Search Results (797)

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

This work investigates the differences between the monthly and 13-month smoothed sunspot numbers since 1749, using data from sunspot number (version 2). The distribution of the differences is centered near zero, with 74% of all values lying between −20 and +20, and only 1% exceeding ±70. Positive and negative differences are nearly balanced in total number, although the distribution of the monthly differences is moderately asymmetric (skewness = −0.55) and high kurtosis (>3), confirming leptokurtic behavior with sharper peak around zero and heavier tails than a Gaussian distribution. Spikes, defined in each tail using the 95th and 5th percentile, occurred in nearly all solar cycles studied, predominantly around solar cycle maxima and in cycles with higher solar activity. Moreover, the five most extreme cases with a difference of more than 100 occur in five solar cycles, ranging from below to above average. Additionally, we analyze the recent behavior of Solar Cycle 25. The significant increase in the monthly sunspot number in August 2024 (it reached 216, the highest since 2001) raised questions about the potential future intensity of Solar Cycle 25. As the sunspot number series evolved, this difference between the maximum monthly and 13-month smoothed series decreased to 55.1 (with data through October 2025), placing Solar Cycle 25 within the historical relationship between maximum monthly and 13-month smoothed sunspot number, and the largest monthly deviations. Our results show that spikes are a recurrent feature of solar activity and can provide useful diagnostics for shorter-term solar cycle variability. Full article

Background: Neoprene knee sleeves are commonly used to enhance joint stability and mechanical performance during resistance training. However, the specific influence of sleeve density on the force–velocity–power (F–V–P) profile during multi-joint lower-body exercises such as the back squat remains unclear. This study aimed to compare the theoretical F–V–P parameters derived from back squat performance while wearing low-density (LD) versus high-density (HD) knee sleeves. Methods: Fifteen resistance-trained males completed an incremental back squat test under both LD and HD conditions. A linear position transducer recorded barbell displacement and velocity. Individual force–velocity relationships were modelled to determine maximal theoretical force (F₀), velocity (V₀), power (Pmax), and the F–V slope. Paired-sample t-tests, linear mixed models, and Cohen’s d effect sizes were calculated. Clinical relevance was assessed using a threshold defined as 0.2 × the standard deviation of the HD condition. Bayesian analyses were conducted to estimate the probability and magnitude of the observed effects. Results: No statistically significant differences were observed between sleeve conditions for F₀, V₀, Pmax, or F–V slope (p > 0.05, d ≤ 0.37). Nonetheless, HD sleeves yielded slightly higher mean values for F₀, V₀, and Pmax, exceeding the predefined threshold for practical relevance. Bayesian models showed moderate probabilities (~0.80) that HD sleeves outperformed LD, though with limited chances of crossing the clinical significance threshold. Conclusions: Although HD sleeves do not produce systematic changes in F–V–P parameters, their increased material stiffness may provide small yet practically meaningful mechanical advantages in high-force resistance training contexts. Full article

With the increasing integration of renewable energy into power grids, voltage source converter-based high-voltage direct current (VSC-HVDC) stations often adopt hybrid grid-following (GFL) and grid-forming (GFM) control strategies to improve adaptability to varying grid strengths. In many existing schemes, the hybrid coefficient changes abruptly, which may produce large transient current overshoots and compromise the safe and stable operation of converters. An adaptive hybrid GFL-GFM control framework equipped with a hybrid coefficient transition regulation is proposed. Small-signal state–space models are established and eigenvalue analysis confirms stability over the considered short-circuit ratio (SCR) range. The regulating method is activated only during coefficient transitions and is inactive in steady-state, thereby preserving the operating-point eigenvalue properties. Dynamic equations of the converter current change rate are derived to reveal the key role of the hybrid-coefficient change rate in driving transient current overshoots, based on which a real-time hybrid coefficient regulating method is developed to shape coefficient transitions. Simulations on a 500 kV/2100 MW VSC-HVDC project demonstrate reduced transient current overshoot and power oscillations during SCR variations, with robustness under moderate parameter deviations as well as representative SCR assessment error and update delay. Full article

Tropical cyclones typically weaken rapidly during poleward propagation due to decreasing sea surface temperatures and increasing vertical wind shear. Super Typhoon Oscar (1995) deviated from this pattern by maintaining Category-5 intensity at an anomalously high latitude. This study investigates the oceanic mechanisms driving this resilience by integrating satellite SST data with atmospheric (ERA5) and oceanic (HYCOM) reanalysis products. Our analysis shows that the storm track intersected a persistent marine heatwave (MHW) characterized by a deep thermal anomaly extending to approximately 150 m. This elevated heat content formed a strong stratification barrier at the base of the mixed layer (~32 m) that prevented the typical entrainment of cold thermocline water. Instead, storm-induced turbulence mixed warm subsurface water upward to effectively mitigate the negative cold-wake feedback. This process sustained extreme upward enthalpy fluxes exceeding 210 W m⁻² and generated a regime of thermodynamic compensation that enabled the storm to maintain its structure despite an unfavorable atmospheric environment with moderate-to-strong vertical wind shear (15–20 m s⁻¹). These results indicate that the three-dimensional ocean structure acts as a more reliable predictor of typhoon intensity than SST alone in regions affected by MHWs. As MHWs deepen under climate warming, this cold-wake mitigation mechanism is likely to become a significant factor influencing future high-latitude cyclone hazards. Full article

Objective: We aimed to study acquired esotropia in adults and its risk factors, compile treatments performed and describe surgical technique used, with a novel indication. Methods: We conducted a retrospective study of patients with insidious distant esotropia along with distant horizontal diplopia (angles 2–30 PD with wide fusion amplitude): Refractively emmetropic, moderately myopic and mildly hyperopic. No systemic alterations. Results: 30 cases were included, average age: 38.13 ± 14.95. Mean time elapsed from the onset of symptoms to surgical treatment was 22.52. Mean spherical equivalent is −3.19 ± 2.83. Mean preoperative horizontal deviation was 18.58 ± 5.45 PD in distant vision and 5.48 ± 8.35 PD in close vision (p < 0.001). 100% of cases reported diplopia in distance vision. 20% required prismatic treatment (<10 PD) and 80% surgical (>10 PD) by lateral rectus resection, with an average of 4.82 ± 1.23 mm. Sensory result was successful in 100% of the cases and motor in 75%. Conclusions: We are facing a new type of acquired esotropia in adults that can be individualized by its clinical and therapeutic characteristics. Our prismatic and surgical treatment has been successful. Full article

Amid China’s “Manufacturing Power” push, full-chain digital restructuring in the Guangdong–Hong Kong–Macao Greater Bay Area remains hampered by mismatches among technology, organization, and environment. We therefore explored how shop floor actors perceive and shape this Technology–Organization–Environment (TOE) interplay. Semi-structured interviews with frontline operators, maintainers, and supply chain staff from GBA manufacturers were inductively coded, yielding 36 concepts, 10 categories, and 3 core TOE aggregates that were woven into a grounded model. The analysis shows that industrial internet platforms and smart equipment only create value when matched by flexible shop floor structures, cross-department data protocols, and skilled teams; otherwise, data silos, simulation–production deviations, and “buy-but-not-build” procurement stall adoption. Market pressure for customized, short-lead-time products and divergent municipal pilot policies further intensify the TOE balancing act, particularly for SMEs with weak absorptive capacity. By revealing a grassroots “technology-driven → organization-adapted → environment-adjusted” spiral that is moderated by frontline feedback, the study extends the TOE framework to micro-level, regional innovation theory and offers policy–practice levers for differentiated, cross-city manufacturing upgrading. Full article

Environmental time series, such as near-surface air temperature, exhibit strong multi-scale structure and persistent autocorrelation. Accurate forecasting therefore requires careful consideration of both temporal scale separation and serial dependence. In this study, we evaluate a unified framework that integrates Kolmogorov–Zurbenko (KZ) filtering with two classes of models: (i) classical regression with Cochrane–Orcutt autocorrelation correction, and (ii) an autocorrelation-adjusted Long Short-Term Memory (LSTM) network that learns an embedded correlation coefficient ($\rho$). All models are assessed using standardized meteorological predictors of T2M under walk-forward validation. The LSTM trained on raw predictors shows moderate performance (RMSE = 0.73, $R^2=0.46$, DW = 0.79), which improves after KZ filtering (RMSE = 0.59, $R^2=0.63$, DW = 1.84). Classical regression applied to KZ-decomposed predictors and corrected using the Cochrane–Orcutt procedure achieves substantially higher accuracy (RMSE = 0.41, $R^2=0.89$, DW $\approx 2.0$), outperforming the LSTM in both predictive precision and residual behavior. Visual diagnostics further confirm tighter predicted–actual alignment and near-white residuals in the classical models, whereas the LSTM retains small systematic deviations even after filtering. Overall, the results demonstrate that addressing multi-scale structures and autocorrelation had a greater impact than increasing model complexity. Integrating spectral decomposition with autocorrelation correction thus produces more reliable, statistically valid forecasts, demonstrating that classical regression with KZ filtering can surpass LSTM models in both accuracy and interpretability. These findings emphasize the value of combining time series–aware pre-processing with both traditional and neural network approaches for environmental prediction. Full article

Background: Despite its crucial role in overall health, oral health is frequently overlooked within healthcare systems, partly due to the misconception that oral diseases are neither life-threatening nor directly disabling. This perception has led to an underestimation of the psychological, social, and economic burden associated with oral diseases. Τhe present study aimed to assess oral health status and oral health-related quality of life among dental patients attending a public Special Care Center in Greece. Methods: A cross-sectional study was conducted among 400 dental patients aged 18 years and older who visited a public Special Care Center for a routine check-up or a dental problem between September and October 2024. Data was collected through personal interviews and clinical examinations after informed consent was obtained. Oral health-related quality of life was evaluated using the Oral Health Impact Profile-14 (OHIP-14) and the Oral Impacts on Daily Performance (OIDP) questionnaires. Categorical variables were presented as absolute and relative frequencies, while quantitative variables were summarized as mean, standard deviation, median, minimum, and maximum. Normality was assessed using the Kolmogorov–Smirnov test. Bivariate analyses and multivariate linear regression models were performed, with statistical significance set at p < 0.05. Statistical analyses were conducted using IBM SPSS 23.0. Results: The majority of participants were female (56.3%) with a mean age of 50.4 years (SD = 14.9). Overall oral health-related quality of life was moderate (OHIP-14: Mean = 21.0, SD = 14.8; OIDP: Mean = 14.0, SD = 12.8). Patients who attended the center due to a dental problem reported significantly poorer oral health outcomes than those attending routine check-ups (p < 0.001). Poorer self-rated oral health, having ≥12 missing teeth, prosthetic restoration, and foreign nationality were significantly associated with worse oral health-related quality of life. Conclusions: Dental patients attending the Special Care Center demonstrated moderate oral health status, which was associated with psychological distress, physical disability, and social limitations. These findings underline the need for targeted public oral health interventions, especially for vulnerable population groups. Full article

Pan-Homophonic events denote fluctuations in stock prices that are triggered by phonetic similarities between event keywords and stock tickers. As a relatively novel and under-researched phenomenon, they mirror a subtle yet influential behavioral deviation within financial markets. Centering on the case of Chuandazhisheng, this study delves into how such events produce dynamic and time-varying impacts on stock prices. A linguistic amplitude segmentation method is devised to discriminate between high- and low-intensity events based on information entropy. To separate pan-homophonic-driven price movements from broader market trends, the Relational Stock Ranking (RSR) model is integrated with a Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework to establish an adjusted price benchmark. The empirical analysis reveals a sequential price response: initial moderate fluctuations in the low-amplitude phase often yield to more prominent volatility in the high-amplitude phase. While price surges typically occur within one or two days of the event, they generally revert within approximately three weeks. Moreover, repeated exposures to homo- phonic stimuli seem to attenuate the response, indicating a decaying spillover pattern. These findings contribute to a more profound understanding of the intersection between linguistic cues and market behavior and provide practical insights for investor education, information filtering, and regulatory supervision. Full article

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks. Full article

Accurate and quantitative pain assessment remains a major challenge in clinical medicine, especially for patients unable to verbalize discomfort. Conventional methods based on self-reports or clinician observation are subjective and inconsistent. This study introduces a novel automated facial pain assessment framework built on a dual-attention convolutional neural network (CNN) that achieves clinically calibrated, high-reliability performance and interpretability. The architecture combines multi-head spatial attention to localize pain-relevant facial regions with an enhanced channel attention block employing triple-pooling (average, max, and standard deviation) to capture discriminative intensity features. Regularization through label smoothing (α = 0.1) and AdamW optimization ensures calibrated, stable convergence. Evaluated on a clinically annotated dataset using subject-wise stratified sampling, the proposed model achieved a test accuracy of 90.19% ± 0.94%, with an average 5-fold cross-validation accuracy of 83.60% ± 1.55%. The model further attained an F1-score of 0.90 and Cohen’s κ = 0.876, with macro- and micro-AUCs of 0.991 and 0.992, respectively. The evaluation covers five pain classes (No Pain, Mid Pain, Moderate Pain, Severe Pain, and Very Pain) using subject-wise splits comprising 5840 total images and 1160 test samples. Comparative benchmarking and ablation experiments confirm each module’s contribution, while Grad-CAM visualizations highlight physiologically relevant facial regions. The results demonstrate a robust, explainable, and reproducible framework suitable for integration into real-world automated pain-monitoring systems. Inspired by biological pain perception mechanisms and human facial muscle responses, the proposed framework aligns with biomimetic sensing principles by emulating how localized facial cues contribute to pain interpretation. Full article

The study assesses the level and concentration of revitalisation measures in Poland’s county-level cities across two dimensions: spatial–cultural and social. We compiled comparable indicators from the Local Data Bank (2020–2023) and municipal revitalisation programmes for 63 cities, constructing ten stimulus variables (five spatial–cultural; five social). Indicators were normalised to (0–1) and aggregated into two synthetic indices—IRSC (spatial–cultural) and IRS (social)—followed by a standard-deviation-based classification into four types/groups. Results show pronounced inter-city variation with no clear voivodeship pattern. Several cities emerge as consistent leaders across dimensions, while others perform unevenly—e.g., cases with high IRSC but moderate IRS, and vice versa—highlighting different strategic emphases of programmes. We also note large disparities in financial effort (per area and per resident) and low counts of actions per unit in many cities, contrasted with a few high-activity cases. The findings indicate that roughly one-third of cities leverage revitalisation effectively in both dimensions. The study advocates complementing synthetic, comparative assessment with practice-informed models that adapt solutions proven in top-performing cities, rather than relying solely on unified, centrally framed approaches. Full article

The ecological status of lakes based on ichthyofauna, as defined by the Water Framework Directive, is assessed using intercalibrated methods. However, the methods adopted (in Poland, the Lake Fish Index LFI-EN method, based on results of one-off fishing with multi-mesh gillnets) are labor-intensive and do not allow for frequent repeat testing. Therefore, the concept of a simple model describing changes in the relative number of single traces in the vertical profile (according to the TS target strength distribution) in a lake is presented, as well as an index (the sum of deviations from such a model), enabling quantification of the similarity of TS distributions in lakes with this model. Preliminary analyses were conducted on acoustic data collected in Lake Dejguny. This lake—the condition of which could be estimated based on historical data using the relationships between LFI and the degree of lake eutrophication (expressed by Carlson’s TSI)—was assessed as having a good status in 2006, whereas in 2021, (based on LFI-EN) it had a moderate status. The study tested the TS distribution model, calculated as the arithmetic mean of the relative number of single traces in 2 m-thick layers. It was also shown that the proposed indicator can effectively signal deterioration of ecological status—the sum of the absolute values of the TS distribution deviations in 2021 (moderate status) from the model was more than seven times greater than the sum of the deviations of the distributions from which the model was built (good status). The obtained results confirmed the hypothesis about the possibility of determining a characteristic distribution of single traces in the vertical profile when the lake was classified as being in good condition. Full article

This study analyzed how different downward loads and rotational kinematics influence NiTi rotary instrumentation outcomes. Heat-treated NiTi instruments were used to prepare extracted human single-rooted premolars with a moderate canal curvature. Instrumentation was performed using an automated endodontic instrumentation device with controlled downward loading and torque/force sensing, under different downward load settings (1, 2, and 3 N), employing either continuous rotation (CR) or optimum torque reverse (OTR) motion, which is a torque-sensitive reciprocation. Instrumentation was completed without instrument fracture or ledge formation in all six groups. OTR-3N specimens displayed a significantly lower upward force (i.e., screw-in force) than OTR-2N specimens (p < 0.05). OTR-1N specimens required a significantly longer instrumentation time than CR-1N specimens and the other OTR specimens (p < 0.05). At 1 mm from the apex, CR-2N specimens showed a significantly larger canal-centering ratio (i.e., larger deviation) than OTR-2N specimens (p < 0.05). Overall, applying a downward load of 2–3 N in OTR mode provided shaping efficiency similar to CR, but with a reduced screw-in force and enhanced canal-centering in the apical region, supporting the use of OTR as a promising alternative to CR for curved canal preparation using heat-treated NiTi instruments. Full article