Time parallelization, also known as PinT (parallel-in-time), is a new research direction for the development of algorithms used for solving very large-scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early as 1964, it was not until 2004, when processor clock speeds reached their physical limit, that research in PinT took off. A distinctive characteristic of parallelization in time is that information flow only goes forward in time, meaning that time evolution processes seem necessarily to be sequential. Nevertheless, many algorithms have been developed for PinT computations over the past two decades, and they are often grouped into four basic classes according to how the techniques work and are used: shooting-type methods; waveform relaxation methods based on domain decomposition; multigrid methods in space–time; and direct time parallel methods. However, over the past few years, it has been recognized that highly successful PinT algorithms for parabolic problems struggle when applied to hyperbolic problems. We will therefore focus on this important aspect, first by providing a summary of the fundamental differences between parabolic and hyperbolic problems for time parallelization. We then group PinT algorithms into two basic groups. The first group contains four effective PinT techniques for hyperbolic problems: Schwarz waveform relaxation (SWR) with its relation to tent pitching; parallel integral deferred correction; ParaExp; and ParaDiag. While the methods in the first group also work well for parabolic problems, we then present PinT methods specifically designed for parabolic problems in the second group: Parareal; the parallel full approximation scheme in space–time (PFASST); multigrid reduction in time (MGRiT); and space–time multigrid (STMG). We complement our analysis with numerical illustrations using four time-dependent PDEs: the heat equation; the advection–diffusion equation; Burgers’ equation; and the second-order wave equation.

Bovine mastitis harms milk quality and cattle health. Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP) are well-known milk-derived bioactive peptides with anti-inflammatory activity. However, the impact of VPP and IPP on mastitis remain unknown. This study aimed to investigate the anti-inflammatory effects and the underlying mechanisms of VPP and IPP in lipopolysaccharide (LPS)-induced inflammation. When cells were treated with LPS (1 µg/mL) for 24 h, the protein levels of pro-inflammatory factors (tumor necrosis factor-α (TNF-α), interleukin(IL)-1β and IL-6)) and chemokine (monocyte chemotactic protein-1 (MCP-1)) were markedly increased, and the protein level of anti-inflammatory cytokine (IL-10) was reduced. Both VPP and IPP with concentrations of 50 and 100 µM reversed these phenomena and further inhibited the protein expression of β-casein induced by LPS. In a mouse mastitis model, different concentrations of VPP and IPP (300, 600 µM/kg) pretreatment alleviated histopathological lesions in the mammary gland and suppressed the mRNA expression of TNFα, IL1β, and IL6 induced by LPS. VPP and IPP also maintained the integrity of the blood–milk barrier in mice. RNA-seq analyses indicated that enriched phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase (MAPK) signaling pathways likely contribute to the changes observed (P < 0.05 and |log2 fold change (FC)| ≥ 1). Notably, fibronectin was identified as an important hub by protein–protein interaction (PPI) analysis and molecular docking combined with molecular dynamics simulation. In summary, VPP and IPP exerted a protective effect on LPS-induced inflammation by regulating PI3K/AKT signaling pathway via fibronectin.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems.

We show that attention constraints on decision-makers create barriers to financial inclusion. Using administrative data on retail loan-screening processes, we find that attention-constrained loan officers exert less effort reviewing applicants of lower socioeconomic status (SES) and reject them more frequently. More importantly, when externally imposed increases in loan officers’ workloads tighten attention constraints, loan officers are even more prone to quickly reject low-SES applicants but quickly accept very high-SES applicants without careful review. Such selective attention allocation further widens the approval rate gap between high- and low-SES applicants—a unique prediction of this attention-based mechanism.

Evidence-based insertion and maintenance bundles are effective in reducing the incidence of central line-associated bloodstream infections (CLABSI) in intensive care unit (ICU) settings. We studied the adoption and compliance of CLABSI prevention bundle programs and CLABSI rates in ICUs in a large network of acute care hospitals across Canada.

We study the melting process of a solid under microgravity, driven solely by lateral vibrations that are perpendicular to the applied temperature gradient due to the absence of gravity-induced convection. Using direct numerical simulations with the phase-field method, we examine two-dimensional vibration-induced melting in a square cavity over four orders of magnitude of vibrational Rayleigh numbers, $10^5\le Ra_{{vib}}\le 10^9$. Our results show that as melting progresses, the flow structure transitions from a periodic-circulation regime with diffusion-dominated heat transfer to a columnar regime with vibroconvection. The mean height of the liquid–solid interface follows a power-law dependency with time, $\bar {\xi } \sim \tilde t^{1/(2-2\alpha )}$, where $\alpha = 0$ in the periodic-circulation regime and $\alpha = 1/2$ in the columnar regime. We further observe that within the columnar regime, the morphological evolution of the liquid–solid interface is influenced by the interaction of columnar thermal plumes in the central regions and the peripheral flow near the sidewalls. Specifically, we offer a comprehensive analysis of the plume merging behaviour, which is governed by the aspect ratio ($\bar {\xi }$) of the liquid layer and the intensity of vibration, quantified by the effective vibrational Rayleigh number $Ra_{vib}^{eff}$. We identify the relationship between the number of columnar plumes $K_m$ and $Ra_{vib}^{eff}$, finding that $K_m \sim \bar {\xi }^{-1} (Ra_{vib}^{eff})^{\gamma }$ with the fitting scaling exponent $\gamma = 0.150 \pm 0.025$. We subsequently quantify the characteristics of the interface roughness amplitude evolution in microgravity vibroconvection. Our results indicate that the roughness amplitude exhibits a power-law dependence on the mean height of the liquid layer. Drawing from the Stefan boundary condition, we theoretically deduce this dependence under the assumption of a non-uniform heat flux distribution at the interface, where the theory is corroborated by our numerical simulations.

To meet the development needs of aeroengines for high thrust-to-weight ratios and fuel-air ratios, a high temperature rise triple-swirler main combustor was designed with a total fuel-air ratio of 0.037, utilising advanced technologies including staged combustion, multi-point injection and multi-inclined hole cooling. Fluent software was used to conduct numerical simulations under both takeoff and idle conditions, thereby obtaining the distribution characteristics of the velocity and temperature fields within the combustor, as well as the generation of pollutants. The simulation results indicate that under takeoff conditions, the high temperature rise triple-swirler combustor achieves a total pressure loss coefficient of less than 6% and a combustion efficiency exceeding 99%. Under takeoff conditions, the OTDF and RTDF values are 0.144 and 0.0738, respectively. The mole fraction of NOx emissions is 3,700ppm, while the mole fraction of soot emissions is 2.55×10−5ppm. Under idle conditions, the triple-swirler combustor maintains a total pressure loss coefficient of less than 6% and a combustion efficiency greater than 99.9%. The OTDF and RTDF values are 0.131 and 0.0624, respectively. The mole fractions of CO and UHC emissions are both 0×10−32ppm at the calculation limit of Fluent software.

Maura Dykstra's 2022 monograph Uncertainty in the Empire of Routine: The Administrative Revolution of the Eighteenth-Century Qing State (Harvard Asia Center, 2022) has attracted controversy in the academic community. This paper analyses the book's use of documents from the Ba County Archive, held in the SIchuan Provincial Archive. While reviewing the monograph's arguments drawn from these materials, the paper also introduces the Ba Archives and the methodologies that may be employed to interpret them.

Tea can improve the progression of some metabolic diseases through anti-inflammatory and antioxidant effects, but its impact on non-alcoholic fatty liver disease (NAFLD) is still controversial. The aim of this paper is to identify the relationship between tea and NAFLD by Mendelian randomisation (MR) and complete clinical validation using National Health and Nutrition Examination Survey (NHANES) database. MR used data from Genome Wide Association Study, with inverse-variance weighted (IVW) as principal analytical methods. The reliability of the results was verified by a series of sensitivity and heterogeneity tests. Subsequently, clinical validation was conducted using NHANES (2005–2018), involving 22 257 participants, grouped by the type of tea. Green tea drinkers were categorised into four groups (Q1–Q4) by quartiles of green tea intake, from lowest to highest (similar for black tea drinkers and other tea drinkers). Models were constructed by logistic regression to estimate the role of tea consumption (Q1–4) on NAFLD. Finally, using fibrosis-4 index (FIB-4) to evaluate the severity of hepatic fibrosis, the effect of tea consumption (Q1–4) on the degree of hepatic fibrosis was investigated by linear regression. IVW method (OR = 0·43, 95 % CI: 0·21, 0·85, P = 0·01) and weighted median method (OR = 0·35, 95 % CI: 0·14, 0·91, P = 0·03) revealed there was a causal relationship between tea and NAFLD. An array of sensitivity analyses validated the reliability of results. Analysis of NHANES indicated tea drinker present a slightly lower prevalence of NAFLD than non-tea drinker (green tea drinkers: 47·6 %, black tea drinkers: 46·3 %, other tea drinker: 43·2 %, non-tea drinkers: 48·1 %, P < 0·05). After adjusting for confounders, compared with the lowest black tea consumption (Q1), the population with the highest black tea consumption (Q4) was independently related to lower presence of NAFLD (Q4: OR = 0·69, 95 % CI: 0·50, 0·93, P < 0·05), such association remained stable in the overweight subgroup. As further analysed, Q4 also displayed a significant negative correlation with the level of hepatic fibrosis in patients with NAFLD (β = –0·073, 95 % CI: –0·126, −0·020, P < 0·01).Tea reduces the morbidity of NAFLD and ameliorates hepatic fibrosis degree in those already suffering from the disease.

Thanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

Recent studies suggest an association between greater dietary inflammatory index (DII) and higher biological ageing. As α-Klotho has been considered as a longevity protein, we examined whether α-Klotho plays a role in the association between DII and ageing. We included 3054 participants from the National Health and Nutrition Examination Survey. The associations of DII with biological and phenotypic age were assessed by multivariable linear regression, and the mediating role of α-Klotho was evaluated by mediation analyses. Participants’ mean age was 58·0 years (sd 11·0), with a median DII score of 1·85 and interquartile range from 0·44 to 2·79. After adjusting for age, sex, race/ethnicity, BMI, education, marital status, poverty income ratio, serum cotinine, alcohol, physical activity, a higher DII was associated with both older biological age and phenotypic age, with per DII score increment being associated with a 1·01-year increase in biological age (1·01 (95 % CI: 1·005, 1·02)) and 1·01-year increase in phenotypic age (1·01 (1·001, 1·02)). Negative associations of DII with α-Klotho (β = –1·01 pg/ml, 95 % CI: –1·02, –1·006) and α-Klotho with biological age (β= –1·07 years, 95 % CI: –1·13, –1·02) and phenotypic age (β= –1·03 years, 95 % CI: –1·05, –1·01) were found. Furthermore, α-Klotho mediated 10·13 % (P < 0·001) and 9·61 % (P < 0·001) of the association of DII with biological and phenotypic age, respectively. Higher DII was associated with older biological and phenotypic age, and the potential detrimental effects could be partly mediated through α-Klotho.

We report the unified constitutive law of vibroconvective turbulence in microgravity, i.e. $Nu \sim a^{-1} Re_{os}^\beta$ where the Nusselt number $Nu$ measures the global heat transport, $a$ is the dimensionless vibration amplitude, $Re_{os}$ is the oscillational Reynolds number and $\beta$ is the universal exponent. We find that the dynamics of boundary layers plays an essential role in vibroconvective heat transport and the $Nu$-scaling exponent $\beta$ is determined by the competition between the thermal boundary layer (TBL) and vibration-induced oscillating boundary layer (OBL). Then a physical model is proposed to explain the change of scaling exponent from $\beta =2$ in the TBL-dominant regime to $\beta = 4/3$ in the OBL-dominant regime. Our finding elucidates the emergence of universal constitutive laws in vibroconvective turbulence, and opens up a new avenue for generating a controllable effective heat transport under microgravity or even microfluidic environment in which the gravity effect is nearly absent.

Gentiana straminea Maxim. (Gentianaceae) is an important traditional Tibetan herb that is mainly distributed on the Qinghai-Tibetan Plateau. Despite its agricultural and pharmacological importance, there remains a paucity of microsatellite markers, particularly expressed sequence tag-simple sequence repeat (EST-SSR) markers, available for this local endemic species. In this study, based on previous Illumina transcriptome data of G. straminea, a total of 96 EST-SSR markers were initially designed and tested. Thirty-two of 96 loci (33.33%) were successfully amplified and verified for validation. Among them, 10 were polymorphic and had clear bands. The polymorphism information content values were 0.09–0.799, the number of alleles per locus ranged from 3 to 14, and the levels of observed and expected heterozygosity were 0.078–0.722 and 0.238–0.884, respectively, which suggested a high level of information. Moreover, cross-amplification was successful for 10 loci in two other related species, Gentiana macrophylla Pallas and Gentiana dahurica Fischer. These EST-SSR markers provide a valuable tool for investigating the genetic diversity related to quantitative traits and population genetic studies on G. straminea and related species in sect. Cruciata Gaudin.

Gradual typing integrates static and dynamic typing by introducing a dynamic type and a consistency relation. A problem of gradual type systems is that dynamic types can easily hide erroneous data flows since consistency relations are not transitive. Therefore, a more rigorous static check is required to reveal these hidden data flows statically. However, in order to preserve the expressiveness of gradually typed languages, static checks for gradually typed languages cannot simply reject programs with potentially erroneous data flows. By contrast, a more reasonable request is to show how these data flows can affect the execution of the program. In this paper, we propose and formalize Static Blame, a framework that can reveal hidden data flows for gradually typed programs and establish the correspondence between static-time data flows and runtime behavior. With this correspondence, we build a classification of potential errors detected from hidden data flows and formally characterize the possible impact of potential errors in each category on program execution, without simply rejecting the whole program. We implemented Static Blame on Grift, an academic gradually typed language, and evaluated the effectiveness of Static Blame by mutation analysis to verify our theoretical results. Our findings revealed that Static Blame exhibits a notable level of precision and recall in detecting type-related bugs. Furthermore, we conducted a manual classification to elucidate the reasons behind instances of failure. We also evaluated the performance of Static Blame, showing a quadratic growth in run time as program size increases.

Accurately predicting neurosyphilis prior to a lumbar puncture (LP) is critical for the prompt management of neurosyphilis. However, a valid and reliable model for this purpose is still lacking. This study aimed to develop a nomogram for the accurate identification of neurosyphilis in patients with syphilis. The training cohort included 9,504 syphilis patients who underwent initial neurosyphilis evaluation between 2009 and 2020, while the validation cohort comprised 526 patients whose data were prospectively collected from January 2021 to September 2021. Neurosyphilis was observed in 35.8% (3,400/9,504) of the training cohort and 37.6% (198/526) of the validation cohort. The nomogram incorporated factors such as age, male gender, neurological and psychiatric symptoms, serum RPR, a mucous plaque of the larynx and nose, a history of other STD infections, and co-diabetes. The model exhibited good performance with concordance indexes of 0.84 (95% CI, 0.83–0.85) and 0.82 (95% CI, 0.78–0.86) in the training and validation cohorts, respectively, along with well-fitted calibration curves. This study developed a precise nomogram to predict neurosyphilis risk in syphilis patients, with potential implications for early detection prior to an LP.

Adherence to post-exposure prophylaxis and post-exposure vaccination (PEV) is an important measure to prevent rabies. The purpose of this study was to explore the adherence to the vaccination protocol and its influencing factors among rabies-exposed patients in Shenzhen, China. A cross-sectional survey was conducted in a tertiary hospital in Shenzhen, China, to obtain epidemiological characteristics of patients; knowledge, attitude, and practice scores of rabies prevention; and medical records. A total of 326 patients requiring full rabies PEV were included in this study, and only 62% (202) completed the full course of vaccination according to the norms of the vaccination guidelines. After multifactor logistic regression, the factors influencing adherence to vaccination were as follows: age 31 to 40 years, time spent to reach the nearest rabies prevention clinic was >60 min, the time of injury was at night to early morning, the place of injury was a school/laboratory, the animal causing injury was a cat, the health status of the animal causing injury could not be determined, and patients with higher practice scores (all p<0.05). Understanding the factors influencing rabies vaccination adherence among rabies-exposed patients in urban areas of China and promote changes in patients’ practice toward rabies prevention is essential for rabies elimination by 2030.