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Citrusspp. trees are not fully dormant during the winter months in the northern hemisphere; therefore, they are susceptible to sporadic freeze events of various magnitudes that could decline tree productivity or be lethal. In Feb 2021, winter storm Uri produced freezing air temperatures for nearly 72 hours, which created several degrees of injuries to citrus orchards in southern Texas. Producers in the area implemented combinations of multiple horticultural practices aimed at remediating injuries from the cold spell to stimulate root and tree recovery. However, there is a gap in our understanding of how practices such as compost application (CA) combined with varying rates of nitrogen (N) might facilitate tree recovery. Therefore, we conducted a 2-year field experiment using two CAs as soil amendments in combination with three N rates (112, 168, and 224 kg·ha⁻¹N) to evaluate fruit yield and internal quality, root growth, and recovery of ‘Rio Red’ grapefruits (Citrus×paradisiMacf.) and ‘Marrs’ sweet oranges (Citrus sinensis). The yields of both crops exhibited modest improvement with CA in 2022; however, it was nonsignificant. Moreover, CA elicited more beneficial effects than N rates alone when rehabilitating trees and improving fruit internal quality after freezing events. Grapefruit brix was 4% higher in fruits harvested from trees treated with compost, and grapefruit roots exhibited a two-fold dry weight increment with CA. Sweet oranges from trees in the CA treatment had 22% lower acidity compared with that of untreated trees. Overall, our results indicate that citrus producers in southern Texas and other subtropical citrus-producing regions might facilitate the rehabilitation of tree injuries attributed to mild to moderate freeze events with moderate financial gains with the timely application of compost, which enhanced tree fitness and ameliorated fruit productivity declines during subsequent harvests. 

Abstract Fire scene reconstruction and determining the fire evolution (i.e., item-to-item ignition events) using the postfire compartment is an extremely difficult task because of the time-integrated nature of the observed damages. Bayesian methods are ideal for making inferences amongst hypotheses given observations and are able to naturally incorporate uncertainties. A Bayesian methodology for determining probabilities to items that may have initiated the fire in a compartment from damage signatures is developed. Exercise of this methodology requires uncertainty quantification of these damage signatures. A simple compartment configuration was used to quantify the uncertainty in damage predictions by firedynamicssimulator (fds) and, a compartment evolution program, jt-risk as compared to experimentally derived damage signatures. Surrogate sensors spaced within the compartment use heat flux data collected over the course of the simulations to inform damage models. Experimental repeatability showed up to 4% uncertainty in damage signatures between replicates. Uncertainties for fds and jt-risk ranged from 12% up to 32% when compared to experimental damages. Separately, the evolution physics of a simple three-fuel-package problem with surrogate damage sensors were characterized in a compartment using experimental data, fds, and jt-risk predictions. A simple ignition model was used for each of the fuel packages. The Bayesian methodology was exercised using the damage signatures collected, cycling through each of the three fuel packages, and combined with the previously quantified uncertainties. Only reconstruction using experimental data was able to confidently predict the true hypothesis from the three scenarios. 

Abstract The directional flame thermometer (DFT) is a robust device used to measure heat fluxes in harsh environments such as fire scenarios but is large when compared to other standard heat flux measurement devices. To better understand the uncertainties associated with heat flux measurements in these environments, a Bayesian framework is utilized to propagate uncertainties of both known and unknown parameters describing the thermal model of a modified, smaller DFT. Construction of the modified DFT is described along with a derivation of the thermal model used to predict the incident heat flux to its sensing surface. Parameters of the model are calibrated to data collected using a Schmidt–Boelter (SB) gauge with an accuracy of ±3% at incident heat fluxes of 5 kW/m2, 10 kW/m2, and 15 kW/m2. Markov Chain Monte Carlo simulations were used to obtain posterior distributions for the free parameters of the thermal model as well as the modeling uncertainty. The parameter calibration process produced values for the free parameters that were similar to those presented in the literature with relative uncertainties at 5 kW/m2, 10 kW/m2, and 15 kW/m2 of 17%, 9%, and 7%, respectively. The derived model produced root-mean-squared errors between the prediction and SB gauge output of 0.37, 0.77, and 1.13 kW/m2 for the 5, 10, and 15 kW/m2 cases, respectively, compared to 0.53, 1.12, and 1.66 kW/m2 for the energy storage method (ESM) described in ASTM E3057. 

Planets with radii between that of the Earth and Neptune (hereafter referred to as `sub-Neptunes') are found in close-in orbits around more than half of all Sun-like stars1,2. However, their composition, formation and evolution remain poorly understood3. The study of multiplanetary systems offers an opportunity to investigate the outcomes of planet formation and evolution while controlling for initial conditions and environment. Those in resonance (with their orbital periods related by a ratio of small integers) are particularly valuable because they imply a system architecture practically unchanged since its birth. Here we present the observations of six transiting planets around the bright nearby star HD 110067. We find that the planets follow a chain of resonant orbits. A dynamical study of the innermost planet triplet allowed the prediction and later confirmation of the orbits of the rest of the planets in the system. The six planets are found to be sub-Neptunes with radii ranging from 1.94R⊕ to 2.85R⊕. Three of the planets have measured masses, yielding low bulk densities that suggest the presence of large hydrogen-dominated atmospheres. 

ABSTRACT Eclipsing binaries are important benchmark objects to test and calibrate stellar structure and evolution models. This is especially true for binaries with a fully convective M-dwarf component for which direct measurements of these stars’ masses and radii are difficult using other techniques. Within the potential of M-dwarfs to be exoplanet host stars, the accuracy of theoretical predictions of their radius and effective temperature as a function of their mass is an active topic of discussion. Not only the parameters of transiting exoplanets but also the success of future atmospheric characterization relies on accurate theoretical predictions. We present the analysis of five eclipsing binaries with low-mass stellar companions out of a subsample of 23, for which we obtained ultra-high-precision light curves using the CHEOPS satellite. The observation of their primary and secondary eclipses are combined with spectroscopic measurements to precisely model the primary parameters and derive the M-dwarfs mass, radius, surface gravity, and effective temperature estimates using the PYCHEOPS data analysis software. Combining these results to the same set of parameters derived from TESS light curves, we find very good agreement (better than 1 per cent for radius and better than 0.2 per cent for surface gravity). We also analyse the importance of precise orbits from radial velocity measurements and find them to be crucial to derive M-dwarf radii in a regime below 5 per cent accuracy. These results add five valuable data points to the mass–radius diagram of fully convective M-dwarfs.