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An IR–vacuum ultraviolet (VUV) ion-dip spectroscopy method is utilized to examine the IR spectrum of acetaldehyde oxide (CH3CHOO) in the overtone CH stretch (2νCH) spectral region. IR activation creates a depletion of the ground state population that reduces the VUV photoionization signal on the parent mass channel. IR activation of the more stable and populated syn-CH3CHOO conformer results in rapid unimolecular decay to OH + vinoxy products and makes the most significant contribution to the observed spectrum. The resultant IR–VUV ion-dip spectrum of CH3CHOO is similar to that obtained previously for syn-CH3CHOO using IR action spectroscopy with UV laser-induced fluorescence detection of OH products. The prominent IR features at 5984 and 6081 cm−1 are also observed using UV + VUV photoionization of OH products. Complementary theoretical calculations utilizing a general implementation of second-order vibrational perturbation theory provide new insights on the vibrational transitions that give rise to the experimental spectrum in the overtone CH stretch region. The introduction of physically motivated small shifts of the harmonic frequencies yields remarkably improved agreement between experiment and theory in the overtone CH stretch region. The prominent features are assigned as highly mixed states with contributions from two quanta of CH stretch and/or a combination of CH stretch with an overtone in mode 4. The generality of this approach is demonstrated by applying it to three different levels of electronic structure theory/basis sets, all of which provide spectra that are virtually indistinguishable despite showing large deviations prior to introducing the shifts to the harmonic frequencies. [ABSTRACT FROM AUTHOR]

The crystallization process of methane hydrates in a confined geometry resembling seabed porous silica sedimentary conditions has been studied using molecular dynamics simulations. With this objective in mind, a fully atomistic quartz silica slit pore has been designed, and the temperature stability of a methane hydrate crystalline seed in the presence of water and guest molecule methane has been analyzed. NaCl ion pairs have been added in different concentrations, simulating salinity conditions up to values higher than average oceanic conditions. The structure obtained when the hydrate crystallizes inside the pore is discussed, paying special attention to the presence of ionic doping inside the hydrate and the subsequent induced structural distortion. The shift in the hydrate stability conditions due to the increasing water salinity is discussed and compared with the case of unconfined hydrate, concluding that the influence of the confinement geometry and pore hydrophilicity produces a larger deviation in the confined hydrate phase equilibria. [ABSTRACT FROM AUTHOR]

As the physicochemical properties of ultrafine bubble systems are governed by their size, it is crucial to determine the size and distribution of such bubble systems. At present, the size or size distribution of nanometer-sized bubbles in suspension is often measured by either dynamic light scattering or the nanoparticle tracking analysis. Both techniques determine the bubble size via the Einstein–Stokes equation based on the theory of the Brownian motion. However, it is not yet clear to which extent the Einstein–Stokes equation is applicable for such ultrafine bubbles. In this work, using atomic molecular dynamics simulation, we evaluate the applicability of the Einstein–Stokes equation for gas nanobubbles with a diameter less than 10 nm, and for a comparative analysis, both vacuum nanobubbles and copper nanoparticles are also considered. The simulation results demonstrate that the diffusion coefficient for rigid nanoparticles in water is found to be highly consistent with the Einstein–Stokes equation, with slight deviation only found for nanoparticle with a radius less than 1 nm. For nanobubbles, including both methane and vacuum nanobubbles, however, large deviation from the Einstein–Stokes equation is found for the bubble radius larger than 3 nm. The deviation is attributed to the deformability of large nanobubbles that leads to a cushioning effect for collision-induced bubble diffusion. [ABSTRACT FROM AUTHOR]

The goals of this paper are to prove a near-martingale optional stopping theorem and establish solvability and large deviations for a class of anticipating linear stochastic differential equations. For a class of anticipating linear stochastic differential equations, we prove the existence and uniqueness of solutions using two approaches: (1) Ayed–Kuo differential formula using an ansatz, and (2) a braiding technique by interpreting the integral in the Skorokhod sense. We establish a Freidlin–Wentzell type large deviations result for the solution of such equations. In addition, we prove large deviation results for small noise where the initial conditions are random. [ABSTRACT FROM AUTHOR]

We consider a nonstationary random walk on a compact metrizable abelian group. Under a classical strict aperiodicity assumption we establish a weak-* convergence to the Haar measure, Ergodic Theorem and Large Deviation Type Estimate. [ABSTRACT FROM AUTHOR]

We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained using Wasserstein, sample robust optimization, and phi-divergence-based ambiguity sets within our DRO formulations, and explore cross-validation approaches for sizing these ambiguity sets. Through numerical experiments, we validate our theoretical results, study the effectiveness of our approaches for sizing ambiguity sets, and illustrate the benefits of our DRO formulations in the limited data regime even when the prediction model is misspecified. [ABSTRACT FROM AUTHOR]

Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over‐year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage. Plain Language Summary: Drought in water systems is a major challenge in managing water resources. Reservoirs are important as they can lessen the impacts of drought on water availability for many users. However, they are impacted by drought as well. We use a novel and generally applicable method to identify when reservoir storage is unusually low, potentially from drought, at 250 reservoirs across the conterminous U.S. We find that the maximum amount of water stored in reservoirs is decreasing across the U.S. We also find that periods of unusually low storage are becoming longer and more severe in western and central U.S. regions as well as for certain types of reservoirs. This suggests that reservoir storage may be less reliable and more vulnerable to extreme conditions and may be further impacted by changing climate and hydrology across the U.S. and by sediment building up behind reservoirs. Key Points: Low‐storage periods are longer, more severe, and more variable in over‐year storage reservoirs and in the western and central CONUSLonger periods of low storage for some regions in recent years suggests decreased reservoir reliability in a changing hydroclimateMaximum annual storage is also declining across CONUS, furthered by storage losses from sedimentation [ABSTRACT FROM AUTHOR]

Despite the significant advancements facilitated by previous research in introducing a plethora of retinal biomarkers, there is a lack of research addressing the clinical need for quantifying different biomarkers and prioritizing their importance for guiding clinical decision making in the context of retinal diseases. To address this issue, our study introduces a novel framework for quantifying biomarkers derived from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images in retinal diseases. We extract 452 feature parameters from five feature types, including local binary patterns (LBP) features of OCT and OCTA, capillary and large vessel features, and the foveal avascular zone (FAZ) feature. Leveraging this extensive feature set, we construct a classification model using a statistically relevant p value for feature selection to predict retinal diseases. We obtain a high accuracy of 0.912 and F1-score of 0.906 in the task of disease classification using this framework. We find that OCT and OCTA's LBP features provide a significant contribution of 77.12% to the significance of biomarkers in predicting retinal diseases, suggesting their potential as latent indicators for clinical diagnosis. This study employs a quantitative analysis framework to identify potential biomarkers for retinal diseases in OCT and OCTA images. Our findings suggest that LBP parameters, skewness and kurtosis values of capillary, the maximum, mean, median, and standard deviation of large vessel, as well as the eccentricity, compactness, flatness, and anisotropy index of FAZ, may serve as significant indicators of retinal conditions. [ABSTRACT FROM AUTHOR]

Starting from a microscopic particle model whose hydrodynamic limit under hyperbolic space-time scaling is a 1D conservation law, we derive the large deviation rate function encoding the probability to observe a density profile which is a non entropic shock, and compare this large deviation rate function with the classical Jensen-Varadhan functional, valid for the totally asymmetric exclusion process and the weakly asymmetric exclusion process in the strong asymmetry limit. We find that these two functionals have no reason to coincide, and in this sense Jensen-Varadhan functional is not universal. However, they do coincide in a small Mach number limit, suggesting that universality is restored in this regime. We then compute the leading order correction to the Jensen-Varadhan functional. [ABSTRACT FROM AUTHOR]

The present work focuses on the convergence order of one point large deviations rate functions for the backward Euler method of stochastic delay differential equations (SDDEs) with small noise. The drift and diffusion coefficients of SDDEs are allowed to grow super-linearly with respect to both the state variables and the delay variables. It is shown that the backward Euler method satisfies the one point large deviations principle (LDP) with good rate function. Further, the local uniform convergence orders of the one point large deviations rate function are derived by means of the equivalent characterizations for the original and numerical rate functions. These theoretical results are finally supported by a series of numerical experiments. [ABSTRACT FROM AUTHOR]

This study addresses issues such as low efficiency in maintaining a constant temperature, uneven temperature distribution, uneven mixture, uneven reaction, and large deviations in experimental data observed in the ethyl acetate saponification reaction and iodine clock reaction. To address these problems, improvements are made in two aspects: the reaction vessel and the mixing method of reactants. The use of a jacketed beaker paired with a reaction flask as the reaction vessel and a multi-site magnetic stirrer for rapid mixing of the reactants is proposed. This improvement ensures a uniform and stable temperature in the solution, enhances the uniform dispersion and rapid reaction of the reactants, and enables accurate measurements of solution conductivity and the time for color change. The obtained data provides accurate values for reaction order, rate constant, and activation energy. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to analyze the spatiotemporal dynamic evolution characteristics and driving mechanisms of impervious surfaces in the central Yunnan urban agglomeration, and to provide a theoretical basis for related research and assisting government and relevant departments in formulating urban agglomeration planning and development strategies, [Methods] GISD 30-meter impervious surface data from the central Yunnan urban agglomeration for the years 2000 to 2020 were utilized. Spatial analysis methods such as dynamic degree, standard deviation ellipse, quadrant analysis, and geographical detector were employed to reveal the spatiotemporal patterns of impervious surface changes and their driving mechanisms. [Results] (1) With respect to temporal scale evolution, over the past two decades, the overall expansion rate of impervious surfaces in the central Yunnan urban agglomeration exhibited a fluctuating trend characterized by decline-rise-decline, with an annual average expansion rate of 66.77 km². The annual average dynamic degree reached to 4.36%, indicating a continuous decline in the dynamism of impervious surfaces. The impervious surface area in the urban agglomeration expanded by 1 402.2 km² during this period, with a growth rate of 91.5%. (2) With respect to spatial scale evolution, the expansion of impervious surfaces within various cities of the central Yunnan urban agglomeration over the last two decades displayed significant spatial disparities. For instance. Kunming witnessed substantial expansion of impervious surfaces. while Chuxiong Prefecture exhibited comparatively minimal expansion. On the whole, the urban. agglomeration expanded towards the southern and southeastern directions, with the migration center remaining relatively stable, (3) With respect to directionality, the expansion of impervious surfaces in the central Yunnan urban agglomeration over the past 20 years showed a clear directional trend. primarily expanding in the northeast to southwest direction. The largest standard deviation ellipse area in year 2005 was marked, signifying the onset of rapid impervious surface expansion. Over the 20-year period, the center of impervious surface expansion remained relatively stable consistently shifting southward with relatively minor overall changes. (4) Analysis of driving mechanisms indicated that population factors had a significant impact on impervious surface expansion, whereas factors such as LAI, slope, precipitation, temperature. and NDVI had less significant effects. The interaction strength between population and other factors was notably stronger than that between other factors. Conclusion Over the past two decades, impervious surfaces in the central Yunnan urban agglomeration had continued to expand, exerting a significant influence on the resource and environmental carrying capacity of the urban agglomeration and the spatial land use pattern. To support the high-quality leapfrog development of the central Yunnan urban agglomeration, an emphasis on optimizing the spatial layout of impervious surfaces is crucial. [ABSTRACT FROM AUTHOR]

The North Pacific sea surface salinity (SSS) decadal variability (NPSDV) and its potential forcing were evaluated from 25 coupled models of the Coupled Model Intercomparison Project phase 6 (CMIP6) considering the prospects for decadal climate predictions. The results indicated that the CMIP6 models generally reproduced the spatial patterns of NPSDV. The large standard deviation of the SSS anomaly over the strong current regions, such as the Kuroshio-Oyashio Extension (KOE), North Pacific Current (NPC), California Current System (CCS), and Alaskan Coastal Current (ACC), is reflected in the two leading modes of NPSDV: a dipole with out-of-phase loadings in the KOE-NPC versus CCS-ACC and a monopole with positive loading over the KOE-NPC. The order of modes is sensitive to individual models that exhibit discrepancies, especially in temporal phases and power spectra. An autoregressive model of order-1 was used to reconstruct the NPSDV with several forcing terms. The generally weaker influence of forcings in an autoregressive model of order-1 is partly related to the overestimated response time of NPSDV relative to forcings. Most NPSDV variances originate from the persistence of SSS anomalies, but the dominant forcing factors are diverse among models. The model diversity for the NPSDV simulation mainly arises from the influence of the tropical El Niño-Southern Oscillation through teleconnection on the North Pacific Oscillation or Aleutian Low with timescale dependence. Conversely, models that can reproduce the NPSDV well are not dependent on those with larger impacts from the North Pacific oceanic processes. [ABSTRACT FROM AUTHOR]

Tracked robots are useful in disaster recovery missions owing to their high traversability. However, even tracked robots find traversing terrains with obstacles such as rocks difficult. Particularly, unfixed obstacles pose problems. Therefore, this study aims to generate motion for tracked robots to go over unfixed obstacles on slopes by utilizing a reinforcement learning (RL) approach. In RL, rewards are important to make robots learn optimal policies for various tasks. Hence, a new definition of going over an unfixed obstacle is proposed. Based on the proposed definition, a sparse reward is designed to motivate the robot to go over the obstacle while avoiding the large sliding-down phenomenon responsible for the failure of going over and the large directional deviation of the robot. In our experiment, a robot is trained in a dynamic simulator and it attempts to go over a spherical unfixed obstacle under various environmental conditions: radius of the obstacle, slope degree, and initial position of the obstacle. We verify that the success rate converges to a high value and the robot successfully generates motions to go over the spherical unfixed obstacle. [ABSTRACT FROM AUTHOR]

Satellite laser ranging (SLR) is the only stand-alone and external tool for verifying the accuracy of microwave-based GNSS orbits. On February 2, 2023, the International Laser Ranging Service initiated the SLR global tracking campaign for the complete BeiDou-3 medium Earth orbit (MEO) constellation. This initiative provides an opportunity to unveil the potential issue for BeiDou-3 MEO orbit modeling. An examination of the SLR validation results of precise BeiDou-3 MEO orbits across different IGS/Multi-GNSS Experiment (MGEX) analysis centres (ACs) reveals that the SLR residual standard deviation for BeiDou-3 SECM-B MEOs C225 and C226 orbits reaches 18 cm. This figure is approximately four to nine times those for other BeiDou-3 MEOs. This contradicts the fact that the orbit quality of C225 and C226 is comparable to other satellites. By analyzing the correlation between SLR residuals and de-trend clock residuals, detector-specific bias performance, and the distribution characteristics of SLR residuals relative to satellite azimuth and nadir angles, we diagnose that abnormal SLR validation results for C225 and C226 originate from a large deviation in the official horizontal offsets for the laser retroreflector arrays (LRAs) in the satellite reference frame. LRA horizontal offsets for C225 and C226 are estimated based on SLR residuals. With our adjusted LRA offset estimates, the SLR residual metrics for C225 and C226 orbits (across various IGS/MGEX ACs) are comparable to the results obtained for other BeiDou-3 MEOs. Additionally, our adjustments enable the identification of their orbit modeling errors across different IGS/MGEX ACs according to the pattern of SLR residual variation. From the study, spacecraft manufacturers should be encouraged to conduct a comprehensive review of ground calibrations for BeiDou LRA offsets. This is crucial for optimizing BeiDou orbit model and enhancing its applications in high-precision geosciences. [ABSTRACT FROM AUTHOR]

We consider local probabilities of lower deviations for branching process Zn = Xn,1 + ⋯ + Xn,Zn−1 in random environment η. We assume that η is a sequence of independent identically distributed variables and for fixed η the variables Xi,j are independent and have geometric distributions. We suppose that steps ξi of the associated random walk Sn = ξ1 + ⋯ + ξn has positive mean and satisfies left-side Cramér condition: E exp(hξi) < ∞ if h− < h < 0 for some h− < − 1. Under these assumptions we find the asymptotic of the local probabilities P(Zn = ⌊exp(θn)⌋), n → ∞, for θ ∈ (max(m−, 0); m(− 1)) and for θ in a neighbourhood of m(− 1), where m− and m(− 1) are some constants. [ABSTRACT FROM AUTHOR]

We prove local limit theorems on large deviations for the moment Tn of attaining the level n ∈ ℕ by the random walk in a random environment. Exact asymptotics of probabilities P(Tn = k) are obtained for values of the parameter k corresponding to the large deviations zone. [ABSTRACT FROM AUTHOR]

Let (Zn) be a supercritical branching process with immigration in an independent and identically distributed random environment. Under necessary moment conditions, we show the exact convergence rate in the central limit theorem on log Zn and establish the corresponding local limit theorem by using the moments of the natural submartingale and the convergence rates of its logarithm. By similar approach and with the help of a change of measure, we also present the so-called integrolocal theorem and integral large deviation theorem to characterize the precise asymptotics of the upper large deviations. [ABSTRACT FROM AUTHOR]

Purpose: This is a clinical study to compare immediate and staged impression methods in a complete digital workflow for single‐unit implants in the posterior area. Materials and Methods: Sixty patients requiring single‐unit implant crowns were enrolled. Forty patients were assigned to the test group, immediate digital impression after implant surgery with crown delivery 4 months later. The remaining 20 patients were assigned to the control group, staged digital impressions 4 months after implant surgery, and crown delivery 1 month later. Both workflows involved free‐model CAD‐CAM crown fabrications. The crowns were scanned before and after clinical adjustment using an intraoral scanner (TRIOS Color; 3Shape). Two 3D digital models were trimmed and superimposed to evaluate the dimensional changes using Geomagic Control software. Chairside times for the entire workflow were recorded. Kruskal–Wallis was performed to compare crown adjustments between two groups, while One‐way ANOVA was used to compare chairside time durations between the test and control groups. Results: All crowns were delivered without refabrication. The average maximum occlusion adjustment of crowns was −353.2 ± 207.1 μm in the test group and −212.7 ± 150.5 μm in the control group (p = 0.02). The average area of occlusal adjustment, measured as an area of deviation larger than 100 μm, was 14.8 ± 15.3 and 8.4 ± 8.1 mm2 in the test and control groups, respectively (p = 0.056). There were no significant differences in the mesial and distal contact adjustment amounts, or the maximum deviations of the proximal area, between the two groups. The mean chair‐side time was 50.25 ± 13.48 and 51.20 ± 5.34 min in the test and control groups, respectively (p = 0.763). Conclusions: The immediate impression method in the digital workflow for single‐unit implants required more occlusal adjustments of crowns but showed similar chairside times compared to the staged impression method. [ABSTRACT FROM AUTHOR]

The accurate evaluation of aerodynamic characteristics is a prerequisite and foundation for the design of high-performance aerodynamic shapes, navigation guidance, and strength of projectiles. The nonlinearity of aerodynamic calculations for a projectile is high, and the modeling and simulation are difficult, especially under the high-angle of attack flight conditions. Small variations in flight conditions, and structural parameters, etc., may cause large deviations in aerodynamic responses. Taking a small cylindrical projectile as an example, and to realize its attitude control, it is necessary to conduct aerodynamic characteristics analysis on it and analyze the main influencing factors of its aerodynamic characteristics parameters. In this paper, the finite volume method is used to solve the three-dimensional unsteady N-S equation, combined with the SST k-ω turbulence model, the overlapping grid technology, and the forced pitching vibration method, and the aerodynamic characteristics analysis model of the projectile is established, which realizes the accurate simulation of the surrounding flow field, aerodynamic coefficients, and dynamic derivative of the projectile under different flight conditions. On this basis, the Sobol global sensitivity analysis method based on the augmented radial basis function surrogate model of aerodynamics characteristics and Latin hypercube sampling is used to efficiently analyze and obtain the main influence parameters of cylindrical projectile aerodynamic characteristics. This paper provides a basic theory and fast algorithm for subsequent engineering system design, which has important theoretical and engineering value. [ABSTRACT FROM AUTHOR]

One of the most basic quantities relevant to planning observations and assessing detection bias is the signal-to-noise ratio (SNR). Remarkably, the SNR of an idealized radial velocity (RV) signal has not been previously derived beyond scaling behaviours and ignoring orbital eccentricity. In this work, we derive the RV SNR for three relevant cases to observers. First, we consider a single mass orbiting a star, revealing the expected result that |$\mathrm{SNR}\propto K \sqrt{T}$|⁠ , where T is the observing window, but an additional dependence on eccentricity and argument of periastron. We show that the RV method is biased towards companions with their semimajor axes aligned to the observer, which is physically intuitive, but also less obviously that the marginalized bias to eccentricity is negligible until one reaches very high eccentricities. Secondly, we derive the SNR necessary to discriminate eccentric companions from 2:1 resonance circular orbits, although our result is only valid for eccentricities |$e\lesssim 0.3$|⁠. We find that the discriminatory SNR is |$\tfrac{9}{8} e^2 (1-e^2)^{-1/2}$| times that of the eccentric planet solution's SNR, and is thus typically an order of magnitude less. Finally, we have obtained a semi-empirical expression for the SNR of the idealized Rossiter–McLaughlin (RM) effect, revealing the bias with respect to spin–orbit alignment angle. Our formula is valid to within 10 per cent accuracy in 95.45 per cent of the training samples used (for |$b\le 0.8$|⁠), but larger deviations occur when comparing to different RM models. [ABSTRACT FROM AUTHOR]

In modern digital photogrammetry, an image is usually registered via a digital matrix with an array of colour filters. From the registration of the image until feature points are detected on the image, the image is subjected to a series of calculations, i.e., demosaicing and conversion to greyscale, among others. These algorithms respond differently to the varying light spectrum of the scene, which consequently results in the feature location changing. In this study, the effect of scene illumination on the localisation of a feature in an image is presented. The demosaicing and greyscale conversion algorithms that produce the largest and smallest deviation of the feature from the reference point were assessed. Twelve different illumination settings from polychromatic light to monochromatic light were developed and performed, and five different demosaicing algorithms and five different methods of converting a colour image to greyscale were analysed. A total of 300 different cases were examined. As the study shows, the lowest deviation in the polychromatic light domain was achieved for light with a colour temperature of 5600 K and 5000 K, while in the monochromatic light domain, it was achieved for light with a green colour. Demosaicing methods have a significant effect on the localisation of a feature, and so the smallest feature deviation was achieved for smooth hue-type demosaicing, while for greyscale conversion, it was achieved for the mean type. Demosaicing and greyscale conversion methods for monochrome light had no effect. The article discusses the problem and concludes with recommendations and suggestions in the area of illuminating the scene with artificial light and the application of the algorithms, in order to achieve the highest accuracy using photogrammetric methods. [ABSTRACT FROM AUTHOR]

Although spine stereotactic body radiation therapy (SBRT) is considered a standard of care in the mobile spine, mature evidence reporting outcomes specific to sacral metastases is lacking. Furthermore, there is a need to validate the existing sacral SBRT international consensus contouring guidelines to define the optimal contouring approach. We report mature rates of local failure (LF), adverse events, and the effect of contouring deviations in the largest experience to date specific to sacrum SBRT. Consecutive patients who underwent sacral SBRT from 2010 to 2021 were retrospectively reviewed. The primary endpoint was magnetic resonance imaging–based LF with a focus on adherence to target volume contouring recommendations. Secondary endpoints included vertebral compression fracture and neural toxicity. Of the 215 sacrum segments treated in 112 patients, most received 30 Gy/4 fractions (51%), 24 Gy/2 fractions (31%), or 30 Gy/5 fractions (10%). Sixteen percent of segments were nonadherent to the consensus guideline with a more restricted target volume (undercontoured). The median follow-up was 21.4 months (range, 1.5-116.9 months). The cumulative incidence of LF at 1 and 2 years was 18.4% and 23.1%, respectively. In those with guideline adherent versus nonadherent contours, the LF rate at 1 year was 15.1% versus 31.4% and at 2 years 18.8% versus 40.0% (hazard ratio [HR], 2.5; 95% CI, 1.4-4.6; P =.003), respectively. On multivariable analysis, guideline nonadherence (HR, 2.4; 95% CI, 1.3-4.7; P =.008), radioresistant histology (HR, 2.4; 95% CI, 1.4-4.1; P <.001), and extraosseous extension (HR, 2.5; 95% CI, 1.3-4.7; P =.005) predicted for an increased risk of LF. The cumulative incidence of vertebral compression fracture was 7.1% at 1 year and 12.3% at 2 years. Seven patients (6.3%) developed peripheral nerve toxicity, of whom 4 had been previously radiated. Sacral SBRT is associated with high efficacy rates and an acceptable toxicity profile. Adhering to consensus guidelines for target volume delineation is recommended to reduce the risk of LF. [ABSTRACT FROM AUTHOR]

This paper focuses on systems of nonlinear second-order stochastic differential equations with multiscales. The motivation for our study stems from mathematical physics and statistical mechanics, for example, Langevin dynamics and stochastic acceleration in a random environment. Our aim is to carry out asymptotic analysis to establish large deviations principles. Our focus is on obtaining the desired results for systems under weaker conditions. When the fast-varying process is a diffusion, neither Lipschitz continuity nor linear growth needs to be assumed. Our approach is based on combinations of the intuition from Smoluchowski--Kramers approximation and the methods initiated in [A. A. Puhalskii, Ann. Probab., 44 (2016), pp. 3111--3186] relying on the concepts of relatively large deviations compactness and the identification of rate functions. When the fast-varying process is under a general setup with no specified structure, the paper establishes the large deviations principle of the underlying system under the assumption on the local large deviations principles of the corresponding first-order system. [ABSTRACT FROM AUTHOR]

In this work, we introduce the random weighting method to the nonlinear regression model and study the asymptotic properties for the randomly weighted least squares estimator with dependent errors. The results reveal that this new estimator is consistent. Moreover, some simulations are also carried out to show the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

Intensity-modulated radiation therapy for cancer is considered to be effective when dealing with complicated tumour shapes because the dose distribution for each irradiation can be modulated. Fluence map optimization is often formulated as an optimization problem with dose volume constraints (DVCs). A linear programming (LP) method that approximated DVCs was proposed, and it was modified to the successive LP method (SLPM) to find a feasible treatment plan in a wider region. In the present paper, we propose a numerical method called SLPM-R (the SLPM with robustness) that enhances the SLPM using a robust optimization approach. We mathematically prove that the proposed method with extended LP problems has the favourable properties of the SLPM, even taking uncertainty in the influence matrix into consideration. In particular, when the optimal value of the LP problem is non-positive, the proposed SLPM-R guarantees that the output solution can satisfy all DVCs. Through numerical experiments, we observed that the proposed method found a feasible plan that the SLPM could not find. In addition, for a test case that even the SLPM-R failed, the largest deviations of 5.65 Gray in the SLPM was reduced to 3.15 Gray by the SLPM-R. [ABSTRACT FROM AUTHOR]

Stochastic variational inequalities (SVIs) have been used widely in modelling various optimization and equilibrium problems subject to data uncertainty. The sample average approximation (SAA) solution is an asymptotically consistent point estimator for the true solution to a stochastic variational inequality. Some central limit results and large deviation estimates for the SAA solution have been obtained. The purpose of this paper is to study the convergences in regimes of moderate deviations for the SAA solution. Using the delta method and the exponential approximation, we establish some results on moderate deviations. We apply the results to the hypotheses testing for the SVIs, and prove that the rejection region constructed by the central limit theorem has the probability of the type II error with exponential decay speed. We also give some simulations and numerical results for the tail probabilities. [ABSTRACT FROM AUTHOR]

Oceanographic connectivity in an effective network of protected areas is crucial for restoring and stabilising marine populations. However, temporal variability in connectivity is rarely considered as a criterion in designing and evaluating marine conservation planning. In this study, indicators were defined to characterise the temporal variability in occurrence, flux, and frequency of connectivity in a northwestern Mediterranean Sea area. Indicators were tested on semi-theoretically-estimated connections provided by the runs of a passive particle transport model in a climatological year and in three years between 2006–2020, showing large deviation from the climatological year. The indicators allowed comparing the temporal variability in connectivity of four zones, highlighted differences in connectivity due to their locations and the mesoscale hydrodynamics, and identified areas that require further investigation. The three indicators also showed that the temporal variability in connectivity was influenced by the duration and depth of particle transport, although no consistent pattern was observed in the indicator variations of the compared zones. Provided that specific objectives will be given when parameterising transport models (i.e., selection of focus species and time period), indicators of temporal variability in connectivity have potential to support spatial conservation planning, prioritise the protection of marine resources, and measure the effectiveness of Marine Protected Areas, in line with a long-term vision of ocean management. [ABSTRACT FROM AUTHOR]

In the title complex, [U(C10H7N3O3)O2(CH3OH)]n, the UVI cation has a typical penta­gonal-bipyramidal environment with the equatorial plane defined by one N and two O atoms of one doubly deprotonated 2-[5-(2-hy­droxy­phen­yl)-1H-1,2,4-triazol-3-yl]acetic acid ligand, a carboxyl­ate O atom of the symmetry-related ligand and the O atom of the methanol mol­ecule [U--N/Oeq 2.256 (4)-2.504 (5) Å]. The axial positions are occupied by two oxide O atoms. The equatorial atoms are almost coplanar, with the largest deviation from the mean plane being 0.121 Å for one of the O atoms. The benzene and triazole rings of the tetra­dentate chelating-bridging ligand are twisted by approximately 21.6 (2)° with respect to each other. The carboxyl­ate group of the ligand bridges two uranyl cations, forming a neutral zigzag chain reinforced by a strong O--H...O hydrogen bond. In the crystal, adjacent chains are linked into two-dimensional sheets parallel to the ac plane by C/N--H...N/O hydrogen bonding and π-π inter­actions. Further weak C--H...O contacts consolidate the three-dimensional supra­molecular architecture. In the solid state, the compound shows a broad medium intensity LMCT transition centred around 463 nm, which is responsible for its red colour. [ABSTRACT FROM AUTHOR]

Eigenvalues of Wigner matrices has been a major topic of investigation. A particularly important subclass of such random matrices, useful in many applications, are what are known as sparse or diluted random matrices, where each entry in a Wigner matrix is multiplied by an independent Bernoulli random variable with mean p$p$. Alternatively, such a matrix can be viewed as the adjacency matrix of an Erdős–Rényi graph Gn,p$\mathcal {G}_{n,p}$ equipped with independent and identically distributed (i.i.d.) edge‐weights. An observable of particular interest is the largest eigenvalue. In this paper, we study the large deviations behavior of the largest eigenvalue of such matrices, a topic that has received considerable attention over the years. While certain techniques have been devised for the case when p$p$ is fixed or perhaps going to zero not too fast with the matrix size, we focus on the case p=dn$p = \frac{d}{n}$, that is, constant average degree regime of sparsity, which is a central example due to its connections to many models in statistical mechanics and other applications. Most known techniques break down in this regime and even the typical behavior of the spectrum of such random matrices is not very well understood. So far, results were known only for the Erdős–Rényi graph Gn,dn$\mathcal {G}_{n,\frac{d}{n}}$without edge‐weights and with Gaussian edge‐weights. In the present article, we consider the effect of general weight distributions. More specifically, we consider entry distributions whose tail probabilities decay at rate e−tα$e^{-t^\alpha }$ with α>0$\alpha >0$, where the regimes 0<α<2$0<\alpha < 2$ and α>2$\alpha > 2$ correspond to tails heavier and lighter than the Gaussian tail, respectively. While in many natural settings the large deviations behavior is expected to depend crucially on the entry distribution, we establish a surprising and rare universal behavior showing that this is not the case when α>2$\alpha > 2$. In contrast, in the α<2$\alpha < 2$ case, the large deviation rate function is no longer universal and is given by the solution to a variational problem, the description of which involves a generalization of the Motzkin–Straus theorem, a classical result from spectral graph theory. As a byproduct of our large deviation results, we also establish the law of large numbers behavior for the largest eigenvalue, which also seems to be new and difficult to obtain using existing methods. In particular, we show that the typical value of the largest eigenvalue exhibits a phase transition at α=2$\alpha = 2$, that is, corresponding to the Gaussian distribution. [ABSTRACT FROM AUTHOR]

During the service process of a bearingless motorized spindle (BLMS), its parameters change with both time and external conditions, leading to a decrease in the accuracy of the motorized spindle. Therefore, it is difficult to accurately describe the dynamic performance of a motorized spindle during its actual operation using deterministic parameters. In this paper, the interactions between the thermal deformation and vibration of a motorized spindle are explored. A dynamic model of the motorized spindle based on multibody dynamics and time-varying parameters is established, and a solution method for the dynamic model with uncertain parameters is investigated. Firstly, the reasons for the vibration in the BLMS are analyzed, and the influences of thermal deformation on the thermal eccentricity and inhomogeneous air gap of the BLMS are studied. A vibration model of the BLMS is established and solved to acquire the radial vibration displacement. Secondly, a discrete multibody dynamics model of the BLMS is built, and the center trajectory of the motorized spindle is attained by solving the multibody dynamics model. A prototype experimental platform of the BLMS is designed, and vibration tests are carried out. The experimental results show that the vibration amplitude of the BLMS increases with the running time and the maximum displacement exhibits a large deviation from the simulation results using the determined parameters, while there is a small deviation from the simulation results using uncertain parameters; this indicates that the solution of the multibody dynamics model of the BLMS described by uncertain parameters is closer to the experimental data. These research findings can provide a reference for the optimized design of BLMSs. [ABSTRACT FROM AUTHOR]

Let S(t)=1πImlogζ12+it$S(t) = \frac{1}{\pi }\operatorname{Im}\log \zeta \left(\frac{1}{2}+it\right)$. We prove an unconditional lower bound on the measure of the sets {t∈[T,2T]:S(t)⩾V}$\lbrace t\in [T,2T] \colon S(t) \geqslant V\rbrace$ for loglogT⩽V≪logTloglogT1/3$\sqrt {\log \log T} \leqslant V \ll \left(\frac{\log T}{\log \log T}\right)^{1/3}$. For V⩽(logT)1/3−ε$V \leqslant (\log T)^{1/3-\varepsilon }$, our bound has a Gaussian shape with variance proportional to loglogT$\log \log T$. At the endpoint, V≍logTloglogT1/3$V \asymp \left(\frac{\log T}{\log \log T}\right)^{1/3}$, our result implies the best known Ω$\Omega$‐theorem for S(t)$S(t)$ that is due to Tsang. We also explain how the method breaks down for V≫logTloglogT1/3$V \gg \left(\frac{\log T}{\log \log T}\right)^{1/3}$ given our current knowledge about the zeros of the zeta function. Conditionally on the Riemann hypothesis, we extend our results to the range loglogT⩽V≪logTloglogT1/2$\sqrt {\log \log T} \leqslant V \ll \left(\frac{\log T}{\log \log T}\right)^{1/2}$. [ABSTRACT FROM AUTHOR]

I propose an affine model of short rates that incorporates a random walk with stochastic drift. This framework enables my model to capture the behavior of monetary authorities in the short rate market, allowing for minor deviations while reacting strongly to deviations large enough to threaten production. Importantly, my model facilitates the derivation of closed-form bond prices, thereby providing an analytical solution for bond-option prices. I compare my model with nine standard short rate models found in the literature. Among these, five are single-factor models and four are multifactor models. Remarkably, my model outperforms all competing short rate models, including the constant elasticity of volatility, stochastic mean, and stochastic volatility models. Moreover, it yields interest rate forecasts consistent with common term structure priors and surpasses the performance of the naive random walk model. Additionally, my stochastic mean model can explain the unspanned risks documented in the literature. [ABSTRACT FROM AUTHOR]

The number of grains per sea rice panicle is an important parameter directly related to rice yield, and it is also a very important agronomic trait in research related to sea rice breeding. However, the grain number per sea rice panicle still mainly relies on manual calculation, which has the disadvantages of being time-consuming, error-prone, and labor-intensive. In this study, a novel method was developed for the automatic calculation of the grain number per rice panicle based on a deep convolutional neural network. Firstly, some sea rice panicle images were collected in complex field environment and annotated to establish the sea rice panicle image data set. Then, a sea grain detection model was developed using the Faster R-CNN embedded with a feature pyramid network (FPN) for grain identification and location. Also, ROI Align was used to replace ROI pooling to solve the problem of relatively large deviations in the prediction frame when the model detected small grains. Finally, the mAP (mean Average Precision) and accuracy of the sea grain detection model were 90.1% and 94.9%, demonstrating that the proposed method had high accuracy in identifying and locating sea grains. The sea rice grain detection model can quickly and accurately predict the number of grains per panicle, providing an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large error and redundancy in the multi-node data acquisition of multi-greenhouse photo growth environmental information, a three-level fusion algorithm based on adaptive weighting, an LMBP network, and an improved D-S theory is proposed. The box-and-line graph method recognizes the original data and then replaces it based on the mean value method; the air temperature, humidity, and light intensity measurements are unbiased estimations of the true value to be estimated, so the first level of fusion chooses the adaptive weighted average algorithm to find the optimal weights of each sensor under the condition of minimizing the total mean-square error and obtains the optimal estimation of the weights of the homogeneous sensors of a greenhouse. The Levenberg–Marquardt algorithm was chosen for the second level of fusion to optimize the weight modification of the BP neural network, i.e., the LMBP network, and the three environmental factors corresponding to "suitable", "uncertain" and "unsuitable" potato growth environments were trained for the three environmental factors in the reproductive periods. The output of the hidden layer was converted into probability by the Softmax function. The third level is based on the global fusion of evidence theory (also known as D-S theory), and the network output is used as evidence to obtain a consistent description of the multi-greenhouse potato cultivation environment and the overall scheduling of farming activities, which better solves the problem of the difficulty in obtaining basic probability assignments in the evidence theory; in the case of a conflict between the evidence, the BPA of the conflicting evidence is reallocated, i.e., the D-S theory is improved. Example validation shows that the total mean square error of the adaptive weighted fusion value is smaller than the variance of each sensor estimation, and sensors with lower variance are assigned lower weights, which makes the fusion result not have a large deviation due to the failure of individual sensors; when the fusion result of a greenhouse feature level is "unsuitable", the fusion result of each data level is considered comprehensively, and the remote control agency makes a decision, which makes full use of the complementary nature of multi-sensor information resources and solves the problem of fusion of multi-source environmental information and the problem of combining conflicting environmental evaluation factors. Compared with the traditional D-S theory, the improved D-S theory reduces the probability of the "uncertainty" index in the fusion result again. The three-level fusion algorithm in this paper does not sacrifice data accuracy and greatly reduces the noise and redundancy of the original data, laying a foundation for big data analysis. [ABSTRACT FROM AUTHOR]

• The infeasibility of the fuel-optimal powered landing problem is analyzed. • A maximum-thrust regulation strategy is proposed to reduce infeasibility. • A trajectory-tracking method with a disturbance compensator is proposed. • The disturbance compensator requires only the current state information. Convex optimization has great potential for onboard trajectory generation which can eliminate large state deviations and improve landing precision. In practical applications, the convex optimization problem is often infeasible under disturbances. In this paper, we analyze the reason for the infeasibility of the fuel-optimal trajectory optimization problem. The analysis result shows that the thrust saturation degrade the anti-disturbance performance and then lead to infeasibility. Based on this analysis, we improved the anti-disturbance performance of convex optimization-based guidance from two aspects: (1) a maximum-thrust regulation strategy is embedded into the original fuel-optimal problem to avoid thrust saturation; (2) a trajectory tracking method with a disturbance compensator is performed to attenuate disturbance effects and to make the initial state of each optimization cycle close to the existing feasible trajectory. Numerical simulations demonstrate that the proposed method can improve the infeasibility and can realize the high-precision landing under disturbances. [ABSTRACT FROM AUTHOR]

Acid digestion is the most ideal detection method for the determination of copper in copper concentrate, which is commonly treated with hydrochloric acid and nitric acid. However, for some insoluble copper-containing material samples, there is always black residue that cannot be treated in the later stage of dissolution, and adding saturated ammonium fluoride after adjusting acidly before titration can produce dark purple flocculent precipitation, witch seriously affect the titration analysis, resulting in large deviation and inaccurate analysis results. In this study, copper-containing material waste dissolved by acid dissolution, insoluble material was filtered and separated, and the residue was analyzed and corrected. The conditions such as the amount of hydrofluoric acid and the amount of nitric acid used in the dissolution of filtered filter residue were optimized by experiments, and the final experimental method was established. The determination results were consistent with the results of laboratory outsourcing, and the relative standard deviation (RSD) was 0.060% -- 0.16%, which showed that this method was accurate, rapid, stable and reliable for the determination of copper content in copper materials in the range of 12.5%--24.5%. [ABSTRACT FROM AUTHOR]

The study of longest increasing subsequences (LIS) in permutations led to that of Young diagrams via Robinson–Schensted's (RS) correspondence. In a celebrated paper, Vershik and Kerov obtained a limit theorem for such diagrams and found that the LIS of a uniform permutation of size n$$ n $$ behaves as 2n$$ 2\sqrt{n} $$. Independently and much later, Hoppen et al. introduced the theory of permutons as a scaling limit of permutations. In this paper, we extend in some sense the RS correspondence of permutations to the space of permutons. When the "RS‐tableaux" of a permuton are non‐trivial, we show that the RS‐tableaux of random permutations sampled from this permuton exhibit a linear behavior, in the sense that their first rows and columns have lengths of linear order. In particular, the LIS of such permutations behaves as a multiple of n$$ n $$. We also prove some large deviation results for these convergences. Finally, by studying asymptotic properties of Fomin's algorithm for permutations, we show that the RS‐tableaux of a permuton satisfy a partial differential equation. [ABSTRACT FROM AUTHOR]

Purpose: To develop neural network (NN)‐based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér–Rao bound. Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. Results: In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér–Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least‐squares fitting, while state‐of‐the‐art NNs show larger deviations. Conclusion: The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy. [ABSTRACT FROM AUTHOR]

Reactions between methanol and the hydroxyl radical are of significant interest for combustion-, atmospheric-, and astrochemistry. While the two primary product channels (the formation of H2O with either CH3O· or ·CH2OH) have been the subject of numerous studies, the possibility of other products has seen little attention. Here, we present a comprehensive thermochemical survey of the stationary points and plausible products of the reaction, featuring 29 geometries optimized at the UCCSD(T)-F12b/aug-cc-pVTZ level, followed by accurate composite ab initio computations for all stationary points (including ·CH2OH dissociation and isomerization) and five product channels, with a detailed evaluation of basis set convergence and efficiency. The computations reveal that the formation of methanediol and the hydroxymethoxy radical is thermodynamically favorable and the endothermicity of formaldehyde formation is low enough to be a plausible product channel. We also observe unexpectedly large energy deviations between the partially-spin-adapted ROHF-RCCSD(T) method and ROHF-UCCSD(T) as well as between UHF-UCCSDT(Q) and ROHF-UCCSDT(Q) results. [ABSTRACT FROM AUTHOR]

A formula for the single-particle current–voltage characteristic of "dirty" (with low concentrations of identical nonmagnetic impurities in the disordered insulator layer) SIN junctions (S is a superconductor, I is a disordered insulator, and N is a normal metal) has been obtained in the region of low temperatures and voltages 0 ≤ |eV| T Δ0, where e is the elementary charge, Δ0 is the superconducting gap in the S edge of a junction at the temperature T = 0, V is the voltage across the junction, c 1 is the dimensionless concentration of impurities in the I layer, β = (ε0 – μ)/Δ0 is the dimensionless deviation of the single-impurity electron energy level ε0 relevant for this problem (on identical impurities in the I layer) from the electron chemical potential μ of the junction. It has been shown that an anomalously large sign-alternating deviation of the single-particle current–voltage characteristic of the dirty SIN junction from the value calculated by the existing theoretical expression is observed in a certain limited region Ωd(c, β) on the (c, β) parameter plane in the presence of random narrow-band quantum jumpers in the disordered I layer. A numerical example has demonstrated that the relative deviation of the single-particle current–voltage characteristic at typical values of the parameters of the dirty SIN junction in the region Ωd(c, β) can reach several orders of magnitude, which ensures the possibility of the experimental observation of this effect. The conditions for the applicability of the considered model of the dirty SIN junction have been discussed and the scheme of the corresponding experiment has been proposed. [ABSTRACT FROM AUTHOR]

In Bayesian statistics, posterior contraction rates (PCRs) quantify the speed at which the posterior distribution concentrates on arbitrarily small neighborhoods of a true model, in a suitable way, as the sample size goes to infinity. In this paper, we develop a new approach to PCRs, with respect to strong norm distances on parameter spaces of functions. Critical to our approach is the combination of a local Lipschitz-continuity for the posterior distribution with a dynamic formulation of the Wasserstein distance, which allows to set forth an interesting connection between PCRs and some classical problems arising in mathematical analysis, probability and statistics, e.g., Laplace methods for approximating integrals, Sanov's large deviation principles in the Wasserstein distance, rates of convergence of mean Glivenko–Cantelli theorems, and estimates of weighted Poincaré–Wirtinger constants. We first present a theorem on PCRs for a model in the regular infinite-dimensional exponential family, which exploits sufficient statistics of the model, and then extend such a theorem to a general dominated model. These results rely on the development of novel techniques to evaluate Laplace integrals and weighted Poincaré–Wirtinger constants in infinite-dimension, which are of independent interest. The proposed approach is applied to the regular parametric model, the multinomial model, the finite-dimensional and the infinite-dimensional logistic-Gaussian model and the infinite-dimensional linear regression. In general, our approach leads to optimal PCRs in finite-dimensional models, whereas for infinite-dimensional models it is shown explicitly how the prior distribution affect PCRs. [ABSTRACT FROM AUTHOR]

Consider a risk model in which the claims follow a non stationary arrival process that satisfies a large deviation principle. Supposing that the claim sizes form a sequence of i.i.d. random variables with subexponential tail, precise large deviations for the aggregate claims are obtained, by allowing the claim-sizes and claim inter-arrival (waiting) times to be arbitrarily dependent. [ABSTRACT FROM AUTHOR]

In biological, glassy, and active systems, various tracers exhibit Laplace-like, i.e., exponential, spreading of the diffusing packet of particles. The limitations of the central limit theorem in fully capturing the behaviors of such diffusive processes, especially in the tails, have been studied using the continuous time random walk model. For cases when the jump length distribution is super-exponential, e.g., a Gaussian, we use large deviations theory and relate it to the appearance of exponential tails. When the jump length distribution is sub-exponential, the packet of spreading particles is described by the big jump principle. We demonstrate the applicability of our approach for finite time, indicating that rare events and the asymptotics of the large deviations rate function can be sampled for large length scales within a reasonably short measurement time. The universality of Laplace tails appears everywhere [ABSTRACT FROM AUTHOR]

The paper presents a study on the dimensional and weight characterization of 3D printed polyethylene terephthalate specimens designed specifically for compliant mechanisms. Compliant mechanisms find applications in various industries, including medical, consumer goods, education, and research and development. Ensuring these mechanisms meet the required functional needs is crucial, particularly when customizing medical devices. The research focuses on material selection, a critical aspect for 3D printed compliant mechanisms. To investigate the influence of the material deposition angle on the quality and precision of 3D printing, the study considers traction and bending specimens. For each specimen type, three sets of five specimens are manufactured, incorporating different deposition angles: 0°, ±45°, and 90°. In total, 30 specimens are produced, labeled, weighted, and measured. The results reveal that specimens printed at a deposition angle of 45° exhibit the smallest dimensional and mass deviations, indicating higher precision and accuracy. Those printed at 0° follow closely in terms of deviation, while specimens printed at 90° display larger deviations. [ABSTRACT FROM AUTHOR]

The phenology of critical biological events in aquatic ecosystems is rapidly shifting due to climate change. Growing variability in phenological cues can increase the likelihood of trophic mismatches (i.e., mismatches in the timing of peak prey and predator abundances), causing recruitment failures in important fisheries. We assessed changes in the spawning phenology of walleye (Sander vitreus) in 194 Midwest US lakes to investigate factors influencing walleye phenological responses to climate change and associated climate variability, including ice‐off timing, lake physical characteristics, and population stocking history. Ice‐off phenology shifted earlier, about three times faster than walleye spawning phenology over time. Spawning phenology deviations from historic averages increased in magnitude over time, and large deviations were associated with poor offspring survival. Our results foreshadow the risks of increasingly frequent natural recruitment failures due to mismatches between historically tightly coupled spawning and ice‐off phenology. [ABSTRACT FROM AUTHOR]

The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users' private information. Many intruders actively seek such private data either for sale or other inappropriate purposes. Similarly, national and international organizations have country-level and company-level private information that could be accessed by different network attacks. Therefore, the need for a Network Intruder Detection System (NIDS) becomes essential for protecting these networks and organizations. In the evolution of NIDS, Artificial Intelligence (AI) assisted tools and methods have been widely adopted to provide effective solutions. However, the development of NIDS still faces challenges at the dataset and machine learning levels, such as large deviations in numeric features, the presence of numerous irrelevant categorical features resulting in reduced cardinality, and class imbalance in multiclass-level data. To address these challenges and offer a unified solution to NIDS development, this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment. Cardinality reduction was applied to categorical features through the binning method. Subsequently, the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method. Finally, the preprocessed, refined, and oversampled feature set was divided into training and test sets with an 80-20 ratio, and two experiments were conducted. In Experiment 1, the binary classification was executed using four machine learning classifiers, with the extra trees classifier achieving the highest accuracy of 97.23% and an AUC of 0.9961. In Experiment 2, multiclass classification was performed, and the extra trees classifier emerged as the most effective, achieving an accuracy of 81.27% and an AUC of 0.97. The results were evaluated based on training, testing, and total time, and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS. [ABSTRACT FROM AUTHOR]

Oxygen isotope ratios and elemental compositions of porphyritic chondrules and olivine and pyroxene fragments in the Asuka-881020 CH chondrite were analyzed. The oxygen isotope ratios inside individual porphyritic chondrules are homogeneous within the uncertainty, except for relict grains of olivine and low-Ca pyroxene that have distinct oxygen isotope ratios. The average oxygen isotope ratios of the individual chondrules plot along and above the primitive chondrule mineral (PCM) line with Δ17O (=δ17O – 0.52 × δ18O) values from −4.7 ‰ to +4.1 ‰. The olivine and pyroxene fragments, which have Δ17O values ranging from −2.1 ‰ to +3.2 ‰, are likely to be fragments of the porphyritic chondrules. Unlike the non-porphyritic chondrules in CH and CB chondrites and chondrules in other carbonaceous chondrites, the type I and II chondrules do not show a systematic difference in the Δ17O values. Furthermore, the Δ17O values of the type I chondrules increase from −4.7 ‰ to +4.1 ‰ with increasing Mg# (=molar [MgO]/[MgO + FeO] × 100) from 96 to 99. We argue that the positive Δ17O-Mg# trend is explained by an addition of 16O-poor carbon-rich organics as a reducing agent to the relatively 16O-rich precursor silicate, which is a new environment for chondrule formation. This hypothesis is supported by the two lines of evidence observed in the present study. (1) The chondrules and fragments with higher Δ17O values show larger deviations from the PCM line towards low δ18O, suggesting oxygen isotope mass fractionation between the chondrule melt and CO or CO 2. (2) Olivine phenocrysts in the chondrules with high Δ17O values contain Ni-poor Fe-metal particles surrounded by silica-rich glass, which may be reduction products during the chondrule formation. Thus, it is suggested that the porphyritic chondrules in CH and CB chondrites have different origins from chondrules in any other chondrite types, even from the non-porphyritic chondrules in CH and CB chondrites. [ABSTRACT FROM AUTHOR]

Most mammals adapt thermal physiology around 37°C and large deviations from their range, as observed in severe hypothermia and hyperthermia, resulting in organ dysfunction and individual death. A prominent exception is mammalian hibernation. Mammalian hibernators resist the long-term duration of severe low body temperature that is lethal to non-hibernators, including humans and mice. This cold resistance is supported, at least in part, by intrinsic cellular properties, since primary or immortalized cells from several hibernator species can survive longer than those from non-hibernators when cultured at cold temperatures. Recent studies have suggested that cold-induced cell death fulfills the hallmarks of ferroptosis, a type of necrotic cell death that accompanies extensive lipid peroxidation by iron-ion-mediated reactions. In this review, we summarize the current knowledge of cold resistance of mammalian hibernators at the cellular and molecular levels to organ and systemic levels and discuss key pathways that confer cold resistance in mammals. [ABSTRACT FROM AUTHOR]

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. [ABSTRACT FROM AUTHOR]