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All sources NASA NTRS DOE OSTI

DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗

DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗

NASA NTRSDate not supplied

Milwaukee Urban Development: Assessing the Drivers of Urban Flood Vulnerability in Milwaukee using the Integrated Valuation of Ecosystem Services and Tradeoffs Urban Flood Risk Mitigation Model (InVEST)

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗

NASA NTRS2022

Milwaukee Urban Development: Assessing the Drivers of Urban Flooding Vulnerability in Milwaukee Using NASA Earth Observations

Madeleine Tango↗

DOE OSTI2025

High-field side scrape-off layer density profile measurements and implications for high-field side LHCD coupling in DIII-D

The high-field side (HFS) scrape-off layer (SOL) is an often under-diagnosed region of tokamak plasmas. Situated in a region with favorable curvature, the HFS SOL has minimal turbulence-induced radial transport compared to the low-field side. Using profile reflectometry, the HFS SOL density profile is measured with high temporal resolution for a wide range of DIII-D plasma discharges for the first time. The magnetic configuration, particularly the location of the secondary separatrix (SS) largely determines the HFS SOL density profile. Additionally, density perturbations induced by edge-localized modes (ELMs) are observed in the HFS SOL, and the impact of ELMs on the SOL density profile is characterized for a range of magnetic configurations. It is found that ELM-induced HFS SOL perturbations are generally localized to within the SS and ELM-induced changes to the HFS wall density are minimized in near double-null configurations. The characterization of the HFS SOL density profile on DIII-D is crucial for the effective coupling of the planned HFS lower hybrid current drive (LHCD) launcher. Experimental HFS SOL measurements are used as input to full-wave simulations of LHCD coupling. Furthermore, this work simulates the effect of the magnetic configuration on LHCD coupling. It also simulates LHCD coupling in ELMs and high q min discharges. During such discharges, the LHCD coupling is predicted to be resilient to ELM-induced SOL density perturbations.

high-field side↗

DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗

DOE OSTI2022

Improved Architectures and Training Algorithms for Deep Operator Networks

Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can also be effective in learning the solution operator of partial differential equations (PDEs) in an entirely self-supervised manner. In this work we analyze the training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel theory, and reveal a bias that favors the approximation of functions with larger magnitudes. To correct this bias we propose to adaptively re-weight the importance of each training example, and demonstrate how this procedure can effectively balance the magnitude of back-propagated gradients during training via gradient descent. We also propose a novel network architecture that is more resilient to vanishing gradient pathologies. Taken together, our developments provide new insights into the training of DeepONets and consistently improve their predictive accuracy by a factor of 10-50x, demonstrated in the challenging setting of learning PDE solution operators in the absence of paired input-output observations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems

Marine renewable energy (MRE) resources are highly predictable and persistent sources of energy, when compared to other renewable sources like wind and solar. These lend them favorably for potential grid applications, particularly for coastal/island power systems where their generation potential is high. Island power systems, on the other hand, are either supported by onsite generation or by transported energy from the mainland grid. Therefore, robustness of grid operations depend heavily on the diversity of onsite generation resources and the reliability of the power transportation medium. Issues relating to either of these two factors may lead to impediments in smooth and reliable operation of the power system. Analyzing and quantifying operational risks for such island power systems with diverse non-conventional generation portfolios through conventional techniques can also prove to be cumbersome, often requiring multiple different inputs. Therefore, in this paper, we firstly present a novel, purely data-driven formulation which quantifies the operational reliability of such island power systems through minimal input data. Specifically, our proposed methodology only relies on historical knowledge of typical hourly load and generation profiles to quantify associated operational risks. Subsequently, we use our proposed formulation to evaluate the effectiveness of MRE resources (over other renewable resources like wind and solar) in providing resilience benefits to island power systems. The proposed formulation is demonstrated with a case study for an island power system in Nantucket, MA.

Chalishazar, Vishvas H.↗

DOE OSTI2026

Robust Heat-Flux Sensors for Coal-Fired Boiler Extreme Environments

In this project, robust heat-flux measurement systems were developed. The heat-flux sensors utilize thermoelectric effects to directly transduce the heat-flux inputs to analog electrical voltage signals. They were constructed from dedicated materials that can withstand temperatures of at least 1000°C and maintain adequate performance at these conditions for prolonged periods of time. The proposed approaches took into account numerous considerations, including system cost, sensor head resilience, sensor footprint, data accuracy, response time, and maintenance requirements. Through modern thermoelectric materials design, methodical materials selection and rigorous testing in materials characterization labs and medium-scale fire research facilities, we have demonstrated functioning laboratory prototypes, upon which one could base industrial heat-flux sensing platforms capable of operating in the challenging high-temperature, corrosive environments of the boilers of coal-fired power plants. A distributed sensor array for heat-flux measurements throughout the furnace water-wall, the superheater area and the economizer coils can provide critical data for the power plant control systems to increase efficiency, improve safety and reduce down times. For example, the combined heat-flux sensor/control systems can contribute to the optimization of burner and boiler operations under flexible loads, the optimization of heat-exchange conditions and overall reduction of heat rate and emissions, the prediction of imminent overheating conditions, and the optimization of the soot-blowing protocols.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗

NASA NTRS2020

Riley County Water Resources Project Summary - Comparing Runoff Curve Calculation Methods to Inform Local Resiliency Initiatives in Riley County, Kansas

Riley County, Kansas, has observed increased levels of flooding, potentially due to changes in land use/land cover (LULC) and seasonal vegetation variation. This study contrasts two methods of generating runoff curve numbers (CN) from 2006-2020. (1) The traditional Soil Conservation Service CN calculation method uses a look-up table and tracked LULC to determine runoff changes. These tables allow for land cover-specific CN and account for various farming techniques but lack flexibility in calculations for various seasons or plant health. (2) A dynamic method employs normalized difference vegetation index (NDVI) compiled over the rainy season each year to calculate CN using seasonal vegetation. This method allows for a more precise analysis of runoff variability within and between rainy seasons because it can be updated with greater temporal detail and captures higher spatial resolutions by using NDVI as a proxy for LULC. This study further uses inputs from the United States Geological Survey (USGS) National Land Cover Database (NLCD), the United States Department of Agriculture (USDA) Cropland Data Layer, and Landsat imagery to create more precise LULC raster datasets including both urban cover and crop-specific land use and curve number maps of the area. Results can guide decision makers in the City of Manhattan, Riley County Department of Planning and Development, Riley County Conservation District, the Kansas Forest Service, and the Kansas Department of Health and Environment toward informed decisions on resiliency strategies to address future flooding.

DEVELOP Project Summary↗

DOE OSTI2022

RLC4CLR (Reinforcement Learning Controller for Critical Load Restoration Problems)

RLC4CLR demonstrates using a reinforcement learning controller (RLC) to solve a critical load restoration (CLR) problem, which improves the grid resilience after a substation outage event. RLC4CLR consists of two parts. (1) RL environment: This environment encapsulates the CLR problem to be solved and provides interfacing functions to follow the standard OpenAI Gym format. A power system simulator, i.e., OpenDSS, is included to provide the power flow solution. Controller inputs and outputs (RL state and action) as well as the reward are defined in this environment as well. In summary, the RL environment is the problem formulation from which the RL agent can learn. (2) RL training script: The training script enables the RL agent to learn its control policy by interacting with the RL environment. For RL training, an open-sourced RL library, i.e., RLlib, is leveraged which is based on a distributed computing framework (Ray). The training script is designed to be able to be run on both local machine or the NREL HPC system. Other components of RLC4CLR include input data, e.g., grid model (standard IEEE test feeders), and other files used for results analysis.

Zhang, Xiangyu↗

DOE OSTI2021

Individual and Collective Strategies to Limit the Impacts of Large Power Outages of Long Duration

As modern society becomes ever more dependent on the availability of electric power, the costs that could arise from individual and social vulnerability to large outages of long duration (LLD-outages) increases. During such an outage, even a small amount of power would be very valuable. Here, this article compares individual and collective strategies for providing limited amounts of electric power to residential customers in a hypothetical New England community during a large electric power outage of long duration. We develop estimates of the emergency load required for survival and assess the cost of strategies to address outages that last 5, 10, and 20 days in either winter or summer. We find that the cost of collective solutions could be as much as 10 to 40 times less than individual solutions (less than $2 per month per home). However, collective solutions would require community-wide coordination, and if local distribution system lines are destroyed, only individual back-up systems could provide contingency power until those lines are repaired. Costs might be reduced if more robust distributed generation were employed that could be operated continuously with the ability to sell power back to the grid. Our cost-effectiveness analysis only assesses what could be done, developing estimates of preparedness cost. A decision about what should be done would require additional input from a range of stakeholders as well as some form of analytical deliberative process.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Survey and Gap Prioritization of U.S. Electric Vehicle Charge Management Deployments

The goal of this study was to survey and characterize the scope of current technical and programmatic knowledge pertaining to EV charge management technologies and practices in the US and relevant international jurisdictions. This characterization of existing field demonstrations and knowledge derived were used to determine gaps in the SCM demonstration landscape. Addressing these gaps through research and demonstration could increase confidence in the U.S. that load management and EV charge control could achieve overarching societal benefits. A survey of charge management deployments and input from stakeholders was completed to determine the state-of-the-art of smart charge management (SCM) where SCM is defined as controlling the amount of power exchanged between chargers and EVs to meet customers' charging needs while also responding to external power demand or pricing signals to provide load management, resilience, or other benefits to the customer and electric grid. The survey was the basis of the gap analysis in this report and determines which areas are well understood, with high confidence, and which areas need further investigation. Existing examples of EV charge management are characterized here to determine aspects that are ready for widespread deployment and have been demonstrated in the field. These include demonstration studies, pilots, programs, and EV-specific tariffs. In all, 110 examples of charge management were characterized. The data sources were public literature and utility filings as well as targeted interviews. In addition, 43 interviews with stakeholders were conducted with a consistent set of questions used in each interview. This study prioritized gaps in demonstrated SCM capabilities based on 1) Urgency of the particular use-case to offset traditional grid assets, 2) Impact, extensibility, and scaling of results across the entire spectrum of 3000+ utility service territories including projected technical and market potential for a given grid service, and 3) Value of federal funding in addressing the gap, including potential to leverage and/or add scope to existing field demonstrations funded by other non-federal funding mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2021

San Diego Urban Development: Utilizing NASA Earth Observations to Identify Drivers of Extreme Urban Heat and Generate a High-Resolution Vulnerability Index for Urban Planning and Climate Resiliency in San Diego, California

Exposure to heat exacerbated by an increase in urbanization as well as increasing global temperatures has become a growing concern for cities and their residents. Excess heat can cause increased heat-related morbidity, mortality, and energy costs. Vulnerability to heat-related illnesses is oftentimes correlated to demographics, socioeconomic status, and pre-existing health conditions. The City of San Diego, California boasts 1.4 million residents and, like many other major cities, has experienced increases in heat-related hospitalizations and mortality. The burden of urban heat is also not equal amongst communities; areas with lower income and communities of color bear a disproportionate burden. In partnership with the City of San Diego, and American Geophysical Union’s (AGU) Thriving Earth Exchange, the DEVELOP team used Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) imagery to identify areas of highest heat based on land surface temperature from 2015-2020. Our analyses showed that health demographics such as obesity and cardiovascular health were the strongest indicators for heat vulnerability. In addition, various inputs (land use/land cover, tree canopy, and building intensity derived from the City of San Diego data along with albedo from Landsat 8) were used in the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model to investigate changes in cooling rates in current and future scenarios for the city. The model results showed that cooling is expected to occur due to a 5% increase in tree canopy. The City of San Diego can use these results to inform the development of the Climate Resilient San Diego plan and prioritize at-risk communities for cooling interventions.

John Dialesandro↗

DOE OSTI2023

Universal Passive Synchronization Method for Grid-Forming Inverters Without Mode Transition

Power systems are transforming with increasing levels of inverter-based resources (IBRs). This transformation requires critical roles of grid-forming (GFM) inverters replacing synchronous generators for bulk power system stabilization and ancillary services, also allowing flexible power system operation, such as microgrid that is operated by multiple GFM IBRs to achieve system resilience against contingencies. To realize the resilient power systems allowing flexible in-and-out operation of GFM IBRs potentially programmed with different primary controls, a synchronization method universally applicable, i.e., independent of control types, would be beneficial to ease the integration process, but it has not been actively studied. To fill the gap, this paper proposes a universal synchronization method that achieves a passive synchronization to enable a smooth transition in a grid with off-nominal system parameters, i.e., voltage and frequency. The logic proposed requires no modification on the primary control, thus applicable to any type of GFMs with a voltage reference input. To validate the concept, a simulation of an IEEE 13-bus benchmark system modified with 3 GFM inverters is presented. It simulates an inverter-driven black start scenario in which GFM inverters autonomously turn on and connect to the grid under heavy loading, using the synchronization logic. The case study demonstrates that GFM inverters can tune their voltage reference to smoothly synchronize without severe transients, and contribute to a seamless black start of the grid under unbalanced load conditions. Two GFM methods - Droop and dispatchable virtual oscillator control - are used for the demo to validate feasibility and interoperability of the passive synchronization.

black start↗

DOE OSTI2021

The future evolution of energy-water-agriculture interconnectivity across the US

Abstract Energy, water, and agricultural resources across the globe are highly interconnected. This interconnectivity poses science challenges, such as understanding and modeling interconnections, as well as practical challenges, such as efficiently managing interdependent resource systems. Using the US as an example, this study seeks to define and explore how interconnectivity evolves over space and time under a range of influences. Concepts from graph theory and input–output analysis are used to visualize and quantify key intersectoral linkages using two new indices: the ‘Interconnectivity Magnitude Index’ and the ‘Interconnectivity Spread Index’. Using the Global Change Analysis Model (GCAM-USA), we explore the future evolution of these indices under four scenarios that explore a range of forces, including socioeconomic and technological change. Analysis is conducted at both national and state level spatial scales from 2015 to 2100. Results from a Reference scenario show that resource interconnectivity in the US is primarily driven by water use amongst different sectors, while changes in interconnectivity are driven by a decoupling of the water and electricity systems, as power plants become more water-efficient over time. High population and GDP growth results in relatively more decoupling of sectors, as a larger share of water and energy is used outside of interconnected sector feedback loops. Lower socioeconomic growth results in the opposite trend. Transitioning to a low-carbon economy increases interconnectivity because of the expansion of purpose-grown biomass, which strengthens the connections between water and energy. The results highlight that while some regions may experience similar sectoral stress projections, the composition of the intersectoral connectivity leading to that sectoral stress may call for distinctly different multi-sector co-management strategies. The methodology we introduce here can be applied in diverse geographical and sectoral contexts to enable better understanding of where, when, and how coupling or decoupling between sectors could evolve and be better managed.

Khan, Zarrar (ORCID:0000000281478553)↗