Search Results (148)

Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into organizational coaching processes to enhance competence development and strategic alignment. AI-supported coaching in this research is treated as an emerging organizational technology whose potential organizational value depends less on model capability and more on governance design, decision rights, and auditable evaluation outputs. Following a mixed-methods, multi-phase design, the research combined a Systematic Literature Review (SLR) with the construction of a layered design architecture in which OSCAR serves as the primary coaching-process scaffold, complemented by KSA for competency specification, Situational Leadership for adaptive guidance, and KPIs for monitoring and governance. The framework structures AI-supported coaching across 10 interrelated phases, from contextual anchoring to review and measurement, while preserving iterative re-entry to earlier phases whenever review evidence, contextual change, or insufficient progress makes adjustment necessary. Prototyping demonstrated feasibility and coherence across models, while the focus group provided qualitative expert feedback on the framework’s clarity, governance needs, and perceived usefulness for competence development. At this stage, however, the KPI structures generated by the framework and the descriptive comparison across AI tools should be interpreted as prototype-level outputs rather than as empirically validated performance measures or evidence of added value over baseline approaches. Because the evaluation relied on two fictional prototyping scenarios and a small expert-oriented focus group (n = 6), the findings should be interpreted as evidence of prototype demonstration and qualitative refinement rather than of real-world effectiveness or organizational impact. The study also does not include a control group or comparison with traditional human coaching, so the added value of the AI-supported framework over alternative coaching arrangements remains a question for future empirical testing. Findings suggest that AI can usefully support organizational coaching by personalizing dialogue, structuring reflection, and generating auditable development artefacts, provided ethical safeguards and human oversight remain integral. The research contributes a preliminarily validated, ethics-informed, and governance-aware framework for AI adoption in organizational coaching and offers practical insights for embedding AI-enabled development in learning organizations. Full article

The European Urban Wastewater Treatment Directive revision introduced the energy neutrality concept, accelerating the transition of wastewater treatment plants (WWTPs) towards a 100% renewable energy share. Energy audits must be initially conducted to assess current energy consumption levels, identifying deviations from benchmarking values, and energy efficiency measures must be implemented. Strategies should be then diversified according to WWTP size: anaerobic digestion (AD) is a core technology for large-scale plants. The refurbishment of conventional digesters into “enhanced” AD, including sludge pretreatment, co-digestion, or two-stage AD, significantly increases energy yields, providing most of the required electricity/heat. Enhanced AD can be complemented by photovoltaic (PV) panels and thermal energy recovery from effluents. For medium-scale plants, instead, PV implementation is a key solution for electricity production, coupled with hydroenergy recovery and, eventually, wind turbines, while heat can be provided by solar thermal panels or thermal energy recovery from effluents. Hybrid systems, which integrate multiple renewable sources, are often the best solution to reach energy neutrality, improving the system’s resiliency; however, dedicated mathematical models are needed to size and operate the different components, considering local factors. Future research must connect theoretical and in-field studies to allow a wider implementation of hybrid systems. Full article

The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of standardized indicators derived from verifiable corporate disclosures. To fill this research gap, the present study develops three quantifiable indices—Intelligence (INT), Sustainability (SUS), and Resilience & Human-centric (RES)—by extracting data from the annual reports and ESG disclosures of 100 Chinese A-share manufacturing enterprises (covering 2022–2024). Fixed-effects panel regression models are employed to assess the impact of these indices on financial performance (ROA, ROE, EPS), market valuation (Tobin’s Q), and sustainability outcomes (ESG ratings). Our findings reveal that INT is the most significant predictor of profitability, with statistically significant positive effects on ROA and ROE—effects that are particularly pronounced among high-tech enterprises. This supports the view that digital capabilities serve as strategic assets. SUS also demonstrates a positive influence on performance, especially in non-high-tech enterprises, where eco-efficiency, regulatory compliance, and ESG-linked financing help offset technological disadvantages. RES contributes to operational and financial stability by enhancing human capital, safety protocols, and organizational practices that reduce performance volatility. Collectively, these results indicate that different types of enterprises follow distinct yet converging pathways toward Industry 6.0: high-tech enterprises capitalize on intelligence to generate innovation rents, while non-high-tech enterprises increasingly rely on sustainability and resilience as strategies to build legitimacy. This study makes significant contributions in three aspects: Methodologically, it differs from previous research that relies on questionnaires and interviews. Instead, it quantifies Industry 6.0 through auditable large-sample key indicators, enhancing the objectivity and operability of the indicators. Empirically, it provides the first empirical evidence on the development path of Industry 6.0 based on data from Chinese manufacturing enterprises. In practical terms, it offers clear references for enterprises and policymakers on the core indicators and their construction framework that should be prioritized during the transformation to Industry 6.0. By linking the index derived from enterprise disclosures with quantifiable performance results, this study effectively bridges the gap between theoretical conceptions and practical applications. It further emphasizes that Industry 6.0 is not merely a technological upgrade but a systematic transformation driven by digitalization, sustainability, and resilience aimed at enhancing enterprise performance and achieving sustainable industrial development. Full article

Sixth-generation (6G) networks are expected to provide ubiquitous connectivity, AI-native orchestration, and seamless integration across terrestrial and non-terrestrial infrastructures. However, these capabilities introduce new privacy challenges related to the classification and protection of personal, quasi-personal, and non-personal data in complex data-driven environments. This paper presents a systematic review of 78 peer-reviewed studies published between 2019 and 2025. Following a PRISMA-based methodology, this review analyzes privacy-enhancing technologies (PETs), regulatory compliance frameworks, and architectural patterns for privacy preservation in 6G networks. The findings show that differential privacy (DP) and federated learning (FL) dominate current research, accounting for nearly 52% of the reviewed studies. Blockchain auditing and zero-knowledge proofs (ZKPs) collectively represent approximately 30%, while the remaining mechanisms, including physical-layer security (PLS), trusted execution environments (TEEs), homomorphic encryption (HE), secure multi-party computation (SMPC), and anonymization, account for roughly 18%. These mechanisms exhibit varying levels of privacy strength, utility preservation, latency, and energy cost. At the same time, evolving regulatory frameworks, including GDPR, PDPL, CCPA/CPRA, LGPD, and PIPL, increasingly extend privacy obligations to quasi-personal and aggregated data. Building on these findings, this paper proposes a unified taxonomy that clarifies the boundary between personal and non-personal data. It also provides a cross-layer mapping between PETs and compliance requirements across the Core/SBA, RAN, Edge/MEC, and NTN layers. Finally, this paper presents a forward-looking roadmap for 2025–2030, highlighting hybrid PET pipelines, post-quantum auditability, and AI-driven compliance automation as key directions for privacy-preserving 6G standardization. Full article

Learning is fundamentally human, even as Artificial Intelligence (AI) challenges human exclusivity. AI, along with Virtual Reality (VR), emerges as a powerful tool that is set to transform higher education, the institutional embodiment of this pursuit at its highest level. These technologies offer the potential not to replace the human factor, but to enhance our ability to create more adaptive, immersive, and truly human-centric learning experiences, aligning powerfully with the emerging vision of Education 5.0, which emphasizes ethical, collaborative learning ecosystems. This research maps how AI and VR tools act as a disruptive force, examining additionally their capabilities and limitations. Moreover, it explores how AI and VR interact to overcome traditional pedagogy’s constraints, fostering environments where technology serves human learning goals. Employing a comprehensive two-month audit of over 60 AI, VR, and AI-VR hybrid tools, the study assesses their functionalities and properties such as technical complexity, cost structures, integration capabilities, and compliance with ethical standards. Findings reveal that AI and VR systems provide significant opportunities for the future of education by providing personalized and captivating environments that encourage experiential learning and improve student motivation across disciplines. Nonetheless, numerous challenges limit widespread adoption, such as advanced infrastructure requirements and strategic planning. By articulating a structured evaluative framework and highlighting emerging trends, this paper provides practical guidance for educational stakeholders seeking to select and implement AI and VR tools in higher education. Full article

Background/Objectives: Artificial intelligence (AI) models are increasingly applied across the assisted reproductive technology (ART) workflow, including male-factor assessment, ovarian stimulation, endometrial receptivity evaluation, embryo selection and prediction of pregnancy outcomes. However, many systems remain difficult to interpret, raising concerns regarding transparency, clinical integration and patient communication. Explainable artificial intelligence (XAI) aims to address these limitations by making model behavior more accessible to clinicians and embryologists. This review aimed to provide a narrative, concept-driven synthesis of how XAI has been implemented in ART, to critically examine methodological quality and clinical relevance and to outline priorities for responsible translation into practice. Methods: A structured narrative review was conducted using PubMed/MEDLINE as the primary database, supplemented by targeted reference-list screening of key primary studies and recent cross-disciplinary reviews relevant to AI in ART. Studies were curated and classified according to stage of the ART workflow, data modality, model family, explanation technique and validation strategy. Methodological features, performance reporting and implementation considerations were qualitatively appraised. Results: Most XAI applications in ART fall into two dominant categories: (i) feature-attribution methods such as SHAP and LIME applied to tabular clinical and laboratory data and (ii) saliency-based approaches, including Grad-CAM and related techniques, applied to embryo and ultrasound imaging. These methods can improve transparency and support counselling by clarifying which variables or image regions influence predictions. However, the majority of studies are retrospective and single centre, with limited external validation and heterogeneous outcome definitions, often prioritising clinical pregnancy over live birth. Calibration, decision-analytic evaluation and prospective assessment remain uncommon. XAI outputs are frequently interpreted as biologically causal despite being derived from observational data, highlighting the need for cautious clinical framing. Conclusions: XAI in ART has progressed from proof-of-concept demonstrations to early clinically oriented tools, but robust validation, standardised reporting and thoughtful workflow integration are still needed. Explanations can enhance auditability and communication, yet they do not compensate for methodological weakness. Future progress will depend on higher-quality multi-centre data, evaluation beyond discrimination metrics and governance frameworks that ensure transparency, fairness and sustained performance in real-world practice. Full article

Ambient Assisted Living (AAL) systems have become increasingly relevant as aging populations intensify the demand for technologies that promote autonomy, safety, and quality of life. However, the widespread adoption of audiovisual sensing in smart homes raises critical concerns regarding data protection, privacy, and user trust. Ensuring secure processing while maintaining accurate activity recognition remains a key challenge. This work introduces DistSense, a distributed Peer-to-Peer (P2P) system designed to enhance activity detection in domestic environments through collaborative inference among intelligent audiovisual sensors. DistSense prioritizes privacy by performing local processing, sharing only high-level events, and leveraging distributed ledger mechanisms to ensure data integrity and auditability and support cross-device validation. This collaborative strategy reduces false positives caused by occlusions, illumination variability, and acoustic noise. To assess the system, functional tests were conducted for each module, followed by two use cases evaluated in both simulated and real edge hardware environments. The trained models achieved 88% accuracy for audio and 80% for video, and the system demonstrated effective performance in detecting daily activities and domestic hazards under varying noise conditions. Results indicate that DistSense successfully balances security, user acceptance, and inference robustness, positioning it as a viable solution for privacy-preserving activity monitoring in smart home contexts. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

As global digital transformation accelerates, artificial intelligence (AI) and blockchain technologies have evolved from theoretical concepts into practical tools within the field of accounting, particularly in financial reporting. This study conducts a systematic review of 62 sources drawn from major academic databases to develop a comprehensive framework for classifying application scenarios. The findings indicate that the application of artificial intelligence and blockchain technology can help improve the efficiency of financial report generation, enhance the reliability of data, and promote innovation in the auditing process. Nevertheless, persistent challenges remain, including concerns related to data security, technological limitations, and regulatory gaps. The study proposes a structured roadmap for the implementation of these technologies, underscoring their transformative potential in advancing the digital evolution of accounting, while also identifying key directions for future research. Full article

Our previous visual privacy framework leveraging Graph Convolutional Networks (GCNs) and Federated Learning (FL) has been shown to achieve state-of-the-art (SOTA) predictive performance. However, it neglects the systemic requirements of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF). To address this critical gap, this paper proposes the Trustworthy Visual Privacy Auditing (TVPA) system, which transitions conventional static detection models into a dynamic and secure governance ecosystem. We first establish system resilience against adversarial threats by proposing an active auditing mechanism called Resilient Federated Protection (RFP) to embed unique model parameter watermarks within client-side updates. The RFP mechanism enables the federated aggregator to verify node legitimacy and automatically isolate malicious clients attempting poisoning attacks. Then, to ensure strict accountability, we design an immutable audit log mechanism in the RFP mechanism that utilizes a Cryptographic Hash Chain (CHC) to record and verify the provenance of every model update, creating a transparent chain of custody. Furthermore, the prediction mechanism is enhanced by Causal Governance (CG) that integrates causal inference to provide counterfactual reasoning for explaining the root causes of privacy risks rather than merely flagging associations. Experiments on the VISPR dataset demonstrate that our TVPA system can synthesize high-performance recognition with robust security, auditability, and causal explainability to provide trustworthy AI governance. Full article

The rapid advancement of artificial intelligence relies on massive high-quality data, yet increasingly stringent data privacy regulations have exacerbated the problem of data silos. Federated learning enables collaborative training under privacy protection by exchanging model parameters rather than transmitting raw data. Nevertheless, its traditional centralized architecture still suffers from limitations such as single points of failure, lack of trust, and insufficient incentives. The integration of blockchain and federated learning opens new pathways for decentralized, auditable, and secure machine learning systems. This paper systematically reviews research progress in blockchain-enabled federated learning, analyzing technological evolution from three perspectives: system architecture, incentive mechanisms, and privacy enhancement. It further explores critical challenges including efficiency bottlenecks, storage overhead, and the inherent tension between transparency and privacy, while identifying key research directions for building scalable, efficient, and trustworthy decentralized learning systems. Full article

Surgical wards constitute a significant contributor to global medical waste (MW), accounting for over one-third of total healthcare sector trash. Medical interventions produce hazardous, infectious, and potentially toxic byproducts, making effective MW management crucial, especially where current mechanisms are insufficient. Substantial disparities persist between high-income and low- and middle-income countries regarding MW infrastructure, enforcement, and adoption of safe, sustainable treatment technologies. Proper segregation, recycling, treatment, and disposal are key to protecting public health, environmental integrity, and promoting healthcare sustainability. Waste treatment technologies divide into thermal and physico-chemical processes, requiring thorough evaluation of advantages, disadvantages, and suitability for each waste type. This narrative review updates MW knowledge by synthesizing data from scientific literature, institutional documents, and regulatory sources. Key quantitative data indicate operating rooms generate up to 30% of total hospital waste, with recyclable materials representing over 40% of that volume. Improper segregation rates remain high, and incineration remains dominant despite sustainability concerns. The Romanian case study highlights progressive EU alignment, enforcing standardized MW classification, color-coded segregation, and specialized disposal protocols in surgical wards. Despite legal compliance, Romania is advancing incrementally, with systematic audits, digital tracking, and national outcome-based evaluations yet to be fully established. The Plastic Surgery Unit at Oradea County Emergency Clinical Hospital demonstrates good protocol adherence; however, strengthening data feedback mechanisms would enhance hospital-wide performance optimization and strategic waste reduction. Training and monitoring represent important areas for continued development. Coordinated professional engagement, modernized infrastructure, and enforceable audits are identified as critical priorities for improving MW handling in surgical environments. Future research should emphasize management innovation, evidence-based policy formulation, and a systematic strategy to achieve sustainable MW. Full article

Ensuring ongoing endpoint security compliance across diverse, hybrid IT infrastructures poses a continual operational challenge, especially in enterprise Linux systems, where manual verification methods are difficult to scale and prone to inconsistency. This study offers an empirical assessment of an automated methodology for monitoring endpoint compliance and security, applied within a mid-sized IT consulting firm. The suggested methodology incorporates automated compliance scanning, malware detection, endpoint verification, and remediation utilising open-source technology, all orchestrated through centralised automation and reporting systems. The evaluation follows an observational comparative methodology, contrasting manual compliance operations with automated enforcement across 60 Linux endpoints (30 Fedora and 30 Ubuntu systems) over two equivalent eight-week operational periods. The analysis emphasises operational parameters such as administrative workload, configuration uniformity, and audit preparedness. The findings demonstrate that automation reduced manual compliance-related tasks by roughly 70–80%, enhanced configuration consistency across endpoints through continuous enforcement, and enabled automated production of audit-ready compliance reports. The findings provide concrete evidence that operational security automation can markedly improve endpoint compliance management in business Linux and hybrid IT environments. Full article

The escalating scale and complexity of cybercrime necessitate standardized digital forensic protocols to ensure the integrity and admissibility of digital evidence. This study empirically assesses the use of ISO/IEC 27037 and ISO/IEC 27041 through three real-world digital forensic case studies conducted in organizational settings. A multi-case methodology was employed, encompassing a multinational corporate criminal investigation, an internal employee misbehaviour probe, and an examination into mobile- and cloud-based data leaks. The effect of synchronized standard implementation was evaluated using audit-based and quantitative indicators that measure forensic process quality as a system attribute. The findings demonstrate that the systematic implementation of ISO/IEC 27037 and ISO/IEC 27041 improves investigative traceability, documentation quality, and evidentiary robustness. In the worldwide case study, documentation completeness increased by 18%, and all digital evidence was deemed admissible in judicial proceedings, surpassing the institutional baseline admissibility rate of 82%. In other instances, evidence gathered within the same framework was acknowledged in organizational or disciplinary review processes, resulting in similar enhancements in documentation quality and procedural consistency, notwithstanding technological and organizational limitations. The paper develops and empirically substantiates an integrated procedural validation model that connects evidence-handling practices with method and instrument validation. The results indicate that the synchronized implementation of ISO/IEC forensic standards improves the transparency, dependability, and auditability of digital forensic investigations. Full article

The global maritime industry, a critical pillar of international trade, continues to face persistent challenges in ensuring the integrity, security, and transparency of containerized cargo data, particularly during ocean transport. Traditional container tracking systems at sea often lack the reliability and resilience required to prevent data tampering, cyber threats, and operational inefficiencies. As supply chains become more complex and interconnected, the demand for robust, end-to-end data security solutions becomes more pressing. A promising technological advancement in this area is the convergence of smart containers, equipped with Internet of Things (IoT) sensors for real-time condition monitoring, and blockchain technology (BCT) for secure data validation. These IoT devices facilitate continuous tracking of critical parameters such as location, temperature, humidity, tilt, and the like. However, the data they generate remains vulnerable to cyberattacks, signal disruptions, and unauthorized alterations. Blockchain’s decentralized and tamper-evident architecture addresses these vulnerabilities by enabling secure data immutability, transparent audit trails, and enhanced stakeholder trust. Despite its potential, the practical integration of blockchain with smart container systems in maritime logistics remains largely underexplored. To bridge this gap, this paper proposes a blockchain-enabled smart container monitoring system that combines container real-time data with secure physical tracking. Furthermore, to ensure scalability and efficient in data storage, hybrid on/off-chain architecture is introduced, balancing blockchain integrity with performance and resource optimization. Full article