Workflow Process Research Articles

The Cherenkov Telescope Array Observatory workflow management system

The Cherenkov Telescope Array Observatory (CTAO) is the next generation ground-based observatory for gamma-ray astronomy at very high energies. It is expected to produce about 2 PB of raw data each year and to manage a global data volume which will grow through the years to reach about 100 PB in 2030. In addition, CTAO will require a high computing capacity for data processing and Monte Carlo simulations, of the order of hundreds of millions of CPU HS06 hours per year. To meet these requirements, CTAO will adopt a distributed computing model using 4 academic data centers, and will use the DIRAC framework as its workload management system. In the past ten years, to optimize the instrument design and study its performances, CTAO has used the European Grid Infrastructure (EGI) to run massive Monte Carlo campaigns. In order to handle these campaigns and to automatize simulation and data processing workflows, we have developed a production system prototype based on DIRAC. Recently, we have also developed a user interface allowing for the configuration and submission of complex workflows. In this contribution we present the production system prototype, its user interface for workflow management as well as its application to CTAO workflows.

Adaptive data reduction workflows for astronomy: The ESO Data Processing System (EDPS)

Context. Astronomical data reduction is usually done via processing pipelines that consist of a series of individual processing steps that can be executed one at a time. These processing steps are then strung together into workflows and fed with data to address a particular processing goal. Examples of such pipeline processing goals are the quality control of incoming data from telescopes, unsupervised production of science and calibration products for an archive, and supervised data reduction to serve the specific science goals of a scientist. For each of these goals, individual workflows need to be developed. These workflows need to evolve when the pipeline, observing strategies, or calibration plans change. Writing and maintaining such a collection of workflows is therefore a complex and expensive task. Aims. In this paper we propose a data processing system that automatically derives processing workflows for different use cases from a single specification of a cascade of processing steps. Methods. The system works by using formalised descriptions of data processing pipelines that specify the input and output of each processing step. Inputs can be existing data or the output of a previous step. The rules for selecting the most appropriate input data are directly attached to the description. Results. A version of the proposed system has been implemented as the ESO Data Processing System (EDPS) in the Python language. The specification of processing cascades and data organisation rules use a restrictive set of Python classes, attributes, and functions. Conclusions. The EDPS implementation of the proposed system was used to demonstrate that it is possible to automatically derive from a single specification of a pipeline processing cascade the workflows that the European Southern Observatory uses for quality control, archive production, and specialised science reduction. The EDPS will be used to replace all data reduction systems using different workflow specifications that are currently in use at the European Southern Observatory.

JIRIAF: JLAB Integrated Research Infrastructure Across Facilities

The JIRIAF project aims to combine geographically diverse computing facilities into an integrated science infrastructure. This project starts by dynamically evaluating temporarily unallocated or idled compute resources from multiple providers. These resources are integrated to handle additional workloads without affecting local running jobs. This paper describes our approach to launch best-effort batch tasks that exploit these underutilized resources. Our system measures the real-time behavior of jobs running on a machine and learns to distinguish typical performance from outliers. Unsupervised ML techniques are used to analyze hardware-level performance measures, followed by a real-time crosscorrelation analysis to determine which applications cause performance degradation. We then facilitate bad behavior by throttling these processes. We demonstrate that problematic performance interference can be detected and acted on, which makes it possible to continue to share resources between applications and simultaneously maintain high utilization levels in a computing cluster. For a case study, we relocated the CLAS12 data processing workflow to a remote data processing facility, preventing file migration and temporal data persistency.

Detection of monkeypox virus based on a convenient and sensitive single-step RPA-CRISPR/Cas12a strategy.

The global outbreak of monkeypox virus (MPXV) has highlighted the need for rapid molecular diagnostics techniques. In this study, a single-step recombinase polymerase amplification (RPA)-CRISPR/Cas12a system was developed for rapid and sensitive detection of MPXV. The limit of detection of this assay was 1 copy per μL of extracted nucleic acids. A heating lysis method was integrated to further simplify the sample processing workflow and shorten the assay time to 40 min from sample to result. The reaction mixture can be lyophilized to improve its accessibility in resource-limited settings. The analysis results of the proposed single-step RPA-CRISPR/Cas12a assay for clinical MPXV positive and negative samples were 100% consistent with standard PCR assay. These results demonstrate the feasibility and efficiency of this method for rapid and accurate MPXV detection in real-world settings, showcasing its potential utility in urgent and practical settings.

Generative interpolation via a diffusion probabilistic model

Seismic data interpolation is essential in a seismic data processing workflow, recovering data from sparse sampling. Traditional and deep-learning-based methods have been widely used in the seismic data interpolation field and have achieved remarkable results. In this paper, we develop a seismic data interpolation method through the novel application of diffusion probabilistic models (DPMs). DPM transforms the complex end-to-end mapping problem into a progressive denoising problem, enhancing the ability to reconstruct complex situations of missing data, such as large proportions and large-gap missing data. The interpolation process begins with a standard Gaussian distribution and seismic data with missing traces and then removes noise iteratively with a UNet trained for different noise levels. Our DPM-based interpolation method allows interpolation for various missing cases, including regularly missing, irregularly missing, consecutively missing, noisy missing, and different ratios of missing cases. The ability to generalize to different seismic data sets is also discussed in this paper. Numerical results of synthetic and field data indicate satisfactory interpolation performance of the DPM-based interpolation method in comparison with the f- x prediction filtering method, the curvelet transform method, the low-dimensional manifold method (LDMM), and the coordinate attention-based UNet method, particularly in cases with large proportions and large-gap missing data. Diffusion is all we need for seismic data interpolation.

Reflection seismic imaging of subsurface geologic structures in the Bayan Obo mining area, China

A dense 2D seismic reflection survey was conducted at the Bayan Obo rare-earth-elements-Nb-Fe deposit in China to explore its geologic features and mineral endowment. A composite processing workflow was established for prestack depth imaging of this data set, which involved static correction to compensate for shifts caused by lateral inhomogeneities in near-surface layers and rugged topography. Standard methods were used to attenuate different types of noise and improve the signal-to-noise ratio of the data. A velocity model in the depth domain was obtained from refraction tomography and velocity analysis, and then used in migration. The preliminary interpretation of the final image revealed the mapping of mineralization at its true position in depth, indicating a relatively deep extension (approximately1.5 km). The results demonstrate the potential of seismic imaging as a complementary exploration tool to gravity and electromagnetic methods for mineral exploration purposes in the Bayan Obo mining area, providing a high-resolution seismic image and allowing for depth characterization of the mineral deposits. In addition, the images provide important information for guiding the selection of drilling borehole locations for deep exploration.

UAV photogrammetry for red tide monitoring and geo-localization via onboard GNSS/IMU data

As one of the worst ecological disasters in the world’s oceans, red tide seriously threatens marine ecology. Red tide monitoring is essential for preserving the ocean ecosystem. However, traditional photogrammetric processing workflows, e.g. SfM (Structure from Motion) and MVS (Multi-view Stereo) based image orientation and dense matching, do not apply to offshore images due to the low texture of ocean color. The primary contribution of this study is a UAV (Unmanned Aerial Vehicle)-based photogrammetric solution for red tide monitoring and direct geo-localization. First, a direct geo-localization model has been created, which solely uses onboard sensor data for the 3D coordinate calculation of 2D image targets based on the collinear equation. Second, two UAVs are chosen as photogrammetric systems that can supply the necessary data for the direct geo-localization model, including high-resolution images, camera intrinsic and extrinsic parameters. Third, a human annotation technique has been utilized to identify red tide regions whose ground polygons can be further estimated using the direct geo-localization model, taking into account how difficult it is to detect red tides automatically. Finally, the accuracy of direct geo-localization and the viability of the proposed solution has been evaluated and verified using real UAV datasets. The experimental results show that the direct geo-localization precision is better than 20 m when only onboard sensor data is used. The suggested workflow is appropriate for monitoring red tides, which can provide critical information for managing marine ecosystems. The executable toolkit would be made publicly available at https://github.com/json87/PhotoDigitizer.

Overview of Apache NiFi

Apache NiFi is an open-source data integration tool designed to automate the flow of data between systems. It provides a user-friendly interface to design data flows and move data between different systems, making it easier to collect, distribute, and manage data in real-time. NiFi supports the concept of data flow, allowing users to define and visualize the movement of data from various sources to various destinations.At its core, Apache NiFi is a data integration tool that facilitates the automated flow of data between systems. It was developed by the National Security Agency (NSA) and later open-sourced as part of the Apache Software Foundation. NiFi is designed to be scalable, flexible, and easy to use, making it suitable for a variety of data integration scenarios.Providing a user-friendly interface for designing, managing, and monitoring data flows, NiFi facilitates the seamless movement of data across different environments. Utilizing a visual design paradigm, users can construct data flows by connecting a variety of processors that perform tasks such as data ingestion, transformation, and routing. NiFi's scalability, provenance tracking, and security features make it well-suited for handling complex data integration scenarios. With a robust community and ongoing development, Apache NiFi continues to play a crucial role in simplifying and optimizing data movement and processing workflows

Assessment and Optimizing Task Performance through Artificial Intelligence Systems in Hangzhou Ruinan Information Technology Co., Ltd

This research study employs a quantitative-descriptive approach to evaluate and enhance task performance through the utilization of Artificial Intelligence (AI) systems at Hangzhou Ruinan Information Technology Co., Ltd., a key player in the technology hub of Hangzhou City, China. The study comprises four main parts: examining AI system characteristics and provisioning, assessing actual task performance, investigating factors influencing system effectiveness, and identifying challenges impacting performance. Participants include management, IT professionals, and employees working with AI systems, providing valuable insights into system implementation and operation. Data analysis, utilizing weighted mean and verbal description techniques, offers both quantitative and qualitative perspectives on AI's role in optimizing task performance. Findings reveal strong scalability, efficient overall performance, and positive impacts on workflow processes and resource utilization. Security measures, especially in data protection, and usability aspects require attention for further enhancement. The study underscores the importance of reliable data, seamless user adoption, enhanced expertise, and consistent system performance in optimizing task performance through AI within the organization. Conclusions emphasize the need to strengthen security measures, enhance usability, and address areas for improvement. The study underscores the value of AI in achieving the organization's goal of enhancing task performance and productivity. Recommendations include initiatives to enhance data quality, streamline user acceptance, invest in employee skill development, and improve scalability and performance. These recommendations aim to guide Hangzhou Ruinan Information Technology Co., Ltd. in refining its AI systems to drive efficiency and excellence in its operations.

Striving for excellence: Study of readiness to organizational change in public sector

The restructuring of organizational structure, along with the improvement of workflow and business processes within the public sector organizations, is conducted to achieve effective and efficient governance. This research utilizes social exchange theory to examine the influence of internal communication satisfaction and perceived organizational support on employee’s readiness to change, mediated by employee engagement to support the renewal of organization in the public sector in Indonesia. The data analysis results from 387 respondents from the Tax Authority employees show that 7 hypotheses have a positive and significant influence, while 3 hypotheses are found to be not significant. The data were gathered via a survey method by sending online questionnaires to the respondents. Using the Lisrel 8.8 program, structural equation modeling (SEM) was used to test the hypotheses.

Transdisciplinary integration of knowledge in the environment of a virtual stem center

The subject of the article is the role of ontological systems in improving the processes of structuring and analyzing scientific content, especially in the context of STEM education.
 The goal of the work is to research and analyze the application of ontological systems in the context of managing educational materials in STEM education. The use of such approaches is focused on developing effective methods for structuring and presenting educational knowledge in the STEM education system, emphasizing the importance of integrating different scientific disciplines to optimize the educational process. In accordance with the purpose, the following tasks were set: to develop a methodology for creating and implementing ontological systems in STEM education and to develop the architecture of a virtual STEM center that would provide the implementation of the proposed principles.
 The research is based on the following methods: For the development of the architecture of the virtual STEM center, we used UML diagrams. We developed UML diagrams of roles and activities that illustrate the interaction of different users and systems, as well as demonstrate work processes and interactions in multi-agent systems. Special attention is paid to activity diagrams, which reflect the processing of user requests and the interaction of the stemua.science agent with other components of the STEM center.
 The following results were obtained: A modular system architecture of the virtual STEM center was developed and described using UML diagrams, which includes roles such as the STEM center administrator, editor, author, and user, as well as the administrator of the CIT "Polyhedron". The interaction of these roles with the virtual STEM center is described in detail, revealing the mechanisms of their interaction and joint work aimed at creating, filling, and editing content in the transdisciplinary STEM center.
 The process of optimizing work processes in the modular system of the virtual STEM center is also considered. Ways to fill and use the T-STEM center in an ontological form have been identified. The interaction of software entities of the T-STEM center in an ontological form has been analyzed.
 Conclusions: Based on the conducted research, it is concluded that the use of ontological systems in the context of managing educational materials in STEM education is an effective method for structuring and presenting scientific content, promoting the integration of various scientific disciplines, and optimizing the learning process. It is determined that ontological systems are an effective method for structuring and presenting scientific content, facilitating the integration of various scientific disciplines, and optimizing the learning process. The modular architecture of the system is found to facilitate efficient interaction among different roles and automate workflow processes. Integration with a multi-agent system allows for the use of external data sources and ensures interoperability with other systems. For the further development of the system, research is needed to enhance the efficiency of role interactions and workflow automation. Additionally, research on integrating the system with other STEM education systems is necessary.

Peningkatan Efisiensi Dokumentasi Melalui Perancangan Sistem Pengarsipan Surat Pada Kantor Kelurahan Sempaja Timur

In this era of globalization, technological advancements have rapidly progressed, easing human tasks with the current technology that typically gets things done swiftly. Information technology in institutional settings serves as a means of knowledge and information management. Kelurahan Sempaja Timur is a local government office that serves the community in all matters related to governance. This office holds a plethora of data concerning incoming and outgoing correspondence. However, the archiving of these documents is still done manually, leading to a buildup of letters over time. Hence, there's a need for a system capable of handling the archiving of documents, facilitating both storage and swift, detailed retrieval when needed. This will expedite subsequent workflow processes.

Spinach Yield Mapping Using Multispectral UAV Imagery

Crop yield prediction and mapping are essential for crop management and decision-making. The development of unmanned aerial vehicles (UAVs) accelerates the acquisition of high-resolution imagery for the evaluation and monitoring of crop growth and development. In this study, a cost-effective yield mapping workflow was developed in a commercial setting. Two UAVs were deployed to rapidly obtain RGB and multispectral images of spinach at different growth stages in multiple spinach fields to assess crop physiological attributes in the field environment. Based on the imagery, an image processing workflow for farm field orthoimage rotation and segmentation was proposed. Then, twelve vegetation indices (VIs) were extracted from the processed images. The most robust VIs were selected by comparing the correlations among the VIs and yields and regression tree and stepwise multiple linear regression. Excess Green Index (ExG) and Normalized Difference Vegetation Index (NDVI) were identified as two of the most robust VIs for predicting spinach yield at various stages of growth. A linear regression model was developed using 45 manually harvested calibration locations and a coefficient of determination (R2) of 0.98. The root mean squared error (RMSE) was 0.19 kg/m with a mean absolute percentage error (MAPE) of 9.0 % when using a validation dataset. The produced yield maps provided the basis for a farm harvesting decision-making process and for a general crop management strategy.

Development of a Rapid Adeno-Associated Virus (AAV) Identity Testing Platform through Comprehensive Intact Mass Analysis of Full-Length AAV Capsid Proteins.

Adeno-associated viruses (AAVs) are commonly used as vectors for the delivery of gene therapy targets. Characterization of AAV capsid proteins (VPs) and their post-translational modifications (PTMs) have become a critical attribute monitored to evaluate product quality. Liquid chromatography-mass spectrometry (LC-MS) analysis of intact AAV VPs provides both quick and reliable serotype identification as well as proteoform information on each VP. Incorporating these analytical strategies into rapid good manufacturing practice (GMP)-compliant workflows containing robust, but simplified, data processing methods is necessary to ensure effective product quality control (QC) during production. Here, we present a GMP-compliant LC-MS workflow for the rapid identification and in-depth characterization of AAVs. Hydrophilic interaction liquid chromatography (HILIC) MS with difluoroacetic acid as a mobile phase modifier is utilized to achieve the intact separation and identification of AAV VPs and their potential proteoforms. Peptide mapping is performed to confirm PTMs identified during intact VP analysis and for in-depth PTM characterization. The intact separations platform is then incorporated into a data processing workflow developed using GMP-compliant software capable of rapid AAV serotype identification and, if desired, specific serotype PTM monitoring and characterization. Such a platform provides product QC capabilities that are easily accessible in a regulatory setting.

Design and implementation of an automatic nursing assessment system based on CDSS technology

BackgroundVarious quantitative and quality assessment tools are currently used in nursing to evaluate a patient’s physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones. MethodsWe developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events’ incidence and the average time for regular daily assessment before and after the implementation. ResultsAfter implementation of the system, the adverse nursing events’ incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s. ConclusionsThe automatic assessment system helps to reduce nurses’ workload and the incidence of adverse nursing events.

Rockfall alarm system for railway monitoring: Integrating seismic detection, localization, and characterization

Rockfalls pose a threat to human infrastructure below cliffs. Sensitive and reactive alarm systems are needed for rail traffic safety because small rockfalls ([Formula: see text]) impacting the railroad may cause train derailment. We develop a seismic processing workflow for rockfall early warning, powered by dense arrays deployed along the track. The method is evaluated by dropping rocks from a controlled height and triggering rockfalls on a cliff. We indicate that seismic arrays are highly sensitive to small impacts and are able to detect them, locate them, and estimate their magnitude. The detection can be performed in near real-time with a simple algorithm because small-scale rockfalls produce impulsive waveforms near the impact. Precise localization with matched field processing is able to track the trajectory of a rockfall. Impacts against the steel rails may be recognized by their source signature. The seismic amplitudes are related to the rockfall volume by the Hertz law, which may be used to estimate their volume. These results indicate the potential of seismic-driven near real-time rockfall alarm systems.

EXPLORING THE LIMITS OF TERRESTRIAL LASER SCANNERS ON AEROSPACE MATERIALS

Abstract. Terrestrial laser scanners are powerful measurement devices commonly used for 3D modelling tasks generating large volumes of data with fast acquisition as a first priority. However, these scanners can alternatively be used to produce near real-time, engineering quality spatial data concerning the changing state of manufactured components. This paper provides a comprehensive analysis of two terrestrial laser scanners capturing aerospace materials and components, and their associated quality measures. In order to explore the limitations of the tested TLS instruments, a mechanical jig was designed incorporating both a rotation and translation stage. This study involved three elements of a point cloud processing workflow: data capture, registration and feature extraction. Sphere-based 7DoF registration is applied using two different commercially available software packages with varying levels of user control. To analyse the quality of the registration, control points extracted from captured point clouds were compared to nominal values measured using a laser tracker. The quality of the registration was consistent, with differences kept between 0.4 mm and 0.6 mm. To evaluate the quality of the captured point clouds, two different tests were conducted. This included planar fit tests on an aluminium drilling template, and sphere fitting tests on white 1.5” spherical targets in magnetic nests. One half of the aluminium drilling template was coated with matte spray to reduce erroneous laser reflections. Finally, the registered point clouds were input to a developed algorithm which automatically extracted drilling holes from the drilling template. Previous scanning work performed on aerospace materials showed evidence of optical rattling caused by high intensity reflections from the interior holes in a drilling template. Further exploration showed that the amount of optical rattle varies systematically with incidence angle. This work demonstrates a systematic offset in the location of extracted hole centres in the drilling template. This offset is dependent on laser incidence angle, and can therefore be accounted for when locating manufacturing components from a known scanning position.

Integrated workflow incorporating the Hurst exponent and interval inversion for evaluating groundwater formations

A novel well-log-analysis approach is presented for an improved prediction of petrophysical properties in groundwater formations. Geophysical well logs are simultaneously processed for quantifying the lithology, storage capacity, and water flow parameters. A fully automated data processing workflow is proposed, the feasibility of which is assured by an appropriate starting model set by the joint application of factor analysis and the Hurst exponent, and a solution of a highly overdetermined inverse problem. The Hurst exponent is used for zone boundary detection, which assists the series expansion-based interval inversion method applied for estimation of the petrophysical parameters of clastic formations. The hydraulic conductivity as a well log is directly derived from the inversion results. The workflow is tested using both synthetic data contaminated with 5% Gaussian distributed noise and real data collected from a thermal water well in Baktalórántháza, eastern Hungary. At the test site, the Hurst exponent extracted from the wireline logs allows one to divide the processed interval into subzones around the Pleistocene-Miocene boundary. The observed wireline logs are inverted to estimate the volumetric parameters (porosity, shale content, water saturation, etc.) of the same zones. The predicted parameters, including hydraulic conductivity, reveal that Pleistocene sediments contain good aquifers with formation quality varying with depth. The shale volume and hydraulic conductivity logs show a proper match with the core data, which confirms the results of the comprehensive analysis. The suggested workflow is recommended for the evaluation of groundwater formations located in different depth domains, from unsaturated sediments to geothermal reservoirs.

Dune belt restoration effectiveness assessed by UAV topographic surveys (northern Adriatic coast, Italy)

Abstract. Unoccupied aerial vehicle (UAV) monitoring surveys are used to assess a dune restoration project in the protected natural area of the Bevano River mouth on the northern Adriatic coast (Ravenna, Italy). The impacts of the installed fences to aid dune development are quantified in terms of sand volume and vegetation cover changes over 5 years using a systematic data processing workflow based on structure-from-motion (SfM) photogrammetry and the Geomorphic Change Detection (GCD) toolset applied to two drone surveys in 2016 and 2021. Accuracy assessment is performed using statistical analysis between ground-truth and model elevation data. Results show that the fence proves to be effective in promoting recovery and growth since significant sand deposition was observed along the dune foot and front – a total area of 3799 m2, volume of 1109 m3, and average depositional depth of 0.29 m. Progradation of around 3–5 m of the foredune and embryo development were also evident. There was a decrease in blowout features of about 155 m2 due to increased deposition and vegetation colonization. There was also an average percent increase of 160 % on wave-induced driftwood and/or debris along the beach and of 9.6 % vegetation within the fence based on the cover analysis on selected transects. Erosion of around 1439 m2 is apparent mostly at the northern portion of the structure, which could be accounted for by the aerodynamic and morphodynamic conditions around the fence and its configuration to trap sediments and efficiency in doing so. Overall, dune fencing coupled with limiting debris cleaning along the protected coast was effective. The proposed workflow can aid in creating transferable guidelines to stakeholders in integrated coastal zone management (ICZM) implementation on Mediterranean low-lying sandy coasts.

“They just looked at me like I was human”: The experiences of parenting women and providers with substance use disorder treatment

BackgroundThe current US addiction treatment system does not effectively meet the needs of pregnant and parenting women with substance use disorder (SUD). The aim of this research was to identify barriers and facilitators to engagement and retention in SUD residential treatment for pregnant and parenting women. This research was part of a co-design process to collaboratively create a more patient-centered long-term residential program. Design and methodsThe study conducted semi-structured individual interviews with both parenting women with lived experience (WWLE) in residential SUD treatment and SUD treatment providers. Interviews aimed to elicit participants' experiences either receiving or providing care. The study team analyzed data in NVivo-12 using a deductive codebook based on the six principles of trauma informed care (TIC). ResultsWe conducted a total of 32 interviews (WWLE =13, SUD providers =19). The study identified four major themes: 1) peer relationships provide inspiration and diminish shame; 2) providing individuals safe space to stumble in recovery creates opportunities for growth and builds self-efficacy; 3) reasonable, clear boundaries create a structured, protective environment for early recovery; 4) nonjudgmental connections facilitate engagement and build trust. We identified small pivotal moments along the continuum of care that showed how the elements in the four themes enhanced engagement and retention in treatment. These interactions, along the care continuum, are either structural (workflow process) or relational (interpersonal). ConclusionThis research increases understanding of the interplay of the structural and relational barriers and facilitators to engagement and retention in treatment. These seemingly minor positive or negative interactions along the care continuum are pivotal to fully operationalizing TIC and optimizing women's engagement in treatment. Improvement strategies that integrate the voices of WWLE and collaboratively co-design a more patient-centered system are critical steps to improving engagement in SUD treatment and more equitable SUD treatment services.