Search Results (564)

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious. Full article

Nitric acid production is a cornerstone of the chemical industry, yet it presents considerable environmental challenges, primarily due to greenhouse gas emissions such as nitrous oxide (N₂O) and nitrogen oxides (NO_x). This manuscript critically examines the key performance indicators (KPIs) that define the gate-to-gate environmental sustainability of nitric acid plants. Quantitative metrics and related benchmarks achieved in modern plants, e.g., energy efficiency (ca. 2 GJ exported per ton of HNO₃) and NO_x/N₂O reduction (95–99%), are presented. Strategies to enhance these KPIs are discussed, including process integration, intensification, advanced emission control technologies, and operational optimization. Special attention is given to the chemical conversion processes of NO_x and N₂O, highlighting their roles in minimizing overall emissions. The review also synthesizes recent literature to showcase emerging trends, regulatory developments, and technological innovations that facilitate the transition toward more sustainable nitric acid production. Finally, the article identifies current research gaps and outlines future directions for the field. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

The current landscape of Environmental, Social, and Governance (ESG) ratings is fragmented by methodological inconsistencies, lack of standardization, and substantial divergences among rating providers. These discrepancies hinder comparability, reduce transparency, and undermine the reliability of ESG assessments, limiting their effectiveness for both investors and corporate decision-makers. To address these issues, this study introduces a standardized approach to ESG rating construction, aimed at enhancing the objectivity and interpretability of corporate sustainability evaluations. The methodology integrates the Global Reporting Initiative standards with the United Nations Sustainable Development Goals, thereby identifying a coherent set of key performance indicators across the ESG pillars. By relying solely on publicly available data and incorporating mechanisms for managing missing information, the model provides a transparent and reproducible framework for sustainability assessment. Its validity is demonstrated through an empirical application to firms in the financial and manufacturing sectors across Europe and the United States, with benchmarking against established ratings from providers. Rather than replicating existing ESG scores, the model offers a transparent and reproducible alternative built on disclosed performance data, without relying on forward-looking statements, corporate promises, or commercial data providers. By penalizing non-disclosure and enabling sector-specific sensitivity analysis, the framework supports more accountable and customizable sustainability assessments, helping align ESG evaluations with strategic and regulatory priorities. Full article

Occupational exposure to sharps waste represents a critical challenge for public health systems, directly affecting healthcare workers’ safety, institutional costs, and environmental sustainability. This study aimed to analyze sharps waste management practices and to structure improvement actions for biosafety governance in Brazilian Emergency Care Units (ECUs) through the application of the Lean Six Sigma (LSS) and DMAIC method (Define, Measure, Analyze, Improve, and Control). A single multiple-case study was conducted across three public units in different regions of Brazil, combining direct observation, regulatory checklists based on ANVISA Resolution No. 222/2018 (RDC), and cause–and–effect (5M) analysis. The diagnostic phase identified recurrent nonconformities in labeling, documentation, and internal transport routes, primarily due to managerial and behavioral gaps. Based on these findings, the DMAIC framework supported the development of a low-cost, evidence-based action plan that outlined proposed interventions, including visual checklists, standardized internal routes, and key performance indicators (KPIs), intended to strengthen biosafety traceability and occupational safety. The se proposed actions are expected to support continuous learning, staff engagement, and a culture of shared responsibility for safe practices. Overall, the study provides a structured basis for future implementation and empirical validation of continuous improvement initiatives, aimed at enhancing public health governance and occupational safety in resource-constrained healthcare environments. Full article

This article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), digital twin (DT) technology, and a programmable logic controller–based architecture aligned with the requirements of the NIS2 Directive. The study considers a five-axis industrial manipulator modeled as a set of decoupled linear single-input single-output systems subject to parametric uncertainty and external disturbances. For position control of each axis, closed-loop robust systems with QFT-based controllers and prefilters are designed, and the dynamic behavior of the system is evaluated using predefined key performance indicators (KPIs), including tracking errors in joint space and tool space, maximum error, root-mean-square error, and three-dimensional positional deviation. The proposed architecture executes robust control algorithms in the MATLAB/Simulink environment, while a programmable logic controller provides deterministic communication, time synchronization, and secure data exchange. The synchronized digital twin, implemented in the FANUC ROBOGUIDE environment, reproduces the robot’s kinematics and dynamics in real time, enabling realistic hardware-in-the-loop validation with a real programmable logic controller. This work represents one of the first architectures that simultaneously integrates robust control, real programmable logic controller-based execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. The conducted simulation and digital twin-based experimental studies under nominal and worst-case dynamic models, as well as scenarios with externally applied single-axis disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. In addition, the study analyzes how the proposed architecture supports the implementation of key NIS2 principles, including command traceability, disturbance resilience, access control, and capabilities for incident analysis and event traceability in robotic manufacturing systems. Full article

A Security Operation Center (SOC) is a security control center for monitoring, detecting, analyzing, and responding to cybersecurity threats. PT (Perseroan Terbatas) Non-Bank Financial Company (NBFC) has implemented an SOC to secure its information systems, but challenges remain to be solved. These include the absence of impact analysis on financial and regulatory requirements, cost, and effort estimation for recovery; established Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) for monitoring security controls; and an official program for insider threats. This study evaluates SOC effectiveness at PT NBFC using the ISO 27005:2018 and NIST SP 800-30 frameworks. The research results in a proposed SOC assessment framework, integrating risk assessment, risk treatment, risk acceptance, and monitoring. Additionally, a maturity level assessment was conducted for ISO 27005:2018, NIST SP 800-30, and the proposed framework. The proposed framework achieves good maturity, with two domains meeting the target maturity value and one domain reaching level 4 (Managed and Measurable). By incorporating domains from both ISO 27005:2018 and NIST SP 800-30, the new framework offers a more comprehensive risk management approach, covering strategic, managerial, and technical aspects. Full article

Background: The objective of this work was to support the implementation of the European Health Technology Assessment Regulation (EU HTAR) and optimize performance of the evolving EU HTA system. Therefore, an inclusive multi-stakeholder framework of key performance indicators (KPI) for success measurement was developed. Methods: A modified Delphi-procedure was applied as follows: (1) development of a generic KPI pool at the Fall Convention 2024 of the European Access Academy (EAA); (2) review of initial pool and identification of additional KPIs; (3) development of prioritized KPIs covering patient, clinician, Health Technology Developer (HTD), and System/Member State (MS) perspectives, and (4) consolidation of the stakeholder-centric KPIs after EAA’s Spring Convention 2025. Results: Steps 1 and 2 of the Delphi procedure revealed 14 generic KPI domains. Steps 3 and 4 resulted in four prioritized KPIs for patients (patient input; utilization of patient-centric outcome measures; time to access; equity); six for clinicians (population/intervention/comparator/outcomes (PICO); addressing uncertainty; clinician involvement; transparency; equity and time to access); four for HTDs (PICO; joint scientific consultation (JSC) process; joint clinical assessment (JCA) process; time to national decision making); five from a system/MS perspective (PICO; learning and training the health system; reducing duplication; equity and time to access). The scope of, e.g., the PICO-related KPI, differed between stakeholder groups. Also, several KPIs intentionally reached beyond the remit of EU HTA as they are also dependent on MS-specific factors including national health systems and budgets. Discussion and Conclusions: The KPI framework developed here presents a step towards the generation of systematic multi-stakeholder evidence to support a successful implementation of the EU HTAR. The relevance of the identified stakeholder-centric KPIs is confirmed by their alignment with the Health System Goals suggested in the context of “Performance measurement for health improvement” by the World Health Organisation. Implementation of the framework, i.e., measurement of KPIs, is envisioned to provide evidence to inform the 2028 revision of the EU HTAR. Full article

Traditional sustainability frameworks in large scale production systems, such as Process Industry (PI) ones, often overlook operational resilience, creating a “resiliency gap” where systems optimized for efficiency remain vulnerable to disruptions. This study addresses this gap by proposing and empirically validating a Quadruple Bottom Line (4BL) framework that integrates resilience as the fourth pillar alongside economic, environmental, and social goals. The purpose is to evaluate the impact that Autonomic Computing (AC) can imply in this perspective. A Procedural Action Research (PAR) methodology was conducted across four distinct PI industrial cases (asphalt, steel, pharma, and aluminum). This involved the ECOGRAI framework to qualitatively link strategic companies’ objectives to shop-floor Key Performance Indicators (KPIs), guiding the assessment of AC systems. The results show benefits at a business level observed following the introduction of AC systems, which were implemented for enhancing resilience by managing ML model drift. Key findings include reduction in plant downtimes, decreases in waste (steel), reductions in gas consumption, and improved operator trust. This research provides empirical evidence that AC can make resilience an actionable component of industrial strategy, leading to measurable improvements across all four pillars of the 4BL framework. Its contribution is methodological and operational, aiming to demonstrate feasibility and causal plausibility. Full article

Growing consumer demand for sustainable food products has amplified the use of “green” marketing claims, yet transnational agri-food supply chains face a critical “perception–reality gap” due to data fragmentation and the absence of independent verification, fostering significant greenwashing risks. This study explores how the synergistic integration of Blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) can bridge this gap. Utilizing a PRISMA-inspired qualitative systemic analysis and scenario modeling, we propose the “Converging Technologies for Sustainable Agri-Food” (CTSAF) model, formalized through a mathematical Green Claim Veracity Index (Vi) and AI-driven anomaly detection algorithms. The analysis evaluates three maturity-level scenarios against expert-calibrated Key Performance Indicators (KPIs). Results demonstrate that while traditional and blockchain-only systems remain vulnerable to the “Oracle Problem”, the integrated CTSAF model (Scenario III) achieves “Very High” performance in data accuracy and audit efficiency. By transforming passive record-keeping into an autonomous governance layer, this framework provides a strategic roadmap for substantiating environmental claims in alignment with the EU Green Claims Directive and the Digital Product Passport framework. Full article

Despite ongoing advances in maritime safety research, ship accidents persist, with significant consequences for human life, marine ecosystems, and port operations. Because many accidents occur in or near ports, assessing a vessel’s ability to enter or depart safely remains critical. Although ports apply local navigational rules, safety criteria could be strengthened by adopting more adaptive and data-informed approaches. This study presents a mathematical framework that links Key Performance Indicators (KPIs) to a Ship Risk Profile (SRP) for collision/contact/grounding risk indication. Expert-based KPI importance weights were derived using the Average Rank Transformation into Weight method in linear (ARTIW-L) and nonlinear (ARTIW-N) forms and aggregated into a nominal SRP. Using routinely monitored KPIs largely drawn from the Baltic and International Maritime Council and Port State Control/flag-related measures, the results indicate that critical equipment and systems failures and human/organisational factors—particularly occupational health and safety and human resource management deficiencies—are the most influential contributors to the normalised accident-risk index. The proposed framework provides port authorities and maritime stakeholders with an interpretable basis for more proactive risk-informed decision-making and targeted safety improvements. Full article

This paper presents the development and implementation of a consumer Digital Twin (DT) for energy demand prediction under the SENDER (Sustainable Consumer Engagement and Demand Response) project, funded by HORIZON 2020. This project aims to engage consumers in the energy sector with innovative energy service applications to achieve proactive Demand Response (DR) and optimized usage of Renewable Energy Sources (RES). The proposed DT model is designed to digitally represent occupant behaviors and energy consumption patterns using Artificial Neural Networks (ANN), which enable continuous learning by processing real-time and historical data in different pilot sites and seasons. The DT development incorporates the International Energy Agency (IEA)—Energy in Buildings and Communities (EBC) Annex 66 and Drivers-Needs-Actions-Systems (DNAS) framework to standardize occupant behavior modeling. The research methodology consists of the following steps: (i) a mock-up simulation environment for three pilot sites was created, (ii) the DT was trained and calibrated using the artificial data from the previous step, and (iii) the DT model was validated with real data from the Alginet pilot site in Spain. Results showed a strong correlation between DT predictions and mock-up data, with a maximum deviation of ±2%. Finally, a set of selected Key Performance Indicators (KPIs) was defined and categorized in order to evaluate the system’s technical effectiveness. Full article

Data-driven quality control (QC) systems for the hot forming of steel parts increasingly rely on deep learning models deployed at the network edge, making multivariate sensor time series a critical asset for both local decisions and management information system (MIS) reporting. However, these models are vulnerable to adversarial perturbations and realistic signal disturbances, which can induce misclassification and distort key performance indicators (KPIs) such as first-pass yield (FPY), scrap-related losses, and latency service-level objectives (SLOs). To address this risk, this study introduces a Digital-Twin-Conditioned Diffusion Purification (DTCDP) framework that constrains latent diffusion-based denoising using process states from a lightweight digital twin of the hot-forming line. At each reverse-denoising step, the twin provides physics residuals that are converted into a scalar penalty, and the diffusion latent is updated with a guidance term. This directly bends the sampling trajectory toward reconstructions that adhere to process constraints while removing adversarial perturbations. DTCDP operates as an edge-side preprocessing module that purifies sensor sequences before they are consumed by existing long short-term memory (LSTM)-based QC models, while exposing purification metadata and physics-guidance diagnostics to the plant MIS. In a four-week production dataset comprising more than 40,000 bars, with white-box ℓ∞ attacks crafted on multivariate sensor time series using Fast Gradient Sign Method and Projected Gradient Descent at perturbation budgets of 1–3% of the physical range, combined with additional realistic disturbances, DTCDP improves the robust classification performance of an LSTM-based QC model from 61.0% to 81.5% robust accuracy, while keeping clean accuracy (≈93%) and FPY on clean data (≈97%) essentially unchanged. These results indicate that physics-aware, digital-twin-guided diffusion purification can enhance the adversarial robustness of edge QC in hot forming without compromising operational KPIs. Full article

This article presents the results of the Innovation Support Program (ISP), designed to enhance the market readiness of 12 bio-based innovators from six European rural regions: Northern Sweden, Mazovia (Poland), Upper Austria, Pays de la Loire (France), Strumica (Macedonia), and Andalusia (Spain). Over three years, the ISP applied a modular and flexible methodology, beginning with a cross-regional needs analysis to identify knowledge gaps, followed by a call for Expressions of Interest to select promising bio-based solutions, and concluding with tailored support delivered through regional Task Forces. These provided mentoring and capacity-building activities focusing on business modeling, market analysis, and funding opportunities. The program identified market access as a major barrier to scaling up and noted that many solutions followed Social and Solidarity Economy principles, prioritizing social and environmental impact over profit. Through targeted assistance and knowledge exchange, the ISP strengthened local innovation capacity and contributed measurable progress in companies’ Technology Readiness Levels (TRLs) and Key Performance Indicators (KPIs). Positioned within the framework of the EU Bioeconomy Strategy, the ISP demonstrates how combining regional insights with a structured support framework can effectively accelerate the scaling of bio-based solutions, highlighting the need for iterative, long-term support to sustain regional bioeconomy growth. Full article

Public procurement in many European Union member states remains strongly price-oriented, often at the expense of delivery performance, innovation, and effective risk management. This study examines how the Best Value Approach (BVA) operates within a post-transition, legality-focused administrative environment through a document-based embedded case study of a major public construction contract in the Czech Republic. By analysing artefacts from the Selection, Clarification, and Execution phases, the study traces how BVA’s core governance mechanisms—expert signalling, vendor-led risk ownership, and information-centric oversight—functioned under locally constraining conditions. The findings show that BVA improved capability sorting, surfaced risks earlier, and enhanced transparency through structured reporting instruments such as Weekly Risk Reports (WRRs), Directors’ Reports (DRs), and Key Performance Indicators (KPI)s. However, the performance effects were partial. Three boundary conditions attenuated BVA’s mechanisms: a 40% price weighting that constrained qualitative differentiation, the omission of a formal Value-Added (VA) pathway for supplier-initiated optimisation, and the absence of continuous expert facilitation to support methodological fidelity. A documented execution-phase cost variance of approximately five percent further indicates residual volatility where key BVA complements are incomplete. The study integrates Principal–Agent theory, New Public Governance, and institutional isomorphism to explain why BVA’s governance architecture activated only in attenuated form and identifies the institutional conditions that moderate its effectiveness. While limited to a single revelatory case, the findings support analytical generalisation to similarly price-dominant, audit-driven procurement regimes in post-transition EU member states and offer practical guidance for evaluation design, innovation pathways, and facilitation models. Full article