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Transport-Support workflow Composition and optimization for Big Data Movement in High-Performance Networks

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2017年第12期28卷 3656-3670页

作者： Yun, Daqing Wu, Chase Q. Zhu, Michelle M. Harrisburg Univ Sci & Technol Comp & Informat Sci Program Harrisburg PA 17101 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07029 USA Mongolian State Univ Dept Comp Sci Montclair NJ 07043 USA

High-performance networks (HPNs) are being increasingly developed and deployed to support the transfer of big data. However, such HPN-based technologies and services have not been fully utilized as their use often requires considerable networking and system domain knowledge and many application users are even not aware of their existence. This work develops an integrated solution to discover system and network resources and compose end-to-end paths for big data movement. We first develop profiling and modeling approaches to characterize various types of resources distributed in end systems, edge segments, and backbone networks. A comprehensive set of performance metrics and network parameters are considered in different phases including device deployment, circuit setup, and data transfer. Based on these profiles and models, we then formulate a class of transport-support workflow optimization problems to compose the best end-to-end path that meets various performance requirements. We prove this problem to be NP-complete and design pseudo-polynomial optimal algorithms. We conduct extensive simulations to evaluate the proposed algorithms in comparison with a greedy approach, and also carry out real-life experiments across different network segments in production HPNs to evaluate the validity of the constructed cost models and illustrate the efficacy of the proposed transport solution.

关键词： Big data transfer performance modeling workflow optimization high-performance networks

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Novel PSO-Based Algorithm for workflow Time and Energy optimization in a Heterogeneous Fog Computing Environment

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IEEE ACCESS 2024年 12卷 41517-41530页

作者： Chafi, Saad-Eddine Balboul, Younes Fattah, Mohammed Mazer, Said El Bekkali, Moulhime Sidi Mohamed Ben Abdellah Univ IASSE Lab Fes 30000 Morocco Moulay Ismail Univ IMAGE Lab Meknes 50050 Morocco

The dynamic and heterogeneous nature of fog computing environments presents significant challenges for efficient resource management, especially in the context of workflow optimization. To address this problem, we propose a novel particle swarm optimization (PSO)-based algorithm for workflow time and energy optimization in heterogeneous fog computing environments. Our algorithm leverages the collective intelligence of particles to efficiently explore the solution space and adapt to the dynamic and uncertain nature of fog computing resources. We evaluate the performance of our algorithm using simulations and experiments, demonstrating significant improvements in workflow completion time, energy consumption, and resource utilization compared to existing PSO-based algorithms and state-of-the-art methods. Our main contributions are twofold: a novel PSO-based algorithm that effectively addresses the challenges of workflow optimization in fog computing environments and empirical evidence of its efficacy and potential impact on real-world scenarios. Our results provide valuable insights for practitioners and researchers in the field of fog computing, demonstrating the feasibility of efficient resource management in dynamic and heterogeneous environments.

关键词： Fog computing workflow optimization particle swarm optimization (PSO) heterogeneous computing energy optimization dynamic environments resource utilization

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Data-Intensive workflow optimization based on Application Task Graph Partitioning in Heterogeneous Computing Systems 4

Data-Intensive Workflow Optimization based on Application Ta...

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IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ahmad, Saima Gulzar Liew, Chee Sun Rafique, M. Mustafa Munir, Ehsan Ullah Khan, Samee U. Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia IBM Res Dublin Ireland COMSATS Inst Informat Technol Dept Comp Sci Wah Cantt Pakistan North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA

ISBN: (纸本)9781479967193

Stream based data processing model is proven to be an established method to optimize data-intensive applications. Data-intensive applications involve movement of huge amount of data between execution nodes that incurs large costs. Data-streaming model improves the execution performance of such applications. In the stream-based data processing model, performance is usually measured by throughput and latency. optimization of these performance metrics in heterogeneous computing environment becomes more challenging due to the difference in the computing capacity of execution nodes and variations in the data transfer capability of communication links between these nodes. This paper presents a dual objective Partitioning based Data-intensive workflow optimization Algorithm (PDWA) for heterogeneous computing systems. The proposed PDWA provides significantly reduced latency with increase in the throughput. In the proposed algorithm, the application task graph is partitioned such that the interpartition data movement is minimal. Such optimized partitioning enhances the throughput. Each partition is mapped to the execution node that gives minimum execution time for that particular partition. PDWA also exploits partial task duplication to reduce the latency. We evaluated the proposed algorithm with synthesized benchmarks and workflows from the real-world workloads, and the proposed algorithm shows 60% reduced latency with 47% improvement in the throughput as compared to the approach when workflows are not partitioned.

关键词： workflow optimization Partitioning task graph Heterogeneous computing Stream-data processing

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An optimal three-tier prioritization-based multiflow scheduling in cloud-assisted smart healthcare

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2025年 238卷

作者： Sarthak Verma, Anshul Verma, Pradeepika Banaras Hindu Univ Dept Comp Sci Varanasi India Indian Inst Technol Technol Innovat Hub Patna India

Internet of Things is significantly advancing the development of modern interconnected networks. Coordinated with cloud computing, this technology becomes even more powerful, cost-effective, and reliable. These advancements are rapidly being integrated into modern healthcare through innovations such as smart ambulances, remote monitoring systems, and smart hospitals, enhancing tracking, analysis, and alerting capabilities. However, these innovations also bring challenges, particularly in task allocation and resource management for safety-critical systems that must meet stringent quality of service while efficiently utilizing resources. This paper introduces a new heuristic Three-Tier Prioritization based Multiflow Scheduling (TTPMS) approach for smart healthcare in cloud, utilizing the adaptive multi-criteria decision-making. The proposed TTPMS algorithm prioritizes tasks across three levels, considering factors such as urgency, deadlines, budget, and impact value within the workflow, and then dynamically selects the most suitable virtual machine for allocation. Performance comparisons were made against traditional approaches like the Prioritized Sorted Task-Based Algorithm (PSTBA) and the Max-Min algorithm. Experiments conducted using the Eclipse IDE with Java, demonstrated that the proposed approach significantly outperforms traditional algorithms across multiple metrics, including success rates for deadlines and budgets, as well as the resource utilization. It achieved a 98% deadline adherence rate, outperforming Max-Min (93%) and PSTBA (60%). Additionally, TTPMS surpassed budget adherence metrics, achieving a 76% success rate compared to PSTBA (72%) and Max-Min (70%). For combined adherence to both deadlines and budgets, TTPMS achieved a 74% success rate, outperforming PSTBA (33%) and Max-Min (63%). These results highlight the effectiveness of TTPMS in scheduling the healthcare applications.

关键词： Task scheduling MCDM Smart healthcare workflow optimization Cloud resource allocation Heuristics Healthcare service prioritization

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First Experience Using a Novel Variable Loop Catheter for Mapping and Pulsed Field Ablation of Atrial Fibrillation

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PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY 2025年第5期48卷 471-479页

作者： Fink, Thomas Sciacca, Vanessa Bannmann, Kevin Moersdorf, Maximilian Beyer, Sebastian Parlato, Alessandro Guckel, Denise El Hamriti, Mustapha Khalaph, Moneeb Braun, Martin Didenko, Maxim Imnadze, Guram Linz, Dominik Vernooy, Kevin Sommer, Philipp Sohns, Christian Herz & Diabet Zentrum NRW Clin Electrophysiol Bad Oeynhausen Germany Maastricht Univ Med Ctr Cardiovasc Res Inst Maastricht CARIM Dept Cardiol Maastricht Netherlands Univ Pisa Cardiovasc Div Pisa Italy Univ Copenhagen Fac Hlth & Med Sci Dept Biomed Sci Copenhagen Denmark Univ Adelaide Royal Adelaide Hosp Ctr Heart Rhythm Disorders Adelaide SA Australia

Background and Aims: A novel multielectrode variable loop catheter (VLC) has been introduced for atrial fibrillation (AF) ablation enabling 3D electroanatomic mapping and concomitant pulsed field ablation (PFA). This study sought to investigate the VLC under routine clinical conditions for AF ablation. Methods: Consecutive patients with symptomatic AF undergoing first-time AF ablation were prospectively enrolled. All procedures were carried out using the VLC. Electroanatomic mapping pre and post-ablation was conducted with the VLC and a high-density multipolar mapping catheter. The general ablation protocol consisted of four ablation pulses per pulmonary vein (PV). All procedures were conducted in conscious sedation. Results: Forty-five patients (mean age 66.3 +/- 6.1 years, 68.9% paroxysmal AF) were analyzed. Procedure duration was 66.3 +/- 13.1 min. Acute pulmonary vein isolation (PVI) was achieved in 45 patients without periprocedural complications. Remapping after the initial 16 ablation pulses revealed sustained electrical conduction to at least one PV in six patients (13.3%). Repeat ablation was conducted and with an average of 7.5 +/- 4.5 additional pulses. PV intubation during mapping was achieved in 168/180 PVs with the VLC (93.3%) and in 180/180PVs (100%) with the high-density mapping catheter (p < 0.001). Incomplete PV intubation during mapping did not result in incomplete PVI, as demonstrated by remapping utilizing the high-density mapping catheter. Adequate correlation between left atrial post-ablation remapping of low voltage areas and ablated regions was demonstrated in all patients. Conclusion: PFA-guided AF ablation using the novel VLC is safe and effective. The integration into a 3D-electroanatomic mapping system enables adequate mapping during PFA procedures.

关键词： atrial fibrillation high-density mapping laboratory analysis pulmonary vein isolation workflow optimization

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Impact of optimized and conventional facility designs on outpatient abdominal MRI workflow efficiency

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SCIENTIFIC REPORTS 2025年第1期15卷 1-8页

作者： Herold, Alexander Tsai, Leo L. Lo, Wei-Ching Tabari, Azadeh Wichtmann, Barbara D. Sharp, Andrew Clifford, Bryan Lang, Min Hartman, Sean P. Melski, Lauren M. Waqas, Majeed Weber, Michael Pianykh, Oleg Huang, Susie Y. Harisinghani, Mukesh G. Catalano, Onofrio A. Harvard Univ Massachusetts Gen Hosp Dept Radiol Med Sch Boston MA USA Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy Vienna Austria Siemens Med Solut USA Boston MA USA Univ Hosp Bonn Dept Neuroradiol Bonn Germany Harvard Med Sch Massachusetts Gen Hosp Dept Radiol Div Abdominal Imaging 55 Fruit St White Bldg Room 270 Boston MA 02114 USA

Purpose: The goal of this study was to evaluate the outpatient workflow efficiency of an optimized facility (OF) compared to an established reference facility (RF) for abdominal magnetic resonance imaging (MRI). Methods: In this retrospective study, we analyzed 2,723 contrast-enhanced liver and prostate MRI examinations conducted between March 2022 and April 2024. All examinations were performed on 3T scanners (MAGNETOM Vida, Siemens Healthineers) at two different imaging facilities within our institution. The optimized facility featured a three-bay setup, with each bay consisting of one magnet, two dockable tables, and one dedicated preparation room, while the reference facility utilized a single scanner-single table setup with one dedicated preparation room. workflow metrics were extracted from scanner logs and electronic health records. Three-way ANOVA and chi-square tests were used to assess the impact of facility design, body region, and date on workflow metrics. Results: The OF significantly reduced mean table turnaround times (4.6 min vs. 8.3 min, p < 0.001) and achieved shorter total process cycle times for both liver (30.6 min vs. 32.7 min, p < 0.01) and prostate exams (32.5 min vs. 36.4 min, p < 0.001) compared to the RF. Additionally, the OF achieved turnaround times of <= 1 min in 37.2% of exams, compared to just 0.6% at the RF (p < 0.001). On-time performance was also notably higher at the OF (79.4% vs. 66.0%, p < 0.001). Furthermore, the mean time from patient arrival to exam start was reduced by 9 min at the OF (p < 0.001). Minor differences in acquisition times were observed between facilities, with both benefiting from deep learning reconstruction techniques. Conclusion: The optimized MRI facility demonstrated superior outpatient workflow efficiency compared to an already efficient reference facility, particularly in table turnover time, resulting in increased patient throughput for abdominal MRI examinations. These findings highlight that even high

关键词： Abdominal MRI workflow optimization Turnaround time Dockable tables Deep learning

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Optimized GMP-grade production of non-viral Sleeping Beauty-generated CARCIK cells for enhanced fitness and clinical scalability

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JOURNAL OF TRANSLATIONAL MEDICINE 2025年第1期23卷 1-14页

作者： Pisani, Ilaria Melita, Giusi de Souza, Patricia Borges Galimberti, Stefania Savino, Angela Maria Sarno, Jolanda Landoni, Beatrice Crippa, Stefano Gotti, Elisa Cuofano, Carolina Pedrini, Olga Capelli, Chiara Matera, Giada Belotti, Daniela Cesana, Stefania Cabiati, Benedetta Quaroni, Michele Colombo, Valentina Mazza, Massimiliano Vergani, Barbara Gaimari, Anna Nicolini, Fabio Tazzari, Marcella Bocchini, Martine Serafini, Marta Rambaldi, Alessandro Rambaldi, Benedetta Dastoli, Giuseppe Biondi, Andrea Gaipa, Giuseppe Introna, Martino Golay, Josee Tettamanti, Sarah Fdn IRCCS San Gerardo dei Tintori Tettamanti Ctr Monza Italy IRCCS Ist Romagnolo Studio Tumori IRST Dino Amado Adv Cellular Therapies & Rare Tumors Unit Meldola Italy Univ Milano Bicocca Bicocca Bioinformat Biostat & Bioimaging Ctr B4 Sch Med & Surg Monza Italy Univ Milano Bicocca Dept Med & Surg Milan Italy ASST Papa Giovanni XXIII Ctr Cellular Therapy G Lanzani Div Hematol I-24122 Bergamo Italy Fdn IRCCS San Gerardo Tintori Lab Terapia Cellulare & Genica Stefano Verri Monza Italy Univ Milan Dept Oncol & Hematol Milan Italy Azienda Socio Sanit Terr Papa Giovanni XXIII Dept Oncol Hematol Bergamo Italy

BackgroundStrict adherence to GMP guidelines and regulatory compliance is crucial when transitioning from research to clinical-grade production of ATMPs like CAR T cells. The success of CAR T cell therapy in treating hematological malignancies highlights the need for closed or automated systems to ensure quality and efficacy. Recent evidence also suggests that ex vivo culture conditions can significantly impact CAR T cell *** present our optimized methodology for expanding Sleeping Beauty transposon-engineered Chimeric Antigen Receptor-Cytokine-Induced Killer (CARCIK) cells using G-Rex devices and evaluate its impact on CARCIK cell phenotype and T cell *** on our previously validated protocol, we introduced key simplifications to optimize the CARCIK differentiation process. Delaying the nucleofection step eliminated the need for feeder cells while maintaining efficient CAR expression and high cell viability. Transitioning from T-flasks to G-Rex bioreactors reduced operator hands-on time from 21 to 28 days to 14-17 days and resulted in a less differentiated CARCIK cell product. Metabolic and transcriptional analyses showed that the novel protocol improves CARCIK cell fitness and in vivo efficacy against B-cell lymphoma. The novel method was validated in Good Manufacturing Practices (GMP) conditions at our two Cell Factories and yielded enough numbers of CARCIK-CD19 cells for clinical *** non-viral CARCIK cell production using G-Rex bioreactors and refined timing adjustments has streamlined the workflow, enhanced cell fitness, and resulted in a highly effective therapeutic product with demonstrated in vivo efficacy in mice. These improvements reduced manipulation and contamination risks, while optimizing logistics and space efficiency, facilitating allogeneic CARCIK generation for a current phase I/II clinical trial (NCT05869279) in patients with R/R CD19 + non-Hodgkin Lymphoma (B-cell NHL) and Chronic Lymp

关键词： Good manufacturing practices (GMP) CAR T cells Hematological malignancies Sleeping beauty transposon workflow optimization T cell fitness

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Original The effect of one-room CT guided brachytherapy on procedure time and cost in the treatment of cervical cancer

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BRACHYTHERAPY 2025年第1期24卷 30-35页

作者： van Well, Evelien B. Showalter, Timothy N. Giannouli, Stavroula Nioutsikou, Elena Rovers, Maroeska M. Govers, Tim M. Radboud Univ Nijmegen Dept Med Imaging Med Ctr Nijmegen Netherlands Univ Virginia Sch Med Dept Radiat Oncol Charlottesville VA USA Siemens Healthineers Co Varian Kokermolen 2 NL-3994 Houten Netherlands Siemens Healthineers GmbH Siemenstr 1 D-91301 Forchheim Germany

PURPOSE: Brachytherapy is associated with improved overall survival in cervical cancer patients, but the utilization seems hindered by high costs and relatively low reimbursement, particularly in the US. A one-room brachytherapy suite with CT (ORBT) could optimize the treatment workflow. By eliminating transport and waiting times, limiting applicator movement, and providing real-time applicator placement feedback, treatment time and costs could potentially be reduced. This study assesses the potential value of implementing ORBT in cervical cancer treatment. METHODS AND MATERIALS: A variable cost model was developed to compare current (multi-room) brachytherapy workflows (MBRT) to ORBT, taking into account staff utilization, staff, equipment and consumables costs and room expenses. Two current care scenarios were simulated;applicator placement performed in the operating room (S1), and applicator placement performed in a brachytherapy suite (S2). For both scenarios literature reported fraction times of MBRT were compared to a range of ORBT times. Sensitivity analyses were performed to determine the influence of input parameters. RESULTS: In scenario one, the results showed yearly savings of $45,572 up to $339,439 (USD), assuming a 5% and 20% reduction in fraction duration, respectively, in ORBT compared to MRBT. In scenario two, ORBT does not result in costs savings at 5% to 15% improvement. Therefore, only when ORBT results in a > 20% improvement of fraction time, cost will be saved. CONCLUSIONS: The results indicate that reducing procedure time (using ORBT) can lead to cost savings, depending on the current workflow. Savings seem to depend mostly on applicator placement location, number of patients per year, and involved personnel. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of American Brachytherapy Society. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)

关键词： Cervical cancer Brachytherapy workflow optimization Cost assessment

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Towards a Regulatory Framework for workflow Improvement in Electronic Medical Records

Towards a Regulatory Framework for Workflow Improvement in E...

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Future of Health Leadership, Informatics and Policy conference

作者： Arslan, Faiza Marcus, Jonathan Khatami, Alireza Guergachi, Aziz Keshavjee, Karim Univ Toronto Dalla Lana Sch Publ Hlth Inst Hlth Policy & Management Toronto ON Canada Dr Jonathan Marcus Med Profess Corp Toronto ON Canada Toronto Metropolitan Univ Ted Rogers Sch Management Dept Informat Technol Management Toronto ON Canada York Univ Dept Math & Stat Toronto ON Canada

ISBN: (纸本)9781643684956;9781643684949

Physicians have to complete several time-consuming and burnout-inducing tasks in their EMRs for everyday care of patients. Poor workflow design generates increased effort for physicians. In this study, we measure time doctors take to retrieve and review information in the patient chart at the beginning of a visit;one of approximately 12 tasks a doctor must do in the EMR during the visit. Information retrieval takes approximately 40 minutes per day. Automation could save 75% of that time. We estimate that if every family doctor in Canada could save 30 minutes through automation of just this one process, we could free up time equivalent to >3000 physicians and >5 million patients;enough to absorb the vast majority of patients who currently do not have a doctor. We know of no more powerful intervention than workflow automation in Canadian EMRs to increase the supply of doctors while simultaneously reducing a major cause of burnout. We recommend an accelerated research program to identify additional opportunities for workflow automation and a regulatory program to ensure that every physician has access to workflow automation in their EMR.

关键词： Electronic medical records workflow optimization burnout workflow automation regulation

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Reduction in Radiologist Interpretation Time of Serial CT and MR Imaging Findings with Deep Learning Identification of Relevant Priors, Series and Finding Locations

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ACADEMIC RADIOLOGY 2023年第10期30卷 2269-2279页

作者： Weikert, Thomas Litt, Harold I. Moore, William H. Abed, Mohammed Azour, Lea Noor, Abass M. Friebe, Liene Linna, Nathaniel Yerebakan, Halid Ziya Shinagawa, Yoshihisa Hermosillo, Gerardo Allen-Raffl, Simon Ranganath, Mahesh Sauter, Alexander W. Univ Basel Univ Hosp Basel Dept Radiol Petersgraben 4 CH-4031 Basel Switzerland Univ Penn Hosp Univ Penn Perelman Sch Med Dept Radiol 3400 Spruce St Philadelphia PA 19104 USA NYU Langone Hlth NYU Grossman Sch Med Dept Radiol 660 First Ave New York NY 10016 USA Siemens Med Solut USA 40 Liberty Blvd Malvern PA 19355 USA Univ Tubingen Univ Hosp Tuebingen Dept Radiol Hoppe Seyler Str 3 D-72076 Tubingen Germany

Rationale and Objectives: Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies. Materials and Methods: The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processing and descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL. Results: Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65 seconds;p < 0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%;p < 0.001). Less mouse clicks (-17.2%) were needed for finding eva

关键词： workflow optimization Deep learning Natural language processing MRI CT

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