Understanding the vertical coherence of the pressure structure and its interaction with velocity fields is critical for elucidating the mechanisms of acoustic generation and radiation in hypersonic turbulent boundary layers. This study employs linear coherence analysis to examine the self-similar coherent structures in the velocity and pressure fields within a Mach 6 hypersonic boundary layer, considering a range of wall-to-recovery temperature ratios. The influence of wall cooling on the geometric characteristics of these structures, such as inclination angles and three-dimensional aspect ratios, is evaluated. Specifically, the streamwise velocity exhibits self-similar coherent structures with the streamwise/wall-normal aspect ratio ranging from 16.5 to 38.7, showing a linear increases with decreasing wall temperatures. Similar linear dependence between the streamwise/wall-normal aspect ratio and the wall temperatures are observed for the Helmholtz-decomposed streamwise velocity and the pressure field. In terms of velocity–pressure coupling, the solenoidal component exhibits stronger interactions with the pressure fields in the near-wall region, while the dilatational component has stronger interactions with the pressure field at large scales with the increase of height. Such coupling generally follows the distance-from-the-wall scaling of the pressure field, except in cooled wall cases. Using the linear stochastic estimation, the pressure field across the boundary layer is predicted by inputting the near-wall pressure/velocity signal along with the transfer kernel. The result demonstrates that near-wall pressure signals provide the most accurate description of the pressure field in higher regions of the boundary layer. As wall-mounted sensors can measure near-wall pressure fluctuations, this study presents a potential approach to predict the off-wall pressure field correlated with the near-wall structures based on wall-pressure measurements.

People, across a wide range of personal and professional domains, need to accurately detect whether the state of the world has changed. Previous research has documented a systematic pattern of over- and under-reaction to signals of change due to system neglect, the tendency to overweight the signals and underweight the system producing the signals. We investigate whether experience, and hence the potential to learn, improves people’s ability to detect change. Participants in our study made probabilistic judgments across 20 trials, each consisting of 10 periods, all in a single system that crossed three levels of diagnosticity (a measure of the informativeness of the signal) with four levels of transition probability (a measure of the stability of the environment). We found that the system-neglect pattern was only modestly attenuated by experience. Although average performance did not increase with experience overall, the degree of learning varied substantially across the 12 systems we investigated, with participants showing significant improvement in some high diagnosticity conditions and none in others. We examine this variation in learning through the lens of a simple linear adjustment heuristic, which we term the “δ-ϵ” model. We show that some systems produce consistent feedback in the sense that the best δ and ϵ responses for one trial also do well on other trials. We show that learning is related to the consistency of feedback, as well as a participant’s “scope for learning” how close their initial judgments are to optimal behavior.

Objectives/Goals: Ventilator-associated pneumonia (VAP) is an infection caused by bacteria, viruses, or fungi during mechanical ventilation. We analyzed a cohort of COVID-19 patients admitted to the intensive care unit with respiratory failure with different VAP outcomes. We hypothesize that the multiomics data can help predict VAP development within this cohort. Methods/Study Population: We recruited participants from a cohort on a NYU IRB protocol (i22–00616), who had COVID19 respiratory failure, admitted to ICU, and required invasive mechanical ventilation (n = 245). We collected and analyzed research specimens (bronchoalveolar lavage [BAL, n = 213], tracheal aspirates [n = 246], background [n = 18]) and clinical cultures (sputum and BAL) for 245 participants. A panel of experts adjudicated VAP within the cohort, resulting in 92 VAP diagnoses. We annotated metatranscriptome (Illumina NovaSeq) using a Kraken/Bracken database, and KEGG for functional annotation of transcriptome data (Illumina HiSeq). We used edgeR (v.4.0.16) to analyze differential expression of metatranscriptome and transcriptome data. Results/Anticipated Results: We diagnosed VAP in n = 92 (38%) participants. We found significant differences in days of overall hospital stay (p Discussion/Significance of Impact: VAP is a serious complication of mechanical ventilation, and oral commensals alter the lung microbiome and host immunity. We identified a transcriptome-metatranscriptome signature that identifies those at VAP risk. VAP was associate with both pro- and anti-inflammatory gene expression resulting in increased risk for lower airway infection.

Although active flow control based on deep reinforcement learning (DRL) has been demonstrated extensively in numerical environments, practical implementation of real-time DRL control in experiments remains challenging, largely because of the critical time requirement imposed on data acquisition and neural-network computation. In this study, a high-speed field-programmable gate array (FPGA) -based experimental DRL (FeDRL) control framework is developed, capable of achieving a control frequency of 1–10 kHz, two orders higher than that of the existing CPU-based framework (10 Hz). The feasibility of the FeDRL framework is tested in a rather challenging case of supersonic backward-facing step flow at Mach 2, with an array of plasma synthetic jets and a hot-wire acting as the actuator and sensor, respectively. The closed-loop control law is represented by a radial basis function network and optimised by a classical value-based algorithm (i.e. deep Q-network). Results show that, with only ten seconds of training, the agent is able to find a satisfying control law that increases the mixing in the shear layer by 21.2 %. Such a high training efficiency has never been reported in previous experiments (typical time cost: hours).

This study delves into the intricate relationship between chief executive officers' (CEOs') experiences of poverty and the digital transformation of their firms. Employing comprehensive data collection on CEOs' birthplaces and leveraging advanced text analytics to quantify digitalization, our analysis encompasses a wide array of listed companies in China. The findings reveal that CEOs' impoverished experiences exert a detrimental influence on their firms' digital transformation efforts, primarily due to a lack of motivation and social resources necessary for such initiatives. However, this adverse effect can be ameliorated when CEOs gain access to substantial social resources in later life. Our conclusions are robust, supported by rigorous testing, and underscore not only the impact of CEOs' early-life poverty on corporate digitalization but also the potential for overcoming these challenges through the acquisition of external social resources and connections in adulthood. This study contributes significantly to existing literature and offers practical implications for enhancing corporate digital transformation strategies.

Fine particulate matter (PM2·5) is a known risk factor for heart failure (HF), while plant-based dietary patterns may help reduce HF risk. This study examined the combined impact of PM2·5 exposure and a plant-based diet on HF incidence. A total of 190 092 participants from the UK Biobank were included in this study. HF cases were identified through linkage to the UK National Health Services register, with follow-up lasting until October 2022 in England, August 2022 in Scotland and May 2022 in Wales. Annual mean PM2·5 concentration was obtained using a land use regression model, while the healthful plant-based diet index (hPDI) was calculated using the Oxford WebQ tool based on two or more 24-hour dietary assessments of seventeen major food groups. Cox proportional hazard models assessed the associations of PM2·5 and hPDI with HF risk, and interactions were evaluated on additive and multiplicative scales. During a median of 13·4-year follow-up, 4351 HF cases were recorded. Participants in the highest PM2·5 tertile had a 23 % increased HF risk (hazard ratio: 1·23, 95 % CI: 1·14, 1·32) compared with those in the lowest tertile. Moderate or high hPDI was associated with reduced HF risk relative to low hPDI. The lowest HF risk was observed in individuals with high hPDI and low PM2·5 exposure, underscoring the protective role of a plant-based diet, particularly in areas with lower PM2·5 levels. A healthy plant-based diet may mitigate HF risk, especially in populations exposed to lower PM2·5 levels.

The rise of online voicing and campaigns empowered by digital technologies and online social media is rejuvenating retail investor activism that has been mostly ignored in the traditional offline setting. This article argues that online activism that is initiated by retail investors will affect managerial attention intensity and attention priority on environmental issues, thus promoting green innovation. Using a Chinese-listed companies database with 13,795 firm-year observations over the period from 2011 to 2018, our results confirm that online environmental activism induces corporate green innovation. Online activism is more effective when the retail investor base holds larger shares in total and presents questions with a more intensely negative tone. Additionally, the above-mentioned moderating effects are stronger in digital firms. Our study offers insights into the online patterns of shareholder activism in the digital era and highlights the role of minority voicing in promoting corporate sustainable transformation.

Mukai’s program in [16] seeks to recover a K3 surface X from any curve C on it by exhibiting it as a Fourier–Mukai partner to a Brill–Noether locus of vector bundles on the curve. In the case X has Picard number one and the curve $C\in |H|$ is primitive, this was confirmed by Feyzbakhsh in [11, 13] for $g\geq 11$ and $g\neq 12$. More recently, Feyzbakhsh has shown in [12] that certain moduli spaces of stable bundles on X are isomorphic to the Brill–Noether locus of curves in $|H|$ if g is sufficiently large. In this paper, we work with irreducible curves in a nonprimitive ample linear system $|mH|$ and prove that Mukai’s program is valid for any irreducible curve when $g\neq 2$, $mg\geq 11$ and $mg\neq 12$. Furthermore, we introduce the destabilising regions to improve Feyzbakhsh’s analysis in [12]. We show that there are hyper-Kähler varieties as Brill–Noether loci of curves in every dimension.

The whitefly, Bemisia tabaci is a cryptic species complex in which one member, Middle East-Asia Minor 1 (MEAM1) has invaded globally. After invading large countries like Australia, China, and the USA, MEAM1 spread rapidly across each country. In contrast, our analysis of MEAM1 in India showed a very different pattern. Despite the detection of MEAM1 being contemporaneous with invasions in Australia, the USA, and China, MEAM1 has not spread widely and instead remains restricted to the southern regions. An assessment of Indian MEAM1 genetic diversity showed a level of diversity equivalent to that found in its presumed home range and significantly higher than that expected across the invaded range. The high level of diversity and restricted distribution raises the prospect that its home range extends into India. Similarly, while the levels of diversity in Australia and the USA conformed to that expected for the invaded range, China did not. It suggests that China may also be part of its home range. We also observed that diversity across the invaded range was primarily accounted for by a single haplotype, Hap1, which accounted for 79.8% of all records. It was only the invasion of Hap1 that enabled outbreaks to occur and MEAM1’s discovery.

The safety of human-collaborative operations with robots depends on monitoring the external torque of the robot, in which there are toque sensor-based and torque sensor-free methods. Economically, the classic method for estimating joint external torque is the first-order momentum observer (MOB) based on a physic model without torque sensors. However, uncertainties in the dynamic model, which encompasses parameters identification error and joint friction, affect the torque estimation accuracy. To address this issue, this paper proposes using the backpropagation neural network (BPNN) method to estimate joint external torque without the delicate physical model by utilizing the powerful machine learning ability to handle the uncertainties of the MOB method and improve the accuracy of torque estimation. Using data obtained from the torque sensor to train the BPNN to build up a digital torque model, the trained BPNN can perceive force in practical applications without relying on the torque sensor. In the end, by contrast to the classic first-order MOB, the result demonstrates that BPNN achieves higher estimation accuracy compared to the MOB.

Scabies is a neglected tropical disease caused by the ectoparasitic mite, Sarcoptes scabiei var. hominis (S. scabiei). Common scabies, the most prevalent clinical subtype of scabies, is characterized by pruritus, multiple skin lesions and low mite burden. In contrast, crusted scabies, an extremely contagious variant, is characterized by hyperkeratosis and high mite burden, with or without pruritus. Scabies can be diagnosed based on clinical manifestations, with confirmation obtained through microscopic identification of diagnostic features of S. scabiei. However, owing to the diversity and non-specific nature of its clinical manifestations and insufficient knowledge regarding early-stage clinical manifestations, the diagnosis of crusted scabies continues to be delayed. Herein, we present three cases of scabies with varying degrees of crusting and mite burden. Three patients with physical and microscopic results suggesting scabies were selected for this study. Case 1 had mild crusting and low mite burden, case 2 had severe crusting and high mite burden and case 3 had mild crusting and high mite burden. In this case report, ‘the initial stage of crusted scabies’ refers to the progression from common to crusted scabies. The discussion regarding the diagnostic characteristics of the initial stage of crusted scabies is expected to aid the early diagnosis of crusted scabies.

In this paper, we study the rapid transition in Richtmyer–Meshkov instability (RMI) with reshock through three-dimensional double-layer swirling vortex rings. The rapid transition in RMI with reshock has an essential influence on the evolution of supernovas and the ignition of inertial confinement fusion, which has been confirmed in numerical simulations and experiments in shock-tube and high-energy-density facilities over the past few years. Vortex evolution has been confirmed to dominate the late-time nonlinear development of the perturbed interface. However, few studies have investigated the three-dimensional characteristics and nonlinear interactions among vortex structures during the transition to turbulent flows. The coexistence of co-rotating and counter-rotating vortices is hypothesized to induce successive large-scale strain fields, which are the main driving sources for rapid development. The three-dimensional effect is reflected in the presence of local swirling motion in the azimuthal direction, and it decreases the translation velocity of a vortex ring. Large-, middle- and small-scale strain fields are employed to describe the development process of RMI with reshock, e.g. vorticity deposited by the reshock, formation of the coexistence of the co-rotating and counter-rotating vortices, iterative cascade under the amplification of the strain fields and viscous dissipation to internal energy. This provides theoretical suggestions for designing practical applications, such as the estimation of the hydrodynamic instability and mixing during the late-time acceleration phase of the inertial confinement fusion.

Prehistoric humans seem to have preferred inhabiting small river basins, which were closer in distance to most settlements compared to larger rivers. The Holocene landscape evolution is considered to have played a pivotal role in shaping the spatiotemporal patterns of these settlements. In this study, we conducted comprehensive research on the relationship between landscape evolution and settlement distribution within the Huangshui River basin, which is a representative small river in Central China with numerous early settlements, including a prehistoric city known as the Wangjinglou site (WJL). Using geoarchaeological investigations, optically stimulated luminescence dating, pollen analysis, and grain-size analysis, we analyzed the characteristics of the Holocene environment. The results indicate the presence of two distinct geomorphic systems, namely the red clay hills and the river valley. The red clay hills, formed in the Neogene, represent remnants of the Songshan piedmont alluvial fan that was eroded by rivers. There are three grades of terraces within the river valley. T3 is a strath terrace and formed around 8.0 ka. Both T2 and T1 are fill terraces, which were developed around 4.0 ka and during the historical period, respectively. The sedimentary features and pollen analysis indicate the existence of an ancient lake-swamp on the platform during 11.0–9.0 ka. This waterbody gradually shrank during 9.0–8.0 ka, and ultimately disappeared after 8.0 ka. Since then, the development of large-scale areas of water ceased on the higher geomorphic units. River floods also cannot reach the top of these high geomorphic units, where numerous prehistoric settlements are located, including the Xia–Shang cities of the WJL site. Our research demonstrates that landscape stability supported the long-term and sustainable development of ancient cultures and facilitated the establishment of the WJL ancient cities in the region.