Mathematical literacy plays a crucial role as a life skill. However, the level of mathematical literacy in Indonesia remains relatively low. One alternative learning approach considered to support the development of mathematical literacy is the Realistic Mathematics Education (RME) approach. Learning activities are also influenced by an individual’s confidence in their own abilities, commonly referred to as self-efficacy. This study aims to examine the effect of the RME approach on mathematical literacy, the impact of self-efficacy on mathematical literacy, and the interaction between the RME approach and self- efficacy in relation to mathematical literacy. This research employs a quantitative design with a quasi- experimental method, using a non-equivalent pre-test–post-test control group design. The findings indicate that students taught using the RME approach demonstrated stronger mathematical literacy than those taught through direct instruction; students with higher levels of self-efficacy also showed higher levels of mathematical literacy; and there was no significant interaction between the learning approach and self-efficacy on mathematical literacy. Future researchers are advised to ensure student attendance at every session, broaden the research sample, and give careful consideration to the allocation of learning time.
Quantum Watermarking (QWM) is an emerging field that combines quantum information theory with digital watermarking techniques to enhance the security of digital content. By leveraging quantum properties such as superposition, entanglement, and the no-cloning theorem, QWM provides robust protection against unauthorised access, tampering, and duplication—offering a significant advantage over classical methods. As the volume and value of digital data grow, the need for advanced intellectual property protection becomes increasingly vital, making QWM a promising approach to next-generation data security. This paper presents a comprehensive review of the theoretical foundations of QWM, its core principles, and various classification methods based on quantum states, embedding techniques, and application domains such as authentication, copyright protection, and tamper detection. It also explores the mathematical models underlying QWM, including quantum state representations and error correction protocols, along with recent advances in experimental validations. Despite its potential, QWM faces challenges such as noise sensitivity, scalability limitations, and high computational resource requirements. Issues related to secure key management and implementation vulnerabilities also remain. Nevertheless, this review aims to serve as a foundational reference for researchers and practitioners, highlighting the promise of QWM in reshaping digital content security, particularly in sensitive domains like digital forensics and healthcare.
Underground cable (UC) joints are among the most failure-prone components in power distribution networks, particularly under thermal stress. This review explores a range of jointing methods and diagnostic approaches used to assess thermal degradation (TD) in UC joints. The discussion begins with the structural components of high-voltage cables, followed by a comparison of mechanical, electromagnetic, heat-shrink, cold-shrink, and exothermic welding techniques. Each method is evaluated based on materials, performance, application principles, and known limitations. The paper also analyses key contributors to TD, including electrical overloading, improper joint construction, and environmental exposure, with attention to material-level effects such as insulation embrittlement and conductor oxidation. Several diagnostic strategies are assessed, including infrared thermography (IRT), partial discharge (PD) testing, real-time thermal monitoring, and emerging tools like acoustic emission (AE) and AI-driven sensing. A comparative evaluation highlights the trade-offs between sensitivity, practicality, and field performance under various conditions. Accordingly, this review synthesises recent and foundational research to address three questions: (1) What are the dominant causes and pathways of TD in UC joints? (2) How do joint technologies and installation/maintenance practices influence thermal aging risk? (3) Which diagnostic techniques are most effective for detecting TD, and what are their limitations in field deployment? The findings emphasise the importance of integrated monitoring, standardised practices, and appropriate material selection to enhance long-term reliability. Future research should focus on the integration of advanced diagnostic technologies such as AI-driven predictive maintenance systems and real-time thermal monitoring to improve the detection of early-stage TD.
The rapid growth of the Internet of Things (IoT) has intensified the demand for lightweight cryptographic solutions that deliver high security without compromising energy and area constraints. This paper presents a highly efficient AES -128 hardware architecture specifically optimised for resource-constrained IoT devices. The proposed design employs reusing a 128 -bit AES framework resource, enabling an extensive reduction in functional units across all transformation stages. Additionally, the elimination of intermediate registers and the reuse of a single round computation block for all encryption rounds significantly reduce area while balancing the trade -offs between throughput, frequency and power. A key innovation in this work is the implementation of a compact S-box using composite Galois Field GF28 arithmetic to enhance the substitution efficiency while maintaining cryptographic strength. The physical layout implemented on 32 nm technology reveals that the proposed architecture achieves a core area of only 0.015 mm , an operating 2 frequency of 200 MHz, with 0.014 Gbps throughput, and a low power consumption of 1.4 mW , offering a well-balanced trade-off among performance, security, and hardware efficiency. A comparative analysis with state-of-the-art designs demonstrates the superiority of this approach for achieving low-cost, energy-aware cryptographic protection. The proposed solution is highly suitable for secure resource constrained IoT deployments.
Multiclass classification is essential in machine learning as it allows systems to differentiate between three or more categories using intricate, multidimensional data. This paper examines the theoretical basis and real-world applications of multiclass classification, highlighting feature selection methods and the efficacy of classifiers. Essential principles, including significant inter -class variance, minimal inter-feature correlation, robust target relevance, and feature consistency, are examined for their contributions to discovering optimal predictors across various datasets. The experimentation is implemented with a dry bean dataset, utilising classifiers such as K-Nearest Neighbors (KNN) and Naive Bayes to distinguish bean types based on their morphological characteristics. KNN excels in basic pattern recognition, and Naive Bayes implements classification probabilistically, which is appropriate, widespread and accurate in high-dimensional environments. The study also stresses the importance of performance metrics such as accuracy, and dimensionality reduction as useful analytic tools. The method of feature selection and classifier combined achieved the accuracy measures from the lowest value of 85.67% to the highest value of 88.58%. Although these results look good for the automated classification of beans, performing thorough evaluati on with balanced metrics for all classes would increase the trust in the robustness of the technique, especially for the minority bean types.
