Search Results (358)

Our new Theoretically Dynamic Regression (TDR) modeling methodology was recently applied in three types of real data modeling cases using physically based dynamic model structures with low-order linear regression static functions. Two of the modeling cases achieved the validation set modeling goal of $r_{fit,val} \ge 0.9$. However, the third case, consisting of eleven (11) type one (1) sensor glucose data sets, and thus, eleven individual models, all fail considerably short of this modeling goal and the average $r_{fit,val}$, ${\overline{r}}_{fit,val}$ = 0.68. For this case, the dynamic forms are highly complex 60 min forecast, second-order-plus-dead-time-plus-lead (SOPDTPL) structures, and the static form is a twelve (12) input first-order linear regression structure. Using these dynamic structure results, the objective is to significantly increase $r_{fit}$ for each of the eleven (11) modeling cases using the recently developed Wiener-Physically-Informed-Neural-Network (W-PINN) approach as the static modeling structure. Two W-PINN stage-two static structures are evaluated–one developed using the JMP® Pro Version 16, Artificial Neural Network (ANN) toolbox and the other developed using a novel ANN methodology coded in Python version, 3.12.3. The JMP ${\overline{r}}_{fit,val}$ = 0.74 with a maximum of 0.84. The Python ${\overline{r}}_{fit,val}$ = 0.82 with a maximum of 0.93. Incorporating bias correction, using current and past SGC residuals, the Python estimator improved the average ${\overline{r}}_{fit,val}$ from 0.82 to 0.87 with the maximum still 0.93. Full article

The solenoid valve component is the core part affecting the total mass of space propulsion system, and the accuracy of the solenoid valve mass model directly impacts the accuracy of the system mass estimation and optimization design. This study focuses on the solenoid valves used in gas path control for cold gas propulsion systems. The relationship between the gas flow rate and volume flow rate of the solenoid valve is derived. By analyzing the parameters affecting the mass of the solenoid valves, a general calculation mass model of the gas solenoid valve used in cold gas propulsion is proposed based on strength theory. Combining with the existing general calculation mass model for liquid solenoid valves and collecting mass data of 16 gas solenoid valves and 33 liquid solenoid valves used in space propulsion system, the mass calculation formulas of the gas and liquid solenoid valves are obtained by employing several mathematical fitting methods, including quadratic polynomial surface, Manski formula, bivariate power function, and pressure-corrected polynomial. The accuracy of different mass model formulas is compared to assess their performance in calculating the solenoid valve mass. The results show that the quadratic surface formula can better reflect the relationship between the mass of the gas solenoid valves and the valve parameters within the medium volume flow range of 1 × 10⁻⁹ to 3.9 × 10⁻³ m³/s and the proof pressure range of 0.4 to 49.74 MPa. For the calculation of liquid solenoid valve mass, the accuracy of quadratic polynomial surface fitting, bivariate power function equation, and univariate polynomial equation with pressure correction is comparable within the liquid volume flow range of 1.8 × 10⁻⁷ to 1.28 × 10⁻⁴ m³/s and the inlet pressure range of 0.99 to 4.24 MPa; the appropriate calculation formula can be selected based on the pressure conditions in the liquid solenoid valve chamber in practical applications. Sensitivity analysis shows a consistent trend for gas and liquid solenoid valves: proof pressure (gas valves) or inlet working pressure (liquid valves) are the dominant factors affecting valve mass, while volume flow rate has a moderate impact. The proposed solenoid valve mass model in this study can be used to calculate the mass of gas solenoid valves for space cold gas propulsion systems and liquid solenoid valves for liquid rocket thrusters with thrust below 1000 N, providing an important reference for the mass modeling and optimization design of the space propulsion systems. Full article

Electrical strain gauges are essential for monitoring wind turbine tower loads and fatigue, but accurate load measurements from these sensors require calibration over time to correct the zero-drift found in long-term measured signals. Calibration is often performed using nacelle rotation events for cable untwisting, where the tower mechanical load is known; however, non-uniform solar heating during these events can introduce thermal stresses that are misinterpreted as drift, causing systematic errors. This study evaluates six preprocessing methods for correcting zero-drift and thermal stresses in strain gauges, using measurements from two tower cross-sections—one with temperature sensors and one without. Performance is quantified using the scatter of the 10 min mean bending moments in the fore–aft and side-to-side directions and the cumulative fatigue damage over the monitoring periods. Results show that modelling the thermal stresses using a linear regression model with temperature measurements as inputs yields the most physically consistent load curves. If temperature measurements are unavailable, the effects of thermal stresses can be partly mitigated by restricting calibration to nighttime events or using solar-position variables in a regression model (instead of temperatures). As expected, the choice of preprocessing method significantly impacts load curves, but its influence on fatigue damage estimates is limited. Full article

Accurate precipitation data is vital for hydrological modeling, climate research, and water resource management, especially in arid regions like the United Arab Emirates (UAE), where rainfall is sparse and highly variable. This study assesses the performance of the Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP) dataset against ground-based gauge data and three satellite precipitation products—CMORPH, IMERG, and GSMaP—across the UAE from 2004 to 2020. Evaluation metrics include statistical, categorical, and extreme precipitation indices. MSWEP shows a moderate correlation with gauge data (mean CC = 0.62), performing better than CMORPH (0.54) but below IMERG (0.68). It also yields lower RMSE and MAE than CMORPH and GSMaP, indicating improved error metrics. However, MSWEP overestimates light rainfall and underestimates extreme events, reflected in a lower KGE (0.42) and weak performance in the 95th percentile rainfall, especially in coastal and mountainous areas. Seasonal analysis reveals overestimation in winter and underestimation during summer convective storms. While MSWEP offers strong global coverage and temporal consistency, its application in arid environments like the UAE requires bias correction. These findings highlight the need for integrating multiple datasets and regional adjustments to enhance rainfall estimation accuracy for hydrological and climate-related applications. Full article

In this paper, we propose a mobile target tracking method for airport movement areas based on an adaptive interacting multiple model framework combined with a strong tracking unscented Kalman filter, referred to as the AIMM-STUKF algorithm. The objective is to enhance real-time tracking accuracy, improve model adaptability, and strengthen robustness against abrupt disturbances in complex airport environments. The proposed AIMM-STUKF adopts a standard STUKF formulation within the overall tracking framework, thereby enhancing responsiveness to maneuvering targets. An exponential correction factor is further constructed based on posterior model probability differences to adaptively adjust the Markov transition matrix, enabling self-adaptive mode switching. In addition, airport map information is incorporated to impose constraints on the position components of the filtered state estimates, enhancing the adaptability of the algorithm to the airport operational environment. Experimental validation is conducted through Monte Carlo simulations using representative trajectories that reflect realistic airport operational characteristics. Comparative results with the standard IMM-UKF and two existing AIMM-UKF algorithms demonstrate that the proposed AIMM-STUKF achieves superior performance in terms of tracking accuracy, model matching consistency, mode-switching responsiveness, and robustness against sudden disturbances. Full article

Construction price indices play a critical role in shaping construction activity and determining the economic success of building projects in Germany, where they can serve as central inputs to cost planning and to updating trade-level project budgets over the planning and construction horizon. This paper develops a forecasting framework for 35 sub-construction price indices for office buildings, providing granular inputs for cost escalation and risk assessment. We employ regularized vector autoregressive models with exogenous variables (VARX) implemented via the BigVAR package and estimate them in a model-vintage design for an unbalanced panel. These high-dimensional models are benchmarked against compact VARX and vector error-correction models (VECM) that jointly forecast each target index with a small macroeconomic block consisting of the gross domestic product (GDP) and the three-month interbank rate. Candidate specifications are evaluated using mean absolute percentage error (MAPE) and out-of-sample root mean square error (RMSE), and the final forecasting model for each index is selected based on ex post MAPE. The results show that regularized VARX models capture dynamic interdependencies among the sub-indices and, for most series, outperform the VARX and VECM benchmarks. The resulting forecasts provide practitioners with trade-specific escalation factors that can support budgeting, contract design, and the mitigation of cost risk in office-building projects. Full article

Advances in robotic technology for hand rehabilitation, particularly soft robotic gloves, have significant potential to improve patient outcomes. While vision-based algorithms pave the way for fast and convenient hand pose estimation, most current models struggle to accurately track hand movements when soft robotic gloves are used, primarily due to severe occlusion. This limitation reduces the applicability of soft robotic gloves in digital and remote rehabilitation assessment. Furthermore, traditional clinical assessments like the Fugl-Meyer Assessment (FMA) rely on manual measurements and subjective scoring scales, lacking the efficiency and quantitative accuracy needed to monitor hand function recovery in data-driven personalised rehabilitation. Consequently, few integrated evaluation systems provide reliable quantitative assessments. In this work, we propose an RGB-based evaluation system for soft robotic glove applications, which is aimed at bridging these gaps in assessing hand function. By incorporating the Hand Mesh Reconstruction (HaMeR) model fine-tuned with motion capture data, our hand estimation framework overcomes occlusion and enables accurate continuous tracking of hand movements with reduced errors. The resulting functional metrics include conventional clinical benchmarks such as the mean per joint angle error (MPJAE) and range of motion (ROM), providing quantitative, consistent measures of rehabilitation progress and achieving tracking errors lower than 10°. In addition, we introduce adapted benchmarks such as the angle percentage of correct keypoints (APCK), mean per joint angular velocity error (MPJAVE) and angular spectral arc length (SPARC) error to characterise movement stability and smoothness. This extensible and adaptable solution demonstrates the potential of vision-based systems for future clinical and home-based rehabilitation assessment. Full article

Real-time precipitation monitoring through satellite remote sensing represents a critical technological frontier for operational hydrology in data-scarce mountainous regions. Following a comprehensive evaluation of reanalysis precipitation products in the downstream Yarlung Zangpo watershed, this investigation advances understanding by systematically assessing eight satellite-based precipitation retrieval algorithms against ground truth observations from 18 meteorological stations (2014–2022). Multi-temporal performance analysis employed statistical metrics including correlation analysis, root mean square error, mean absolute error, and bias assessment to characterize algorithm reliability across annual, monthly, and seasonal scales. Representative monthly spatial analysis (January, April, July) and comprehensive 12 month × 18 station heatmap visualization revealed pronounced seasonal performance variations and elevation-dependent error patterns. Satellite retrieval algorithms demonstrated systematic underestimation tendencies, with observational precipitation averaging 2358 mm/yr, substantially exceeding remote sensing estimates across six of eight products. IMERG_EarlyRun and IMERG_LateRun achieved optimal performance with annual correlation coefficients of 0.41/0.37 and minimal bias (relative bias: −3.0%/1.4%), substantially outperforming other products. Unexpectedly, IMERG_FinalRun exhibited severe deterioration (correlation: 0.37, relative bias: −73.8%) compared to Early/Late Run products despite comprehensive gauge adjustment, indicating critical limitations of statistical correction procedures in data-sparse mountainous environments. Temporal analysis revealed substantial year-to-year performance variability across all products, with algorithm accuracy strongly modulated by annual precipitation characteristics and underlying meteorological conditions. Station-level assessment demonstrated that 100% of stations showed underestimation for IMERG_FinalRun versus balanced patterns for IMERG_EarlyRun/LateRun (53% underestimation, 47% overestimation), confirming systematic gauge-adjustment failures. Supplementary terrain–precipitation analysis indicated GSMaP_MVK_G shows superior spatial pattern representation, while IMERG_LateRun excels in capturing temporal variations, suggesting multi-product integration strategies for comprehensive monitoring. Comparative assessment with previous reanalysis evaluation establishes that satellite products offer superior real-time availability but exhibit greater temporal variability compared to model-based approaches’ consistent performance. IMERG_EarlyRun and IMERG_LateRun are recommended for operational real-time applications, GSMaP_MVK_G for terrain-sensitive spatial analysis, and reanalysis products for seasonal assessment, while IMERG_FinalRun and FY2 require substantial improvement before deployment in high-altitude watershed management systems. Full article

Despite advancements in deep learning-based Monocular Depth Estimation (MDE), applying these models to video sequences remains challenging due to geometric ambiguities in texture-less regions and temporal instability caused by independent per-frame inference. To address these limitations, we propose STF-Depth, a novel post-processing framework that enhances depth quality by logically fusing heterogeneous information—geometric, semantic, and panoptic—without requiring additional retraining. Our approach introduces a robust RANSAC-based Vanishing Point Estimation to guide Dynamic Depth Gradient Correction for background separation, alongside Adaptive Instance Re-ordering to clarify occlusion relationships. Experimental results on the KITTI, NYU Depth V2, and TartanAir datasets demonstrate that STF-Depth functions as a universal plug-and-play module. Notably, it achieved a 25.7% reduction in Absolute Relative error (AbsRel) and significantly enhanced temporal consistency compared to state-of-the-art backbone models. These findings confirm the framework’s practicality for real-world applications requiring geometric precision and video stability, such as autonomous driving, robotics, and augmented reality (AR). Full article

Session-based recommender systems (SBRs) are critically impaired by exposure bias in observational training logs, causing models to overfit to logging policies rather than true user preferences. This bias distorts offline evaluation and harms generalization, particularly for long-tail items. To address this, we propose the Propensity- and Temporal-consistency Enhanced Graph Transformer (PTE-GT), a principled framework that enhances a recent interval-aware graph transformer backbone with two synergistic training-time modules. This Graph Neural Network -based architecture is adept at modeling the complex, graph-structured nature of session data, capturing intricate item transitions that sequential models might miss. First, we introduce a propensity-aware (PA) optimization objective based on the self-normalized inverse propensity scoring (SNIPS) estimator. This module leverages logs containing randomized exposure or logged behavior-policy propensities to learn an unbiased risk estimate, correcting for the biased data distribution. Second, we design a lightweight, view-free temporal consistency (TC) contrastive regularizer that enforces alignment between session prefixes and suffixes, improving representation robustness without computationally expensive graph augmentations, which are often a bottleneck for graph-based contrastive methods. We conduct comprehensive evaluations on three public session-based benchmarks—KuaiRand, the OTTO e-commerce challenge dataset (OTTO), and the YOOCHOOSE-1/64 split (YOOCHOOSE)—and additionally on the publicly available Open Bandit Dataset (OBD) containing logged bandit propensities. Our results demonstrate that PTE-GT significantly outperforms strong baselines. Critically, on datasets with randomized exposure or logged propensities, our unbiased evaluation protocol, using SNIPS-weighted metrics, reveals a substantial performance leap that is masked by standard, biased metrics. Our method also shows marked improvements in model calibration and long-tail item recommendation. Full article

This study examines the determinants of food price inflation in Morocco using a comprehensive econometric framework based on an Autoregressive Distributed Lag (ARDL) model. Relying on monthly data and controlling for major structural shocks, the analysis captures both the short-run dynamics and long-run equilibrium relationships between food prices and key macroeconomic, external, and climatic variables. The estimation results reveal strong inflation inertia, indicating that past food prices are the most significant driver of current price changes. External cost variables, including the nominal effective exchange rate, world oil prices, and international cereal prices, are mostly insignificant in the short run, suggesting a muted and delayed pass-through. Import volumes exert a marginal but lagged effect, while rainfall emerges as a consistent determinant, highlighting Morocco’s structural vulnerability to climatic variability. The error-correction term is negative and significant, confirming the existence of a stable long-run relationship. Long-run estimates show that oil prices and precipitation remain relevant drivers of food price dynamics, whereas the exchange rate appears largely neutral, reflecting the impact of subsidies, managed exchange rate arrangements, and domestic supply-chain characteristics. Nonlinear NARDL estimations provide no evidence of asymmetric exchange rate pass-through. The findings underscore some policy recommendations to enhance agricultural resilience, strengthen climate adaptation, and improve supply-chain efficiency for food price stability. Full article

The paper proposes a pavement safety index, the estimated available friction at the expected travel speed, FRS(v), to model the composed effect of low-slip speed friction and macrotexture on roadway crashes. This index seems to capture the relative contributions of microtexture and macrotexture across different operating speeds. Speed-dependent available friction at 40, 55, and 70 mph was estimated using the speed-correction procedure in ASTM E1960-07 and integrated into Safety Performance Function (SPF) development. Comparison of the resulting SPF models suggests that FRS values corresponding to typical operating speeds can capture the combined influence of SFN (40) and macrotexture on expected crashes for freeways and rural two-lane, two-way highways. For freeways, the estimated available friction at 70 mph (FRS113) produced the most appropriate SPF, evidenced by the lowest AIC. For rural two-lane, two-way highways, the estimated available friction at 40 mph (FRS65) resulted in the lowest AIC value, consistent with the typical operating speeds on these facilities. In contrast, none of the speed-specific friction estimates produced satisfactory model performance for urban and suburban arterials, likely due to the wide variation in traveling speeds and geometric characteristics on these facilities. The applicability of the proposed metric was demonstrated through the development of illustrative investigatory friction levels based on observed crash data, and the identification of candidate roadway segments for friction improvement interventions, and the estimation of the corresponding return on investment for these interventions. Full article

Accurate estimation of the open-circuit voltage (OCV) as a function of state of charge (SOC) is critical for reliable battery-management system (BMS) design in lithium-ion battery applications. However, at low temperatures, polarization effects distort the measured OCV–SOC profile due to premature voltage cutoffs during low-rate testing. This paper presents an offsetting-based correction method that reconstructs the truncated portions of the OCV curve by extrapolating the charge/discharge data beyond the cutoff points using simple voltage offsets. The approach is applied entirely in post-processing, requiring no modification to standard test protocols. Experimental validation using Samsung EB575152 Li-ion cells across a wide temperature range (−25 °C to 50 °C) demonstrates that the method restores the full OCV span, reduces apparent capacity loss, and improves consistency across cells and temperatures. The proposed technique offers a practical and effective enhancement to standard OCV testing procedures for temperature-aware SOC modeling. Full article

Nudging remains a cost-effective data assimilation technique in coupled climate models, yet conventional schemes with fixed spatial strengths struggle to represent heterogeneous ocean processes. This study introduces an adaptive nudging framework in which a spatially varying gain matrix dynamically balances model and observational errors, providing a more physically consistent determination of nudging coefficients. Implemented in the SPEEDY-NEMO coupled model, the method is systematically evaluated against a traditional latitude-dependent scheme. Results show substantial improvements in subsurface temperature assimilation across key regions, including the Niño3.4, tropical Indian Ocean, North Pacific, North Atlantic, and northeastern Pacific. The most pronounced gains occur above and within the thermocline, where strong stratification renders fixed nudging strengths inadequate, yielding a 20–30% reduction in RMSE and a 30–50% increase in correlation. In mid- to high-latitude regions, improvements extend to greater depths, consistent with deeper thermocline structures. The adaptive framework corrects both systematic bias and variance, enhancing not only the mean state but also variability representation. Additional benefits are found in salinity, currents, and sea surface height, demonstrating that spatially adaptive nudging provides a more effective and practical alternative for improving ocean state estimation in coupled models. Full article

Renewable energy efficiency (REE) remains critically low across many Sub-Saharan African (SSA) countries, yet the existing literature provides limited empirical clarity on how governance quality shapes efficiency outcomes and through which mechanisms these effects operate. This study addresses this gap by examining the influence of governance quality on REE in 23 SSA countries from 2005 to 2023, drawing on institutional theory and innovation diffusion theory. The analysis investigates three mediating channels, renewable investment, green policy, and green technology, using a multidimensional empirical framework that integrates the Malmquist Productivity Index (MPI), Two-Step System GMM, Generalized Estimating Equations (GEE), Generalized Least Squares (GLS), and Panel-Corrected Standard Errors (PCSE). Results consistently show that governance quality significantly enhances REE through investment, policy, and technological pathways. To capture nonlinearities and heterogeneous responses often overlooked in traditional models, we complement the econometric estimations with causal machine-learning simulations (Double Machine Learning and Causal Forests). These counterfactual analyses reveal that governance improvements and renewable-policy adoption produce the highest efficiency gains in mid-governance countries with stronger absorptive capacity. While the study offers policy-relevant insights, limitations remain, due to data constraints, unobserved institutional dynamics, and the uneven maturity of green-technology systems across the region. Nevertheless, the findings underscore that strengthening governance and fostering innovation are fundamental to accelerating a sustainable and inclusive green-energy transition in Sub-Saharan Africa. Full article