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.

This study presents a comprehensive analysis on the extreme positive and negative events of wall shear stress and heat flux fluctuations in compressible turbulent boundary layers (TBLs) solved by direct numerical simulations. To examine the compressibility effects, we focus on the extreme events in two representative cases, i.e. a supersonic TBL of Mach number $M=2$ and a hypersonic TBL of $M=8$, by scrutinizing the coherent structures and their correlated dynamics based on conditional analysis. As characterized by the spatial distribution of wall shear stress and heat flux, the extreme events are indicated to be closely related to the structural organization of wall streaks, in addition to the occurrence of the alternating positive and negative structures (APNSs) in the hypersonic TBL. These two types of coherent structures are strikingly different, namely the nature of wall streaks and APNSs are shown to be related to the solenoidal and dilatational fluid motions, respectively. Quantitative analysis using a volumetric conditional average is performed to identify and extract the coherent structures that directly account for the extreme events. It is found that in the supersonic TBL, the essential ingredients of the conditional field are hairpin-like vortices, whose combinations can induce wall streaks, whereas in the hypersonic TBL, the essential ingredients become hairpin-like vortices as well as near-wall APNSs. To quantify the momentum and energy transport mechanisms underlying the extreme events, we proposed a novel decomposition method for extreme skin friction and heat flux, based on the integral identities of conditionally averaged governing equations. Taking advantage of this decomposition method, the dominant transport mechanisms of the hairpin-like vortices and APNSs are revealed. Specifically, the momentum and energy transports undertaken by the hairpin-like vortices are attributed to multiple comparable mechanisms, whereas those by the APNSs are convection dominated. In that, the dominant transport mechanisms in extreme events between the supersonic and hypersonic TBLs are indicated to be totally different.

This study examined how linguistic complexity features contribute to second language (L2) processing effort by analyzing the Dutch English-L2 learners’ eye movements from GECO and MECO, two eye-tracking corpora. Processing effort was operationalized as reading rate, mean fixation duration, regression rate, skipping rate, and mean saccade amplitude. In Study 1, the lexical, syntactic, and discoursal indices in 272 snippets of a novel in GECO were regressed against these eye-movement measures. The results showed that the one-component partial least square regression (PLS-R) models could explain 11%–37% of the variance in these eye-movement measures and outperformed eight readability formulas (six traditional and two recent cognitively inspired formulas based on the readers’ perception on text difficulty) in predicting L2 processing effort. In Study 2, the eye-tracking data from MECO were used to evaluate whether the findings from Study 1 could be applied more broadly. The results revealed that although the predictability of these PLS-R components decreased, they still performed better than the readability formulas. These findings suggest that the linguistic indices identified can be used to predict L2 text processing effort and provide useful implications for developing systems to assess text difficulty for L2 learners.

A 198.8 m deep borehole was drilled through ice to subglacial bedrock in the northwestern marginal part of Princess Elizabeth Land, ~12 km south of Zhongshan Station, in January–February 2019. Three years later, in February 2022, the borehole temperature profile was measured, and the geothermal heat flow (GHF) was estimated using a 1-D time-dependent energy-balance equation. For a depth corresponding to the base of the ice sheet, the GHF was calculated as 72.6 ± 2.3 mW m−2 and temperature −4.53 ± 0.27°C. The regional averages estimated for this area based, generally, on tectonic setting vary from 55 to 66 mW m−2. A higher GHF is interpreted to originate mostly from the occurrence of metamorphic complexes intruded by heat-producing elements in the subglacial bedrock below the drill site.

Contestation over the structure and location of final sovereign authority—the right to make and enforce binding rules—occupies a central role in political development. Historically, war often settled these debates and institutionalized the victor’s vision of sovereignty. Yet sovereign authority requires more than institutions; it ultimately rests on the recognition of the governed. How does war shape imagined sovereignty? We explore the effect of warfare in the United States, where the debate over two competing visions of sovereignty erupted into the American Civil War. We exploit the grammatical shift in the “United States” from a plural to a singular noun as a measure of imagined sovereignty, drawing upon two large textual corpuses: newspapers (1800–99) and congressional speeches (1851–99). We demonstrate that war shapes imagined sovereignty, but for the North only. Our results further suggest that Northern Republicans played an important role as ideational entrepreneurs in bringing about this shift.