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Abstract: With recent advances in astronomical observations, major progress has been made in determining the pressure of neutron star matter at high density. This pressure is constrained by the neutron star deformability, determined from gravitational waves emitted in a neutron-star merger, and the mass-radii relation of two neutron stars, determined from a new X-ray observatory on the International Space Station. Previous studies have relied on nuclear theory calculations to constrain the equation of state at low density. Here we use a combination of constraints composed of three astronomical observations and twelve nuclear experimental constraints that extend over a wide range of densities. A Bayesian inference framework is then used to obtain a comprehensive nuclear equation of state. This data-centric result provides benchmarks for theoretical calculations and modeling of nuclear matter and neutron stars. Furthermore, it provides insights into the microscopic degrees of freedom of the nuclear matter equation of state and on the composition of neutron stars and their cooling via neutrino radiation. 

Context: Stalk lodging causes up to 43 % of yield losses in maize (Zea mays L.) worldwide, significantly worsening food and feed shortages. Stalk lodging resistance is a complex trait specified by several structural, material, and geometric phenotypes. However, the identity, relative contribution, and genetic tractability of these intermediate phenotypes remain unknown. Objective: The study is designed to identify and evaluate plant-, organ-, and tissue-level intermediate phenotypes associated with stalk lodging resistance following standardized phenotyping protocols and to understand the variation and genetic tractability of these intermediate phenotypes. Methods: We examined 16 diverse maize hybrids in two environments to identify and evaluate intermediate phenotypes associated with stalk flexural stiffness, a reliable indicator of stalk lodging resistance, at physiological maturity. Engineering-informed and machine learning models were employed to understand relationships among intermediate phenotypes and stalk flexural stiffness. Results: Stalk flexural stiffness showed significant genetic variation and high heritability (0.64) in the evaluated hybrids. Significant genetic variation and comparable heritability for the cross-sectional moment of inertia and Young’s modulus indicated that geometric and material properties are under tight genetic control and play a combinatorial role in determining stalk lodging resistance. Among the twelve internode-level traits measured on the bottom and the ear internode, most traits exhibited significant genetic variation among hybrids, moderate to high heritability, and considerable effect of genotype × environment interaction. The marginal statistical model based on structural engineering beam theory revealed that 74–80 % of the phenotypic variation for flexural stiffness was explained by accounting for the major diameter, minor diameter, and rind thickness of the stalks. The machine learning model explained a relatively modest proportion (58–62 %) of the variation for flexural stiffness. 

Within the Transport Model Evaluation Project (TMEP), we present a detailed study of the performance of different transport models in Sn+Sn collisions at 270⁢𝐴MeV, which are representative reactions used to study the equation of state at suprasaturation densities. We put particular emphasis on the production of pions and Δ resonances, which have been used as probes of the nuclear symmetry energy. In this paper, we aim to understand the differences in the results of different codes for a given physics model to estimate the uncertainties of transport model studies in the intermediate energy range. Thus, we prescribe a common and rather simple physics model, and follow in detail the results of four Boltzmann-Uehling-Uhlenbeck (BUU) models and six quantum molecular dynamics (QMD) models. The nucleonic evolution of the collision and the nucleonic observables in these codes do not completely converge, but the differences among the codes can be understood as being due to several reasons: the basic differences between BUU and QMD models in the representation of the phase-space distributions, computational differences in the mean-field evaluation, and differences in the adopted strategies for the Pauli blocking in the collision integrals. For pionic observables, we find that a higher maximum density leads to an enhanced pion yield and a reduced 𝜋−/𝜋+ yield ratio, while a more effective Pauli blocking generally leads to a slightly suppressed pion yield and an enhanced 𝜋−/𝜋+ yield ratio. We specifically investigate the effect of the Coulomb force and find that it increases the total 𝜋−/𝜋+ yield ratio but reduces the ratio at high pion energies, although differences in its implementations do not have a dominating role in the differences among the codes. Taking into account only the results of codes that strictly follow the homework specifications, we find a convergence of the codes in the final charged-pion yield ratio to a 1⁢𝜎 deviation of about 5%. However, the uncertainty is expected to be reduced to about 1.6% if the same or similar strategies and ingredients, i.e., an improved Pauli blocking and calculation of the nonlinear term in the mean-field potential, are similarly used in all codes. As a result of this work, we identify the sensitive aspects of a simulation with respect to pion observables, and suggest optimal procedures in some cases. This work provides benchmark calculations of heavy-ion collisions to be complemented in the future by simulations with more realistic physics models, which include the momentum-dependence of isoscalar and isovector mean-field potentials and pion in-medium effects. 

Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performed face detection, face recognition, facial action unit detection, head pose detection, facial expression recognition, posture recognition, actigraphy analysis, sound pressure and light level detection, and visitation frequency detection. We were able to detect patient's face (Mean average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their postures (F1=0.94). We also found that all facial expressions, 11 activity features, visitation frequency during the day, visitation frequency during the night, light levels, and sound pressure levels during the night were significantly different between delirious and non-delirious patients (p-value<0.05). In summary, we showed that granular and autonomous monitoring of critically ill patients and their environment is feasible and can be used for characterizing critical care conditions and related environment factors.