| A Multimodal Approach to Heritage Preservation in the Context of Climate Change |
Cultural heritage sites face accelerating degradation due to climate change yet tradi- tional monitoring relies on unimodal analysis (visual inspection or environmental sen- sors alone) that fails to capture the complex interplay between environmental stres- sors and material deterioration. We propose a lightweight multimodal architecture that fuses sensor data (temperature humidity) with visual imagery to predict degradation severity at heritage sites. Our approach adapts PerceiverIO with two key innovations: (1) simplified encoders (64D latent space) that prevent overfitting on small datasets (n=37 training samples) and (2) Adaptive Barlow Twins loss that encourages modality complementarity rather than redundancy. On data from Strasbourg Cathedral our model achieves 76.9% accu- racy a 43% improvement over standard multimodal architectures (VisualBERT Trans- former) and 25% over vanilla PerceiverIO. Ablation studies reveal that sensor-only achieves 61.5% while image-only reaches 46.2% confirming successful multimodal synergy. A systematic hyperparameter study identifies an optimal moderate correlation target (τ =0.3) that balances align- ment and complementarity achieving 69.2% accuracy compared to other τ values (τ =0.1/0.5/0.7: 53.8% τ =0.9: 61.5%). This work demonstrates that architectural sim- plicity combined with contrastive regularization enables effective multimodal learning in data-scarce heritage monitoring contexts providing a foundation for AI-driven con- servation decision support systems.
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Submitted 15 October 2025;
originally announced |
| Forecasting the effect of heat stress index and climate change on cloud data center energy consumption |
In this paper we estimate the effect of heat stress index (a measure which takes into account rising temperatures as well as humidity) on data center energy consumption. We use forecasting models to predict future energy use by data centers taking into account rising temperature scenarios. We compare those estimates with baseline forecasted energy consumption (without heat stress index or rising temperature correction) and present the result that there is a sizeable and significant difference in the two forecasts. We show that rising temperatures will cause a negative impact on data center energy consumption increasing it by about 8 percent and conclude that data center energy consumption analyses and forecasts must include the effects of heat stress index and rising temperatures and other climate change related effects.
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Submitted 9 November 2019;
originally announced |
| A Two-Stage Stochastic Optimization Framework for Environmentally Sensitive Oil Spill Response Resource Allocation in the Arctic |
The risk of oil spills in the Alaskan Arctic has become an urgent environmental and logistical concern as maritime traffic increases under climate driven sea ice retreat. Traditional deterministic response planning models fail to represent key uncertainties including variable spill magnitudes changing environmental sensitivity and infrastructure limitations. This study develops a two-stage stochastic mixed integer linear programming framework that jointly optimizes the location of oil spill response stations and the allocation of heterogeneous resources across multiple probabilistic spill scenarios. The model integrates a weighted objective that combines spill volume environmental sensitivity index (ESI) response time and costs for station setup deployment and inter station transfer. Separate importance weights for coverage and cost together with internal ecological weights allow decision makers to balance ecological protection and operational efficiency. Data was compiled from Alaska Department of Environmental Conservation spill records and National Oceanic and Atmospheric Administration ESI layers and are converted into model ready scenarios through harmonization and sampling. The model is solved with the Gurobi optimizer and sensitivity analysis is performed over 324 combinations of importance and ecological weights. Results show about a 35.45% percent improvement in response effectiveness over deterministic methods as confirmed by the value of the stochastic solution and reveal clear tradeoffs between cost and ecological coverage. The framework provides a data driven decision support tool for Arctic emergency planners that simultaneously accounts for uncertainty environmental sensitivity and realistic logistical constraints.
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Submitted 27 November 2025;
originally announced |
| DeFault: Deep-learning-based Fault Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site |
The carbon capture utilization and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection serve as indispensable tools offering vital insights into subsurface structures and fluid migration pathways. Accurate identification and localization of seismic events however face significant challenges including the necessity for high-quality seismic data and advanced computational methods. To address these challenges we introduce a novel deep learning method DeFault specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain-adaptation DeFault allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using DeFault the passive seismic sources are automatically clustered based on their recording time and spatial locations and subsequently faults and fractures are delineated accordingly. We demonstrate the efficacy of DeFault on a field case study involving CO2 injection related microseismic data from the Decatur Illinois area. Our approach accurately and efficiently relocated passive seismic events identified faults and aided in the prevention of potential geological hazards. Our results highlight the potential of DeFault as a valuable tool for passive seismic monitoring emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS illustrating machine learnings capacity to refine these methods. Ultimately our work bear significant implications for CCUS technology deployment an essential strategy in combating climate change.
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Submitted 4 November 2024; v1 submitted 7 November 2023;
originally announced |
| Reviewing climate change and agricultural market competitiveness |
The paper is a collection of knowledge regarding the phenomenon of climate change competitiveness and literature linking the two phenomena to agricultural market competitiveness. The objective is to investigate the peer reviewed and grey literature on the subject to explore the link between climate change and agricultural market competitiveness and also explore an appropriate technique to validate the presumed relationship empirically. The paper concludes by identifying implications for developing an agricultural competitiveness index while incorporating the climate change impacts to enhance the potential of agricultural markets for optimizing the agricultural sectors competitiveness.
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Submitted 31 August 2020;
originally announced |
