Workflow Optimization Research Articles

AI-based security functions co optimization in the SFC

Dealing with large-scale attack traffic and complex attack scenarios can be challenging for a single attack detection system in the 6G era. The ubiquitous artificial intelligence security services enabled by the AI-based Security Functions (AISF) and Service Function Chain (SFC) become strong candidate to solve this problem. However, supervised learning-based AISF requires a significant amount of manually labeled data and is unable to adapt to changing attack scenarios once it is deployed. To this end, we propose an AISF co-optimization method in the SFC. The goal is to use unlabeled network traffic samples to update the model based on pseudo-labeling and co-training. First, we model the AISF chain composed of AISFs with various detection targets and feature subspaces. We also define its detection capability evaluation metrics and final detection result. Then, we design an AISF co-optimization flow including online detection and online optimization workflows. In online optimization, we design the dynamic threshold of normalized comprehensive confidence to generate pseudo-labels for network traffic samples and combine labeled and pseudo-labeled samples to train the new models of AISFs. These models replace the previous ones and continue to detect and optimize. Experimental results in a prototype system show that compared with a single AISF, the AISF chain has higher detection capabilities that can even detect unknown attacks to a certain extent, and co-optimization can make better use of unlabeled network traffic samples than individual optimization.

Performance aware algorithm design for elastic resource workflow management of cluster consolidation to handle enterprise big data

<span lang="EN-US">Integration and deployment of big data and business analytics application with cloud computing are more attractive as a service and are trending practice. This hybrid workflow is rapidly increasing and will trigger a revolution for enterprise data handling, information retrieval and computing. This paper presents hybrid workflow management framework for big data and multi cloud computing systems in a two-step approach. Linear optimization-based resource assessment algorithm is planned in the first step. Cluster oriented elastic resource allocation and workflow management techniques are concentrated in the second step. This paper also focus on performance evaluation parameters includes execution time, through put with multi task work flow optimization model. The proposed framework is efficiently managed the implementation of hybrid workflows by finetuning the evaluation attributes and provides improvement in terms of response time an average of 6%.</span>

1311: Workflow optimization in cervical cancer: a decision rule for bladder filling using ultrasound.

1311: Workflow optimization in cervical cancer: a decision rule for bladder filling using ultrasound.

2410: Online adaptive radiotherapy for anal cancer - time mapping and potentials for workflow optimization

2410: Online adaptive radiotherapy for anal cancer - time mapping and potentials for workflow optimization

Overcoming Limited Access to Virus Infection Rapid Testing: Development of a Lateral Flow Test for SARS-CoV-2 with Locally Available Resources.

The COVID-19 pandemic highlighted testing inequities in developing countries. Lack of lateral flow test (LFT) manufacturing capacity was a major COVID-19 response bottleneck in low- and middle-income regions. Here we report the development of an open-access LFT for SARS-CoV-2 detection comparable to commercial tests that requires only locally available supplies. The main critical resource is a locally developed horse polyclonal antibody (pAb) whose sensitivity and selectivity are greatly enhanced by affinity purification. We demonstrate that these Abs can perform similarly to commercial monoclonal antibodies (mAbs), as well as mAbs and other pAbs developed against the same antigen. We report a workflow for test optimization using nasopharyngeal swabs collected for RT-qPCR, spiked with the inactivated virus to determine analytical performance characteristics as the limit of detection, among others. Our final prototype showed a performance similar to available tests (sensitivity of 83.3% compared to RT-qPCR, and 90.9% compared to commercial antigen tests). Finally, we discuss the possibility and the challenges of utilizing affinity-purified pAbs as an alternative for the local development of antigen tests in an outbreak context and as a tool to address inequalities in access to rapid tests.

Revolutionizing Workflows: The Role of Efficiency in Organizational Success

This research investigates the impact of workflow optimization and resource management on organizational success at PT. Citra Shipyard, with a particular focus on the mediating role of operational efficiency. Using a quantitative research design and random sampling of 80 employees, data were collected through structured questionnaires and analyzed with Smart PLS (Partial Least Squares Structural Equation Modeling). The study found that both workflow optimization and resource management significantly influence operational efficiency, which, in turn, has a strong effect on organizational success. Specifically, workflow optimization and resource management were shown to enhance operational efficiency, thereby driving greater organizational success. These results emphasize the importance of integrating effective workflow and resource management strategies to improve operational outcomes and achieve sustained competitive advantage in the shipbuilding industry. This research provides valuable insights for PT. Citra Shipyard to refine its operational practices and enhance overall performance.

Integration and Optimization of Digital Workflows in the Printing Industry

Integration and Optimization of Digital Workflows in the Printing Industry

Workflow and Short-Term Functional Outcomes in Simultaneous Acute Code Stroke Activation and Stroke Reperfusion Therapy.

The burden of simultaneous acute code stroke activation (ACSA) is not known. We aim to assess the effect of simultaneous ACSA on workflow metrics and home time at 90 days in patients undergoing reperfusion therapies in the emergency department. Simultaneous ACSA was defined as code activation within 60 min of the arrival of any patient receiving intravenous thrombolysis, within 150 min of the arrival of any patient receiving endovascular thrombectomy, within 45 min of the arrival of any patient receiving no reperfusion therapies (based on mean local door-to-needle and door-to-puncture times). Simultaneous ACSA was further graded as 1, 2 and 3. We assessed workflow metrics as door-to-CT (DTC) time, in minutes, and functional outcome as home time at 90 days. A total of 2605 patients were assessed as ACSA at a mean ± SD activations of 130.8 ± 17.1/month and 859 (33%) were simultaneous. Among all ACSA, 545 (20.9%) underwent acute reperfusion therapy with a mean age of 70.6 ± 14.2 years, 45.9% (n = 254) were female with a median (IQR) NIHSS of 13 (8-18). A total of 220 (40.4%) patients underwent simultaneous treatments. The median DTC time, in minutes, was prolonged in grade 3 simultaneous ACSA (18 (13, 28)) compared to non-simultaneous ACSA (15 (11, 21) β = 0.23, p < 0.0001). There was no difference in the median home time at 90 days between the simultaneous (58, 0-84.5 days) and non-simultaneous (54, 0-85 days) patients. Simultaneous ACSA is frequent in patients receiving acute reperfusion therapies. An optimal workflow in high-volume centers may help mitigate the clinical and system burden associated with simultaneity.

Machine learning‐driven implementation of workflow optimization in cloud computing for IoT applications

AbstractThe optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML‐driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large‐scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.

Efficient patient preparation in computed tomography coronary angiography

In the coming years, asignificant increase in the number of computed tomography coronary angiographies (CCTA) is expected, leading to an additional burden on existing structures. Discussion of effective processes and patient preparation in the context of high-volume CCTA. The relevance of patient education and optimized workflows in clinical practice, including pharmacological heart rate control and vasodilation. To conduct resource-efficient and effective patient examinations, comprehensive and clear patient education is crucial. This can be provided to the patient during scheduling or delivered digitally, so that the patient arrives with athorough understanding of the examination process. In addition, targeted optimization of workflows tailored to CCTA is important. For high image quality, the administration of β‑blockers is necessary in most cases. This can be done intravenously to save time. The administration of nitrates immediately before the examination is recommended by current guidelines for all patients without contraindications and is ideally applied sublingually shortly before the examination. The anticipated increase in CCTA will significantly strain existing structures not only in terms of reporting. Especially standardized and structured workflows pre- and periprocedurally are essential for handling ahigh number of examinations, while maintaining sufficient image quality in clinical practice.

Grade control drillhole spacing and mining selectivity determination using high resolution simulations applied on distinctly heterogeneous open pit mines

The grade control drillhole spacing and mining selectivity decisions are typically made using the resource model estimated from exploration and infill drilling data. Once production starts, large quantities of grade control data are collected to delineate ore and waste boundaries for ore control. In this work, a grade control drillhole spacing and mining selectivity optimization workflow is presented which allows the practitioner to use site specific knowledge to determine the most profitable ore control mining scenario. The densely gridded production data is used to simulate a ground truth block model from which scenarios of variable drillhole spacing and selectivity are evaluated against their corresponding costs to determine the maximum profit scenario. Eleven mining scenarios are evaluated using year production data from three distinctly heterogeneous mine with drillhole spacing and mining selectivity varying from 3 × 3 × 3 m (27 m3) to 30 × 30 × 15 m (13,500 m3). The profit differences from the optimum scenario varied by millions of dollars (1–8%) against the next best case depending on the heterogeneity of the deposit. Practitioners could apply this workflow to inform grade control drillhole spacing and mining selectivity decisions for different domains within the mine or multiple pits especially if distinctly heterogeneous volumes exist.

Spherical-based porous architectures: In silico design and validation

Porous (or cellular) materials, like wood and bones, are abundantly found in nature for structural purposes. Thanks to the advancements of evolving technologies, the fabrication of intricate cellular structures was possible. Notably, functionally graded porous structures offer superior mechanical and physical properties compared to their uniform counterparts. Additive manufacturing, especially light-based 3D printing (3DP) technologies, has significantly augmented the production of such materials thanks to their high speed, resolution, and cost-efficiency. However, selecting the most suitable porous architecture for one's necessities remains challenging, requiring an optimised workflow supporting 3DP methods with in silico tools to predict the functional properties of the developed porous architecture. This study introduces a preliminary step toward this workflow, involving in silico design and mechanical validation of porous architectures manufactured via low-force stereolithography 3DP. The work proposes for the first time spherical-based functionally graded porous geometries, filling the existing gap in the literature. The proposed structures, categorised upon their pore size, porosity, and pore size gradients, demonstrate potential applications in the biomedical field (e.g., tissue engineering scaffolds and soft bioreactor systems) and outperform other existing porous architectures in terms of energy absorption capabilities. These findings lay the foundation for further workflow optimisation. By coupling our fast method of generation of porous structures and a trained machine learning model to predict desired physical properties, we aim to produce functionalised cellular architectures for biomedical applications.

Designing the Second Target Station with a Coupled Neutronics-Mechanical Optimization Workflow

The Second Target Station (STS) at the Spallation Neutron Source of the U.S. Department of Energy’s Oak Ridge National Laboratory is being designed to become the world’s highest peak brightness source of cold neutrons. As the STS project evolves, neutronics and other engineering analyses will inform many design iterations. To facilitate this process, a fully automated optimization workflow was developed to convert a parameterized computer-aided-design model of the target into an unstructured mesh geometry model and then to run a neutronics calculation and (optionally) a mechanical analysis for each design iteration. This workflow enables efficient, high-fidelity modeling; simulation; and optimization of new designs, as has been demonstrated for the optimization of the STS neutron moderators. In this paper, we present the results of our first major effort to automate the design optimization process for a spallation target. In the first analysis, the goal is to find optimal dimensions of a monolithic tungsten target coupled with an optimal super-Gaussian proton beam profile to deliver maximum brightness of the resulting neutron beams while maintaining good mechanical properties of the target. In the second analysis, geometric and beam parameters are optimized for an alternative design with tungsten plates, which can reach superior mechanical performance without compromising the neutronics performance.

Artificial intelligence in state and municipal management: challenges and prospects

Relevance. In the modern world, artificial intelligence (AI) is one of the fastest growing areas of information technology, which is increasingly penetrating into various spheres of human activity. In the context of public and municipal management, AI is a technology that is able to automate and optimize work processes, improve the quality of services provided to citizens, and increase the level of security and transparency of government work.The purpose is to analyze the possibilities and risks of using artificial intelligence in state and municipal management, as well as to determine the prospects for its development in this area.Objectives: to analyze the advantages of using artificial intelligence in state and municipal management; to study the risks and challenges associated with its implementation; to analyze trends in the development of artificial intelligence in this area, as well as to determine the prospects for its use.Methodology. Empirical, theoretical, statistical and graphical representation methods were used in the course of scientific research.Results. The study revealed the advantages of AI in public administration (improving efficiency, improving the quality of services, transparency, security); risks and challenges were studied (lack of infrastructure, risk of privacy violations, problems of transparency of algorithms, risk of discrimination); trends in the development of AI were analyzed (e-government, optimization of workflows, personalization of services, monitoring of social media, security); the prospects of using AI (smart cities, virtual assistants, hybrid systems, decision support, cooperation with business, international cooperation) have been identified.Conclusions. Artificial intelligence can significantly improve the efficiency and quality of public and municipal administration. However, for the successful implementation of AI, it is necessary to solve problems such as the lack of infrastructure and qualified specialists, ensuring data security, transparency of algorithms and compliance with the regulatory framework. To do this, it is necessary to conduct further research, develop integrated approaches and take into account world experience. The prospects for using AI include the creation of smart cities, the use of virtual assistants, hybrid systems, decision support systems, business cooperation and international cooperation.

OptiPRM: A Targeted Immunopeptidomics LC-MS Workflow With Ultra-High Sensitivity for the Detection of Mutation-Derived Tumor Neoepitopes From Limited Input Material

Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is the only method to directly prove actual peptide presentation and we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for the improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft from as little as 2.5× 106cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, makes the identification of actionable neoepitopes possible from sample sizes usually available in the clinic.

An innovative model to enhance engineer-to-order companies operational efficiency conceived using axiomatic design theory

ABSTRACT The paper introduces the E.OpEx (ETO Operation Excellence) Model, a new framework to improve operational efficiency in Engineer-to-Order (ETO) manufacturing companies. The study used a Systematic Literature Review (SLR) and surveys to identify critical challenges in the ETO sector, which were prioritized through criticality analysis. The research applied Axiomatic Design (AD) theory to address these challenges and create structured solutions. The E.OpEx Model offers practical guidelines for resource management, workflow optimization, and customer satisfaction, aligning with the operational goals of ETO companies. Input from a Focus Group (FG) of industry experts was crucial in refining the model and identifying areas for improvement and practical integration with existing methodologies. This research also contributes to the application of AD to tackle operational challenges and proposes a model that enhances practices and lays the foundation for future innovations. Future research will focus on validating the E.OpEx Model in diverse ETO settings.

Challenges of Workflow Optimization in Mammography

Challenges of Workflow Optimization in Mammography

Stable and Highly Emissive Infrared Yb-Doped Perovskite Quantum Cutters Engineered by Machine Learning.

Quantum cutting (QC) allows the conversion of high-energy photons into lower-energy photons, exhibiting great potential for infrared communications. Yb-doped perovskite nanocrystals can achieve an efficient QC process with extremely high photoluminescence quantum yield (PLQY) thanks to the favorable Yb3+ incorporation in the perovskite structure. However, conventionally used oleic acid-oleylamine-based ligand pairs cause instability issues due to highly dynamic binding to surface states that have curbed their potential applications. Herein, zwitterionic type C3-sulfobetaine 3-(N,N-Dimethylpalmitylammonio)propanesulfonate molecule is utilized to build a strong binding state on the nanocrystals' surface through a new phosphine oxide synthesis route. Leveraging machine learning and Bayesian Optimization workflow to determine optimal synthesis conditions, near-infrared PLQY above 190% is achieved. The high PLQY is well maintained after over three months of aging, under high-flux continuous UV irradiation, and long continuous annealing. This is the first report of highly efficient and stable perovskite quantum cutters, which will drive the study of fundamental physics phenomena and near-infrared quantum communications.

Life Cycle Optimization of CO2 Huff ’n’ Puff in Shale Oil Reservoir Coupling Carbon Tax and Embedded Discrete Fracture Model

Summary Amidst escalating environmental pressures, energy-intensive industries, particularly the oil and gas sector, are compelled to transition toward sustainable and low-carbon operations, adhering to the constraints of the environmental economy. While conventional reservoirs have been extensively developed, unconventional reservoirs, such as shale reservoirs, are poised to be the focal point in the future. Carbon dioxide enhanced oil recovery (CO2-EOR), a potent development tool proven effective in shale reservoirs, offers substantial carbon storage potential while significantly augmenting production. However, prior studies have solely optimized shale oil CO2-EOR production based on a singular optimization algorithm with net present value (NPV) as the objective function. In this study, we propose a novel NPV concept incorporating a carbon tax, which incorporates carbon taxes regulated by governments or organizations, thereby guiding carbon offsetting in oil reservoirs. We employ the embedded discrete fracture model (EDFM) approach to strike a balance between the accuracy of shale reservoir fracture simulation and computational efficiency, thereby enhancing timely technical guidance in the field. Subsequently, we compare the existing mainstream reservoir optimization algorithms and introduce a novel life cycle CO2 huff ’n’ puff (HnP) optimization workflow based on low-carbon NPV. The optimized NPV of the target reservoir witnessed an increase of 116.30%, while the optimization time was reduced by 89.47%, and the CO2 storage capacity was augmented by 12.58%. The workflow accelerates the simulation of the CO2 HnP in shale reservoirs, optimizing the production efficiency and CO2 storage capacity of shale reservoirs, and facilitating comprehensive and efficient production guidance for the production site.

Pharmacogenetic educational needs and the role of pharmacogenetics in primary care: a focus group study with multiple perspectives.

Pharmacogenomics (PGx) is a well-established concept of how genes impact medication response, with many studies demonstrating reductions in medication side effects, improved efficacy and cost effectiveness. Despite these benefits, implementation of PGx in daily practice remains limited. Studies on the implementation of PGx in clinical practice have previously found that inadequate knowledge is one of the main barriers. Details regarding specifically which educational needs exist among family medicine clinicians requires further study. The aim of this study was to identify both the perceived role that pharmacogenomics (PGx) could play in primary care practice, the knowledge gaps that family medicine clinicians experience, and the skills they require to use PGx in their daily practice. To achieve this aim, the attitudes, knowledge, barriers, skills needed, and preferred educational program were explored in a family medicine clinician focus group study via a semi-structured interview and knowledge quiz. Second, multidisciplinary focus groups provided information on the level of knowledge and necessary skills to use PGx in patient care. After gathering key recorded information from both focus groups, the perceived role pharmacogenomics could possibly play in primary care, the predominant knowledge gaps, and the most appropriate educational program was determined by qualitative analysis. Four themes emerged regarding the PGx educational needs and the role of PGx in family medicine: 1) need for PGx competences, 2) insight into the roles and responsibilities of PGx services, 3) optimization of PGx workflow through artificial intelligence integrated in the electronic health record, and 4) the ethical dilemmas and psychological effects related to PGx. These themes reflect a shift in the role of PGx in family medicine with implications for education. The results obtained from this study will help improve the implementation of PGx in daily practice, and consequently, may result in increased utilization of PGx, thereby resulting in improved medication efficacy and reduced side effects.