Search Results (44)

Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems. Full article

Purpose: This study systematically reviews research on six emerging technologies in external auditing, Big Data, Blockchain, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Robotic Process Automation (RPA), to clarify what is currently known and to identify where the main gaps remain. Rather than treating each technology in isolation, this study brings them together under a single integrative review to provide a consolidated reference point for scholars assessing their impact on external audit practices. Design/Methodology/Approach: Following a structured systematic review protocol, searches were conducted in Scopus, ScienceDirect and SpringerLink (2000–2024) using technology-related keywords combined with “audit”, “auditor” and “auditing”. After applying explicit inclusion and exclusion criteria, 471 records were reduced to 32 ABS-listed journal articles, which were analysed thematically. Findings: The review shows that research on emerging technologies in external auditing is still fragmented, with substantial variation in the depth and maturity of evidence across the six technologies. The strongest empirical base is concentrated in Big Data analytics and ML-based predictive models (including more advanced Deep Learning variants), whereas Blockchain and RPA work remains predominantly conceptual or confined to small-scale design-science implementations. Across technologies, most studies are single-country and either rely on auditors’ self-reported perceptions of adoption and impact or evaluate model performance without tracing effects on audit strategies and engagement outcomes, which limits external validity and construct measurement. Very few articles explicitly integrate the Audit Risk Model or other formal theories, and almost no work examines multi-technology “audit stacks” or generative AI, leaving substantial gaps in understanding how these tools jointly reshape inherent, control and detection risk across the audit cycle. Originality/Value: By integrating six technologies within a single external audit framework, the review offers a technology-specific evidence map and a targeted future research agenda that can guide scholars, audit firms and regulators in designing studies and policies aligned with actual gaps in the current literature. Full article

Background: This paper is a systematic review of 100 peer-reviewed articles (2015–2025) related to artificial intelligence (AI) applications in the auditing field, and includes machine learning, natural language processing, robotic process automation, and other AI methods. Purpose: The paper delves into the integration of these AI technologies into the audit workflow; empirical implications of these technologies on audit effectiveness; efficiency and quality; and technical, organizational, and regulatory obstacles that suggest more widespread adoption is still limited. Methods: Five large-scale databases and other sources were searched and selected using PRISMA; structured data were extracted, assessed in quality and narrative, and thematically analyzed. Results: The discussion indicates that machine learning-based anomaly detection and predictive analytics, document analysis through NLP, and automation through RPA are becoming part of planning, risk assessments, control tests, and substantive procedures/reporting, with improvements in detection capabilities, coverage and efficiency reported in various empirical and design science studies. The review also presents common architectural models of AI-enabled audit processes, including layered data and governance, model development and oversight, orchestration and automation, auditor-facing applications, and human-in-the-loop controls. Conclusions: The article proposes an AI-based audit workflow reference architecture and summarizes evidence on opportunities, threats, and implementation obstacles, highlighting gaps in longitudinal assessment, comparative evaluation of AI methods, and regulatory recommendations. The results have practical implications for auditors, standard-setters, and system designers seeking to revise the audit approach and regulations to enable AI-driven assurance. Full article

Tax administrations are undergoing rapid digitalisation, while sustainability requirements are increasingly embedded in corporate governance frameworks. These parallel transformations are raising new expectations for transfer pricing (TP) documentation, which must be accurate, transparent, and audit-ready. This paper investigates the extent to which artificial intelligence (AI)—specifically natural language processing (NLP), robotic process automation (RPA), and machine-learning techniques—can support a sustainability-oriented governance framework for TP documentation in multinational enterprises. Using a longitudinal case study of the OMEGA Group, operating across 21 jurisdictions, we analyse an AI-enabled documentation architecture that streamlines data extraction, enhances comparability analysis, and strengthens audit preparedness, in line with the OECD Transfer Pricing Guidelines and relevant European Union regulatory requirements. The empirical evidence indicates substantial improvements in documentation efficiency (−68.3%), a significant reduction in processing errors (−81.5%), and higher audit acceptance rates (+27%). Beyond compliance, AI-driven digital workflows contribute to sustainability objectives by reducing resource consumption, improving data traceability, and facilitating alignment with CSRD-related reporting requirements. Overall, the findings demonstrate that AI-enabled TP documentation can evolve into a strategic pillar of sustainable tax governance, provided that its outputs remain explainable, auditable, and grounded in professional judgment. The study proposes an integrated governance framework that connects digital transformation, regulatory compliance, and sustainability within contemporary TP management practices. Full article

Ergonomic load in human–autonomy teams is commonly treated as a static score or a post-hoc audit, even though modern sensing and communication enable real-time regulation of operator effort. We model ergonomic load as a dissipative dynamical state inferred online from multimodal effort proxies and task context, and couple it to autonomy through load-dependent gain moderation and compliance shaping. The method is evaluated on public human–swarm and human–robot interaction traces together with effort-proximal wearable and myographic datasets using a unified, windowed pipeline and controlled stress tests that emulate latency, downsampling, packet loss, and channel dropouts. On a large human–swarm benchmark, the estimator achieves strong discrimination and calibration for rare high-load events (up to AUROC $0.87$, AUPRC $0.41$, ECE $0.031$ at $q=0.90$) and degrades predictably under delay, with a knee around 300–$400\mathrm{ms}$ (AUROC $0.87\to 0.80$, ECE $0.031\to 0.061$ at $500\mathrm{ms}$). Embedding the estimate in the adaptation schedule reduces overload incidence and oscillatory redistribution while preserving coordination proxies in surrogate closed-loop simulation: overload time drops from $7.8\%$ to $4.1\%$ (relative reduction $\approx 47\%$) with throughput maintained near baseline ($1.00\to 0.97$) and oscillation power reduced ($0.26\to 0.14$) under nominal timing. These results provide a reproducible pathway for making ergonomics a control-relevant feedback signal, together with explicit operational constraints on estimator calibration (target ECE $\le 0.05$) and end-to-end latency (effective $\tau \le 300\mathrm{ms}$) required to avoid regime switching and maintain stable, interpretable adaptation. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

In the era of rapid technological progress, Robotic Process Automation (RPA) has emerged as a pivotal tool across professional domains. Organizations pursue automation to boost efficiency and productivity, control costs, and reduce errors. RPA software automates repetitive, rules-based tasks previously performed by employees, and its effectiveness depends on integration across the business–IT–people interface. We adopted a mixed-methods study combining a PRISMA-guided multivocal review of peer-reviewed and gray sources with semi-structured practitioner interviews to capture firsthand insights and diverse perspectives. Triangulation of these phases examines RPA governance, auditing, and policy. The study clarifies the relationship between business processes and IT and offers guidance that supports procedural standardization, regulatory compliance, employee engagement, role clarity, and effective change management—thereby increasing the likelihood of successful RPA initiatives while prudently mitigating associated risks. Full article

Advanced technologies, such as robotic process automation, blockchain, and machine learning, increase audit efficiency. Nonetheless, some Supreme Audit Institutions (SAIs) have not undergone digital transformation. This research aimed to develop a comprehensive framework for supreme audit institutions to adopt and integrate emerging technologies into their auditing processes using a hybrid theoretical approach based on the TAM (Technology Acceptance Model) and TOE (Technology–Organisation–Environment) models. The framework was informed by insights from nineteen highly experienced experts in the field from eight countries. Through a two-round Delphi questionnaire, the experts provided valuable input on the key factors, challenges, and strategies for successful technology adoption by public sector audit organisations. The findings of this research reveal that technology adoption in SAIs starts with solid management support led by the chief technology officer. They must evaluate the IT infrastructure and readiness for advanced technologies, considering the budget and funding. Integrating solutions like the SAI of Ghana’s Audit Management Information System can significantly enhance audit efficiency. Continuous staff training is essential to build a positive attitude toward new technologies, covering areas like data algorithm auditing and big data analysis. Assessing the complexity and compatibility of new technologies ensures ease of use and cost-effectiveness. Continuous support from technology providers and monitoring advancements will keep SAIs aligned with technological developments, enhancing their auditing capabilities. Full article

This paper introduces a configuration and integration model for mobile robots deployed in emergency and special operations scenarios. The proposed method is designed for implementation within the operational technology (OT) domain, enforcing security protocols that ensure both data encryption and network isolation. The primary objective is to establish a dedicated operational environment encompassing a command and control center where the robotic network server resides, alongside real-time data storage from network clients and remote control of field-deployed mobile robots. Building on this infrastructure, operational strategies are developed to enable an efficient robotic response in critical situations. By leveraging remote robotic networks, significant benefits are achieved in terms of personnel safety and mission efficiency, minimizing response time and reducing the risk of injury to human operators during hazardous interventions. Unlike generic IoT or IoRT systems, this work focuses on secure robotic integration within segmented OT infrastructures. The technologies employed create a synergistic system that ensures data integrity, encryption, and safe user interaction through a web-based interface. Additionally, the system includes mobile robots and a read-only application positioned within a demilitarized zone (DMZ), allowing for secure data monitoring without granting control access to the robotic network, thus enabling cyber-physical isolation and auditability. Full article

This umbrella review synthesizes international guidelines on the surgical management of rectal cancer to provide unified recommendations tailored to local healthcare organizations. This review emphasizes the importance of surgical centralization in high-volume centers, which maximizes outcomes, reduces morbidity, and increases survival rates. Minimally invasive approaches, such as laparoscopy and robotic surgery, are highlighted for their perioperative benefits, although careful patient selection and surgical expertise are required. Mechanical bowel preparation combined with oral antibiotics is recommended to effectively reduce complications, including surgical site infections and anastomotic leakage. Enhanced Recovery After Surgery protocols have been shown to significantly improve postoperative recovery and reduce hospital stay duration. Comprehensive perioperative care, including venous thromboembolism prophylaxis and infection control, is essential for optimal patient outcomes. This review underscores the need for structured training, certification, and regular audits for advanced techniques such as robotic surgery and transanal total mesorectal excision. Implementation of a national database is recommended to support ongoing improvements in rectal cancer surgery. This review centralizes evidence-based recommendations to guide surgical decision-making and harmonize the multidisciplinary care for patients with rectal cancer. Full article

This study presents a three-dimensional collective motion model that integrates auditory and visual sensing modalities, inspired by organisms like bats that rely on these senses for navigation. Most existing models of collective motion consider vision-based sensing, likely reflecting an inherent human bias towards visual perception. However, many organisms utilize multiple sensory modalities, and this study explores how the integration of these distinct sensory inputs influences group behavior. We investigate a generalized scenario of three-dimensional motion, an area not previously explored for combining sensory information. Through numerical simulations, we investigate the combined impact of auditory and visual sensing on group behavior, contrasting these effects with those observed when relying solely on vision or audition. The results demonstrate that composite sensing allows particles to interact with more neighbors, thereby gaining more information. This interaction allows the formation of a single, large, perfectly aligned group using a narrow sensing region, achievable by taking advantage of the mechanics of both auditory and visual sensing. Our findings demonstrate the importance of integrating multiple sensory modalities in shaping emergent group behavior, with potential applications in both biological studies and the development of robotic swarms. Full article

This paper proposes efficient implementations of robot audition systems, specifically focusing on deployments using HARK, an open-source software (OSS) platform designed for robot audition. Although robot audition systems are versatile and suitable for various scenarios, efficiently deploying them can be challenging due to their high computational demands and extensive processing times. For scenarios involving intensive high-dimensional data processing with large-scale microphone arrays, our generalizable GPU-based implementation significantly reduced processing time, enabling real-time Sound Source Localization (SSL) and Sound Source Separation (SSS) using a 60-channel microphone array across two distinct GPU platforms. Specifically, our implementation achieved speedups of 23.3× for SSL and 3.0× for SSS on a high-performance server equipped with an NVIDIA A100 80 GB GPU. Additionally, on the Jetson AGX Orin 32 GB, which represents embedded environments, it achieved speedups of 14.8× for SSL and 1.6× for SSS. For edge computing scenarios, we developed an adaptable FPGA-based implementation of HARK using High-Level Synthesis (HLS) on M-KUBOS, a Multi-Access Edge Computing (MEC) FPGA Multiprocessor System on a Chip (MPSoC) device. Utilizing an eight-channel microphone array, this implementation achieved a 1.2× speedup for SSL and a 1.1× speedup for SSS, along with a 1.1× improvement in overall energy efficiency. Full article

Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of Time–Frequency Fusion (TFF) blocks to extract both time and frequency domain features from time series data. The block applies the Fast Fourier Transform to convert the input to the frequency domain, followed by two separate dense layers to process the resulting real and imaginary components, respectively. The frequency and time features are then combined to reconstruct the input. A U-shaped architecture is implemented to link corresponding TFF blocks of the encoder and decoder at the same level through skip connections. The semi-supervised model is trained using data exclusively from normal operations. Significant errors were generated during testing for anomalies with data distributions deviating from the training samples. Consequently, a threshold based on the magnitude of reconstruction errors was implemented to identify anomalies. Experimental validation was conducted using a custom dataset, including physical attacks as abnormal cases. The proposed framework achieved an accuracy and recall of approximately 0.93 and 0.83, respectively. A comparison with other benchmark models further verified its superior performance. Full article

With the rapid industrialization utilizing multi-rotor drones in recent years, an increase in urban flights is expected in the near future. This may potentially result in noise pollution due to the operation of drones. This study investigates the near- and far-field acoustic characteristics of low-noise propellers inspired by Gurney flaps. In addition, we examine the impact of these low-noise propellers on the sound source localization performance of drones equipped with a microphone array, which are expected to be used for rescuing people in disasters. Results from in-flight noise measurements indicate significant noise reduction mainly in frequency bands above 1 kHz in both the near- and far-field. An improvement in the success rate of sound source localization with low-noise propellers was also observed. However, the influence of the position of the microphone array with respect to the propellers is more pronounced than that of propeller shape manipulation, suggesting the importance of considering the positional relationships. Computational fluid dynamics analysis of the flow field around the propellers suggests potential mechanisms for noise reduction in the developed low-noise propellers. The results obtained in this study hold potential for contributing to the development of integrated drones aimed at reducing noise and improving sound source localization performance. Full article

Mobile service robots employ vision systems to discern objects in their workspaces for navigation or object detection. The lighting conditions of the surroundings affect a robot’s ability to discern and navigate in its work environment. Robot inclusivity principles can be used to determine the suitability of a site’s lighting condition for robot performance. This paper proposes a novel framework for autonomously auditing the Robot Inclusivity Index of indoor environments based on the lighting condition (RII-lux). The framework considers the factors of light intensity and the presence of glare to define the RII-Lux of a particular location in an environment. The auditing framework is implemented on a robot to autonomously generate a heatmap visually representing the variation in RII-Lux of an environment. The applicability of the proposed framework for generating true-to-life RII-Lux heatmaps has been validated through experimental results. Full article