Workflow Engine Research Articles

Graphic Facilitation in the Engineering Workflow: Adoption Framework, Barriers, and Future Roadmap

Engineering processes involve multiple interrelated activities comprehended through workflows. An increase in remote working accelerated by Covid-19 has pushed an immediate shift to technology-enabled processes, fundamentally shifting workflow dynamics causing information overload. Graphic facilitation as a high-artifact representation methodology for processes, concepts, or ideations, employing an organized cluster of images, arrows, diagrams, sketches, doodles, and text elements, offers a unique application for information management in an engineering context. The article further investigates the adoption framework focusing on the stakeholders. This study applies a systematic literature review approach to present essential literature across multiple databases and provides an adoption framework systematically exploring six years of popular peer-reviewed journal bibliographic and global patent grants data. The study reveals that the discussions are still emerging, and the domain is not thoroughly explored in the engineering context. The patent dataset showcases insignificant development; however, emerging approaches are summarized, and newer tools are presented along with an adoption framework as a roadmap to further exploration. Graphic facilitation demonstrates the potential capacity to support individuals and groups in different, creative, and innovative ways to address information overload. The presented outline will serve as a template for engineering leaders toward reviewing, experimenting with, and incorporating across the engineering workflows. The approach addresses information overload and presents its use cases along with entry-level barriers to graphic facilitation and suggests an exploratory future research scope, including enhanced patent data analytics that synergizes with the latest technologies mitigating barriers.

Unlocking the properties of a presalt carbonate reservoir offshore Brazil with facies-constrained geostatistical inversion

A continuing concern regarding presalt carbonate reservoirs offshore Brazil is how to derive accurate quantitative estimates of reservoir properties. It is challenging to understand the link between the facies model and the variation in elastic properties, recover a reliable model of elastic properties from seismic, estimate porosities and permeabilities to use in reservoir simulations, and ultimately close the loop in integrated geology and engineering workflows. This case study describes our use of geostatistical inversion as a tool to unlock reservoir properties. We show how the integration of diverse information from various sources and at different scales is used to produce a meaningful range of probabilistic realizations of this Brazilian deepwater presalt reservoir. We do this while respecting the reservoir properties observed at the locations of drilled wells. We also present a workflow for an optimized implementation of the inversion results at the modeling stage, resulting in fast and geologically consistent history matching in an extremely challenging reservoir management environment. In this way, we achieve accurate history-matched cases on a field level and on a well-by-well basis while remaining within the uncertainty limits. Therefore, we produce geologically plausible and reliable scenarios.

Diagnostic Fracture Injection Tests Analysis and Numerical Simulation in Montney Shale Formation

Unconventional oil and gas formations are abundant, have become an increasingly important part of the global energy supply, and are attracting increasing attention from the industry. Predicting key reservoir properties plays a significant role in both geological science and subsurface engineering workflows. With the advent of horizontal well drilling and multiple-stage hydraulic fracturing, the Montney Shale formation is one of the most promising and productive shale plays in Canada. However, very few academic papers discuss its in situ stress, reservoir pressure, and permeability, which are essential for the development of the Montney Shale. The objective of this study is to analyze the geo-stress, the pore pressure, and several key reservoir properties by using diagnostic fracture injection test (DFIT) data from the Montney Shale. One horizontal well from the Wapiti field has been analyzed with a set of DFIT data, and its results show that the general pressure and Gdp/dG responses from Well-A indicate a signature of height recession/transverse storage. In the study, the Tangent Line method, the Compliance method, and the Variable Compliance method have been applied to estimate the key reservoir properties. As a result, the Well-A DFIT analysis estimates that the closure pressure is ranging from 34.367 to 39.344 MPa, contributing to the stress gradient from 14.09 to 16.13 KPa/m for the formation. The pore pressure is ranging from 20.82 to 24.58 MPa, contributing to the pore pressure gradient from 8.54 to 10.07 KPa/m for the formation. The porosity is ranging from 3% to 6%. These reservoir properties are contoured cross the Montney Shale formation. Using the DFIT’s numerical simulation and history matching, the reservoir permeability is 0.024 md, fracture length is 13.44 m, and fracture geometries are analyzed by different models. Moreover, the physics behind the DFIT are analyzed and discussed in detail. For the first time, three different analysis methods have been applied to estimate a series of key reservoir properties for the case wells in the Montney Shale formation. This approach can not only reduce the potential prediction error caused by a single method application but also increase the persuasiveness of the assessment and save time, ensuring the efficient implementation of engineering operations. Given the significance of quantifying in situ stress and reservoir pore pressure in unconventional hydrocarbon exploration and development, this study could help the operator to quickly understand the stress regimes, the fracture geometry, and the formation properties of the Montney Shale formation in the Wapiti field. Furthermore, the interpreted results demonstrated in this paper are adding substantial business value to the asset, especially in terms of improving the hydraulic fracturing design and, thus, accelerating the cashflow from production.

Configurable Readout Error Mitigation in Quantum Workflows

Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain.

High efficiency sorting and outgrowth for single-cell cloning of mammalian cell lines

Single-cell selection and cloning is required for multiple bioprocessing and cell engineering workflows. Dispensing efficiency and outgrowth were optimized for multiple common suspension (CHO ES, Expi293F, and Jurkat) and adherent (MCF-7, A549, CHO-K1, and HEK293) cell lines. Single-cell sorting using a low pressure microfluidic cell sorter, the WOLF Cell Sorter, was compared with limiting dilution at 0.5 cells/well to demonstrate the increased efficiency of using flow cytometry selection of cells. In this work, there was an average single cell deposition on Day 0 of 89.1% across all the cell lines tested compared to 41.2% when using limiting dilution. After growth for 14 days, 66.7% of single-cell clones sorted with the WOLF Cell Sorter survived and only 23.8% when using limiting dilution. Using the WOLF Cell Sorter for cell line development results in higher viable single-cell colonies and the ability to select subpopulations of single-cells using multiple parameters.

Comparing observed and unobserved fishing characteristics in the drift gillnet fishery for swordfish

Central to the success of partial coverage observer programs is the assumption that observed fishing behaviors are representative of unobserved fishing behaviors. Traditionally, this was a difficult assumption to test beyond comparing catch compositions for observed and unobserved trips. With the proliferation of vessel monitoring systems (VMS) however, a suite of trip- and set-level fishing characteristics can be engineered from vessel locations that facilitate quantitative comparisons between observed and unobserved behaviors. The U.S. West Coast drift gillnet (DGN) fishery for swordfish (Xiphias gladius) is a small fishery with a long history of bycatch concerns and an observer coverage rate around 20–30 %. We developed an integrated data set composed of VMS, logbook, landings, and observer data for the DGN fishery from 2013 to 2019. We used a suite of machine learning models to classify unobserved fishing behaviors and we characterized these fishing behaviors at the trip- and set-level. Analyses of the trip- and set-level metrics (e.g., trip duration, fishing depth, sea surface temperatures, distance from port, and catch per unit effort) revealed that operational characteristics of the fishery were largely similar for observer and unobserved trips and vessels, but also highly variable. In the fall season, observed trip distances were an average of 53 % (std. error 20 %) longer than unobserved trips for the same vessels. These longer trips occurred in water that was, on average, about 39 % (std. error 16 %) deeper. However, characteristics like amounts of fishing effort, revenue, and water temperatures were generally similar between observed and unobserved trips. We present a methodical workflow for data integration and feature engineering and a simple approach for assessing differences across fleet behaviors in space and time that could be applied to many fisheries with limited observer coverage. This approach could also be used to target specific strata within fisheries that may benefit from increases or shifts in observer coverage.

Automated QSPR modeling and data curation of physicochemical properties using KNIME platform: Prediction of partition coefficients

In the last two decades, the volumes of chemical and biological data are constantly increasing. The problem of converting data sets into knowledge is both expensive and time-consuming, as a result a workflow technology with platforms such as KNIME, was built up to facilitate searching through multiple heterogeneous data sources and filtering for specific criteria then extracting hidden information from these large data. Before any QSAR modeling, a manual data curation is extremely recommended. However, this can be done, for small datasets, but for the extensive data accumulated recently in public databases a manual process of big data will be hardly feasible. In this work, we suggest using KNIME as an automated solution for workflow in data curation, development, and validation of predictive QSAR models from a huge dataset.In this study, we used 250250 structures from NCI database, only 3520 compounds could successfully pass through our workflow safely with their corresponding experimental log P, this property was investigated as a case study, to improve some existing log P calculation algorithms.

Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis.

A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.

Cloud AI: A comparison of specimen image data extraction processes

The Natural History Museum (NHM) of London has embarked on an ambitious programme to digitise the 80 million specimens in its collection, releasing them through the NHM data portal and the global biodiversity research community. As part of the digitisation process, data is transcribed from specimen labels to capture the vital taxonomic and collection event data. Accurate human transcription is slow and the NHM, like many institutions, has been exploring machine learning (ML) for automated specimen analysis and label data capture. This process requires many different models, chained in series: semantic segmentation to identify specimen and label regions of interest; optical character recognition to identify text on labels; natural language processing to extract entities from the text. As part of SYNTHESYS+, the NHM has been building the Specimen Data Refinery (SDR) (Smith et al. 2019) - a workflow engine for chaining ML models, each performing one atomic task in the data extraction process. The SDR is now in public beta, and we present evaluation metrics from our initial testing. Alongside the SDR project, the NHM has been exploring cloud-based artificial intelligence tools for specimen digitisation, using Google and Amazon technologies. We present an analysis of these different approaches, comparing the results from third-party AI services with models developed specifically for the biodiversity and natural history collection domains. With large corporates providing comparatively low-cost access to AI compute resources and models transferrable to many specimen image digitisation tasks, is developing bespoke solutions still required?

Embedded Parameter Information in Conditional Generative Adversarial Networks for Compressor Airfoil Design

The use of conditional generative adversarial networks has become popular with the advancement of computer power, and in particular graphics processing units. Large hardware and software companies such as Google, IBM, and Facebook have been experimenting in this field for over a decade for facial recognition, image classification and pattern recognition, and deep fake facial and object design. One essential key to their success is access to large quantities of tagged and classified images, with millions of images typically being used. In contrast, relatively few similar advances have been seen in the engineering sector, mainly because engineering analyses that produce suitable images are often very expensive processes that absorb a considerable amount of effort to generate. In addition, feeding synthetically generated image data back into traditional engineering workflows is not entirely straightforward. In this paper we show how adversarial networks can be used if order images are available and focused on the problem in hand. In particular we show how such images can then be augmented with histograms and glyphs to enhance the image content with pictorial representations of numerical data. This is shown to significantly assist the network training process on our data when used in contexts where numerical data categorizing individual images are available and numerical performance measures of products must be predicted. Crucially, it allows for follow-on analyses without the need to use artificially generated flow fields.

THE APPROACH AND THE SOFTWARE TOOL TO CALCULATE SEMANTIC QUALITY MEASURES OF BUSINESS PROCESS MODELS

Business process models are essential business process management artifacts that help describe visually ongoing business activities to facilitate communication between information technology and business stakeholders. Business process models are used to find inefficient spots within described workflows and resolve detected shortcomings by automation via configurable software solutions or unified workflow engines. However, this is impossible when using syntactically or semantically poor business process models. It is the same as building a house using the blueprint with windows on the floor and typos in text labels. Therefore, it is extremely important to keep created business process models clear and relevant to the actual workflows they describe. Hence, in this paper, we propose the approach and the software tool to calculate semantic quality measures of business process models. The proposed approach uses a special procedure to extract the modeling domain statements using natural language processing techniques. According to the proposed approach, the initial textual descriptions of business process models should be tokenized. Then obtained tokens should be turned to the lower case style and cleansed to remove non-alphabetic tokens and stop words. Finally, the remaining tokens should be stemmed and the existing duplicates should be removed. The same procedure is then repeated for text labels attached to the business process model activities. Then, tokens present in the result of textual description’s processing but missing in the result of labels’ processing are considered incomplete (i.e. incorrect in the modeling domain). Similarly, tokens present in the result of labels’ processing but missing in the result of textual description’s processing are considered invalid (i.e. irrelevant to the modeling domain). Therefore, respective semantic quality measures can be calculated. The software tool is created using the Python programming language because of its powerful natural language processing packages.

Definition, Characterization, and Modeling of Hybrid Modular‐Monolithic Process Plants

AbstractThe modularization of production plants and their automation in the pharmaceutical and (bio‐)chemical industry is currently gaining attention and is subject of standardization efforts. The combination of modular and monolithic (bio‐)chemical plants and related engineering processes, i.e., hybrid process plants and hybrid engineering, respectively, offers advantages in production flexibility and time to operation. However, combining the different approaches is full of hurdles and unsolved issues. In this paper, hybrid process plants are defined and characterized in a process systems framework. Modeling and simulation requirements are elaborated for the engineering workflow of hybrid process plants. The contribution concludes with summarizing current research topics and pointing to a future research agenda.

Abstract 1912: A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data

Abstract Cell-free DNA (cfDNA) emerges as a promising liquid biopsy biomarker for cancer diagnosis and patient monitoring. Complementing mutation-based assays, cfDNA carries information about epigenetic modifications from decaying cells. This information is encoded in the shape of the cfDNA fragments. Specifically, fragments from cancer tend to be shorter than those originating from other adult cells, enabling a distinction between cancer patients and healthy individuals. Additional cfDNA features such as fragment end motifs and information on nucleosome positioning provide further insight into cancer biology. These cfDNA measures are typically inferred from low-pass whole genome sequencing and subsequent bioinformatics processing. A key bioinformatics step is the alignment of DNA sequencing reads to the reference genome, which critically depends on preprocessing steps such as read trimming and alignment filters. A good understanding of preprocessing settings is thus crucial to derive accurate information from cfDNA. We therefore investigated to what extent preprocessing choices affect cfDNA analysis. To this end, we have built a robust bioinformatics pipeline that evaluates a range of possible preprocessing settings. The pipeline is implemented in a bioinformatics workflow engine, enabling a scalable and reproducible workflow. We are currently evaluating the effect of preprocessing on global and regional fragmentation patterns, detection of nucleosome positioning and identification of fragment end motifs. The different preprocessing settings are also evaluated for their ability to distinguish cancer from control samples using cfDNA fragmentation information. We are benchmarking the pipeline using cfDNA datasets from multiple centres to ensure our findings are generalizable over different platforms and experimental procedures. Our investigations will allow us to build a versatile bioinformatics preprocessing pipeline for the analysis of cell-free DNA fragmentation data. Citation Format: Ivna Ivankovic, Zsolt Balázs, Todor Gitchev, Daniel Trejo Banos, Norbert Moldovan, Panagiotis Balermpas, Jonas Willmann, Nicolaus Andratschke, Florent Moulière, Michael Krauthammer. A versatile computational pipeline for the preprocessing of cell-free DNA fragmentation data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1912.

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

AbstractIn this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

A streamlined strain engineering workflow with genome-wide screening detects enhanced protein secretion in Komagataella phaffii.

Expression of secreted recombinant proteins burdens the protein secretion machinery, limiting production. Here, we describe an approach to improving protein production by the non-conventional yeast Komagataella phaffii comprised of genome-wide screening for effective gene disruptions, combining them in a single strain, and recovering growth reduction by adaptive evolution. For the screen, we designed a multiwell-formatted, streamlined workflow to high-throughput assay of secretion of a single-chain small antibody, which is cumbersome to detect but serves as a good model of proteins that are difficult to secrete. Using the consolidated screening system, we evaluated >19,000 mutant strains from a mutant library prepared by a modified random gene-disruption method, and identified six factors for which disruption led to increased antibody production. We then combined the disruptions, up to quadruple gene knockouts, which appeared to contribute independently, in a single strain and observed an additive effect. Target protein and promoter were basically interchangeable for the effects of knockout genes screened. We finally used adaptive evolution to recover reduced cell growth by multiple gene knockouts and examine the possibility for further enhancing protein secretion. Our successful, three-part approach holds promise as a method for improving protein production by non-conventional microorganisms.

A Rapid, Whole Genome Sequencing Assay for Detection and Characterization of Novel Coronavirus (SARS-CoV-2) Clinical Specimens Using Nanopore Sequencing.

A new human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged at the end of 2019 in Wuhan, China that caused a range of disease severities; including fever, shortness of breath, and coughing. This disease, now known as coronavirus disease 2019 (COVID-19), quickly spread throughout the world, and was declared a pandemic by the World Health Organization in March of 2020. As the disease continues to spread, providing rapid characterization has proven crucial to better inform the design and execution of control measures, such as decontamination methods, diagnostic tests, antiviral drugs, and prophylactic vaccines for long-term control. Our work at the United States Army’s Combat Capabilities Development Command Chemical Biological Center (DEVCOM CBC) is focused on engineering workflows to efficiently identify, characterize, and evaluate the threat level of any potential biological threat in the field and more remote, lower resource settings, such as forward operating bases. While we have successfully established untargeted sequencing approaches for detection of pathogens for rapid identification, our current work entails a more in-depth sequencing analysis for use in evolutionary monitoring. We are developing and validating a SARS-CoV-2 nanopore sequencing assay, based on the ARTIC protocol. The standard ARTIC, Illumina, and nanopore sequencing protocols for SARS-CoV-2 are elaborate and time consuming. The new protocol integrates Oxford Nanopore Technology’s Rapid Sequencing Kit following targeted RT-PCR of RNA extracted from human clinical specimens. This approach decreases sample manipulations and preparation times. Our current bioinformatics pipeline utilizes Centrifuge as the classifier for quick identification of SARS-CoV-2 and RAMPART software for verification and mapping of reads to the full SARS-CoV-2 genome. ARTIC rapid sequencing results, of previous RT-PCR confirmed patient samples, showed that the modified protocol produces high quality data, with up to 98.9% genome coverage at >1,000x depth for samples with presumably higher viral loads. Furthermore, whole genome assembly and subsequent mutational analysis of six of these sequences identified existing and unique mutations to this cluster, including three in the Spike protein: V308L, P521R, and D614G. This work suggests that an accessible, portable, and relatively fast sample-to-sequence process to characterize viral outbreaks is feasible and effective.

Engineering Poster E5: Digitalisation and automation for the development of modern engineering workflows

Poster E5 Automation, as part of the Energy Industry’s Digital Transformation, is producing significant time savings in modern engineering workflows whilst minimising opportunity for human error. A challenge we face is ensuring these automated workflows are accessible to people in the industry without programming experience. Xodus’ solution to this problem is FLUX. FLUX works as an object-oriented, ‘no-code’, web-based platform which tracks, stores and executes both technical and non-technical workflows. Workflows are first automated in-house by developing scripts, which are then hosted on FLUX allowing them to be easily shared across the business without requiring programming knowledge. A popular use case for FLUX is in the concept engineering phase of a project when performing parametric analyses. Multiple simulation cases (typically thousands) assessing all the different possible combinations of design parameters can be interrogated at once. FLUX can then automatically output an interactive parallel coordinate plot to highlight the impact of each parameter on the overall system. Platforms such as FLUX are an effective solution to increasing accessibility to automation for the industry via ‘no-code’ mechanisms, the ability to interface through an intuitive digital platform where project data is stored, exchanged and analysed through automated calculations is a step forward in engineering workflow efficiency. To access the poster click the link on the right. To read the full paper click here

ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation

Image processing in cryogenic electron tomography (cryoET) is currently at a similar state as Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years ago. Its data processing workflows are far from being well defined and the user experience is still not smooth. Moreover, file formats of different software packages and their associated metadata are not standardized, mainly since different packages are developed by different groups, focusing on different steps of the data processing pipeline.The Scipion framework, originally developed for SPA (de la Rosa-Trevín et al., 2016), has a generic python workflow engine that gives it the versatility to be extended to other fields, as demonstrated for model building (Martínez et al., 2020). In this article, we provide an extension of Scipion based on a set of tomography plugins (referred to as ScipionTomo hereafter), with a similar purpose: to allow users to be focused on the data processing and analysis instead of having to deal with multiple software installation issues and the inconvenience of switching from one to another, converting metadata files, managing possible incompatibilities, scripting (writing a simple program in a language that the computer must convert to machine language each time the program is run), etcetera. Additionally, having all the software available in an integrated platform allows comparing the results of different algorithms trying to solve the same problem. In this way, the commonalities and differences between estimated parameters shed light on which results can be more trusted than others. ScipionTomo is developed by a collaborative multidisciplinary team composed of Scipion team engineers, structural biologists, and in some cases, the developers whose software packages have been integrated. It is open to anyone in the field willing to contribute to this project.The result is a framework extension that combines the acquired knowledge of Scipion developers in close collaboration with third-party developers, and the on-demand design of functionalities requested by beta testers applying this solution to actual biological problems.

A New Multi-Target Compiler Architecture for Edge-Devices and Cloud Management

Edge computing is the concept where the computation is handled at edge-devices. The transfer of the computation from servers to edge-devices will decrease the massive amount of data transfer generated by edge-devices. There are several efficient management tools for setup and connection purposes, but these management tools cannot provide a unified programming system from a single source code/project. Even though it is possible to control each device efficiently, a global view of the computation is missing in a programming project that includes several edge-devices for computation and data analysis purposes, and the devices need to be programmed individually. A generic workflow engine might automate part of the problem using standard interfaces and predefined objects running on edge-devices. Nevertheless, the approach fails in fine-tuning each edge-device since the computation cannot be moved easily among devices. This paper introduces a new compiler architecture to control and program edge-devices from a single source code. The source code can be distributed to multiple edge-devices using simple compiler directives, and the transfer and communication of the source code with multiple devices are handled transparently. Fine-tuning the source code and code movement between devices becomes very efficient in editing and time. The proposed architecture is a lightweight system with fine-tuned computation and distribution among devices.

Digitalisation and automation for the development of modern engineering workflows

Automation, as part of the Energy Industry’s Digital Transformation, is producing significant time savings in modern engineering workflows whilst minimising opportunity for human error. A challenge we face is ensuring these automated workflows are accessible to people in the industry without programming experience. Xodus’ solution to this problem is FLUX. FLUX works as an object-oriented, ‘no-code’, web-based platform which tracks, stores and executes both technical and non-technical workflows. Workflows are first automated in-house by developing scripts, which are then hosted on FLUX allowing them to be easily shared across the business without requiring programming knowledge. A popular use case for FLUX is in the concept engineering phase of a project when performing parametric analyses. Multiple simulation cases (typically thousands) assessing all the different possible combinations of design parameters can be interrogated at once. FLUX can then automatically output an interactive parallel coordinate plot to highlight the impact of each parameter on the overall system. Platforms such as FLUX are an effective solution to increasing accessibility to automation for the industry via ‘no-code’ mechanisms, the ability to interface through an intuitive digital platform where project data is stored, exchanged and analysed through automated calculations is a step forward in engineering workflow efficiency.