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Objective: The purpose of this study was to evaluate the effect of different teaching methods of geriatric nursing on the mastery of geriatric knowledge among nursing students and their attitude toward the elderly. Methods: Relevant randomized controlled trials (RCTs) and quasi-experimental studies on teaching methods to improve nursing students' knowledge and attitude were systematically retrieved in electronic databases. The time scale of retrieval spans from the database establishment to January 2024, and the database consists of PubMed, the Cochrane Library, Web of Science, Embase, China National Knowledge Infrastructure Database (CNKI), China Biological literature database (CBM), Wanfang Database and VIP Database. Network meta-analysis was performed by Stata 16.0 software. Results: Thirty-nine studies involving 5310 nursing students met our inclusion criteria, and a total of 6 teaching methods were analyzed. According to the surface under the cumulative ranking (SUCRA) ranking, problem-based learning (PBL) was most effective in enhancing the knowledge mastery of geriatric nursing, while simulation-based learning (SBL) demonstrated the best application effect in improving nursing students' attitude toward the elderly. When considering both knowledge acquisition and attitude improvement simultaneously, service learning combined with traditional teaching method (SL+TTM) was found to exhibit the most optimal effectiveness. Conclusion: Educators in geriatric nursing education should prioritize the adoption of PBL, SBL and SL + TTM to enhance nursing students' knowledge and attitude. Protocol registry: PROSPERO (CRD42023442001). [ABSTRACT FROM AUTHOR]

Aim/objective: Given an escalated interest in fostering environmental protection, scholars have associated green entrepreneurial behavior as a stimulating factor and the cornerstone of green entrepreneurial performance. Nevertheless, the underlying mechanism that nurtures university students' green entrepreneurial behavior is yet to be explored in the extant literature. Our study proposes the antecedent effects of institutional support and green knowledge transfer to enhance university students' green entrepreneurial behavior. Moreover, we also expand the boundary conditions of these relationships and suggest the mediating effect of university students' absorptive capacity and the moderating effect of environmental responsibility. Methodology: The study samples university graduates in Chinese universities (N = 434) by adopting a lagged research design spanning over three months. We assessed the proposed model through the multivariate analytical technique. Findings: The findings indicate that institutional support and green knowledge transfer significantly elevate university students' green entrepreneurial behavior. Further, these relationships are intervened considerably through absorptive capacity's mediating effect and environmental responsibility's moderating effect. Implications: By investigating the crucial roles of institutional support and green knowledge transfer in culminating university students' green entrepreneurial behavior, our study extends the boundary conditions of these relationships and investigates the hitherto unexplored moderated mediation model. [ABSTRACT FROM AUTHOR]

Background: In the rapidly evolving field of nuclear medicine, the paramount importance of radiation protection, safety, and quality systems cannot be overstated. This document provides a comprehensive analysis of the intricate regulatory frameworks and guidelines, meticulously crafted and updated by national and international regulatory bodies to ensure the utmost safety and efficiency in the practice of nuclear medicine. Methods: We explore the dynamic nature of these regulations, emphasizing their adaptability in accommodating technological advancements and the integration of nuclear medicine with other medical and scientific disciplines. Results: Audits, both internal and external, are spotlighted for their pivotal role in assessing and ensuring compliance with established standards, promoting a culture of continuous improvement and excellence. We delve into the significant contributions of entities like the International Atomic Energy Agency (IAEA) and relevant professional societies in offering universally applicable guidelines that amalgamate the latest in scientific research, ethical considerations, and practical applicability. Conclusions: The document underscores the essence of international collaborations in pooling expertise, resources, and insights, fostering a global community of practice where knowledge and innovations are shared. Readers will gain an in-depth understanding of the practical applications, challenges, and opportunities presented by these regulatory frameworks and audit processes. The ultimate goal is to inspire and inform ongoing efforts to enhance safety, quality, and effectiveness in nuclear medicine globally. [ABSTRACT FROM AUTHOR]

With the development of augmented reality (AR) technology, AR navigation tools have gradually received increasing attention. Few previous studies have comprehensively discussed the advantages and disadvantages of AR and 2D maps from a spatial cognition perspective. Through three experiments, this article investigated the effects of different AR navigation display methods on wayfinding performance and spatial knowledge acquisition. First, we compared participants' performances in using AR and 2D map navigation aids for wayfinding and spatial knowledge tasks. Second, we developed a combined map and compared the usability of this map with that of an AR map. Then, we applied the focus + context technique to improve the combined map's minimap and verified the improved map's effectiveness by laboratory simulation. The AR map is superior to the 2D map in acquiring spatial knowledge. AR displays are crucial for helping individuals acquire landmark and route knowledge. The improved map expands the range of exocentric spatial information and highlights the landmark locations to provide comprehensive information about the environment; thus, the improved map can further assist in acquiring spatial knowledge. [ABSTRACT FROM AUTHOR]

Qualitative data analysis is recognized as a threshold concept in research education and can be conceptually challenging for doctoral students. While retreats are common approaches to support dissertation writing, we propose an unconventional approach for doctoral education with the use of retreats for qualitative data analysis. Analytic autoethnography was used to examine what features of an off-campus retreat supported data analysis of dissertation research, With the use of a focused agenda, the retreat space offered opportunities for icebreakers to stimulate synthesis thinking, student-led analytic activities, and reflective writing. Data were collected from documents, analytic artifacts, photographs, and reflective journals. We identified three themes pertaining to retreat features to support qualitative analytic work: Analytic Immersion, Analytic Support, and Analytic Reflection. Findings suggest that retreat spaces can be used to support doctoral students navigating the challenges of knowledge acquisition associated with qualitative data analysis. We recommend four key considerations when designing a qualitative analysis retreat: (1) create a space for analytic immersion; (2) design activities to cultivate student agency; (3) situate faculty for optimal student mentoring and support; and (4) allocate time and space reflective practice. This paper contributes to the ongoing conversation of threshold concepts in doctoral education and offers a new approach for supporting students during data analysis. [ABSTRACT FROM AUTHOR]

Digital games (DGs) have the potential to immerse learners in simulated real‐world environments that foster contextualised and active learning experiences. These also offer opportunities for performance assessments by providing an environment for students to carry out tasks requiring the application of knowledge and skills learned in the classroom. Although widely used for teaching and learning, there are challenges in using digital game applications for assessment. Designing assessments around tasks in complex learning environments like DGs is not always easy, particularly for educators new to using these technologies for teaching. This paper presents a game‐based assessment framework (GBAF) that is designed to be educator‐friendly and useful for the design of assessments for immersive learning environments. The application of the framework to the design of embedded and external (multiple‐choice questions) assessments is also presented. The GBAF offered an easy and structured basis for the design of two distinct assessments for a computer game. It provides educators with useful guidelines for designing assessments around pre‐existing games. Practitioner notesWhat is already known about this topicImmersive technologies are increasingly being used for teaching and learning due to their benefits to knowledge and skills acquisition. However, for educators interested in using these tools for formal classroom teaching, there is a lack of resources and guidance on assessment designs and implementations. The most commonly used assessment design framework, the evidence‐centred design (ECD), is complicated to use, requiring advanced machine learning skills to implement. This has so far limited its use to large‐scale computerised and game‐based testing.What this paper addsThis paper presents a framework developed to guide educators through the design and implementation of assessments when teaching with immersive learning applications. Grounded in the constructive alignment principles and the ECD framework, the game‐based assessment framework is designed to help educators align assessment tasks to game tasks and the intended learning outcomes. It also provides step‐by‐step guidelines for the identification of appropriate immersive applications. It shows the practical application of the framework to the design of embedded (in‐game) assessments and external (multiple‐choice tests) assessments.Implications for practice and/or policyThe framework presented in this paper provides a structured and pedagogically sound basis for the design of assessments when measuring performance, or skills and knowledge acquisition with immersive learning applications. [ABSTRACT FROM AUTHOR]

Understanding local history is fundamental to fostering a comprehensive global viewpoint. As technological advances shape our pedagogical tools, Virtual Reality (VR) stands out for its potential educational impact. Though its promise in educational settings is widely acknowledged, especially in science, technology, engineering and mathematics (STEM) fields, there is a noticeable decrease in research exploring VR's efficacy in arts. The present study examines the effects of VR-mediated interventions on cultural education. In greater detail, secondary school adolescents (N = 52) embarked on a journey into local history through an immersive 360° VR experience. As part of our research approach, we conducted pre- and post-intervention assessments to gauge participants' grasp of the content and further distributed psychometric instruments to evaluate their reception of VR as an instructional approach. The analysis indicates that VR's immersive elements enhance knowledge acquisition but the impact is modulated by the complexity of the subject matter. Additionally, the study reveals that a tailored, context-sensitive, instructional design is paramount for optimising learning outcomes and mitigating educational inequities. This work challenges the "one-size-fits-all" approach to educational VR, advocating for a more targeted instructional approach. Consequently, it emphasises the need for educators and VR developers to collaboratively tailor interventions that are both culturally and contextually relevant. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided VR reconstruction of the murals in Mogao Grottoes, Pilgrimage To Pureland, to offer the general public an accessible and engaging way to experience the Dunhuang murals. We explored an immersive VR reconstruction approach that can efficiently convert complex 2D mural art into 3D digital VR content while maintaining its originality. Specifically, we reconstructed the Mt. Wutai pilgrimage mural in Cave 61 into a plot-based interactive VR environment that offers users a more accessible and immersive narrative to visit and appreciate Dunhuang murals' complex religious, historical, and artistic value. We conducted a user study with twenty participants to further investigate how this reconstruction approach and the interactive narratives would affect users' immersive experiences and knowledge acquisition, compared with other forms of virtual access as the control group. Findings from our user study revealed that participants from the VR experimental group reported a significantly higher level of immersion and understanding of the knowledge. To conclude, our 2D-to-VR reconstruction methods provided a VR reconstruction approach and insights for creating engaging user experiences and improving users understanding of cultural and historical concepts while preserving the originality of the cultural heritage. [ABSTRACT FROM AUTHOR]

Primary school children frequently use digital devices, which can be infected by computer viruses. In this mixed methods paper with two studies (N = 278 + 114), we examined 8-year-olds' preconceptions about computer viruses and protection against them; how to teach these children about said topics using three different, 30-min-long, content-equivalent lessons; and what knowledge the children can acquire. We found that participants had limited prior knowledge of computer viruses and almost no knowledge about protection against them. However, they rarely had misconceptions. They learnt, and retained over a month, key general points and a few specific points about this domain. Acquired knowledge was still somewhat patchy, most likely represented in 'pieces' rather than as complex, theory-like chunks. Nevertheless, all three approaches produced notable learning gains (d > 1.78). A lesson organized around a narrative 5-min video and six < 1 min video snippets was the most effective: compared to a lesson organized around two 5-min videos (d = 0.89) and a teacher-led lesson without videos (d = 0.54). The findings are consistent with contemporary instructional design theories and 'knowledge in pieces' conceptual change frameworks. They imply that the topic of computer viruses should be included in second-graders' curricula. [ABSTRACT FROM AUTHOR]

Purpose: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. Methods: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. Results: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. Conclusion: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management. [ABSTRACT FROM AUTHOR]

We collect Chinese A-share listed companies from 2013 to 2022 as samples and use the multi-period difference-in-difference model (DID) to study the impact of multilingual ESG report disclosure on the enthusiasm of foreign investors. We find that Chinese companies disclose ESG reports in both Chinese and English stimulate the enthusiasm of foreign investors to hold shares. The main manifestations are the expansion of the company's foreign shareholding quota and the increase in the number of shareholders. Further research show that disclosure of multilingual ESG reports makes up for the readability of company annual reports for foreign investors. In the case of companies with poor analyst attention and comparability of accounting information, and companies that hire non-big four auditing firms to audit financial reports, multilingual ESG report disclosures are more positive for foreign shareholdings. The participation of the central investor service center in corporate governance is weak, the degree of regional cultural integration is low, and the disclosure of English ESG reports by Chinese enterprises is conducive to promoting the enthusiasm of foreign shareholding. The research conclusions provide theoretical guidance and empirical reference for enterprises to expand information disclosure methods to foreign investors and attract overseas capital investment. [ABSTRACT FROM AUTHOR]

The African Growth Opportunity Act (AGOA) has been a crucial trade and development initiative, offering preferential access to qualified Sub-Saharan African (SSA) countries to the United States market since its enactment in 2000. This paper presents a comprehensive review of scholarly articles and policy reports that analyze the impact of AGOA on the economic performance of SSA countries. Employing various econometric methods and data analysis techniques, researchers have investigated the effects of AGOA on trade flows, foreign direct investment (FDI) inflows, employment, economic growth, and poverty levels. The findings reveal that AGOA has positively affected the region's trade, particularly in apparel, textiles, and agriculture. However, its influence on promoting export diversification and attracting FDI is nuanced, with substantial heterogeneity among the beneficiary countries and industries within each country. While some SSA countries have experienced substantial export growth and FDI inflows, others have not fully leveraged the benefits of AGOA due to absorptive capacity constraints and governance challenges. AGOA's effectiveness in promoting broad-based employment, GDP growth, and poverty reduction remains an active area of inquiry, necessitating further research to understand the policy's sustained impact and inform future trade policy designs for SSA countries. [ABSTRACT FROM AUTHOR]

Early adolescence is a period characterized by enormous biological, cognitive, sexual, emotional, and social changes. Sexual curiosity and the desire to acquire sexual health (SH) information are part of these developments. Understanding the SH knowledge acquisition process is critical for designing interventions that can best support very young adolescents (VYAs). This study explored the SH knowledge acquisition processes among VYA girls aged 10 to 14 years who attended the DREAMs Girl Only Clubs (GOCs) and those who did not. The GOCs were a part of a larger comprehensive HIV prevention project called DREAMS (Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe) which provided an evidence-based core package of interventions to VYAs to prevent HIV. In-depth interviews were conducted with 43 VYA girls aged 10–14 years in two rural southern districts of Zomba and Machinga in Malawi. Twenty-three VYA girls were GOC participants and 20 VYAs were not. Guided by the Social Ecological Model, a thematic analysis approach was used to analyze the data with the assistance of Nvivo 12 software. The SH knowledge acquisition processes were the interaction of various factors at the microsystem (self-efficacy, attitudes, trust and the beginning of menstruation), mesosystem (communication of SH issues between VYAs and their immediate family and peers), and exosystem levels (availability of life skills programs and mother-groups in schools and availability of GOCs). Compared to Non-GOC participants, GOC participants demonstrated an in-depth knowledge of SH issues and positive sexual behaviors such as HIV testing. Limitations to SH knowledge acquisition were adult messages' focus on sexual relationship avoidance and on girls who have started menstruation; the perception of adults not being knowledgeable about SH and school teachers hiding some SH information. VYAs' SH interventions that provide VYAs with accurate, consistent, and age-appropriate SH information such as the GOCs have the potential to address the limitations that other sources have in reaching VYAs. Integrating such interventions with programs that empower parents, other adults, and teachers with comprehensive SH information and with skills on how to deliver SH information to VYAs can enhance VYAs' SH knowledge acquisition and influence positive behavior change. [ABSTRACT FROM AUTHOR]

Library data contains many students' reading records that reflect their general knowledge acquisition. The purpose of this study is to deeply mine the library book-borrowing data, with concerns on different book catalogues and properties to predict the students' extracurricular interests. An intelligent computing framework is proposed by the fusion of a neural network architecture and a partial differential equations (PDE) function module. In model designs, the architecture is constructed as an adaptive learning backpropagation neural network (BPNN), with automatic tuning of its hyperparameters. The PDE module is embedded into the network structure to enhance the loss functions of each neural perceptron. For model evaluation, a novel comprehensive index is designed using the calculus of information entropy. Empirical experiments are conducted on a diverse and multimodal time-series dataset of library book borrowing records to demonstrate the effectiveness of the proposed methodology. Results validate that the proposed framework is capable of revealing the students' extracurricular reading interests by processing related book borrowing records, and expected to be applied to "big data" analysis for a wide range of various libraries. [ABSTRACT FROM AUTHOR]

Objectives To determine factors influencing reader agreement in breast screening and investigate the relationship between agreement level and patient outcomes. Methods Reader pair agreement for 83 265 sets of mammograms from the Scottish Breast Screening service (2015-2020) was evaluated using Cohen's kappa statistic. Each mammography examination was read by two readers, per routine screening practice, with the second initially blinded but able to choose to view the first reader's opinion. If the two readers disagreed, a third reader arbitrated. Variation in reader agreement was examined by: whether the reader acted as the first or second reader, reader experience, and recall, cancer detection and arbitration recall rate. Results Readers' opinions varied by whether they acted as the first or second reader. Furthermore, reader 2 was more likely to agree with reader 1 if reader 1 was more experienced than they were, and less likely to agree if they themselves were more experienced than reader 1 (P  < .001). Agreement was not significantly associated with cancer detection rate, overall recall rate or arbitration recall rates (P  > .05). Lower agreement between readers led to a higher arbiter workload (P  < .001). Conclusions In mammography screening, the second reader's opinion is influenced by the first reader's opinion, with the degree of influence dependent on the readers' relative experience levels. Advances in knowledge While less-experienced readers relied on their more experienced reading partner, no adverse impact on service outcomes was observed. Allowing access to the first reader's opinion may benefit newly qualified readers, but reduces independent evaluation, which may lower cancer detection rates. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine the role of open innovation practices to promote globalization. For this purpose, this study considered two open innovation practices: external knowledge and supplier involvement. A survey was carried out to collect primary data from the respondents. This study preferred Chinese small and medium-sized enterprises (SMEs) to examine the role of open innovation in globalization. Data were collected from the employees of SMEs in China. By employing Structural Equation Modeling, this study addressed the important insights for academicians and practitioners. It is concluded that; open innovation practices have key role to promote globalization. The promotion of open innovation practices among the Chinese SMEs can increase the globalization potential. The strategies of open innovation led to increase in business collaboration across the border business organizations. The business collaboration between the SMEs of various countries is the best way to enhance globalization by increasing the level of social capital. Therefore, those institutions who want to promote globalization should promote open innovation practices among the business organizations. [ABSTRACT FROM AUTHOR]

There is substantial evidence that incorporating interactive environments for game-based instruction has a significant potential to support the development of computational thinking and programming skills in primary education students. However, it is not clear whether a simulation game (SG) with different user interface elements, created via three-dimensional (3D) virtual worlds and visual programming environments to project various problem-solving exercises in a simulated reality, can significantly influence students to think and practice "computationally" their solution plans into code. The current study aims to identify any possible added value of each instructional approach by measuring students' game experience and learning performance. This quasi-experimental study involved ninety participants (n = 90) aged between 10 and 11 years that consisted of two comparison conditions. The experimental group (n = 45) received training using OpenSimulator in combination with Scratch4SL, whereas the control group (n = 45) was trained using Scratch. The findings indicate considerable differences in students' game experience and satisfaction, but no statistically significant difference in their learning performance and knowledge gain was identified. This study provides several design implications for user interface and game elements to inform educational practitioners and instructors about the benefits that each approach can offer students for better knowledge acquisition and deeper disciplinary understanding. [ABSTRACT FROM AUTHOR]

Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while playing. The present work aims to fill the gap in the existing literature by showcasing the power of learning analytics methods to reveal and represent students' behavior when participating in a computer-supported educational escape room. Specifically, we make use of sequence mining methods to analyze the temporal and sequential aspects of the activities carried out by students during these novel educational games. We further use clustering to identify different player profiles according to the sequential unfolding of students' actions and analyze how these profiles relate to knowledge acquisition. Our results show that students' behavior differed significantly in their use of hints in the escape room and resulted in differences in their knowledge acquisition levels. [ABSTRACT FROM AUTHOR]

The spillover effect of university-based agricultural research and development (R&D) has been recognized as a crucial factor contributing to the enhanced performance of the agricultural industry. Nonetheless, the psychological impact of organizational inertia on individuals and groups may shape the influence of such spillovers for agribusiness firms. To date, there has been limited exploration of the interplay between university agricultural R&D spillovers and agribusiness firms. Utilizing panel data from Chinese listed agribusiness firms between 2009 and 2019, our empirical investigation reveals a negative short-term relationship between university agricultural R&D spillovers and agribusiness firm performance due to the similarity in knowledge backgrounds. In the short term, organizational inertia, stemming from familiar and parallel knowledge, renders university agricultural R&D spillovers unfavorable to agribusiness firm performance, thereby reducing their value to the enterprises. Conversely, the long-term influence of university agricultural R&D spillovers on agricultural enterprise performance is positive, as organizational inertia dissipates over time. Additionally, our findings indicate that university non-agricultural R&D spillovers serve to positively moderate the relationship between agricultural R&D spillovers and agribusiness firm performance in the short term, while exerting a negative moderating effect in the long term. Lastly, our analysis reveals a negative correlation between the effect of university agricultural R&D spillovers and the geographical distance between agribusiness firms and universities. This suggests that proximity to academic institutions may play a role in shaping the impact of R&D spillovers on agribusiness performance. In summary, our study highlights the complex dynamics between university agricultural R&D spillovers and agribusiness firm performance, revealing both short-term and long-term effects. We also underscore the importance of considering the influence of organizational inertia and the moderating role of non-agricultural R&D spillovers. Understanding these relationships is crucial for informing strategic decisions and fostering innovation within the agricultural industry. [ABSTRACT FROM AUTHOR]

Drawing upon cases studies from Southeast Asia, especially Singapore and Malaysia, this article addresses the following questions pertaining to the rise of China and its impact upon Chinese voluntary associations (CVAs) in the region over the past two decades. By employing theoretical insights of positionality, nudging and de‐territorialisation and by focusing on various strategies pursued respectively by CVAs and the state, we conclude: (i) the growing economic ties between China and Southeast Asia serve as the platform through which the reconstruction of the CVAs take place; (ii) the reconstitution of the CVAs has been significantly driven by their own initiatives to compete in a new economy, in which knowledge of and connection with a rising China as an expanding market and a culture has advantages; (iii) the states in both China and Southeast Asia have played a part in the reconfiguration of the CVAs, motivated by their respective political and economic agendas; and (iv) it is imperative to go beyond the conventional approaches in understanding CVAs (internal structure and external connections) that have dominated much of the existing literature; and we argue that it is in the interactions and intersections between the internal dynamics and external political economy that a new type of CVAs has emerged. [ABSTRACT FROM AUTHOR]

As a result of alkali ASP flooding in oil and gas fields, strata and pipelines become seriously scaled, which poses a threat to the normal operation of crude oil production. We propose an intelligent knowledge reasoning model for dynamic scaling prediction in order to address the problems of high directivity, poor generalization ability, and poor application effect of existing scaling prediction methods. The model framework includes the knowledge acquisition layer which mainly relates to the manual acquisition of scaling prediction knowledge and the intelligent training of the knowledge base, and it includes the knowledge modeling layer that provides a set of standard domain common ontology and knowledge organization system using the ontology modeling technology, it also includes the knowledge inference layer which is the application layer of the model. The three layers collaborate and finally complete the scaling prediction through inference and expression. A total of 238 wells were selected for experimentation in the northern development area of the Xingshugang Oilfield. Experimental results indicate that the model has the highest accuracy of 91.87%. Additionally, the time series prediction trend for the six ions matches the trend of change in ion concentration in the scaling state, verifying the accuracy of the model's predictions. [ABSTRACT FROM AUTHOR]

The establishment of the correspondence between the important features of the topside weld pool and the penetration state is a key link in penetration control. This article presents a scheme of variable gap welding penetration control based on rough-fuzzy modeling. By utilizing the basic concepts of variable precision rough sets and conditional information entropy, relevant algorithms for attribute reduction and rule extraction in decision information systems are addressed. Raw data for modeling are obtained through current–gap combination experiments, and then rough-set-based knowledge acquisition algorithms are used to generate classification rules for the penetration state. Based on the correspondence between the important features of the topside pool and the penetration state, a fuzzy control model is established. Welding experiments under variable gaps are conducted to examine the proposed control scheme, and the results indicate that the weld has good penetration and meets the requirements of welding specifications. [ABSTRACT FROM AUTHOR]

Background: Letter knowledge is crucial in the first stages of reading development. It supports learning letter‐sound mappings and the identification of the letters that make up words. Previous studies have investigated the longitudinal impact of early letter knowledge on children's further word reading abilities. This study employed an artificial orthography learning paradigm to explore whether the rate of letter learning modulates children's reading and word identification skills. Methods: In an initial training phase, 8‐year‐old Spanish children (N = 30) learned nine artificial letters and their corresponding sounds (two vowels and six consonants). The letter learning rate was set according to the number of attempts needed to name at least seven letters (i.e., 80% correct). These ranged from 1 to 4. In a second training phase, children visualized words made up of the trained letters while listening to their pronunciations. Some words included a context‐dependent syllable (i.e., leading to grapheme‐to‐phoneme inconsistency), and others had an inconsistent syllable (i.e., phoneme‐to‐grapheme inconsistency). The post‐test consisted of a reading aloud task and an orthographic‐choice task in which the target word was presented with a distractor equal to the target except for the substitution of a letter. Results: Children showed a high accuracy rate in the post‐test tasks, regardless of whether words contained context‐dependent or inconsistent syllables. Critically, the letter learning rate predicted both reading aloud and identification accuracy of words in the artificial orthography. Conclusions: We provide evidence for the vital role of letter knowledge acquisition ability in children's decoding and word identification skills. Training children on this ability facilitates serial letter‐sound mapping and word identification skills. Artificial orthography paradigms are optimal for exploring children's potential to achieve specific literacy skills. Highlights: What is already known about this topicChildren's understanding of the alphabetic code in a given language is key to developing reading abilities.Letter knowledge is involved in word decoding by fostering letter‐sound associations.Children's letter knowledge is a crucial predictor of further decoding skills. What this paper addsWe employed an artificial orthography learning paradigm to mimic children's process of reading acquisition, minimizing previous knowledge effects.The speed at which children acquire letter knowledge modulates not only decoding but also word identification outcomes.The results obtained in the artificial orthography paradigm replicate and extend the findings obtained in developmental studies with real orthographies. Implications for theory, policy or practiceLetter knowledge training is an important means to improve children's decoding abilities and to facilitate the accurate identification of words.Children who struggle to learn letter sounds in the early years might be at risk of developing reading difficulties and should receive specific letter training.Artificial learning paradigms can be an important tool to test children's learning ability when they have already been exposed to letters and words in their orthography. [ABSTRACT FROM AUTHOR]

Purpose: OpenWHO is the World Health Organization's online learning platform that was launched in 2017. The COVID-19 pandemic led to massive growth in the number of courses, enrolments and reach of the platform. The platform is built on a stable and scalable basis that can host a large volume of learners. The authors aim to identify key factors that led to this growth. Design/methodology/approach: In this research paper, the authors examined OpenWHO metadata, end-of-course surveys and internal processes using a quantitative approach. Findings: OpenWHO metadata showed that the platform has hosted over 190 health courses in 65 languages and over seven million course enrolments. Since the onset of the pandemic, there have been more women, older people and people from middle income countries accessing courses than before. Following data analysis of the platform metadata and course production process, it was found that several key factors contributed to the growth of the platform. First, OpenWHO has a standardised course production pathway that ensures efficiency, consistency and quality. Further, providing courses in different languages increased its reach to a variety of populations throughout the world. For this, multi-language translation is achieved through a network of translators and an automated system to ensure the efficient translation of learning products. Lastly, it was found that access was promoted for learners with disabilities by optimising accessibility in course production. Data analysis of learner feedback surveys for selected courses showed that the courses were well received in that learners found it useful to complete courses that were self-paced and flexible. In addition, results indicated that preferred learning methods included videos, downloadable documents, slides, quizzes and learning exercises. Originality/value: Lessons learnt from the WHO's learning response will help prepare researchers for the next health emergency to ensure timely, equitable access to quality health knowledge for everyone. Findings of this study will provide valuable insights for educators, policymakers and researchers in the field who intend to use online learning to optimise knowledge acquisition and performance. [ABSTRACT FROM AUTHOR]

James Poskett's book, "Horizons: a global history of science," challenges the traditional narrative of the advent of modern science by examining the contributions of non-European scholars and traditions. The book explores how the modern sciences were constructed through exchange, circulation, and confrontation with other scholarly traditions. It highlights the global history of social and political engineering that supports the development of modern science, while also acknowledging the imbalances and asymmetries that have been established through processes of globalization and knowledge predation. The book offers a mosaic version of the grand canonical narrative, with a more cosmopolitan perspective, and calls for a shared history that includes diverse perspectives and practices of knowledge. [Extracted from the article]

Motivation Information retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query–article annotations that are difficult to obtain in biomedicine. As a result, most biomedical IR systems only conduct lexical matching. In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine. Results To train MedCPT, we collected an unprecedented scale of 255 million user click logs from PubMed. With such data, we use contrastive learning to train a pair of closely integrated retriever and re-ranker. Experimental results show that MedCPT sets new state-of-the-art performance on six biomedical IR tasks, outperforming various baselines including much larger models, such as GPT-3-sized cpt-text-XL. In addition, MedCPT also generates better biomedical article and sentence representations for semantic evaluations. As such, MedCPT can be readily applied to various real-world biomedical IR tasks. Availability and implementation The MedCPT code and model are available at https://github.com/ncbi/MedCPT. [ABSTRACT FROM AUTHOR]

Introduction: Previous reviews on active learning in dental education have not comprehensibly summarized the research activity on this topic as they have largely focused on specific active learning strategies. This scoping review aimed to map the breadth and depth of the research activity on active learning strategies in undergraduate classroom dental education. Methods: The review was guided by Arksey & O'Malley's multi-step framework and followed the PRISMA Extension Scoping Reviews guidelines. MEDLINE, ERIC, EMBASE, and Scopus databases were searched from January 2005 to October 2022. Peer-reviewed, primary research articles published in English were selected. Reference lists of relevant studies were verified to improve the search. Two trained researchers independently screened titles, abstracts, and full-texts articles for eligibility and extracted the relevant data. Results: In total, 93 studies were included in the review. All studies performed outcome evaluations, including reaction evaluation alone (n = 32; 34.4%), learning evaluation alone (n = 19; 20.4%), and reaction and learning evaluations combined (n = 42; 45.1%). Most studies used quantitative approaches (n = 85; 91.3%), performed post-intervention evaluations (n = 70; 75.3%), and measured student satisfaction (n = 73; 78.5%) and knowledge acquisition (n = 61; 65.6%) using direct and indirect (self-report) measures. Only 4 studies (4.3%) reported faculty data in addition to student data. Flipped learning, group discussion, problem-based learning, and team-based learning were the active learning strategies most frequently evaluated (≥6 studies). Overall, most studies found that active learning improved satisfaction and knowledge acquisition and was superior to traditional lectures based on direct and indirect outcome measures. Conclusion: Active learning has the potential to enhance student learning in undergraduate classroom dental education; however, robust process and outcome evaluation designs are needed to demonstrate its effectiveness in this educational context. Further research is warranted to evaluate the impact of active learning strategies on skill development and behavioral change in order to support the competency-based approach in dental education. [ABSTRACT FROM AUTHOR]

Introduction: Virtual simulation within health professions education can be categorized as two-dimensional or three-dimensional, dependent upon how users interact with the scenario. A core difference between these two categories is the degree of immersion experienced by the user. The importance of immersion for knowledge-based learning is unclear. Hence, this pilot study compares two virtual simulators to determine the impact of immersion on knowledge acquisition and retention and user experience. Methods: This randomized crossover trial consisted of 25 fifth-year medical students attending two consecutive teaching sessions using two-dimensional and three-dimensional virtual simulation, respectively. Multiple-choice questions, completed immediately before and after, and one-month after the sessions, were employed to determine knowledge acquisition and retention. Questionnaires, consisting of Likert-scale and open-ended questions, evaluated user experience. Quantitative data was analyzed using a Student's t-test and qualitative data was analyzed using thematic analysis. Results: Both interventions demonstrated statistically significant levels of knowledge acquisition and retention, though there was no significant difference in the extent of learning between the simulators. The two interventions offered valuable and acceptable approaches to virtual simulation, though Likert-scale responses indicated that participants significantly preferred three-dimensional virtual simulation. Free-form responses revealed themes of education and technology, with subthemes of desirability, learning, curriculum integration, fidelity, hardware and software. Conclusions: The findings indicate that higher levels of immersion do not appear to offer greater educational benefit, with two-dimensional simulation possibly offering an equally valuable learning experience to three-dimensional simulation. Participants appeared to significantly prefer three-dimensional virtual simulation, though potential uses for both simulators were identified. [ABSTRACT FROM AUTHOR]

Background. The Back School program has been recommended in many countries around the world for patients with low back pain (LBP) to help improve self-efficacy to enhance their prognosis. However, few studies have reported on the application of the Back School in East Asia, including China. This study aimed to explore the Back School's effect on Chinese adults with chronic LBP based on four areas: posture, knowledge of LBP, physical activity and body performance. Material and methods. There were 10 participants in the intervention group and 11 in the control group. Baseline data was collected prior to the intervention, including upper body physical examination, core and lower limb muscle examination, Roland-Morris Disability Questionnaire, LBP Knowledge Questionnaire and Global Physical Activity Questionnaire. Physical indicators and questionnaires were retaken after the 8-week Back School intervention. The differences between the two groups were compared before and after the intervention. Results. There was a statistically significant increase in McGill trunk flexion test results and knowledge of LBP (especially basic knowledge and treatment sections) in the intervention group. Conclusions. The Back School-based intervention model has a positive impact on muscle performance in the core area and knowledge acquisition of LBP in Chinese patients with chronic LBP. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN's superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices. [ABSTRACT FROM AUTHOR]

Positioned in the study of firm heterogeneity in industrial districts (IDs), this study analyses how mergers and acquisitions (M&A) differ between local and non-local acquirers, impacting district evolution. Focusing on the Castellon ceramic tile district transformation, from acquisitions over 2012–2022, M&A and their implication for the local district are discussed, cross-fertilizing strategy and IDs literatures. Findings, from using mixed-methods, reveal that in IDs, the M&A process remarkably differs between local and non-local firms. While local firms tend to specialize in the related focal business, targeting local acquisitions to achieve internal synergies from a better valuation and integration of acquisitions, foreign firms seek access to tacit knowledge through relational and network synergies through diversification. Effects on the territory from M&A are also discussed. [ABSTRACT FROM AUTHOR]

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances. [ABSTRACT FROM AUTHOR]

It has become a common way for enterprises to use external knowledge to reduce the development bottleneck caused by internal resource constraints. How does external knowledge develop inside enterprises and then promote the innovation performance? Drawing upon resource-based view and dynamic capability theory, this study proposes a chain mediation model of how firms' opportunity identification and R&D, as two distinctive microfoundations of dynamic capabilities, mediate the relationship between external knowledge acquisition and firms' innovation performance. Through the questionnaire survey of Chinese high-tech enterprises, the results show that both external market knowledge acquisition and external technological knowledge acquisition influence the level of opportunity identification and internal R&D activities, so that the internal knowledge base of the enterprise can be updated or reconstructed, and the innovation performance can be improved. This further confirms that enterprises transform external knowledge with the role of dynamic capabilities, which enriches the research on open innovation from the perspective of dynamic capabilities. Plain Language Summary: How external knowledge acquisition contribute to innovation performance The single-use of resource-based view makes it difficult to explain how external knowledge acquisition enhances innovation performance. Drawing upon resource-based view and dynamic capability theory, this study proposes a chain mediation model of how firms' opportunity identification and internal R&D, as two distinctive microfoundations of dynamic capabilities, mediate the relationship between external knowledge acquisition and firms' innovation performance. Through the questionnaire survey of Chinese high-tech enterprises, the hierarchical regression results show that both external market knowledge acquisition and external technological knowledge acquisition influence the level of opportunity identification and internal R&D activities, so that the internal knowledge base of the enterprise can be updated or reconstructed, and the innovation performance can be improved. This further confirms that enterprises transform external knowledge with the role of dynamic capabilities, which enriches the research on open innovation from the perspective of dynamic capabilities. [ABSTRACT FROM AUTHOR]

Livestreaming commerce (LSC) has transformed the retail business model, introducing it as an integral channel of distribution in social commerce. Numerous existing contemporary research literature highlights the absence of LSC-underpinning theories. The paramount aim of the present study is to furnish a comprehensive reference for forthcoming analyses by conducting an all-encompassing meta-review of the literature. The present study adopted VOSviewer software package to conduct a bibliometric analysis of 75 articles in domain. The conducted comprehensive literature analysis reveals current LSC research segments, classifying significant discoveries into three broad areas of specialization: streamer-focused research, consumer-focused research, and platform-focused research. These subdomains contribute to the existing knowledge base by identifying underpinnings theory, methodologies, and eight core themes that have emerged: behavior, credibility, trust, adoption, costs, information dissemination, purchase intention and knowledge acquisition. A comprehensive study is a valuable tool for pinpointing pivotal research areas that have been extensively examined while establishing a strong foundation for detecting potential obstacles and emerging patterns that could influence forthcoming investigations. This study aims to fill the gap by conducting a comprehensive review of this emerging phenomenon. This study categorizes key findings into three specialization and identifies underpinnings theory, and methodologies for each subdomain. By exploring key themes in the field, this study sheds light on important areas for future research in LSC. Plain Language Summary: Study using bibliometric analysis to review the existing live streaming commerce (LSC) research The aim of the present study is to furnish a comprehensive reference for forthcoming LSC research by conducting an all-encompassing the exist literature. The present study analysis 75 typical research articles in domain and identified three broad areas of specialization: streamer-focused research, consumer-focused research, and platform-focused research. These subdomains contribute to the existing knowledge base by identifying underpinnings theory, methodologies, and detect eight core themes that have emerged: behavior, credibility, trust, adoption, costs, information dissemination, purchase intention and knowledge acquisition. This study aims to fill the gap by conducting a comprehensive review of this emerging phenomenon. [ABSTRACT FROM AUTHOR]

Modern cutting-edge technologies have changed the appearance of the world and are presently working diligently to turn this partially automated world into a fully autonomous one. The primary players in this quest are the Internet of Things (IoT), big data, and artificial intelligence. These technologies are game changers in almost all walks of life, including business, entertainment, healthcare, agriculture, education, etc. In the education sector, these technologies play their role to some extent, but there is still some room that needs to be filled in order to utilize the full potential of these technologies. For instance, there is very limited work in the literature that utilizes these technologies for developing college students' innovative and entrepreneurial skills. Thus, the main goal of this research is to investigate how big data analysis and IoT might be used by the professional associations of college students to encourage their growth of innovative and entrepreneurial skills. In this study, we tackled this issue in two ways: first, we thoroughly examined how IoT can help college students' professional associations for developing their innovative and entrepreneurial skills. More specifically, we used IoT to collect data on innovative and entrepreneurial skills from associations of college students. Secondly, we used a big data analytic algorithm, i.e., C4.5 decision tree, to calculate the knowledge acquisition rate for the professional associations of students' training in the field of entrepreneurship and innovation. The results are carefully evaluated, and a thorough comparison is made with relevant work from the literature. The experimental results demonstrate that our proposed method has a high degree of data classification accuracy and a high data throughput capacity as compared to state-of-the-art approaches. The proposed IoT and big data-enabled approach can significantly enhance college students' professional practice and employment capabilities. [ABSTRACT FROM AUTHOR]

This study investigates the students' behaviour, especially regarding their cognitive and psychological endowments' effect on their cognitive absorption, attachment comprehensivity, and learning achievement through various types of learning media. Specifically, it highlights that the resulting learning performance fundamentally starts from individual endowment positions, regardless of the available types of learning media. The authors find that the cognitive and psychological endowments' functional superiority dominates the learning achievement compared to the MR role types. Furthermore, we demonstrate that individuals' beliefs, attitudes, and behaviour are superior to their cognitive and psychological endowments because motivation, inner spirit, and faithfulness in appropriation defeat their material knowledge acquisition in their learning processes, such as absorption, achievement, and attachment. This research presents its novelty through learning attachment behaviour due to supremely personal psychological and cognitive endowment effects. Furthermore, the students with the highest negative experiences impact low absorption, comprehensive attachment, and learning achievement. Finally, the authors reveal that individually endowed mindsets and attitudes contribute to higher learning performance than the MR role types. Thus, it demonstrates that the MR role types produce a low incremental performance, which is nothing more than maximising valuable information. [ABSTRACT FROM AUTHOR]

This study analyzes the market value of listed companies in Mainland China across different industries, including capital-intensive, labor-intensive, technology-intensive, and other industries. A generalized neoclassical investment model that considers physical capital, labor, and knowledge capital as input variables is built to theoretically decompose firm value. The empirical results indicate that knowledge capital accounts for an increasing proportion of the market value of companies, rising sharply from 21.5% in 2009 to 37.9% in 2018. In contrast, the share of labor in enterprise market value has been decreasing year by year, dropping from 56.5% in 2009 to 36.4% in 2018. The share of physical capital in enterprise market value remains relatively stable. Based on these findings, the study simulates the optimal investment behaviors and their influence on the firm value of various types of enterprises, providing valuable insights for investment decision-making for managers in different industries. [ABSTRACT FROM AUTHOR]

Nowadays, due to the increasing number of cyberattacks, cybersecurity education, training, and awareness are considered crucial for preparing current and future IT professionals. Thus, it is essential for educational institutions to foster well-designed learning strategies in the field of cybersecurity that will not only focus on theory-based learning interventions but also on encapsulating authentic learning practices. In this context, the paper at hand presents a ubiquitous scenario-based learning (SBL) intervention, blended with IoT devices for introducing the topic of the man-in-the-middle attack to 1st-grade students in vocational education. The learning scenario enables two-way plain text communication through a LoRa network. For securing the transmission and assure confidentiality, basic encryption techniques are enabled for the transmitted messages. Meanwhile, an eavesdropper, acting as the man-in-the-middle attacker, tries to intercept the communication, by applying different decryption techniques. For this purpose, a u-Learning app was developed. The app was evaluated by ninety 1st-grade students of an educational institute of vocational training, in terms of effectiveness, efficiency, knowledge acquisition, and learners' satisfaction. Among others, the results show that the effectiveness and the efficiency of the proposed learning process were 92.03%, and 89.63%, respectively. Finally, learners' satisfaction was high, and their knowledge acquisition was improved. [ABSTRACT FROM AUTHOR]

This paper explores the prospects for a Pritchardean epistemological disjunctivist account of memory knowledge. We begin by providing an overview of Duncan Pritchard's epistemological disjunctivist account of perceptual knowledge, as well as the theoretical advantages of such an account. Drawing on that account, we present and motivate our own Pritchardean epistemological disjunctivist account of memory knowledge. After distinguishing different sorts of memory and the different roles that memory can play in knowledge acquisition, we set out our account and argue that the case for epistemological disjunctivism enjoys parity with the case for epistemological disjunctivism of perceptual knowledge. We also consider objections to our account. The first is from Joe Milburn and Andrew Moon, who argue against a general epistemological disjunctivism of memory on the basis of differences in memory types. The second objection arises from a Radford-like case whereby one's true belief is from memory even though one lacks rational support that is reflectively accessible to one. [ABSTRACT FROM AUTHOR]

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies. [ABSTRACT FROM AUTHOR]

This article uses neoinstitutional theory to comparatively analyze different actors' institutional work with "problematic school absenteeism". Data was generated around four cases, each consisting of absent students and actors who work with absenteeism. The results describe how a dichotomous view of students' well-being and knowledge development is established among actors, fueled by bureaucratic and professional logics that challenge the idea of integrating a concern for students' knowledge development and a care for their well-being. [ABSTRACT FROM AUTHOR]

Objective: The protective effect of the Mediterranean Diet (MeDi) is undisputed. However, adherence to MeDi has decreased in recent years, particularly in young people. The aim of this study was to evaluate adherence to MeDi in medical students and to assess the influence of knowledge acquisition as well as other factors on dietary compliance. Design: A cross-sectional study was conducted on medical students. The data were obtained through anonymous surveys that collected demographic characteristics, medical history, alcohol and tobacco consumption, physical activity and adherence to MeDi – using 14-point Mediterranean Diet Adherence Score (MEDAS) –. Adherence to MeDi and related factors were evaluated by univariate and multivariable analysis. Participants: Medical students from the first to the sixth year of the 2018–2019 academic year. Setting: The study was conducted at the university of Las Palmas de Gran Canaria. Results: Of 589 respondents (73 % women) mean aged 22 years (range 18–39), 58·9 % showed good adherence to MeDi. Adherence was significantly associated with age (P = 0·017) but not with sex or the presence of comorbidities. Independently, adherence to MeDi was higher in last academic courses (OR = 2·1; 95 % CI = 1·3, 3·2; P = 0·001), in those who consumed alcohol more frequently (OR = 1·5; 95 % CI = 1·0, 2·1; P = 0·039) and in those who practiced more exercise (OR = 1·5; 95 % CI = 1·2, 1·9; P < 0·001). Conclusions: Half of all medical students did not have a good adherence to MeDi. Adherence was higher at older age in higher academic years and related to greater physical activity. It would be convenient to quantify dietary knowledge as well as implement nutritional educational programmes, favouring a healthy lifestyle. [ABSTRACT FROM AUTHOR]