Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Adapting Self-Regulated Learning in an Age of Generative Artificial Intelligence Chatbots

The increasing use of generative artificial intelligence (GenAI) has led to a rise in conversations about how teachers and students should adopt these tools to enhance the learning process. Self-regulated learning (SRL) research is important for addressing this question. A popular form of GenAI is the large language model chatbot, which allows users to seek answers to their queries. This article seeks to adapt current SRL models to understand student learning with these chatbots. This is achieved by classifying the prompts supplied by a learner to an educational chatbot into learning actions and processes using the process–action library. Subsequently, through process mining, we can analyze these data to provide valuable insights for learners, educators, instructional designers, and researchers into the possible applications of chatbots for SRL

Physics education research: the teaching and learning of quantum mechanics

Undergraduate students, in their first formal encounter with Quantum Mechanics, are introduced to the shocking concepts of the quantum world. A large part of introductory quantum mechanics builds on physical theory and the corresponding mathematical formalism from Linear Algebra and Differential Equations. Students need to have a good appreciation of the physics and equipped with the relevant mathematical skills. Thus, when physics concepts are taught, students need to have been equipped with the relevant skills in mathematics in their long-term and working memory so that they are not distracted by learning the mathematics while, at the same time, learning the physics. This thesis seeks to confirm the hypothesis that the focus on mathematics distracts students from interpreting the physical picture of a quantum mechanical question. It also explores the possibility that certain personality types are more ready to learn the subject such as quantum mechanics. In response to the collected data and analysis, strategies in introducing mathematics in quantum physics are proposed and creating visuals to enhance learning and retention by stimulating working memory for different personality groups.Bachelor of Science in Physic

A Parrondo paradoxical interplay of reciprocity and reputation in social dynamics

Our study investigates the role of reciprocity and reputation in shaping social dynamics within networks. We uncover this by creating a model network with agents who follow unique beliefs and rules. While relying solely on either reciprocity or reputation often leads to negative outcomes for a group, combining these strategies leads to unexpectedly positive results. This finding, akin to the counterintuitive Parrondo's paradox, illustrates the hidden potential of integrating different social strategies in boosting group welfare. This work has implications for understanding and leveraging the physics of social dynamics, such as understanding the ‘Goldilocks domain’ for different population sizes and Burt's theory for structural holes. This article sheds light on the nuanced interrelations between reciprocity and reputation and emphasizes their impact on social welfare, offering valuable insights for taking the first steps in enhancing collective welfare in social networks.Ministry of Education (MOE)The authors thank the Ministry of Education, Singapore Academic Research Fund (AcRF) Tier 2, Grant No. MOE-T2EP50120-0021 for kind support

A Simple-FSDT-Based Isogeometric Method for Piezoelectric Functionally Graded Plates

An efficient isogeometric analysis method (IGA) based on a simple first-order shear deformation theory is presented to study free vibration, static bending response, dynamic response, and active control of functionally graded plates (FGPs) integrated with piezoelectric layers. Based on the neutral surface, isogeometric finite element motion equations of piezoelectric functionally graded plates (PFGPs) are derived using the linear piezoelectric constitutive equation and Hamilton’s principle. The convergence and accuracy of the method for PFGPs with various mechanical and electrical boundary conditions have been investigated via free vibration analysis. In the dynamic analysis, both time-varying mechanical and electrical loads are involved. A closed-loop control method, including displacement feedback control and velocity feedback control, is applied to the static bending control and the dynamic vibration control analysis. The numerical results obtained are accurate and reliable through comparisons with various numerical and analytical examples

A Simple-FSDT-Based Isogeometric Method for Piezoelectric Functionally Graded Plates

An efficient isogeometric analysis method (IGA) based on a simple first-order shear deformation theory is presented to study free vibration, static bending response, dynamic response, and active control of functionally graded plates (FGPs) integrated with piezoelectric layers. Based on the neutral surface, isogeometric finite element motion equations of piezoelectric functionally graded plates (PFGPs) are derived using the linear piezoelectric constitutive equation and Hamilton’s principle. The convergence and accuracy of the method for PFGPs with various mechanical and electrical boundary conditions have been investigated via free vibration analysis. In the dynamic analysis, both time-varying mechanical and electrical loads are involved. A closed-loop control method, including displacement feedback control and velocity feedback control, is applied to the static bending control and the dynamic vibration control analysis. The numerical results obtained are accurate and reliable through comparisons with various numerical and analytical examples

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia

Parrondo effect: Exploring the nature-inspired framework on periodic functions

Recently, a population model has been analyzed using the framework of Parrondo’s paradox to explain how behavior-switching organisms can achieve long-term survival, despite each behavior individually resulting in extinction. By incorporating environmental noise, the model has been shown to be robust to natural variations. Apart from the role of noise, the apparent ubiquity of quasi-periodicity in nature also motivates a more comprehensive understanding of periodically-coupled models of Parrondo’s paradox. Such models can enable a wider range of applications of the Parrondo effect to biological and social systems. In this paper, we modify the canonical Parrondo’s games to show how the Parrondo effect can still be achieved despite the increased complexity in periodically-noisy environments. Our results suggest the extension of Parrondo’s paradox to real-world phenomena strongly subjected to periodic variations, such as ecological systems experiencing seasonal changes, disease in wildlife and humans, or resource management

Parrondo effect: Exploring the nature-inspired framework on periodic functions

Recently, a population model has been analyzed using the framework of Parrondo’s paradox to explain how behavior-switching organisms can achieve long-term survival, despite each behavior individually resulting in extinction. By incorporating environmental noise, the model has been shown to be robust to natural variations. Apart from the role of noise, the apparent ubiquity of quasi-periodicity in nature also motivates a more comprehensive understanding of periodically-coupled models of Parrondo’s paradox. Such models can enable a wider range of applications of the Parrondo effect to biological and social systems. In this paper, we modify the canonical Parrondo’s games to show how the Parrondo effect can still be achieved despite the increased complexity in periodically-noisy environments. Our results suggest the extension of Parrondo’s paradox to real-world phenomena strongly subjected to periodic variations, such as ecological systems experiencing seasonal changes, disease in wildlife and humans, or resource management