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Experimental investigation of PWHT and normalizing effects on SMAW low-carbon steel joint properties
Full text linkThe influence of post-weld heat treatment (PWHT) followed by normalizing on the mechanical properties of AH36 low-carbon steel is significant, particularly in the context of marine applications, such as shipbuilding welded joints. According to the extant literature, PWHT has been demonstrated to reduce residual stresses and enhance microstructural uniformity. However, the suitable PWHT temperatures for AH36 steel welds to balance strength, ductility, and toughness prior to normalizing remain underexplored. The objective of this study is to ascertain the suitable PWHT temperatures prior to normalizing, with the aim of improving weld performance in marine environments. A parametric study was conducted on AH36 steel specimens welded using shielded metal arc welding. The specimens were subjected to PWHT at 0°C (as-welded), 450°C, 600°C, and 750°C, followed by normalizing. Tensile, bending, and Charpy impact tests were utilized to assess the mechanical properties against established maritime safety standards. The results show that 600°C is the optimal PWHT temperature, effectively reducing residual stresses and promoting microstructural homogeneity. This, in turn, ensures that welds meet safety standards while preserving mechanical integrity. Higher temperatures increased the risk of brittleness, while lower temperatures provided insufficient stress relief. This study demonstrates that precise selection of PWHT temperature prior to normalizing is critical for ensuring reliable welds in marine structures. It identifies the optimal condition that maximizes strength, ductility, and impact toughness of AH36 steel while satisfying the Indonesian Classification Bureau (BKI) maritime safety standards.
Sustainable competitiveness through community empowerment and the success factors of SMEs?
No full textThis study aims to examine the relationship between sustainable competitiveness and community empowerment in the context of small and medium enterprises (SMEs) by conducting a systematic literature review using the PRISMA guidelines. The review identified relevant studies published between 2021 and 2025 in the Scopus database and analyzed them using the VOS Viewer bibliometrics tool. This study explores the role of empowerment in improving Sustainable Competitiveness. Findings show that the topics of sustainable competitiveness and community empowerment are still rarely discussed in an integrated manner, although both are significantly interrelated. The analysis identified six main dimensions that contribute to sustainable competitiveness in SMEs: Economic, Social, Environmental, Technological, Organizational, and Human Resources that correlate with sustainable competitiveness in SMEs. In various literature, sustainable competitiveness in SMEs is often discussed and has a close relationship with the use of technology in SMEs businesses. However, on the other hand, it also needs to be supported by the role of the community, which is included in the social dimension, which also plays an important role in SMEs that support business sustainability, foster cooperation networks, and improve the welfare of local communities. These findings provide a basis for insight and information to develop targeted business strategies and public policies aimed at enhancing sustainable competitiveness in SMEs through community engagement
Performance Analysis of a Micro Underwater Remotely Operated Vehicle (ROV)
Full text linkUnderwater Remote Operated Vehicle (ROV) is a tethered marine robot that are widely employed for scientific and commercial applications. Several industries are working on underwater robots to increase the productivity, monitoring and surveillance especially in the petroleum and gas industries. These operations are often performed by human divers; however, the underwater environment poses hazards and pressure-related limits, making them costly and risky. As a result, ROVs have been designed to replace divers themselves. It is a tethered underwater robot that the operator controls manually using a PS2 controller. This project is to design and develop a micro underwater ROV for monitoring applications. The ROV are designed to withstand pressure underwater by selection of suitable material for its frame and other components will be equipped including pressure/depth sensor, MPU6050 IMU sensor and waterproof endoscope camera. Standard testing procedures are employed to assess the ROV's performance in buoyancy and control efficiency tests for the propulsion system in real environment, including laboratory pool. The developed ROV prototype shows promising performance with achieved 90% negative buoyancy is crucial for the ROV to perform effective submerge and raise operations and also with stable velocity and acceleration in forward, backward, and submerging. The steering tests highlighted that the ROV is more flexible and faster in maneuvering concerning turning performance as the horizontal thrusters’ configurations are positioned at 45° at the back of the ROV. The outcomes of this project are anticipated to bring substantial advantages to industries associated with underwater applications
The impact of the inclination angle of perforated screen facade on daylight performance in the tropics
Full text linkDaylighting is one of the fundamental aspects of green building principles. Utilizing daylighting in a building offers numerous benefits, including energy efficiency, enhanced comfort, improved workplace productivity, better health, and increased economic value. However, buildings with glazed facades can experience excessive illuminance, uneven daylight distribution, and glare without proper shading devices. Perforated screen facade (PSF) is one of the shading devices widely used in buildings with glass facades. PSF minimizes direct solar radiation and enhances daylighting performance while preserving outdoor views. This study focused on one design variable of PSF, the inclination angle, which had not been widely explored in previous research within the context of a tropical climate. The research aimed to evaluate the impact of the PSF inclination angle on daylight performance. The research method was experimental, using radiance-based simulation as a tool. The daylight availability and visual comfort of office buildings with vertical PSF were compared with inclined PSF. The daylight performance metrics analyzed included mean illuminance, useful daylight illuminance, and spatial disturbing glare. The results indicated that implementing an inclined PSF resulted in mean illuminance ranging from 1065 to 1105 lx, useful daylight illuminance between 95.08% and 95.55%, and spatial disturbing glare between 5.1% and 6.5%. Increasing the PSF inclination angle raises the mean illuminance and spatial disturbing glare and reduces the useful daylight illuminance. PSF can be applied with an inclination angle to buildings in the tropics, providing broader possibilities for facade design exploration
Signal quality comparison of customer base and branching methods in fiber to the home network design
Full text linkOne communication medium that is well-known for its outstanding and reliable performance is fiber optic. A social example of its application is the Fiber to the Home (FTTH) network. The goal of this study is to evaluate the signal quality of the customer base method and the branching method, two FTTH-building techniques based on the PT.PLN Icon Plus standards, in order to identify the most practical approach for use in the Air Hitam 2 cluster. Two scenarios were used in this study at the Fiber Access Terminal (FAT) with 1:16 and 1:8 splitters. The fiber optic cable path design findings demonstrate that the branching approach is a wise decision, utilizing optical fiber cables for a total of 9 Km, with the greatest cable distance being 2.5 Km from the Optical Line Terminal (OLT) to the end FAT. According to theory, in the 1:16 splitter situation and the 1:8 splitter scenario, the optical power received by the Optical Network Terminal (ONT) is -19.13 dBm and -16.03 dBm, respectively, with an OLT transmit power of 3 dBm. For these cases, the simulation results are -17.98 dBm and -20.27 dBm. Additionally, the budget value for the rising time reaches 0.253 ns. The bit error rate values in the 1:16 and 1:8 splitter scenarios are 3.157 × 10-10, and 1.63507 × 10-28, respectively, while the Q factor values are 6.18233 and 11.014, respectively. Based on theory and simulation, these findings suggest that the branching strategy can deliver good performance
Bibliometric analysis of research trends in rigid pavement over the last decade
Full text linkThis study presents a bibliometric analysis of research trends in rigid pavement over the last decade, aiming to identify publication trends, research output distribution, main themes, citation patterns, and research gaps. The PRISMA method was employed, and statistical analysis was conducted using bibliometric software. By collecting bibliographic data from academic publications, this research reveals a significant growth in rigid pavement publications, reflecting increased global interest in this field. Major research themes include pavement design, material characterization, construction techniques, maintenance, and performance evaluation. Citation pattern analysis is used to identify influential works in this field. However, this study has limitations in data coverage and is susceptible to biases inherent in bibliometric analysis. Nevertheless, it contributes significantly to understanding the research landscape of rigid pavement, providing valuable insights for researchers, practitioners, and policymakers. Future research could deepen qualitative analysis, track the evolution of research themes, and explore interdisciplinary frameworks to enrich our understanding of rigid pavements
Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection
Full text linkEnsuring airport security is of paramount importance to safeguard the lives of passengers and prevent acts of terrorism. In this context, developing advanced technology for early terrorist detection is crucial. This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. Our system utilizes state-of-the-art artificial intelligence techniques to analyze facial features. Our research uses VGG architecture and pre-trained with face data as a CNN model. This model is used to extract face embedding features from the dataset. These embedding features are then compressed with Principal Component Analysis (PCA) to obtain the meaningful feature as training data for the ANN algorithm. We trained our system using data from 500 identities data with 60 data for each identity. This training enables our system to recognize known terrorists and individuals on watchlists by comparing the facial features of individuals passing through security checkpoints with those in the database. The proposed CNN-ANN-based face recognition system not only enhances airport security but also significantly reduces the processing time for security checks. It can quickly identify potential threats, allowing security personnel to take appropriate actions in real time ensuring a rapid response to security concerns. We present the architecture, training methodology, and evaluation of the CNN-ANN model, achieving a high accuracy of 91.16% and precision of 91.36%. Through this research, we aim to increase airport security and strengthen efforts to combat terrorism, making air travel safer and more secure for all passengers.
Forecast of sugar demand in retail using SARIMA and decomposition models case study: a retail store in Indonesia
Full text linkThis study discusses forecasting demand in a retail store, focusing on sugar, which is a staple food in Indonesia, as the research object. Despite its importance and forecast challenge, there is no research has been done on sugar at the retail level. This study aims to find the most suitable forecast model that can capture data patterns well to give a good prediction of sugar sales in a retail store in Indonesia by comparing SARIMA and decomposition models. This study uses a stationary test and ACF pattern analyses to prepare the data, a residual test to avoid forecast bias, cross-validation to check the forecast model performance, and MAPE as the performance indicator. SARIMA (0,0,0)(0,1,1)8 and multiplicative decomposition with 3 periods of double-moving average models are chosen. Both models have similar patterns but different slopes because the decomposition model is more sensitive to data patterns, resulting in different MAPEs, which are 15.22% and 13.64%. Despite the popularity of SARIMA, decomposition can be an interesting alternative to use since it can capture trend data patterns better. However, the short forecast period is preferable for the decomposition model to avoid high trend slope prediction in the long run, leading to more frequent forecast activity and higher resources compared to SARIMA
Characterization of Eichhornia crassipes bio-adsorbent activated by H3PO4 for the removal of lead ion (Pb2+) from wastewater of battery industry
Full text linkLead ion (Pb2+) contamination from battery industry wastewater affects significant environmental and health risks. This study explored the use of H3PO4-activated water hyacinth (WH) bio-adsorbent as an effective solution for removing Pb2+. The WH bio-adsorbent was prepared by activating dried water hyacinth stems with 1.2 M H3PO4, enhancing adsorption properties. SEM-EDX analysis revealed significant morphological changes, with increased porosity and oxygen-containing functional groups (O-H, C-O-P), which improved adsorption capacity. Adsorption kinetics followed a pseudo-second-order model (R2 = 0.99981), indicating that chemisorption dominated the Pb2+ removal process. Adsorption isotherms firmly fit the Langmuir model (R2 = 0.96), confirming monolayer adsorption on a homogeneous surface. The effect of pH was also investigated, with maximum adsorption efficiency (96.928%) observed at pH 7. FTIR analysis showed changes in functional groups before and after adsorption, confirming the ion exchange mechanism between Pb2+ and the activated bio-adsorbent. The findings suggest that H3PO4 activation increases the surface area and raises the chemical activity of WH, providing new insights into the dual mechanism of physical and chemical modifications for lead removal. This study addresses a critical gap in optimizing adsorbents for heavy metal removal, demonstrating the potential of H3PO4-activated WH for industrial wastewater treatment
Comparative study of CNN techniques for tuberculosis detection using chest X-ray images from Indonesia
Full text linkConvolutional neural networks (CNNs) represent a popular deep-learning approach for image classification tasks. They have been extensively employed in studies aimed at classifying tuberculosis (TB), coronavirus disease 2019 (COVID-19), and normal conditions on chest X-ray images. However, there is limited research utilizing Indonesian data, and the integration of CNN models into user-friendly interfaces accessible to healthcare professionals remains uncommon. This study addresses these gaps by employing three CNN architectures—AlexNet, LeNet, and a modified model—to classify TB, COVID-19, and normal condition images. Training data were sourced from both a local hospital in Indonesia (RSUP dr. Rivai Abdullah) and an additional online dataset. Results indicate that AlexNet achieved the highest accuracy, with rates of 97.52%, 64.45%, and 92.43% on the Kaggle dataset, the RSUP Dr. Rivai Abdullah dataset, and the combined dataset, respectively. Subsequently, this model was integrated into a user interface and deployed for testing using new data from the RSUP Dr. Rivai Abdullah dataset. The web-based interface, powered by the Gradio library, successfully detected 7 out of 10 new cases with 70% accuracy. This implementation may enable medical professionals to make preliminary diagnoses