Some comments on Monte Carlo and molecular dynamics methods

We highlight some links between molecular dynamics and Monte Carlo algorithms used to simulate condensed matter systems. Special attention is paid to the question of sampling the desired statistical ensemble

Folding kinetics of a polymer [corrigendum]

In our original article (Phys. Chem. Chem. Phys., 2012, 14, 60446053) a convergence problem resulted in an averaging error in computing the entropy from a set of Wang-Landau Monte-Carlo simulations. Here we report corrected results for the freezing temperature of the homopolymer chain as a function of the range of the non-bonded interaction. We find that the previously reported forward-flux sampling (FFS) and brute-force (BF) simulation results are in agreement with the revised Wang-Landau (WL) calculations. This confirms the utility of FFS for computing crystallisation rates in systems of this kind.Comment: 2 pages, 4 figure

Hey ChatGPT, give me a title for a paper about degree apathy and student use of AI for assignment writing

ChatGPT could allow students to plagiarize the content of their coursework with little risk of detection. Little is known about undergraduate willingness to use AI tools. In this study, psychology undergraduates (N = 160) from the United Kingdom, indicated their willingness to use, and history of using, ChatGPT to write university assignments. Almost a third (32%) indicated that they would use such tools; 15% indicated that they had used them already. Neither personality (conscientiousness, agreeableness, Machiavellianism, narcissism), academic performance, nor study skills self-efficacy could predict future use of AI tools. A novel Degree Apathy Scale was the only significant predictor. Willingness to use AI tools was greater when the risk of getting caught was low, and punishment was light, particularly for those high in degree apathy. Findings suggest that degree apathy is a key risk factor in academic misconduct. Wider research and pedagogical applications of degree apathy are discussed

Translating the 10 golden rules of reforestation for coral reef restoration

Efforts are accelerating to protect and restore ecosystems globally. With trillions of dollars in ecosystem services at stake, no clear framework exists for developing or prioritizing approaches to restore coral reefs even as efforts and investment opportunities to do so grow worldwide. Restoration may buy time for climate change mitigation, but it lacks rigorous guidance to meet objectives of scalability and effectiveness. Lessons from restoration of terrestrial ecosystems can and should be rapidly adopted for coral reef restoration. We propose how the 10 golden rules of effective forest restoration can be translated to accelerate efforts to restore coral reefs based on established principles of resilience, management, and local stewardship. We summarize steps to undertake reef restoration as a management strategy in the context of the diverse ecosystem service values that coral reefs provide. Outlining a clear blueprint is timely as more stakeholders seek to undertake restoration as the UN Decade on Ecosystem Restoration begins

Enhancement of island size by dynamic substrate disorder in simulations of graphene growth

We demonstrate a new mechanism in the early stages of sub-monolayer epitaxial island growth, using Monte Carlo simulations motivated by experimental observations on the growth of graphene on copper foil. In our model, the substrate is “dynamically rough”, by which we mean (i) the interaction strength between Cu and C varies randomly from site to site, and (ii) these variable strengths themselves migrate from site to site. The dynamic roughness provides a simple representation of the near-molten state of the Cu substrate in the case of real graphene growth. Counterintuitively, the graphene island size increases when dynamic roughness is included, compared to a static and smooth substrate. We attribute this effect to destabilisation of small graphene islands by fluctuations in the substrate, allowing them to break up and join larger islands which are more stable against roughness. In the case of static roughness, when process (ii) is switched off, island growth is strongly inhibited and the scale-free behaviour of island size distributions, present in the smooth-static and rough-dynamic cases, is destroyed. The effects of the dynamic substrate roughness cannot be mimicked by parameter changes in the static cases

A Learning Analytics Approach to Scaffolding Scientific Modeling in the Classroom

Scientific modeling is increasingly important both in K-12 science education and the broader scientific community, but there are significant gaps in both our understanding of how people learn modeling and how we can support them in this process. This work takes a comprehensive look at how students use digital modeling tools in science classrooms, particularly with EcoSurvey, a tool developed to support students in creating a model of the components and interactions in the local ecosystem. This tool has been developed using an iterative process and deployed in three consecutive school years as part of a design-based implementation research project in high school biology classrooms. During this time, I have developed new techniques for analyzing students&rsquo; models and modeling activity along with the impact of feedback and recommender systems. These approaches have demonstrated significant power in creating a picture of students&rsquo; modeling activity in real time. In addition, I have determined the effects of certain design decisions on student tool utilization through iterative deployment, and found that explicit scaffolds can have a significant impact on students&rsquo; models and modeling practices. Finally, I have begun to map how student activity can be related to their learning of modeling as a science and engineering practices. Through this work, I have demonstrated the power of real-time activity analytics to provide insight on the appropriate level of student support to give. This work advances learning analytics, the study of scientific modeling in the classroom, and modeling tool design.</p