Search Results (243)

Cost code verification in state-funded construction projects remains a labor-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent units of measurement (UoMs), and locally modified price components. This study evaluates a deterministic GPT-based assistant designed to automate Vietnam’s regulatory verification. The assistant was developed and iteratively refined across four Action Research cycles. Also, the system enforces strict rule sequencing and dataset grounding via Python-governed computations. Rather than relying on probabilistic or semantic reasoning, the system performs strictly deterministic checks on code validity, UoM alignment, and unit price conformity in material (MTR), labor (LBR), and machinery (MCR), given the provincial unit price books (UPBs). Deterministic equality is evaluated either on raw numerical values or on values transformed through explicitly declared, rule-governed operations, preserving auditability without introducing tolerance-based or inferential reasoning. A dedicated exact-match mechanism, which is activated only when a code is invalid, enables the recovery of typographical errors only when a project item’s full price vector well matches a normative entry. Using twenty real construction estimates (16,100 rows) and twelve controlled error-injection cases, the study demonstrates that the assistant executes verification steps with high reliability across diverse spreadsheet structures, avoiding ambiguity and maintaining full auditability. Deterministic extraction and normalization routines facilitate robust handling of displaced headers, merged cells, and non-standard labeling, while structured reporting provides line-by-line traceability aligned with professional verification workflows. Practitioner feedback confirms that the system reduces manual tracing effort, improves evaluation consistency, and supports documentation compliance with human judgment. This research contributes a framework for large language model (LLM)-orchestrated verification, demonstrating how Action Research can align AI tools with domain expectations. Furthermore, it establishes a methodology for deploying LLMs in safety-critical and regulation-driven environments. Limitations—including narrow diagnostic scope, unlisted quotation exclusion, single-province UPB compliance, and sensitivity to extreme spreadsheet irregularities—define directions for future deterministic extensions. Overall, the findings illustrate how tightly constrained LLM configurations can augment, rather than replace, professional cost verification practices in public-sector construction. Full article

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

Background. Enhanced Recovery After Surgery (ERAS) is a structured, multidisciplinary programme designed to optimise the entire perioperative pathway through evidence-based, patient-centred, standardised care. The objective of this report is to determine whether it is feasible to implement an ERAS^® program in orthopaedic surgery within our institution for primary THA and TKA. Methods. This single-centre quality-improvement project followed the ERAS^® Society certification framework to create and implement an enhanced-recovery pathway for primary total hip and knee arthroplasty. The three-phase roadmap comprised baseline pathway mapping and audit, pilot implementation and refinement, and full roll-out, punctuated by four multidisciplinary seminars. Key aspects of this programme included preoperative education, minimal fasting and early return to feeding, rational choice of regional anaesthesia techniques, administration of multimodal analgesia, reduction in urinary and surgical catheterization, active management of the risk of blood loss and deep vein thrombosis, optimisation of surgical workflow and techniques, and early mobilisation of patients. Global- and element-level compliance was tracked prospectively; ≥70% compliance was required for certification. External ERAS^® Society review at month 15 confirmed data integrity, sustainability planning, and successful certification. Continuous feedback loops drove micro-teaching and order-set optimisation throughout deployment phases. Results. Our ERAS programme for primary THA and TKA was introduced in April 2022. The definitive programme contained 24 mandatory elements grouped into three perioperative areas. After the fourth seminar, the rate of compliance was 81%. The certification was obtained in June 2023. Conclusions. Implementing an ERAS^® programme for primary total hip and knee arthroplasty is feasible within a high-volume academic institution when supported by multidisciplinary teamwork, robust data collection, and iterative feedback mechanisms. Further high-quality outcome-focused research is required to evaluate the clinical impact of individual ERAS components and to validate a personalised ERAS programme incorporating emerging technologies. Full article

This paper reviews Artificial Intelligence techniques for distributed energy management, focusing on integrating machine learning, reinforcement learning, and multi-agent systems within IoT-Edge-Cloud architectures. As energy infrastructures become increasingly decentralized and heterogeneous, AI must operate under strict latency, privacy, and resource constraints while remaining transparent and auditable. The study examines predictive models ranging from statistical time series approaches to machine learning regressors and deep neural architectures, assessing their suitability for embedded deployment and federated learning. Optimization methods—including heuristic strategies, metaheuristics, model predictive control, and reinforcement learning—are analyzed in terms of computational feasibility and real-time responsiveness. Explainability is treated as a fundamental requirement, supported by model-agnostic techniques that enable trust, regulatory compliance, and interpretable coordination in multi-agent environments. The review synthesizes advances in MARL for decentralized control, communication protocols enabling interoperability, and hardware-aware design for low-power edge devices. Benchmarking guidelines and key performance indicators are introduced to evaluate accuracy, latency, robustness, and transparency across distributed deployments. Key challenges remain in stabilizing explanations for RL policies, balancing model complexity with latency budgets, and ensuring scalable, privacy-preserving learning under non-stationary conditions. The paper concludes by outlining a conceptual framework for explainable, distributed energy intelligence and identifying research opportunities to build resilient, transparent smart energy ecosystems. Full article

Forest certification in developing countries faces significant challenges due to weak institutions, limited market incentives, and complex trade conditions. This study investigates the status and key constraints of the Myanmar forest certification sector through a survey of 180 stakeholders from government organizations, NGOs, INGOs, third-party certification bodies, and private plantation owners, complemented by quantitative analysis and qualitative interviews. The results indicate a moderate level of familiarity with the Myanmar forest certification standard and high awareness of the Myanmar Forest Certification Committee; however, progress remains slow due to limited transparency, poor institutional coordination, financial and technical constraints, and insufficient stakeholder involvement. Non-compliances issues identified during pilot audits were primarily related to incomplete documentation, unclear land tenure, and weaknesses in environmental assessment. Geopolitical factors continue to limit Myanmar’s participation in certified timber markets and weaken efforts to improve traceability. Experiences from Indonesia, Malaysia, and Vietnam highlight that developing credible national certification systems requires time, clear legal frameworks, and strong cooperation among stakeholders. Strengthening institutional capacity, improving transparency, and aligning national standards with international forest governance frameworks are essential for Myanmar to build trust, achieve sustainable forest management, and regain market access. Full article

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

Traditional Human Resource Management (HRM) systems are criticized for lacking transparency, being inefficient, and offering ample opportunities for fraud because of their centralized design and reliance on manual processes. This work proposes a blockchain-enabled framework for HRM that enhances the transparency, trust, and global mobility of talents by integrating distributed ledgers, consensus protocols, and smart contract networks into Human Resources (HR) functions. A four-layer theoretical model—data, consensus, smart contract, and application layers—is developed and comparatively examined against traditional HR systems to show how blockchain principles can be systematically mapped into HR processes. This study shows how blockchain-driven HRM can ensure tamper-evident employee records, automate contractual and payroll operations, and enhance auditability and compliance. By informing the framework with established technology adoption perspectives, this paper extends both the theoretical and managerial understanding of blockchain in HR. In comparison with previous studies that were limited to either recruitment or credential verification, this article presents an overarching, cross-layer synthesis that connects blockchain architectures with end-to-end HR functions, thus providing a clear conceptual foundation for its future enterprise adoption in the digital economy. Full article

A robust audit services market is essential for ensuring financial transparency, regulatory compliance, and investor confidence. As a dimension of organizational sustainability, the capacity of audit firms to remain competitive and resilient under market pressures is increasingly relevant. However, existing research has paid insufficient attention to the stability of audit firms and the survival dynamics of mid-sized players. The present study addresses this gap by examining the volatility of the U.S. audit services market and the sustained dominance of the Big Four firms over the 2019–2023 period. Based on data from Accounting Today’s annual rankings, the study employs Kaplan–Meier survival analysis to assess the probability of audit firms remaining in the Top 100 over time. Furthermore, K-means clustering is used to identify structural factors contributing to firm exit, including revenue, number of employees, branches, and partners. The results indicate that, while the Big Four retained stable leadership, 19 firms exited the rankings, with revenue and number of specialists being the most influential exit factors. These findings provide insights for enhancing risk assessment, strategic planning, and regulatory design. Moreover, the study contributes to broader discussions on organizational sustainability and long-term competitiveness within the context of the U.S. audit sector, while offering insights that may be informative for understanding similar dynamics in other markets rather than aiming for direct global generalization. Full article

Compliant cross-border data flows face persistent challenges from fragmented regulatory regimes, inconsistent enforcement, and limited trust among stakeholders. Current approaches typically rely on centralized oversight or excessive data disclosure, both compromising regulatory interoperability and operational security. This paper introduces a trust-oriented blockchain architecture that enables secure cross-border data exchange while ensuring verifiable compliance without revealing sensitive content. The architecture decouples policy enforcement, privacy-preserving validation, and cross-jurisdiction auditability, enabling entities to share cryptographically verifiable compliance proofs rather than raw data. To capture the behavioral dynamics across heterogeneous regulatory environments, we incorporate a strategic interaction layer that models how domestic firms, foreign enterprises, and cross-border data platforms adjust decisions under varying incentive structures. These insights guide the design of an adaptive compliance verification pipeline that maintains trust equilibrium across participants. Our design records only cryptographic digests and structured compliance evidence on-chain, while off-chain components execute privacy-preserving checks using secure computation and decentralized storage. Through a case-driven evaluation, we show that the proposed architecture reduces governance friction, enhances institutional trust, and achieves interoperable compliance validation with minimal disclosure overhead. Through component-level evaluation and architectural analysis, this work establishes a technical foundation for secure, transparent, and regulation-aligned cross-border data governance. The framework provides a blueprint for future multi-party pilot deployments in operational environments. Full article

This paper presents the design, implementation, and evaluation of a low-code document management and approval system developed on the Microsoft Power Platform. The solution integrates Power Apps, Power Automate, SharePoint Online, and Azure Active Directory to enable secure, traceable, and device-independent workflows for managing organizational documents. By combining graphical interfaces, automated approval logic, and enterprise-grade identity management, the system supports real-time collaboration and compliance with records’ governance standards. A comparative analysis with traditional enterprise content management and open-source web architectures demonstrates substantial advantages in deployment speed, scalability, and auditability. Empirical results from a six-week pilot involving multiple users indicate a reduction in approval cycle time, high user satisfaction, and strong cost-efficiency relative to conventional development models. The findings highlight how low-code ecosystems operationalize digital transformation by empowering non-technical users to automate complex workflows while maintaining security and governance integrity. This work contributes to the understanding of information system democratization, showing that low-code platforms can extend digital participation, improve organizational agility, and support sustainable operational efficiency across distributed environments. Full article

The increasing complexity and digitization of scientific research require Electronic Laboratory Notebooks (ELNs) that are adaptable, sustainable, and compliant across heterogeneous laboratory environments. In response to the limitations of proprietary, inflexible, and cost-intensive ELN solutions, this study systematically derives comprehensive requirements and proposes a modular solution concept for self-configurable ELNs that is explicitly platform-agnostic and broadly accessible. The methodological approach combines a structured requirements analysis with a modular architectural design, followed by theoretical validation through stepwise implementation walkthroughs on Microsoft SharePoint and Google Workspace. These walkthroughs demonstrate the feasibility of deploying self-configurable ELN modules using widely available low-code/no-code tools and native platform extensibility mechanisms. Based on a rigorous literature-driven analysis, key requirements, including modularity, usability, regulatory compliance, interoperability, scalability, auditability, and cost efficiency, are explicitly mapped to concrete architectural features within the proposed framework. The results show that essential ELN functionalities can, in principle, be realized across diverse digital platforms, enabling researchers and local administrators to independently assemble, configure, and adapt ELNs to their specific operational and regulatory contexts. Beyond technical feasibility, the proposed approach fundamentally democratizes ELN deployment and substantially mitigates vendor lock-in by leveraging existing digital infrastructures. Identified limitations, particularly with respect to advanced workflow orchestration and real-time data integration, delineate clear directions for future development. Overall, this work provides a systematic theoretical validation of a modular, self-configurable ELN concept, establishing it as a robust, scalable, and future-ready foundation for digital laboratory infrastructures. Full article

Explainability is increasingly expected to support not only interpretation, but also accountability, human oversight, and auditability in high-risk Artificial Intelligence (AI) systems. However, in many deployments, explanations are generated as isolated technical reports, remaining weakly connected to decision provenance, governance actions, audit logs, and regulatory documentation. This short communication introduces XAI-Compliance-by-Design, a modular engineering framework for explainable artificial intelligence (XAI) systems that routes explainability outputs and related technical traces into structured, audit-ready evidence throughout the AI lifecycle, designed to align with key obligations under the European Union Artificial Intelligence Act (EU AI Act) and the General Data Protection Regulation (GDPR). The framework specifies (i) a modular architecture that separates technical evidence generation from governance consumption through explicit interface points for emitting, storing, and querying evidence, and (ii) a Technical–Regulatory Correspondence Matrix—a mapping table linking regulatory anchors to concrete evidence artefacts and governance triggers. As this communication does not report measured results, it also introduces an Evidence-by-Design evaluation protocol defining measurable indicators, baseline configurations, and required artefacts to enable reproducible empirical validation in future work. Overall, the contribution is a practical blueprint that clarifies what evidence must be produced, where it is generated in the pipeline, and how it supports continuous compliance and auditability efforts without relying on post hoc explanations. Full article

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose a Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy-driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, and leveraging immutable ledgers and smart contracts, the framework ensures tamper-proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations such as alerts; actual device isolation is performed by external controllers like SDN or NAC systems. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. A working prototype of the proposed BENSAM framework was developed that demonstrates end-to-end network scanning, device profiling, traffic monitoring, policy enforcement, and blockchain-based immutable logging. This implementation is publicly released and is available on GitHub. It analyzes common network vulnerabilities (e.g., open ports, remote access, and disabled firewalls), attacks (including spoofing, flooding, and DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for the future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain-enhanced approach streamlines security analysis, extends the framework for AI threat detection with improved accuracy, and reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution. Full article

Effective municipal waste management is fundamental to environmental sustainability and the circular economy. This case study assesses the operational effectiveness of the Recycling/Civic Amenity Site (CAS) network in Białostocki county, Poland, during the 2014–2018 national waste management transition. A multi-criteria assessment was employed, integrating compliance audits, infrastructure checks, and spatial analysis of waste type distributions to evaluate CAS operations. The findings reveal a socio-economic divergence between more urbanised (town-and-village) and purely rural (village) municipalities, which is directly reflected in their distinct waste composition patterns. The town-and-village areas produced homogeneous, high-quality packaging waste streams that support recycling goals. Conversely, the village municipalities generated more commingled, heterogeneous streams that challenge recycling efforts. An optimised CAS model was proposed for the county to enhance sustainability by adaptively differentiating CAS services to local needs. However, a direct stock-take of all 16 CASs revealed significant infrastructural disparities, limiting the model’s potential. The study concludes that overcoming both the qualitative waste stream divergence and quantitative infrastructure disparities through tailored strategies is essential for meeting national recycling targets and achieving long-term sustainability. The methodology provides a replicable framework for pinpointing the root causes of inefficient operations, offering local authorities evidence-based tools to optimise CAS design and ensure infrastructure investments directly support overarching sustainability goals. Full article

Cloud-based machine learning systems are increasingly used in sectors such as healthcare, finance, and public services, where they influence decisions with significant social consequences. While these technologies offer scalability and efficiency, they raise significant concerns regarding security, privacy, and compliance. One of the central issues is algorithmic bias, which can emerge from data, design choices, or system interactions, and is often amplified when deployed at scale through cloud infrastructures. This study examines the relationship between algorithmic bias, social equity, and cloud-based innovation. Drawing on a survey of public perceptions, we find strong recognition of the risks posed by biased systems, including diminished trust, harm to vulnerable populations, and erosion of fairness. Participants overwhelmingly supported regulatory oversight, developer accountability, and greater transparency in algorithmic decision-making. Building on these findings, this paper proposes measures to integrate fairness auditing, representative datasets, and bias mitigation techniques into cloud security and compliance frameworks. We argue that addressing bias is not only an ethical responsibility but also an essential requirement for safeguarding public trust and meeting evolving legal and regulatory standards. Full article