Search Results (1,346)

Current ML lifecycle frameworks provide limited support for continuous stakeholder alignment and infrastructure evolution, particularly in sensor-based AI systems. We present CAPTURE, a seven-phase framework (Consult, Articulate, Protocol, Terraform, Utilize, Reify, Evolve) that integrates stakeholder-centered requirements engineering with MLOps practices to address these gaps. framework (Consult, Articulate, Protocol, Terraform, Utilize, Reify, Evolve) that integrates stakeholder-centered requirements engineering with MLOps practices to address these gaps. The framework was synthesized from four established standards (ISO/IEC 22989, ISO 9241-210, CRISP-ML(Q), SE4ML) and validated through a longitudinal five-year case study of a psychomotor skill learning system alongside semi-structured interviews with ten domain experts. The evaluation demonstrates that CAPTURE supports governance of iterative development and strategic evolution through explicit decision gates. Expert assessments confirm the necessity of the intermediate stakeholder-alignment layer and substantiate the participatory modeling approach. By connecting technical MLOps with human-centered design, CAPTURE reduces the risk that sensor-based AI systems become ungoverned, non-compliant, or misaligned with user
needs over time. Full article

The purpose of this study was to characterise the effect of cranberry (Vaccinium macrocarpon) juice on unicellular and multicellular systems, specifically food spoilage yeasts (Wickerhamomyces anomalus, Dekkera bruxellensis and Rhodotorula mucilaginosa) and intestinal cells (IEC-6 and Caco-2 cells). The effects of both raw cranberry juice and juice digested in vitro were investigated. The juices were analysed for polyphenol content using high-performance liquid chromatography coupled with mass spectrometry. The cranberry juice was evaluated for its impact on yeast surface hydrophobicity and anti-adhesive action using the MATH test and luminometry/microscopy, respectively. We also assessed the effects of raw and digested cranberry juices on IEC-6 and Caco-2 cells by measuring cell viability, metabolic modulation, genotoxicity, and antioxidant activity. Chromatographic analysis of the raw cranberry juice revealed the presence of diverse bioactive compounds, identified as hydroxybenzoic and hydroxycinnamic acids, flavonols, and anthocyanins. After digestion, the cranberry juice remained a rich source of phenolic acids. The yeast strain R. mucilaginosa was characterised by the highest hydrophobicity and adhesive abilities, but cell adhesion in the presence of raw cranberry juice was several times lower for all the tested strains. Both tested cranberry juices reduced ROS levels and were well tolerated by intestinal epithelial cells, without significant cytotoxic or genotoxic effects. Our findings provide new insights into the safety of using cranberry juice across unicellular and multicellular systems. However, further validation in real-world settings is necessary before practical applications. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

The ground impedance (insulation resistance R_isol) of photovoltaic (PV) modules is usually measured only in the dry state, even though arrays frequently operate under dew-wet or rain-wet conditions, when leakage paths can change. We measured dry insulation resistance R_dry and IEC 61215 MQT 15 wet leakage resistance R_wet for N = 37 field-aged crystalline-silicon modules from utility-scale plants and related the results to the IEC 40 MΩ·m² criterion (R_wet × A ≥ 40). The measurements used 1000 V DC and a 2 min dwell; R_wet was obtained in a salted bath with a solution resistivity < 3500 Ω·cm. The median R_dry was 42.4 GΩ, whereas the median R_wet was 462.5 MΩ, resulting in a median R_dry/R_wet ratio of ~110×. Three modules (8.1%) failed the 40 MΩ·m² limit already in the dry state, whereas eight modules (21.6%) failed under IEC-wet conditions; five were dry-pass/wet-fail cases that would have passed dry screening. For a representative area A = 1.8 m², a practical conservative dry triage threshold of approximately 55.5 GΩ identifies modules needing IEC-wet verification rather than serving as a stand-alone limit. Overall, combining dry and IEC-wet measurements improves safety and supports sustainability through resource-efficient repowering/revamping and end-of-life decisions in large PV fleets, particularly in hot climates. Full article

This paper examines the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for voltage regulation in a small-scale wind turbine (SWT) system intended for off-grid rural electrification in Uzbekistan. The proposed architecture consists of a wind turbine, a permanent-magnet DC generator, and a buck converter supplying a regulated 48 V DC load. While ANFIS-based control has been reported previously for wind energy systems, the novelty of this work lies in its focused application to a DC-generator-based SWT topology using real wind data from the Bukhara region, together with a rigorous quantitative comparison against a conventional PI controller under both constant- and reconstructed variable-wind conditions. Dynamic performance was evaluated through MATLAB/Simulink simulations incorporating IEC-compliant wind turbulence modeling. Quantitative results show that the ANFIS controller achieves faster settling, reduced voltage ripple, and improved disturbance rejection compared to PI control. The findings demonstrate the technical feasibility of ANFIS-based voltage regulation for decentralized DC wind energy systems, while recognizing that economic viability and environmental benefits require further system-level and experimental assessment. Full article

The increasing complexity of modern electrical networks poses significant challenges in terms of monitoring, maintenance, and operational efficiency. However, current planning approaches often lack a unified integration of real-time data and predictive modeling. In this context, Digital Twins (DTs) emerge as a promising solution, as they enable the creation of virtual replicas of physical assets. This research addresses the lack of standardized technical frameworks by proposing a novel mathematical optimization model for grid planning based on DTs. The proposed methodology integrates comprehensive architecture (frontend/backend), specific data standards (IEC 61850), and a linear optimization formulation to minimize operational costs and enhance reliability. Case studies such as DTEK Grids and American Electric Power are analyzed to validate the approach. The results demonstrate that the proposed framework can reduce planning errors by approximately 15% and improve fault prediction accuracy to 99%, validating the DTs as a key tool for the digital transformation of the energy sector towards Industry 5.0. Full article

This paper analyses the impact of inlet air precooling on the efficiency and electricity consumption of an open-type evaporative cooling tower. An Indirect Evaporative Cooler (IEC) was used to reduce the inlet air temperature, and its influence on system efficiency was experimentally evaluated. Although IEC units and the Maisotsenko cycle are increasingly discussed in the literature, no research to date has considered their effect on evaporative tower efficiency under actual operating conditions. For this purpose, a test stand was constructed comprising an open cooling tower and an IEC unit. The system operated automatically for 2952 h, corresponding to a full cooling season in Poland. Two sets of data collected during cooling tower operation were analysed: without precooling (Stage I) and with precooling using IEC (Stage II). Measurements were recorded every 10 s. Additionally, tests were conducted at elevated thermal loads and peak ambient temperatures. The comparative analysis concluded that air precooling using IEC reduced the cooling tower’s electricity consumption by approximately 15% and increased the SCOP of the cooling tower by 30%. This demonstrates the significant potential of the proposed solution. Full article

In electrical networks, the coordination and selectivity of protective devices are key to improving reliability and ensuring operational safety. Protections play a fundamental role in maintaining system stability and detecting faults within the power system. This study presents an optimized genetic algorithm (OGA) as a method to optimize the configurations of overcurrent protections in high voltage distribution systems. The OGA obtained the best results in all tested systems, demonstrating its effectiveness in coordinating protections according to IEC 60255-151:2009. In addition, simulations performed with the integration of Python and PowerFactory DigSILENT software validated the correct coordination of the protections, showing that the OGA not only optimizes response times, but also guarantees greater selectivity and reliability in the protection of the electrical system in an efficient way. Full article

Titanium dioxide (TiO₂) nano- and submicrometric particles’ widespread use in different sectors raised concerns about human and environmental exposure. The validation of analytical methods is essential to ensure reliability in risk assessment studies. In this study, a single-particle inductively coupled plasma mass spectrometry (spICP-MS) method was validated for the detection, quantification, and dimensional characterization of TiO₂ particles in biological tissues. Tissue samples collected after exposure to TiO₂ particles underwent mild acidic digestion using a HNO₃/H₂O₂ mixture to achieve complete matrix decomposition while preserving particle integrity. The resulting digests were analyzed by ICP-MS operated in single-particle mode to quantify and size TiO₂ particles. Method validation was conducted according to ISO/IEC 17025:2017 and included linearity, repeatability, recovery, and detection limit assessments. The limit of detection for TiO₂ particles was 0.04 µg/g, and 55.7 nm was the size the detection limit. Repeatability was within 0.5–11.5% for both TiO₂ mass concentrations and particle size determination. The validated method was applied to tissues from inhalation-exposed subjects, showing TiO₂ levels of 80 ± 20 µg TiO₂/g and particle number concentrations of 5.0 × 10⁵ ± 1.2 × 10⁵ part. TiO₂/mg. Detected TiO₂ particles’ mean diameter ranged from 230 to 330 nm. The developed and validated spICP-MS method provides robust and sensitive quantification of TiO₂ particles in biological matrices, supporting its use in human biomonitoring and exposure assessment studies. Full article

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems. Full article

This study aims to assess the energy efficiency of LED luminaires used in public road lighting by comparing manufacturer-declared photometric and electrical parameters with laboratory simulation results. The research also evaluates the performance of these luminaires across various road types and installation configurations to determine compliance with national and international standards. Eleven LED luminaires were tested using a rotating mirror goniophotometer in an ISO/IEC 17025-accredited laboratory. Simulations were conducted using Dialux Evo software across six road types (M1–M6) and three installation configurations (unilateral, bilateral, and staggered). Key parameters analyzed included brog (L_m), overall uniformity (U₀), longitudinal uniformity (U_l), luminous efficacy (lm/W), power factor, and total harmonic distortion (THD) in voltage and current. Discrepancies were found between manufacturer-declared and simulation results, especially in higher-class roads (M1–M3), where up to 28.57% of luminaires failed to meet the minimum luminance requirements when tested. The study highlights the importance of validating manufacturer specifications through accredited laboratory testing. Overall, LED technology improves energy efficiency in public lighting, and inconsistencies in the power factor and luminance performance suggest the need for stricter regulatory oversight and more rigorous quality control. Simulation tools like Dialux Evo prove essential for optimizing lighting designs tailored to specific road types and traffic conditions. Full article

The present literature review identifies substantial research and applied potential in the combined utilization of internationally recognized information security standards, Bayesian networks, and AI-based assistants to enhance cyber resilience in Unmanned Aerial Systems (UAS) operations within the specific category defined by the SORA (Specific Operations Risk Assessment) methodology. The analysis reveals that while the existing literature individually addresses key components such as ISO/IEC 27001, NIST SP 800-53, MITRE ATT&CK, Bayesian models, and AI techniques, integrated methodologies that unify these elements into a comprehensive and operationally applicable framework are lacking. Particularly underrepresented is the connection to the Cyber Safety Extension of SORA, as well as the synergistic application of quantitative analysis and automation through intelligent systems. The review concludes that a systematic effort is required to develop a holistic framework that reflects the dynamic regulatory demands, operational environments, and contemporary threats facing drone technologies. Full article

This study developed high-performance anti-flashover silicone coatings using sol–gel-synthesized CaCO₃/SiO₂ hierarchical fillers optimized via L₁₆(4⁵) orthogonal design. The optimal filler (Sample 5) was prepared under 70 vol% ethanol, with nTEOS:nCaCO₃ = 1:1 and 0.2 mol/L NH₃·H₂O, at 45 °C, for 18 h, featuring covalent Si-O-Ca bonding, a dual-scale microstructure (2–4 μm CaCO₃ cores + 20–40 nm SiO₂ nodules), a 14.44 m²/g specific surface area, and bimodal porosity (8–80 nm). Composite C7 (30 wt% filler, 3 wt% KH-570, 1:2 resin-to-filler ratio) achieved superhydrophobicity (a 153° contact angle via Cassie-Baxter stabilization), ultrahigh electrical insulation (3.20 × 10¹⁴ Ω·cm volume resistivity, 1.60 × 10¹³ Ω surface resistivity), and robust mechanical properties (Shore 3H hardness, 5B adhesion). Standardized IEC 60507:2020 tests showed that C7’s flashover voltages (14.8 kV for KMnO₄, 14.3 kV for NaCl/KMnO₄, 13 kV for NaCl) exceeded that of neat silicone resin (NSR) and conventional CaCO₃-filled composite (SR-CC) by >135%. Additionally, C7 retained superhydrophobicity after 500 h UV aging and maintained a 124° contact angle after 12 months of outdoor exposure. The superior performance stems from synergistic hierarchical topology, tortuous discharge paths, and interfacial passivation. This work establishes a microstructure-driven design paradigm for grid protection materials in harsh environments. Full article

The flexible positioning platform is a critical structural component in the ultra-high acceleration macro–micro motion platform, enabling precise positioning across multiple scales. However, under high-frequency start–stop cycles and prolonged multi-condition operation, it is prone to fatigue damage induced by random vibrations, which poses a threat to system reliability. This study proposes a method for evaluating and optimizing the platform’s performance under random vibration based on power spectral density (PSD) analysis. In accordance with the IEC 60068-2-64 standard, representative load spectra from Tables A.8 and A.6 were selected as excitation inputs. Frequency-domain analyses of stress, strain, and displacement were conducted using ANSYS Workbench 2022R1 in conjunction with the nCode platform, incorporating the Gaussian three-sigma probability interval. The results reveal that stress and deformation are highly concentrated in the hinge region, indicating a structural vulnerability. Fatigue life predictions were carried out using the Dirlik method and Miner’s linear damage rule under various PSD loading conditions. The findings demonstrate that hinge stiffness is a key factor influencing vibration resistance and service life. This research provides theoretical support for the design optimization of flexible structures operating in complex random vibration environments. Full article

Accurate biomass and carbon estimation in tropical plantation forests requires species-specific allometric models. Silver Oak (Grevillea robusta A. Cunn. ex R. Br.), cultivar “AVAONE,” is widely planted in northeastern Thailand, yet locally calibrated equations remain limited. This study developed species- and site-specific allometric models using destructive sampling of eight trees (n = 8) aged 2–9 years from a single plantation in Pak Chong District, Nakhon Ratchasima Province, without independent validation. Each tree was separated into stem, branches, leaves, and roots to determine fresh and dry biomass, and carbon concentrations were measured using a LECO CHN628 analyzer in an ISO/IEC 17025-accredited laboratory. Aboveground biomass increased from 17.49 kg at age 2 to 860.42 kg at age 9, with the most rapid gains occurring between ages 6 and 9. Tree height stabilized at approximately 19–20 m after age 7, while diameter continued to increase. Stems accounted for the largest proportion of dry biomass, followed by branches and roots. Carbon concentrations ranged from 45.561% to 48.704%, close to the IPCC default value of 47%. Power-law models based on D²H showed clear relationships with biomass, with R² values ranging from 0.7365 to 0.9372 for individual components and 0.8409 for aboveground biomass. These locally derived equations provide preliminary, site-specific relationships for estimating biomass and carbon stocks in Silver Oak AVAONE plantations and offer a baseline for future studies with expanded sampling and independent validation. Full article