Workflow Systems Research Articles

A roadmap for a middleware as a federation service for integrative data retrieval of agricultural data.

Agriculture is confronted with several challenges such as climate change, the loss of biodiversity and stagnating productivity. The massive increasing amount of data and new digital technologies promise to overcome them, but they necessitate careful data integration and data management to make them usable. The FAIRagro consortium is part of the National Research Data Infrastructure (NFDI) in Germany and will develop FAIR compliant infrastructure services for the agrosystems science community, which will be integrated in the existing research data infrastructure service landscape. Here we present the initial steps of designing and implementing the FAIRagro middleware infrastructure to connect existing data infrastructures. The middleware will feature services for the seamless data integration across diverse infrastructures. Data and metadata are streamlined for research in agrosystems science by downstream processing in the central FAIRagro Search and Inventory Portal and the data integration and analysis workflow system "SciWIn".

Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection.

Temperate bacteriophages, such as lambda, incorporate environmental cues including host abundance and nutrient conditions to make optimal decisions between propagation and dormancy. A higher phage-to-host ratio or multiplicity of infection (MOI) during λ infection strongly biases toward lysogeny. However, a comprehensive understanding of this decision-making process and the impact of phage replication prior to the decision is yet to be achieved. Here, we used fluorescence microscopy to quantitatively track the spatiotemporal progression of viral DNA replication in individual cells with different cell fates. The implementation of this fluorescent reporter system and quantitative analysis workflow opens a new avenue for future studies to delve deeper into various types of virus-host interactions at a high resolution.

Optimization of a Hydrogen-Fueled Parametric Strut Injector Using an Automated Workflow Computational Fluid Dynamics Method

Abstract A strut injector for burning hydrogen has been optimized using an automated workflow system. In order to choose the optimal set of injectors to cover the design space, an optimized design of experiments (DOE) method was used to automatically choose the parameters that best spanned the design space. One hundred candidate designs were chosen, and a script was used to generate a series of stereolithography (STL) files for each design. The STL files were then uploaded to a supercomputer for computational fluid dynamics analysis. For each of the 100 designs, a four step process was followed to generate the required data, and this included an automated mesh generation step, a field initialization step, a mesh adaptation step, and finally an large eddy simulation all within the yales2 numerical framework. In order to reduce the time required for postprocessing and the amount of data required, the simulations relied heavily on an on-the-fly postprocessing methodology, which reduced the complex time-unsteady flow fields to a small number of quantified outputs of interest that measured the suitability of each design such as the pressure drop across the injector and the efficiency of the mixing process. At the conclusion of these simulations, automated scripts translated these outputs into a smaller set of parameters that could be used to compare each design and allow subsequent optimization and surrogate modeling. Several surrogate modeling methods were attempted with mixed results; however, a simple classification methodology quickly identified the parameters of interest.

Recording provenance of workflow runs with RO-Crate.

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain.

Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification

Workflows orchestrate a collection of computing tasks to form a complex workflow logic. Different from the traditional monolithic workflow management systems, modern workflow systems often manifest high throughput, concurrency and scalability. As service-based systems, execution time monitoring is an important part of maintaining the performance for those systems. We developed a trace profiling approach that leverages quantitative verification (also known as probabilistic model checking) to analyse complex time metrics for workflow traces. The strength of probabilistic model checking lies in the ability of expressing various temporal properties for a stochastic system model and performing automated quantitative verification. We employ semi-Makrov chains (SMCs) as the formal model and consider the first passage times (FPT) measures in the SMCs. Our approach maintains simple mergeable data summaries of the workflow executions and computes the moment parameters for FPT efficiently. We describe an application of our approach to AWS Step Functions, a notable workflow web service. An empirical evaluation shows that our approach is efficient for computer high-order FPT moments for sizeable workflows in practice. It can compute up to the fourth moment for a large workflow model with 10,000 states within 70 s.

A case report of system configuration issue in medical imaging due to system upgrade- changes in hardware and software.

Although the rapid growth in the efficiency of medical imaging is undeniable, the expansion of health information technology (HIT) into medical imaging has not been as seamless or well-integrated as it was thought to be. The socio-technical complexities in medical imaging associated with HIT systems can cause risks to patient harm and inconvenience, both individually and collectively, often in new, unforeseen, and unexpected ways. This study reflects a retrospectively collected single incident report related to medical imaging HIT systems, aiming to develop a set of preventive and corrective strategies. A combination of multiple deductive approaches (existing frameworks), i.e., HIT Classification Systems and 18-step medical imaging process workflow and inductive method (content analysis), were used to analyze the incident. The incident was identified as a "system configuration"-related software issue, contributed by system upgrade- changes in hardware and software. The incident was determined to occur during steps 10-12, i.e., "study selection and retrieval," "calling up of patient's referral," and "image review and interpretation," causing severe disruptions in the clinical workflow for several weeks. We propose 16 preventive and corrective strategies grouped under four key areas based on the socio-technical aspects associated with HIT systems. The key areas are (i) preparation and integration for upgraded systems, (ii) training for medical imaging specialists, (iii) contingency planning/immediate backup system, and (iv) system design and configuration. These strategies are expected to help healthcare staff, analysts, reporters, researchers, and relevant stakeholders improve care delivery and patient safety in medical imaging in the context of any system upgrades.

WEB-BASED BPS PARTNER MONITORING INFORMATION SYSTEM FOR SOUTH COAST BASED

In the digital era, effective and efficient data management is very important, especially in government organizations such as the Central Statistics Agency (BPS). This project aims to design and implement a web-based BPS Pesisir Selatan Partner Monitoring Information System, which aims to facilitate monitoring and management of BPS partner data in Pesisir Selatan Regency. This system is built using the Waterfall method, which allows gradual software development starting from the analysis, design, implementation, to testing stages. For system design, the Unified Modeling Language (UML) is used to visually model system requirements and workflows. The PHP programming language was chosen for the development of this system because of its flexibility in developing web applications and its support for various databases. The result of this system development is a web application that can be used by BPS to monitor partner performance and activities in real-time, accelerate the decision-making process, and improve work efficiency. With this system, it is hoped that the management of BPS Pesisir Selatan partners can be carried out in a more structured and accurate manner.

Flow cytometry assay modifications: Recommendations for method validation based on CLSI H62 guidelines.

The Clinical and Laboratory Standards Institute (CLSI) H62-Validation of Assays Performed by Flow Cytometry guideline, released in 2021, provides recommendations for platform workflow and quality system essentials, instrument setup and standardization, assay development and optimization and fit-for-purpose analytical method validation. In addition, CLSI H62 includes some recommendations for the validation strategies after a validated flow cytometric method has been modified. This manuscript builds on those recommendations and discusses the impact of different types of assay modifications on assay performance. Recommendations regarding which validation parameters to evaluate depending on the type of modification are provided. The impact of assay modification on the assay's intended use is discussed. When recommending minor deviations from the CLSI H62 process for a laboratory-initiated assay revision (e.g., specimen numbers for sensitivity, specificity, or precision studies), a rationale based on expert opinion is provided with the understanding that not every laboratory, assay type, and circumstance can be comprehensively addressed in this paper. These recommendations are meant as a practical recommendation and are not intended to be restrictive, prescriptive, or understood as necessarily sufficient to meet every specific requirement from regulatory bodies (e.g., FDA or New York State Department of Health).

Hand Gesture Recognition Using Deep Learning

Hand gesture recognition (HGR) has gained significant attention due to its potential for various applications. This paper explores the use of deep learning, specifically Convolutional Neural Networks (CNNs), for HGR using the TensorFlow library. We investigate existing research on CNN-based HGR, focusing on image classification tasks. We then provide a brief overview of CNNs and their suitability for image recognition. Subsequently, we describe the typical workflow of a deep learning-based HGR system, including data preprocessing, hand detection, feature extraction with CNNs, and classification. We highlight the advantages of using TensorFlow to build and train CNN models for HGR. Finally, we conclude by summarizing the key findings from related work and mentioning the specific dataset and number of gestures classified in our research. This work contributes to the growing body of research on CNN-based HGR using TensorFlow and emphasizes its potential for developing accurate and efficient HGR systems.

A mobile application for STEMI care optimization: Pilot implementation project report

BackgroundClinical outcomes of patients presenting with STEMI are significantly improved by reducing time from vessel occlusion to coronary blood flow restoration. In an effort to improve outcomes, we developed a secure mobile application, STEMIcathAID, and designed a pilot project implementing the app into the workflow for STEMI patients transfer. The aim of the study is to assess the impact of the app on key metrics for STEMI transfer before (historic) and after app launch. MethodsThe pilot project included physicians, nurses and technicians from the Emergency Medicine and Nursing Departments at the referring center, the catheterization laboratory and transfer center. From July 2021 to February 2023, the referring center activated STEMIcathAID alarms in parallel with the previously established STEMI activation with traditional phone call to transfer center. ResultsOne hundred eleven suspected STEMI calls were activated through the app with 66 accepted and 45 rejected cases; thirty-one STEMI cases with available device time were compared with 42 STEMIs activated through the traditional pathway before the app implementation. Median door-to-device time for STEMIcathAID-assisted transfer decreased from 106 to 86 min (p < 0.001). The significant improvement, 20 min (19%), of the key metric for interhospital transfer resulted in all STEMI cases meeting the AHA goal of door-to-device time ≤ 120 min. In addition, median door-in-door-out time at the referral hospital decreased from 56 to 50 min (p = 0.01). ConclusionsImplementation of a mobile app into STEMI workflow of a large urban healthcare system significantly improved the quality of care for transfer of STEMI patients. Trial registrationAHA Get With The Guidelines-Coronary Artery Disease® (GWTG-CAD) registry is a national quality improvement program and is not subject to the institutional review board approval.

The Application of Large Language Models in The Workflow of the Chinese Internet Credit System and Accountability Analysis

The Application of Large Language Models in The Workflow of the Chinese Internet Credit System and Accountability Analysis

Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice

Background and ObjectivePreterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice. MethodsFor this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model's predictions were then fine-tuned by post-processing. ResultsU-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model's adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals. ConclusionsAs automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes.

BioFlow-Insight: facilitating reuse of Nextflow workflows with structure reconstruction and visualization.

Bioinformatics workflows are increasingly used for sharing analyses, serving as a cornerstone for enhancing the reproducibility and shareability of bioinformatics analyses. In particular, Nextflow is a commonly used workflow system, permitting the creation of large workflows while offering substantial flexibility. An increasing number of Nextflow workflows are being shared on repositories such as GitHub. However, this tremendous opportunity to reuse existing code remains largely underutilized. In cause, the increasing complexity of workflows constitute a major obstacle to code reuse. Consequently, there is a rising need for tools that can help bioinformaticians extract valuable information from their own and others' workflows. To facilitate workflow inspection and reuse, we developed BioFlow-Insight to automatically analyze the code of Nextflow workflows and generate useful information, particularly in the form of visual graphs depicting the workflow's structure and representing its individual analysis steps. BioFlow-Insight is an open-source tool, available as both a command-line interface and a web service. It is accessible at https://pypi.org/project/bioflow-insight/ and https://bioflow-insight.pasteur.cloud/.

An Analysis of Performance Appraisal in China from The Perspectives of Scholars

The importance of performance appraisal is widely acknowledged, and its implementation varies from country to country. China's performance evaluation is conducted once a year, and a new version of the civil service evaluation system will be developed in 2020. This study examines academic perspectives on performance appraisal in China, which often vary in approach, content, incentive structures, and outcomes. Due to the lack of accuracy and subjectivity, the concept of "unscientific indicator design" in China has been greatly criticized. Chinese civil servants must have a high ideological and political quality, strong working ability, maintain a good work style, outstanding work performance, and achieve excellent results in the annual assessment. These criteria have a wide range and have the potential to lead to unfair evaluations that can negatively impact employee motivation. Many experts put forward opinions and suggestions on improving the content of performance appraisal in China; The Dutch academics suggest a transparent and detailed workflow system for registering and measuring performance, while the Brazilian academics suggest that employee performance should be taken seriously during evaluation. This requires recognizing strengths and weaknesses, making suggestions for improvement, and identifying their career prospects based on the results of the performance review. This study mainly examines the scholars' views on the similarities and differences of performance evaluation. In addition, it assesses current concerns within the appraisal system. This data will provide the basis for the exploration and development of broader, more scientific and logical performance evaluation systems.

Container Yard Layout Design Problem with an Underground Logistics System

In recent years, underground logistics systems have attracted more and more attention from scholars and are considered to be a promising new green and intelligent transportation mode. This paper proposes a yard design problem considering an underground container logistics system. The structure and workflow of the underground container logistics system are analyzed, and key features are recognized for the yard design problem, such as the container block layout direction, the lane configuration in the yard, and the number of container blocks. We formulate the problem into mathematical models under different scenarios of the key features with the comprehensive objective of maximizing the total throughput and minimizing the total operation cost simultaneously. An improved tabu search algorithm is designed to solve the problem. Experimental results show that the proposed algorithm can generate a satisfactory layout design solution for a real-size instance. Our research studies different container yard design options for introducing the underground logistics system into port terminals, which provides an important scientific foundation for promoting the application of underground container logistics systems.

Comparison of the Accuracy and Efficiency of Two Dynamic Navigation System Workflow for Fiber-post Removal: Small versus Large Field-of-view Registration Workflows

IntroductionThis study investigates the feasibility of a dynamic navigation system (DNS) small field of view workflow (DNS-SFOVw) for fiber-post removal and compares its accuracy and efficiency to the conventional large field of view workflow (DNS-LFOVw). MethodsFifty-six extracted human maxillary molars were divided into DNS-SFOVw (n = 28) and DNS-LFOVw (n = 28). The palatal canal was restored with an intraradicular RelyX fiber post and luted with RelyX Unicem; a core buildup was used. Teeth were mounted in a 3D-printed surgical jaw. A preoperative cone-beam computed tomography (CBCT) scan was taken with a 40 × 40 mm FOV for the DNS-SFOVw and a single arch CBCT scan for the DNS-LFOVw. The drilling entry point, trajectory, angle, and depth were planned in the X-guide software. The DNS registration method for the DNS-SFOVw was virtual-based registration on teeth, and the marker point-based method was used for the DNS-LFOVw. The fiber posts were drilled out under DNS guidance. A postoperative CBCT scan was taken. Three-dimensional deviations, angular deflection, number of mishaps, registration, and total operation time were calculated. ResultsThe DNS-SFOVw was as accurate as DNS-LFOVw (P > .05). The DNS-LFOVw registration time was less than DNS-SFOVw (P < .05). There was no difference in the number of mishaps (P > .05). Both DNS-SFOVw and DNS-LFOVw were time-efficient, with DNS-LFOVw taking less total operational time (P < .05). ConclusionWithin the limitations of this in-vitro study, the DNS-SFOVw was as accurate as the DNS-LFOVw for fiberpost removal. Both DNS-LFOVw and DNS-SFOVw were time-efficient in removing fiber-posts.

Sapporo: A workflow execution service that encourages the reuse of workflows in various languages in bioinformatics.

The increased demand for efficient computation in data analysis encourages researchers in biomedical science to use workflow systems. Workflow systems, or so-called workflow languages, are used for the description and execution of a set of data analysis steps. Workflow systems increase the productivity of researchers, specifically in fields that use high-throughput DNA sequencing applications, where scalable computation is required. As systems have improved the portability of data analysis workflows, research communities are able to share workflows to reduce the cost of building ordinary analysis procedures. However, having multiple workflow systems in a research field has resulted in the distribution of efforts across different workflow system communities. As each workflow system has its unique characteristics, it is not feasible to learn every single system in order to use publicly shared workflows. Thus, we developed Sapporo, an application to provide a unified layer of workflow execution upon the differences of various workflow systems. Sapporo has two components: an application programming interface (API) that receives the request of a workflow run and a browser-based client for the API. The API follows the Workflow Execution Service API standard proposed by the Global Alliance for Genomics and Health. The current implementation supports the execution of workflows in four languages: Common Workflow Language, Workflow Description Language, Snakemake, and Nextflow. With its extensible and scalable design, Sapporo can support the research community in utilizing valuable resources for data analysis.

Enhancing wireless sensor network connectivity and coverage using Hybrid GWO‐HSA algorithm

SummaryWireless sensor networks (WSNs) are essential in environmental monitoring, healthcare, and industrial automation. Persistent connectivity and coverage challenges in WSN stem from intermittent node connectivity due to obstacles, signal interference, node failures, and compromised data reliability. Existing solutions, while useful, exhibit limitations in fully addressing these concerns. To confront these challenges, a proposed system introduces the Hybrid Grey Optimizer–Harmony Search Algorithm (Hybrid GWO‐HSA), merging adaptive routing protocols and efficient deployment techniques. The Hybrid GWO‐HSA system conducts an initial environmental analysis to pinpoint factors affecting node communication. It strategically deploys additional nodes to bridge coverage gaps, using the Grey Wolf Algorithm's capabilities to optimize node placement. Moreover, it employs the Harmony Search Algorithm to dynamically adjust communication paths based on real‐time network conditions, ensuring robust data transmission. The system workflow involves an environmental assessment followed by node deployment guided by the Grey Wolf Algorithm. Subsequently, the Harmony Search adapts communication paths to enhance connectivity. Simulations and practical experiments across diverse environments validate the Hybrid GWO‐HSA system's effectiveness. Results showcase substantial improvements: network lifetime of 13,200 s, a network delay of 37 ms, a coverage rate of 0.88, and an energy consumption of 590 J. This Hybrid GWO‐HSA‐based system establishes a resilient and efficient WSN infrastructure vital for reliable data collection and transmission in challenging settings. The Hybrid GWO‐HSA system offers a comprehensive approach to WSN connectivity and coverage issues by leveraging firefly and genetic algorithms, and it significantly enhances WSN performance and reliability across multifaceted application domains.

Assessing Telemedicine Demand and Viability in Indonesian Geriatric Clinics: A Comprehensive HOT FIT and Sociotechnical Analysis.

The growing elderly population in Indonesia presents challenges for the healthcare system, prompting the exploration of telemedicine as a solution. However, its effective implementation in Indonesia faces obstacles. This research aimed to develop a comprehensive geriatric telemedicine framework in Padang City by studying multiple stakeholders. We employed qualitative methods, including in- -depth interviews, across two hospitals, a Health Office, and a Community Health Center, involving 18 elderly participants. The study identified ten key dimensions for geriatric telemedicine services: technology, Human-Computer Interface (HCI), infrastructure, system workflow, clinical content, people (diverse roles), organization (ecosystem, service workflow, internal and external regulations), and financing (social security agency on health and independent). We used the Human-Organization- Technology Fit and Sociotechnical System approaches for analysis. The study suggests implications for future implementation and advocates for broader participant involvement, information technology (IT) studies for system development, and longitudinal evaluations to assess the impact on elderly health outcomes.

Mapping fault geomorphology with drone-based lidar

The advent of sub-meter resolution topographic surveying has revolutionized active fault mapping. Light detection and ranging (lidar) collected using crewed airborne laser scanning (ALS) can provide ground coverage of entire fault systems but is expensive, while Structure-from-Motion (SfM) photogrammetry from uncrewed aerial vehicles (UAVs) is popular for mapping smaller sites but cannot image beneath vegetation. Here, we present a new UAV laser scanning (ULS) system which overcomes these limitations to survey fault-related topography cost-effectively, at desirable spatial resolutions, and even beneath dense vegetation. In describing our system, data acquisition and processing workflows, we provide a practical guide for other researchers interested in developing their own ULS capabilities. We showcase ULS data collected over faults from a variety of terrain and vegetation types across the Canadian Cordillera and compare them to conventional ALS and SfM data. Due to the lower, slower UAV flights, ULS offers improved ground return density (~260 points/m2 for the capture of a paleoseismic trenching site and ~10–72 points/m2 for larger, multi-kilometer fault surveys) over conventional ALS (~3–9 points/m2) as well as better vegetation penetration than both ALS and SfM. The resulting ~20–50 cm-resolution ULS terrain models reveal fine-scale tectonic landforms that would otherwise be challenging to image.