Engineering Expertise Research Articles

Cell scientist to watch – Gokul Upadhyayula

ABSTRACT Srigokul (Gokul) Upadhyayula is Scientific Director of the Advanced BioImaging Center (ABC) and an Assistant Professor in Residence of Cell Biology, Development and Physiology at the University of California (UC), Berkeley, USA. Gokul completed his PhD in the bioengineering department with Valentine Vullev at UC Riverside, where he built his first microscope. Inspired to apply his fascination with microscopy and image analysis to understanding biological phenomena, he subsequently joined the group of Tomas Kirchhausen at Harvard Medical School as a postdoc and collaborated with Eric Betzig at Janelia Research Campus on major advances in adaptive optical lattice light-sheet microscopy (LLSM) technology to study fundamental cellular processes such as clathrin-mediated endocytosis and nuclear pore reformation. In 2019, he moved to UC Berkeley to build the ABC together with Eric Betzig. The ABC aims to enhance the accessibility and adoption of data-heavy imaging technologies like LLSM. Gokul's team combines expertise in biology, chemistry, engineering, microscopy and computational science to provide resources and tools that allow biologists to extract meaningful insights from imaging data. We spoke with Gokul over Zoom to learn about his career path, the challenges that come with handling massive bioimaging datasets and the vision of the ABC for the future of bioimaging.

Emergency Knowledge Translation, COVID-19 and indoor air: evaluating a virtual ventilation and filtration consultation program for community spaces in Ontario

BackgroundAn October, 2021 review of Public Health Ontario's COVID-19 guidance for congregate settings such as shelters and long-term care homes demonstrated that this guidance did not include references to ventilation or filtration. In April 2022, an interdisciplinary team with expertise in indoor air quality (IAQ), engineering, epidemiology, community programming and knowledge translation launched a virtual ventilation and filtration consultation program for community spaces in Toronto, Ontario. The program gives people working in community spaces direct access to IAQ experts through 25-min online appointments. The program aims to help reduce the risk of COVID-19 transmission in community spaces, and was designed to help compensate for gaps in public health guidance and action.MethodsRepresentatives from participating organizations (n. 27) received a link to an online survey via email in April 2023. Survey questions explored the impacts of the program on topics such as: purchase and use of portable air filters; maintenance and use of bathroom fans; and, maintenance and modification of HVAC systems. Survey participation was anonymous, and no demographic information was collected from participants.ResultsRepresentatives from 11 organizations completed the survey (40%). Of those who responded, nine (82%) made changes as a result of the program, with eight (73%) making two or more changes such as purchasing portable air filters and increasing routine maintenance of HVAC systems.ConclusionsWhen presented with brief access to expert support and tailored plain language guidance, people working in community spaces increased their use of ventilation and filtration strategies for COVID-19 infection prevention and control.

Algorithm-Enhanced Engineering English Education in the Era of Artificial Intelligence: A Data-Driven Approach

The era of artificial Intelligence (AI), the landscape of language training, especially in the vicinity of Engineering English, is gift technique a transformative shift. This paper offers a unique data-driven technique to Engineering English training, stronger via modern day algorithms, to address the precise stressful situations and opportunities furnished through AI enhancements. Our approach integrates algorithmic answers with conventional language coaching methodologies to create a dynamic, adaptive gaining knowledge of environment sustainable-made to the unique wishes of engineering students. Primary method is the usage of AI-driven analytics to investigate students’ language talent and studying patterns. Using leveraging natural language processing and machine mastering algorithms, we are able to customize the curriculum and offer focused practise that aligns with each student’s linguistic and technical level. This consists of the development of specialised vocabulary, comprehension of technical files, and effective verbal exchange in expert engineering contexts. A tremendous element of this study is the collection and evaluation of data on student performance and engagement. This information-driven feedback loop allows non-prevent refinement of coaching techniques and substances, ensuring that the instructional content material cloth remains relevant and effective in the unexpectedly evolving discipline of engineering.

Accelerating Museum AI Research and Application at the UK Natural History Museum: The NHM AI Lab Programme

The United Kingdom's Natural History Museum (NHM) AI Lab Programme represents a pioneering initiative aimed at harnessing the power of artificial intelligence (AI) to bridge the gap between the museum's extensive collection and cutting-edge AI technologies. Despite its immense potential, the application of AI in museum research remains nascent (e.g., He et al. 2024), with some individual research groups pursuing independent projects without cohesive collaboration with AI experts who know or have experience in similar endeavours. Moreover, differing standards in utilising AI among researchers add complexity to the field. The NHM AI Lab Programme addresses these challenges by co-creating AI pilot projects that bring together the NHM's collection, academic researchers, and AI experts. The NHM AI Lab Programme serves as a nexus for interdisciplinary collaboration, offering expertise in AI, machine learning, data science, and software engineering to support NHM researchers. Through one-to-one consultations and collaborative research projects, the NHM AI Lab Programme facilitates the integration of innovative AI-driven technologies into streamlining digitisation workflows and enhancing Earth and Life Science research at the NHM. In less than a year since its inception, our Programme has achieved several milestones, hosting around 20 diverse projects. These include research projects such as the application of AI for the automatic detection and identification of nannofossils in chalk, the classification of ancient shark and dinosaur teeth, the prediction of mammal disease outbreaks, and the extraction of data from historical bird egg records. Additional projects focus on the automation of mineral analysis and the detection of secondary impact craters on planetary surfaces using AI. Some led to journal publications (e.g., He et al. 2024), while others streamlined NHM researchers' workflows, enhancing their processes of research and digitisation. Moreover, several initiatives have paved the way for new funding streams and collaborative ventures, as well as promising commercial prospects. Certain projects have pioneered the creation or transformation of datasets to meet AI-ready standards, such as data quality, consistency, accessibility, usability, and data governance protocols, helping to embed AI practices into NHM research. This AI Lab Programme can act as a model for other institutions addressing a similar challenge of bridging the gap between AI and their research and collections. This presentation provides insights into the establishment and operation of the NHM AI Lab Programme, shares experiences, highlights successful collaborations, discusses challenges encountered, and outlines future directions.

Application of BIM and Virtual Reality of System Integration Design and Development in Medical Building Projects: A Case Study in China

Design outcomes in the Medical Building Projects (MBPs) must satisfy the demand of the medical staff and stakeholders. However, they often do not have the relevant engineering expertise, so the design team is prone to misunderstandings when communicating with them using traditional two-dimensional (2D) Computer-Aided Design (CAD) drawings. The medical staff may not readily visualize and understand the design and then provide feedback to the designer through professional medical requirements or advice, which results in the need to redesign and carry out additional work during the design phase, thus delaying the progress of the MBPs. To address these challenges, this study aims to develop an integrated system based on BIM, advanced rendering engines, and VR technology for use in non-immersive and immersive VR environments. Moreover, in this study, the integrated system was applied to the infection-building project of the People's Hospital of Qingbaijiang District, Chengdu City, and semi-structured interviews were distributed to 16 engineers, designers, contractors, and medical staff who had been involved in the whole project to validate the practicality and effectiveness of the integrated system. The results indicate that this system integration is an efficient visual communication and simulation tool in medical design. Key advantages include enhanced communication efficiency between the design team and medical stakeholders, improved visual interaction, and streamlined decision-making processes within a 3D VR/BIM environment. The successful implementation of this integrated system is expected to significantly aid design teams and stakeholders in effectively managing medical design tasks in future projects.

Bicycle Infrastructure Safety Assessment From the Perspective of Urban Development Specialists and Engineers

The safety of bicycle infrastructure is a primary factor influencing bicycle travel. While cyclists’ perspectives on infrastructure safety are extensively studied, they are merely the end users. Decisions on infrastructure design are made by engineers and urban development specialists. Therefore, it is crucial to determine if these professionals’ safety assessments align with those of cyclists. A qualitative survey was conducted with 5 expert engineers and 5 urban development specialists, each having 5 to 20 years of experience in transportation infrastructure planning. Kendall’s coefficient of concordance W was used to assess the compatibility of their opinions. The results showed significant compatibility: W = 0.697 for engineers and W = 0.511 for urban development specialists. Seventeen cycling infrastructure installation schemes were evaluated. Both engineers (M = 10.0, SD = 0.0) and urban development specialists (M = 9.8, SD = 0.44) indicated the DT_2 option as providing the greatest sense of security, where the bicycle path is physically separated from both the carriageway and pedestrian path. The key findings reveal agreement on the safety of straight-street segments of bicycle infrastructure but diverging opinions at intersections zones. Urban development specialists are influenced by existing practices and legal frameworks lacking detailed cycling infrastructure guidelines at intersections. Engineers align more closely with cyclists’ perceptions, emphasising physical separation and speed reduction measures. The study concludes that urban development specialists need to better understand cyclists’ needs and prioritize safer infrastructure solutions.

In Silico Decoding of Parkinson's: Speech & Writing Analysis.

Background: Parkinson's disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1-4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD's early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.

Preserving Doñana Park requires sound science, wise governance, and expert engineering

Preserving Doñana Park requires sound science, wise governance, and expert engineering

Challenges in Applying System Dynamics to Address Scoping and Estimating Problems

System dynamics (SD) is a fundamental model that facilitates research by defining and modeling a project’s scope and estimation. Scoping and estimation is a set of approaches that ensure engineering managers that a project’s accuracy and cost estimation are properly specified and mapped. Engineering management’s primary project planning responsibilities include project scoping and estimating. Insufficient project scoping and estimation criteria significantly fail the project plan. The study authors collaborated with five distinctive journal institutions to address project scoping and estimation challenges. The authors thoroughly analyzed one core research issue and five secondary research questions from each article. In terms of core issues, most project managers dislike SD modeling because of its complexity, especially if they lacked engineering, programming, mathematical analysis, or IT expertise. The first secondary question revealed that project managers’ awareness of scoping and estimating problems ranges from 78.3333% to 91.6666%. The secondary question revealed that 83.3333% to 90% of managers are familiar with SD, which can help resolve scoping and estimation issues in project management. The third secondary question showed that 83.3333% to 91.6666% of managers are aware of SD during the project’s scoping and estimating phases. The fourth secondary question found that 83.333% to 91.6666% of managers can use mathematical equations and programming for SD design during the scoping and estimating phases. The authors developed a novel SD scoping and estimating model since the last question indicated that there were no downloadable models from the five journals.

Evaluating the implementation of the mechanical engineering student skills competency

This study aims to evaluate the implementation of the Expertise Competency Test (UKK) for students of Vocational High Schools (SMK) in the Machining Engineering Expertise Program implemented by the First Party Professional Certification Agency (LSP P1) in Banten Province. The evaluation was conducted using the CIPP model (Context, Input, Process, Product, and Outcome). This study is useful for assessing the performance of LSP P1 in implementing the UKK and evaluating the impact of UKK graduates on their acceptance in the industrial world. The research was conducted in three vocational schools in Banten Province with the research subjects including the head of LSP P1, the head of the machining engineering expertise programme, and competency assessors. The research instruments used were questionnaires, observations, documents and interviews, which had been validated by expert judgement. The data analysis technique used was descriptive quantitative. The results showed that the implementation of UKK by LSP P1 in Banten vocational schools received the following evaluations: 1) Context aspect scored 3.36 (very good category), 2) Input aspect scored 3.47 (very good category), 3) Process aspect scored 3.36 (very good category), 4) Product aspect scored 3.26 (very good category), and 5) Outcome aspect scored 2.94 (good category). In conclusion, the implementation of the UKK by LSP P1 in the Machining Engineering Expertise Programme at SMK Banten Province has been running very well in most aspects, although there is room for improvement in the Outcome aspect related to the impact of graduates in the industrial world. The contribution of this research is to provide an overview of the effectiveness of expertise competency certification in SMKs as well as input to improve the quality of graduates who are better prepared to compete in the world of work.

The Role of Video Cameras and Emerging Technologies in Disaster Response to Increase Sustainability of Societies: Insights on the 2023 Türkiye–Syria Earthquake

New technologies are being used to facilitate the recognition process during and after earthquakes. These advanced tools are essential to keep track of what is left from of the destruction suffered by the built stock. Among the new technologies are video recordings captured during seismic events, footage from drones, and satellite imagery acquired before and after the event. This review paper presents a series of examples collected from the 2023 Türkiye–Syria earthquakes to illustrate how these new technologies offer a unique and efficient way to capture, document, and transfer information among experts in seismology, earthquake engineering, and disaster management. Whenever possible, these examples are accompanied by simple qualitative explanations to enhance understanding. To demonstrate the potential of video cameras and drone imagery for quantitative analysis, in addition to the various simple examples provided, two case studies are provided—one on road blockages, and another on intensity assessment and wave attenuation as observed in video cameras. These technologies are critical and merit considerable focus, particularly video cameras, which have not received much attention recently, on helping to understand seismic wave passage and their impact on the built environment. Enhancing our use of video cameras in this context can significantly contribute to the sustainability and resilience of our society. With the rapid advancement of image analysis, we advocate for a collaborative platform for accessing and utilizing imagery materials, aiding current and future generations in analysing the causes of such tragedies.

Neural architecture search for image super-resolution: A review on the emerging state-of-the-art

Nowadays, complex and expensive neural architectures are seen by many as a way to improve the performance of existing models in image recognition, voice recognition, translation, and other tasks. Such a perspective caused an increased interest in expert architecture engineering within Deep Learning. Fueled by this interest, neural architecture search originated as a promising way to automate the tedious process of constructing a deep neural network by hand. Over the last five years, we have seen an increasing number of works focusing all efforts on studying the impact of automating deep neural network design. The spotlight has recently turned from automatically discovering classification models to other more complex tasks. Motivated by a desire for high-resolution images in real-world user-centered and expert computer vision applications, architecture search for super-resolution image restoration centers in approaches capable of automatically finding efficient and well-performing models. Here, we present a survey that, beyond delving into an overview of modern approaches to automatic neural network design, focuses on the recollection and study of neural architecture search approaches that have directed their efforts at the super-resolution image restoration tasks and future lines of research found within this emerging area of study.

Assessment of timber engineering needs and practices in Bhutan: A comprehensive study

A survey was conducted to explore the intensity of timber usage and evaluate the knowledge and experiences amongst working professional timber handlers in Bhutan. Subsequently, a need assessment survey was carried out to understand the need and demand for certification courses in timber engineering in the country. Despite having an abundant timber resource and massive usage, Bhutan still needs to gain expertise in timber engineering. This study involved 90 respondents from timber processing, construction and manufacturing sectors across 12 districts in the country. Twenty-one respondents are timber technocrats who have significantly contributed to timber associate infrastructure, while 61 are from the Wood-based Industries, specifically from the timber manufacturing sectors. The key findings from this survey observed several barriers to timber use that include limited knowledge amongst designers, clients and construction companies, alongside concerns about durability and fire resistance. As per the findings, these challenges could be addressed through a dedicated timber engineering course, an idea supported by participants, with 80% of manufacturing professionals and 55–60% of others. The respondents also highlighted the demand for timber testing facilities in the country. This research underscores the need for timber engineering expertise in Bhutan's construction industry.

IMPLEMENTATION OF THE TEACHING FACTORY MODEL TO ENHANCE COMPETENCE IN COMPUTER AND NETWORK ENGINEERING EXPERTISE AT SMK ACEH SELATAN

The teaching factory learning model has become an important strategy in enhancing students' expertise in Vocational High Schools (SMK) in South Aceh. This study aims to evaluate the effectiveness of this learning model in improving students' skills, particularly in the concentration of computer and network engineering expertise. The research shows that products, whether goods or services, in the teaching factory learning model play a crucial role as the main medium in delivering expertise to students. Block schedules are used to optimize resources efficiently, while Job Sheets regulate the sequence of materials to achieve the desired competencies. Supporting factors in the implementation of the teaching factory include support from school management, active student participation, and trust from the community as consumers of the products. However, some obstacles have also been identified, such as lack of socialization, suboptimal teaching staff, and limited facilities and infrastructure. Practical learning processes are conducted according to real work procedures, with a focus on student-centered learning, self-learning, and learning by doing principles. This allows students to learn through direct practice, not just theory. Thus, the teaching factory learning model at SMK in South Aceh contributes to enhancing students' expertise in the concentration of computer and network engineering.

Protonic Ceramic Electrochemical Cell Manufacturing at Idaho National Laboratory

Protonic Ceramic Electrochemical Cells (PCECs), operating at intermediate temperatures of 400-600oC, show great potential for hydrogen production and versatile chemical manufacturing. As this technology rapidly evolves, scaling up production in both quantity and size is crucial. This step bridges the gap between fundamental academic research on small button cells (e.g., 10 mm to 1 inch) and higher-technology readiness level development of industry-standard units (> 10x10 cm2) for stack assembly. At Idaho National Laboratory, we've made significant strides in this transition by applying advanced manufacturing technologies and our expertise in materials science and engineering. Our approach integrates materials, structural, and interface engineering research and development into the cell fabrication process, guided by a stringent quality assurance and quality control (QA&QC) system. This ensures the reliable, repeatable, and durable performance of PCECs.

(Invited) Electrolysis of Liquid Dairy Cow Manure into Clean Reusable Water

Livestock wastewater management is a critical concern in the United States, with an annual production of 1.2 to 1.37 billion tons of waste, surpassing human waste by three to twenty times. This mismanagement poses significant threats to freshwater sources, ecosystems, and public health. Agriwater’s project proposes an innovative solution using electrocoagulation (EC) treatment. The EC technique is a process involving the intentional corrosion of aluminum or iron electrodes to introduce trivalent ions, facilitating the co-precipitation and coagulation of contaminants, making their removal as sludge easier. The project's primary objective is to use electrolysis to convert liquid animal waste into clean water for irrigation, drinking, and farm maintenance. This solution is vital for various farms, including those facing drought, zero-discharge, and EPA-permitted farms discharging manure into rivers. Preliminary research shows EC's potential to significantly reduce turbidity and phosphate levels in livestock wastewater, forming the basis for scalable on-site treatment. The project team, with expertise in electrolysis, wastewater treatment, and agricultural engineering, ensures the development of an industrial-grade water treatment system. This project is in collaboration with Oak Ridge National Laboratory and the University of Tennessee's Dairy Farm and addresses immediate environmental challenges that contribute to a more sustainable and responsible agricultural industry.

Capturing Usability Problems for People Living With Dementia by Applying the DEMIGNED Principles in Usability Evaluation Methods: Mixed Methods Study.

Dementia-related impairments can cause complex barriers to access, use, and adopt digital health technologies (DHTs). These barriers can contribute to digital health inequities. Therefore, literature-based design principles called DEMIGNED have been developed to support the design and evaluation of DHTs for this rapidly increasing population. This study aims to apply the DEMIGNED principles in usability evaluation methods to (1) capture usability problems on a mobile website providing information resources for people visiting a memory clinic, including those living with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia, and (2) investigate the realness of usability problems captured by the DEMIGNED principles in expert testing, specifically for mobile websites that act as a means of providing DHTs. First, a heuristic evaluation was conducted, with the DEMIGNED principles serving as domain-specific guidelines, with 3 double experts (experienced in both usability and dementia) and 2 usability engineering experts. Second, think-aloud sessions were conducted with patients visiting a memory clinic who were living with SCD, MCI, or dementia. The heuristic evaluation resulted in 36 unique usability problems. A representative sample of 7 people visiting a memory clinic participated in a think-aloud session, including 4 (57%) with SCD, 1 (14%) with MCI, and 2 (29%) with dementia. The analysis of the think-aloud sessions revealed 181 encounters with usability problems. Of these encounters, 144 (79.6%) could be mapped to 18 usability problems identified in the heuristic evaluation. The remaining 37 (20.4%) encounters from the user testing revealed another 10 unique usability problems. Usability problems frequently described in the think-aloud sessions encompassed difficulties with using the search function, discrepancies between the user's expectations and the content organization, the need for scrolling, information overload, and unclear system feedback. By applying the DEMIGNED principles in expert testing, evaluators were able to capture 79.6% (144/181) of all usability problem encounters in the user testing of a mobile website for people visiting a memory clinic, including people living with dementia. Regarding unique usability problems, 50% (18/36) of the unique usability problems identified during the heuristic evaluation were captured by the user-testing sessions. Future research should look into the applicability of the DEMIGNED principles to other digital health functionalities to increase the accessibility of digital health and decrease digital health inequity for this complex and rapidly increasing population.

Investigating the Influence of Counterflow Regions on the Hydrodynamic Performance of Biomimetic Robotic Fish.

Biomimetic robotic fish are a novel approach to studying quiet, highly agile, and efficient underwater propulsion systems, attracting significant interest from experts in robotics and engineering. These versatile robots showcase their ability to operate effectively in various water conditions. Nevertheless, the comprehension of the swimming mechanics and the evolution of the flow field of flexible robots in counterflow regions is still unknown. This paper presents a framework for the self-propulsion of robotic fish that imitates biological characteristics. The method utilizes computational fluid dynamics to analyze the hydrodynamic efficiency of the organisms at different frequencies of tail movement, under both still and opposing flow circumstances. Moreover, this study clarifies the mechanisms that explain how changes in the aquatic environment affect the speed and efficiency of propulsion. It also examines the most effective swimming tactics for places with counterflow. The results suggest that the propulsion effectiveness of robotic fish in counterflow locations does not consistently correspond to various tail-beat frequencies. By utilizing vorticity maps, a comparative analysis can identify situations when counterflow zones improve the efficiency of propulsion.

Pengembangan Media Interactive Assisted Learning (IVAL) dalam Mata Kuliah Asesmen untuk Mahasiswa Tunagrahita Ringan

The development of an innovative interactive assisted learning (IVAL) model for mildly mentally retarded students is an effort to create solutions related to the need for digital-based media to stimulate mildly mentally retarded students in assisting the learning process, in this context, in early childhood learning assessment courses at the PG-PAUD STKIP Muhammadiyah Kuningan. In fact, it has not used digital media to help students with special needs due to limited innovation of digital-based media. Through the research and development process carried out, the results of this research creates an android application entitled "Interactive Assisted Learning (IVAL) which is suitable to be applied as a learning medium for students with special needs and mild mental retardation to stimulate students in receiving, processing and storing information in brain memory. The feasibility of IVAL media is proven from the validation results of software engineering experts 73.3%, media experts 80.8% and PAUD lecturer practitioners 71.4%. The evidence of effectiveness is proven from the results of trials in the PG-PAUD Study Program which shows that the ability of students with special needs mild mental retardation is up to 90.3% with a gain score of 0.70 in the medium category and in class of assessment subjects, it is increased to 90.1% with a gain score of 0.70 in the medium category.

Automated design and optimization of distributed filter circuits using reinforcement learning

Abstract Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are not only time-consuming and labor-intensive but also rely heavily on the expertise and experience of electronics engineers, making it difficult to adapt to rapidly changing design requirements. Additionally, these commercial tools struggle with precise adjustments when parameters are sensitive to numerical changes, resulting in limited optimization effectiveness. This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Thus, it significantly reduces the subjectivity and constraints associated with circuit design. The experimental findings demonstrate clear improvements in design efficiency and quality when comparing the proposed method with traditional engineer-driven methods. Furthermore, the proposed method achieves superior performance when designing complex or rapidly evolving DFCs, highlighting the substantial potential of RL in circuit design automation. In particular, compared to the existing DFC automation design method CircuitGNN, our method achieves an average performance improvement of 8.72%. Additionally, the execution efficiency of our method is 2000 times higher than CircuitGNN on the CPU and 241 times higher on the GPU.