Effective Workflow Research Articles

A Comprehensive Review on the Synergy between Emergency Services, Nurses, Assistant Nurses, and Laboratory Teams in Critical Care

Emergency services as well as the nurses, assistant nurses and the laboratory teams, must work hand in hand when providing critical care. In critically-acclaimed cases where time is of the essence, interdisciplinary coordination enhances the diagnosis, delivery of treatments and patient outcomes. The emergency patient is kept safe, rapidly moved, and treated by triage and early care, followed by ward nurses and assistant nurses who can perform continuous observations, supported by laboratory personnel who provide essential information needed for treatment. However, collaboration is often faced with barriers such as communication breakdown, organizational structure, and lack of standard use of technology. Works released between 2010 and 2020 indicate that standardization of information transfer, such as the SBAR model, and embracing clinical information technology, such as EHR, improves team coordination, minimizes adverse events, and shortens reaction time. Also, interdisciplinary training is another important practice that helps ensure that different departments have enough trust for one another, enabling better integration. Since the changes in attitudes towards interdisciplinary collaboration, new technologies such as data sharing and diagnostics have enhanced the flow of information between teams. However, the patchy implementation throughout facilities has hindered this. Other areas that may need to be tackled to improve collaboration and support these initiatives include workload disparities, the number of staff, and other resources available to research and analyse different topics. This review systematically presents data regarding the collaboration of these teams. It highlights the implementation of common processes and information exchange in Main Communication Protocols and effective workflow for coordinating the care for critically ill patients as pillars for better outcomes in patient care in critical care settings.

Assessment of the Mechanical Properties and Fragment Characteristics of a 3D-Printed Forearm Orthosis.

Distal radius fractures (DRF) are one of the most prevalent injuries a person may sustain. The current treatment of DRF involves the use of casts made from Plaster of Paris or fiberglass. The application of these materials is a serious endeavor that influences their intended use, and should be conducted by specially trained personnel. In this research, with the use of the full-body 3D scanner Vitus Smart, 3D modelling software Rhinoceros 3D, and 3D printer Creality CR-10 max, an easy, yet effective workflow of orthosis fabrication was developed. Furthermore, samples that represent segments of the orthosis were subjected to static loading. Lastly, fragments that occurred due to excessive force were characterized with the use of a digital microscope. It was observed that with the implementation of the designed workflow, a faster 3D printing process was present. Samples subjected to mechanical loading had values that exceeded those of conventional Plaster of Paris; the minimum recorded value was 681 N, while the highest was 914 N. Microscopic characterization enabled a clear insight into the occurrence of fragments, as well as their potential risk. Therefore, in this research, an insight into different stages of fabrication, characterization of undesirable events, as well as the risks they may pose were presented.

Fast Building Instance Proxy Reconstruction for Large Urban Scenes.

Digitalization of large-scale urban scenes (in particular buildings) has been a long-standing open problem, which attributes to the challenges in data acquisition, such as incomplete scene coverage, lack of semantics, low efficiency, and low reliability in path planning. In this paper, we address these challenges in urban building reconstruction from aerial images, and we propose an effective workflow and a few novel algorithms for efficient 3D building instance proxy reconstruction for large urban scenes. Specifically, we propose a novel learning-based approach to instance segmentation of urban buildings from aerial images followed by a voting-based algorithm to fuse the multi-view instance information to a sparse point cloud (reconstructed using a standard Structure from Motion pipeline). Our method enables effective instance segmentation of the building instances from the point cloud. We also introduce a layer-based surface reconstruction method dedicated to the 3D reconstruction of building proxies from extremely sparse point clouds. Extensive experiments on both synthetic and real-world aerial images of large urban scenes have demonstrated the effectiveness of our approach. The generated scene proxy models can already provide a promising 3D surface representation of the buildings in large urban scenes, and when applied to aerial path planning, the instance-enhanced building proxy models can significantly improve data completeness and accuracy, yielding highly detailed 3D building models.

Abstract 429: The usability study of non‐contrast CT based AI to reduce the time from hospital door to reperfusion in anterior large vessel occlusion

Purpose The purpose of this study is to evaluate the time reduction from hospital door to endovascular treatment (EVT) when using a non‐contrast CT (NCCT) basis artificial intelligence (AI) solution to classify and notify patients with anterior large vessel occlusion (LVO). It has the advantage of improving the prognosis by reducing the time required for treatment if patients of suspected LVO can be quickly screened and notified to clinicians in step of the NCCT test. Therefore, it was compared the time from the door of emergency room (ER) to reperfusion before and after to use of AI solution for evaluating clinical effectiveness aspects. Materials and Methods Patients over 19 years of age who visited a thrombectomy capable stroke center (TSC) with acute stroke symptoms and underwent EVT were enrolled this study. For the Post‐AI group, cases were collected prospectively after applying the AI solution [2022.05.01~2023.12.31], and cases with similar age, gender, and NIHSS with Post‐AI group were retrospectively collected as the Pre‐AI group [2020.05.01~2022.04.31]. The main comparison point was time difference from ER door to endovascular treatment between Pre‐AI and Post‐AI groups. Additionally, time from ER door to CT scan, CT scan to Stroke team treatment (STT), and STT to EVT were also compared between groups. Differences in time between groups were analyzed using an unpaired t‐test with Welch's correction. Results A total of 25 EVT cases were enrolled as the Post‐AI group, and 70 cases were retrospectively selected after 1:3 propensity score matching with age, gender and NIHSS in EVT cases. As a result of primary endpoint, time from ER door to EVT was significantly different between Pre‐AI (174.7±75.0 min.) and Post‐AI (147.7±31.6 min) [p=0.0155]. In detail results of time comparison, the time from CT scan to Stroke team treatment (20.2±7.9 min. vs. 35.4±41.3 min., p=0.0043) and the time from CT scan to EVT (127.7±29.2 min. vs. 153.9±71.1 min., p=0.0127) were significantly reduced in Post‐AI group. Conclusions and Limitations It has been confirmed that quickly screening LVO patients and notifying clinicians by an AI solution at the very front of the clinical process can significantly reduce the time from ER admission to EVT, and it will influence to prognosis outcome. Therefore, it is highly recommended to use for effective workflow in clinical environment. This study demonstrated how AI improved the hyperacute endovascular treatment workflow, by showing the impact on reducing ER door‐to‐reperfusion time. Although the study results were limited in generalizability as it was evaluated in a single TSC, we expect that it will be particularly valuable in remote regions where clinical experts may be limited.

Institutional shaping of CDE implementation in BIM-enabled AEC projects

The common data environment (CDE) for information sharing is one of the fundamental requirements for effective workflow management to harness the potential of building information modeling (BIM). While studies are replete on BIM from a technology adoption perspective, notably lacking in the literature is the understanding of the influence of institutions and their pressures on CDE adoption as an information management platform. This study aims to address this gap. Using the action research approach, a flagship university construction project in India was studied to understand the interplay of institutions and CDE adoption. Institutional theory was used as a theoretical lens to illuminate and analyze the implementation issues of CDE. The findings show that CDE implementation undergoes a series of transformations due to institutional pressures. The responses to institutional pressures are conformance, coupling and decoupling, and mutations. Insights of this study guide practitioners to be aware of the processes through which institutional pressures contextualize and transform the intended change. Accordingly, findings can help managers and public sector agents on aspects related to nudging and shaping the implementation of change through action strategies and tactics. The study extends the BIM adoption/localization literature by highlighting that institutional pressures modify the objectives of CDE. It theorizes the processes through which institutional pressures contextualize and transform a change agenda.

Establishment of a workflow for high-throughput identification of anti-inflammatory peptides from sea cucumbers

Developing an effective workflow for screening anti-inflammatory peptides is crucial for discovering novel food-derived anti-inflammatory peptides and optimizing the screening and identification process of bioactive peptides. Virtual screening identified three major yolk proteins as target precursor proteins for anti-inflammatory peptides in sea cucumbers. A portfolio of 170 peptides was identified from hydrolysates after 9 h of alcalase treatment by combining antioxidant activity determination and peptidomics analysis. Among these, 12 high-confidence anti-inflammatory peptides were identified through virtual screening. Three of these peptides were shown to effectively inhibit the production of NO and the release of pro-inflammatory cytokines in RAW264.7 cells. Molecular docking demonstrated that these three peptides exerted their anti-inflammatory effects primarily by binding to the active sites of cyclooxygenase-2 and inducible nitric oxide synthase through hydrophobic interactions. This study provided a reference workflow for screening anti-inflammatory peptides, facilitating the discovery of novel anti-inflammatory peptides and the high-value utilization of sea cucumber cooking liquid.

AI-driven Business Model Innovation - Where Technology Meets Strategy

Business Model Innovation (BMI) involves redefining how organizations create, deliver, and capture value. With the advent of Artificial Intelligence (AI), businesses are increasingly leveraging these technologies to transform their models. The current research investigates the impact of AI on BMI using primary data collected from various industries. It employs statistical analyses to understand the extent of AI adoption, its effects on business model components and various key business metrics. The study identifies key trends in AI adoption across functions and industries. Key findings reveal significant correlations between AI usage and improvements in key business metrics such as operational efficiency, customer engagement, and revenue streams thus resulting in an organization’s competitive advantage. Businesses can make better-informed decisions, establish more effective workflows, and produce more powerful marketing campaigns as a result of AI adoption. It also provides a competitive edge and lays the framework for future growth. This academic work makes a substantial contribution to the discourse on AI for business model innovation, where technology is leveraged to optimize strategy.

A Novel Rational PROTACs Design and Validation via AI-Driven Drug Design Approach.

The rational design of novel drug candidates presents a formidable challenge in modern drug discovery. Proteolysis-targeting chimeras (PROTACs) drug design is particularly demanding due to their limited crystal structure availability and design of a viable small molecule to bridge the protein of interest (POI) and ubiquitin-protein ligase (E3). An integrated approach that combines superimposition techniques and deep neural networks is demonstrated in this study to leverage the power of deep learning and structural biology to generate structurally diverse molecules with enhanced binding affinities. The superimposition technique ensures the congruence of initial and new protein-ligand pairs, which are evaluated via subsequent comprehensive screening using the root-mean-square deviation (RMSD), binding free energy (BFE), and buried solvent-accessible surface area (SASA). The final candidates are subjected to the incorporation of molecular dynamics (MD) and free energy perturbation (FEP) simulations to provide a quantitative evaluation of relative binding energies, reinforcing the efficacy and reliability of the generated molecules. The outcomes of the generated novel PROTACs molecules exhibit comparable structural attributes while demonstrating superior binding affinities within the binding pockets when contrasted with those of the established cocrystal ternary complexes. To enhance the generalizability of the workflow, we chose the ternary structure of the cellular inhibitor of apoptosis protein 1 (cIAP1) and Bruton's Tyrosine Kinase (BTK) for validating the chemical properties generated from the processes. The new linker molecules additionally showed superior affinity from the simulations. In summary, this methodology serves as an effective workflow to align computational predictions with current limitations, thereby introducing a novel paradigm in AI-driven drug design.

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.

Selecting the Best Radiology Workflow Efficiency Applications

In the rapidly evolving digital radiology landscape, a surge in solutions has emerged including more than 500 artificial intelligence applications that have received 510 k clearance by the FDA. Moreover, there is an extensive number of non-regulated applications, specifically designed to enhance workflow efficiency within radiology departments. These efficiency applications offer tremendous opportunities to resolve operational pain points and improve efficiency for radiology practices worldwide. However, selecting the most effective workflow efficiency applications presents a major challenge due to the multitude of available solutions and unclear evaluation criteria. In this article, we share our perspective on how to structure the broad field of workflow efficiency applications and how to objectively assess individual solutions. Along the different stages of the radiology workflow, we highlight 31 key operational pain points that radiology practices face and match them with features of workflow efficiency apps aiming to address them. A framework to guide practices in assessing and curating workflow efficiency applications is introduced, addressing key dimensions, including a solution’s pain point coverage, efficiency claim strength, evidence and credibility, ease of integration, and usability. We apply this framework in a large-scale analysis of workflow efficiency applications in the market, differentiating comprehensive workflow efficiency ecosystems seeking to address a multitude of pain points through a unified solution from workflow efficiency niche apps following a targeted approach to address individual pain points. Furthermore, we propose an approach to quantify the financial benefits generated by different types of applications that can be leveraged for return-on-investment calculations.

Illustrating an Effective Workflow for Accelerated Materials Discovery

Illustrating an Effective Workflow for Accelerated Materials Discovery

ITS 95 - Harnessing the Power of Robotic Process Automation for Efficient and Effective Workflow of Infection Preventionists

ITS 95 - Harnessing the Power of Robotic Process Automation for Efficient and Effective Workflow of Infection Preventionists

Enabling Simultaneous PDF File Access on Android Mobile Device

Pdf (portable record format) files use is growing each day for college kids, instructors, and for all of us because it's far person pleasant clean to examine, open and proportion. purpose: This assignment goals to beautify the user experience by means of growing a cell application for Android devices that allows users to open and manipulate a couple of PDF files simultaneously or simultaneously. Our answer leverages Android's sturdy multitasking talents to allow the simultaneous establishing of multiple PDF files within a unmarried app, presenting a unbroken and effective workflow. Key capabilities encompass: Tabbed Interface: The application presents PDF files in a tabbed interface, allowing users to replace between documents results easily. every tab represents a different PDF document, making navigation intuitive. report management: The app affords gear for organizing and handling PDF documents efficaciously. users can create folders, rename documents, and perform actions like reproduction, move, and delete. Customization: users can customize the viewing revel in through adjusting settings like zoom degree, page layout, and coloration schemes, making sure a relaxed analyzing revel in. Our assignment addresses the growing need for green PDF record control on Android devices, catering to experts, college students, and anybody who relies on PDFs for his or her daily obligations. by means of imparting a unbroken and function-wealthy answer, we aim to enhance mobile productiveness and convenience in coping with PDF documents. This assignment represents a full-size leap forward in cell PDF file management, imparting Android customers with a versatile and user-friendly device for handling multiple PDF documents simultaneously. whether or not for paintings, have a look at, or entertainment, our application empowers customers to make the most of their Android devices for PDF-related responsibilities

An application of AHP and fuzzy entropy-TOPSIS methods to optimize upstream petroleum investment in representative African basins

The growing demand of China for petroleum heightens the complexities and prospects in worldwide investments, necessitating refined and strategic investment approaches. Evaluating the potential of different hydrocarbon-potential areas needs more comprehensive scientific evaluation models. This study aims to establish a Comprehensive Investment Potential of Petroleum (CIPP) framework for targeted sedimentary basins by using an integrated approach that combines the Analytic Hierarchy Process (AHP) and the Entropy-Weighted Fuzzy TOPSIS models. We focus particularly on representative African basins to inform strategic decision-making for the Chinese overseas petroleum enterprises. We firstly interpret the geological condition of these petroleum basins by researching multiple databases and proprietary research data. Then, we use a combined approach of ranking-classification-correlation analysis to evaluate 17 representative basins, taking into account both overall and individual key performance indicators. Our findings suggest the Illizi Basin and the Offshore Côte d'Ivoire Basin could be the most favorable for investment and development. Those like Southwest African Basin warrant cautious consideration. The new evaluation model and computational workflow offer an effective workflow for assessing multiple petroleum basins. This work provides not just practical investment strategies for companies aiming for African petroleum basins, but also a transferable methodology for optimizing investment decisions.

Effect of antinuclear antibody positivity on antineutrophil cytoplasmic antibody results by indirect immunofluorescence assay.

The indirect immunofluorescence assay (IIFA) utilizing antineutrophil cytoplasmic antibodies (ANCA) is widely used as a diagnostic test for autoimmune vasculitis. The presence of antinuclear antibodies (ANA) might lead to a misleading interpretation of ANCA. This study aims to explore the impact of the presence of ANA on the interpretation of ANCA. This retrospective research examined samples negative for antiMPO and antiPR3 ANCA by IIFA and explored correlations between the ANA-IIFA results and the ANCA interpretation frequencies. Our analysis involved the use of suitable statistical methods, including Chi-square and kappa statistics. Up to 75.2% of the ANCA-IIFA-positive samples exhibited a positive p-ANCA pattern when using the ethanol-fixed substrate, with c-ANCA positivity at 24.8%. In the ANA-IIFA-positive samples, ~77.3% displayed p-ANCA patterns on ethanol-fixed substrates. A comparison between the ANA-IIFA titers and the p-ANCA results revealed that p-ANCA positivity was notably more common in samples with higher titers, and this correlation was found to be statistically significant. Positive ANA results by IIFA tests are linked to a higher incidence of p-ANCA interpretation, particularly in cases with higher titer patterns. This insight aids laboratories in establishing effective workflows to investigate potential p-ANCA interference.

Exploring the primary magnetic parameters affecting chemical fractions of heavy metal(loid)s in lake sediment through an interpretable workflow

The magnetic properties of lake sediments account for close relationships with heavy metal(loid)s (HMs), but little is known about their relationships with chemical fractions (CFs) of HMs. Establishing an effective workflow to predict HMs risk among various machine learning (ML) methods in conjunction with magnetic measurement remains challenging. This study evaluated the simulation efficiency of nine ML methods in predicting the risk assessment code (RAC) and ratio of the secondary and primary phases (RSP) of HMs with magnetic parameters in sediment cores of a shallow lake. The sediment cores were collected and sliced, and the total amount and CFs of HMs, as well as magnetic parameters, were determined. Support vector machine (SVM) outperformed other models, as evidenced by coefficient of determination (R2) > 0.8. Interpretable machine learning (IML) methods were employed to identify key indicators of RAC and RSP among the magnetic parameters. Values of χARM, HIRM, χARM/χ, and χARM/SIRM of sediments ranging in 220–500 × 10−8 m3/kg, 30–40 × 10−5Am2/kg, 15–25, and 0.5–1, respectively, indicated the potential ecological risks of Cd, Hg, and Sb. This study offers new perspectives on the risk assessment of HMs in lake sediments by combining magnetic measurement with IML workflow.

A bidirectional workflow scheduling approach with feedback mechanism in clouds

Workflow scheduling poses a significant challenge in cloud computing environments, offering the potential to optimize execution time and reduce economic costs. In this paper, we present BFWS, a bidirectional workflow scheduling approach with a feedback mechanism, designed to address the deadline-constrained cost-aware workflow scheduling problem. BFWS is built upon a bidirectional scheduling framework that facilitates resource allocation from both the forward and the backward directions of the workflow. This framework incorporates a dynamic sub-deadline prediction method, leveraging prior knowledge and real-time scheduling progress to enhance the accuracy of task sub-deadline predictions. Additionally, a cost-aware resource selection operator is integrated into the framework, enabling the selection of appropriate virtual machines based on considerations of cost and time constraints. To further enhance decision-making and optimize performance, we introduce a feedback mechanism that enables BFWS to adapt and improve over time. Through extensive experiments using real-world workflows, the superior performance of BFWS is validated compared to the state-of-the-art algorithms. Moreover, the effectiveness of the bidirectional scheduling approach and the feedback mechanism is demonstrated individually through rigorous validation experiments. These findings highlight the potential of BFWS in achieving efficient and effective workflow scheduling in cloud computing environments.

Molecular Gas-Phase Conformational Ensembles.

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

GIS Tips & Tricks Slivers be Gone!

One of the most annoying aspects of building large polygon datasets by heads-up digitizing occurs when there are small overlaps and/or gaps where the polygons meet. Edge-matching to eliminate slivers between digitized polygons can be a laborious and tedious task. These "slivers", especially voids, can be very difficult to detect by visual means, so the GIS workflow to resolve these issues generally involves building topology, constructing a ruleset, and running advanced GIS tools; a heady operation for a beginning GIS analyst and particularly cumbersome when tracking a few slivers. This month's GIS Tip demonstrates a quick and effective workflow to avoid the build topology route.

Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery

The emergence of powerful machine learning methodology together with an increasing amount of data collected during clinical routine have fostered a growing role of artificial intelligence (AI) in medicine. Algorithms have become part of clinical care enhancing image reconstruction, detecting cancer or predicting individual risk to support treatment decisions and patient management. The entry into clinical care is determined by technological feasibility, integration into effective workflows, and immediacy of benefits. At the same time, research is advancing the integration of imaging data and other modalities such as genomics, and the linking of observations made at large scale with the understanding of underlying biological processes. AI will have impact in imaging and precision medicine not only because of the successful application of techniques established in other domains, but primarily because of the effective joint development of new technology and corresponding advance of diagnosis and care.