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DOE OSTI2019

Glomerular filtrate proteins in acute cardiorenal syndrome

Acute cardiorenal syndrome (CRS-1) is a morbid complication of acute cardiovascular disease. Heart-to-kidney signals transmitted by “cardiorenal connectors” have been postulated, but investigation into CRS-1 has been limited by technical limitations and a paucity of models. To address these limitations, we developed a translational model of CRS-1, cardiac arrest and cardiopulmonary resuscitation (CA/CPR), and now report findings from nanoscale mass spectrometry proteomic exploration of glomerular filtrate 2 hours after CA/CPR or sham procedure. Filtrate acquisition was confirmed by imaging, molecular weight and charge distribution, and exclusion of protein specific to surrounding cells. Filtration of proteins specific to the heart was detected following CA/CPR and confirmed with mass spectrometry performed using urine collections from mice with deficient tubular endocytosis. Cardiac LIM protein was a CA/CPR-specific filtrate component. Cardiac arrest induced plasma release of cardiac LIM protein in mice and critically ill human cardiac arrest survivors, and administration of recombinant cardiac LIM protein to mice altered renal function. These findings demonstrate that glomerular filtrate is accessible to nanoscale proteomics and elucidate the population of proteins filtered 2 hours after CA/CPR. The identification of cardiac-specific proteins in renal filtrate suggests a novel signaling mechanism in CRS-1. We expect these findings to advance understanding of CRS-1.

Wakasaki, Rumie (ORCID:0000000202291919)↗

DOE OSTI2016

Integrated Risk-Informed Decision-Making for an ALMR PRISM

Decision-making is the process of identifying decision alternatives, assessing those alternatives based on predefined metrics, selecting an alternative (i.e., making a decision), and then implementing that alternative. The generation of decisions requires a structured, coherent process, or a decision-making process. The overall objective for this work is that the generalized framework is adopted into an autonomous decision-making framework and tailored to specific requirements for various applications. In this context, automation is the use of computing resources to make decisions and implement a structured decision-making process with limited or no human intervention. The overriding goal of automation is to replace or supplement human decision makers with reconfigurable decision-making modules that can perform a given set of tasks rationally, consistently, and reliably. Risk-informed decision-making requires a probabilistic assessment of the likelihood of success given the status of the plant/systems and component health, and a deterministic assessment between plant operating parameters and reactor protection parameters to prevent unnecessary trips and challenges to plant safety systems. The probabilistic portion of the decision-making engine of the supervisory control system is based on the control actions associated with an ALMR PRISM. Newly incorporated into the probabilistic models are the prognostic/diagnostic models developed by Pacific Northwest National Laboratory. These allow decisions to incorporate the health of components into the decision–making process. Once the control options are identified and ranked based on the likelihood of success, the supervisory control system transmits the options to the deterministic portion of the platform. The deterministic portion of the decision-making engine uses thermal-hydraulic modeling and components for an advanced liquid-metal reactor Power Reactor Inherently Safe Module. The deterministic multi-attribute decision-making framework uses various sensor data (e.g., reactor outlet temperature, steam generator drum level) and calculates its position within the challenge state, its trajectory, and its margin within the controllable domain using utility functions to evaluate current and projected plant state space for different control decisions. The metrics that are evaluated are based on reactor trip set points. The integration of the deterministic calculations using multi-physics analyses and probabilistic safety calculations allows for the examination and quantification of margin recovery strategies. This also provides validation of the control options identified from the probabilistic assessment. Thus, the thermalhydraulics analyses are used to validate the control options identified from the probabilistic assessment. Future work includes evaluating other possible metrics and computational efficiencies, and developing a user interface to mimic display panels at a modern nuclear power plant.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Augmented Human Analysis (AHA)

Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.

42 ENGINEERING↗

DOE OSTI2023

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office: Preprint

analysis↗

DOE OSTI2022

Development of Land‐River Two‐Way Hydrologic Coupling for Floodplain Inundation in the Energy Exascale Earth System Model

Abstract Floodplain inundation links river and land systems through significant water, sediment, and nutrient exchanges. However, these two‐way interactions between land and river are currently missing in most Earth System Models. In this study, we introduced the two‐way hydrological coupling between the land component, E3SM Land Model, and the river component, Model for Scale Adaptive River Transport, in Energy Exascale Earth System Model (E3SM) to study the impacts of floodplain inundation on land and river processes. We calibrated the river channel geometry and developed a new data‐driven inundation scheme to improve the simulation of inundation dynamics in E3SM. The new inundation scheme captures 96% of the spatial variation of inundation area in a satellite inundation product at global scale, in contrast with 7% when the default inundation scheme of E3SM was used. Global simulations including the new inundation scheme performed at resolution with and without land‐river two‐way coupling was used to quantify the impact of coupling. Comparisons show that two‐way coupling modifies the water and energy cycle in 20% of the global land cells. Specifically, riverine inundation is reduced by two‐way coupling, but inland inundation is intensified. Wetter periods are more impacted by the two‐way coupling at the global scale, while regions with different climates exhibit different sensitivities. The two‐way exchange of water between the land and river components of E3SM provides the foundation for enabling two‐way coupling of land‐river sediment and biogeochemical fluxes. These capabilities will be used to improve understanding of the interactions between water and biogeochemical cycles and their response to human perturbations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Chapter 14: Sustainable Biomass Conversion Process Assessment

Process intensification and integration for sustainable design play an essential role in addressing and alleviating severe sustainable development challenges. It is prudent to evaluate a process design in its early stage development to ensure the process can achieve sustainability goals. The primary goal of a sustainability evaluation is to assess the impact of systems on areas sought to be protected and maintained over time, such as human well-being and ecosystems (Schaubroeck, T. and Rugani, B. (2017). A revision of what life cycle sustainability assessment should entail: towards modeling the net impact on human well-being. Journal of Industrial Ecology 21 (6): 1464-1477). Parenthetically, sustainable process design to convert biomass to fuels and chemicals plays an integral part in the bioeconomy sustainability. It is recognized that the integration of sustainability in process design is core to the mission in developing renewable fuels and should be considered a necessary practice in biorefinery design. This study demonstrates the use of Gauging Reaction Effectiveness for ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) as a tool to help track progress on process sustainability performance attributed to conversion improvement. With the incorporation of a wide range of indicators, GREENSCOPE captures the multi-dimensional aspect of the process design and operation. As opposed to conventional techno-economic analysis and life cycle assessment (in which the focuses are limited to economic and environmental aspects), GREENSCOPE enables the consideration of process intensification and integration for sustainable design. A high-octane gasoline production from woody biomass feedstock is used as a case study to demonstrate the approach.

biomass↗

DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗

DOE OSTI2022

Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

We present a machine learning framework (GP-NODE) for Bayesian model discovery from partial, noisy and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling and Gaussian Process priors over the observed system states. This allows us to exploit temporal correlations in the observed data, and efficiently infer posterior distributions over plausible models with quantified uncertainty. The use of the Finnish Horseshoe as a sparsity-promoting prior for free model parameters also enables the discovery of parsimonious representations for the latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed GP-NODE method including predator–prey systems, systems biology and a 50-dimensional human motion dynamical system. This article is part of the theme issue ‘Data-driven prediction in dynamical systems’.

Science & Technology - Other Topics↗

DOE OSTI2025

The Second Skin: A Wearable Sensor Suite that Enables Real-Time Human Biomechanics Tracking Through Deep Learning

Objective: Real-time determination of human kinematics and kinetics could advance biomechanics research and enable valuable applications of biofeedback and generalizable exoskeleton control. Here, this work aims to investigate a taskindependent, user-independent method for obtaining precise realtime joint state estimation across lower-body joints during a wide variety of tasks. Methods: We developed a generalizable sensing approach using a suit comprised of inertial measurement units (IMUs) and pressure insoles. With the suit, we collected a dataset of 33 tasks commonly performed during construction and hazardous waste cleanup (N = 10). We then trained deep learning user-independent, task-agnostic models to estimate joint lowerbody kinematics and dynamics using only worn sensor data. We likewise computed joint kinematics and dynamics analytically from sensor data to serve as a comparison tool for model results. Results: Our models achieved overall angle estimation root-meansquared-errors (RMSE) of 6.56±.92°, 8.60±1.01°, 7.58±.89°, and 6.00±.73° compared to 13.9±.1.3°, 15.31±1.0°, 10.76±.70°, and 7.56±.48° via analytical methods at the lower back, hip, knee, and ankle, respectively. Likewise, our models achieved overall normalized moment estimation RMSEs of .207±.069 Nm/kg, .242±.044 Nm/kg, .202±.038 Nm/kg, and .193±.034 Nm/kg compared to .306±.036 Nm/kg, .407±.021 Nm/kg, 1.18 ±.022 Nm/kg, and 1.73±.071 Nm/kg via analytical methods at the lower back, hip, knee, and ankle, respectively. Conclusion: These results are comparable to other state-of-the-art wearable sensing systems, establishing deep learning as a viable sensing approach that generalizes to new users and tasks. Significance: This work shows promise for enabling accurate real-world biomechanical data collection and enhancement of biofeedback systems and wearable robot control.

Casey, Ryan T. F. [Georgia Institute of Technology↗

DOE OSTI2023

cotH Genes Are Necessary for Normal Spore Formation and Virulence in Mucor lusitanicus

Mucormycosis is an invasive fungal infection caused by certain members of the fungal order of Mucorales. The species most frequently identified as the etiological agents of mucormycosis belong to the genera Rhizopus , Lichtheimia , and Mucor . The frequency of systemic mucormycosis has been increasing, mainly because of increasing numbers of susceptible patients. Furthermore, Mucorales display intrinsic resistance to the majority of routinely used antifungal agents (e.g., echinocandins and short-tailed azoles), which limits the number of possible therapeutic options. All the above-mentioned issues urge the improvement of molecular identification methods and the discovery of new antifungal targets and strategies. Spore coat proteins (CotH) constitute a kinase family present in many pathogenic bacteria and fungi and participate in the spore formation in these organisms. Moreover, some of them can act as virulence factors being receptors of the human GRP78 protein during Rhizopus delemar-induced mucormycosis. We identified 17 cotH -like genes in the Mucor lusitanicus genome database. Successful disruption of five cotH genes in Mucor was performed using the CRISPR-Cas9 system. The CotH3 and CotH4 proteins play a role in adaptation to different temperatures as well as in developing the cell wall structure. We also show CotH4 protein is involved in spore wall formation by affecting the total chitin content and, thus, the composition of the spore wall. The role of CotH3 and CotH4 proteins in virulence was confirmed in two invertebrate models and a diabetic ketoacidosis (DKA) mouse model.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Autonomous Unmanned Aerial Vehicles for Examining Current and Evolving Distribution of Contamination from the FDNPP Accident - 17020

Estimates as well as subsequent measurements of the radiation released following the events at the Fukushima Daiichi Nuclear Power Plant in March 2011 place the incident as the second greatest nuclear event to have ever occurred - duly rated at the maximum of Level 7 on the INES Event Scale. The releases of radioactivity from the plant were detected worldwide, however the multi-reactor nature of the events at the FDNPP has complicated the understanding of the radionuclide release inventory and the subsequent distribution of the material. As a result of this highly-complex release scenario, it is important to understand the current and evolving state of contamination within the rapidly evolving, topographically extreme and frequently typhoon impacted environment on Japan's eastern coast. Existing systems rely either on high-altitude manned aircraft to perform such contamination surveys, correcting results to ground-level, or, on humans manually measuring the dose-rate on foot. In order to overcome the shortcomings of both methods, a low-altitude unmanned aerial vehicle (UAV) has been developed at the University of Bristol and successfully deployed to the affected areas surrounding the FDNPP. In contrast to these other methods; the use of low-altitude UAVs provides a spatial resolution previously achievable only through the deployment of humans on the ground, with the potential for significant radiation exposure. This high-resolution monitoring allows for subsequent modelling to inform our understanding of the behaviour and long-term stability of the fallout material. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

MO-DE-207A-01: Impact of Statistical Weights On Detection of Low-Contrast Details in Model-Based Iterative CT Reconstruction

Purpose: Penalized-weighted least-square reconstruction has become an important research topic in CT, to reduce dose without affecting image quality. Two components impact image quality in this reconstruction: the statistical weights and the use of an edge-preserving penalty term. We are interested in assessing the influence of statistical weights on their own, without the edge-preserving feature. Methods: The influence of statistical weights on image quality was assessed in terms of low-contrast detail detection using LROC analysis. The task amounted to detect and localize a 6-mm lesion with random contrast inside the FORBILD head phantom. A two-alternative forced-choice experiment was used with two human observers performing the task. Reconstructions without and with statistical weights were compared, both using the same quadratic penalty term. The beam energy was set to 30keV to amplify spatial differences in attenuation and thereby the role of statistical weights. A fan-beam data acquisition geometry was used. Results: Visual inspection of images clearly showed a difference in noise between the two reconstructions methods. As expected, the reconstruction without statistical weights exhibited noise streaks. The other reconstruction appeared better in this aspect, but presented other disturbing noise patterns and artifacts induced by the weights. The LROC analysis yield the following 95-percent confidence interval for the difference in reader-averaged AUC (reconstruction without weights minus reconstruction with weights): [0.0026,0.0599]. The mean AUC value was 0.9094. Conclusion: We have investigated the impact of statistical weights without the use of edge-preserving penalty in penalized weighted least-square reconstruction. A decrease rather than increase in image quality was observed when using statistical weights. Thus, the observers were better able to cope with the noise streaks than the noise patterns and artifacts induced by the statistical weights. It may be that different results would be obtained if the penalty term was used with a pixel-dependent weight. F Noo receives research support from Siemens Healthcare GmbH.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Networks and interfaces as catalysts for polymer materials innovation

Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Research on the ATLAS Experiment at the University of Illinois: Final Report

The goal of particle physics is to understand the Universe at its most fundamental level, including the constituents of matter and their interactions, and the nature of space and time itself. The quest to understand the fundamental building blocks of nature, and their interactions, is one of the most ambitious and enduring of human endeavors. Recent developments in particle physics and cosmology raise the exciting prospect that we are on the threshold of a major step forward in this pursuit. Facilities such as CERN's Large Hadron Collider (LHC) provide a unique window into the subatomic world to pursue this understanding. Mark Neubauer and his research group at the University of Illinois were supported over a nine-month period (07/01/2017 through 03/31/2018) to perform high-energy particle physics research on the ATLAS experiment at the LHC. The primary focus of this research was to search for physics beyond the standard model through an analysis of high-energy proton-proton collision data from the ATLAS experiment, with an emphasis on the Higgs boson as a sensitive tool for this search, and development of fast particle tracking systems for use the ATLAS trigger system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Quantifying Occupant Health and Productivity for Deep Retrofits: Beyond Traditional IEQ Studies

As energy-efficiency standards for both new construction and existing building renovations become more stringent, further reductions become less tractable and more expensive. With increasing interest and rising investment in healthy built environments, integrating energy efficiency with improved occupant experience (i.e., health, comfort, and work performance) offers a new path to drive deep energy retrofits. However, the difficulty in quantifying and verifying health and productivity gains from building upgrades leads to lower valuation of human benefits in practice. Building codes and standards have progressive energy targets but often give minimum requirements to occupant health for factors such as ventilation and thermal comfort. State and federal laws in the United States that govern the energy service performance contract (ESPC) have no clear and consistent guidance on quantifying non-energy benefits and incorporating them into a formal cost-effectiveness analysis. This paper presents a methodology to measure a building’s potential performance improvement regarding occupant health based on existing evidence correlating IEQ to occupant outcomes. Health improvement measures are integrated with a building energy model to identify and measure the impact of holistic improvement strategies. The net present value of these measures is determined based on the investment costs required to attain those improvements, energy savings, and personnel gains. We will discuss results from one pilot building, which showed nearly $50 per square foot 20-year net present value using the integrated approach.

Keene, Kevin M.↗

DOE OSTI2017

Prognostic significance of high YY1AP1 and PCNA expression in colon adenocarcinoma

Highlights: • YY1AP1 expression might play an important role in the tumorgenesis of colon adenocarcinoma. • YY1AP1 expression is related with colon adenocarcinoma patient M-class/distant metastasis. • High YY1AP1 indicates poor overall survival of colon adenocarcinoma patients. • Cox proportional-hazards model analysis indicated YY1AP1 as an independent predictor for overall survival. To investigate the relationship between YY1AP1 and various clinicopathological features of colon adenocarcinoma (COAD), we conducted immunohistochemical (IHC) analyses of human tissue microarrays. We found that YY1AP1 protein expression was significantly higher in tumor tissue of the colon and liver, and was significantly lower in tumor tissue of the kidney. An analysis that employed the SurvExpress database indicated that increased expression of YY1AP1 mRNA was significantly associated with the overall survival of COAD patients. To clarify the validity of YY1AP1 or PCNA as determined by the IHC analysis was performed on 59 paired samples from COAD and adjacent normal tissue. Statistically significant differences of immunoreactivity for YY1AP1 or PCNA protein expression was observed between COAD tissue and adjacent normal tissue. High protein expression levels of YY1AP1 and PCNA were also found to be significantly correlated with M-class and distant metastasis. We also determined that YY1AP1 was correlated with PCNA expression in COAD samples, and Kaplan-Meier survival curves indicated that YY1AP1 protein expression was significantly associated with poor survival. Finally, a univariate analysis demonstrated that YY1AP1 protein expression was related to YY1AP1 score, and multivariate analysis revealed that the YY1AP1 protein expression level was an independent risk factor of overall COAD survival. Taken together, our findings indicate that YY1AP1 expression plays an important role in the tumorigenesis and progression of COAD and could serve as a clinical prognostic indicator for COAD.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗