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.

Accurate and reliable brain tumor classification from magnetic resonance imaging (MRI) is a critical component of computer-aided diagnosis systems, directly impacting clinical decision-making and patient outcomes. This study presents ResSGA-Net, a hybrid deep learning framework that integrates a ResNet50 backbone with dual attention mechanisms (global and gated) and a Swin Transformer to capture both fine-grained local features and long-range contextual dependencies effectively. A fusion strategy is employed to unify convolutional, attention-refined, and transformer-enhanced representations into a robust feature space for multi-class classification. The proposed model is evaluated on two publicly available benchmark datasets, including a four-class and a three-class brain tumor classification task, using stratified cross-validation. Extensive quantitative analysis demonstrates that ResSGA-Net achieves state-of-the-art performance, with accuracies exceeding 98% on Dataset I and strong generalization on Dataset II (accuracy of 93.18% and macro-averaged AUC of 0.989). Comprehensive statistical significance testing confirms that the observed improvements are highly significant and not attributable to random chance. Ablation studies further validate the individual contributions of attention mechanisms and data augmentation strategies, demonstrating that performance gains arise from tumor-specific feature learning rather than artificial data diversity. Qualitative analyses, including confusion matrices, training dynamics, ROC curves, and confidence-based visualizations, confirm stable convergence, robust generalization, and reliable decision confidence across tumor classes. These results indicate that ResSGA-Net provides an accurate, stable, and clinically meaningful solution for automated brain tumor classification, with strong potential for integration into real-world diagnostic imaging workflows.