Does knowledge make a difference? Understanding how the lay public and experts assess the credibility of information on novel foods.

Drawing on Metzger's dual-processing model of credibility assessment, this study examines how individuals with varying topical knowledge (laypersons vs experts) assess the credibility of information on novel foods. Online focus group discussions reveal that both groups share similar motivations for assessing the credibility of information on novel foods (e.g. personal relevance and concerns about the impact of unverified information on others). However, they differ in the barriers they encounter during the assessment of information credibility. Both groups employ analytical (e.g. evaluating content quality) and intuitive methods (e.g. looking at source credibility) to assess the credibility of novel food-related information. However, they differ in the cues used for credibility assessment. Laypersons tend to rely on superficial heuristics (e.g. social endorsement cues or surface features), whereas experts rely more on content features and scientific knowledge to evaluate information credibility. Theoretical and practical implications are discussed.

Exposure to climate change information predicts public support for solar geoengineering in Singapore and the United States.

Solar geoengineering is a controversial climate policy measure that could lower global temperature by increasing the amount of light reflected by the Earth. As scientists and policymakers increasingly consider this idea, an understanding of the level and drivers of public support for its research and potential deployment will be key. This study focuses on the role of climate change information in public support for research and deployment of stratospheric aerosol injection (SAI) in Singapore (n = 503) and the United States (n = 505). Findings were consistent with the idea that exposure to information underlies support for research and deployment. That finding was stronger in the United States, where climate change is a more contentious issue, than in Singapore. Cost concern was negatively related to support for funding and perceived risk was negatively related to support for deployment. Perceived government efficacy was a more positive predictor of support for funding in Singapore than in the United States. Additionally, relatively low support for local deployment was consistent with a NIMBY mindset. This was the first study to quantify the role of climate change information in SAI policy support, which has practical implications for using the media and interpersonal channels to communicate about SAI policy measures.

Exploring the general public's and experts' risk and benefit perceptions of cultured meat in Singapore: A mental models approach.

Despite the recent approval of cultured meat products in Singapore, the understanding of public perceptions towards this novel food technology remains limited. Utilizing attitude formation theory and the mental models approach, this study compares the mental models of the general public and experts regarding their risk and benefit perceptions of cultured meat. Through four online focus group discussions with 40 participants, we found convergences in the mental models of experts and the general public concerning perceived individual- and societal-level benefits of cultured meat (e.g., health benefits and food security) as well as their perceived individual-level risks of cultured meat (e.g., potential health issues and affordability). However, divergences in understanding societal-level risks were noted; the public expressed concerns about the challenges of cultured meat to religious and racial dietary customs, while experts highlighted potential investment uncertainties due to unclear consumer acceptance of cultured meat. Theoretical and practical implications are discussed.

A forward modeling approach to analyzing galaxy clustering with SimBIG.

We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the SimBIG forward modeling framework. SimBIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply SimBIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, [Formula: see text], to [Formula: see text]. We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Quijote[Formula: see text]-body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of [Formula: see text]CDM cosmological parameters: [Formula: see text]. We derive significant constraints on [Formula: see text] and [Formula: see text], which are consistent with previous works. Our constraint on [Formula: see text] is 27% more precise than standard [Formula: see text] analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, [Formula: see text]. This improvement is equivalent to the statistical gain expected from a standard [Formula: see text] analysis of galaxy sample [Formula: see text]60% larger than CMASS. While we focus on [Formula: see text] in this work for validation and comparison to the literature, SimBIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent SimBIG analyses of summary statistics beyond [Formula: see text].

Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation.

We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approximating the Green's function expansion that relates the initial conditions of the simulations to its outcome at later times in the deeply nonlinear regime. We test the accuracy of this approximation by assessing its performance on well-understood simple cases that have either known exact solutions or well-understood expansions. These scenarios include spherical configurations, isolated plane waves, and two interacting plane waves: initial conditions that are very different from the Gaussian random fields used for training. We find our model generalizes well to these well-understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data. These tests also provide a useful diagnostic for finding the model's strengths and weaknesses, and identifying strategies for model improvement. We also test the model on initial conditions that contain only transverse modes, a family of modes that differ not only in their phases but also in their evolution from the longitudinal growing modes used in the training set. When the network encounters these initial conditions that are orthogonal to the training set, the model fails completely. In addition to these simple configurations, we evaluate the model's predictions for the density, displacement, and momentum power spectra with standard initial conditions for N-body simulations. We compare these summary statistics against N-body results and an approximate, fast simulation method called COLA (COmoving Lagrangian Acceleration). Our model achieves percent level accuracy at nonlinear scales of k ∼ 1 Mpc - 1 h , representing a significant improvement over COLA.

Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev-Zeldovich flux-mass scatter.

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux-cluster mass relation (YSZ - M), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines YSZ and concentration of ionized gas (cgas): M ∝ Yconc3/5 ≡ YSZ3/5(1 - A cgas). Yconc reduces the scatter in the predicted M by ∼20 - 30% for large clusters (M ≳ 1014 h-1 M⊙), as compared to using just YSZ. We show that the dependence on cgas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Yconc on clusters from CAMELS simulations and show that Yconc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

Perceived behavioral control as a moderator: Scientists' attitude, norms, and willingness to engage the public.

Scientists play important roles in conducting public engagement, but evidence shows that scientists perceive great challenges in doing so. Drawing broadly from the theory of planned behavior (TPB), this study examines factors predicting scientists' willingness to conduct public engagement. This study further examines how perceived behavioral control (PBC) of conducting public engagement would moderate the relationships between the proposed predictors and scientists' willingness to conduct public engagement. Using survey data collected from 706 scientists based in Singapore, this study found that attitude toward and personal norms of conducting public engagement, as well as PBC, significantly predicted scientists' willingness to conduct public engagement. Notably, PBC interacted with attitude toward conducting public engagement, the perceived descriptive norms, the perceived positive media influence, and the perceived negative external norms of conducting public engagement, as well as personal norms of conducting public engagement to predict scientists' willingness to conduct public engagement. We postulated the key role that the perception of the ease or difficulty plays in motivating scientists to conduct the skill-intensive endeavor explains the significant moderating effects. The theoretical implications on the TPB and the practical implications for public engagement are further discussed.

Exploring channels and gaps in information dissemination and acquisition among energy scientists and the public: The southeast Asian context.

This study investigates the flow of energy-related information, which plays a vital role in promoting the public understanding and support for various energy sources. Through 12 focus group discussions with the public and energy experts, this study found that energy information flows from scientists to the public through both direct (e.g., roadshows, scientists' blogs) and indirect (via agents, e.g., school, news media) channels. However, communication gaps remain between scientists and the public. First, the public commonly obtains information from personal experience and the media but not directly from scientists. Second, while the public stressed the importance of mass media and social media, only a few experts reported writing news commentaries or making social media posts about energy. Third, while scientists emphasize their relationships with the government and other agencies in disseminating information, the public shows relatively weak trust in these agencies. Implications are made for future research and public communication on energy issues.

Fake news, real risks: How online discussion and sources of fact-check influence public risk perceptions toward nuclear energy.

This study seeks to understand how online discussion, fact-checking, and sources of fact-checks will influence individuals' risk perceptions toward nuclear energy when they are exposed to fake news. Using a 2 × 3 experimental design, 320 participants were randomly assigned to one of the six experimental conditions. Results showed an interaction effect between online discussion and exposure to fact-checking, in which online discussion lowered individuals' risk perception toward nuclear energy when a fact-check was unavailable. Of those who participated in the online discussion, those who viewed a fact-check posted by traditional media have higher risk perception as compared to those who viewed a fact-check posted by a fact-check organization. Our findings indicate that different fact-checking sources can have differential effects on public risk perceptions, depending on whether online discussion is involved. To curb the spread of fake news, different fact-checking strategies will need to be deployed depending on the situation.

Thinking, not talking, predicts knowledge level: Effects of media attention and reflective integration on public knowledge of nuclear energy.

By applying the cognitive mediation model, this study seeks to investigate factors influencing public knowledge of nuclear energy in Singapore. In addition, this study seeks to extend the cognitive mediation model by explicating the knowledge variable into four facets - general science knowledge, perceived familiarity, content nuclear knowledge and contextual nuclear knowledge. Using data collected from an interviewer-led door-to-door survey with 1000 Singapore citizens and permanent residents (PRs), we found that attention to TV news, website news and social media news stimulated news elaboration and interpersonal discussion. However, attention to print newspaper was neither associated with news elaboration nor interpersonal discussion. We also found that news elaboration could enhance factual knowledge such as general science knowledge, content nuclear knowledge and contextual nuclear knowledge, while interpersonal discussion could enhance perceived familiarity. Theoretical and practical implications are discussed.

The relationship between knee loading during gait and cartilage thickness in nontraumatic and posttraumatic knee osteoarthritis.

The relationship between knee moments and markers of knee osteoarthritis progression has not been examined in different knee osteoarthritis subtypes. The objective was to examine relationships between external knee moments during gait and tibiofemoral cartilage thickness in patients with nontraumatic and posttraumatic knee osteoarthritis. For this cross-sectional study, participants with knee osteoarthritis were classified into two groups: nontraumatic (n = 22; mean age 60 years) and posttraumatic (n = 19; mean age 56 years, history of anterior cruciate ligament rupture). Gait data were collected with a three-dimensional motion capture system sampled at 100 Hz and force plates sampled at 2000 Hz. External knee moments were calculated using inverse dynamics. Cartilage thickness was determined with magnetic resonance imaging (T1-weighted, 3D sagittal gradient-echo sequence). Linear regression analyses examined relationships between cartilage thickness with knee moments, group, and their interaction. A higher knee adduction moment impulse was negatively associated with medial to lateral cartilage thickness ratio (B = -1.97). This relationship differed between participants in the nontraumatic osteoarthritis group (r = -0.56) and posttraumatic osteoarthritis group (r = -0.30). A higher late stance knee extension moment was associated with greater medial femoral condyle cartilage thickness (B = -0.86) and medial to lateral cartilage thickness (B = -0.73). These relationships also differed between participants in the nontraumatic osteoarthritis group (r = -0.61 and r = -0.51, respectively) and posttraumatic osteoarthritis group (r = 0.10 and r = 0.25, respectively). Clinical Significance: The relationship between knee moments with tibiofemoral cartilage thickness differs between patients with nontraumatic and posttraumatic knee osteoarthritis. The potential influence of mechanical knee loading on articular cartilage may also differ between these subtypes.

A Bayesian neural network predicts the dissolution of compact planetary systems.

We introduce a Bayesian neural network model that can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both nonresonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to [Formula: see text] times faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK (https://github.com/dtamayo/spock) package, with training code open sourced (https://github.com/MilesCranmer/bnn_chaos_model).

Using the Marked Power Spectrum to Detect the Signature of Neutrinos in Large-Scale Structure.

Cosmological neutrinos have their greatest influence in voids: These are the regions with the highest neutrino to dark matter density ratios. The marked power spectrum can be used to emphasize low-density regions over high-density regions and, therefore, is potentially much more sensitive than the power spectrum to the effects of neutrino masses. Using 22 000 N-body simulations from the Quijote suite, we quantify the information content in the marked power spectrum of the matter field and show that it outperforms the standard power spectrum by setting constraints improved by a factor larger than 2 on all cosmological parameters. The combination of marked and standard power spectra allows us to place a 4.3σ constraint on the minimum sum of the neutrino masses with a volume equal to 1 (Gpc h^{-1})^{3} and without cosmic microwave background priors. Combinations of different marked power spectra yield a 6σ constraint within the same conditions.

Promoting Healthier Eating via Parental Communication: Development and Validation of the Active and Restrictive Parental Guidance Questionnaire (PARQ).

Parents are important sources of influence in the development of healthy eating among children and adolescents. Besides gatekeeping and modeling, parents serve as health educators and promoters, using intentional and persuasive communication to encourage healthier eating preferences and behaviors in children. Despite this, a lack of reliable and valid measures has limited the research on how parent-driven interpersonal communication about foods influence child food consumption outcomes. Building on the research in parental mediation of media consumption, and parenting practices in public health nutrition, this study details the development and validation of the active and restrictive parental guidance questionnaire with a sample of 246 children and adolescents at the scale development phase and another sample of 1,113 children and adolescents at the scale validation phase. Findings show that parents employ four communicative strategies to encourage a healthier diet: active guidance, general discussion, preventive restrictive guidance, and promotive restrictive guidance. The new measure was shown to have good validity and measurement model fit. Implications for future research are discussed.

Examining Organizational, Cultural, and Individual-Level Factors Related to Workplace Safety and Health: A Systematic Review and Metric Analysis.

One major gap in existing health communication research is that few studies have synthesized findings from the literature to map out what are the key factors related to workplace (a) safety awareness, (b) safety risks, (c) health awareness, and (d) health risks. This study bridges the gap by systematically reviewing what these organizational, cultural, and individual-level factors are, and examine the impact of workplace safety and health publications using traditional and alternative metrics in academic and non-academic settings. Through an iterative process of coding, the results revealed six categories of organizational (management commitment, management support, organizational safety communication, safety management systems, physical work environment, and organizational environment), two cultural (interpersonal support and organizational culture), and four individual-level (perception, motivation, attitude, and behavior) factors. In terms of impact, articles that were most impactful in academia (e.g., high citation count) may not necessarily receive the same amount of online attention from the public. Theoretical and practical implications for health communication were discussed.

Comparing the knowledge gap hypothesis in the United States and Singapore: The case of nanotechnology.

This study examines the knowledge gap hypothesis in the United States and Singapore in the context of nanotechnology. This study proposes that academic discipline serves as a better indicator than education levels in predicting nanotechnology knowledge gaps. To reflect the contemporary media landscape, this study examines how attention to online media and documentaries alongside traditional news outlets affect individuals' nanotechnology knowledge. In both countries, online media and documentaries, as well as traditional news outlets, were related to nanotechnology knowledge to various extents. While the knowledge gap hypothesis was not observed in Singapore, results revealed that increased media attention and interpersonal discussion widened knowledge gaps between individuals from science and non-science disciplines in the United States. Education levels failed to reveal a consistent moderation effect. Taken together, the interaction analyses revealed that academic discipline predicted nanotechnology knowledge gaps more consistently than education levels in the United States. Theoretical and practical implications are discussed.

Predicting the long-term stability of compact multiplanet systems.

We combine analytical understanding of resonant dynamics in two-planet systems with machine-learning techniques to train a model capable of robustly classifying stability in compact multiplanet systems over long timescales of [Formula: see text] orbits. Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first [Formula: see text] orbits, thus achieving speed-ups of up to [Formula: see text] over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems. Our model, trained on ∼100,000 three-planet systems sampled at discrete resonances, generalizes both to a sample spanning a continuous period-ratio range, as well as to a large five-planet sample with qualitatively different configurations to our training dataset. Our approach significantly outperforms previous methods based on systems' angular momentum deficit, chaos indicators, and parametrized fits to numerical integrations. We use SPOCK to constrain the free eccentricities between the inner and outer pairs of planets in the Kepler-431 system of three approximately Earth-sized planets to both be below 0.05. Our stability analysis provides significantly stronger eccentricity constraints than currently achievable through either radial velocity or transit-duration measurements for small planets and within a factor of a few of systems that exhibit transit-timing variations (TTVs). Given that current exoplanet-detection strategies now rarely allow for strong TTV constraints [S. Hadden, T. Barclay, M. J. Payne, M. J. Holman, Astrophys. J. 158, 146 (2019)], SPOCK enables a powerful complementary method for precisely characterizing compact multiplanet systems. We publicly release SPOCK for community use.