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ABSTRACT Climate extremes—e.g., drought, atmospheric rivers, heat waves—are increasing in severity and frequency across the western United States of America (USA). Tree‐ring widths reflect the concurrent and legacy effects of such climate extremes, yet our ability to predict extreme tree growth is often poor. Could tree‐ring data themselves identify the most important climate variables driving extreme low‐ and high‐growth states? How does the importance of these climate drivers differ across species and time? To address these questions, we explored the spatial synchrony of extreme low‐ and high‐growth years, the symmetry of climate effects on the probability of low‐ and high‐growth years, and how climate drivers of extreme growth vary across tree species. We compiled ring widths for seven species (four gymnosperms and three angiosperms) from 604 sites in the western USA and classified each annual ring as representing extreme low, extreme high, or nominal growth. We used classification random forest (RF) models to evaluate the importance of 30 seasonal climate variables for predicting extreme growth, including precipitation, temperature, and vapor pressure deficit (VPD) during and up to four years prior to ring formation. For four species (three gymnosperms, one angiosperm) for which climate was predictive of growth, the RF models correctly classified 89%–98% and 80%–95% of low‐ and high‐growth years, respectively. For these species, asymmetric climate responses dominated. Current‐year winter hydroclimate (precipitation and VPD) was most important for predicting low growth, but prediction of high growth required multiple years of favorable moisture conditions, and the occurrence of low‐growth years was more synchronous across space than high‐growth years. Summer climate and temperature (regardless of season) were only weakly predictive of growth extremes. Our results motivate ecologically relevant definitions of drought such that current winter moisture stress exerts a dominant role in governing growth reductions in multiple tree species broadly distributed across the western USA. 

Background/Objectives: Hydrophobic semi-solid or liquid biodegradable polymers have shown unique advantages as injectable matrices for sustained release of a wide range of drugs. Here we report the design, synthesis, and characterization of a new low-melt liquid copolymer based on poly(ε-caprolactone) (PCL) and establish its utility as a versatile delivery platform. Methods: The copolymer, mPA20, consisting of short PCL blocks connected via acid-labile acetal linkages, was synthesized using a one-pot reaction and its properties were comprehensively characterized. Results: mPA20 is an amorphous, injectable liquid at physiological temperature and can undergo pH-sensitive hydrolytic degradation. mPA20 bearing methacrylate end groups can be photo-crosslinked into solid matrices with tunable mechanical properties. A hydrophobic fluorophore, Nile Red (NR), was solubilized in mPA20 without any solvent. Sustained release of NR into aqueous medium was achieved using mPA20, either as an injectable liquid depot or a photo-crosslinked solid matrix. Further, mPA20 self-emulsified in water to form nanodroplets, which were subsequently photo-crosslinked into nanogels. Both the nanodroplets and nanogels mediated efficient intracellular delivery of NR with no cytotoxicity. Conclusions: mPA20, a new photo-crosslinkable, hydrophobic liquid copolymer with pH-sensitive degradability, is highly adaptable as either an injectable or implantable depot or nanoscale carrier for the controlled release and intracellular delivery of poorly soluble drugs. 

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. 

Abstract We present first results from James Webb Space Telescope Near-Infrared Spectrograph, Mid-Infrared Instrument, and Keck Cosmic Webb Imager integral field spectroscopy of the powerful but highly obscured host galaxy of the jetted radio source Cygnus A. We detect 169 infrared emission lines at 1.7–27μm and explore the kinematics and physical properties of the extended narrow-line region (NLR) in unprecedented detail. The density-stratified NLR appears to be shaped by the initial blow-out and ongoing interaction of the radio jet with the interstellar medium, creating a multiphase bicone with a layered structure composed of molecular and ionized gas. The NLR spectrum, with strong coronal emission at kiloparsec scale, is well modeled by active galactic nucleus photoionization. We find evidence that the NLR is rotating around the radio axis, perhaps mediated by magnetic fields and driven by angular momentum transfer from the radio jet. The overall velocity field of the NLR is well described by 250 km s⁻¹outflow along biconical spiral flow lines, combining both rotation and outflow signatures. There is particularly bright [Feii]λ1.644μm emission from a dense, high-velocity dispersion, photoionized clump of clouds found near the projected radio axis. Outflows of 600–2000 km s⁻¹are found in bullets and streamers of ionized gas that may be ablated by the radio jet from these clouds, driving a local outflow rate of 40M_⊙yr⁻¹. 

The Brillouin sphere is defined as the smallest sphere, centered at the origin of the geocentric coordinate system, that incorporates all the condensed matter composing the planet. The Brillouin sphere touches the Earth at a single point, and the radial line that begins at the origin and passes through that point is called the singular radial line. For about 60 years there has been a persistent anxiety about whether or not a spherical harmonic (SH) expansion of the external gravitational potential,V, will converge beneath the Brillouin sphere. Recently, it was proven that the probability of such convergence is zero. One of these proofs provided an asymptotic relation, called Costin’s formula, for the upper bound,E_N, on the absolute value of the prediction error,e_N, of a SH series model, $V_N(\theta ,\lambda ,r)$, truncated at some maximum degree, $N=n_{max}$. When the SH series is restricted to (or projected onto) a particular radial line, it reduces to a Taylor series (TS) in $1/r$. Costin’s formula is $E_N\simeq B{N}^{-b}(R/r{)}^N$, whereRis the radius of the Brillouin sphere. This formula depends on two positive parameters:b, which controls the decay of error amplitude as a function ofNwhenris fixed, and a scale factorB. We show here that Costin’s formula derives from a similar asymptotic relation for the upper bound,A_non the absolute value of the TS coefficients,a_n, for the same radial line. This formula, $A_n\simeq K{n}^{-k}$, depends on degree,n, and two positive parameters,kandK, that are analogous tobandB. We use synthetic planets, for which we can compute the potential,V, and also the radial component of gravitational acceleration, $g_r=\partial V/\partial r$, to hundreds of significant digits, to validate both of these asymptotic formulas. Let superscriptVrefer to asymptotic parameters associated with the coefficients and prediction errors for gravitational potential, and superscriptgto the coefficients and predictions errors associated withg_r. For polyhedral planets of uniform density we show that $b^V=k^V=7/2$and $b^g=k^g=5/2$almost everywhere. We show that the frequency of oscillation (around zero) of the TS coefficients and the series prediction errors, for a given radial line, is controlled by the geocentric angle,α, between that radial line and the singular radial line. We also derive useful identities connecting $K^V,B^V,K^g$, andB^g. These identities are expressed in terms of quotients of the various scale factors. The only other quantities involved in these identities areαandR. The phenomenology of ‘series divergence’ and prediction error (whenr < R) can be described as a function of the truncation degree,N, or the depth,d, beneath the Brillouin sphere. For a fixed $r\text{⩽}R$, asNincreases from very low values, the upper error boundE_Nshrinks until it reaches its minimum (best) value whenNreaches some particular or optimum value, $N_{\mathrm{opt}}$. When $N>N_{\mathrm{opt}}$, prediction error grows asNcontinues to increase. Eventually, when $N\gg N_{\mathrm{opt}}$, prediction errors increase exponentially with risingN. If we fix the value ofNand allow $R/r$to vary, then we find that prediction error in free space beneath the Brillouin sphere increases exponentially with depth,d, beneath the Brillouin sphere. Because $b^g=b^V-1$everywhere, divergence driven prediction error intensifies more rapidly forg_rthan forV, both in terms of its dependence onNandd. If we fix bothNandd, and focus on the ‘lateral’ variations in prediction error, we observe that divergence and prediction error tend to increase (as doesB) as we approach high-amplitude topography. 

Ni-Cr based super-alloys have exceptional corrosion resistance, which is further improved with Mo alloying. The correlation between passive layer performance and composition was studied to gain a deeper mechanistic understanding of the role of Mo by comparing the behavior of Ni-22Cr to Ni-22Cr-6Mo (wt%) alloys. The passive layers were formed using galvanostatic holds to create fast and slow growth conditions using high and low current densities. A potentiostatic hold was added to initiate exposure aging. The passive film was characterized using electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), atomic emission spectro-electrochemistry (AESEC), and X-ray photoelectron spectroscopy (XPS). Combined electrochemical and XPS characterization offered insight in cation concentrations and stratification, bonding states (oxide, hydroxide), and their modulation as a function of electrochemical conditions and performance. Most importantly: (i) Mo addition enhanced Cr(III) bound in oxide, (ii) fast growth conditions resulted in less corrosion resistant films, and (iii) exposure aging increased Cr-enrichment and reduced stratification of Mo-cations. The correlation between passive film performance and Cr, Ni, and Mo oxidation states, bonding, oxide-hydroxide contributions, and stratification is discussed. Generally accepted correlations, such as Cr-cation concentration and performance of the passive layer, have to be reexamined in order to account for the complex chemical make-up of the passive layer. 

Abstract Extreme climate events are becoming more frequent, with poorly understood implications for carbon sequestration by terrestrial ecosystems. A better understanding will critically depend on accurate and precise quantification of ecosystems responses to these events. Taking the 2019 US Midwest floods as a case study, we investigate current capabilities for tracking regional flux anomalies with “top‐down” inversion analyses that assimilate atmospheric CO₂observations. For this analysis, we develop a regionally nested version of the NASA Carbon Monitoring System‐Flux system for North America (CMS‐Flux‐NA) that allows high resolution atmospheric transport (0.5° × 0.625°). Relative to a 2018 baseline, we find the 2019 US Midwest growing season net carbon uptake is reduced by 11–57 TgC (3%–16%, range across assimilated CO₂data sets). These estimates are found to be consistent with independent “bottom‐up” estimates of carbon uptake based on vegetation remote sensing (15–78 TgC). We then investigate current limitations in tracking regional carbon budgets using “top‐down” methods. In a set of observing system simulation experiments, we show that the ability of atmospheric CO₂inversions to capture regional carbon flux anomalies is still limited by observational coverage gaps for both in situ and satellite observations. Future space‐based missions that allow for daily observational coverage across North America would largely mitigate these observational gaps, allowing for improved top‐down estimates of ecosystem responses to extreme climate events. 

Forest dynamics in arid and semiarid regions are sensitive to water availability, which is becoming increasingly scarce as global climate changes. The timing and magnitude of precipitation in the semiarid southwestern U.S. (“Southwest”) has changed since the 21st century began. The region is projected to become hotter and drier as the century proceeds, with implications for carbon storage, pest outbreaks, and wildfire resilience. Our goal was to quantify the importance of summer monsoon precipitation for forested ecosystems across this region. We developed an isotope mixing model in a Bayesian framework to characterize summer (monsoon) precipitation soil water recharge and water use by three foundation tree species (Populus tremuloides [aspen], Pinus edulis [piñon], and Juniperus osteosperma [Utah juniper]). In 2016, soil depths recharged by monsoon precipitation and tree reliance on monsoon moisture varied across the Southwest with clear differences between species. Monsoon precipitation recharged soil at piñon-juniper (PJ) and aspen sites to depths of at least 60 cm. All trees in the study relied primarily on intermediate to deep (10- 60 cm) moisture both before and after the onset of the monsoon. Though trees continued to primarily rely on intermediate to deep moisture after the monsoon, all species increased reliance on shallow soil moisture to varying degrees. Aspens increased reliance on shallow soil moisture by 13% to 20%. Utah junipers and co-dominant ñons increased their reliance on shallow soil moisture by about 6% to 12%. Nonetheless, approximately half of the post-monsoon moisture in sampled piñon (38-58%) and juniper (47- 53%) stems could be attributed to the monsoon. The monsoon contributed lower amounts to aspen stem water (24-45%) across the study area with the largest impacts at sites with recent precipitation. Therefore, monsoon precipitation is a key driver of growing season moisture that semiarid forests rely on across the Southwest. This monsoon reliance is of critical importance now more than ever as higher global temperatures lead to an increasingly unpredictable and weaker North American Monsoon.