Search Results (666)

Real-time monitoring of wind turbine aeroelastic damping is crucial for dynamically adjusting operational strategies and enhancing turbine stability and economic efficiency. However, since aeroelastic damping cannot be directly measured and effective industrial methodologies remain limited, this study proposes an innovative hybrid prediction framework for aeroelastic damping of wind turbine blades based on field-measured turbine data. First, a general model for calculating blade root reaction forces was developed using blade element momentum theory, considering multiple influencing factors such as motor torque, gravitational force, and centrifugal force. Linear regression and decision tree algorithms were employed to identify key coefficients in the theoretical model, thereby providing accurate hub load inputs for finite element (FE) calculations of tower aeroelastic damping through blade physical modeling. Second, a full-scale FE model of the wind turbine was constructed in Abaqus, where dynamic responses were computed using hub axial forces as inputs and compared with field data to obtain aeroelastic damping values, yielding high-quality labeled data for training machine learning models. Finally, an aeroelastic damping dataset was generated through data analysis and downsampling, and a long short-term memory recurrent neural network was trained as the prediction model. Simulation results demonstrated high accuracy, with mean prediction errors below 2.2% and maximum errors below 2.5% on real turbine datasets. In addition, experimental validation further confirmed the effectiveness of the proposed method. The model features a computationally efficient architecture, strong real-time applicability for high-dimensional inputs, and considerable potential for practical implementation. Full article

Background: Rugby union and rugby league are among the most physically demanding contact team sports, in which playing positions impose distinct physical requirements. The aim of the review was to collect and synthesize available data on positional differences in physical fitness between forwards and backs in adult male rugby players. Methods: The systematic review was conducted in accordance with PRISMA 2020 and SWiM guidelines and was prospectively registered in the OSF database (10.17605/OSF.IO/E4Y37). Four electronic databases were searched: PubMed, SPORTDiscus, Web of Science, and Scopus up to June 2026. The search identified 3058 records, which were reduced to 1343 after language and document-type limits and then screened. Eligibility criteria were applied within the PICOs framework. Methodological quality was assessed using JBI Critical Appraisal Checklists and the AXIS tool as a validation instrument. Due to considerable methodological heterogeneity, narrative synthesis with directional meta-synthesis was applied. Results: Thirty-seven studies were included (n > 2000 players; 15 countries; 1996–2025). Forwards achieved higher values of absolute muscular strength and power, body mass, and sprint momentum, whereas backs performed better in sprint tests (10–50 m), maximum sprint velocity, aerobic fitness (VO₂max, Yo-Yo IRTL1), CMJ height, and relative power. Following normalization to body mass, positional differences in strength and power were attenuated or reversed. In the only study that isolated the change in direction deficit, no clear positional difference was observed. Conclusions: Body mass appears to be the principal explanatory factor underlying positional differences in absolute strength and power. Position-specific fitness profiles are observable as early as the U20 category and become more pronounced with increasing competitive level. Strength and conditioning coaches should compare positional groups using body mass normalized ratio or, where justified, allometrically scaled measures rather than absolute values. Full article

We review the current understanding of transversely polarized quarks inside longitudinally polarized nucleons from the perspective of the longi-transversity distribution function and transverse-momentum-dependent (TMD) factorization. Employing the recently developed TMD evolution formalism for the unpolarized and longi-transversity distribution functions, we present the theoretical formalism for the relevant physical observables, namely the $A_{LL}^{\cos 2\varphi }$ asymmetry in the Drell–Yan process and the $A_{UL}^{\sin 2{\varphi }_h}$ asymmetry in the semi-inclusive deep inelastic scattering process. The phenomenological results are presented numerically. Full article

Laboratory experiments were conducted to investigate the effects of the relative depth ratio, $D_r$, on flow in an asymmetric compound channel with a partially vegetated floodplain. Five cases covering $D_r=0.15-0.52$ were examined. Partial-width vegetation produced two lateral shear layers: the main-channel/floodplain (MCFP) layer and the non-vegetated/vegetated-floodplain (NVV) layer. As $D_r$ increased, streamwise velocity and discharge were redistributed from the main channel toward the floodplain: the main-channel discharge fraction decreased from 83.7% to 56.6%, while the combined floodplain fraction increased from 16.31% to 43.33%. The dimensionless shear parameters ${\lambda }_{MCFP}$ and ${\lambda }_{NVV}$ decreased from 0.584 to 0.064 and from 0.741 to 0.316, respectively, with ${\lambda }_{NVV}>{\lambda }_{MCFP}$ in all cases. The maximum local Reynolds shear stress occurred near the NVV interface. For the four cases measured using an acoustic Doppler velocimeter (ADV), mean transverse advection dominated the total depth-averaged transverse momentum exchange, and its case-wise maximum magnitude exceeded those of the Reynolds-stress and dispersive contributions by factors of 71.4–356.3 and 74.2–556.7, respectively. Spectral analysis indicated that large-scale coherent motions over the floodplain were strongest under shallow-flow conditions and weakened as $D_r$ increased. These findings show that $D_r$ regulates the relative roles of the two shear layers. Full article

Commodity prices are notoriously hard to forecast, and whether the returns of commodity exchange-traded funds (ETFs) can be predicted remains an open question. We compare three deep learning models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, for forecasting the returns of six Deutsche Bank commodity ETFs covering agriculture (DBA), base metals (DBB), broad commodities (DBC), energy (DBE), oil (DBO), and precious metals (DBP). Using daily price data from January 2007 to December 2025, we predict daytime returns (open to close) and overnight returns (previous close to open) separately, over five horizons of 1, 5, 30, 60, and 180 trading days. Each model sees a 20-day window of price-based features, returns, rolling averages and volatilities, momentum, and recent lags, built from all six ETFs. All models are trained on a strict chronological split and judged by two simple, decision-oriented measures: how often they call the direction correctly, and the risk-adjusted return (annualized Sharpe ratio) of a stylized long–short strategy that ignores transaction costs. Formal significance tests with HAC corrections for overlapping targets, bootstrap confidence intervals, and comparisons with ARIMA, random forest, and simpler benchmarks corroborate strong predictability in overnight DBP and daytime DBB at medium horizons. Predictability turns out to be highly specific to the asset, the trading session, and the horizon. Overnight returns of the precious metals ETF (DBP) are by far the most predictable: the correct direction is called 71.6% of the time at 60 days and 76.7% at 180 days, with Sharpe ratios reaching about 15. Base metals (DBB) daytime returns are predictable at 30 days and oil (DBO) daytime returns at 180 days, whereas one-day-ahead forecasts and agricultural returns (DBA) stay essentially unpredictable. The Transformer has a slight edge at longer horizons and the GRU at shorter ones. Key directional accuracy and Sharpe ratio results are confirmed by Newey–West HAC significance tests and Diebold–Mariano forecast comparison tests with the Harvey–Leybourne–Newbold small-sample correction; HAC standard errors at the 180-day horizon exceed naïve OLS errors by a factor of approximately 7.4, and we explicitly flag results that do not survive this correction. A three-fold expanding walk-forward validation scheme corroborates the main findings, with DBP overnight and DBO daytime predictability persisting across all evaluation windows. Deep learning architectures statistically and economically outperform logistic regression, ridge regression, and momentum baselines on the most predictable configurations. An anomalous failure of all models on DBA daytime returns at the 180-day horizon is diagnosed as a regime-driven artefact associated with post-2021 commodity inflation, not a general feature of agricultural return dynamics. The broader lesson is that splitting returns into daytime and overnight components exposes predictable structure that conventional close-to-close returns hide. Full article

During the production of high-sulfur-content gas–water wells, elemental sulfur saturated in natural gas gradually precipitates as solid particles with decreasing wellbore temperature and pressure. When these sulfur particles cannot be continuously carried upward in the gas–liquid–solid three-phase flow formed with natural gas and formation water, they tend to deposit in the wellbore, potentially blocking the production string and even severely restricting the gas well’s deliverability. Current research on predictive models for the critical gas flow velocity required to carry sulfur particles remain inadequate. Therefore, accurately predicting this critical velocity and adjusting production to prevent deposition are crucial for managing high-sulfur gas wells. The primary innovation of this study lies in the development of a predictive model for the critical gas flow velocity required for sulfur particle entrainment. Grounded in the “gas–liquid coalescence–liquid film entrainment” coupling mechanism revealed by preliminary experiments, this model is established through a mechanical analysis of sulfur particles within liquid films in vertical and inclined pipes. Recognizing liquid film thickness and velocity as pivotal parameters for model solving, auxiliary models for predicting these two parameters in inclined pipe annular flow were developed based on experimental results and the momentum balance principle. The proposed model comprehensively incorporates factors such as well inclination angle, pipe diameter, liquid flow rate, and sulfur particle size, rendering it applicable to diverse well configurations including vertical, horizontal, and deviated wells. Evaluation against 48 sets of experimental data yielded a Mean Absolute Percentage Error (MAPE) of 2.28%, demonstrating high predictive accuracy. Furthermore, an engineering calculation program for the critical gas flow velocity was developed. A case study involving a well in the Puguang Gas Field was conducted to predict and diagnose sulfur deposition conditions, thereby verifying the model’s practical utility. This research provides a scientific basis for the safe and efficient development of high-sulfur gas fields. Full article

The wake evolution of vertical-axis wind turbines (VAWTs) plays a critical role in turbine-array performance, yet the wake variations associated with rotor geometry, operating condition, and Reynolds-number-related factors remain insufficiently understood. In this study, the wake characteristics of H-type VAWTs were systematically investigated under varying rotor diameters (D = 2, 3, 4 m), chord lengths (c = 0.1–0.4 m), and incoming wind speeds ($v_{\infty }$ = 6–10 m/s) using a two-dimensional mid-span CFD approach based on Improved Delayed Detached Eddy Simulation (IDDES) built on the SST k-ω model. The simulations are intended to examine mid-span wake mechanisms rather than to directly predict full three-dimensional far-wake recovery, turbine-array interaction, or engineering layout performance involving tip vortices, spanwise momentum transport, and three-dimensional breakdown of coherent structures. The results show that, for the selected reference geometry and within the tested inflow-speed range, the lateral mean-velocity profiles at the same downstream location collapse reasonably well after normalization by $v_{\infty }$ and D, indicating weak sensitivity to incoming wind speed under these conditions rather than general Reynolds-number independence of VAWT wakes. For cases with different solidities, the observed wake differences should be interpreted as the combined effects of rotor solidity and the corresponding near-optimal operating condition. Overall, this study provides a mechanism-oriented numerical assessment of wake behavior in H-type VAWTs. Full article

The continued implementation of the coordinated development strategy for the Beijing–Tianjin–Hebei region has placed higher demands on regional transportation integration and smart transportation development. However, regional disparities in smart transportation development and their spatiotemporal evolution within the Beijing–Tianjin–Hebei urban agglomeration have not yet been comprehensively investigated. This study focused on 13 cities in the Beijing–Tianjin–Hebei urban agglomeration during 2016–2023. A comprehensive evaluation index system was constructed to systematically examine regional disparities, spatiotemporal evolution, and the driving factors of smart transportation development. The results showed the following: (1) The overall level of smart transportation development increased steadily during the study period, exhibiting a hierarchical pattern featuring a leading core, differentiated levels of development, and persistent lagging areas. (2) Overall regional disparity declined from 0.147 to 0.137, indicating a gradual convergence trend. Inter-regional disparities remained the primary contributor to overall imbalance, accounting for up to 81.1%. (3) Smart transportation development demonstrated significant negative spatial autocorrelation. Although spatial dependence weakened over time, the interspersed distribution of high- and low-development cities remained largely unchanged. Moreover, smart transportation development exhibited strong path dependence and neighborhood effects, with limited upward mobility and stronger constraining effects from low-level neighboring areas than promoting effects from high-level neighbors. (4) Digital human capital, economic development, and R&D investment significantly promoted smart transportation development, whereas government support for science and technology and urbanization had not yet effectively generated development momentum. This study advances understanding of the disparities and evolutionary patterns in smart transportation development within the Beijing–Tianjin–Hebei urban agglomeration and provides empirical evidence to inform the design of differentiated policies under the coordinated regional development strategy. Full article

Container sea–rail intermodal transport is pivotal to the low-carbon transformation of global supply chains. However, traditional performance evaluation systems are prone to circular reasoning fallacies due to the nesting of input and output indicators and frequently suffer from spurious regression when analyzing high-dimensional macro time series under small-sample constraints. To address these endogeneity and attribution challenges, this study proposes a four-step progressive causal inference framework. Taking Tianjin Port—a pioneering hub of China’s “road-to-rail” freight restructuring policy—as the empirical subject, we use quarterly operational data covering a complete cycle from 2017Q1 to 2024Q4. First, we construct a strictly exogenous high-quality development index based on turnover efficiency, logistics cost reduction, and carbon emission mitigation, which completely isolates scale input factors. Second, from an initial pool of 35 operational and macroeconomic indicators, 17 candidate variables are rigorously pre-screened according to statistical consistency and logistics system theory. Third, an adaptive Double Machine Learning (DML) model integrated with leave-one-out cross-fitting is applied to disentangle complex collinearity among variables. The results show that DML effectively eliminates confounding noise, accurately identifies 15 true causal drivers, and excludes spurious correlations such as redundant macro-infrastructure investment. Furthermore, a causally weighted composite index reveals that the intermodal system exhibits strong resilience to global supply chain fluctuations and has undergone a four-stage evolution. Its development momentum has fundamentally shifted from extensive scale expansion to a refined mode driven by the synergy of efficiency and service quality. This study provides a robust methodological paradigm for port performance evaluation and targeted decision support for resource allocation. Full article

This study examines whether corporate governance helps explain cross-sectional stock return variation on the Tokyo Stock Exchange. We construct 32 portfolios sorted by firm size, book-to-market equity, profitability, investment, and a governance indicator distinguishing institutional and participatory structures. Using monthly data from 2010–2017, we estimate Fama–French five-, six-, and seven-factor models with ARIMAX specifications to address serial correlation. Unlike most existing Japan-focused studies that examine corporate governance primarily through firm-level regressions or simple portfolio sorts without incorporating it as a risk factor, this study adopts a more comprehensive approach by constructing governance-sorted portfolios and including a governance-mimicking factor (IMP) as an additional risk factor within multi-factor asset pricing models. We find governance has strong explanatory power, second only to market risk, and is associated with lower returns for firms with greater shareholder participation. Furthermore, governance alters size and value effects, while momentum is largely insignificant. In sum, the findings provide valuable information that portfolio managers, analysts, and investors may use for optimizing portfolio choices. Full article

We develop a differential-geometric framework for variable-mass particles moving along non-lightlike curves with non-vanishing curvature in Minkowski 3-space ${\mathbb{E}}_1^3$, employing the Frenet–Serret apparatus adapted to a Lorentzian signature. The force is defined as the time derivative of momentum, $F=d\left(m\mathbf{v}\right)/dt$, incorporating mass variation through a Meshchersky-type reactive term; no covariant four-momentum formulation is assumed. Explicit closed-form expressions are derived for the momentum vector $\mathbf{P}\left(t\right)$, force $\mathbf{F}\left(t\right)$, and yank $\mathbf{Y}\left(t\right)=d\mathbf{F}/dt$ for three distinct causal types of regular Frenet curves: spacelike curves with a spacelike principal normal, spacelike curves with a timelike principal normal, and timelike curves. The tangential yank component carries the causal sign factor ${\delta }_{\mathbf{B}}$, reflecting the type of curve. A theorem on the evolution of kinetic energy separates the inertial contribution $mv\dot{v}$ from the reactive contribution ${\displaystyle \frac{1}{2}}\dot{m}{v}^2$ due to mass variation. A radial decomposition of the force in the osculating plane generalizes Siacci’s classical theorem to Lorentzian geometry and variable-mass systems. When the rectifying coordinate b is non-zero, a corresponding decomposition of the yank is also obtained. Three illustrative physical scenarios are discussed: rocket motion with variable mass (with potential future relevance to trajectory prediction, stability analysis, and motion-anomaly assessment in unmanned systems), a geometric analogy for orbital parameter changes, and particle motion in a magnetic monopole field. Two fully worked examples (a Lorentzian helix and a logarithmic spiral) provide explicit closed-form expressions for all geometric and dynamical quantities, accompanied by numerical plots. The results recover the Euclidean case in the appropriate signature limit. Full article

The present study investigates how bed roughness affects the velocity field in a laboratory water channel designed for testing hydrokinetic turbines. The main aim is to evaluate the impact of bed morphology on flow hydrodynamics and, consequently, on the turbines’ operating conditions. Experimental studies were carried out in two hydraulic regimes—smooth channel bed and bed with artificially created irregularities—at flow velocities of 0.3 and 0.4 m/s, with a depth of 180 mm. The results indicate that bed roughness significantly affects the velocity field, leading to increased turbulent fluctuations, the formation of vortex structures, and momentum redistribution. There is also localized velocity acceleration within the measurement region caused by local acceleration between the bed irregularities, which is influenced by the geometry of the water channel. A clear vertical velocity distribution is established, with larger fluctuations being registered in the surface layer, while near the channel bed, the flow is more stable. The results obtained emphasize the importance of bed roughness as a key factor in laboratory modeling and analysis of hydrokinetic turbine performance, with a direct impact on their efficiency and load. Full article

Improving regional economic resilience is a point addressed in the sustainable development goals (SDGs; i.e., SDG 8 and SDG 11). The Yangtze River Delta (YRD) has demonstrated excellent economic resilience during the COVID-19 pandemic, largely due to the persistent green transition of the YRD in the past two decades. This paper uses a single-case method combined with the perspective of evolutionary economic geography to systematically investigate the process of green transition in the YRD (2000–2023) at both vertical and horizontal levels and proposes an integrated multi-dimensional system framework to reveal the collaborative logic of the overall green transition action and the internal mechanism of enhancing economic resilience in the YRD. The findings indicate that the combination of external factors such as contradiction change, magnifying crises, economic stabilization, and policy steering has driven the historical inevitability of green transition in China. Under such conditions, the YRD not only completed development in terms of primitive accumulation of space (coordinated development, i.e., chassis), industry (orderly upgrade, i.e., engine), and governance (equal supply, i.e., lubricant) earlier but also ensured the stability of this triangle, injecting sustained strong momentum into the rapid recovery of the economy under the impact. The solidification of green concepts further enhances the sustainability and strength of the YRD’s economic resilience. These findings provide beneficial experience on how to resume production after the pandemic or lay out cities in developing countries that are still in rapid urbanization in advance. Full article

Accurately predicting the terminal state of shaped charge jets (SCJs) is crucial for optimizing their penetration performance. The core challenge lies in a deep understanding of the complete physical chain from shock compression to unloading expansion. This paper presents a hybrid analytical–numerical model for SCJ formation that incorporates a shock-pressure-driven unloading term. Unlike classical PER theory, the proposed model explicitly introduces an unloading term and derives a quantitative expression for the momentum conversion factor $\left({\Pi }_{DMCF}\right)$ to quantitatively characterize the momentum redistribution during collapse. Our analysis finds that ${\Pi }_{DMCF}$ exhibits a typical S-shaped evolution law as the dimensionless Mach number $\left(Ma\right)$ varies. This study uses a logistic function with two characteristic parameters, $M{a}_0$ and $k$, to accurately fit the data. The research results indicate that the model parameters have clear physical connotations: $M{a}_0$ characterizes the critical condition for the material to transition from “strength-dominated” to “kinetic-energy-dominated” behavior, while k reflects the degree of abrupt transition. After calibrating the model parameters using high-fidelity numerical simulations, the jet morphology and velocity data obtained from X-ray flash photography experiments are compared and verified, confirming that the model can significantly improve the prediction accuracy. Especially for Ti₅₅Al₂₀V₅Zr₅Nb₁₅ HEA, the prediction error in the jet velocity is less than 4%, and the theoretically predicted shock pressure is highly correlated with the numerical results $\left({\mathrm{R}}^2=0.943\right)$. A further mechanistic analysis indicates that the proposed model successfully decodes the unique response of the HEA: its high dynamic strength results in a larger value, causing its momentum conversion efficiency to fall within a lower range under typical impact conditions. The theoretical framework constructed in this study provides a hybrid analytical–numerical and highly reliable theoretical tool for the accurate prediction of SCJs, as well as for the material selection and design of high-performance liners. Full article

This systematic literature review examines 31 peer-reviewed articles released in 2021–2025. It offers a coherent summary of intrusion detection and prevention systems based on machine learning and deep learning of industrial communication protocols. The review categorizes the studies depending on research focus, experimental setup, datasets, and analytical methods. According to the quantitative analysis results, the most suitable model for use in this case is the hybrid deep learning architecture, which includes the combination of Transformer-LSTM models and MODLSTM models, with 29% of the reviewed studies using these models and achieving detection rates of over 99%. Federated learning was mentioned in about 9.7% of the studies, and for 67% of them, real-world data was not available, indicating a lack of access to real-world data. These models are prevalently implemented to identify Denial-of-Service, Man-in-the-Middle, and data injection attacks. The results show that Modbus/TCP is the most studied protocol, which indicates how common it is in industrial systems. Meanwhile, other more recent protocols like MQTT and OPC UA are gaining momentum. Another insight revealed by this review is the tendency towards the use of more realistic validation techniques. Hardware-in-the-loop simulations and physical testbeds are in use in many studies. Integrated solutions which comprise a combination of edge, fog, and cloud computing are gaining popularity. Federated learning (utilized in 6.45% of the selected corpus) and software-defined networking are two emerging directions. Although these developments have taken place, there are still critical gaps, including the scarcity of real-world datasets combined with a lack of robust approaches to address scalability and privacy complications. Furthermore, recent IIoT protocols have not been thoroughly evaluated. The study highlights the need for adaptive and lightweight frameworks and the importance of implementing mechanisms that ensure privacy. There is also a need to have standardized evaluation criteria. These factors combined are instrumental for creating secure, resilient, and interoperable industrial networks during the Industry 4.0 period. Full article