Search Results (666)

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Global satellite missions with the capability to measure ocean surface currents are continually being proposed. This new observation type is expected to significantly improve ocean model analysis and forecast skill. The potential impact of assimilating sea surface currents from the proposed wide-swath Ocean Dynamics and Surface Exchange with the Atmosphere (ODYSEA) mission is investigated in this study. An Observing System Simulation Experiment (OSSE) is set up with a 1 km Navy Coastal Ocean Model (NCOM) analysis-forecasting system in the Gulf of America domain over a 4-month time period. When compared to an experiment with only the standard data streams of temperature, salinity, and sea surface height anomaly observations from in situ and satellite platforms assimilated, the inclusion of ODYSEA-like sea surface current observations leads to a 13% and 17% reduction in the domain and time averaged root mean squared error (RMSE) for surface u and v components, respectively, as well as an improvement in the current velocity throughout the upper water column. The assimilation of the sea surface current observations also leads to an improvement in the model sea surface height, although there is a negligible to slight degradation in the temperature and salinity at depth, which is likely due to the explicit geostrophic assumption made within the velocity assimilation methodology. Full article

Particle Image Velocimetry (PIV) has recently evolved from a costly, specialized technique into an accessible method thanks to affordable hardware and open-source software. This work introduces a time calibration validation method tailored for low-cost or Do-It-Yourself (DIY) PIV systems. By utilizing inexpensive components such as light-dependent resistors (LDRs), basic resistors, and data acquisition devices or microcontrollers, the study enables accurate timing analysis of light pulses from synchronized lasers or LEDs. Experimental data obtained in real time using a National Instruments USB-6003 DAQ device confirm the system’s ability to detect light pulses with high temporal resolution. Through voltage signal interpretation, the synchronization accuracy of light sources is validated across different sampling rates. Moreover, the study demonstrates how the internal frequency settings of PIVlab, an open-source PIV software package, can be customized to enhance acquisition flexibility. Timing deviations of up to 20% were identified across selected default frequency settings. The proposed method ensures that low-cost systems maintain sufficient accuracy for phase-sensitive flow measurements, such as oscillatory flow or wave action, contributing to the broader adoption of PIV in resource-limited environments. It presents a low-cost method for validating timing accuracy in PIV systems, employs widely available components and is adaptable to multiple platforms, and enables precise synchronization checks critical for flow visualization. Full article

The Robot Operating System (ROS) is widely used in modern robotics, but its open architecture makes it vulnerable to numerous cyber threats. Although machine learning (ML)-based intrusion detection systems (IDSs) demonstrate strong classification performance on ROS-specific datasets, reliance on topology-dependent identifiers such as source and destination IP addresses, port numbers, and Flow IDs remains a critical limitation in current research. This reliance may encourage algorithms to exploit scenario-specific endpoint signatures instead of relying primarily on transferable behavioral patterns. Consequently, classification scores may be artificially inflated due to data leakage. This study addresses this issue by quantitatively measuring the impact of data leakage and introducing a topology-independent, explainable ROS framework that provides a more realistic, leakage-aware, and topology-independent evaluation framework. The evaluation involved testing the LightGBM, XGBoost, and CatBoost algorithms on ROSIDS23. Additionally, Random Forest and Gradient Boosting were included to verify the presence of data leakage. In our ablation study, models that included topology features achieved near-perfect Macro-F1 values of 0.999 to 1.000. In contrast, removing topology-dependent features reduced the Macro-F1 score to about 0.66. This finding shows that topology descriptors, rather than just transferable attack behaviors, can significantly influence the near-perfect scores seen with topology-preserving protocols. Even without topology data, ML models effectively captured temporal behavioral patterns and detected DoS attacks with nearly perfect performance, reaching F1 scores of 0.99 or higher. However, semantic attacks like Unauthorized Subscribe remained tough to classify, with F1 scores of 0.43 or lower. Additionally, SHapley Additive exPlanations (SHAP) analysis improves the interpretability of IDSs by identifying the main behavioral features that drive model decisions and suggesting feature-level directions for rule-based defense configurations in ROS environments. Full article

Green Supply Chain Management (GSCM) and sustainable economic development are two areas that have been studied extensively by scholars. However, there continues to exist a lack of cohesion or integration across academic fields regarding how GSCM can act as a catalyst for economic sustainability. This systematic literature review attempts to create a cohesive body of knowledge by exploring the drivers, barriers, and outcome measures associated with GSCM specifically within the context of creating sustainable economic growth in the long term. A structured literature review approach was used; this included conducting an extensive search of all relevant articles using multiple databases, followed by a thorough review and thematic analysis based upon the dimensions outlined above. The results indicate that GSCM is primarily influenced by the pressure of regulatory requirements and expectations of stakeholders. Financial constraints and technology gaps remain significant obstacles to the effective implementation of GSCM. Additionally, our analyses indicate that GSCM will enhance both environmental and economic performance when it is practiced with circular economy strategies and digital technologies such as AI and big data. The review shows that small- to medium-sized enterprises and firms in emerging economies face different practicalities than other types of organizations in terms of implementing GSCM strategically. However, SMEs and firms in emerging economies may benefit proportionally more than others from adopting GSCM strategically. Industry-specific case studies show that the success of GSCM practices varies widely depending on the sector; therefore, consideration of context is required. Additionally, the various theoretical frameworks discussed throughout the literature have developed from linear models towards more dynamic system-based models, indicating a developing discipline. In conclusion, we find that GSCM does not solely serve as an operational tool; rather, it acts as a strategic enabler of sustainable economic development, provided that it is implemented appropriately relative to organizational and regional context. Full article

High-precision rolling bearing applications are widely used in aerospace, new energy vehicles and high-end equipment. However, high-precision bearing manufacturing has always been one of the hot topics of research. The main problem is that the grinding accuracy of rolling bearings is too divergent. In this study, the improvement of grinding accuracy of high-precision rolling bearings was mainly studied. An on-line roundness measurement system was developed and its accuracy was analyzed. The same bearing precision grade, different bearing brands, different bearing sizes and different measurement methods were mainly used for cross-precision measurement comparison. Meanwhile, a static analysis was conducted on the measuring claw. Results indicate that the on-line measurement system can achieve an accuracy of 3 µm. The error rate was less than 11% compared with the current mature measurement technology. Under the action of the same normal measuring force, the deformation of the measuring claw of invar was larger than that of the measuring claw of 45 steel, which was relatively increased by 31%. The conclusion of this study will provide reliable data analysis and a theoretical basis for research in the field of bearing. Full article

In recent years, the touch panel industry has experienced rapid growth. With technological maturation and progressive cost reduction, touch technology has been widely adopted in human–machine interfaces. Currently, touch panels are predominantly employed in smartphones and tablet devices, and the industry is increasingly pursuing thinner, lighter designs, driving the development of diverse touch technologies, including one-glass solution (OGS), on-cell, and in-cell architectures. To enhance competitive advantage within the touch panel industry, it is essential to improve production efficiency and elevate product quality; consequently, yield has become a critical metric for evaluating industrial competitiveness. This study adopts the electrical test yield of Touch-on-Lens (TOL) touch-sensing glass as the primary performance indicator. A Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework is applied to systematically address impedance-related quality defects occurring during manufacturing. First, key quality characteristics (KQCs) of the TOL touch-sensing glass process are rigorously defined. Subsequently, measurement system analysis (MSA) and process capability assessment are conducted. Next, the Taguchi method is employed to identify the most influential process factors affecting electrical test yield. Finally, response surface methodology (RSM) is utilized to determine the optimal combination of process parameter settings that maximize electrical test yield. Results from the empirical case study demonstrate that the electrical test yield improved significantly—from 90.2% to 93.6%. This outcome validates that the integrated application of the Six Sigma DMAIC methodology, combined with the Taguchi method and RSM, effectively enhances the electrical test yield of TOL sensing glass. The proposed approach offers a robust, data-driven improvement framework applicable to touch panel manufacturers seeking to optimize sensing-glass fabrication processes—thereby supporting broader industry efforts to improve product quality and reduce manufacturing costs. Full article

This paper documents a fundamental sectoral divergence in capital–employment relationships using UK quarterly data (2014Q1–2024Q4, N = 44). While manufacturing automation studies consistently find negative employment effects, we show that information-intensive service sectors (SIC J: Information and Communication; K: Financial and Insurance; M: Professional/Scientific/Technical) exhibit robust positive co-movement between capital formation and employment. Structural vector autoregression analysis reveals persistent positive employment responses following capital shocks, with effects peaking at 5–6 quarters and remaining significant through 10 quarters. This pattern holds across eight alternative specifications with varying lag structure, variable ordering, and subsample periods. Granger causality tests reveal bidirectional temporal relationships (capital → employment: F = 3.932, p = 0.028; employment → capital: F = 5.659, p = 0.007), indicating joint determination from anticipated demand growth rather than unidirectional technology-driven dynamics. This finding—while complicating causal interpretation—strengthens the contribution by providing honest empirical characterization of coordination mechanisms in information-intensive sectors. Our capital formation proxy measures all investment in AI-intensive sectors (buildings, equipment, conventional IT, emerging AI systems) rather than AI expenditure specifically, creating measurement ambiguity we acknowledge transparently. The sectoral focus (J+K+M sectors with 22–34% AI adoption rates exceeding the 15% economy-wide average) provides indicative evidence that patterns relate to advanced technology deployment, but measurement breadth prevents definitive AI-specific conclusions. The contribution lies not in establishing AI-specific causality—which aggregate time-series methods cannot achieve—but in documenting robust sectoral heterogeneity using methodology comparable to manufacturing displacement studies. The positive association in information-intensive services contrasts sharply with manufacturing’s negative relationship, suggesting technology–employment dynamics vary fundamentally across sectors with different task structures. Three limitations constrain interpretation: (i) recursive identification cannot definitively rule out common demand shocks, (ii) the 44-quarter sample provides limited statistical power for precise magnitude estimation, and (iii) external validity to other countries, time periods, or service sectors remains uncertain. The findings motivate sector-specific rather than economy-wide technology policy approaches, recognizing that extrapolating manufacturing evidence to service-dominated economies may systematically mischaracterize employment dynamics. Full article

The economic resilience of agricultural enterprises is increasingly relevant for maintaining processing continuity and food quality in highly perishable agro-food chains. This study examines the associations between financial knowledge, financial management competency, business liquidity, and operational food-processing continuity in Türkiye’s tea sector. A quantitative cross-sectional design was employed, using structured survey data from 203 senior managers across 86 public and private tea-processing firms in Rize Province. The data were analysed using Ordinary Least Squares regression, mediation analysis, exploratory factor analysis, and robustness checks in accordance with OECD/INFE guidelines. Results indicate a significant deficit in theoretical financial knowledge (mean score: 4.47/10) alongside widespread overconfidence among 85% of managers. Applied financial management competency is positively associated with perceived business liquidity (β = 0.336, p < 0.001), suggesting that practical budgeting, cash-flow planning, and financial decision-making capabilities are relevant to maintaining operational funding capacity. In contrast, cash-flow difficulties are not significantly explained by firm-level financial knowledge, managerial competency, liquidity, or ownership structure (R² = 0.014, p = 0.722), indicating that these difficulties may reflect broader seasonal and sector-wide financing constraints. The findings challenge the assumption of a linear relationship between theoretical financial knowledge and managerial outcomes. They suggest a dual policy approach that combines applied financial management training with structural financing mechanisms to ensure the continuity of fresh leaf procurement and processing. While the study does not directly measure food safety, post-harvest losses, or SDG outcomes, the results have potential implications for reducing processing disruptions and supporting more resilient agro-food processing systems. Full article

The quarantine pathogen Phytophthora ramorum has a high potential for dispersal due to its airborne inoculum, its wide range of hosts, and its ability to spread through the trade of nursery plants. For these reasons, it represents a serious threat to ornamental nursery production and, consequently, to urban, natural and semi-natural ecosystems. This oomycete pathogen (EU1 lineage, A1 mating type) has been detected on Viburnum tinus in a commercial nursery located in the Pistoia nursery district (PND) (Tuscany, central Italy), one of the main nursery areas for the production of ornamentals in Europe. Artificial inoculations were carried out in the laboratory under controlled conditions, following a standard detached-leaf assay protocol, on leaves of 16 ornamental shrub species commonly marketed by the PND. Disease severity was assessed, and susceptibility categories (high, moderate, low, and non-susceptible) were defined based on data collected at 7 and 14 days post-inoculation and validated through statistical analysis. Inoculated species exhibited variable levels of disease severity. The results confirmed the pathogen’s high virulence on Viburnum tinus and Rhododendron hybrid ‘Madame Masson’. The following species were also found to be highly susceptible: Ilex aquifolium, Loropetalum chinense, Magnolia stellata, Osmanthus fragrans, and Trachelospermum jasminoides. Camellia japonica, Nerium oleander, Osmanthus heterophyllus, Prunus laurocerasus, and Rhododendron obtusum showed moderate susceptibility. Arbutus unedo, Laurus nobilis, Photinia fraseri and Syringa vulgaris exhibited low susceptibility. At the end of the trial, no infected species fell into the non-susceptible categories. The oomycete proved particularly aggressive on Ilex aquifolium, the most susceptible host among those tested. This high susceptibility is a new finding that could have significant epidemiological implications. Our findings emphasize the need for rigorous phytosanitary surveillance in nursery systems, based on constant monitoring and the adoption of high-throughput diagnostic protocols, in order to implement effective and rapid control measures. Full article

Radio occultation (RO) has become a key technique for monitoring the ionosphere by deriving electron density (Ne) profiles and total electron content (TEC) from GNSS signals. This study assesses the newly deployed PlanetiQ GNOMES constellation by validating its ionospheric Ne profiles and profile-based TEC against collocated measurements from ionosondes and the COSMIC-2 mission under both quiet and disturbed geomagnetic conditions. Data matching for the statistical validation uses conservative spatial thresholds of less than 1° in latitude and longitude and temporal limits of 30 min for ionosondes and 1 h for COSMIC-2, supported by a dedicated sensitivity analysis, whereas storm-time case studies apply tighter temporal collocation and explicit control of the ray path geometry. Quantitative agreement is evaluated using root mean square error (RMSE), mean and absolute mean differences, correlation coefficients, regression analysis, and normalized percentage differences for key F-layer parameters, including the maximum Ne of the F2 layer (NmF2), the peak height of the F2 layer (hmF2), and the critical frequency of the F2 layer (foF2), along with altitude-dependent Ne profiles. PlanetiQ shows strong consistency with ionosonde profiles, with RMSE ranging from 2.94 × 10⁴ to 2.76 × 10⁵ el/cm³, correlations typically exceeding 0.90, and normalized absolute mean differences often near or below about 10–20%, although lower correlations of about 0.31 and 0.69 are found at Poker Flat and Awase, respectively, reflecting complex local structures and regional variability. Comparisons with COSMIC-2 during quiet conditions yield RMSE values between 7.06 × 10⁴ and 2.16 × 10⁵ el/cm³, correlations from 0.94 to 0.99, and percentage differences that generally remain within a few tens of percent, while storm-time analyses show RMSE between 1.12 × 10⁵ and 3.70 × 10⁵ el/cm³ with correlations from 0.80 to 0.99, confirming robust agreement across a wide range of geophysical conditions. Regression results demonstrate slopes near 1.00 and correlation coefficients above 0.90 for NmF2 and foF2 between PlanetiQ and both ionosondes and COSMIC-2, whereas hmF2 exhibits larger scatter, particularly during geomagnetic disturbances; additional binning by spatial and temporal separation indicates that temporal mismatches generally have a stronger impact on discrepancies than horizontal distance. Overall, the results demonstrate that PlanetiQ ionospheric RO data provide accurate and consistent measurements of key ionospheric parameters, comparable to those from COSMIC-2 and ionosondes, and can reliably complement existing observing systems for monitoring ionospheric variability and space-weather impacts. Full article

Single-molecule Förster resonance energy transfer (smFRET) enables direct measurement of nanoscale conformational dynamics and heterogeneity in biomolecules, but quantitative interpretation of smFRET data critically depends on well-controlled excitation geometry, low background fluorescence, robust calibration, and reproducible data-analysis workflows. Prism-based total internal reflection fluorescence (pTIRF) microscopy provides important advantages for such measurements by physically separating excitation and emission paths and generating a highly confined evanescent field, yet practical guidance for implementing reproducible, quantitative pTIRF systems remains fragmented. Here we present a comprehensive, standardized framework for the design, alignment, calibration, validation, and operation of a prism-based TIRF microscope optimized for single-molecule fluorescence measurements. We describe the complete optical architecture for dual-color excitation and detection, establish alignment invariants that ensure reproducible evanescent excitation and stable donor–acceptor channel registration, and detail surface preparation, flow control, and photostabilization strategies required for reliable long-term imaging. Quantitative benchmarking protocols are introduced to evaluate signal-to-noise ratio, photobleaching kinetics, and spectral crosstalk, providing objective criteria for defining optimal operating conditions and instrument performance limits. Finally, we integrate these experimental procedures with an end-to-end single-molecule data-analysis workflow encompassing channel registration, automated and manual trajectory selection, FRET calculation, and kinetic analysis using hidden Markov modeling. The utility of the platform is demonstrated through smFRET measurements of conformational dynamics in a model nucleic acid system. Together, this work provides a reproducible and accessible methodology for implementing prism-based TIRF microscopy as a robust quantitative platform for single-molecule fluorescence studies across a wide range of biomolecular systems. Full article

Improving the irrigation water effective utilization coefficient (IWEUC, η) is important for improving agricultural water management and reducing pressure on agricultural water resources. However, in humid agricultural regions, the main factors associated with observed IWEUC improvement remain uncertain. They could be either interannual climate variability or structural improvements in irrigation engineering and management. Using measurement data from 202 representative irrigation districts in Hunan Province, China, from 2014 to 2022, this study combined the head–tail measurement method, comparative trend analysis, and correlation analysis to distinguish climate-related variability from structural change. Province-wide η increased from 0.4884 to 0.5502, and differences among large, medium-sized, and small districts narrowed over time. Structural differences also remained evident. Pumped systems generally showed higher efficiency and faster improvement than gravity-fed systems, and medium-sized gravity-fed districts remained the main low-efficiency category. No stable significant relationships were found between η and hydrometeorological indicators, whereas water-saving investment showed a positive association with η, with the strongest positive association observed in medium-sized gravity-fed districts. These results suggest that IWEUC improvement in humid agricultural regions is more closely associated with structural modernization related to engineering investment and management improvement than with annual hydrometeorological variability. Full article

Burden of diseases measured as disability-adjusted life years (DALYs) per 100,000 people can be mined from public domain data, when they are made available by population health surveillance systems. This can be analysed to allow insightful comparisons with the national average, and to understand differences in trends between the sexes, age groups, time periods, geographic regions, and sub-regions. In this illustrative case study, we have analysed the Scottish burden of disease database to understand what ailed the population of the Grampian region before the COVID-19 pandemic. We have identified that selected cancers, ischaemic heart disease, Alzheimer’s disease and other dementias are amongst the highest contributors to the burden; that drug use disorders and colorectal cancer are showing worsening trends and require health promotion and disease prevention measures from ages 15 and 25, respectively, especially in Aberdeen City; and that males are more vulnerable to atrial fibrillation and flutter, diabetes mellitus, oesophageal cancer, and self-harm, while females are more vulnerable to cerebrovascular and chronic obstructive pulmonary diseases. We demonstrate the usefulness of our analysis and methodology for the wider health system, allowing targeted medical research investments and coordinated response from public health and health service delivery. We also show the need for up-to-date surveillance data, forecasts, and evidence on the impact of interventions to be made available widely. Full article

Soil CO₂ emissions are widely used to trace fluid circulation in the crust, as faults and fracture networks act as preferential pathways for fluid ascent from depth. Their spatial distribution may reveal tectonic lineaments controlling fluid migration, while temporal variations may reflect stress changes associated with seismogenic processes. In active volcanic systems, however, identifying tectonic influences is challenging because volcanic and hydrothermal activity can mask tectonically controlled signals. Vulcano Island is particularly suitable for investigating these interactions, as it is characterized by both persistent volcanic–hydrothermal activity and a tectonic setting shaped by major regional fault systems. In this study, we analyze continuous soil CO₂ flux records and periodic surveys conducted over a fixed measurement grid during the last 20 years. Continuous records show that a clear tectonic signal is recognizable only at the Faraglione site, where the most pronounced increase in soil CO₂ flux occurred after the 16 August 2010 M 4.8 earthquake. Spatial analysis reveals two anomalous phases following this event, in September 2010 and January 2011, both showing a NNW-SSE alignment consistent with the regional structural framework. Analysis of data collected during the 2021 unrest confirms that the tectonic framework exerts strong control on fluid release both during quiescence and during phases of enhanced volcanic activity. Full article