Purpose: This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better understanding of the field.Design/methodology/approach: This study makes use of the Bibliometric Analysis method and gathered 725 papers from the Web of Science and Scopus databases in the management and accounting, business, financial, economic and social science, as well as decision sciences fields from 2017 to 2021 using the R-Package Bibliometrix Analysis “biblioshiny”.Findings: The findings revealed that blockchain research in terms of auditing has already increased and started to spark a quick rise in popularity, but is still in its initial phases with important quality though less in quantity. Moreover, the Journal of Emerging Technologies in Accounting is the most prolific journal with 2019 as the highest publication year, with the United States and China as the most cited countries in this field. Furthermore, in this field, there are much research topics involving blockchain, audit and smart contracts; and there is less involving data analytics, governance, hyperledger, distributed ledger and financial reporting. Additionally, Sheldon (2019) and Smith and Castonguay (2020) are the most productive authors in the field in terms of the H-index.Research limitations/implications: This study has certain limitations such as the fact that it only looked at 105 papers in the domains of finance, business, economics, accounting, management as well as multidisciplinary science. Moreover, the research’s data and dates have an impact on the results dependability. As this is an original topic, fresh studies are anticipated to remain to shine a spotlight on and suggest answers to blockchain’s implications on auditing. Additionally, the period of time was limited to only the last five years, from 2017 to 2021. As a result, extensive study into the topic is required since there is currently a research deficit in the blockchain field in the setting of auditing. So, new research is required to offer new frameworks and understandings for describing the blockchain function in auditing, including processes, techniques, security, as well as timeliness. Investigations in unique circumstances and research employing innovative research methodologies for discovering the new issue would be valuable in acquiring a higher grasp of the complexities faced.Originality/value: This research contributed to the field by assessing the present state of the art of research on the usage and use of BT in finding research gaps, the audit profession and, most importantly, recommending a future direction for researchers in the subject.

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience.

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings.

This study examines the evolution and impact of Big Data technologies across sectors, emphasizing key algorithms, emerging trends, and organizational challenges in their adoption. Special attention is given to ethical concerns related to data privacy, security, and scalability, underscoring the importance of responsible governance frameworks. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure transparency and methodological rigor. A comprehensive literature search identified 83 peer-reviewed articles from high-indexed journals, and a complementary bibliometric analysis of 1108 Scopus-sourced articles (2015–2024) was conducted using R Biblioshiny. This dual-method approach offers both qualitative depth and quantitative insights into major trends, influential sources, and leading countries in Big Data research. Key findings reveal that real-time data processing and AI integration have significantly enhanced data management capabilities, supporting faster and more informed organizational decision-making. This study concludes by highlighting the importance of ethical governance and recommending future research on sector-specific adoption patterns and strategic frameworks that maximize Big Data’s value while safeguarding privacy and trust.

Data encryption is a core mechanism in modern security services for protecting confidential data at rest and in transit. This work introduces the Super Encryption Standard (SES), a symmetric block cipher that follows the overall workflow of the Advanced Encryption Standard (AES) but adopts a key-dependent design to enlarge the effective key space and improve execution efficiency. The SES accepts a user-supplied key file and a selectable block dimension, from which it derives per-block round material and a dynamic substitution box generated using SHA-512. Each round relies only on XOR and a conditional half-byte swap driven by key-derived row and column vectors, enabling lightweight diffusion and confusion with low implementation cost. Experimental evaluation using multiple color images of different sizes shows that the proposed SES algorithm achieves faster encryption than the AES baseline and produces a ciphertext that behaves statistically like random noise. The encrypted images exhibit very low correlation between adjacent pixels, strong sensitivity to even minor changes in the plaintext and in the key, and resistance to standard statistical and differential attacks. Analysis of the SES substitution box also indicates favorable differential and linear properties that are comparable to those of the AES. The SES further supports a very wide key range, scaling well beyond typical fixed-length keys, which substantially increases brute-force difficulty. Therefore, the SES is a promising cipher for image encryption and related data-protection applications.