Emerging Technologies and Engineering Journal

Reviewing The Effectiveness of Different Methods of Applying Post-Tensioned Metal Straps (PTMS) In Enhancing the Flexural Strength of Normal Concrete Beams After Strengthening

Serwan Khwrshid Rafiq, Wrya Abdullah — Tue, 30 Apr 2024 00:00:00 -0600

Concrete's strength and deformability can be enhanced by adding external lateral confinement. Several materials and confinement techniques have been investigated throughout the development of confined concrete, including Post-Tensioned Metal Straps (PTMS). This study compares three methods of using PTMS to strengthen normal Reinforced Concrete (R.C) beams in bending. First, the method of completely wrapping the beams longitudinally with metal straps is assessed. Secondly, the application of lateral metal straps is examined. Finally, the method of combining metal straps with steel channels is investigated. The assessment criteria included price, effort required for application, applicability of the method, strength enhancement, and ductility of the enhancement. It is concluded that wrapping the beam longitudinally with metal straps is the most cost-effective and easiest method. However, the use of metal straps with steel channels offers greater applicability, strength, and ductility, as it can be applied to any concrete beam. Finally, it is demonstrated that ANSYS can accurately model beams strengthened with metal straps, as evidenced by the good match between the failure modes of beams tested experimentally and analytically.

Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability

Waqas Ali, Mariam Sabir — Tue, 30 Apr 2024 00:00:00 -0600

This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. The LSTM model is trained and evaluated on a dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The dataset's distribution of bug categories and statuses further provides valuable insights into common software development issues.

Integrating Internet of Things Technologies for Dynamic Sustainability in Architectural Design

Sardar Sulaiman Shareef, Hozan Latif Rauf — Tue, 30 Apr 2024 00:00:00 -0600

The application of Internet of Things (IoT) technology is a significant step toward improving the sustainability and responsiveness of the built environment. The current work introduces the Adaptive and Sustainable IoT Integration Model (ASIIM), a novel framework designed to enhance the dynamic sustainability and adaptation of IoT in architecture in order to maximize its potential. Through a comprehensive review of the literature, this study analyzes the current state of IoT applications in architectural professions, highlighting the key benefits of IoT in improving building performance, occupant comfort, and energy efficiency. The ASIIM framework emerges as a comprehensive approach that encompasses key strategies for combining user interface systems, sustainability measures, IoT-enabled adaptive features, and fundamental design principles in order to promote a more responsive and sustainable architecture design. The paper identifies key obstacles to IoT integration, such as interoperability, data protection, and device sustainability, and offers collaborative ways to overcome them. The findings demonstrate the transformative potential of IoT in architecture, suggesting a future in which buildings will become dynamic systems that can adapt to the needs of both the surrounding environment and their occupants rather than static structures. This study contributes to the expanding body of knowledge on sustainable architecture by offering insights and a theoretical foundation for further study and practice in the integration of IoT technology in architectural design.

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

Maloth Bhavsingh, K Samunnisa, A Mallareddy — Tue, 30 Apr 2024 00:00:00 -0600

This study presents a new strategy to improve security and efficiency in Machine-Type Communication (MTC) networks, addressing the drawbacks of the existing Adaptive Hierarchical Group-based Mutual Authentication and Key Agreement (AHGMAKA) protocol. The AHGMAKA protocol, crucial for securing communication within groups of devices with limited resources, has been found to cause significant operational delays and inefficiencies. Our proposed solution integrates advanced cryptographic methods, including an optimized Authenticated Message Authentication Code (AMAC) and lightweight encryption, sophisticated optimization algorithms for dynamic grouping, and an efficient, lightweight group management protocol. It also introduces adaptive network management strategies to customize performance according to the needs of MTC networks. The effectiveness of this approach has been validated through empirical analysis, showing considerable improvements in operational performance and energy efficiency. These improvements mark a significant step toward achieving an optimal balance between efficiency and security for MTC networks. However, the research acknowledges ongoing challenges, including the trade-off between security and efficiency and the issue of compatibility with older devices, suggesting these as areas for future study. The paper outlines potential research paths, including using machine learning for better group management, adopting post-quantum cryptographic methods, applying hardware acceleration, and pushing to standardize these technologies. This work significantly advances the field of secure and efficient communication in MTC, a critical component of the growing Internet of Things (IoT) landscape, setting the stage for future breakthroughs.

Different Statistical Modeling to Predict Compressive Strength of High-Strength Concrete Modified with Palm Oil Fuel Ash

Tue, 30 Apr 2024 00:00:00 -0600

The present study focuses on proposing various statistical models, such as linear regression (LR), nonlinear regression (NLR), and artificial neural network (ANN), to forecast the compressive strength of environmentally friendly high-strength concrete, incorporating waste agricultural material like palm oil fuel ash (POFA). A dataset of 105 experimental observations was compiled from existing literature to achieve this goal, which was subsequently partitioned into training and testing subsets. Each model was developed based on the training data and evaluated using the testing data. The performance of each proposed model was gauged using diverse statistical metrics like the coefficient of determination, mean absolute error, root mean square error, and scatter index to identify the most effective model. The findings indicate that using POFA with a finer particle size exerts a greater influence on the concrete's properties. The replacement was done using the weight method, and the predicted equation worked with the variation of the used rate of POFA from 0 to 60% of total binder weight. Substituting a portion of cement with POFA leads to a reduction in the heat of hydration and an extension of the setting time. The optimal percentage of POFA is 30%, yielding mechanical properties superior to those of the control mixture, particularly in the later stages of development. Among the models considered, the ANN demonstrates superior efficiency and accuracy in predicting the compressive strength of conventional concrete modified with POFA compared to LR and NLR models. This is evident in the ANN's higher R2 values of 52% and 16%, respectively, and a lower scatter index below 0.1%.