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Recent Submissions
Some of these are not like the others: Relative thermal sensitivity among anuran species of the Southeast United States
DuBose, Traci P.; Moore, Chloe E.; Farallo, Vincent R.; Benson, Abigail L.; Hopkins, William A.; Silknetter, Sam; Mims, Meryl C. (Wiley, 2025-08-14)
Estimating how close a species is to its upper thermal limits (i.e., warming tolerance, a thermal sensitivity index) and how that proximity changes across space enables spatially explicit identification of species with increased extinction risk as temperatures increase. Yet, thermal sensitivity is often difficult to calculate because it is the result of many traits. We aimed to synthesize multiple traits into a single estimate of relative terrestrial thermal sensitivity for 13 anuran species in the southeastern United States. We employed models that incorporate traits and microclimate variation to (1) estimate species warming tolerance (the difference between species critical thermal maximum and modeled operative temperature, an estimate of body temperature) and (2) investigate how warming tolerance varied with latitude (whereby latitude represents different temperature regimes and external drivers of thermal sensitivity). We ran mechanistic niche models across a 12° latitudinal gradient and 10 years to estimate individual operative temperature. We calculated the minimum, 25th percentile (hottest quarter), and median daily minimum warming tolerance. Estimates of minimum warming tolerance spanned −5 to 10°C (Lithobates palustris and Gastrophryne carolinensis respectively) and differed among species. For most species, modeled operative temperatures exceeded species' critical thermal maximum during extreme warm temperatures (i.e., heat waves) in part of their range, and warming tolerance increased with latitude. During heat waves, five species had lower warming tolerance at higher latitudes, and three species' warming tolerance did not change with latitude. We identified species that are approaching their thermal limits in the Southeast and characterized spatial patterns of warming tolerance. Increased temperatures could increase anuran extinction risk, posing an additional challenge for threatened anuran species. Spatial patterns of warming tolerance were not consistent among species in our study, highlighting that patterns identified at higher taxonomic categories could be inconsistent at lower taxonomic categories.
Sustainable Timber Supply Chain Optimization
Adams, Irma; Canuel, Austin; Lee, Minseo; Büyüktahtakın, İ. Esra (2026-02-23)
The U.S. timber supply chain faces mounting challenges related to capacity constraints, sustainability, and supply resilience at a time when federal policy calls for a rapid expansion of domestic timber production. Following the March 2025 executive order to reduce reliance on foreign timber imports, achieving near-term production targets requires a nationwide redesign of supply chain infrastructure under significant data and operational uncertainty. This study develops a data-driven optimization framework to support short-term, actionable planning for the U.S. timber supply chain. We propose a hybrid machine learning–mixed-integer linear programming (ML–MILP) model that captures the flow of timber from mills through distribution centers to demand points, with the objective of minimizing total transportation and facility-opening costs. U.S.–wide implementation is complicated by incomplete and fragmented data, particularly for mill counts, production levels, and facility locations. To address these gaps, we leverage machine learning models, including gradient boosting, ridge regression, and weighted K-Means clustering, to reconstruct a comprehensive national dataset and generate candidate distribution center locations informed by socioeconomic and environmental factors. The resulting MILP generates an infrastructure and flow plan and is evaluated through sensitivity and scenario-based analyses reflecting demand growth, transportation disruptions, and disaster impacts. Results highlight the dominant role of transportation costs, diminishing returns to capacity expansion, and heightened vulnerability in the South and West regions. Overall, the proposed framework provides policymakers and industry stakeholders with a scalable, sustainability-oriented decision-support tool for guiding domestic timber supply chain expansion under evolving policy objectives.
Behavior-Specific Filtering for Enhanced Pig Behavior Classification in Precision Livestock Farming
Zhang, Zhen; Ha, Dong Sam; Morota, Gota; Shin, Sook (Academy & Industry Research Collaboration, 2025-07-19)
This study proposes a behavior-specific filtering method to improve behavior classification accuracy in Precision Livestock Farming. While traditional filtering methods, such as wavelet denoising, achieved an accuracy of 91.58%, they apply uniform processing to all behaviors. In contrast, the proposed behaviorspecific filtering method combines Wavelet Denoising with a Low Pass Filter, tailored to active and inactive pig behaviors, and achieved a peak accuracy of 94.73%. These results highlight the effectiveness of behaviorspecific filtering in enhancing animal behavior monitoring, supporting better health management and farm efficiency.
Cross-Attention Guided Data Sharing for Knowledge Transfer in Robotic AI Systems
Liu, Hui; Zeng, Yingyan; Qiao, Helen; Piliptchak, Pavel; Jin, Ran (2026)
Cross-robot transfer learning is crucial for building robotic AI systems that can generalize across diverse platforms and tasks by utilizing heterogeneous datasets. However, not all source samples are effective to improve the accuracy of the target AI task; while incompatible samples may lead to negative transfer and degrade model performance. This challenge is particularly in a connected robot fleet where robots differ in configurations, sensors, but are connected via Industrial Internet for sequential or parallel tasks. To address this, we propose Cross-Attention guided Proximal Policy Optimization (CAPPO), a reinforcement learning-based sample selection framework that adaptively identifies the most valuable source samples for a given target AI modeling task. Our method employs cross-attention mechanisms to capture fine-grained relevance between source and target samples, constructing informative state representations for a PPO-based selection policy. A task-driven reward function based on downstream performance improvement is created to enable the agent to learn efficient and adaptive selection strategies. Experimental results on a connected robotic fleet with different AI tasks show that our method consistently outperforms existing baselines under low-budget settings, demonstrating strong and robust knowledge transfer performance to train new robotic AI models.
Contextual Bandits for Contrastive Representation Learning with Progressive Annotation in Manufacturing Robotic Operations
Jin, Xuancheng; Liu, Hui; Zeng, Yingyan; Qiao, Helen; Piliptchak, Pavel; Jin, Ran (2026)
Streaming recognition of robotic-arm degradation is challenging due to severe class imbalance, sparse labels, and the progressive emergence of new states. Existing stream-based active learning methods often rely on fixed feature spaces and static class sets, leading to redundant queries during healthy-only phase and slow adaptation when new states appear. Although contrastive learning can improve representation quality under imbalance, it is typically trained offline and remains decoupled from query selection, limiting the responsiveness to evolving tasks. To address these limitations, we propose a contrastive ensemble active learning (ConEAL) framework that integrates staged contrastive representation learning with contextual and rewardadaptive acquisition. The encoder first learns invariant and compact feature embeddings by training with an unsupervised contrastive loss on healthy state data. As new classes emerge, an anchor-based triplet loss with dynamic anchor updates is applied to improve the separability between classes. Based on the encoder output and predicted posteriors, ConEAL collects normalized acquisition scores from multiple agents. It then updates the agent weights online using a contextual bandit strategy, guided by the reward from each query, while respecting both the global labeling budget and the temporal order of the data stream. Evaluated on an accelerated-wear robotic dataset, ConEAL achieves a superior utility-cost (i.e., classification accuracy and labeling cost) trade-off compared to strong stream-based baselines, attaining earlier regime adaptation and sustained three-state recognition with substantially fewer annotations. The results indicate that the joint adaptation of representations and query policies is an effective strategy for label-efficient degradation monitoring in nonstationary robotic settings.