Large-scale Experimentation Research Articles

Explainable machine learning assisted molecular-level insights for enhanced specific stiffness exploiting the large compositional space of AlCoCrFeNi high entropy alloys

Design of high entropy alloys (HEA) presents a significant challenge due to the large compositional space and composition-specific variation in their functional behavior. The traditional alloy design would include trial-and-error prototyping and high-throughput experimentation, which again is challenging due to large-scale fabrication and experimentation. To address these challenges, this article presents a computational strategy for HEA design based on the seamless integration of quasi-random sampling, molecular dynamics (MD) simulations and machine learning (ML). A limited number of algorithmically chosen molecular-level simulations are performed to create a Gaussian process-based computational mapping between the varying concentrations of constituent elements of the HEA and effective properties like Young’s modulus and density. The computationally efficient ML models are subsequently exploited for large-scale predictions and multi-objective functionality attainment with non-aligned goals. The study reveals that there exists a strong negative correlation between Al concentration and the desired effective properties of AlCoCrFeNi HEA, whereas the Ni concentration exhibits a strong positive correlation. The deformation mechanism further shows that excessive increase of Al concentration leads to a higher percentage of face-centered cubic to body-centered cubic phase transformation which is found to be relatively lower in the HEA with reduced Al concentration. Such physical insights during the deformation process would be crucial in the alloy design process along with the data-driven predictions. As an integral part of this investigation, the developed ML models are interpreted based on Shapley Additive exPlanations, which are essential to explain and understand the model’s mechanism along with meaningful deployment. The data-driven strategy presented here will lead to devising an efficient explainable ML-based bottom-up approach to alloy design for multi-objective non-aligned functionality attainment.

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction. To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.

ScPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.

With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.

Exploiting fly models to investigate rare human neurological disorders.

Rare neurological diseases, while individually are rare, collectively impact millions globally, leading to diverse and often severe neurological symptoms. Often attributed to genetic mutations that disrupt protein function or structure, understanding their genetic basis is crucial for accurate diagnosis and targeted therapies. To investigate the underlying pathogenesis of these conditions, researchers often use non-mammalian model organisms, such as Drosophila (fruit flies), which is valued for their genetic manipulability, cost-efficiency, and preservation of genes and biological functions across evolutionary time. Genetic tools available in Drosophila, including CRISPR-Cas9, offer a means to manipulate gene expression, allowing for a deep exploration of the genetic underpinnings of rare neurological diseases. Drosophila boasts a versatile genetic toolkit, rapid generation turnover, and ease of large-scale experimentation, making it an invaluable resource for identifying potential drug candidates. Researchers can expose flies carrying disease-associated mutations to various compounds, rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and, ultimately, clinical trials. In this comprehensive review, we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis, pathophysiology, and potential therapeutic implications. We discuss rare diseases associated with both neuron-expressed and glial-expressed genes. Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay, mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay, and mutations in IRF2BPL causing seizures, a neurodevelopmental disorder with regression, loss of speech, and abnormal movements. And we explore mutations in EMC1 related to cerebellar atrophy, visual impairment, psychomotor retardation, and gain-of-function mutations in ACOX1 causing Mitchell syndrome. Loss-of-function mutations in ACOX1 result in ACOX1 deficiency, characterized by very-long-chain fatty acid accumulation and glial degeneration. Notably, this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology, offering a platform for the rapid identification of potential therapeutic interventions. Rare neurological diseases involve a wide range of expression systems, and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia. Furthermore, mutations within the same gene may result in varying functional outcomes, such as complete loss of function, partial loss of function, or gain-of-function mutations. The phenotypes observed in patients can differ significantly, underscoring the complexity of these conditions. In conclusion, Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases. By facilitating the modeling of these conditions, Drosophila contributes to a deeper understanding of their genetic basis, pathophysiology, and potential therapies. This approach accelerates the discovery of promising drug candidates, ultimately benefiting patients affected by these complex and understudied diseases.

Survey of burning rate measurements in small solid rocket motors

Burning rate plays a crucial role in determining the performance of solid rocket motors (SRMs). In the traditional approach for solid propellant propulsion, technical activities regarding burning rates are developed at three different operational levels: (i) Strand burners or laboratory-scale devices in general; (ii) Small-scale motors (SSMs); And (iii) full-size or end-item rocket motors. While strand burners are extensively used for propellant development (formulation exploration, ingredients screening, performance verification, and production control) and relatively little is done experimentally at the full-size motor level (being large-scale experimentation too expensive and dangerous), a lot is usually carried out at SSM level to obtain burning rate information under motor operating conditions. In the introductory part of this work, burning rate fundamentals are recalled and burning rate measurement devices are quickly summarized. Then, a survey of subscale motors is conducted and several automated procedures to deduce burning rate from SSM testing are analyzed. Data reduction methods commonly used by leading European companies are based on the thickness-over-time (TOT) definition. Attention is dedicated to procedures used in Italy and France for quality control of the European space launchers (solid propellant boosters of Ariane family and core solid rocket motors of VEGA family). In addition, automated data reduction methods based on mass balance (MB) and often used in USA are investigated. Specific features and general trends of the tested industrial procedures are pointed out. Since for any tests the actual burning rates are unknown, results can only be compared based on the statistical quality of the deduced ballistic data. Mainly reproducibility, ease of application, and suitability for automated computer implementation are of interest to industrial users. The effects of test variability, input data quality, and data reduction methods on result reproducibility are discussed with reference to fire tests of the successful Ariane-5 solid boosters. The systematic analysis of industrial data conducted in this work suggests that improving the actual mix reproducibility and quality of experimental data is more important than perfecting the current data reduction methods. Moreover, the international trends suggest that the fundamental TOT procedures are being replaced by MB procedures or advanced TOT procedures with burning times evaluated using pressure integrals.

Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure

We describe a novel optimization-based approach—generalized synthetic control (GSC)—in which we learn from experiments conducted in a physical retail environment. GSC solves a long-standing problem of learning from experiments conducted in this environment when treatment effects are small, the environment is extremely noisy and nonstationary, and interference and adherence problems are commonplace. The utilization of GSC has demonstrated a remarkable increase in statistical power, approximately one hundredfold (100×) higher than conventional inferential methods. This innovative approach forms the basis of TestOps, a pioneering large-scale experimentation platform designed specifically for physical retailers. TestOps was developed and has been broadly implemented as part of a collaboration between Anheuser Busch Inbev (ABI) and a team of operations researchers and data engineers from the Massachusetts Institute of Technology. TestOps currently runs physical experiments impacting approximately 135 million USD in revenue every month and routinely identifies innovations that result in a 1%–2% increase in sales volume. The vast majority of these innovations would have remained unidentified had we not developed our novel approach to inference. Prior to our implementation, statistically significant conclusions could be drawn on only ∼6% of all experiments, a fraction that has now increased by 10-fold. Given its success, TestOps is being rolled out globally at ABI, driving significant revenue growth and enabling the extraction of valuable insights from large-scale physical experiments. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.

Intersectional Effects of Crystal Features on the Actuation Performance of Dynamic Molecular Crystals.

Despite being researched for decades, shape-shifting molecular crystals have yet to claim their spot as an actuating materials class among the primary functional materials. While the process for developing and commercializing materials can be lengthy, it inevitably starts with building an extensive knowledge base, which for molecular crystal actuators remains scattered and disjointed. Using machine learning for the first time, we identify inherent features and structure-function relationships that fundamentally impact the mechanical response of molecular crystal actuators. Our model can factor in different crystal properties in tandem and decipher their intersectional and combined effects on each actuation performance. This analysis is an open invitation to utilize interdisciplinary expertise in translating the current basic research on molecular crystal actuators into technology-based development that promotes large-scale experimentation and prototyping.

Controlled-release fertilizers from phosphate glass-matrix: A new ecological approach to match nutrients release with plants demand

Physico-chemical properties, structural characterization, and dissolution behaviors of four phosphate glasses modified by incorporating zinc, boron, and copper, acting as eco-friendly fertilizers with controlled release of macro and micronutrients for wheat plants, were investigated. The elaborated glasses were characterized by differential thermal analysis, density measurements, X-ray diffraction, FTIR spectroscopy, and Raman spectroscopy. The dissolution behaviors were investigated using weight and pH measurements. The ionic concentration of leachate solutions was determined using ICP-OES. Results proved that the glass's chemical composition and lattice structure play the mean role in controlling the release of nutrients from the glass. The seemingly different properties between the elaborated glasses depend on the strength's bonds of glass lattice-formers and modifiers as well as their ionic field strength of the incorporated elements. An agronomic valorization was carried out to evaluate the efficiency of these agriglasses on wheat crops. It revealed a positive impact on wheat growth, yield, photosynthetic parameters, and grain mineral content with improvement values ranging from 4 to 89% compared to conventional fertilizers. These results pave the way to applying these fertilizers in large-scale experimentation to confirm their potentiality in crop production and as eco-friendly fertilizers.

Clustering-Based Imputation for Dropout Buyers in Large-Scale Online Experimentation

In online experimentation, appropriate metrics (e.g., purchase) provide strong evidence to support hypotheses and enhance the decision-making process. However, incomplete metrics are frequently occurred in the online experimentation, making the available data to be much fewer than the planned online experiments (e.g., A/B testing). In this work, we introduce the concept of dropout buyers and categorize users with incomplete metric values into two groups: visitors and dropout buyers. For the analysis of incomplete metrics, we propose a clustering-based imputation method using k-nearest neighbors. Our proposed imputation method considers both the experiment-specific features and users’ activities along their shopping paths, allowing different imputation values for different users. To facilitate efficient imputation of large-scale data sets in online experimentation, the proposed method uses a combination of stratification and clustering. The performance of the proposed method is compared to several conventional methods in both simulation studies and a real online experiment at eBay.

Improving the Efficacy and Accessibility of Intracranial Viral Vector Delivery in Non-Human Primates.

Non-human primates (NHPs) are precious resources for cutting-edge neuroscientific research, including large-scale viral vector-based experimentation such as optogenetics. We propose to improve surgical outcomes by enhancing the surgical preparation practices of convection-enhanced delivery (CED), which is an efficient viral vector infusion technique for large brains such as NHPs’. Here, we present both real-time and next-day MRI data of CED in the brains of ten NHPs, and we present a quantitative, inexpensive, and practical bench-side model of the in vivo CED data. Our bench-side model is composed of food coloring infused into a transparent agar phantom, and the spread of infusion is optically monitored over time. Our proposed method approximates CED infusions into the cortex, thalamus, medial temporal lobe, and caudate nucleus of NHPs, confirmed by MRI data acquired with either gadolinium-based or manganese-based contrast agents co-infused with optogenetic viral vectors. These methods and data serve to guide researchers and surgical team members in key surgical preparations for intracranial viral delivery using CED in NHPs, and thus improve expression targeting and efficacy and, as a result, reduce surgical risks.

TERMS: textual emotion recognition in multidimensional space.

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios.

Geometric design optimization of a Savonius wind turbine

Savonius vertical-axis wind turbines are becoming an outstanding alternative for small-scale power generation mainly due to their simple design, high self-starting performance at low wind speeds, omnidirectional capability, and low cost. Over the past few decades, extensive research has suggested turbine configurations that maximize their aerodynamic performance, evaluating different influencing geometric parameters. Nevertheless, few studies have considered running large-scale experimentation, simultaneously assessing different influencing parameters, and implementing mathematical optimization techniques. Consequently, there is a need to establish a set of procedures and techniques that address these gaps. The current research proposes a new design method for a Savonius wind turbine to maximize its aerodynamic performance. The proposed method defines a numerical model for a geometry with four influencing parameters, which automatically achieves solutions to the equations that govern the motion of fluids for different turbines. The model is successfully validated with experimental results available in the literature. The numerical solutions of 340 different turbines build a response surface with excellent quality metrics based on the Kriging technique. Two mathematical optimization techniques are implemented on the response surface to determine the configuration that maximizes its aerodynamic performance. This optimal configuration is characterized under different operating conditions. The results indicate that the optimal turbine has an aspect ratio of 8.38, an overlap ratio of 0.08, a twist angle of 174.05°, and two blades. The behavior shown by the optimal turbine (maximum power coefficient of 0.21 at a wind speed of 12 m/s) is remarkable since its aerodynamic performance is superior to multiple reported turbines. The instantaneous torque that it develops is always positive during its operation, which indicates a greater capacity for self-starting and continuity in its rotation. Likewise, it supplies a maximum rotor power of approximately 160 W at a wind speed of 12 m/s. These results make the optimal turbine an attractive design for electric power generation. The proposed method provides novel insights and powerful tools for researchers to explore various geometries and operating conditions that, together with the established optimization process, accomplish the turbine that maximizes its aerodynamic performance.

Manipulation of free-floating objects using Faraday flows and deep reinforcement learning

The ability to remotely control a free-floating object through surface flows on a fluid medium can facilitate numerous applications. Current studies on this problem have been limited to uni-directional motion control due to the challenging nature of the control problem. Analytical modelling of the object dynamics is difficult due to the high-dimensionality and mixing of the surface flows while the control problem is hard due to the nonlinear slow dynamics of the fluid medium, underactuation, and chaotic regions. This study presents a methodology for manipulation of free-floating objects using large-scale physical experimentation and recent advances in deep reinforcement learning. We demonstrate our methodology through the open-loop control of a free-floating object in water using a robotic arm. Our learned control policy is relatively quick to obtain, highly data efficient, and easily scalable to a higher-dimensional parameter space and/or experimental scenarios. Our results show the potential of data-driven approaches for solving and analyzing highly complex nonlinear control problems.

COPING WITH THE DYNAMICS OF URBAN SUSTAINABILITY: POSITIONING URBAN EXPERIMENTATION PLATFORMS AS TOOLS FOR POLICY MAKING

Abstract. This research considers the creation of resilient organizational models of urban governance to foster urban sustainability. Building on large-scale urban experimentation initiatives, we identify relevant factors in establishing urban experimentation with the help of digital platforms as a means to support such resilient organizational models. Our findings suggest that digital platforms help establish urban experimentation platforms (UXPs) as a core means to innovate towards urban sustainability. UXPs utilize digital platforms to coordinate policy measures and stimulate collective intelligence in order to reach a sustained local socio-technical transformation process. Based on these empirical findings, we use a systems lens to decompose urban experimentation into interconnected sub-systems and then describe some characteristics of their dynamic interaction. Our goal is to develop a System Dynamics model and simulations to support urban public authority decision makers in formulating and implementing policy interventions towards urban sustainability.

Innovation Analytics and Digital Innovation Experimentation: The Rise of Research-driven Online Review Platforms

Big data analytics constitute one of the driving forces of the fourth industrial revolution and represent one of the founding pillars of Industry 4.0. They are increasingly leveraged to create business insights from online reviews of products and services by a wide range of organizations and firms. In this work, we develop a typology of online review platforms (ORPs) and describe a novel platform, research-driven online review platform (RORP), that combines the science and rigor of very large-scale, low-cost, fast-paced, and complex digital experimentation using real-world customers on digital platforms with the power of modern AI-based big data analytics capabilities (BDAC) to generate novel innovation insights for the digital age. Using multiple real-world case studies, we illustrate how RORPs operate and deliver value through innovation analytics, and serve as a powerful tool for digital innovation experimentation, enabling firms to innovate more effectively and transform their business models to adapt to rapidly changing market conditions. We shed light on the BDAC requirements, as well as the benefits and challenges of using RORPs and innovation analytics, particularly in the post-COVID-19 world, and offer strategic and operational implications for entrepreneurs and innovation managers.

Pattern discovery, validation, and online experiments: a methodology for discovering television shows for public health announcements.

Public Health Announcements (PHAs) on television are a means of raising awareness about risk behaviors and chronic conditions. PHAs' scarce airtime puts stress on their target audience reach. We seek to help health campaigns select television shows for their PHAs about smoking, binge drinking, drug overdose, obesity, diabetes, STDs, and other conditions using available statistics. Using Nielsen's TV viewership database for the entire US panel, we presented a novel show discovery methodology for PHAs that combined (i) pattern discovery from high-dimensional data (ii) nonparametric tests for validation, and (iii) online experiments on Facebook. The nonparametric tests verified the robustness of the discovered associations between the popularity of certain shows and health conditions. Findings from fifty (independent) online experiments (where our awareness messages were seen by nearly 1.5 million American adults) empirically demonstrated the value of the methodology. For 2016, the methodology identified several shows whose popularities were genuinely associated with certain health conditions, opening up the possibility of health agencies embracing both big data and large-scale experimentation to address an old problem in a new way. Policy makers can repeatedly apply the methodology as new data streams in, with perhaps different feature sets, pattern discovery techniques, and online experiments running over longer periods. The comparatively lower initial investment in the methodology can pay off by identifying several shows for a potentially national television campaign. As simply a by-product, the initial investment also results in awareness messages that might reach millions of individuals.

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review.

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

Surface Functionalization of Biochar from Oil Palm Empty Fruit Bunch through Hydrothermal Process

The use of biochar as an adsorbent for wastewater treatment purposes has been hindered due to its lower surface area compared to activated carbon. Current research on increasing surface functional groups on biochar surfaces to improve its adsorption performance suggests using high chemical concentration and long period of modification. This study solves these problems by focusing on improving surface functionalities of biochar via the hydrothermal functionalization process. Oil palm empty fruit bunch biochar was functionalized using autoclave with nitric acid as the functionalization agent. Functionalized biochar properties such as Brunauer–Emmett–Teller (BET) surface area and surface functional groups were analyzed and compared with untreated biochar. Fourier Transform Infrared (FTIR) spectroscopic analysis shows a significant increase in absorption by oxygen functional groups and is corroborated with energy dispersive X-ray (EDX) analysis. The process does not result in any major change in surface morphology and reduction in surface area value. Methylene blue (MB) adsorption test shows 7 times increase in adsorption performance. These results show that the simple hydrothermal functionalization process successfully functionalizes the biochar surface and improves its performance without affecting its surface area at lower concentration, and shorter time compared to previous studies. This result, with future large-scale experimentation using real-life equipment in palm oil mills, would provide a better technology that can be implemented in the industry.

A foveated vision framework for visual change detection using motion and textural features

Change detection is an important process in many video-based applications such as anomaly event detection and video surveillance. This paper proposes a foveated vision framework that simulates the human visual system for change detection. It contains two phases—first identifying regions with visual changes due to significant motion, and then, the extraction of detailed information of the change. In phase I, change proposals (CPs) and background are segregated by analyzing the intensity and motion features. In phase II, visual changes are estimated from the CPs by analyzing the photometric and textural features. Each phase of analysis has a unique pre-generated archetype. A probabilistic refinement scheme is used to rectify the labeling of background and change. In each phase of analysis, the result is used to update the archetype immediately. Some well-known and recently proposed background modeling/subtraction algorithms are selected for our comparative study. Experimentations are performed on various video datasets. In some videos, our method can achieve higher accuracy than some recently proposed methods by 30%. In the large-scale experimentation using all the testing videos, our method can achieve higher average accuracy than the second best method by more than 3%.

Open Source Framework for Enabling HPC and Cloud Geoprocessing Services

Geoprocessing is a set of tools that can be used to efficiently address several pressing chal-lenges for the global economy ranging from agricultural productivity, the design of transport networks, to the prediction of climate change and natural disasters. This paper describes an Open Source Framework developed, within three European projects, for Ena-bling High-Performance Computing (HPC) and Cloud geoprocessing services applied to agricultural challenges. The main goals of the European Union projects EUXDAT (EUro-pean e-infrastructure for eXtreme Data Analytics in sustainable developmenT), CYBELE (fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics), and EOPEN (opEn interOperable Platform for unified access and analysis of Earth observatioN data) are to enable the use of large HPC systems, as well as big data management, user-friendly access and visualization of results. In addition, these projects focus on the development of software frameworks, and fuse Earth-observation data, such as Copernicus data, with non-Earth-observation data, such as weather, environmental and social media information. In this paper, we describe the agroclimatic-zones pilot used to validate the framework. Finally, performance metrics collected during the execution (up to 182 times speedup with 256 MPI processes) of the pilot are presented.