In California, hot and dry seasonal wind events such as Santa Ana and Diablo winds are among the predominant wildfire drivers. Such wind events not only cause vegetation to dry out, creating fuel for wildfires, but also damage power system equipment, causing conductors to fall to the ground, resulting in sparks and excessive heat that may ignite wildfires. Historical data highlight that over 10% of total burned areas in Southern California result from wildfires initiated by failures in power system equipment, mainly caused and intensified by high wind gusts produced by Santa Ana wind events. A majority of wind-intensified failures in power system equipment are caused by wind-induced fallen trees. However, the existing literature does not offer any model to estimate the likelihood of wildfire ignitions due to fallen trees. To fill in this knowledge gap, this study proposes a framework to assess the effectiveness of tree removal programs in reducing the risk associated with wildfire events. The framework employs a novel probabilistic seasonal wind hazard model using historical weather data and integrates it with a novel high-fidelity physics-based model for the interdependent failures of conductors and poles subjected to wind-induced fallen trees. These models are then integrated with an empirical vegetation ignition model due to electrical arcs to estimate wildfire risk for two case study areas in Southern and Northern California. The findings indicate that tree removal can significantly reduce the risk of wildfire ignitions in regions with abundant dead ground vegetation and dense woodlands.
Coronary artery disease (CAD), primarily caused by arterial stenosis or occlusion, remains a leading cause of mortality worldwide. Invasive coronary angiography (ICA) is the clinical gold standard for CAD diagnosis and treatment planning, yet its effectiveness critically depends on accurate coronary artery segmentation and stenosis assessment. However, ICA images are often affected by low contrast, severe noise, and complex multi-scale vascular morphology, substantially challenges existing segmentation methods. This study aims to improve the robustness and reliability of ICA-based coronary artery segmentation and stenosis detection by learning structure-consistent representations that jointly capture geometric priors, cross-dataset variability, long-range spatial dependencies, and frequency-aware vascular features. We propose SFD-Mamba2Net, an end-to-end framework for coronary artery segmentation and stenosis detection. A Curvature-Aware Structural Enhancement (CASE) module is introduced in the encoder to highlight elongated vascular structures while suppressing background interference. To alleviate performance degradation induced by inter-dataset discrepancies, a Supervised Domain Adaptation (SDA) strategy is incorporated at the bottleneck to encourage domain-invariant feature learning. Long-range spatial dependencies and complex vessel topology are modeled via an Axial-Alternating Dual-Stream Mamba2 (AA-DS Mamba2) module. In the decoder, a Progressive High-Frequency Perception (PHFP) module integrates multi-level wavelet decomposition with deep convolution to refine high-frequency vascular details while preserving global structural. Experiments on three large-scale ICA datasets show that SFD-Mamba2Net achieves superior segmentation accuracy and strong cross-domain generalization, obtaining Dice scores of 89.31%, 77.01%, and 74.14% on JSPH-ICA, DCA1, and ARCADE, respectively. It also achieves high true positive and positive predictive values in stenosis detection, demonstrating robustness and clinical potential.
Rapid characterization of soil surface is critical for military mobility planning, yet conventional methods require in-situ measurements that are time consuming, costly, and risky in unknown terrain. This study presents a near-real-time soil strength characterization framework that develops three models to predict soil texture, moisture, and Cone Index (CI) at the pixel level using only remotely sensed inputs. The developed workflow was validated at two sites within the Bundy Hill Off-Road Park, Jerome, Michigan, across two seasons (August 2024 and April 2025) using both drone and vehicle-mounted platforms at 50 cm resolution. Against 116 field measurements, the CI prediction achieved Spearman r = 0.81 and root mean squared error (RMSE) = 60.8 psi, outperforming the current baseline Soil Moisture Strength Prediction Model Version II (SMSP II model) (r = 0.69, RMSE = 78.2 psi) across all aggregate metrics. The improvement was most pronounced in the operationally critical marginal range (100–200 psi), where accurate prediction drives GO/NOGO mobility decisions. While CI maps provide an interpretable index of soil bearing capacity, the soil texture and moisture content predictions produced by this framework constitute the terrain inputs required by the Next Generation NATO Reference Mobility Model (NG-NRMM) for stochastic mobility mapping.
Abstract Price volatility in construction materials introduces uncertainty into highway project delivery, contributing to cost overruns, schedule delays, and contractual disputes. To help manage this risk, state DOTs incorporate price adjustment clauses (PACs) into eligible highway construction contracts. Although PACs are widely used, there is limited research examining their benefits, fairness, and implementation challenges under current market conditions. This study investigates stakeholder perceptions of PACs in terms of their advantages, fairness, and practical concerns within the context of US highway construction. Two national surveys were conducted in 2025: one distributed to 50 state DOTs, yielding responses from 40 agencies, and one distributed to highway contractors, yielding 140 responses from diverse firm sizes and regions. Categorical responses were summarized with frequency distributions, and multioption benefit items used weighted scores. Among state DOT respondents to the adoption item, 97% reported including at least one material PAC, and most contractors reported high interest in PAC inclusion. Across both stakeholder groups, volatility mitigation and facilitation of large or multiyear contracts received the highest benefit ratings, while influence on bidding participation and change-order frequency was lower. State DOT respondents indicated that contractors generally view PACs as fair, and 84% of contractor respondents rated PACs somewhat or very fair. More than 80% of state DOT respondents reported slight or no administrative burden. Implementation concerns centered on the regional applicability and timeliness of price indices, and 53% of state DOT respondents reported at least one prospective refinement, most often related to material coverage or threshold settings. Respondents perceived PACs as most useful for controlling exposure to material price volatility in multiyear work, and greater transparency in price index sourcing and adjustment procedures was associated with more favorable fairness perceptions, while administrative workload was generally described as low. This study provides insights that can assist state DOTs in refining PACs to maintain transparency, support fair risk allocation, and limit administrative complexity within current market conditions.
The automotive industry depends on high-quality paint coatings for both aesthetic appeal and functional performance. However, surface imperfections such as scratches, paint runs, and orange peel can arise from process and environmental variations. This study employs machine learning (ML) and exploratory data analysis (EDA) to identify key factors that influence surface defect formation in automotive painting. Using historical production data from Lucky Motor Corporation Limited, models based on linear regression, support vector machines (SVM), and random forests were developed and validated under various process conditions. The best-performing model achieved an R² of 0.94, with a mean absolute error (MAE) of 0.125 and mean squared error (MSE) of 0.033, demonstrating high predictive accuracy. The proposed ML framework offers a data-driven approach for quality control and process optimization, with the potential to enhance production efficiency, reduce waste, and improve overall paint surface quality.
