Potential Collaboration Research Articles

Co-Creation through scholarship. The Queen Mary Academy learner interns' programme

Since 2021, the Queen Mary Academy (QMA) at Queen Mary University of London has been running its Learner Interns Programme (LIP). The aim of the LIP is to support educators to undertake student-voice scholarship projects in collaboration with our diverse learner population by annually hiring interns to work on projects. In 2023-24, over 140 applications were received. The poster will discuss how learning and development, for both staff and students, occurs via instigation of such research projects, valuing learner expertise and agency. Student engagement is an element of the Queen Mary Academic Careers Framework which supports academic career paths, and engagement with the LIP has the potential for educators to develop evidence for this. Examples showed how educators deepen their approach to reflection and identified opportunities to leverage research avenues with students as active partners, contributing to their scholarly profiles. As a complimentary point, examples of how interns develop critical thinking and communication skills within academic contexts were shared, covering how interns’ exposure to research helps prepare them for professional roles. The origin and evolution of the LIP was outlined, alongside approaches to intern recruitment, onboarding, co-responsibility training, and community building, that subsequently provide opportunities for the professional development of support colleagues who are involved. Delegates took away ideas around valuing students as partners within structured internships, with direct quotes from educators and support colleagues describing the impact this initiative has had on their professional development. This approach was displayed as an effective example of educational learning development, underpinned by student involvement, for which others may wish to copy its approach or explore potential future collaborations with Queen Mary.

Bibliometric and visualized analysis of peripartum respiratory complications from 2004 to 2023

BackgroundPeripartum respiratory complications are a major reason for pregnant women’s admission to the ICU, even endangers the maternal life. This bibliometric analysis was designed to elucidate the spectrum of diseases and risk factors for various respiratory complications during the peripartum period, as well as the future research directions in this field.MethodsRelevant publications were downloaded from the Web of Science Core Collection on November 1, 2023. CiteSpace was utilized for conducting the scientometric study.ResultsA total of 2,331 articles and reviews on respiratory complications during pregnancy published between 2004 and 2023 were retrieved, comprising 87,244 co-cited references, originating from 568 institutions across 104 countries/regions. The United States emerged as the leading country in this domain, with Harvard University standing out as the most actively engaged institution. Bibliometric analysis reveals that the current research hotspots include “COVID-19 pandemic,” “venous thromboembolism,” “respiratory distress syndrome” and “cardiovascular diseases.” Meanwhile, “venous thromboembolism,” cytokine storm” and supportive management such as “extracorporeal membrane oxygenation” might represent potential future research directions.ConclusionOver the past two decades, research on respiratory system complications in pregnancy has continually evolved. This study contributes to enabling researchers in the related field to understand future research hotspots and trends, providing information on potential collaborators, institutions, countries, and citation references.

Correspondence between Mawson and Simmers: the missing meteorological report of Ritchie Simmers from the BANZARE voyages of 1929–1931 in Antarctic waters

ABSTRACT The British, Australian and New Zealand Antarctic Research Expedition (BANZARE) operated during the 1929–1930 and 1930–1931 austral summers, primarily as a ship-borne expedition in the Southern Ocean. Involvement of meteorologist Ritchie Simmers from the New Zealand Meteorological Service was part of the New Zealand contribution to the expedition. He collected data assiduously, including two-hourly weather observations during the day and 34 balloon flights tracked by theodolite observations. Simmers worked up considerable data but did not produce anything in the BANZARE scientific report series, as expected by Professor Douglas Mawson, series editor and expedition leader. Based on correspondence in the Mawson Centre at the South Australian Museum between Mawson and Simmers about his pending report, we suggest reasons for the striking omission of meteorological information from the report series. Simmers had a busy, full-time career with the New Zealand Meteorological Service, and not until 1947 was he permitted to work on his report in office hours. He was distracted by other events, including studying at Massachusetts Institute of Technology (M.I.T.). We suggest Simmers’ enthusiasm for the project waned after Mawson rebuffed potential collaboration with Professor Rossby at M.I.T. involving BANZARE meteorological data. Little of Simmers’ data set has been located.

Bibliometric analysis of research hotspots and global trends in patient safety education for nursing students.

Patient safety education for nursing students has recently garnered interest. However, there is a paucity of data on the key areas and global trends over time. This study aims to analyze research patterns in the field of patient safety education for nursing students, identifying key contributors and global trends. A descriptive bibliometric approach was employed to analyze 782 articles related to patient safety education using data from the Web of Science Core Collection and CiteSpace 6.2. R4. Co-occurrence and co-citation analyses were conducted to identify prominent authors, institutions, countries, and common keywords. The analysis revealed contributions from 335 authors and 302 institutions across 78 countries. Levett-Jones T emerged as the most prolific author with 12 articles. The United States and the University System of Ohio were identified as the most productive countries and institutions, with 276 and 26 articles, respectively. Frequently occurring keywords included patient safety, education, medical education, simulation training, and standardized patients. This study provides valuable insights into the global research landscape of patient safety education in nursing, highlighting key contributors and trends. These findings can assist nursing students, educators, and researchers in identifying potential collaborators and informing future research to advance patient safety education in nursing.

Analisis Strategi Manajemen Badan Amil Zakat Nasional Sibolga Berdasarkan Analisis SWOTt

This research aims to evaluate the dominant internal and external factors affecting the effectiveness of managing Zakat, Infaq, and Sedekah (ZIS) at BAZNAS Sibolga. The research method involves SWOT analysis to identify Strengths, Weaknesses, Opportunities, and Threats in ZIS management. The analysis results indicate that Management Strengths and Operational Efficiency are the strongest internal aspects with a score of 92%, while Weaknesses such as Limited Resources and Inconsistent Implementation require more attention. To enhance effectiveness, Potential Collaborations and Government Support are considered key opportunities with scores of 100% and 85%, respectively. Threats such as Policy Changes and Competition need to be anticipated. In conclusion, optimizing Operational Efficiency with a focus on the collection and utilization of ZIS funds is key to improving community welfare and reducing poverty in Sibolga.

Identifying and Visualizing Global Research Trends and Hotspots of Artificial Intelligence in Medical Ultrasound: A Bibliometric Analysis.

Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to identify and visualize global research trends and hotspots of AI in medical ultrasound to provide guidance for further exploitation. This study aims to highlight the global research trends and hotspots of the top 100 most-cited papers related to AI in medical ultrasound by combining quantitative and visualization methods. Articles on AI in medical ultrasound were selected from the WoSCC database and ranked by citation count. After identifying the 100 most-cited papers, we conducted a quantitative and visualized analysis of bibliometric characteristics, including leading research countries, prominent institutions, key authors and journals, author clusters and collaborations, and keyword co-occurrence network analysis. The top 100 highly cited papers from the WoSCC database were published between 1999 and 2021, with total citations ranging from 91 to 1580. The most cited article was published in IEEE Transactions on Medical Imaging. The top three most prolific countries/regions were the United States, mainland China, and the United Kingdom. The most published institutions and journals were Idaho University and IEEE Transactions on Medical Imaging. Twelve authors published more than four papers, with Suri, JS being the most productive author. The most studied topics were "ultrasound", "computer-aided diagnosis", and "segmentation". Ultrasonography of Superficial Organs was the main site that was studied the most. This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the most highly cited literature. It serves as a valuable reference for the development and applications of AI, fostering potential collaborations within this domain.

Visual analysis of research hotspots and trends in traditional Chinese medicine for depression in the 21st century: a bibliometric study based on Citespace and VOSviewer

Background and ObjectivesDepression long been a key concern for scholars worldwide; however, the field of depression has not received sufficient attention in traditional Chinese medicine. It was not until the 21st century that research into depression gradually entered a period of rapid development, with an increasing number of academic studies published in major journals. However, one limitation of this field is that no scholars have yet summarised the development process and key research issues. Therefore, the present study aimed to summarise the research trends and progress in this field, providing relevant information and presenting potential future research directions for subsequent researchers. MethodsLiterature in this field was searched from 1 January 2000 to 20 April 2024 in the Web of Science Core Collection database, to analyse the current status of the literature and publication trends. Bibliographic information, including study authors, organisations, keywords, countries, references, citations, and co-citations, was extracted using CiteSpace and VOSviewer software for quantitative analysis, visual mapping, and scientific evaluation. ResultsA total of 921 papers were included, with a significant increase in the number of publications from 2017 to 2021, and a stable number of more than 140 publications between 2022 and 2023, with publications in these two years accounting for 31.38% of the total. The Journal of Ethnopharmacology had the highest number of publications (97) and citations (2067), as well as the highest number of co-citations (1369). China (847 publications, 13256 citations), Beijing University of Chinese Medicine (90 publications, 1232 citations), and Qin Xuemei (30 publications, 759 citations) were the most prolific and influential countries, organisations, and authors in the field, respectively. Keyword clustering co-occurrence analysis revealed nine different clusters with good homogeneity. The top three clusters were randomised controlled trials, traditional Chinese medicine, and hippocampal neurogenesis. In the timeline analysis of keywords, from 2000 to 2010, keywords in this field were concentrated on hippocampal neurology and forced swimming test as clustering axes of Traditional Chinese Medicine. From 2010 to 2020, the research hotspots focused on randomised controlled trials and hippocampal neurogenesis. After 2020, keywords became more focused on network pharmacology. In addition, the occurrence time of explosive keywords were distributed before 2010 and after 2020. Before 2010, these keywords included the forced swimming test, Tail Suspension Test, Chronic Cold Stress, Neural Regeneration, and Banxia Houpu Decoction. Conversely, network Pparmacology and Molecular Docking arose as key buzzwords starting in 2020. ConclusionsThis study comprehensively analysed and summarised the research hotspots and trends in this field of research in the 21st century from a bibliometric perspective, further generating a series of visual graphs to help researchers understand the current research status, potential collaborators, collaborating institutions, and potential future research hotspots in this field.

Synergy between multidimensional intellectual capital and digital knowledge management: Uncovering innovation performance complexities

This paper aims to deepen our understanding of the contingency and complex interrelations between multidimensional intellectual capital (IC), technology-based knowledge management (KM), and innovation outcomes in the rapidly changing business environment. More particularly, we investigate causal recipes for high innovation performance consisting of traditional IC components (human capital, structural capital, relational capital), the three more recently emerging IC dimensions (renewal capital, entrepreneurial capital, trust capital), and digital KM practices. This study adopts neo-configurational perspective and fuzzy-set qualitative comparative analysis (fsQCA) to explore multiple conjunctural causations of innovation performance using survey data collected from 102 publicly listed enterprises in Taiwan. Although the four identified archetypes (causal recipes) indicate that all IC dimensions and digital KM are potential collaborators contributing to high innovation performance, they also emphasize the critical role of digital KM in leveraging relational and trust capital for superior innovation performance. Human, structural, entrepreneurial, and renewal capital seems to be a supporting chorus for open and collaborative innovations in the digital era.

Preface

The 2024 International Conference on Computational Intelligence and Communication System (CCICS2024), held in Wuhan, China from June 26-28, 2024, is a testament to the ever-evolving nature of fields of computational intelligence and communication technology, has attracted a diverse and brilliant array of researchers, engineers, and scholars from around the globe. CCICS2024 attracted more than 80 participants from around the world, which is a vibrant and engaging event, with a diverse program that included an opening ceremony, keynote speeches, oral presentations, poster sessions, Q&A discussions, and an ending ceremony. The keynote speakers, renowned experts worldwide in their respective fields, provided insightful overviews of the latest developments and trends in domains related to computational intelligence and communication technology. The oral presentations and poster sessions were equally enriching. Researchers and practitioners shared their latest findings, discussed challenges and opportunities, and explored potential collaborations. The lively discussions and interactions among the participants created a vibrant intellectual atmosphere that fostered innovation and knowledge sharing. The submissions received for this conference are vivid evidence of the creativity and innovation that flourishes in related areas. The manuscripts cover a wide range of topics, including computing for emerging technology, signal processing, sensor and detection technology, futuristic communication and network technologies, etc. The diversity and depth of these contributions reflect the vibrant and interdisciplinary nature of relevant industries. All papers submitted to the conference have been reviewed using a double-blind peer-review process. Each paper was tested for similarity with already published content using the Turnitin platform. All sources of similarities were checked and only original content was accepted. This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the above-mentioned topics. Building on the success of this event, we hope CCICS 2024 will be one of the most spectacular events in those areas. We would like to thank the reviewers and the members of the international scientific committees. We also extend our sincere thanks to Prof. Joarder Kamruzzaman, Prof. Yangjun Chen, Prof. Oscar Eduardo Ruiz Salguero, Prof. Jie Yang, Prof. JingTao Yao and Dr. Iacovos Ioannou for their wonderful keynote talks. We are grateful to everyone who has contributed to the realization of CCICS 2024. We also look forward to the next conferences in this series, which will continue to build upon the foundation laid by this one. Organizing Committee of CCICS 2024 Prof. Yuriy S. Shmaliy (Conference Chair) IEEE Fellow, AAIA Fellow, University of Guanajuato, Mexico List of Conference Committee is available in this Pdf.

RESOURCE COMMITMENT AND PARTNER SELECTION IN ASYMMETRIC CORPORATE-STARTUP COLLABORATIONS

Startups and established firms collaborate in order to leverage complementary resources and innovative capabilities to sustain a competitive advantage. By forming alliances, startups are able to mitigate challenges connected to their smallness and lack of legitimacy. However, the underlying criteria by which startups screen and select potential collaboration partners in such asymmetric collaborations remain poorly understood. This paper examines the dynamics of these asymmetric collaborations, exploring the motives, selection criteria, and the willingness of startups to partner from a resource-based perspective. We conduct a conjoint experiment to examine the relationship between diverse resource commitment types and startups’ willingness to partner. The findings of the study confirm a strong influence of financial, technological, managerial, and physical resources in a startup’s decision-making process regarding partner selection. Notably, the attractiveness of a partner was found to diminish when they possess a similar market portfolio, potentially fostering future competition. Consequently, startups must implement effective control and protection mechanisms to prevent opportunistic behaviours from collaboration partners operating in the same market. To contribute to the existing literature, we adopt a resource-based theoretical lens that emphasises the pivotal role of capabilities in a startup’s decision-making process and demonstrates their influential role in forging successful collaborations with established partners. Ultimately, our findings enhance the understanding of the partner selection criteria, offering valuable insights for new ventures seeking beneficial partnerships at the early stages of a collaboration.

Bringing Artificial Intelligence to Research Analytics: Research Highlighter-MatchMaker

It is more significant to identify accurate grant opportunities given the increasing challenges in securing grants these days. Combining Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques, we have developed Research Highlighter-MatchMaker to facilitate the grant application process. The tool aims to provide an overview of research strength and to recommend matched funding opportunities as well as potential collaborators for researchers based on their research track records. It has the potential to expand to provide matching for on-campus researchers and industrial partners. It will also include a chatbot to facilitate the research administration infrastructure and process on campus.

The automated sustainability auditor: Does artificial intelligence curtail greenwashing behavior in Chinese firms?

AbstractCorporate stakeholders are intrigued by the potential collaboration between AI and environmental reporting to maintain competitiveness in the digital and sustainable economy. This exploration is crucial given the persistent pressures driving companies to engage in greenwashing practices for legitimacy. Aiming to shed light on the function of AI in reducing the prevalence of greenwashing by Chinese businesses, the findings, derived from panel data estimation of Chinese A‐share firms, suggest that the implementation of AI has a positive impact on the fight against greenwashing. The investigation presents compelling evidence that greenwashing mechanism control can be accelerated by AI technologies. Organizations that invest strategically in artificial intelligence exhibit a diminished propensity to obfuscate environmental performance by means of AI‐enabled automation and enhanced data‐driven decision‐making. Intriguingly, the study demonstrates that the substantial disparate greenwashing impact of AI depends on ownership structure. In comparison to non‐SOEs, state‐owned enterprises (SOEs) demonstrate diminished AI control over greenwashing. Significantly, the research utilizes a variety of validation methods, such as instrumental variable approach, propensity score matching, and two‐stage least squares, to ensure the validity of the primary findings.

Artificial intelligence and health-related data: The patient's best interest and data ownership dilemma.

The rapid advancement of artificial intelligence (AI) in healthcare has the potential to revolutionize the global healthcare sector and medicine in general. However, integrating AI technologies in healthcare requires access to large amounts of personal health-related data (HRD), which raises concerns regarding confidential personal information considering unregulated and not transparent data ownership. Setting up the patient's welfare as an unquestionable principle, this commentary explores the various ethical aspects of using HRD in AI applications, focusing on informed consent, data ownership, data sharing, financial considerations, accountability, and ethical standards. Three models of potential collaboration between AI-specializing firms and healthcare providers are evaluated: the commercial model, the equitable profit-sharing model, and the public-funded non-profit model. Each model has its advantages and challenges, necessitating a careful balance between ethical considerations, financial implications, and technological advancements. Policymakers and healthcare regulators are urged to establish transparent legislation to safeguard patient privacy, ensure informed consent, and promote the responsible use of HRD in AI applications. This commentary emphasizes the importance of addressing ethical issues to protect basic patient rights, foster responsible collaborations, and ensure the ethical use of health-related data in AI-based healthcare applications. While the coexistence of regulated AI and healthcare professionals is inevitable for validating the cost-effectiveness of AI use in healthcare economics, the transparency of HRD sources is deemed of utmost importance in the best interest of the patient.

Deciphering the global research trends and significance of moral intelligence via bibliometric analysis.

Moral Intelligence (MI) as a concept has gained importance in recent years due to its wide applicability in individual, organizational, and clinical settings or even policy making. The present study employed Bibliometric analysis to understand the emerging topics associated with MI and its global research trend. This paper's primary aim was (i) to explore the temporal and geographic growth trends of the research publication on MI. (ii) to identify the most prolific countries, institutions, and authors, working on MI, (iii) to identify the most frequent terminologies, (iv) to explore research topics and to provide insight into potential collaborations and future directions, and (v) to explore the significance of the concept of moral intelligence. Bibliometric analysis was used to understand the emerging topics associated with MI and its global research trend using the SCOPUS database. VOS viewer and R were employed to analyze the result. Through the analysis conducted, the development of the construct over time was analyzed. Results have shown that Iran and the United States and these two combined account for 53.16% of the total country-wise publications. Switzerland has the highest number of Multi-county publications. Authors from Iran and Switzerland have the most number of publications. Emerging topics like decision-making, machine ethics, moral agents, artificial ethics, co-evolution of human and artificial moral agents, green purchase intention etc were identified. The application of MI in organisational decision-making, education policy, artificial intelligence and measurement of moral intelligence are important areas of application as per the results. Research interest in MI is projected to increase according to the results delineated in this article.

Hospital Performance and Operational Management: A Bibliometric Review of Key Factors Affecting Effectiveness

This study employs bibliometric and co-authorship network analyses to investigate the landscape of research in hospital performance and operational management. Using VOSviewer, key themes were visualized to depict the concentration and evolution of research areas from technology integration to quality management within healthcare settings. The study identifies central researchers and maps collaborative networks, revealing both densely interconnected groups and isolated researchers. Practical implications are discussed for academic researchers, hospital administrators, and policymakers, highlighting areas for strategic development and potential collaboration. Limitations such as selection bias and the exclusion of qualitative impact underscore the need for cautious interpretation of the results. The study provides a foundational tool for stakeholders to understand the current research environment and to guide future healthcare management practices effectively.

Trends in research on nanomedicine in urologic cancer: a bibliometric and visualized analysis

Increasing research efforts are focused on studying the synthesis and mechanisms of nanomedicine in urologic cancer. We performed a bibliometric study of the literature on nanomedicine in urologic cancer over the last 23 years, focusing on aspects such as researchers, institutions, nations, and keywords. We searched for papers in the Web of Science Core Collection from January 1, 2001, to December 29, 2023. Only reviews and original articles written in English were considered. A total of 2386 papers satisfied the given criteria for inclusion. The publications included in the study originated from 90 nations. The United States had the largest number of published papers, accounting for more than 31.01% of the total. The leading institution in this field is the Chinese Academy of Sciences, with a publishing output of 2.35%. Farokhzad, Omid C., is the most prolific author, with 21 articles, and has garnered the most citations, totaling 6271. The latest phrase to enter the top ten most common lists was "gold nanoparticles." We searched for papers in the Web of Science Core Collection from January 1, 2000, to November 28, 2023. Only reviews and original articles written in English were considered. This is the first bibliometric study of nanomedicine in urologic cancer. This article provides a comprehensive analysis of the current state of research on nanomedicine in urologic cancer over the last 23 years. On the basis of this study, future researchers can identify noteworthy publications, journals, and potential collaborators and explore cutting-edge research directions.

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

A novel approach to enterprise technical collaboration: Recommending R&D partners through technological similarity and complementarity

Choosing the right partner is a key factor in the success of enterprise R&D cooperation, directly affecting innovation outcomes and market competitiveness. Technical similarity provides a common language and foundational understanding between enterprises, while technical complementarity offers opportunities for knowledge exchange and innovation. However, no previous research has effectively integrated these two features within a collaborator recommendation framework. This study aims to explore a method that combines technological similarity and complementarity for collaborator recommendations. We introduced the Technological Similarity and Complementarity Enhanced Collaborator Recommendation (TSCE-CR) model, which constructs a heterogeneous corporate collaboration network and designs a tailored loss function. This model effectively integrates features of technological similarity and complementarity, enabling the neural network to capture and elucidate the nonlinear and multidimensional relationships in corporate collaborations. Experimental validation on patent data in the field of artificial intelligence demonstrated that our TSCE-CR model excels in identifying potential collaborators, effectively confirming the critical role of technological complementarity in R&D collaboration. This research provides a flexible framework for future studies on collaborator recommendations and offers reliable decision-making support for enterprises in selecting R&D partners.

High Throughput Characterization of Organic Thin Film Transistors.

Automation is vital to accelerating research. In recent years,the application of self-driving labs to materials discovery and device optimization has highlighted many benefits and challenges inherent to these new technologies. Successful automated workflows offer tangible benefits to fundamental science and industrial scale-up by significantly increasing productivity and reproducibility all while enabling entirely new types of experiments. However, it's implemtation is often time-consuming and cost-prohibitive and necessitates establishing multidisciplinary teams that bring together domain-specific knowledge with specific skillsets in computer science and engineering. This perspective article provides a comprehensive overview of how the research group has adopted "hybrid automation" over the last 8 years by using simple automatic electrical testers (autotesters) as a tool to increase productivity and enhance reproducibility in organic thin film transistor (OTFT) research. From wearable and stretchable electronics to next-generation sensors and displays, OTFTs have the potential to be a key technology that will enable new applications from health to aerospace. The combination of materials chemistry, device manufacturing, thin film characterization and electrical engineering makes OTFT research challenging due to the large parameter space created by both diverse material roles and device architectures. Consequently, this research stands to benefit enormously from automation. By leveraging the multidisciplinary team and taking a user-centered design approach in the design and continued improvement of the autotesters, the group has meaningfully increased productivity, explored research avenues impossible with traditional workflows, and developed as scientists and engineers capable of effectively designing and leveraging automation to build the future of their fields to encourage this approach, the files for replicating the infrastructure are included, and questions and potential collaborations are welcomed.

OpenBIM in the global architecture engineering and construction industry: a literature review of academic research

Much has been said to promote openBIM. However, little has been done to articulate its past, present, and future prospects. This paper aims at reporting a critical review of academic research on openBIM in the global architecture, engineering, and construction (AEC) industry. Through a systematic filtering process, we selected articles that are relevant, comprehensive, and with clear definitions of openBIM. Non-academic publications and studies with insufficient details were excluded. This process allowed for an initial review of 335 publications, narrowed to 49 academic journal articles published from 2009 to 2024. We discovered that in the past 15 years the openBIM literature has been expanding steadily along with openBIM industry applications. However, we identified four limitations of this expansion: (1) lack of interoperability, (2) library readiness, (3) domain knowledge gaps, and (4) lack of liability issues. Continuous improvement of the openBIM domain is needed, including standardizing codes, training talents, and exploring potential collaborations with other technologies.