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DOE OSTI2022

Catalytic production of tetrahydropyran (THP): a biomass-derived, economically competitive solvent with demonstrated use in plastic dissolution

Tetrahydropyran (THP) is a five-carbon heterocyclic ether that is non-carcinogenic, non-peroxide forming, biodegradable, and economically competitive with tetrahydrofuran (THF) as a solvent. In this work, THP has been synthesized from renewable biomass at >99.8% selectivity and 98% yield via hydrogenation of furfural-derived 3,4-dihydropyran (DHP) over Ni/SiO 2 in a continuous flow reactor at 150–200 °C. The apparent activation energy of THP formation is 31 kJ mol -1 , and the reaction orders with respect to the partial pressures of H 2 , DHP, and THP are: 2, 1, and -0.3. The kinetic data has been fitted to a Hougen–Watson model where the rate limiting step is the hydrogenation of adsorbed DHP. Ni/SiO 2 is shown to have a low deactivation rate constant of 0.012 h -1 over 100 h time on stream and can be regenerated in situ. As a performance advantage, THP is shown to be resistant to ring opening polymerization under strongly acidic conditions that THF is not, revealing it to be a superior solvent. Further, the minimum selling price of THP is competitive with the market price of THF ($$900 – 1400 per ton ) at a DHP feedstock cost of $1000 per ton. Conductor-like Screening Model for Real Solvents (COSMO-RS) and molecular dynamics (MD) simulations with 1008 solvents and 8 common plastics have demonstrated that THP can serve as an alternative solvent to THF, 2-methyltetrahydrofuran (MeTHF), and cyclopentyl methyl ether (CPME) for plastic dissolution, especially low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS) and polyvinyl chloride (PVC). This work establishes THP as a green solvent with excellent thermal, chemical and peroxidative stability that can be used for numerous applications, including waste plastic recycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Uncertainty Quantification of Fatigue Behavior of Rough AM Surfaces and Microstructures to Enable Hydrogen Gas Turbine

Modifying fossil-fueled industrial gas turbines to utilize low or zero-carbon fuels, such as hydrogen or hydrogen-natural gas blends, is a complex endeavor. The successful implementation of this technology hinges on three key design criteria: (1) developing new fuel injectors capable of efficiently burning alternative fuels, (2) ensuring manufacturability to meet cost and time-to-market goals, and (3) achieving component durability in the demanding environment of an operating gas turbine. Additive manufacturing (AM) accelerates product development, yet concerns persist regarding the durability of parts with rough AM surfaces. A fully experimental approach to quantify the fatigue performance of rough AM microstructures is both costly and labor-intensive. To address this, ORNL and Solar Turbines Incorporated (Solar) employed a crystal plasticity finite element (CPFE) model to identify the factors influencing AM surface fatigue behavior. These CPFE findings, combined with targeted experimental data, were used to develop a computationally efficient surrogate model suitable for assessing the lifespan of gas turbine engine components.

08 HYDROGEN↗

NASA NTRS1981

Remote sensing for forest applications in New York: Two case studies

Under-utilized forest resources in the Northeast can supply fiber to new markets. Information on forest resources, however, is often too aggregated to estimate available woody material with sufficient detail for planning specific projects. Over the past year, staff of Cornell University's Remote Sensing Program, under a grant from NASA, assisted New York State agency personnel by assessing available woody material for two potential development projects, a wood-fired power plant and a particle board manufacturing plant. These feasibility studies are reviewed, illustrating how information derived from high-altitude aircraft photographs, LANDSAT imagery, and available supporting data were integrated to arrive at estimates of woody material.

Stanturf, J. A.↗

DOE OSTI2021

Design Readiness And Maturity Assessment (DRAMA) Tool for Advanced Reactors

Abstract – This research is developing a formal, repeatable method to assess the readiness and maturity of an advanced nuclear reactor design for licensing and deployment. This design readiness and maturity assessment (DRAMA) tool will be capable of determining the readiness of the design and of all parties needed to bring a particular advanced reactor design to fruition. Beneficiaries and stakeholders include the design team, research organizations (required to collect needed data and develop design tools), standards organizations, research facilities to support gathering of needed data, supply chain and construction organizations, and everyone responsible for legal and regulatory infrastructure (including defining import-export requirements). Recent experience has shown that even the most experienced engineering and construction organizations, with decades of experience in the nuclear power business, have had significant challenges in bringing designs to completion, licensing the designs, and constructing new plants. For new entries into the field, simply understanding the unique environmental and regulatory requirements have been daunting. A significant part of the challenge is that new entries into the market do not know what they don't know. This tool will provide design teams a better understanding of their readiness to proceed to licensing (and other steps in the process), while at the same time providing a valuable metric for other interested organizations, such as funding agencies, national regulators, and international markets. The DRAMA tool will provide an assessment of the likelihood of successfully completing licensing and deployment and will also create the capability to assess the ability of regulatory infrastructure and the supply chain to support the deployment of any design or class of designs. It will also help prioritize research and policy efforts to improve the likelihood of deployment of the next generation of advanced reactors.

Arndt, Steven↗

DOE OSTI2023

Unlocking the Value of Deep Energy Retrofits

This publication is based on an extensive study conducted on behalf of the New York State Energy Research and Development Authority (NYSERDA) and the U S Department of Energy (DOE) to explore financial and risk products to accelerate the implementation of deep energy retrofit (DER) solutions to reduce costs These financial products are intended to speed market development via three fundamental themes: reallocating risk from building owners and lenders to insurers, quantifying and monetizing previously unrecognized value associated with DERs, and providing building owners and lenders with confidence in the performance of building systems Findings are based on a comprehensive analysis and characterization of 100+ existing DER case studies, and more than 40 qualitative interviews with industry experts, including insurers, researchers, building owners, and policymakers In addition, a quantitative model of overall project economics with baseline, high, and low cases was developed This data informed the analysis that resulted in three recommended financial product solutions, which were evaluated for their potential to raise projected cash flows and finance DERs: 1) Building System Performance and Energy Savings Guarantee; 2) Trade Credit Insurance; 3) Ancillary Revenue Contracts. This report identifies many value streams associated with DERs and introduces these three potential financial products, which aim to reallocate risks and reduce barriers to the adoption of advanced envelope DERs.

ancillary renenue contracts↗

DOE OSTI2023

EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.

Suryanarayana, Harish↗

DOE OSTI2020

Vehicle to Grid Frequency Regulation Capacity Optimal Scheduling for Battery Swapping Station Using Deep Q-Network

Battery swapping stations (BSSs) are ideal candidates for fast frequency regulation services (FFRS) due to their large battery stock capacity. In addition, BSSs can precharge batteries for customers and the batteries that are not in charging can provide a stable regulation capacity to the market. However, uncertainties, such as ACE signals and the EV per-hour visit counts, introduce stochastic nonlinear dynamics into the operation of a BSS-based FFRS. Currently, there is no quantification method to ensure its optimal economical operation. To close this gap, in this article, we propose a novel deep Q-learning-based FFRS capacity dynamic scheduling strategy. This method can autonomously schedule the hourly regulation capacity in real time to maximize the BSSx0027;s revenue for providing FFRS. Case studies using real-world data verify the efficacy of the proposed work.

25 ENERGY STORAGE↗

NASA NTRS2016

In-Flight Personalized Medication Management

Current medication selection for treatment of astronauts during spaceflight missions is primarily dictated by the task of efficiently treating the widest possible range of physiological conditions and illnesses with a limited set of medications. Dosage and recommendations on the combination of drugs are based on the assumption of genetically equal drug sensitivity and unchanged metabolism. To our knowledge, there was no pre-flight drug sensitivity testing on a genetic level for any of the previous manned NASA space missions. Although many of the common, binary drug-drug interactions are, most likely, already considered in the ISS Medical kit composition, multi-drug and multi-drug-gene factors are not incorporated in the medication selection or prescription. Furthermore, due to the physiological changes occurring in microgravity environments, astronauts might be susceptible to potential increased drug toxicity as a result of decreased clearance of numerous drugs. In particular, perturbation of CYP450 enzymes which contribute to the hepatic metabolism of the majority of drugs may have significant effects on therapeutic efficacy and increase treatment-related toxicity5. The genes encoding the CYP450 enzymes are highly variable in humans. Inheritable variations of CYP450 hepatic metabolizer enzymes and transport proteins play a crucial role in the inter-individual variability of drug efficiency and risks of adverse drug reactions5. Additionally, there are some reports that document changes in the levels of production of drug-metabolizing enzymes in microgravity. These data can be extrapolated to provide reasonable assumptions of decreased levels of expression for most CYP450 enzymes in human body during prolonged space travel. If the prescribed medication regiment is not fully effective or causes undesirable side effects, the ability of the astronauts to function and maintain peak performance levels during space flight could be seriously compromised. Therefore, technologies capable of predicting and managing medication side effects, interactions, and toxicity of drugs during spaceflight are needed. We propose to develop and customize for NASAs applications available on the market Personalized Prescribing System (PPS) that would provide a comprehensive, non-invasive solution for safer, targeted medication management for every crew member resulting in safer and more effective treatment and, consequently, better performance. PPS will function as both decision support and record-keeping tool for flight surgeons and astronauts in applying the recommended medications for situations arising in flight. The information on individual drug sensitivity will translate into personalized risk assessment for adverse drug reactions and treatment failures for each drug from the medication kit as well as predefined outcome of any combination of them. Dosage recommendations will also be made individually. The mobile app will facilitate ease of use by crew and medical professionals during training and flight missions.

Personalized Medication↗

DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗

DOE OSTI2021

Global Ethanol-Blended-Fuel Vehicle Compatibility Study

The objective of this study is to understand the impact of ethanol-blended fuel at various blending levels (10%, 15%, and 20% vol.) on "in-operation" vehicles built to differing emissions and manufacturing standards around the world. The study focuses on vehicles used in Canada, China, India, Indonesia, Japan, South Korea, and Mexico. Historical experience in the United States and Brazil informs the analysis. The primary study question is: Are vehicles in targeted countries physically and operationally compatible with ethanol blended fuel? For a fuel to be compatible with a vehicle, the fuel must perform its function as part of the integrated fuel-vehicle system, meaning: the car should start easily and drive normally, the fuel should not cause catastrophic fuel system leaks, the fuel should not cause corrosion or degradation of any engine or fuel system components (including emissions control components). The history of E10 use in the United States, beginning in 1978, was evaluated and shows no reliability or operability issues for cars dating back to pre-emissions-control times -- and likely included many cars manufactured in the 1960s. This strongly supports the contention that fuel chemistry and property differences between E0 and E10 are so small that any car made to international standards in the last 50 years will have a very high probability of being fully compatible with E10. This conclusion is supported by the experience in Brazil in the 1970s, where E10 was also introduced, and ethanol blending for conventional cars rapidly ramped up to even higher blend levels. A limited number of fuel system and component manufacturers supply the global market, including Bosch, Continental, Denso, Delphi, and Visteon. To reduce complexity, ethanol-compatible materials began to be integrated in fuel system designs globally. Fuel systems evolved over the following decades to incorporate ethanol-compatible materials with core subsystem families, such as in-tank fuel pumps used across several global vehicle original equipment manufacturers (OEMs). A similarly compelling case can be made that all cars at the Tier 1 (or equivalent) emissions-control technology level or higher are fully compatible with E15 blends, based on the data evaluated by the U.S. Environmental Protection Agency (EPA) and Ricardo in 2010. For cars at this technology level, the minor differences in fuel chemistry and properties between E10 and E15 are not significant. For E20, studies are not as extensive but are still highly significant. A long-term durability study conducted on mileage accumulation dynamometers presents convincing evidence that Tier 2 technology level cars have materials of construction and engine control authority for compatibility with E20, although this conclusion is not as strong as those drawn for E10 and E15, which are also partly based on real-world experience.

09 BIOMASS FUELS↗

DOE OSTI2023

Evaluating Production Implications of Pressure Maintenance in Unconventional Oil and Gas Wells using a Machine Learning Modeling Approach: Case Study in the Permian Basin

This study implements the proprietary machine learning-based model (model) developed under the 2022 study titled “Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach” for forecasting unconventional oil and gas production using well data from the Permian Basin. The model, developed using an exclusive dataset that includes time series production data from an operator in the Permian Basin, is designed to jointly predict daily oil, gas, and water production for horizontal wells as a function of bottomhole pressure drawdown, spatial placement across the study domain, and well-completion attributes. In this study, the model was explicitly applied to explore its utility to evaluate the impact of varying drawdown strategies on the production forecast of a well in the Permian Basin dataset. Managing pressure drawdown has been identified as a way to improve estimated ultimate recovery (EUR) from unconventional shale wells due to the stress-dependent nature of fractures in shale reservoirs. Research has shown that applying a lower pressure drawdown helps to maintain the reservoir conductivity, resulting in higher productivity over the life of a well. Historic bottomhole pressure data from the well over time was used as a benchmark from which to set more and less aggressive pressure decline rates as bounding modeling cases. All pressure decline rates/strategies were forecasted over 5 years, and the model was used to generate oil, water, and gas prediction over the same timeframe. This report presents the results from the production forecast and discusses potential operational and economic implications, with contrasting perspectives between well productivity and profitability given typical oil and gas economics and the volatility in the oil and gas market.

02 PETROLEUM↗

DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗

NASA NTRS1981

Future satellite systems - Market demand assessment

During 1979-80, a market study was performed regarding the future total demand for communications services, and satellite transmission service at the 4/6 GHz, 12/14 GHz, and 20/30 GHz frequencies. Included in the study were a variety of communications traffic characteristics as well as projections of the cost of C and Ku band satellite systems through the year 2000. In connection with the considered study, a total of 15 major study tasks and subtasks were undertaken and were all interrelated in various ways. The telecommunications service forecasts were concerned with a total of 21 data services, 5 voice services, and 5 video services. The traffic volumes within the U.S. for the three basic services were projected for three time periods. It is found that the fixed frequency allocation for domestic satellites combined with potential interference from adjacent satellites means a near term lack of orbital positions above the U.S.

Reiner, P. S.↗

DOE OSTI2021

Porting CMS Heterogeneous Pixel Reconstruction to Kokkos

Programming for a diverse set of compute accelerators in addition to the CPU is a challenge. Maintaining separate source code for each architecture would require lots of effort, and development of new algorithms would be daunting if it had to be repeated many times. Fortunately there are several portability technologies on the market such as Alpaka, Kokkos, and SYCL. These technologies aim to improve the developer productivity by making it possible to use the same source code for many different architectures. In this paper we use heterogeneous pixel reconstruction code from the CMS experiment at the CERNL LHC as a realistic use case of a GPU-targeting HEP reconstruction software, and report experience from prototyping a portable version of it using Kokkos. The development was done in a standalone program that attempts to model many of the complexities of a HEP data processing framework such as CMSSW. We also compare the achieved event processing throughput to the original CUDA code and a CPU version of it.

Childers, Taylor↗

DOE OSTI2025

ComStock Measure Scenario Documentation: Standard Performance Heat Pump Rooftop Unit With New Windows

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2003

Envisioning a 21st Century, National, Spacecraft Servicing and Protection Infrastructure and Demand Potential: A Logical Development of the Earth Orbit Economy

The modern world is extremely dependent on thin strings of several hundred civil, military, and commercial spacecraft/satellites currently stationed in space. They provide a steady stream of commerce, defense, and knowledge data. This dependency will in all likelihood increase significantly during this century. A major disruption of any kind in these essential systems and networks could be socially, economically, and politically catastrophic, on a global scale. The development of a space-based, robotic services economy could be useful in mitigating this growing risk, from an efficiency and security standpoint. This paper attempts to suggest what makes sense to invest in next for the logical, economic development of Earth orbit i.e., after ISS completion. It expands on the results of an advanced market research and analysis study that sampled the opinions of several satellite industry executives and presents these results within a broad policy context. The concept of a spacecraft carrier that serves as the nucleus of a national, space-based or on-orbit, robotic services infrastructure is introduced as the next logical step for United States leadership in space. This is viewed as a reasonable and appropriate followon to the development of ELVs and satellites in the 1950s and 1960s, the Space Shuttle/PRLV in the 1970s and 1980s, and the International Space Station (ISS) in the 1980s, 1990s and 2000s. Large-scale experience in LEO-to-GEO spacecraft/satellite servicing and protection by robotic means is assumed to be an indispensable prerequisite or stepping-stone toward the development and preservation of the large scientific exploration facilities that are envisioned by NASA for operation beyond GEO. A balanced, return on national investment (RONI) strategy for space, focused on the provision of enhanced national/homeland security for increased protection, national economic/industrial expansion for increased revenue, and national scientific exploration for increased knowledge is recommended as the next strong, irrepressible goal toward realizing and achieving the official NASA vision and mission.

Horsham, Gary A.↗

NASA NTRS2007

Vertical Drop Testing and Analysis of the Wasp Helicopter Skid Gear

This report describes an experimental program to assess the impact performance of a skid gear for use on the Wasp kit-built helicopter, which is marketed by HeloWerks, Inc. of Hampton, Virginia. In total, five vertical drop tests were performed. The test article consisted of a skid gear mounted beneath a steel plate. A seating platform was attached to the upper surface of the steel plate, and two 95th percentile Hybrid III male Anthropomorphic Test Devices (ATDs) were seated on the platform and secured using a four-point restraint system. The test article also included ballast weights to ensure the correct position of the Center-of-Gravity (CG). Twenty-six channels of acceleration data were collected per test at 50,000 samples per second. The five drop tests were conducted on two different gear configurations. The details of these test programs are presented, as well as an occupant injury assessment. Finally, a finite element model of the skid gear test article was developed for execution in LS-DYNA, an explicit nonlinear transient dynamic code, for predicting the skid gear and occupant dynamic responses due to impact.

Jackson, Karen E.↗

NASA NTRS2024

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

Vegetation health forecasting (NDVI as a proxy) informs decision-makers about the end of season crop yield productivity but is not well-documented. This study tests improvements in vegetation health forecasting by developing a data-driven Dynamic Agricultural Productivity Indicator ( DAPI ), which simultaneously incorporates satellite-based root zone soil moisture (RZSM) and satellite-based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology-based NDVI forecasts, with the largest improvements in water-limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food-insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Manh Le↗