Search Results (673)

This paper examines whether a residual structural state extracted from cross-asset downside-risk dependence contains incremental information for forecasting next-day market downside risk beyond a strong heterogeneous autoregressive (HAR) benchmark. The empirical analysis uses Binance intraday data from September 2019 to December 2025 and a fixed sample of 24 liquid cryptocurrencies obtained through explicit data-quality screening and sample diagnostics. The forecasting target is the log of an equal-weight cross-sectional downside-risk index constructed from strictly valid asset-level realized downside semivariance measures. The empirical design is deliberately conservative: the market sample is fixed ex ante, the target is evaluated against Bitcoin (BTC) and Ethereum (ETH) dominance diagnostics, and asset-level HAR-type models are estimated recursively to generate ex-ante one-step-ahead residuals, from which rolling residual-dependence matrices and structural signatures are constructed. The selected residual state contains four components: average residual correlation, Frobenius-type deformation, influence concentration, and influential-set turnover. The evidence supports three qualified conclusions. First, the full residual state attains the lowest average QLIKE loss relative to the HAR benchmark, although the corresponding Diebold–Mariano test under the primary QLIKE loss does not reject equal predictive accuracy at conventional levels. Complementary Clark–West evidence on the nested log-scale comparison supports incremental predictive content for the level-state and full-state augmentations. Second, the strongest forecasting evidence comes from the full state rather than from deformation-only specifications. Third, event-window diagnostics show that structural reorganization is most pronounced around stress-entry and extreme-risk episodes, supporting an onset-sensitive rather than a long-lead early-warning interpretation. Overall, the evidence supports a cautious and statistically qualified predictive conclusion: residual market structure may contain incremental information for short-horizon downside-risk forecasting in cryptocurrency markets, especially around stress onset, but the result should not be interpreted as a decisive primary-loss improvement or as evidence that deformation alone dominates a strong benchmark. Full article

Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for secure grid dispatch and renewable-rich system operation, yet it remains difficult because of rapid weather fluctuations and error accumulation in multi-step prediction. This paper proposes a decoupled physics-guided residual-correction framework, built on an attention-based sequence-to-sequence (Seq2Seq) architecture, for deterministic 4 h ahead rolling PV forecasting at 15 min resolution. In the first stage, a physical model maps numerical weather prediction (NWP) inputs to a deterministic baseline trajectory while preserving physical bounds. In the second stage, an Attention-Seq2Seq network learns the structured residual trajectory from historical sequences. The global attention mechanism allows the decoder to focus on the most informative historical states, helping reduce information loss and error accumulation over extended horizons. Experiments on a 22-month real-world PV dataset show that the proposed framework outperforms conventional linear and nonlinear benchmarks, reducing root mean square error (RMSE) and mean absolute error (MAE) by 23.79% and 39.17%, respectively, relative to the physical baseline. The framework also maintains robust instantaneous tracking under rapidly changing cloud conditions and preserves a 30–40% error reduction rate at Steps 12–16, supporting reliable intraday scheduling. Full article

This study presents the design, simulation, and experimental validation of a novel sector-shaped thread rolling machine aimed at improving forming efficiency, structural compactness, and process controllability compared with conventional linear thread rolling systems. A systematic engineering framework integrating mechanism design, curved-die implementation, motion control, finite-element simulation, and experimental verification is established. DEFORM-3D simulations are performed to investigate the effects of friction coefficient and die spacing on material flow and thread profile formation, and the results are used to guide machine construction and parameter optimization. Experimental results demonstrate that the proposed mechanism can simultaneously form four screws within a single rotation cycle, significantly enhancing production efficiency. Under optimized parameters, the relative errors of pitch diameter and helix angle are maintained within 5%, showing good agreement with simulation predictions. The findings confirm the feasibility, controllability, and stable forming capability of the proposed system, providing a practical and efficient solution for next-generation compact and high-productivity thread rolling equipment. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

In this study, nanolubricants based on SAE 5W-30 mineral oil were formulated using oleic acid-functionalized graphene nanoplatelets (GNPs), and their colloidal stability, rheological behaviour, thermal stability, and tribological performance under cold rolling conditions were systematically investigated. The nanolubricants were prepared at GNP concentrations of 0.05, 0.1, 0.2, 0.4, and 0.6 wt%. FT-IR analysis confirmed successful functionalization, evidenced by the characteristic C=O band at approximately 1710 cm⁻¹ and changes in CH₂ stretching vibrations in the 2850–3000 cm⁻¹ range. UV–VIS results indicated initially homogeneous dispersions; however, after three days, relative concentrations decreased to 95%, 90%, and 75% for 0.05, 0.2, and 0.6 wt% GNPs, respectively. Viscosity measurements showed minimal variation at low concentrations, with only a 0.64% increase at 0.2 wt% compared to the base oil. TGA revealed enhanced oxidative stability at low GNP contents, with the oxidation onset temperature increasing from 205.3 °C to 207.2 °C at 0.05 wt%, while a marked decline was observed at higher concentrations (176.8 °C at 0.6 wt%). In cold rolling experiments at a 3% reduction ratio, the rolling force was measured at 1341 N/mm with the neat lubricant, decreasing to 1210 N/mm with a lubricant containing 0.1 wt% GNPs, corresponding to an approximate 10% reduction. Compared with dry conditions, this reduction was approximately 21%. Surface roughness and 3D topography analyses further showed that GNPs-containing lubricants reduced asperities and promoted the formation of a more uniform tribofilm. At low concentrations, the improved lubrication performance of oleic acid-functionalized graphene nanoplatelets is attributed to their homogeneous dispersion in mineral oil, where physically adsorbed oleic acid improves colloidal stability by reducing agglomeration and promotes the formation of a stable tribofilm, facilitating interlayer sliding under boundary lubrication conditions. Overall, the findings demonstrate that oleic acid-functionalized GNPs, when used at optimal concentrations, significantly enhance both lubricant stability and cold rolling performance. Full article

Real-time coordination across OEM–Tier-1 manufacturing networks remains challenging due to delayed shop-floor data, stochastic machine availability, and the need for schedule stability. This paper presents a protocol-agnostic, two-stage mixed-integer linear programming (MILP) framework for real-time family-level scheduling. The method integrates MTConnect-like data streams without requiring adherence to any single communication standard. In Stage 1, a baseline plan is generated using expected capacity; in Stage 2, a rolling-horizon recourse model adapts the plan to observed (possibly lagged) capacity while incorporating a stability penalty to control resequencing. A synthetic OEM–Tier-1 testbed with three machines (two Tier-1, one OEM) is used to benchmark performance under real-time (L = 0) and delayed (L = 5) data scenarios. Across these scenarios, the real-time rolling scheduler improves strict on-time fulfillment by approximately 70% and eliminates terminal backlog relative to static planning, while MILP solve times remain under 0.1 s per cycle. Sensitivity experiments that vary disruption intensity, replanning interval (Δ), and stability weight (λ) show consistent qualitative trends and illustrate how the framework can be tuned to balance service performance against schedule stability without sacrificing computational tractability. Full article

Timely monitoring of tourism demand is essential for destination management, yet official monthly arrival statistics are often released with delays and can be difficult to use for near-real-time decision-making, particularly under structural shocks such as coronavirus disease 2019 (COVID-19). This study develops a fully reproducible, open-data nowcasting pipeline for Ecuador’s international tourist arrivals using a Python workflow. The framework integrates (i) the official monthly arrivals series published by Ecuador’s Ministry of Tourism (MINTUR), (ii) open online attention proxies from Wikipedia pageviews retrieved via the Wikimedia REST application programming interface (API), and (iii) open climate covariates derived from the ERA5-Land land reanalysis. Multiple forecasting models are evaluated under a rolling-origin, one-step-ahead backtest, with a mandatory seasonal naïve benchmark and a regime-aware assessment that separates a stress-test window (2019–2021) from an operational post-COVID window (2022–2025). Forecast accuracy is summarized using root mean squared error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), and statistical significance of performance differences is assessed using the Diebold–Mariano (DM) test. Results show that a ridge-regularized autoregressive model (ridge_ar) achieves the best overall accuracy, reducing RMSE by approximately 79% relative to the seasonal naïve baseline over the full evaluation window. Windowed results confirm robust performance during the shock period and sustained improvements in the post-2022 operational regime, while the incremental benefit of broader exogenous signals is heterogeneous across windows, underscoring the importance of regularization and regime-aware reporting. The proposed approach provides a transparent, low-cost blueprint for reproducible tourism monitoring that is transferable to other destinations using open data and standard computational tools. Full article

Rabbit manure with high-moisture content exhibits complex adhesive and flow behaviors, which make accurate parameterization in discrete element method (DEM) simulations difficult. To improve the reliability of DEM modeling for rabbit manure composting processes, this study calibrated the contact parameters of rabbit manure at 65% moisture content using the angle of repose as the target response. A physical angle of repose test was first conducted using the cylindrical lifting method, yielding a measured value of 38.77°. The Hertz–Mindlin with Johnson–Kendall–Roberts (JKR) contact model was then adopted to represent the adhesive behavior of the material, and a three-stage optimization strategy consisting of a Plackett–Burman screening test, a steepest ascent test, and a Box–Behnken design was applied to identify and optimize the key parameters. The results showed that the particle restitution coefficient, rabbit manure–PLA rolling friction coefficient, and surface energy were the dominant factors affecting the angle of repose. The optimal parameter combination was a particle restitution coefficient of 0.56, a rabbit manure–PLA rolling friction coefficient of 0.375, and a surface energy of 0.243 J/m². Under these conditions, the simulated angle of repose was 39.21°, with a relative error of 1.13%. These calibrated parameters provide a reliable basis for DEM simulation and engineering optimization of rabbit manure composting equipment. Full article

This paper studies the dynamic behavior of a catamaran hovercraft wind farm service vessel (CHWFSV) during the berthing coupling process with a wind turbine tower, aiming to enhance its safety and reliability in engineering applications. By constructing an arc-shaped elastic fender and employing computational fluid dynamics (CFD), it investigates the motion response under transverse waves considering the effects of thrust, air-cushion flow and the elasticity coefficient of the fender. A finite element analysis (FEA) model of the arc-shaped fender, accounting for elastic stress and strain, is developed to study its coupled mechanical behavior under different thrust conditions. The research in this paper is based on numerical CFD simulation with experimental validation. The motion modeling under transverse waves is further verified through uncertainty analysis. The series of research results indicate the following: vessel rolling resonance occurs at λ/L = 1.667 (λ/L denotes the dimensionless wavelength-to-length ratio); increasing air-cushion flow extends the roll period and reduces roll amplitude at λ/L = 0.667, while applying thrust at λ/L = 1.667~3 lowers roll but reduces pitch and heave stability; relatively good berthing performance is achieved when F_CM/∆ = 0.054 and the elastic coefficient is 1.25 × 10⁷ Pa/m (Δ represents the vessel weight). Full article

Oxidative aging drives asphalt pavement degradation, causing critical structural failures. This study evaluated hazelnut (HS) and walnut shell (WS) powders (0–3% w/w; 10–12 μm) as sustainable antioxidants, from valued residues, to mitigate thermo-oxidative aging in CA-24 binders. After evaluating the antioxidant potential (ORAC; Oxygen radical absorbance capacity, and TPC; Total phenolic content), modified binders underwent RTFO (Rolling thin film oven) and PAV (Pressure aging vessel) aging, evaluated by Fraass fragility, Relative Aging Index (RAI), dynamic shear rheometry (G*/sin δ), and Multiple Stress Creep Recovery (MSCR). WS exhibited significantly higher antioxidant capacity (6000 μmol TE g DW⁻¹) and TPC than HS. The 3% treatments demonstrated optimal antioxidant efficacy, reducing long-term RAI by 14% and improving low-temperature flexibility by 3.8 °C (Fraass point −12.3 °C). However, MSCR revealed initial plasticizing effects decreasing elastic recovery (70%) and increasing non-recoverable compliance (Jnr) compromising unaged rutting resistance. Principal component analysis confirmed progressive diversification of aging-induced properties, evidencing complex multivariate trajectories. Ultimately, while nutshell derived phenolic modifiers provide effective concentration-dependent antioxidant protection, practical application requires optimization through targeted phenolic extraction, particle engineering, or elastomeric co-modification. Balancing aging resistance with high temperature stability remains essential for advancing these sustainable biomodification strategies in road infrastructure. Full article

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

Active magnetic levitation bearings incorporate backup bearings that support the rotor during a breakdown, allowing it to maintain its circular movement despite the loss of magnetic force. This safeguards both the stator of the magnetic levitation bearing and the motor stator from harm. Research reveals that ball bearings are susceptible to failure mechanisms, including raceway wear and scoring. The principal cause is the unregulated motion of the rolling parts, which are divided by the cage, once wear manifests, resulting in raceway lag. This leads to significant contact deformation between the rolling elements and the raceway, along with prolonged cumulative impacts between the rolling elements and the cage. Cage-free bearings prevent collisions between the cage and rolling elements; yet, the orbital motion of the rolling elements in these bearings demonstrates a level of independence and randomness relative to traditional caged ball bearings. This presents considerable obstacles to attaining standard orbital motion in cage-free ball bearings. Despite advancements in technology that have largely elucidated the non-linear motion dynamics of ball bearings, several critical hurdles in behavioral characterization persist. This work presents a thorough review of the non-linear motion behavior of ball bearings and the methodologies for their multi-body dynamic characterization. This report proposes future research topics to improve the design of high-performance bearings and augment their reliability. Full article

This paper develops a probabilistic Available Transfer Capability (ATC) framework that quantifies export headroom for renewables across transmission-distribution interfaces under time-varying uncertainty. Static transmission reliability margins can unnecessarily curtail exports. A dynamic transmission reliability margin (TRM) is embedded within ATC using rolling window statistics and adaptive confidence factor scheduling to release capacity in calm periods and tighten margins during volatile transitions. Uncertainty is modeled as net nodal power imbalance variability from load and renewable deviations, together with stochastic thermal limit fluctuations. Correlated multivariate scenarios are generated via Latin Hypercube Sampling with Iman-Conover correlation preservation and propagated through full AC power flow analysis. Validation on the IEEE 39-bus system and New Zealand’s HVDC inter-island corridor recovers 93.31 MW of usable transfer capacity on the IEEE system relative to the pooled Monte Carlo P95 constant-margin baseline, with 78.11 MW attributable to rolling window volatility tracking and 15.20 MW to adaptive confidence factor scheduling, and 59.51 MW (+7.6%) on the New Zealand corridor relative to the corresponding pooled Monte Carlo P95 baseline, with the gain arising primarily from rolling window volatility tracking. Relative to a 95% one-sided reliability target, achieved coverage is 93.9% for IEEE and 91.8% for New Zealand, translating into increased export headroom and reduced curtailment. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article