The compression waves/boundary layer interaction (CWsBLI) in high-speed inlets poses significant challenges for predicting flow separation, rendering traditional shock wave/boundary layer interaction (SWBLI) scaling laws inadequate due to unaccounted effects of the coverage range of compression waves. This study aims to establish a unified scaling framework for CWsBLIs and SWBLIs by proposing an equivalent interaction intensity. Experiments were conducted in a Mach 2.5 supersonic wind tunnel, employing schlieren imaging and pressure measurements to characterise flows induced by curved surfaces at two deflection angles ($10^{\circ }, 12^{\circ }$) and varying coverage ranges of compression waves ($d$). An equivalent transformation method was developed to convert the CWsBLI into an equivalent incident SWBLI (ISWBLI), with interaction intensity derived from pressure gradients considering the coverage range. Key results reveal a critical threshold based on the interaction length of ISWBLI ($L_{\textit{single}}$): when $d \leq L_{\textit{single}}$, the interaction scale remains comparable to ISWBLI; when $d \gt L_{\textit{single}}$, the weakened adverse pressure gradient leads to a reduction in the length scale. The proposed scaling framework unifies the CWsBLIs and SWBLIs, achieving better data collapse compared to the existing methods. This work advances our understanding of complex waves/boundary layer interactions, and provides a prediction method for the length scales of CWsBLIs.

Wall pressure fluctuations (WPFs) over aerodynamic surfaces contribute to the physical origin of noise generation and vibrational loading. Understanding the generation mechanism of WPFs, especially those exhibiting extremely high amplitudes, is important for advancing design and control in practical applications. In this work, we systematically investigate extreme events of WPFs in turbulent boundary layers and the compressibility effects thereon. The compressibility effects, encompassing extrinsic and intrinsic ones, ranging from weak to strong, are achieved by varying Mach numbers and wall temperatures. A series of datasets at moderate Reynolds numbers obtained from direct numerical simulation are analysed. It is found that the intermittency of WPFs depends weakly on extrinsic compressibility effects, whereas intrinsic compressibility effects significantly enhance intermittency at small scales. Coherent structures related to extreme events are identified using volumetric conditional average. Under extrinsic compressibility effects, extreme events are associated with the weak dilatation structures induced by interactions of high- and low-speed motions. When intrinsic compressibility effects dominate, these events are associated with the strong alternating positive and negative dilatation structures embedded in low-speed streaks. Furthermore, Poisson-equation-based pressure decomposition is performed to partition pressure fluctuations into components governed by distinct physical mechanisms. By analysing the proportion of each pressure component in extreme events, it is found that the contributions of the slow pressure and viscous pressure exhibit weak dependence on the compressibility effects, especially the extrinsic ones, and the varying trend of contributions of the rapid pressure with compressibility effects is opposite to that of the compressible pressure component.

We present the flexible delivery of picosecond laser pulses with up to 20 W average power over a 3-m-long sample of anti-resonant hollow-core fiber (AR-HCF) for laser-micromachining applications. Our experiments highlight the importance of optical-mode purity of the AR-HCF for manufacturing precision. We demonstrate that compared with an AR-HCF sample with a capillary to core (d/D) ratio of approximately 0.5, the AR-HCF with a d/D ratio of approximately 0.68 exhibits better capability of high-order-mode suppression, giving rise to improved micromachining quality. Moreover, the AR-HCF delivery system exhibits better pointing stability and setup flexibility than the free-space beam delivery system. These results pave the way to practical applications of AR-HCF in developing advanced equipment for ultrafast laser micromachining.

As the global elderly population expands, the associated risks of longevity intensify, presenting significant challenges to traditional retirement security systems. We study actuarial fairness in tontines under the Volterra mortality framework, integrating long-range dependence mortality models rates with tontine structures. Initially, we establish an optimal tontine model for a homogeneous tontine under this framework. However, according only to individual actuarial fairness can neglect the collective nature of tontines. So we propose a hybrid optimization model that accounts for age and wealth discrepancies affecting payment amounts and the collective fairness. Specially, we first apply the f-value fairness measure in age-heterogeneous tontines for assessing fairness. Our results reveal that while the model ensures actuarial fairness at the group level, relative payments are lower for older age groups. By incorporating dynamic mortality modeling through the Volterra mortality framework, our work demonstrates that this comprehensive scheme significantly enhances the robustness and sustainability of retirement security systems. These findings provide valuable insights for the future integration of dynamic mortality models with innovative retirement income structures.

Seminal theories in political science argue that military service is a critical driver of minority integration. However, a major obstacle bedeviling the study of military service is self-selection: individuals who are better integrated may be more likely to join the military in the first place. We address the selection problem by examining the effects of military conscription during the Vietnam War using an instrumental variables approach. Conscription during 1970–72 was decided on the basis of national draft lotteries that assigned draft numbers based on an individual’s date of birth. Using the draft lottery instrument, we find no evidence of a causal effect of military service on a range of integration outcomes from the 2000 decennial census. At least for the Vietnam era, the link between service and long-term integration is largely driven by self-selection, which points to important scope conditions for the integrationist view.

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI model that analyzes voice to noninvasively predict aspiration. Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. Patients were classified into three groups: aspirators (Penetration-Aspiration Scale, PAS 6–8), probable (PAS 3–5), and non-aspirators (PAS 1–2) based on video fluoroscopic swallow (VFSS) findings. Multivariate analysis evaluated patient demographics, history of head and neck surgery, radiation, neurological illness, obstructive sleep apnea, esophageal disease, body mass index, and vocal cord dysfunction. Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. The model was then validated using an independent, external database. Results/Anticipated Results: Aspirators were found to have quantifiably worse quality of sound with higher jitter and shimmer but lower harmonics noise ratio. NAM modeling classified aspirators and non-aspirators as distinct groups (aspirator NAM risk score 0.528+0.2478 (mean + std) vs. non-aspirator (control) risk score of 0.252+0.241 (mean + std); p Discussion/Significance of Impact: We report the use of voice as a novel, noninvasive biomarker to detect aspiration risk using machine learning techniques. This tool has the potential to be used for the safe and early detection of aspiration in a variety of clinical settings including intensive care units, wards, outpatient clinics, and remote monitoring.

Two potential obstacles stand between the observation of a statistical correlation and the design (and deployment) of an effective intervention, omitted variable bias and reverse causality. Whereas the former has received ample attention, comparably scant focus has been devoted to the latter in the methodological literature. Many existing methods for reverse causality testing commence by postulating a structural model that may suffer from widely recognized issues such as the difficulty of properly setting temporal lags, which are critical to model validity. In this article, we draw upon advances in machine learning, specifically the recently established link between causal direction and the effectiveness of semi-supervised learning algorithms, to develop a novel method for reverse causality testing that circumvents many of the assumptions required by traditional methods. Mathematical analysis and simulation studies were carried out to demonstrate the effectiveness of our method. We also performed tests over a real-world dataset to show how our method may be used to identify causal relationships in practice.

DNA barcoding approaches have been successfully applied for estimating diet composition. However, an accurate quantification in the diets of herbivores remains to be achieved. In the current study, we present a novel methodology that reveals the relationship between the actual proportions (by mass) of each herbage species in the diets and the relative proportions of the ITS2 gene sequences obtained from faecal samples to evaluate the diet composition of sheep in a meadow steppe. Nine common and 12 rare species of plants were employed for formulating 6 diets, along with the addition of feed supplements for improving the growth performance of sheep. Faecal samples were collected for DNA analysis over the period spanning days 7–12. A significant positive correlation (Spearman’s ρ = 0.389) was obtained between the actual proportions (by mass) of the herbage in the diet provided and the relative abundance of ITS2 sequences obtained from the faecal samples. A significant regression coefficient was found between the relative abundance of all common species and their respective herbage mass proportions. The accuracy of the relation equations, evaluated by utilizing the similarity coefficient, showed 84.69% similarity between the actual diet composition and the correct percentage. Taken together, the current study has provided empirical evidence for the accuracy and applicability of ITS2 as a DNA barcode for obtaining quantitative information about the diet composition of sheep grazing in species-rich grasslands.

While environmental concerns are increasingly driving firms’ strategic decisions, insights into why firms make heterogeneous environmental investments are limited. Taking an institutional view, we explore the effect of institutional complexity resulting from multiple but incongruent institutional logics within an organization on firms’ environmental investments. Using China's mixed-ownership reform as a research context, we identify a unique condition in which institutional complexity arises as the privatization process results in two coexisting but incongruent institutional logics – namely, state and financial logic. We further propose that privatization plays both enabling and constraining roles in state-owned enterprises’ (SOEs’) strategic decisions about environmental investments, depending on the relative dominance of each institutional logic, resulting in an inverted U-shaped relationship between privatization and environmental investments. Moreover, we examine the moderating effects of CEO background characteristics and firms’ external environmental context to uncover how these factors influence the relative dominance of state or financial logic in privatized SOEs, thereby reshaping SOEs’ environmental investments. Analyses of multisource panel data from Chinese listed SOEs from 2013 to 2020 support our theoretical propositions. The findings contribute to the literature on how institutional factors affect firm environmental practices and provide new insights to better understand the influence of institutional complexity on firm strategic actions.

Supersonic internal flows often exhibit multiple reflected shocks within a limited distance. These shocks can interact with each other in a complex manner due to the characteristics of the shock wave–turbulent boundary layer interaction (STBLI), including flow distortion and the relaxing boundary layer. This study aims to characterise this type of interaction and to clarify its fluid physics. A separated STBLI zone was established either upstream or downstream, and another weaker STBLI was established in the opposing position to serve as a perturbation. Time-resolved measurements were employed to characterise the mean separation and unsteadiness as the two regions approached each other, as well as their relationship. The experimental results indicated that the STBLI could affect the separation and reattachment of the other STBLI through either the decelerated or relaxing boundary layer. Despite a small deflection angle, the incident shock can amplify the low-frequency oscillations in the downstream STBLI region. Additionally, the interaction in the downstream region can be influenced by both low- and high-frequency oscillations associated with the upstream STBLI through a relaxing boundary layer. Despite the limited correlation observed between the low-frequency fluctuations in the downstream region and the boundary layer flow not far upstream, there still exists some degree of correlation between the low-frequency shock motions even when they are widely separated. Both the ‘upstream mechanism’ and ‘downstream mechanism’ have been observed, and the significance of low-frequency dynamics in the separated flow, relative to that of the upstream flow, is closely associated with interaction intensity.

In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending was inevitable. Such blending posed a potential primary systematic uncertainty for upcoming surveys. Current deblenders predominantly depended on analytical modelling of galaxy profiles, facing limitations due to inflexible and imprecise models. We presented a novel approach, using a U-net structured transformer-based network for deblending astronomical images, which we term the CAT-deblender. It was trained using both RGB and the grz-band images, spanning two distinct data formats present in the Dark Energy Camera Legacy Survey (DECaLS) database, including galaxies with diverse morphologies in the training dataset. Our method necessitated only the approximate central coordinates of each target galaxy, sourced from galaxy detection, bypassing assumptions on neighbouring source counts. Post-deblending, our RGB images retained a high signal-to-noise peak, consistently showing superior structural similarity against ground truth. For multi-band images, the ellipticity of central galaxies and median reconstruction error for r-band consistently lie within $\pm$0.025 to $\pm$0.25, revealing minimal pixel residuals. In our comparison of deblending capabilities focused on flux recovery, our model showed a mere 1% error in magnitude recovery for quadruply blended galaxies, significantly outperforming SExtractor’s higher error rate of 4.8%. Furthermore, by cross-matching with the publicly accessible overlapping galaxy catalogs from the DECaLS database, we successfully deblended 433 overlapping galaxies. Moreover, we have demonstrated effective deblending of 63 733 blended galaxy images, randomly chosen from the DECaLS database.

Acid-activated bentonites are utilized in many applications, including those that depend on their rheological properties and behavior, but little information is available regarding the rheological characteristics of this important industrial material. The purpose of this study was to investigate the effects of solids concentration, salt concentration, and pH value on the shear rate, shear stress, and other flow parameters of acid-activated bentonite suspensions. Activated Na-bentonite was prepared using sulfuric acid. Flow curves of the suspensions were modeled using the Herschel-Bulkley equation, which performed well for this system. The Herschel-Bulkley yield stress increased with the solids concentration and showed a maximum and minimum at the NaCl concentrations of 0.001 M and 0.01 M, respectively, and increased again slightly with further increases in NaCl concentration. The yield stress was at a maximum and a minimum at pH values of ≈5 and ≈7, respectively, followed by a slight increase with pH under alkaline conditions. The variations in dispersion rheological properties can be attributed to the change in the particle-association modes under different conditions.