoniacal nitrogen (AN). Removal efficiencies of pH, TUR, BOD, TSS, and AN were 7.39%, 41.28%, 53.14%, 62.69%, and 30.56% respectively for OPM-OPEFB. Results obtained from characterization show that the coagulation–flocculation mechanism was ruled by the existence of a hydroxyl group and hydrogen bond in cellulose and lignin that increase the rate of absorption and bonding. OPM-OPEFB demonstrates the potential to lower the organic contaminants. Therefore, optimizing contact time and coagulant dosage may enhance the effectiveness of the removal of organic pollutant in the POME

Are Malaysia\u27s Graduates Prepared The Fourth Industrial Revolution Workforce?: A systematic Literature Review

The Fourth Industrial Revolution is defined by artificial technologies and the Internet of Things, leading to the disappearance of some jobs while creating new ones. Most emerging technologies require advanced technical expertise and academic qualifications, significantly impacting employment, education, and TVET training for skilled workers. Skilled labor is essential for economic advancement and achieving a high-income economy, making it crucial to enhance the quality and participation in TVET. This literature review identifies the new skills needed in the workforce for the Fourth Industrial Revolution, focusing on "fourth industrial revolution skills" and "graduate readiness." The literature is categorized into nine Malaysia Future-Proof Skills: 1) Creativity & Innovation, 2) Holistic, Entrepreneurial & Balance, 3) Resilience, 4) Leadership, 5) Compassion & Mindfulness, 6) Value & Ethics, 7) Flexibility & Adaptability, 8) Critical Thinking & Problem Solving, and 9) Communication & Language Proficiency. Utilizing a systematic literature review methodology and the PRISMA procedure, this study synthesizes findings from journals and industry reports. The findings suggest that the nine Future-Proof Skills are consistent with the requirements of businesses that are in search of 4IR professionals. Therefore, further research on human resource perspectives regarding 4IR skills is necessary. Strengthening these skills among Malaysian graduates is vital to fostering high-quality, future-proof talent. To thrive in the machine-human technology era of 4IR, Malaysian graduates must embrace all nine future-proof skills. This study aims to enhance understanding of 4IR skills among graduates, institutions, and industries

Forensic Analysis of Damage in Malaysia Government Structural Assets: Case Studies from JKR Reports

The Public Works Department (PWD)\u27s primary roles include conducting forensic investigations into structural failures and recommending detailed corrective and preventive measures. It also serves as an expert advisory body, providing expert advice and assessments on structural forensics issues to other government departments and agencies. The department ensures the safety and integrity of government assets through expert forensic analysis and guidance. The research objective is to identify the causes of damage to government-owned structures using forensic reports from the PWD Forensic Division. The scope is focused on forensic reports issued specifically by the PWD building\u27s forensic team. Published forensic reports from the PWD Forensic Division were assessed. The research involved identifying and analysing the findings from these reports to determine the variables associated with damage to different building structures. The data was compiled, conceptualised, and statistically analysed to explore correlations between damage factors. There is a notable increase in the number of forensic reports produced each year. Based on the research results, several variables contribute to the structural damage of government buildings. The most frequently observed factor is material deterioration. An in-depth analysis indicates that cracks in the building structure are a primary cause of material deterioration. These cracks often occur alongside other forms of damage such as delamination, spalling, and corrosion. The conclusion drawn from this research is that the number of forensic examination applications for government buildings is likely to increase. The prevalence of cracks found in the majority of forensic reports indicates significant structural changes that are often only visible through detailed examination. This highlights the need for meticulous attention from all technical stakeholders involved in design, project monitoring, and maintenance. Addressing these issues proactively is essential to ensure the structural integrity and longevity of government buildings

UAV Based Efficient Cooperative Spectrum Sensing in CRN: Time-Slot Optimization

Cooperative spectrum sensing in cognitive radio networks provides a strong way to increase the throughput, spectral efficiency, and energy efficiency. However, the increased number of secondary users increases the energy consumption, thereby reducing the energy efficiency. To overcome this, a novel technique called unmanned aerial vehicles based on cooperative spectrum sensing has been proposed to reduce energy consumption and enhance the throughput and energy efficiency in a cognitive radio network. In this paper, a UAV-based cognitive radio network is considered to improve the throughput.  The performance of unmanned aerial vehicles is closely verified with parameters such as sensing time, path radius, and UAV velocity.  Optimization of the number of time slots is considered to further enhance the throughput. Simulation results indicate that the maximum optimal N is 18 when the detection probability is 0.9, with a sensing time of 2 ms. However, as the sensing time increases to 10 ms, the optimal N decreases to 3. Thus, maximum throughput is achieved by either selecting a higher optimal N with a high detection probability and lower sensing time or a lower optimal N with a lower detection probability and higher sensing time. This optimization strategy improves the throughput of virtual cooperative spectrum sensing compared to conventional approaches

Corrosion Analysis Tool Using Pencil Graphite Electrode Sensor with Machine Learning Algorithm

Corrosion 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

Optimizing Solar Panel Cleaning with Kalman Filter-Enhanced Mobile Robotics

This study proposes the use of the Kalman filter method to accurately determine the position of the robot so that it can monitor the efficiency of the solar panels. This method is applied to the mobile solar panel cleaning robot, the Kalman filter is used to process data from the Inertial Measurement Unit (IMU) sensor on the robot specifically on the z-axis to accurately determine the position of the robot in the Cartesian coordinate system. The robot\u27s performance tests show that the accuracy of the displacement measurement of the encoder corresponds to the pulse value. The test results showed that the use of the Kalman filter could significantly reduce the total error in the sensor data, namely when before using the Kalman filter, the total error from the reference axis gradient was 47.17 degrees, while by using the Kalman filter, the total error was 0.23 degrees, which means that the effectiveness of dust cleaning by the robot showed that the robot was able to reach the target coordinates with a high level of accuracy. Then, the mobile solar panel cleaning robot is taken simultaneously to monitor and maintain the efficiency of the solar panel in terms of dust and temperature drop. The efficiency of solar panels with a temperature drop of 5-6 degrees Celsius. The result of this study is a solar panel cleaning robot equipped with the Kalman filter algorithm to lower the temperature and clean dust. The total movement error of the robot was 0.73 for the X coordinates and 0.79 for the Y coordinates. The decrease in temperature had a positive effect on the increase in power by 2% from 85% to 87%. The results of this study show that the performance of the system is maintained in optimal conditions even though temperature fluctuations are successfully treated to increase the efficiency of the system, the temperature reduction according to the standard conditions (STC) is still not optimal, so further research and improvement is needed in the temperature reduction to achieve higher efficiency

Design of RoF-VLC Based DWDM Communication System

Radio over Fiber with Visible Light Communication system (RoF-VLC) is a promising technology that integrates the benefits of optical fiber communication and wireless transmission. This abstract presents a novel approach to enhance the RoF system by integrating VLC with millimeter-wave (mm Wave) technology, leveraging the advantages of both systems. The proposed system utilizes mmWave signals over a single optical fiber link through a Wavelength Division Multiplexing (WDM) technology to enable the simultaneous transmission of multiple VLC system. On the other hand, mm Wave technology leverages the abundant bandwidth available in the millimeter-wave frequency range, enabling high-capacity wireless communication. By combining these technologies, the proposed system achieves high-speed data transmission and increased network capacity. WDM technology plays a crucial role in the proposed system by enabling the simultaneous transmission of VLC and mm Wave signals over a single optical fiber. The RoF-VLC system based on WDM technology is built and simulated using OptiSystem software, with four WDM channels at 450 nm, 450.8 nm, 451.6 nm, and 452.4 nm as an optical source of VLC system and a photodetector as a receiver. Each channel with a 40 GHz radio signal is transmitted over a 40 km of fiber link and a 3 m of VLC channel. This proposed design of RoF-VLC system based on WDM has been analyzed based on the effect of propagation distance (km), modulation format, data rate, and input power. The performance analysis show that this system is achieved by using the values of BER at 2.6355e-009 for channel 1, 7.54389e-010 for channel 2, 5.39904e-010 for channel 3, and 2.45532e-010 for channel 4

IoT Based Water Quality Monitoring System for Swimming Pool

A swimming pool is a recreational facility where people relax and refresh themselves. However, maintaining good water quality is essential, as poor water conditions can negatively impact users\u27 health. In public swimming pools, operators typically rely on manual devices to measure pH and chlorine levels, making it challenging to maintain consistent water quality. This can lead to potential skin irritation and other health issues. Therefore, an automated water quality monitoring system has been developed to continuously track and maintain pool water quality by measuring pH, chlorine concentration, turbidity, and temperature to ensure a safe and comfortable swimming environment. The system was tested on several swimming pools with varying water conditions, evaluating key parameters such as pH, Total Dissolved Solids (TDS), turbidity, and temperature. The analysis of results demonstrates that the developed system effectively and accurately measures these parameters, providing reliable data for maintaining optimal water quality

Haar-VGG: Face Attendance System

Attendance taking is a crucial practice in educational institutions in Malaysia, but the traditional manual method is time-consuming and risky, particularly in the post-Covid era. To address this, a face recognition attendance system using Python is developed. The Viola-Jones algorithm known as Haar is utilized for face detection, and transfer learning on VGGFace is applied for model training, using 195 images from FLW dataset and volunteers among students. The system achieves a validation and testing accuracy of 1.0 through image preprocessing and augmentation. The attendance system includes a user-friendly graphical interface and live webcam feed, enabling instant recognition and recording of attendance. Integration with a MySQL database allows easy access to attendance records for teachers. This advanced system saves time, reduces the risk of virus transmission, and simplifies attendance management, offering a convenient and efficient solution for educational institutions