Blockchain is a technology that has been gaining popularity in the recent years thanks to its immutability and decentralized features, various industries have started to integrate the technology with their business models, and this resulted in various types of blockchain networks being developed. This paper reviews the latest academic advancements of sharding techniques in the various types of sharded blockchain networks that have been implemented in the past 5 years to identify their strengths and weaknesses. The increasing volume of transactions in blockchain networks has caused issues in terms of their scalability, transaction efficiency, storage, and security which obstructs the global adoption of blockchain technology. Sharding in blockchain is a solution that aims to solve the issues through its various types of sharding techniques such as network, transaction, or state sharding along with the various types of networks and systems. The general definition of sharding is to partition the network into subsets known as shards which allows them to validate transactions parallelly and thus, resulting in performance improvement and scalability improvement of the existing network. The existing sharding techniques (before 2020) and latest sharding techniques (2020 – 2024) along with their characteristics, advantages, and disadvantages are also discussed. An analysis based on the performance evaluation of recent sharding techniques was done to further understand the impact brought by the techniques. The result of the review sheds critical insights on the advancements in sharding techniques and fosters innovation and facilitates the widespread adoption of sharding-based solutions in the blockchain ecosystem.

The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment.

Object recognition presents considerable difficulties within the domain of computer vision. Field-Programmable Gate Arrays (FPGAs) offer a flexible hardware platform, having exceptional computing capabilities due to their adaptable topologies, enabling highly parallel, high-performance, and diverse operations that allow for customized reconfiguration of integrated circuits to enhance the effectiveness of object detection accelerators. However, there is a scarcity of assessments that offer a comprehensive analysis of FPGA-based object detection accelerators, and there is currently no comprehensive framework to enable object detection specifically tailored to the unique characteristics of FPGA technology. The You Only Look Once (YOLO) algorithm is an innovative method that combines speed and accuracy in object detection. This study implemented the YOLOv5 algorithm on a Xilinx® Zynq-7000 System on a Chip (SoC) to perform real-time object detection. Using the MS-COCO dataset, the proposed study showed an improvement in resource utilization with approximately 42 thousand (78%) look-up tables, 56 thousand (52%) flip-flops, 65 (46%) BRAMs, and 19 (9%) DSPs at a frequency of 250 MHz, improving the effectiveness compared to previous simulated results.

The Fourth Industrial Revolution (4IR) has ushered in a new era of efficiency across various domains, including the management of Road Traffic Congestion (RTC) in metropolitan cities. Numerous studies have elucidated how smart urban traffic management systems (TMS), encompassing 4IR technologies like Artificial Intelligent (AI), Machine Learning (ML), Internet of Things, and even Software-Define Networks (SDN) or Blockchain can optimize traffic flow and mitigate congestion. Given these results, there is a pressing need to evaluate these approaches and innovations in terms of their performance and sustainability. This review aims to scrutinize current technologies predicated on intelligent traffic management. It will examine the methodologies employed, the benefits derived from these systems, and potential challenges that may arise. The study has identified and categorized research based on the TMS procedure from the sensing process, traffic data evaluation, and solution developments. By providing a comprehensive overview of state-of-the-art in intelligent traffic management, this review seeks to contribute to the ongoing discourse on leveraging 4IR technologies for sustainable urban development.

Recurrent fragility fractures are a big challenge in managing bone health, especially in older adults and people with osteoporosis or related disorders. The prediction of such fractures depends on the overall understanding of various clinical and lifestyle factors contributing to bone fragility. The goal of this analysis is to carry out advanced predictive analytics related to the problem of recurrent fragility fractures concerning several key features about each patient: age, sex, body mass index, physical activities, smoking status, and several others: T-score, along with biomarkers such as Vitamin D3, calcium levels, and the rest. Indeed, the results show that the features most indicative of recurrent fractures include age, T-score, physical activity, and glucocorticoid usage; greater emphasis has been laid on bone mineral density and lifestyle factors. The detail feature importance analysis showed that age and T-score have the highest important values for fracture recurrence prediction, followed by physical activity and glucocorticoid treatment. The study finds that the proposed models achieved an accuracy of 91.3% (LLaMA 7GB) and 90.2% (Gamma 7GB), with ROC AUC scores of 93% and 92%, respectively. The study highlights that age, T-score, and glucocorticoid usage were the most significant predictive factors. These findings enable improved clinical decision-making for fracture prevention. This model can facilitate the identification of high-risk patients with recurrent fragility fractures by clinicians, thus allowing for targeted intervention and personalized treatment to reduce the risk of future fractures. The findings contribute to the development of fracture prediction, incorporating a wide array of clinical data that, in turn, will improve patient outcomes and their quality of life.

This study significantly contributes to the sphere of educational technology by deploying state-of-the-art machine learning and deep learning strategies for meaningful changes in education. The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. That indeed puts into great perspective the huge potential it possesses for accuracy measures while predicting in educational setups. The CNN model, which predicted with an accuracy of 89%, showed quite impressive capability in sentiment analysis to acquire further insight into the emotional status of the students. RCNN, Random Forests, and Decision Trees contribute to the possibility of educational data complexity with valuable insight into the complex interrelationships within ML models and educational contexts. The application of the bagging XGBoost algorithm, which attained a high accuracy of 88%, further stamps its utility toward enhancement of academic performance through strong robust techniques of model aggregation. The dataset that was used in this study was sourced from Kaggle, with 1205 entries of 14 attributes concerning adaptability, sentiment, and academic performance; the reliability and richness of the analytical basis are high. The dataset allows rigorous modeling and validation to be done to ensure the findings are considered robust. This study has several implications for education and develops on the key dimensions: teacher effectiveness, educational leadership, and well-being of the students. From the obtained information about student adaptability and sentiment, the developed system helps educators to make modifications in instructional strategy more efficiently for a particular student to enhance effectiveness in teaching. All these aspects could provide critical insights for the educational leadership to devise data-driven strategies that would enhance the overall school-wide academic performance, as well as create a caring learning atmosphere. The integration of sentiment analysis within the structure of education brings an inclusive, responsive attitude toward ensuring students’ well-being and, thus, a caring educational environment. The study is closely aligned with sustainable ICT in education objectives and offers a transformative approach to integrating AI-driven insights with practice in this field. By integrating notorious ML and DL methodologies with educational challenges, the research puts the basis for future innovations and technology in this area. Ultimately, it contributes to sustainable improvement in the educational system.