Search Results (334)

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

Hybridizing concentrating solar power (CSP) with photovoltaics (PV) offers a pathway to combine low-cost daytime generation with dispatchable nighttime supply. This study compares two CSP–PV hybridization concepts for Midelt, Morocco, under a common tender-style design framework: (i) a co-located configuration in which PV and CSP interact at the grid level and (ii) an EH-integrated configuration in which an electric heater (EH) uses PV electricity to heat molten salt in a topping cycle. The main contribution of this study lies in the two-stage optimization workflow, in which leading candidates are selectively re-simulated at higher temporal resolution. This workflow is applied to a common design framework that compares EH-integrated and co-located concepts while considering multiple PV technologies and a broad set of interdependent sizing variables. A surrogate-assisted genetic algorithm evaluates more than 200,000 candidate designs across PV technology, inverter size, TES capacity, EH capacity, and battery energy storage system (BESS) size. The optimization minimizes the levelized cost of energy (LCOE) subject to a $200 {\mathrm{MW}}_{\mathrm{el}}$ export limit, a CAPEX ceiling, and a nighttime-delivery constraint of ${\mathrm{CF}}_{\mathrm{night}}\ge 39\%$. Candidate designs are screened at 600 s and selectively re-simulated at 120 s, showing that temporal refinement affects not only KPI values but also candidate feasibility, final ranking, and preferred component sizing. The lowest-LCOE solution is the EH-integrated bifacial configuration, achieving 64.5% overall capacity factor, ${\mathrm{CF}}_{\mathrm{night}}=39.1\%$, less than 0.1% curtailment, a specific CAPEX of $4698/kW, and an LCOE of 7.29 ¢/kWh. Pareto-front and parameter-trend analyses further show that stricter nighttime-delivery targets shift the dominant sizing levers and define a neighborhood of near-optimal solutions rather than a single fixed design. Full article

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 growing complexity of enterprise resource planning (ERP) systems necessitates intelligent approaches for dynamically identifying and evaluating key performance indicators (KPIs) that accurately reflect organizational performance. This paper proposes a multi-agent architecture for dynamic KPI management over Oracle E-Business Suite (EBS). The core design combines a dynamic multi-agent analytics layer, an extendable dedicated EBS KPI Model Context Protocol (MCP) server layer, and a data layer. The dynamic multi-agent analytics layer defines a set of independent large language model (LLM) agents, each responsible for a specific task determined by the business requirements of a particular company. The EBS KPI MCP server layer defines the tools required to access and transform Oracle EBS data and exposes them to the AI agents in the upper layer. Above these layers is the user layer, where the user actively participates in the process through a human-in-the-loop approach. Based on this general architecture, we proposed and implemented, as a proof of concept (PoC), a multi-agent system for dynamic business KPI selection, evaluation, and quantification, in which three distinct agents for KPI selection, KPI quantification, and KPI forecasting were instantiated within the multi-agent analytics layer. This demonstrates the practical applicability of the proposed general architecture. The study contributes to intelligent business analytics by showing how coordinated LLM agents can automate KPI lifecycle activities within ERP ecosystems, enabling adaptive, data-driven performance management aligned with evolving organizational needs. Full article

Soft operational faults can noticeably degrade the performance of heat pumps and influence key monitored variables, emphasizing the need for reliable Fault Detection, Diagnosis, and Evaluation (FDDE) strategies. The BEYOND project tackles this challenge by analyzing simultaneous soft faults using a calibrated simulation model informed by data from a dedicated test rig. Achieving reliable results depends on both accurate measurements and proper model calibration. However, sensor uncertainty and errors in sub-models and correlations calibration can compromise model reliability. This work investigates the influence of measurement accuracy and calibration quality on both experimental variables and simulation outcomes for a residential air-to-water heat pump operating in cooling mode, with particular focus on refrigerant charge estimation. Two sensor configurations—“low accuracy” and “high accuracy”—are assessed, representing commercial- and laboratory-grade instruments, respectively, along with two corresponding calibration strategies. In the low-accuracy case, uncertainties around 10% were found for cooling capacity, energy efficiency ratio, and refrigerant mass flow rate, whereas high-accuracy setups reduced these to approximately 3%. Ultimately, the comparison between experimental and model-derived uncertainties confirms that achieving reliable predictions requires a balanced investment in both high-quality instrumentation and careful model calibration. Overall, this study serves as a crucial tool during the preliminary design of an experimental setup, assisting in the selection of a sensor suite that ensures not only the reliability of secondary variables and KPIs but also a robust and accurate calibration of physics-based models using the acquired experimental data. Full article

Rapid urbanization in Saudi Arabia puts increasing pressure on energy, water, mobility, and waste-management systems, strengthening the need for evidence-based smart-city policy under Vision 2030. Rather than offering a descriptive inventory of projects, this paper develops a verification-oriented framework for assessing smart-city initiatives in the Kingdom. The framework is built on four principles: (i) distinguishing national contextual indicators from city-level evidence, (ii) separating stated ambitions from observed outcomes, (iii) applying an evidence-grading rubric that prioritizes publicly verifiable mechanisms and performance indicators over anecdotal or promotional claims, and (iv) introducing a readiness–impact matrix adapted to Saudi climatic, infrastructural, and institutional conditions. The framework is applied to major Saudi smart-city cases, including NEOM, KAEC, Riyadh, Jeddah, Makkah, and Madinah. The analysis shows that the strongest publicly documented evidence is concentrated in selected sectoral applications, particularly demand response and smart-building control in electricity systems, leak detection and pressure management in water networks, and intelligent traffic management in urban transport. These cases indicate plausible pathways for improving service efficiency and reducing resource waste; however, publicly verifiable city-level outcome data remain limited, fragmented, and uneven across cases. In response, the paper proposes a policy playbook centered on KPI transparency, interoperable data governance, cybersecurity safeguards, and public–private partnership templates to improve the measurability, comparability, and scalability of smart-city outcomes. By formalizing verification and cross-case assessment, the study contributes a reproducible methodological basis for evaluating smart-city progress and prioritizing future investments in Saudi Arabia. Full article

Background: Green logistics requires decision-support approaches that jointly address cost efficiency, emissions reduction, service reliability, and reporting transparency under dynamic operating conditions. Existing studies often treat optimization, predictive updating, stakeholder coordination, and emissions traceability separately, limiting integration. Methods: This study develops a simulation-based integrated decision-support framework that combines multi-objective mixed-integer linear programming (MILP), machine learning-based travel-time prediction in a rolling-horizon setting, cooperative allocation using a Shapley value mechanism, and ISO 14083:2023-aligned emissions accounting. A permissioned blockchain layer is included as a post-decision governance mechanism to support traceability. The framework is evaluated using industry-calibrated synthetic scenarios over a 30-day planning horizon with 50 independent simulation runs. Results: Under the tested scenarios, the integrated configuration reduced average CO₂ emissions per route by 27.6% (±2.4%), improved the cost index by 17.3% relative to the baseline, and increased on-time delivery to 96.8%. Robustness analyses showed average key performance indicator (KPI) deviations below 5%. Component-level analysis suggests that the main operational gains arise from the interaction between predictive updating and prescriptive optimization, while the blockchain layer mainly improves auditability. Conclusions: The framework improves environmental and operational performance under the tested simulation scenarios, although real-world validation remains necessary before deployment-level conclusions can be drawn. Full article

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization. Full article

Artificial lighting accounts for roughly 30% of total electricity use in supermarkets and significantly affects product perception, customer experience, and purchasing behavior. Increasing the availability of natural light, combined with appropriate architectural energy retrofitting strategies, offers a major opportunity to reduce electricity demand. This study proposes a data-driven framework for evaluating energy retrofit strategies in commercial buildings, integrating Building Information Modeling (BIM) and Building Energy Modeling (BEM). A parametric methodology is used to evaluate multiple architectural retrofitting scenarios aimed at enhancing daylighting and reducing artificial lighting demand, while improving energy efficiency and environmental performance. The scenarios investigated include variations in skylight geometry and orientation, glazing type, photovoltaic integration, and advanced lighting controls. Three Key Performance Indicators (KPIs)—real energy effectiveness, lighting control performance, and environmental impact—are used to assess how design modifications influence energy use, indoor lighting quality, and environmental performance. The methodology is applied to three real food-retail buildings in Italy. Results show that lighting energy consumption can be reduced by up to 60% in scenarios combining LED technology with smart control systems, while total building electricity savings vary across case studies depending on building characteristics and usage patterns. Environmental impact reductions of approximately 15–20% are achieved, reflecting both operational and life-cycle improvements. The study demonstrates the potential of parametric architectural retrofitting to support multi-criteria decision-making for sustainable refurbishment of food-retail environments. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

This paper proposes a human-centric digital twin (DT) framework balancing energy efficiency with occupant well-being in existing buildings, addressing the lack of actionable insights in data-driven facility management and comfort issues common in fully automated systems. A “Human-in-the-loop” approach using dual-KPIs integrates real-time IoT data and visualization to evaluate sustainable energy use via Indoor Environmental Quality (IEQ). A novel occupancy-inference method tracks efficiency in legacy buildings without granular metering, implemented through a case study of 26 office rooms. Results indicate that the framework successfully identifies significant energy wastage and comfort anomalies without compromising well-being. Integrating real-time analytics with human oversight enables more resilient management than fully automated alternatives, particularly for detecting non-operational heating waste. The occupancy inference method was validated against ground truth, achieving 81% accuracy, with limitations regarding decay lag discussed. This research offers a cost-effective diagnostic tool for legacy buildings lacking sub-metering, lowering DT adoption barriers, and shifting maintenance from reactive to data-driven strategies. The framework leverages human expertise and infers occupancy-normalized energy metrics from standard IEQ sensors, proposing a human-centric DT framework to bridge the gap between raw sensor data and actionable facility management insights. Full article

The aggregation of flexible resources contributes to sustainability because it impacts on CO₂ emissions, enables renewable energy integration, improves network efficiency and makes the electric power system more resilient. The research project DEMAND has tested the potential of bottom-up aggregation of smart prosumers with no intermediation by a third-party balancing service provider. The present work analyzes the electrical and environmental effects of this new type of aggregation in different scenarios, taking into account both simulated data and data obtained from four pilot sites where the DEMAND system has been implemented. The effectiveness of the proposed aggregation method is evaluated through the calculation of some KPIs: power peaks, grid losses, voltage drops and CO₂ emissions. Full article

Nanoimprint lithography (NIL) plays an increasingly important role in modern nanomanufacturing processes, but its effective application in production conditions requires precise tools for evaluating and optimising technological processes. The aim of the study was to develop and model key performance indicators (KPIs) supporting the optimisation of the quality, stability and efficiency of nanoimprint lithography processes. As part of the selection of indicators, a literature review, surveys and in-depth interviews with industry experts were conducted, which enabled the identification of indicators relevant from a technological practice perspective. The proposed KPI classification was directly linked to the stages of the NIL process, creating a basis for operational performance control and process capability analysis. A novel aspect is the proposal of an integrated KPI model that combines the classification of indicators based on the stages of the NIL process with their direct link to technological parameters and measurable quality effects. These indicators have been linked to critical process parameters for different NIL variants, including Thermal NIL, UV-NIL, Roll-to-Roll NIL and Step-and-Repeat NIL, using a process analysis and multi-criteria optimisation approach. Research indicates that the use of an integrated KPI model improves the accuracy of nanostructure mapping, reduced defect density, and increased process efficiency without compromising technological stability. The proposed approach is a universal tool supporting NIL process control, its scaling to industrial applications, and integration with statistical process control and data-driven optimisation methods. Full article

The evolution toward sixth-generation (6G) networks envisions humans as active nodes within a fully interconnected digital ecosystem, supported by data collected from in-body and on-body sensors. Since many of these devices are not equipped to connect directly to 6G networks, Wireless Body Area Networks (WBANs) serve as an essential intermediate layer. However, conventional radio-frequency technologies face limitations in terms of energy efficiency, security, and data integrity, motivating the adoption of lightweight security mechanisms. Physical Layer Security (PLS), and in particular Physical Key Extraction (PKE), offers a promising solution by enabling legitimate devices to derive shared cryptographic keys from the reciprocal properties of the communication channel. Galvanic coupling (GC) communication has recently emerged as an on-body transmission technology alternative to radio-frequency (RF), which exploits low-power electrical signals propagating through biological tissue. Building on prior feasibility studies, this work proposes a PKE framework tailored to GC channels, integrating a lightweight key reconciliation method, based on Hamming (7,4) error-correction codes, and evaluating system performance through dedicated reliability and security Key Performance Indicators (KPIs). Results reveal a trade-off shaped by electrode placement and channel quantization parameters. Among the ones tested, the optimal configuration is achieved with a 3 cm transverse inter-electrode spacing at both transmitter and receiver, and a 3 cm longitudinal separation between transmitter and receiver, by quantizing the channel impulse response with two quantization bits. While this work focuses on validating the method in controlled conditions in order to establish a reliable study framework, future developments will focus on enhanced reconciliation, privacy amplification, and analysis of the GC channel considering physiological and environmental variations. Full article

As large scale digitization continues to reform business processes, one critical challenge organizations are currently facing is managing the staggering amount of data flowing. Further, with large datasets comes the added complexity of insuring a cyber secure environment and shielding the information security management system (ISMS) from undesirable manipulations. Today’s drastic rise of cyberattacks urges the need for effective security frameworks to guard against unauthorized access and malicious acts impeding business operations. The latter of which compelled organizations to adopt holistic information security approaches, commonly implemented via ISMS frameworks. Further, to maintain an effective ISMS, ongoing monitoring and measurements are highly required. Considering the aforementioned points, this paper explores how organizations measure the effectiveness of their ISMS focusing on key performance indicators, metrics, and foundational components involved in information security management by categorizing metrics into governance, risk, and incident response as well as determining the maturity level based on ISO alignment, the presence, specificity and automation of KPIs. Based on empirical interviews with eight diverse organizations, the research findings reveal a wide range of maturity among organizations, from those lacking clear defined KPIs to those with sophisticated multi-layered systems. While special attention is paid to incident-response management, companies with a strong ISMS stand out because they use automated and proactive metrics for strategic reporting, whereas companies with a weaker ISMS often do not have organized KPIs and depend on random manual audits. Based on these results, the present work suggests an analysis framework for evaluating ISMS effectiveness. While previous studies have struggled to define clear ISMS measurement practices, this paper aims to provide insights on measurements by identifying the core building blocks of ISMS and revealing how they are evaluated to drive continual ISMS improvement. Full article