Workflow Research Articles

Rapidly developing NLP applications for content curation

AbstractTime and again we are faced, in a number of collaborative research projects, with the challenge of interconnecting various language processing tools to implement certain industry-driven use cases focusing, for the most part, upon digital content curation processes. In this paper we first describe several of the relevant projects and their technology platforms, followed by a description of the corresponding use cases and their requirements. The content curation platform we focus upon in this article and which has been implemented as a prototype makes use of a large number of NLP services, which we also build upon for other use cases and prototypes. In addition to the implemented NLP services, the article presents a workflow manager for the flexible creation and customisation of processing workflows that make use of the above mentioned NLP services. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on its custom definition language, explaining the communication components and the general system architecture and setup. The paper also addresses challenges in interoperability across different NLP tasks and hardware-based resource use.

System Center Virtual Machine Manager 2022: Enhanced Cloud Integration and Management Capabilities

System Center Virtual Machine Manager (SCVMM) 2022 represents a transformative advancement in private cloud management, offering enhanced capabilities across virtualization, deployment, and cloud integration domains. This comprehensive platform introduces sophisticated hardware-level security protocols, dynamic resource allocation mechanisms, and intelligent orchestration capabilities that significantly improve operational efficiency. The integration with Azure Arc and support for Azure Kubernetes Service demonstrates SCVMM's evolution in hybrid cloud management, while enhanced data protection solutions ensure robust security and recovery capabilities. The platform's implementation of energy-aware management protocols and low-carbon operational methodologies showcases Microsoft's commitment to environmental sustainability without compromising performance. Through advanced predictive analytics, automated workflow management, and streamlined deployment architectures, SCVMM 2022 addresses the growing demands of modern enterprise environments. This article examines the platform's core enhancements, deployment methodologies, cloud integration capabilities, and impact on enterprise operations while providing implementation best practices derived from extensive research and real-world deployments.

Challenges in the Implementation of Technology in effective Hospital Management System: A quantitative study

To provide excellent care to patients and to maintain seamless functioning of healthcare facilities it is crucial to have effective hospital management system. Hospital management system presently is incorporating analytical approach to optimize work flow, for enhancement of resource utilization, and for providing comprehensive insurance assistance to patients. The technologies are highly crucial for workflow optimization, allocation of resources, scheduling of patients, and management of finances. There are various challenges faced by hospital management system like integration of data from diversified sources like billing system, administrative database, EHRs, and data accuracy and consistency. Safeguarding of data security is highly important that need strong security and adhering to rules and regulation as per the “Health Insurance Portability and Accountability Act.” The importance of Information Technology is reducing rising costs of healthcare and improving quality of services. There is a need to address such challenges and identify the best way of technology implementation. A sample of 249 was collected from different departments in Hospital. The factors that identify the Challenges in the Implementation of Technology in effective Hospital Management System are High Initial Costs, Resistance to Change, Training and Skill Development, and Regulatory and Compliance Challenges.

Towards Flexible Control of Production Processes: A Requirements Analysis for Adaptive Workflow Management and Evaluation of Suitable Process Modeling Languages

In the context of Industry 4.0, Artificial Intelligence (AI) methods are used to maximize the efficiency and flexibility of production processes. The adaptive management of such semantic processes can optimize energy and resource efficiency while providing high reliability, but it depends on the representation type of these models. This paper provides a literature review of current Process Modeling Languages (PMLs). Based on a suitable PML, the flexibility of production processes can be increased. Currently, a common understanding of this process flexibility in the context of adaptive workflow management is missing. Therefore, requirements derived from the business environment are presented for process flexibility. To enable the identification of suitable PLMs, requirements regarding this are also raised. Based on these, the PMLs identified in the literature review are evaluated. Thereby, based on a preselection, a detailed examination of the seven most promising languages is performed, including an example from a real smart factory. As a result, a recommendation is made for the use of BPMN, for which it is presented how it can be enriched with separate semantic information that is suitable for the use of AI planning and, thus, enables flexible control.

Empowering Access to Justice through Automated Legal Case Management

This article presents an analytical framework for understanding automated legal case management systems, specifically focusing on Pega's implementation across public defense organizations. A comprehensive examination of system architecture and empirical evidence gathered from multiple jurisdictions demonstrates how workflow automation and intelligent case routing can substantially enhance case processing efficiency while reducing operational costs. This article examines implementation patterns across immigration, family law, and public defense sectors, revealing significant improvements in case management efficiency. This article indicates that automated case management systems markedly improve access to legal services for underserved communities, demonstrated through substantial reductions in case backlogs and increased case handling capacity. A novel framework is used to evaluate legal automation systems that consider technical performance and societal impact metrics. This article extends the current understanding of legal technology implementation by providing detailed insights into system architecture, data flow optimization, and social impact assessment. This article reveals that properly implemented automation systems can transform public legal services, creating more equitable access to justice while maintaining high standards of legal service delivery

Outcomes of a multidisciplinary approach to management of mavacamten in obstructive hypertrophic cardiomyopathy.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Traditional treatments for obstructive hypertrophic cardiomyopathy (oHCM) include β-blockers, calcium channel blockers, and disopyramide. Mavacamten, a novel cardiac myosin inhibitor, is a promising oHCM therapy but has practical challenges limiting its use. This descriptive study aimed to describe a clinic workflow for mavacamten management in a real-world setting, addressing challenges such as cost, drug interactions, and monitoring requirements. The focus was on reducing patient-level costs while ensuring feasibility and efficiency. A retrospective analysis was conducted on 34 patients with oHCM for whom mavacamten was considered between May 2022 and May 2023. The clinic workflow involved cardiologist assessment, pharmacist evaluation of drug interactions, enrollment in the mavacamten risk evaluation and mitigation strategy program, cost reduction measures, and initiation of monitoring through scheduled echocardiograms. Of the 34 patients, 21 (62%) were initiated on mavacamten and followed for up to 1 year on therapy. The median time from referral to prior authorization approval and first fill was 5 and 22 days, respectively. Patients demonstrated high adherence (99.1%) as measured by the proportion of days covered. Echocardiogram follow-up showed improvements in left ventricular outflow tract parameters with no patients having a decrease in left ventricular ejection fraction to less than 50%. The described workflow effectively addressed challenges associated with mavacamten management, emphasizing roles for clinic personnel, cost reduction strategies, and structured patient monitoring. While the workflow's specifics may need adaptation in different settings, this report provides valuable insights for clinics implementing structured mavacamten management approaches.

A Hybrid Framework for Multi-Objective Construction Site Layout Optimization

Effective Construction Site Layout Planning (CSLP) ensures the organized placement and sizing of temporary facilities, enhancing workflow and logistical efficiency. Poorly planned layouts, however, can increase material handling times, create bottlenecks, and reduce productivity, ultimately leading to higher costs. The main objective of this study is to introduce a BIM-based hybrid framework for CSLP that integrates Systematic Layout Planning (SLP) with a Genetic Algorithm (GA), developed through a Design Science Research approach. This Construction Site Optimization Framework (CSOF) addresses CSLP as a multi-objective optimization problem, prioritizing efficient positioning of facilities while accounting for workflow intensity, safety, and manager preferences. The framework’s continuous-space modeling supports a realistic approach, moving beyond fixed-location models. Exploratory case studies demonstrated CSOF’s effectiveness, achieving 30.79% to 40.98% reductions in non-value-adding travel distances and adaptability across varied site conditions. In this way, this research provides a decision-support tool that balances automation with decision-maker input, enhancing layout efficiency and operational flexibility in construction site management.

SingletSeeker: an unsupervised clustering approach for automated singlet discrimination in cytometry.

Flow cytometry is a high-throughput, high-dimensional technique that generates large sets of single-cell data. Prior to analyzing this data, it is common to exclude any events that contain two or more cells, multiplets, to ensure downstream analysis and quantification is of single-cell events, singlets, only. The process of singlet discrimination is critical yet fundamentally subjective and time-consuming; it is performed manually by the user, where the proper exclusion of multiplets depends on the user's expertise and often varies from experiment to experiment. To address this problem, we have developed an algorithm to automatically discriminate singlets from other unwanted events such as multiplets and debris. Using parameters derived from imaging, the algorithm first identifies high-density clusters of events using a density-based clustering algorithm, and then classifies the clusters based on their properties. Multiplets are discarded in the first step, while singlets are distinguished from debris in the second step. The algorithm can use different strategies on imaging feature selection-based user's preferences and imaging features available. In addition, the relative importance of singlets precision vs. sensitivity can be further tweaked via a density coefficient adjustment. Twenty-two datasets from various sites and of various cell types acquired on the BD FACSDiscover™ S8 Cell Sorter with CellView™ Image Technology were used to develop and validate the algorithm across multiple imaging feature sets. A consistent singlets precision >97% with a solid >88% sensitivity has been demonstrated with a LightLoss feature set and the default density coefficient. This work yields a high-precision, high-sensitivity algorithm capable of objective and automated singlet discrimination across multiple cell types using various imaging-derived parameters. A free FlowJo™ Software plugin implementation is available for simple and reproducible singlet discrimination for use at the beginning of any user's workflow.

Healthcare dashboard technologies and data visualization for lipid management: A scoping review

BackgroundLipid disorders significantly increase cardiovascular disease (CVD) risk, the leading cause of mortality worldwide. Effective lipid management is critical for improving health outcomes. Traditional screening methods face challenges due to data complexity and the need for timely decision-making. Data visualization and dashboard technologies offer clear, actionable insights and supporting informed decision-making. This study investigates the use of these technologies in lipid management and their impacts on the quadruple aim of healthcare.MethodsThis scoping review followed the Joanna Briggs Institute (JBI) approach, focusing on studies involving dashboard technologies or data visualization in lipid management. A comprehensive search across multiple databases (Embase, Web of Science, PubMed, Scopus, CINAHL) and gray literature was conducted, including English-language publications from 2014 to 2024. Data were analyzed using quantitative descriptive and qualitative content analysis to evaluate the key features, clinical applications, and outcomes.ResultsTwenty-seven studies met the inclusion criteria, primarily focusing on dashboard utilization by physicians for managing diabetes and CVD, utilizing electronic medical records and clinical guidelines. Key analysis methods included comparing key performance indicators (KPIs) (85.2%) and trend analysis (74.1%). Lipid management workflows emphasized prevention (88.9%) and treatment planning (77.8%). Interventions included care packages (comprehensive sets of interventions for patient care), decision support systems, web-based tools, and mobile health solutions. Regarding Quadruple Aim outcomes: 12 studies focused on improving population health (8 positive, 4 no change), 9 on clinical outcomes (5 positive, 4 no change), 6 on provider work life (5 positive), 5 on patient experience (positive changes in education and time management), and 2 on cost reduction (1 positive, 1 negative).ConclusionsDashboards are important tools in managing lipid disorders in managing lipid disorders, integrating with educational tools, collaborative care models, and decision support systems. Although they are effective in enhancing population health and clinical experiences, their impact on patient outcomes and cost reduction requires further exploration. Future research should focus on detailed evaluations of dashboard impacts on patient outcomes and cost-effectiveness, emphasizing precision prevention of chronic diseases.

Direct Manipulation of Procedural Implicit Surfaces

Procedural implicit surfaces are a popular representation for shape modeling. They provide a simple framework for complex geometric operations such as Booleans, blending and deformations. However, their editability remains a challenging task: as the definition of the shape is purely implicit, direct manipulation of the shape cannot be performed. Thus, parameters of the model are often exposed through abstract sliders, which have to be nontrivially created by the user and understood by others for each individual model to modify. Further, each of these sliders needs to be set one by one to achieve the desired appearance. To circumvent this laborious process while preserving editability, we propose to directly manipulate the implicit surface in the viewport. We let the user naturally interact with the output shape, leveraging points on a co-parameterization we design specifically for implicit surfaces, to guide the parameter updates and reach the desired appearance faster. We leverage our automatic differentiation of the procedural implicit surface to propagate interactions made by the user in the viewport to the shape parameters themselves. We further design a solver that uses such information to guide an intuitive and smooth user workflow. We demonstrate different editing processes across multiple implicit shapes and parameters that would be tedious by tuning sliders.

Phage therapy candidates from Sphae: An automated toolkit for predicting sequencing data.

Phage therapy is a viable alternative for treating bacterial infections amidst the escalating threat of antimicrobial resistance. However, the therapeutic success of phage therapy depends on selecting safe and effective phage candidates. While experimental methods focus on isolating phages and determining their lifecycle and host range, comprehensive genomic screening is critical to identify markers that indicate potential risks, such as toxins, antimicrobial resistance, or temperate lifecycle traits. These analyses are often labor-intensive and time-consuming, limiting the rapid deployment of phage in clinical settings. We developed Sphae, an automated bioinformatics pipeline designed to streamline therapeutic potential of a phage in under ten minutes. Using Snakemake workflow manager, Sphae integrates tools for quality control, assembly, genome assessment, and annotation tailored specifically for phage biology. Sphae automates the detection of key genomic markers, including virulence factors, antimicrobial resistance genes, and lysogeny indicators like integrase, recombinase, and transposase, which could preclude therapeutic use. Benchmarked on 65 phage sequences, 28 phage samples showed therapeutic potential, 8 failed during assembly due to low sequencing depth, 22 samples included prophage or virulent markers, and the remaining 23 samples included multiple phage genomes per sample. This workflow outputs a comprehensive report, enabling rapid assessment of phage safety and suitability for phage therapy under these criteria. Sphae is scalable, portable, facilitating efficient deployment across most high-performance computing (HPC) and cloud platforms, expediting the genomic evaluation process. Sphae is source code and freely available at https://github.com/linsalrob/sphae, with installation supported on Conda, PyPi, Docker containers.

HiFiBGC: an ensemble approach for improved biosynthetic gene cluster detection in PacBio HiFi-read metagenomes

BackgroundMicrobes produce diverse bioactive natural products with applications in fields such as medicine and agriculture. In their genomes, these natural products are encoded by physically clustered genes known as biosynthetic gene clusters (BGCs). Genome and metagenome sequencing advances have enabled high-throughput identification of BGCs as a promising avenue for natural product discovery. BGC mining from (meta)genomes using in silico tools has allowed access to a vast diversity of potentially novel natural products. However, a fundamental limitation has been the ability to assemble complete BGCs, especially from complex metagenomes. With their fragmented assemblies, short-read technologies struggle to recover complete BGCs, such as the long and repetitive nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS). Recent advances in long-read sequencing, such as the High Fidelity (HiFi) technology from PacBio, have reduced this limitation and can help retrieve both accurate and complete BGCs from metagenomes, warranting improvement in the existing BGC identification approach for better utilization of HiFi data.ResultsHere, we present HiFiBGC, a command-line-based workflow to identify BGCs in PacBio HiFi metagenomes. HiFiBGC leverages an ensemble of assemblies from three HiFi-tailored metagenome assemblers and the reads not represented in these assemblies. Based on our analyses of four HiFi metagenomic datasets from four different environments, we show that HiFiBGC identifies, on average, 78% more BGCs than the top-performing single-assembler-based method. This increase is due to HiFiBGC’s ensemble assembly approach, which improves recovery by 25%, as well as from the inclusion of mostly fragmented BGCs identified in the unmapped reads.ConclusionsHiFiBGC is a computational workflow for identifying BGCs in long-read HiFi metagenomes, implemented majorly using Python programming language and workflow manager Snakemake. HiFiBGC is available on GitHub at https://github.com/ay-amityadav/HiFiBGC under the MIT license. The code related to the figures and analyses presented in the manuscript is available at https://github.com/ay-amityadav/HiFiBGC_analyses.

High-efficient white blood cell separation from whole blood using cascaded inertial microfluidics

White blood cells (WBCs) are a crucial component of the human immune system. WBCs contain invaluable information about the health status of the human body. Therefore, separating WBCs is indispensable for the diagnosis of many diseases in clinical setting. The low ratio of WBCs to red blood cells in whole blood has made the isolation of WBCs challenging. As the conventional single-stage microfluidic technology cannot provide sufficient separation purity. We used a cascaded inertial microfluidic chip by consecutively connecting two sinusoidal channels to enhance the purity of WBCs after single processing. The improvement was in part due to the diversion of the sample at the end of the first stage separation, resulting in a lower flow rate in the second stage of processing within the cascaded device. We embedded concave micro-obstacles in sinusoidal channels to adjust their effective working flow rate range and enable the proper operation of both channels simultaneously. Using polystyrene beads mixture (5 and 10 μm) with a primary ratio of 1000 to 1, a single processing step through our cascaded chip improved the purity of 10-μm particles with more than three orders of magnitude of enrichment (from 0.08 % to 99.83 %) with a flow rate of 560 μL/min (Re = 77). Using diluted whole blood ( × 1/10), we achieved 307-fold enrichment of WBCs (0.14 %–43.017 %) in a single process which was accompanied with ∼3 orders of magnitude background removal of RBCs (from 4.8 × 108 to 5.7 × 105 counts/mL). This cascaded manner chip has the capacity to achieve high-efficiency separation of blood cells for clinical diagnosis.

Linked Data in Library Workflows

As the library community continues to explore linked data for cataloging and data management, there are a variety of new opportunities as well as challenges to ensure that data is interoperable and manageable across bibliographic datasets. Data model interoperability is critical to ensure we can continue to manage bibliographic materials at scale across the wide range of material types and metadata providers and consumers. Additionally, the interoperability of the linked data that connects our bibliographic materials together—traditionally seen as authorities—must be evaluated and managed to improve the way users find and interact with library materials. To meet the challenges, OCLC, in partnership with others, is building a robust and interoperable set of linked data that can be used in future-facing data management workflows and discovery interfaces.

Leveraging Microsoft sentinel and logic apps for automated cyber threat response

An integrated approach to automated cyber threat response is explored in this paper, with Microsoft Sentinel's Security Information and Event Management (SIEM) capabilities being leveraged alongside Logic Apps' workflow automation within the Azure ecosystem. Efficient identification and automated mitigation of security incidents are enabled by a combination of AI-driven analytics and advanced threat-hunting capabilities, resulting in a substantial reduction of manual intervention and response times. The approach is demonstrated to contribute scientifically across three core areas: (1) a novel integration of Sentinel's SIEM with Logic Apps is proposed to streamline response workflows using automated playbooks; (2) the effectiveness of the system is assessed through multiple cyber threat scenarios, including automated account blocking and virtual machine isolation in response to identified threats; and (3) Sentinel's performance is evaluated relative to other SIEM solutions, such as Splunk and IBM QRadar, particularly in Azure-centric and hybrid environments. The potential of Microsoft Sentinel and Logic Apps to advance proactive cybersecurity defenses is underscored, while key limitations in scalability and cross-platform adaptability are also identified.

Influence of Distributor Structure on Through-Sea Valve Vibration Characteristics and Improvement Design

To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the distributor. Based on theoretical calculation and simulation analysis, the size and shape of the buffer slot of the flow hole are determined under the condition that the stable working flow rate remains unchanged. An improved electromagnetic hydraulic distributor is developed and tested. The results indicate that this method can effectively control the opening and closing transient noise of the sea valve.

Too much of a good thing: How varying levels of automation impact user performance in a simulated intrusion detection task

Cyber analysts face a demanding task when prioritizing alerts from intrusion detection systems, balancing the challenge of numerous false positives from rule-based methods with the critical need to detect genuine cyber threats, necessitating unwavering vigilance and imposing a significant cognitive burden. In this field, there exists pressure to incorporate artificial intelligence techniques to enhance the automation of analyst workflows, yet without a clear grasp of how elevating the Level of Automation impacts the allocation of attentional and cognitive resources among analysts. This paper describes a simulated AI-assisted intrusion detection task which varies five degrees of automation as well as the sensitivity of the assistant, evaluating performance-based (e.g., accuracy, response time, sensitivity, response bias) and subjective (e.g., surveys on workload and trust) measures. Participants white-listed a series of time-sensitive alerts in a simulated Snort® environment. Our findings indicate that elevating the level of automation altered participants’ behavior, evident in their tendency to display a response bias towards rejecting hits (reduced hit rate and false alarm rate) when overriding an AI’s decision. Additionally, participants subjectively reported experiencing a decreased cognitive workload with a more precise algorithm, irrespective of any variance in their actual performance. Our findings suggest the necessity for additional research before implementing further automation into analyst workflows, as the demands of tasks evolve with escalating levels of automation.

A Study on 128 Slice MDCT unit in a Tertiary Care Teaching Hospital with Special Emphasis on Work Flow and Break-Even Analysis

Purpose: To study the Cost Analysis of 128 Slice MDCT unit in a tertiary care teaching hospital Materials and methods: The study followed a biphasic design, comprising both retrospective and prospective components. The retrospective phase involved a record-based analysis conducted over a 3-month period, while the prospective phase included record review, observational methods, interviews, and material evaluation relevant to the study, carried out over the course of 1 month. Results: The cost of running the 128 Slice MDCT unit, including manpower, was calculated for a 3-month period, with an average of 28 cases per day. The unit handles a total workload of 54 TR (15 TR + 35 TR + 4 TR). Power is supplied by a 120KVA UPS located in the unit's UPS room. Depreciation for the building, with a lifespan of 100 years, is set at 1% of the capital cost. For an area of 51.2 sqm, the construction cost totals Rs 19,18,208/-, with a depreciation cost of Rs 47,955/- over 3 months. According to the CPWD manual, the annual maintenance cost is Rs 5104/- per sq.m., resulting in a maintenance expense of Rs 65,331/-. Additional costs for electricity, water, and linen over the 3-month period were also factored in. The breakeven point for the unit is estimated to be 744 days (approximately 2 years and 14 days). Conclusion: Getting each patient into and out of the procedure room in a timely manner can add minutes and maybe even hours to the daily schedules, allowing for the accommodation of a greater number of cases. Increasing the number of procedures performed per day or week can add to overall income.

Large Language Model Agents as Prognostics and Health Management Copilots

Amid concerns of an aging or diminishing industrial workforce, the recent advancement of large language models (LLMs) presents an opportunity to alleviate potential experience gaps. In this context, we present a practical Prognostics and Health Management (PHM) workflow and self-evaluation framework that leverages LLMs as specialized in-the-loop agents to enhance operational efficiency without subverting human subject matter expertise. Specifically, we automate maintenance recommendations triggered by PHM alerts for monitoring the health of physical assets, using LLM agents to execute structured components of the standard maintenance recommendation protocol, including data processing, failure mode discovery, and evaluation. To illustrate this framework, we provide a case study based on historical data derived from PHM model alerts. We discuss requirements for the design and evaluation of such “PHM Copilots” and formalize key considerations for integrating LLMs into industrial domain applications. Refined deployment of our proposed end-to-end integrated system may enable less experienced and professionals to back-fill existing personnel at reduced costs.

Polyvinyl fluoride: Predicting polarization in a complex soft matter system

We use first-principle density functional theory (DFT) to predict properties for semicrystalline polyvinyl fluoride (PVF) and compare with polyvinylidiene fluoride. We note that the crystalline regions of PVF are complex in the sense that we lack a complete experimental characterization of the detailed atomic organization. We therefore turn to DFT to predict both the structure and associated materials properties, illustrating a possible work flow for complex soft-matter modeling. We rely on the nonempirical consistent-exchange van der Waals density functional version [K. Berland and P. Hyldgaard, ] and identify plausible ground-state and excited-state motifs. From there we predict the elastic response of the crystalline motifs, and an upper limit estimate of the PVF polarization at room temperature. Published by the American Physical Society 2024