Workflow Optimization Research Articles

Relationship of the Perceived Service Quality and Customer Satisfaction on Digital Printing Businesses

The study investigates the relationship between perceived service quality and customer satisfaction among digital printing businesses in Santiago City. The relationship between the demographic profile and customer satisfaction was also examined. This cross-sectional study is based on primary and secondary data. A sample of 369 respondents who availed of the service at least once from the various digital printing businesses in Santiago City participated in the study. A non-probability sampling technique, specifically quota sampling, has been used in identifying participants for the current study. The researchers used the Pearson correlation coefficient to test hypotheses and provide statistical results. The study found essential implications on the relationship between the perceived service quality and customer satisfaction in digital printing businesses; the customers perceived all the service quality dimensions, especially the Empathy dimension. Participants are “Very Satisfied” with the service they received rendered by the Digital Printing Businesses. Service quality and customer satisfaction have a moderate positive correlation. The perceived service quality has a weak positive correlation with the participant’s demographic profile, except the gender. Customer satisfaction has no significant relationship with the participant’s demographic profile. The study presented an analysis of the relationship between perceived service quality and customer satisfaction among digital printing businesses that would be relevant in increasing the level of satisfaction experienced by the customers. Therefore, there is an essential contribution towards providing best practices for operational efficiency, workflow optimization, and quality control, resulting in improved processes and customer satisfaction.

Advancing dental diagnostics: The significance of cone-beam computed tomography (CBCT) in modern dentistry

Dental radiology has historically relied on 2-dimensional (2D) imaging, but recent advancements in Cone-Beam Computed Tomography (CBCT) have revolutionized maxillofacial imaging. This review explores the applications of CBCT, emphasizing its advantages in dental specialties such as implant dentistry, endodontics, orthodontics, and temporomandibular joint evaluation. CBCT offers unparalleled diagnostic accuracy while minimizing radiation exposure, making it a valuable tool in dental practice. The integration of artificial intelligence (AI) and machine learning into CBCT technology presents exciting possibilities for automated image analysis, pathology detection, treatment planning, radiation dose reduction, image enhancement, workflow optimization, predictive analytics, customized treatment plans, teledentistry, and dental education. AI's role in dentistry is poised to enhance diagnostic and treatment capabilities while optimizing CBCT scans, ultimately improving patient care.

Robust optimization of a radiotherapy pretreatment preparation workflow

Objective. Increasing cancer incidence, staff shortage and high burnout rate among radiation oncologists, medical physicists and radiation technicians are putting many departments under strain. Operations research (OR) tools could optimize radiotherapy processes, however, clinical implementation of OR-tools in radiotherapy is scarce since most investigated optimization methods lack robustness against patient-to-patient variation in duration of tasks. By combining OR-tools, a method was developed that optimized deployment of radiotherapy resources by generating robust pretreatment preparation schedules that balance the expected average patient preparation time (F mean) with the risk of working overtime (RoO). The method was evaluated for various settings of an one-stop shop (OSS) outpatient clinic for palliative radiotherapy. Approach. The OSS at our institute sees, scans and treats 3–5 patients within one day. The OSS pretreatment preparation workflow consists of a fixed sequence of tasks, which was manually optimized for radiation oncologist and CT availability. To find more optimal sequences, with shorter F mean and lower RoO, a genetic algorithm was developed which regards these sequences as DNA-strands. The genetic algorithm applied natural selection principles to produce new sequences. A decoder translated sequences to schedules to find the conflicting fitness parameters F mean and RoO. For every generation, fitness of sequences was determined by the distance to the estimated Pareto front of F mean and RoO. Experiments were run in various OSS-settings. Main results. According to our approach, the expected F mean of the current clinical schedule could be reduced with 37%, without increasing RoO. Additional experiments provided insights in trade-offs between F mean, RoO, working shift length, number of patients treated on a single day and staff composition. Significance. Our approach demonstrated that OR-tools could optimize radiotherapy resources by robust pretreatment workflow scheduling. The results strongly support further exploration of scheduling optimization for treatment preparation also outside a one-stop shop or radiotherapy setting.

Non-Small Cell Lung Cancer Testing on Reference Specimens: An Italian Multicenter Experience.

Biomarker testing is mandatory for the clinical management of patients with advanced non-small cell lung cancer (NSCLC). Myriads of technical platforms are now available for biomarker analysis with differences in terms of multiplexing capability, analytical sensitivity, and turnaround time (TAT). We evaluated the technical performance of the diagnostic workflows of 24 representative Italian institutions performing molecular tests on a series of artificial reference specimens built to mimic routine diagnostic samples. Sample sets of eight slides from cell blocks of artificial reference specimens harboring exon19 EGFR (epidermal growth factor receptor) p.E746_AT50del, exon2 KRAS (Kirsten rat sarcoma viral oncogene homologue) p.G12C, ROS1 (c-ros oncogene1)-unknown gene fusion, and MET (MET proto-oncogene, receptor tyrosine kinase) Δ exon14 skipping were distributed to each participating institution. Two independent cell block specimens were validated by the University of Naples FedericoII before shipment. Methodological and molecular data from reference specimens were annotated. Overall, a median DNA concentration of 3.3ng/µL (range 0.1-10.0ng/µL) and 13.4ng/µL (range 2.0-45.8ng/µL) were obtained with automated and manual technical procedures, respectively. RNA concentrations of 5.7ng/µL (range 0.2-11.9ng/µL) and 9.3ng/µL (range 0.5-18.0ng/µL) were also detected. KRAS exon2 p.G12C, EGFR exon19 p.E736_A750del hotspot mutations, and ROS1 aberrant transcripts were identified in all tested cases, whereas 15 out of 16 (93.7%) centers detected MET exon14 skipping mutation. Optimized technical workflows are crucial in the decision-making strategy of patients with NSCLC. Artificial reference specimens enable optimization of diagnostic workflows for predictive molecular analysis in routine clinical practice.

Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer

Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity. However, there are significant challenges when it comes to distributing intricate tasks, such as workflows structured in a Directed Acyclic Graph (DAG), across cloud resources. These challenges include increased energy consumption and delays experienced by end-users due to geographical disparities between users and service providers. To address these issues, this research work introduces an innovative solution that utilizes a multi-objective Grey Wolf Optimizer (GWO) algorithm for optimizing the allocation of complex workflows. The primary objective of this workflow optimization is to achieve the highest possible processor utilization ratio while adhering to latency constraints. This approach consists of a two-step process. In the initial step, it assesses processor utilization by calculating the number of completed transactions within a specified period divided by the average power consumption. This evaluation ensures that processors operate at the most power-efficient utilization levels, resulting in the highest service profit. In the second step, an optimal mapping algorithm assigns workflow tasks to the most efficient destinations. This is accomplished by applying the Mean Grey Wolf Optimization (GWO) algorithm, which is a metaheuristic technique. The goal is to find an optimal pairing of tasks and virtual machines (VMs) that balances multiple objectives. The simulation of this approach was conducted using the Java-based open-source CloudSim simulator version 5.0. The experimental results from a series of trials indicate that the ELSCiW approach leads to various improvements, including a reduction in latency ranging from approximately 5.35% to 12.92%, a decrease in end-to-end delay costs of about 5.03% to 13.80%, a reduction in energy consumption within the range of 4.71% to 11.19%, an enhancement in load balancing of around 14.84% to 37.30%, an improvement in performance degradation of about 12.52% to 35.14%, a decrease in overhead by approximately 17.27% to 26.92%, and an increase in the quality of service of roughly 56.21% to 64.85%. Furthermore, the ELSCiW approach outperforms recent state-of-the-art metaheuristic methods, as confirmed by statistical findings that emphasize the robustness of the proposed algorithm.

An application of a genetic algorithm in co-optimization of geological CO2 storage based on artificial neural networks

Abstract Global warming, driven by human-induced disruptions to the natural carbon dioxide (CO2) cycle, is a pressing concern. To mitigate this, carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources. Deep saline aquifers are of particular interest due to their substantial CO2 storage potential, often located near fossil fuel reservoirs. In this study, a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow. Due to the time-consuming nature of each realization of the numerical simulation, we introduce a surrogate aquifer model derived from extracted data. The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework. Unlike previous studies, which typically focused on single-parameter optimization, our research addresses this gap by performing multi-objective optimization for CO2 storage and breakthrough time in deep saline aquifers using a data-driven model. Our methodology encompasses preprocessing and feature selection, identifying eight pivotal parameters. Evaluation metrics include root mean square error (RMSE), mean absolute percentage error (MAPE) and R2. In predicting CO2 storage values, RMSE, MAPE and R2 in test data were 2.07%, 1.52% and 0.99, respectively, while in blind data, they were 2.5%, 2.05% and 0.99. For the CO2 breakthrough time, RMSE, MAPE and R2 in the test data were 2.1%, 1.77% and 0.93, while in the blind data they were 2.8%, 2.23% and 0.92, respectively. In addressing the substantial computational demands and time-consuming nature of coupling a numerical simulator with an optimization algorithm, we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm. Within this framework, we conducted 5000 comprehensive experiments to rigorously validate the development of the Pareto front, highlighting the depth of our computational approach. The findings of the study promise insights into the interplay between CO2 breakthrough time and storage in aquifer-based carbon capture and storage processes within an integrated framework based on data-driven coupled multi-objective optimization.

Optimization of the Second Target Station cold source moderators using an automated workflow

The Second Target Station (STS) at the US Department of Energy’s Oak Ridge National Laboratory is designed to become the world’s highest peak-brightness spallation source of cold neutrons. Successful completion of the STS, which is currently in the preliminary design phase, will provide transformative new capabilities to examine novel materials for future technologies. At STS, neutrons will be generated by spallation reactions in a solid tungsten target. They will be moderated and thermalized in two cold (20 K) para-hydrogen moderators. Careful optimization of these moderators is essential to the project’s success. To find optimal moderator designs, an advanced optimization workflow integrates high-fidelity neutronics calculations using the Monte Carlo N-Particle (MCNP) transport code MCNP6.2 with state-of-the-art optimization algorithms in the Dakota optimization toolkit. For each design iteration, a parametrized solid CAD geometry is generated in Creo and automatically converted into an unstructured mesh geometry by Attila 4MC for the neutronics calculation with MCNP. Iterations repeat until optimal designs are found. This paper presents the results of a sensitivity and optimization study for the cylindrical and tube moderators. Both moderators can be optimized for maximum peak brightness, maximum time-integrated brightness, or any combination between these extremes. Maximum peak brightness is achieved by using smaller optimal dimensions of the moderators, whereas maximum time-integrated brightness is achieved by using larger dimensions. A Pareto front details the designs that optimally balance both brightness metrics. The Pareto front can be found in only 40–110 iterations with 4–5 design parameters when using the efficient global and Pareto-set optimization algorithms in Dakota. Additionally, important engineering constraints can be taken into account, such as the coupling between the cylindrical moderator radius and aluminum vessel wall thicknesses required to ensure structural integrity of the vessels. This interaction has a significant impact on the resulting optimal designs. Our new, highly efficient, fully automated optimization workflow will be used to optimize additional STS components in the future and can be adopted for design and optimization studies at other experimental neutron and accelerator facilities.

Identifying factors in the provision of intravenous stroke thrombolysis in Malaysia: a multiple case study from the healthcare providers’ perspective

BackgroundTranslation into clinical practice for use of intravenous thrombolysis (IVT) for the management of ischemic stroke remains a challenge especially across low- and middle-income countries, with regional inconsistencies in its rate. This study aimed at identifying factors that influenced the provision of IVT and the variation in its rates in Malaysia.MethodsA multiple case study underpinning the Tailored Implementation for Chronic Diseases framework was carried out in three public hospitals with differing rates of IVT using a multiple method design. Twenty-five in-depth interviews and 12 focus groups discussions were conducted among 89 healthcare providers, along with a survey on hospital resources and a medical records review to identify reasons for not receiving IVT. Qualitative data were analysed using reflective thematic method, before triangulated with quantitative findings.ResultsOf five factors identified, three factors that distinctively influenced the variation of IVT across the hospitals were: 1) leadership through quality stroke champions, 2) team cohesiveness which entailed team dynamics and its degree of alignment and, 3) facilitative work process which included workflow simplification and familiarity with IVT. Two other factors that were consistently identified as barriers in these hospitals included patient factors which largely encompassed delayed presentation, and resource constraints. About 50.0 – 67.6% of ischemic stroke patients missed the opportunity to receive IVT due to delayed presentation.ConclusionsIn addition to the global effort to explore sustainable measures to improve patients’ emergency response for stroke, attempts to improve the provision of IVT for stroke care should also consider the inclusion of interventions targeting on health systems perspectives such as promoting quality leadership, team cohesiveness and workflow optimisation.

Improving laboratory turnaround times in clinical settings: A systematic review of the impact of lean methodology application.

Lean methodology, originally developed in the manufacturing sector, is a process management philosophy focused on maximizing value by eliminating waste. Its application in laboratory settings, particularly concerning laboratory turnaround times (TAT), involves a systematic approach to identifying inefficiencies and optimizing processes to enhance value for end customers. This systematic review was registered in PROSPERO with identification number (CRD42024552350) and reported based on the 2020 PRISMA checklist. An extensive search strategy was performed using PubMed, Scopus, and Embase databases and gray literatures. Advanced searching was used using Boolean operators (AND & OR). After articles were exported to endnote x8, duplications were removed and articles were selected based on titles, abstracts, and full texts. The illegibility of the articles was independently assessed by the three authors (NC, DMB, and BBT), and the disagreements were settled through scientific consensus. Methodological quality was assessed using JBI critical appraisal checklist. In this review, electronic databases search yielded 1261 articles, of which 7 met the inclusion criteria. The review demonstrated, implementation of lean principle into the routine laboratory testing had an overall impact 76.1% on reducing laboratory TAT. Transportation, manual data processing, inefficient workflow, and the heavy workload were identified as the main wasteful procedures. To eliminate these non-value-added steps, several intervention techniques were implemented, including the use of a barcoding system, process redesign, workflow optimization, hiring additional staff, and relocating the sample collection room closer to the result distribution center. Lean implementation is crucial in the medical laboratory industry for optimizing processes, reducing TAT, and ultimately enhancing customer satisfaction. As a result, all clinical laboratories should adopt and implement lean principles in their routine testing processes. The medical laboratory industry should also proactively look for and apply lean tools, provide ongoing training, and foster awareness among laboratory staffs.

Ab. No. 146 Digital Revolution in Physiotherapy Clinical Practice: A Programme Built by a Physiotherapist for Physiotherapists

In clinical practise, what gets measured, gets managed. A streamlined documentation system is necessary to organise the journey of each patient from digital consent to expected timeline of recovery. Even at a single clinic, each practitioner has their own assessment methods, differential and provisional diagnoses, and treatment protocols. As a consultant, monitoring the data of each patient is tedious, which necessitated the need for the ASIYA Documentation System. American therapists spent 57 minutes/day authoring, 17 minutes/day viewing notes. Even after 2 years, notes are viewed. Nigerian report on physiotherapy documentation suggested accuracy needs to be improved to provide optimal continuity of care. 2006 study of an IPD physiotherapy department in Adelaide suggests only 4.3% records had 100% completion, highlighting need for quality in documentation. This online inbuilt programme maintains patient records for data monitoring and organised rehabilitation. This includes a comprehensive patient registration format with an informed consent document, detailed evaluation in which pictures/ videos are uploaded, estimated treatment time, short- and long-term goals, next session plan, detailed treatment including exercise dosages. Patients can log-in to view the number of sessions completed, and the home exercise videos specifically prescribed. Patients receive message and email appointment reminders. Data security measures have also been inbuilt for data protection. Separate home visit management section exists. The system is accessible, user-friendly, accurate ensuring quick information retrieval for timely decision-making and treatment planning. Analytics and reporting enable tracking of outcomes, identifying trends, and makes data-driven improvements. Helps in providing information in legal cases and insurance claims, eliminates need for a receptionist. By implementing ASIYA Clinic Management System, we have successfully addressed key administrative challenges, resulting in enhanced patient care, workflow optimization, staff efficiency, improved clinic performance, since the past 8 years. Future scope includes remote access and telehealth integration.

Prognostic models for colorectal cancer recurrence using carcinoembryonic antigen measurements.

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide. A considerable percentage of patients who undergo surgery with curative intent will experience cancer recurrence. Early identification of individuals with a higher risk of recurrence is crucial for healthcare professionals to intervene promptly and devise appropriate treatment strategies. In this study, we developed prognostic models for CRC recurrence using machine learning models on a limited number of CEA measurements. A dataset of 1927 patients diagnosed with Stage I-III CRC and referred to Zuyderland Hospital for surgery between 2008 and 2016 was utilized. Machine learning models were trained using this comprehensive dataset, which included demographic details, clinicopathological factors, and serial measurements of Carcinoembryonic Antigen (CEA). In this study, the predictive performance of these models was assessed, and the key prognostic factors influencing colorectal cancer (CRC) recurrence were pinpointed. Among the evaluated models, the gradient boosting classifier demonstrated superior performance, achieving an Area Under the Curve (AUC) score of 0.81 and a balanced accuracy rate of 0.73. Recurrence prediction was shown to be feasible with an AUC of 0.71 when using only five post-operative CEA measurements. Furthermore, key factors influencing recurrence were identified and elucidated. This study shows the transformative role of machine learning in recurrence prediction for CRC, particularly by investigating the minimum number of CEA measurements required for effective recurrence prediction. This approach not only contributes to the optimization of clinical workflows but also facilitates the development of more effective, individualized treatment plans, thereby laying the groundwork for future advancements in this area. Future directions involve validating these models in larger and more diverse cohorts. Building on these efforts, our ultimate goal is to develop a risk-based follow-up strategy that can improve patient outcomes and enhance healthcare efficiency.

ВПЛИВ АДИТИВНИХ ТЕХНОЛОГІЙ НА СТРУКТУРУ ЛОГІСТИКИ ВИРОБНИЧИХ ПІДПРИЄМСТВ

In the article the integration of additive manufacturing within the logistics chains utilized by modern manufacturing companies is examined. The study explores how additive technologies, particularly 3D printing, influence logistics processes and supply chains, offering opportunities for optimization and acceleration of production workflows. Specific advantages of 3D printing, such as reduced transportation and storage costs, enhanced production flexibility, and a decrease in required inventory levels, are highlighted, demonstrating the greater efficiency of these technologies compared to traditional manufacturing methods. The article also presents a practical case study of a Ukrainian manufacturing enterprise that has implemented additive technologies, showcasing how these innovations have helped the company streamline its logistics processes, reduce costs, and enhance its market competitiveness. The analysis underlines the significance of further developing and adopting innovations in the field of additive technologies, which can substantially transform the logistics structure of manufacturing enterprises, fostering their successful growth in today’s economic environment. By exploring the practical benefits and potential of 3D printing in logistics, the article provides valuable insights into how companies can leverage these technologies to gain a competitive edge. The study suggests that continued innovation in additive manufacturing will play a crucial role in reshaping the logistics landscape, enabling companies to meet evolving market demands with greater agility and efficiency. The atticle discusses how 3D printing technology enables on-demand production, reducing lead times and minimizing the need for extensive warehousing. The study emphasizes the strategic importance of adopting additive technologies in logistics, predicting that companies that invest in these innovations will be better positioned to navigate the challenges of a rapidly changing economic landscape. By integrating 3D printing into their logistics and production strategies, enterprises can achieve more sustainable and resilient operations, ultimately leading to enhanced customer satisfaction and increased profitability.

Challenges for error-correction coding in DNA data storage: photolithographic synthesis and DNA decay

Photolithographic synthesis and decay – underexplored challenges to DNA data storage – are characterized in this study, uncovering avenues for workflow and codec optimizations. To aid the latter, scenarios to benchmark codecs are presented.

Reducing energy consumption in secondary aluminum manufacturing through the implementation of improved workflow practices

In the recent decades, sustainability has emerged as a crucial concern for governments and industries worldwide. While the political sphere appears to take the lead in the pursuit of a greener world from a societal standpoint, industries play a pivotal role in implementing innovative technologies. Certain industries, however, still heavily rely on natural gas consumption for their operations. Metal manufacturers, in particular, face significant energy demands due to their melting processes, which have historically necessitated the use of combustion. This paper focuses on examining the current workflow of secondary aluminum manufacturing, with a specific emphasis on rolled sheet production. By analyzing combustion intensive equipment involved in the process, the paper identifies inherent deficiencies and energy losses throughout the production process and propose strategies that can effectively reduce the industry's global carbon footprint. Results show that optimizing work flow to retain embodied energy and heat of intermediate products, as well as smart waste heat reuse can result in significant savings in secondary aluminum manufacturing.

Advanced Multi-Layer Watertight Closure versus Conventional Closure in Total Hip and Knee Replacement Surgery.

In total joint replacement procedures, surgeons have increasingly adopted advanced multi-layer, watertight closure. The objective of the study was to compare the clinical and economic outcomes for advanced multi-layer, watertight closure patients to those with conventional closure with sutures and skin staples. Patients aged ≥18 years were included in the study if they underwent total joint arthroplasty of the hip or knee as an elective, primary, inpatient procedure between January 2014 and March 2019. Cohorts having advanced multi-layer, watertight closure or conventional closure were compared using multivariable regression analysis of surgical site infections, length of stay, operating room time, procedure time, discharge status, readmissions, reoperations, and hospital emergency department visits. A total of 1828 patients received at least one total hip or knee replacement, of which 434 (23.7%) had advanced multi-layer, watertight closure and 1394 (76.3%) had conventional closure. Unadjusted time to readmission, when occurring, was considerably longer following advanced multi-layer, watertight closure (89.9 vs 51.1 days, p < 0.0001), and a lower proportion of the advanced multi-layer, watertight closure cohort required reoperation within 90 days (0.0% vs 2.6%, p < 0.0001). Adjusted mean hospital length of stay was approximately half of a day shorter for advanced multi-layer, watertight closure patients (1.10 vs 1.65 days; p < 0.001), and they were also more likely to be discharged to home (Odds Ratio: 4.61; p = 0.002). Among patients undergoing total hip and knee arthroplasty in a highly optimized real-world clinical practice, advanced multi-layer, watertight closure was associated with significantly shorter inpatient length of stay and increased likelihood of being discharged to home compared with conventional closure. These findings suggest that advanced multi-layer, watertight closure is a valuable component of an optimal workflow for total hip or knee replacement, and may be especially valuable for high-risk patients.

A multi-objective optimization workflow of ring-rolling process parameters based on production energy and time

Our industrial era is characterized by a significant attention to environmental aspects and production sustainability. Under this point of view, the minimization of the process energy represents a milestone leading to a consistent reduction of pollution. However, to promptly react to the high production rate market request, companies employ time saving manufacturing strategies, often in contrast with this principle. Considering this, an optimization concerning the reduction of both production time and energy, results to be strategical, especially when energy-intensive processes, such as ring rolling, are considered. Ring rolling is a shaping process performed by a dedicated equipment, composed of a driving roll, an idle roll, and a couple of axial rolls, allowing to obtain seamless rings while simultaneously reducing their height and thickness, while enlarging diameter. The kinematics of each component of the equipment can be set independently. This leads to several process conditions influencing quality, energy, and times, and makes it difficult the selection of the most suitable configuration. In this paper, a multi-objective optimization workflow is presented. Starting from validated FEM simulation campaign results, the proposed methodology allowed to individuate the best process parameters combination for limiting energy and time, encouraging its application to a wider range of energy-consuming processes.

Research on AIGC-driven Innovation Path and Smart Creative Workflow Optimization in Design Field

Abstract AIGC has attracted much attention in the application of art and design professional fields and has gradually become an important auxiliary tool for art and design professionals creation. This paper utilizes AIGC as a starting point for design creativity and develops a text-guided image generation model based on AIGC, which employs a semantically consistent generative adversarial network. We compare different model indexes across different datasets to evaluate the image generation effect of the constructed model. Subsequently, we summarize and optimize the intelligent, creative workflow. Based on this workflow and the image generation model, we generate a series of images as an example of a cosmetic package. We then analyze the audience’s acceptance of these intelligently generated images through a questionnaire survey, thereby exploring the impact of AIGC-driven design and innovation. The GAN-SC model outperforms the comparison algorithms in various datasets, achieving an IS value higher than AttnGAN and DFGAN by 17.7% and 2.7%, respectively, and reducing the FID metrics by 29.43% and 19.6%, respectively. The acceptance rate of the AIGC-based design solution surpasses that of the traditional solution by 4.9%, and it excels across all design dimensions, showcasing the effectiveness of AIGC-driven design innovation.

Boosting RDataFrame performance with transparent bulk event processing

Boosting RDataFrame performance with transparent bulk event processing

A comprehensive preliminary design methodology and an optimization workflow for single-stage radial-outflow turbines and application for 10 MW sCO2 turbines

Development of efficient turbines for sCO2 power cycles is crucial to increase the efficiency, economic feasibility, and competitiveness of future thermal power plants. Radial-outflow turbines are recently considered as a viable power block option for sCO2 power cycles for several applications. This research aims at presenting a detailed and replicable design procedure for radial-outflow turbines. A detailed one-dimensional design method was first developed and validated, then an optimization design workflow was developed using this one-dimensional design method. Utilizing this workflow, the impact of some key design parameters on turbine performance was studied. It was found that the velocity ratio, specific speed, and flow coefficient have significant impact on turbine performance, the reaction degree has moderate impact, and the diameter ratio has the lowest impact. The criterion of selecting the design variables was given and its applicability to different boundary conditions and constraints has also been illustrated. Furthermore, a test case turbine of 10 MW power scale was designed using the developed method, and 3D CFD simulations were performed to study the on-design and off-design performance of the designed turbine. Results show that high efficiency (above 90.0 %) and overall good off-design performance can be obtained from sCO2 radial-outflow turbines in the 10 MW power scale using the developed design methodology when the design parameters are properly selected. The developed 1D design and optimization method is directly applicable to other working fluids and can be easily extended to design multistage radial-outflow turbines.

Robotic-Arm Assisted Total Hip Arthroplasty: Workflow Optimization and Operative Times

Robotic-arm assisted total hip arthroplasty (RATHA) has been demonstrated to offer several benefits, such as increased accuracy in the placement of implants, improved patient outcomes and reduced complications such as dislocations in total hip arthroplasty. However, the potential increase in surgical time may sway some practitioners to hesitate adopting this technology, despite its benefits. Studies of RATHA learning curves have demonstrated that time neutrality can be achieved, but do not describe an efficient workflow. This paper lays out a process to achieve an optimal RATHA workflow and efficiencies in an ambulatory surgery center and presents timing data from 105 cases. We demonstrate that the learning curve for implementing RATHA can be navigated such that providers can offer the clinical benefits of RATHA to their patients without increasing operative or overall perioperative patient time.