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Automatic recognition system for document digitization in nuclear power plants
With the increasing number of data-driven models in nuclear applications, large volumes of numerical data are required to accurately model and predict the health status of a plant component. However, many historical operation logs that contain useful information are not fully utilized due to the lack of a systematic approach of digitization. To overcome this issue, this study proposes an automatic pipeline for extracting information from handwritten tabular documents collected from nuclear power plants. In our pipeline, we first denoise scanned documents with morphological operations, and then extract relevant parts from individual pages using both traditional computer vision and neural network methods. Handwriting recognition is applied to obtain text and numbers. As the most challenging step is how to crop only relevant information, the main focus of our paper is to detect tables and cells from scanned handwritten documents. Here we evaluate the efficiency and accuracy of our proposed method on handwritten operational reports obtained from a real-world case study. The results demonstrate the high accuracy and practicality of our proposed method.
In Situ Monitoring of Groundwater Contamination Using the Kalman Filter
This study presents a Kalman filter-based framework to establish a real-time in situ monitoring system for groundwater contamination based on in situ measurable water quality variables, such as specific conductance (SC) and pH. First, this framework uses principal component analysis (PCA) to identify correlations between the contaminant concentrations of interest and in situ measurable variables. It then applies the Kalman filter to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration-decay models with the previously identified data correlations. We demonstrate our approach with historical groundwater data from the Savannah River Site F-Area: We use SC and pH data to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate these contaminant concentrations based on in situ measurable variables. The estimates remain reliable with less frequent or no direct measurements of the contaminant concentrations, while capturing the dynamics of short- and long-term contaminant concentration changes. In addition, we show that data mining, such as PCA, is useful to understand correlations in groundwater data and to design long-term monitoring systems. As a result, the developed in situ monitoring methodology is expected to improve long-term groundwater monitoring by continuously confirming the contaminant plume's stability and by providing an early warning system for unexpected changes in the plume's migration.
Data publication with the structural biology data grid supports live analysis
Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. In conclusion, it is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.
Four-dimensional chromosome reconstruction elucidates the spatiotemporal reorganization of the mammalian X chromosome
Significance Breakthrough technological advances such as Hi-C have enabled large-scale sequencing of pairwise genomic contacts. However, translating the 2D contacts to 3D spatial models remains challenging. Here we develop the “4DHiC” modeling method to infer 3D information from 2D datasets across time and construct a 3D model of the inactive X-chromosome (Xi). Our model not only demonstrates a spatial phase-separation between A/B and S1/S2 compartments to form “hemispheres,” but also reveals persistent smaller-scale structures hidden under the Xi superstructure. We also simulate Xist RNA spreading dynamics and deduce that the RNA spreads between hemispheres via both surface and core to establish the Xi. Our 4DHiC will serve a growing demand for data-driven methodologies that extract spatial information from biochemical interaction data.
The impact of new d(p,γ)3 rates on Big Bang Nucleosynthesis
We consider the effect on Big Bang Nucleosynthesis (BBN) of new measurements of the d(p,γ) 3 He cross section by the LUNA Collaboration. These have an important effect on the primordial abundance of D/H which is also sensitive to the baryon density at the time of BBN. We have re-evaluated the thermal rate for this reaction, using a world average of cross section data, which we describe with model-independent polynomials; our results are in good agreement with a similar analysis by LUNA. We then perform a full likelihood analysis combining BBN and Planck cosmic microwave background (CMB) likelihood chains using the new rate combined with previous measurements and compare with the results using previous rates. Concordance between BBN and CMB measurements of the anisotropy spectrum using the old rates was excellent. The predicted deuterium abundance at the Planck value of the baryon density was (D/H) BBN+CMB old = (2.57 ± 0.13) × 10 –5 which can be compared with the value determined from quasar absorption systems (D/H) obs = (2.55 ± 0.03) × 10 –5 . Using the new rates we find (D/H) BBN+CMB = (2.51 ± 0.11) × 10 –5 . We thus find consistency among BBN theory, deuterium and 4He observations, and the CMB, when using reaction rates fit in our data-driven approach. We also find that the new reaction data tightens the constraints on the number of relativistic degrees of freedom during BBN, giving the effective number of light neutrino species N ν = 2.880 ± 0.144 in good agreement with the Standard Model of particle physics. Lastly, we note that the observed deuterium abundance continues to be more precise than the BBN+CMB prediction, whose error budget is now dominated by d(d,n) 3 He and d(d,p) 3 H.
AI & Physics-Based Bad Command/Data Detection in Large Power Electronics Systems: Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS)
Detection of bad data from measurement sensors and bad commands from control centers need to be carried out to avoid instabilities within large power electronics systems. Towards the same, in this paper, model and data driven methods are proposed to identify anomalies in measured data and commands received by large power electronics systems. The large power electronics system considered in this paper is a multi-port autonomous reconfigurable solar power plant (MARS), which consists of photovoltaic (PV) and energy storage systems (ESSs) that connect to high-voltage direct current (HVdc) system and transmission ac power grid. The proposed algorithms in the MARS power plant to detect bad data from measurements and bad commands from control centers are evaluated in simulations and hardware-in-the-loop (HIL) tests. Furthermore, it has been observed that the proposed algorithms are able to detect bad measurements and commands in all the use cases evaluated.
Smart Manufacturing Technologies and Data Analytics for Improving Energy Efficiency in Industrial Energy Systems
Smart manufacturing and advanced data analytics can help the manufacturing sector unlock energy efficiency from the equipment level to the entire manufacturing facility and the whole supply chain. These technologies can make manufacturing industries more competitive, with intelligent communication systems, real-time energy savings, and increased energy productivity. Smart manufacturing can give all employees in an organization the actionable information they need, when they need it, so that each person can contribute to the optimal operation of the corporation through informed, data-driven decision making. This paper examines smart technologies and data analytics approaches for improving energy efficiency and reducing energy costs in process-supporting energy systems. It dives into energy-saving improvement opportunities through smart manufacturing technologies and sophisticated data collection and analysis. The energy systems covered in this paper include those with motors and drives, fans, pumps, air compressors, steam, and process heating.
Smart Manufacturing Technologies and Data Analytics for Improving Energy Efficiency in Industrial Energy Systems
Smart manufacturing and advanced data analytics can help the manufacturing sector unlock energy efficiency from the equipment level to the entire manufacturing facility and the whole supply chain. These technologies can make manufacturing industries more competitive, with intelligent communication systems, real-time energy savings, and increased energy productivity. Smart manufacturing can give all employees in an organization the actionable information they need, when they need it, so that each person can contribute to the optimal operation of the corporation through informed, data-driven decision making. This paper examines smart technologies and data analytics approaches for improving energy efficiency and reducing energy costs in process-supporting energy systems. It dives into energy-saving improvement opportunities through smart manufacturing technologies and sophisticated data collection and analysis. The energy systems covered in this paper include those with motors and drives, fans, pumps, air compressors, steam, and process heating.
Renewable Energy Data and Decision Support for Kazakhstan - RE Data Explorer in Action
In 2016, with support from USAID Central Asia and the U.S. Department of State Leadership Compact, and in collaboration with the Government of Kazakhstan and other Central Asian governments, NREL launched the RE Data Explorer for Central Asia, including world-class wind and solar resource data for Kazakhstan and surrounding countries. The RE Data Explorer provides an easy- to-use online platform for users to view, analyze, and download high-quality datasets for informed decision-making. The RE Data Explorer is part of a broader platform called the RE Explorer that provides expertly curated materials and knowledge resources to enable data-driven decisions. This case study documents how the RE Data Explorer was used to support renewable energy decision-making in Kazakhstan.
Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation
The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.
Path-space variational inference for non-equilibrium coarse-grained systems
In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.
PanDA: Production and Distributed Analysis System
The Production and Distributed Analysis (PanDA) system is a data-driven workload management system engineered to operate at the LHC data processing scale. The PanDA system provides a solution for scientific experiments to fully leverage their distributed heterogeneous resources, showcasing scalability, usability, flexibility, and robustness. The system has successfully proven itself through nearly two decades of steady operation in the ATLAS experiment, addressing the intricate requirements such as diverse resources distributed worldwide at about 200 sites, thousands of scientists analyzing the data remotely, the volume of processed data beyond the exabyte scale, dozens of scientific applications to support, and data processing over several billion hours of computing usage per year. PanDA’s flexibility and scalability make it suitable for the High Energy Physics community and wider science domains at the Exascale. Beyond High Energy Physics, PanDA’s relevance extends to other big data sciences, as evidenced by its adoption in the Vera C. Rubin Observatory and the sPHENIX experiment. As the significance of advanced workflows continues to grow, PanDA has transformed into a comprehensive ecosystem, effectively tackling challenges associated with emerging workflows and evolving computing technologies. The paper discusses PanDA’s prominent role in the scientific landscape, detailing its architecture, functionality, deployment strategies, project management approaches, results, and evolution into an ecosystem.
Climate-invariant machine learning
Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.
Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions
Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6-7%/K, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (2-3%/K in the global average). Some other aspect of precipitation events must then change to compensate for this difference. We develop here a new methodology for identifying individual rainstorms and studying their physical characteristics - including starting location, intensity, spatial extent, duration, and trajectory - that allows identifying that compensating mechanism. We apply this technique to precipitation over the contiguous U.S. from both radar-based data products and high-resolution model runs simulating 100 years of business-as-usual warming. In model studies, we find that the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. We show also that projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via "data-driven simulations" that apply model-projected changes to observational data. We therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.
ALPINE Overview
Data-driven sampling enables probabilistic identification of interesting regions in the data automatically, prioritizing important regions. Applied in situ to Nyx, important halo regions are preserved.
Transfer Learning for HVAC System Fault Detection
Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HVAC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. To overcome these challenges, we present a transfer methodology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.
An experimental database of cell performance for vanadium redox flow battery
The continual growth in energy demand has resulted in the deployment of renewable energy generators to reduce the impact of fossil fuel dependence. However, these generators often suffer from intermittency and require energy storage when there is over-generation and the subsequent release of this stored energy at high demand. One promising energy storage technology which can provide a solution to improve energy management and grid stability, is the redox flow battery. Among the numerous flow battery systems, vanadium redox flow battery is the most iconic solution to large scale energy storage, giving a more efficient link between energy production, especially from renewables, and energy demand. The aim of the current database is to characterize the performance of the cell design and to provide training/validation data for physical model or data-driven model. The database includes hundreds of experimental cell performance data of vanadium redox flow battery with various current densities for multiple charge-discharge cycles. All the cell parameters, chemical parameters, material parameters, operation parameters and thermodynamic parameters of the cell system are listed. Coulomb, voltaic and energy efficiencies are also provided. The database will be helpful for researchers in the field of redox flow batteries.