Advanced Machine Learning Research Articles

Exploring novel heat transfer correlations: Machine learning insights for molten salt heat exchangers

The utilization of molten salts in heat transfer applications, specifically within shell-and-tube heat exchangers, has garnered significant attention for its potential in sustainable energy solutions. this study employs advanced machine learning algorithms, including decision tree regressor, support vector regressor, extreme gradient boosting, and random forest, to not only predict the heat transfer behavior of molten salts but also unravel the complex mechanisms underlying this process. Achieving a remarkable accuracy score of 0.985, the Support Vector Regressor leads the predictive models, closely followed by random forest (0.982), Decision Tree Regressor (0.974), and Extreme Gradient Boosting (0.965). The incorporation of Shapley Additive exPlanations values accentuates the Reynolds number’s pivotal role, elucidating a robust correlation with the Nusselt value. These insights transcend mere prediction, offering a profound understanding that can significantly impact the design and optimization of molten salt heat exchangers. The applications of molten salts extend across various sectors, including concentrated solar energy and thermal storage, solidifying their position as a versatile and effective solution in the pursuit of sustainable and efficient energy systems.

AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data‐driven control

AbstractThe mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics‐constrained data‐driven models through optimal sensor selection and placement. The research effort is anchored by a common task framework (CTF) that evaluates the performance of ML algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. The aim is to push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross‐disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data. The CTF further supports sustainable and open‐source challenge datasets, which are foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.

Characterizing hub biomarkers for post-transplant renal fibrosis and unveiling their immunological functions through RNA sequencing and advanced machine learning techniques

BackgroundKidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis.MethodsWe obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs.ResultsThrough WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations.ConclusionFour potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.

AFM signal model for dysarthric speech classification using speech biomarkers.

Neurological disorders include various conditions affecting the brain, spinal cord, and nervous system which results in reduced performance in different organs and muscles throughout the human body. Dysarthia is a neurological disorder that significantly impairs an individual's ability to effectively communicate through speech. Individuals with dysarthria are characterized by muscle weakness that results in slow, slurred, and less intelligible speech production. An efficient identification of speech disorders at the beginning stages helps doctors suggest proper medications. The classification of dysarthric speech assumes a pivotal role as a diagnostic tool, enabling accurate differentiation between healthy speech patterns and those affected by dysarthria. Achieving a clear distinction between dysarthric speech and the speech of healthy individuals is made possible through the application of advanced machine learning techniques. In this work, we conducted feature extraction by utilizing the Amplitude and frequency modulated (AFM) signal model, resulting in the generation of a comprehensive array of unique features. A method involving Fourier-Bessel series expansion is employed to separate various components within a complex speech signal into distinct elements. Subsequently, the Discrete Energy Separation Algorithm is utilized to extract essential parameters, namely the Amplitude envelope and Instantaneous frequency, from each component within the speech signal. To ensure the robustness and applicability of our findings, we harnessed data from various sources, including TORGO, UA Speech, and Parkinson datasets. Furthermore, the classifier's performance was evaluated based on multiple measures such as the area under the curve, F1-Score, sensitivity, and accuracy, encompassing KNN, SVM, LDA, NB, and Boosted Tree. Our analyses resulted in classification accuracies ranging from 85 to 97.8% and the F1-score ranging between 0.90 and 0.97.

A machine learning investigation of the ZnO–water nanofluid flow with magnetic field through convergent and divergent channels: a numerical study

This investigation focuses on how ZnO–H2O nanofluid flow affects the thermal processes in divergent/convergent channels under the effects of magnetohydrodynamics (MHD). Advanced machine learning techniques are utilised to comprehensively quantify the interactions among the implications of nanoparticles on thermal transmission and fluid actions. Our analysis encompasses the MHD and Joule emission effects, transforming the intricate system of nonlinear system of equations to ordinary differential equations (ODEs) following the strategy of non-dimensionalisation. Leveraging the integration of artificial neural networks (ANN) integrated a hybrid approach for numerical solutions. The optimum value of ANN-based fitness function is up to 5.53 × 10 − 5 . The optimal weights and biases of ANN through ABC–WCA are ranging from − 10 to 10 . These results are meticulously compared with shooting method and homotopy perturbation method (HPM) solutions. The absolute error between the ANN, shooting and HPM are equal up to four (4) decimal places. Further, the ANN-based fitness function optimised through ABC-WCA over 250 independent runs for the efficiency of algorithms. It is found that the current results are much more authentic and powerful than the existing less converging and slow analytic and numerical methods.

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening

This work unfolds a robust and interpretable strategy for evaluating the stability and potential photovoltaic application of 6526 multicomponent perovskite oxides, employing a synergetic methodology that intertwines advanced machine learning (ML) algorithms and symbolic regression based on genetic programming. Initially, ML algorithms, namely XGBoost, LightGBM, and random forest, were harnessed, with elemental oxidation state and electronegativity serving as input features, achieving R2 values of 0.98, 0.98, and 0.74, respectively, on the test set for predicting the formation enthalpy, a criterion for perovskite stability. Despite the amplified interpretability offered by SHAP analysis, the inherent “black-box” nature of ML obfuscates a transparent understanding of intrinsic relations between input features and performance. To surmount this, symbolic regression introduced not only elucidates a clear functional relationship between input features and perovskite stability but also achieves a commendable R2 of 0.79 on the test set. Subsequent high-throughput screening, based on perovskite stability ranking, designated the top 500 stable perovskites for band gap calculation using the PBE functional, wherein DyNdHf2O6, CeEuAl2O6, and CeSrAl2O6 emerged as potential candidates for photovoltaic applications and were subjected to further electronic structure simulations employing the HSE06 functional, encompassing density of states, band structure, charge density, and optical absorption spectra. Ultimately, CeEuAl2O6, boasting an optical direct bandgap of 2.31 eV and minimal electron-hole wavefunction overlap, stands out as the prime choice for photovoltaic materials. This research not only pioneers the exploration of enhancing the interpretability of ML but also propels theoretical guidance for the evolution of photovoltaic cells by bridging predictive modeling with high-throughput screening.

Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis

The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.

BUSINESS INTELLIGENCE IN THE ERA OF BIG DATA: A REVIEW OF ANALYTICAL TOOLS AND COMPETITIVE ADVANTAGE

In the contemporary business landscape, the proliferation of Big Data has revolutionized the way organizations gather, process, and utilize information for strategic decision-making. This paper provides a comprehensive overview of the evolving role of Business Intelligence (BI) in harnessing the potential of Big Data and the subsequent impact on gaining a competitive advantage. The review delves into the arsenal of analytical tools that have emerged to handle the vast volumes of data generated in the digital age. From traditional reporting and querying to advanced analytics, machine learning, and predictive modeling, organizations now have a myriad of options to extract valuable insights from their data reservoirs. This paper investigates the efficiency, scalability, and adaptability of these tools in the context of Big Data, emphasizing their role in transforming raw data into actionable intelligence. Furthermore, the paper explores how the integration of BI and Big Data analytics contributes to the development of a competitive edge for businesses. The ability to harness insights from diverse data sources provides organizations with a holistic view of market trends, consumer behavior, and operational efficiency. This, in turn, empowers decision-makers to make informed and timely choices, enhancing overall organizational agility and responsiveness to market dynamics. The study also highlights the challenges associated with implementing BI in the era of Big Data, including issues related to data quality, security, and the need for skilled professionals. Effective solutions to these challenges are discussed, emphasizing the importance of a robust data governance framework and continuous investment in talent development. This paper underscores the pivotal role of Business Intelligence in leveraging the potential of Big Data for gaining a competitive advantage. As organizations navigate the complexities of the modern business landscape, the judicious use of analytical tools and the integration of BI with Big Data analytics stand as key drivers for informed decision-making and sustainable success.
 Keywords: Business Intelligence, Big Data, Analytical Tool, Business, AI, Review.

QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics.
 Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.

From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey.

Air pollution significantly threatens human health and natural ecosystems and requires urgent attention from decision makers. The fight against air pollution begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis and the application of advanced machine learning algorithms. To effectively reduce air pollution, decision makers must focus on reducing primary sources such as industrial plants and obsolete vehicles, as well as policies that encourage the adoption of clean energy sources. In this study, data analysis was performed for the first time to evaluate air pollution based on the SPSS program. Correlation coefficients between meteorological parameters and particulate matter concentrations (PM1, PM2.5, PM10) were calculated in two urban regions of Romania (Craiova and Drobeta-Turnu Severin) and Turkey (Adana). This study establishes strong relationships between PM concentrations and meteorological parameters with correlation coefficients ranging from -0.617 (between temperature and relative humidity) to 0.998 (between PMs). It shows negative correlations between temperature and particulate matter (-0.241 in Romania and -0.173 in Turkey) and the effects of humidity ranging from moderately positive correlations with PMs (up to 0.360 in Turkey), highlighting the valuable insights offered by independent PM sensor networks in assessing and improving air quality.

Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering

Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (Tc) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.

Molecule Maker Lab Institute: Accelerating, advancing, and democratizing molecular innovation

AbstractMany of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI‐experts from the University of Illinois Urbana‐Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI‐enabled synthesis planning, (2) AI‐enabled catalyst development, (3) AI‐enabled molecule manufacturing, and (4) AI‐enabled molecule discovery. The MMLI's new AI‐enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use‐inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI‐enabled tools can help to make chemical synthesis accessible to nonexperts.

Exploring multifaceted factors in chronic kidney disease risk: A comprehensive analysis of biochemistry, lifestyle, and inflammation in elderly Chinese individuals.

This letter praises a recent article in the World Journal of Clinical Cases (Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese), examining factors affecting abnormal renal function in elderly Chinese using advanced machine learning. It highlights the importance of uric acid, age, hemoglobin, body mass index, sport hours, and systolic blood pressure. The study's holistic approach, integrating lifestyle and inflammation, offers a nuanced understanding of chronic kidney disease risk factors. The letter suggests exploring mechanistic pathways of hyperuricemia, the link between anemia and renal function, and the connection between body mass index and estimated glomerular filtration rate. It advocates investigating physical activity's impact on renal health and the independent effects of blood pressure. The study significantly contributes to chronic kidney disease understanding, proposing avenues for further exploration and interventions. Commendations are extended to the authors and the journal.

Machine learning of the chemical elements for enhanced interpretation of depositional environments: Upper Jurassic strata case study in central Saudi Arabia

To explore how machine learning (ML) techniques can enhance the interpretation of depositional environments using chemical elemental data, this study analyzes data from 156 shallow marine strata samples. Several advanced machine learning (ML) techniques were integrated to predict lithofacies and stratigraphic units of shallow marine carbonate strata, focusing on the Arab-D reservoir equivalent outcrops in Central Saudi Arabia. Utilizing a detailed geochemical dataset comprising major, trace, and rare earth elements, we aimed to determine the feasibility of using chemical elemental data in sedimentary geology for predictive modeling. The studied strata are categorized into two lithological units based on depositional settings: one unit comprises rocks that dominantly deposited below the storm wave base with a single lithofacies association, whereas the other includes rocks dominantly deposited above the storm wave base, and consists of two lithofacies associations. The study demonstrates the efficacy of ML algorithms in distinguishing these two lithological units, with a minimum prediction accuracy of 76%. Four ML techniques, including K-nearest neighbors (KNN); random forest classifier (RFC); gradient boosting classifier (GBC); and multi-layer perceptron neural network (MLP-NN) were used, two of them (RFC and GBC) achieved 89% and 92% prediction accuracy. However, a slightly poorer performance was observed in predicting fine-scale lithofacies associations of these lithological units, especially for the lithofacies association representing a transitional interval between the two lithofacies units. Expanding the scope of the study, we tested the ML models against geochemically similar strata from the Oligocene shallow marine reef carbonates. These strata are globally comparable and provide an external validation for the predictive models developed from the Saudi Arabian dataset. GBC and RFC maintained a higher accuracy level than KNN and MLP, demonstrating their robustness and transferability across diverse geological settings. Based on these results, the study underscores the significance of specific oxides and elements as distinctive geochemical markers, elucidating the environmental conditions of the depositional environments and their deposits. This research advances the application of ML in interpreting the depositional environments of shallow marine strata using elemental data, as the majority of the existing researches focus on employing unsupervised machine learning for cluster analysis.

A.I. INTO FASHION PROCESSES

The article aims to provide a comprehensive understanding of Artificial Intelligence (AI) and its integration into fashion processes, focusing on the research, design, development, and manufacturing stages. First, it offers an overview of AI evolution, from its early developments to the contemporary advanced Machine and Deep learning models, attempting to tackle the challenge of ambiguous terminology and aiming to deal with the different interpretations of AI capabilities. Subsequently, a review of the perspectives on the integration of AI tools within fashion processes will be presented. This overview will underscore the growing need for industries to undergo a conscious technological transformation, adopting AI toward a more sustainable and responsible fashion evolution.

Application of Machine Learning in Fracture Analysis of Edge Crack Semi-Infinite Elastic Plate

This paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. The Stress Intensity Factor (SIF) model for a semi-infinite plate with a tip crack is employed in the study, and Finite Element Analysis (FEA) is performed via ABAQUS CAE to build a comprehensive dataset containing numerical simulations data. To improve accuracy and reliability, data preprocessing is implemented, and ANN as a valuable machine learning model is trained with various variables describing crack propagation, stress distribution, and plate structure as input parameters. The suggested method is compared to established fracture analysis methods, proving its accuracy in predicting crack behavior and stress distribution under a variety of loading circumstances. The model provides useful insights into the behavior of edge cracks in semi-infinite elastic plates, enhancing material engineering and structural mechanics. The study demonstrates the potential of combining FEA and machine learning to improve fracture analysis capabilities, and it discusses limitations and future research directions, encouraging the exploration of advanced machine learning techniques and broader fracture scenarios for future fracture mechanics innovation.

Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

The Erzincan (Cimin) grape, which is an endemic product, plays a significant role in the economy of both the region it is cultivated in and the overall country. Therefore, it is crucial to closely monitor and promote this product. The objective of this study was to analyze the spatial distribution of vineyards by utilizing advanced machine learning and deep learning algorithms to classify high-resolution satellite images. A deep learning model based on a 3D Convolutional Neural Network (CNN) was developed for vineyard classification. The proposed model was compared with traditional machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROTF). The accuracy of the classifications was assessed through error matrices, kappa analysis, and McNemar tests. The best overall classification accuracies and kappa values were achieved by the 3D CNN and RF methods, with scores of 86.47% (0.8308) and 70.53% (0.6279) respectively. Notably, when Gabor texture features were incorporated, the accuracy of the RF method increased to 75.94% (0.6364). Nevertheless, the 3D CNN classifier outperformed all others, yielding the highest classification accuracy with an 11% advantage (86.47%). The statistical analysis using McNemar's test confirmed that the χ2 values for all classification outcomes exceeded 3.84 at the 95% confidence interval, indicating a significant enhancement in classification accuracy provided by the 3D CNN classifier. Additionally, the 3D CNN method demonstrated successful classification performance, as evidenced by the minimum-maximum F1-score (0.79-0.97), specificity (0.95-0.99), and accuracy (0.91-0.99) values.

GPOSYSH: Genomic Prediction of Oryza Sativa Yield and Subpopulation Using Hybrid Methods.

Most of the existing studies focus on genetic methods or Machine learning models, in this, we implemented a hybrid combination of genetic methods and machine learning models that accurately predicted phenotypic trait yield, height and subpopulation. Our proposed methodology for genomic prediction of yield in Oryza sativa (rice) involves a two-level classification approach. First, we classify biological sequences and cluster them using the UPGMA algorithm on a phylogenetic tree. Then, we use advanced machine learning techniques like Random Forest, and K-Nearest Neighbours to predict GEBVs with 85- 95% accuracy on rice subpopulations. we achieved an accuracy of 93% when compared with other stated literature in this paper. This approach overcomes limitations and effectively enhances crop breeding by capturing the genotype-phenotype relationship.

Uniform upscaling techniques for eddy covariance FLUXes (UFLUX)

ABSTRACT Data-driven techniques that scale up eddy covariance (EC) fluxes from tower footprints with satellite observations and machine learning algorithms significantly advance our understanding of global carbon, water, and energy cycles. However, few upscaling approaches take a consistent approach to upscaling both carbon and energy fluxes. A lack of uniformity in the upscaling approach could lead to inconsistencies in global interannual variability of fluxes and between types of carbon and energy fluxes. Hence, this study aims to identify obstacles in flux upscaling and propose a uniform upscaling framework UFLUX for gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem exchange (NEE), sensible heat (H), and latent energy (LE). The key findings are as follows: 1) The upscaling performance exhibits a limited improvement from the use of more advanced machine learning approaches (e.g. <0.3 in R2 improvements while using deep neural networks). 2) The spatial density of EC towers is the primary factor determining the effectiveness of upscaling, explaining >50% of the upscaling uncertainty. 3) The UFLUX framework considered the interconnection between fluxes and achieved a competitive validation precision (daily R2 = 0.7 on average of five flux types) when compared with products that upscaled a subset of the fluxes. UFLUX effectively preserved the ecosystem light-use efficiency (0.83 of linear regression slope and the same after), Bowen ratio (0.8), and particularly, the water-use efficiency (0.81), when compared to the only other product (i.e. FLUXCOM) to upscale both carbon and water.