The temporal characteristics of fully localised turbulent bands in transitional channel flow remains unclear due to the difficulty in resolving the large length and time scales involved. Here, we tackle this problem by performing statistical lifetime studies in sufficiently large computational domains. The results show signs of stochastic memoryless decay of a fully localised band, suggesting a chaotic-saddle behaviour of the entire band as a coherent entity. Although the mean lifetime of a turbulent band was reported to increase with the band length, our data suggest that it saturates at a certain length. This saturation results in a characteristic lifetime for a fully developed band with a changing length due to the intermittent chipping and decay of turbulence at the upstream end. This memoryless behaviour is observed down to Reynolds number $Re=630$ in our study and we propose that the onset of the memoryless behaviour is in the range of $Re\simeq 620{-}630$. Our data also show that the time it takes for a perturbed flow to enter the saddle, i.e. to start behaving memorylessly, can be thousands of convective time units, which is comparable to the maximum achievable observation time in existing channel set-ups and may pose difficulties for experiments.

While the traditional Japanese diet has been suggested to increase blood pressure due to its high sodium content, whether the contemporary Japanese diet is associated with blood pressure remains elusive. We developed a traditional Japanese diet score (nine items: white rice, miso soup, soy products, vegetables, mushrooms, seaweeds, fish, salty food, and green tea) and a modified version by substituting white rice with whole-grain rice, reverse scoring for salty food and adding fruits, raw vegetables, and dairy foods using data from 12,213 employees from Japanese companies. Hypertension was defined as a blood pressure of 140/90 mmHg or more or the use of antihypertensive drugs. A multilevel Poisson regression model with a robust variance estimator was used to calculate prevalence ratios (PRs) and 95% confidence intervals (CIs) while adjusting for covariates. The adjusted PRs (95%CIs) of hypertension for the lowest through highest quartiles of the traditional Japanese diet score were 1.00 (reference), 0.94 (0.88–1.02), 0.98 (0.90–1.06), and 0.96 (0.90–1.02), respectively (P for trend=0.29), while those for the modified Japanese diet score were 1.00 (reference), 0.96 (0.94–0.99), 0.95 (0.85–1.05), and 0.94 (0.87–1.01), respectively (P for trend=0.10). In this cross-sectional study, close adherence to the traditional Japanese diet was not associated with the prevalence of hypertension, whereas there was a suggestion of an inverse association between the modified Japanese diet and the prevalence of hypertension.

Retracted research publications reached an all-time high in 2023, and COVID-19 publications may have higher retraction rates than other publications. To better understand the impact of COVID-19 on the research literature, we analyzed 244 retracted publications related to COVID-19 in the PubMed database and the reasons for their retraction. Peer-review manipulation (18.4%) and error (20.9%) were the most common reasons for retraction, with time to retraction occurring far more quickly than in the past (13.2 mos, compared with 32.9 mos in a 2012 study). Publications focused on controversial topics were retracted rapidly (mean time to retraction 10.8 mos) but continued to receive media attention, suggesting that retraction alone may be insufficient to prevent the spread of scientific misinformation. More than half of the retractions resulted from problems that could have been detected prior to publication, including compromise of the peer review process, plagiarism, authorship issues, lack of ethics approvals, or journal errors, suggesting that more robust screening and peer review by journals can help to mitigate the recent rise in retractions.

The Lyman alpha (Lyα) forest in the spectra of z > 5 quasars provides a powerful probe of the late stages of the Epoch of Reionization (EoR).With the recent advent of exquisite datasets such as XQR-30, many models have struggled to reproduce the observed large-scale fluctuations in the Lyα opacity. Here we introduce a Bayesian analysis framework that forward-models large-scale lightcones of intergalactic medium (IGM) properties, and accounts for unresolved sub-structure in the Lyα opacity by calibrating to higher-resolution hydrodynamic simulations. Our models directly connect physically-intuitive galaxy properties with the corresponding IGM evolution, without having to tune “effective” parameters or calibrate out the mean transmission. The forest data, in combination with UV luminosity functions and the CMB optical depth, are able to constrain global IGM properties at percent level precision in our fiducial model. Unlike many other works, we recover the forest observations without invoking a rapid drop in the ionizing emissivity from z ∼ 7 to 5.5, which we attribute to our sub-grid model for recombinations. In this fiducial model, reionization ends at z = 5.44 ± 0.02 and the EoR mid-point is at z = 7.7 ± 0.1. The ionizing escape fraction increases towards faint galaxies, showing a mild redshift evolution at fixed UV magnitude, MUV. Half of the ionizing photons are provided by galaxies fainter than MUV ∼ –12, well below direct detection limits of optical/NIR instruments including JWST. We also show results from an alternative galaxy model that does not allow for a redshift evolution in the ionizing escape fraction. Despite being decisively disfavored by the Bayesian evidence, the posterior of this model is in qualitative agreement with that from our fiducial model. We caution however that our conclusions regarding the early stages of the EoR and which sources reionized the Universe are more model-dependent.

Artificial Intelligence (AI) has the potential to revolutionize public health surveillance by analyzing large datasets to detect outbreaks. However, privacy, consent, and governance issues persist. While the International Health Regulations do not explicitly mention the use of AI in infectious disease surveillance, transparent processes, accountability, and public trust are key for responsibly integrating AI in pandemic preparedness.

In this study, we tackle the challenge of inferring the initial conditions of a Rayleigh–Taylor mixing zone for modelling purposes by analysing zero-dimensional (0-D) turbulent quantities measured at an unspecified time. This approach assesses the extent to which 0-D observations retain the memory of the flow, evaluating their effectiveness in determining initial conditions and, consequently, in predicting the flow’s evolution. To this end, we generated a comprehensive dataset of direct numerical simulations, focusing on miscible fluids with low density contrasts. The initial interface deformations in these simulations are characterised by an annular spectrum parametrised by four non-dimensional numbers. To study the sensitivity of 0-D turbulent quantities to initial perturbation distributions, we developed a surrogate model using a physics-informed neural network (PINN). This model enables computation of the Sobol indices for the turbulent quantities, disentangling the effects of the initial parameters on the growth of the mixing layer. Within a Bayesian framework, we employ a Markov chain Monte Carlo (MCMC) method to determine the posterior distributions of initial conditions and time, given various state variables. This analysis sheds light on inertial and diffusive trajectories, as well as the progressive loss of initial conditions memory during the transition to turbulence. Furthermore, it identifies which turbulent quantities serve as better predictors of Rayleigh–Taylor mixing zone dynamics by more effectively retaining the memory of the flow. By inferring initial conditions and forward propagating the maximum a posteriori (MAP) estimate, we propose a strategy for modelling the Rayleigh–Taylor transition to turbulence.

Let $X$ be a compact Riemann surface. Let $(E,\theta )$ be a stable Higgs bundle of degree $0$ on $X$. Let $h_{\det (E)}$ denote a flat metric of the determinant bundle $\det (E)$. For any $t\gt 0$, there exists a unique harmonic metric $h_t$ of $(E,t\theta )$ such that $\det (h_t)=h_{\det (E)}$. We prove that if the Higgs bundle is induced by a line bundle on the normalization of the spectral curve, then the sequence $h_t$ is convergent to the naturally defined decoupled harmonic metric at the speed of the exponential order. We also obtain a uniform convergence for such a family of Higgs bundles.

This article examines whether artificial intelligence (AI)-driven harm can be classified as moral or natural evil, or whether a new category – artificial evil – is needed. Should AI’s harm be seen as a product of human design, thus maintaining moral responsibility for its creators, or whether AI’s autonomous actions resemble natural evil, where harm arises unintentionally? The concept of artificial evil, combining elements of both moral and natural evil, is presented to better address this dilemma. Just as AI is still a form of intelligence (albeit non-biological), artificial evil would still be evil in the sense that it results in real harm or suffering – it is just that this harm is produced by AI systems rather than by nature or human moral agents directly. The discussion further extends into the realm of defence or theodicy, drawing parallels with the Free Will Defence, questioning if AI autonomy may be justified even if it results in harm, much like human free will. Ultimately, the article calls for a re-evaluation of our ethical frameworks and glossary of terms to address the emerging challenges of AI autonomy and its moral implications.

Rainfall is the main driving factor for soil-active herbicides, influencing their incorporation, leaching and absorption. Studies were conducted to determine the effects of simulated rainfall and hexazinone application rates on Sporobolus indicus var. pyramidalis control, and the impacts of application timing and rates on Sporobolus indicus var. pyramidalis in the field. Greenhouse experiments were established in Florida between 2017 and 2018, comprising hexazinone application rates of 0.56 and 1.12 kg ai ha-1, and seven simulated rainfall accumulation volumes (0, 6, 12, 25, 50, 100 and 200 mm), distributed in a completely randomized design with four replicates and a non-treated control. Field experiments were conducted in a split-plot arrangement, where main plots were application timings at one-week intervals, subplots were two hexazinone application rates (0.56 and 1.12 kg ha-1), and a non-treated control, distributed in a randomized complete block design with four replicates. In the greenhouse experiment, 49 and 92 mm were required to obtain 50% visual control and 35 and 82 mm to reduce biomass by 50% for hexazinone rates of 0.56 and 1.12 kg ai ha-1, respectively. Field experiments showed that hexazinone peak efficacy was from mid-June to mid-August when applications were followed by 10 to 75 mm of rainfall during the first 7 DAT. The recommended rate of hexazinone at 1.12 kg ai ha-1 should be applied as it has an extended window of optimum application timing.

The goniadid polychaete Goniadella gracilis (Verrill, 1873), currently considered as a non-indigenous species in European waters, is reported for the first time in Italian waters of the Tyrrhenian and Ionian Seas. A detailed description of individuals of this species from the Mediterranean Sea is provided, including the pigmentation pattern of the specimens, which to our knowledge has never been reported before in literature. In addition, new molecular data, 18S and COI sequences, from Mediterranean specimens are presented and compared with data from the North-Eastern Atlantic. On the basis of the available evidence, the status of G. gracilis in the Mediterranean basin is discussed, and the species is here proposed to be considered as a questionable species in the area.

This paper explores the crisis of 596 which interrupted Augustine of Canterbury's mission to the Anglo-Saxons, necessitating his return to Rome. Bede's interpretation of this as a moment of psychological failure is discounted. Political changes in Merovingian Gaul associated with the death of Childebert are reconsidered. A new economic explanation is advanced based on the wording of Gregory the Great's letter of encouragement to Augustine and his fellow missionaries, consideration of the management of the papal estates in Gaul and the behaviour of Virgilius, metropolitan of Arles.

This article argues that critical and emerging technologies, evolving geopolitical dynamics and the urgent need to pursue the green agenda are changing the traditional approach of the European Union (EU) and its Member States towards their trade and security strategies, and this is particularly evident in the domain of export controls. In search of a balance between green energy, security and technological progress, this article explores the potential for a more cohesive and comprehensive regulatory framework for export controls at the EU level. It takes the debate beyond a technical level of export control lists to discuss geopolitical and strategic assumptions surrounding inter-State cooperation on the regulation of critical and emerging technologies and their components. The article underscores, in particular, the potential unintended repercussions of controls for the EU's technological future and the prospects of the green transition in Europe and beyond. Finally, it advocates for what is often overlooked in discussions: the necessity for the EU to set clear, long-term objectives for its export controls and to align them with the purposes and objectives of other EU economic instruments.

International law and global governance regimes for environmental health challenges have been slow to reflect the intertwined relationship between the environment and human health. Historical legacies have caused artificial fragmentation between the two that has resulted in distinct fields of international law and institutions for the environment and health. However, new global paradigms for thinking about environmental health have emerged to foster synthesis under global health law, including One Health and Planetary Health approaches, as well as through international human rights law like the recognition of the right to a clean, safe, and healthy environment. Guided by equity, new international law and global governance reforms, including the proposed Pandemic Agreement and Plastics Treaty, are opportunities to synthesize the intersecting dimensions of the environment and global health. However, future paths towards cohesion must explicitly incorporate human rights in environmental health governance, including the rights of Indigenous Peoples, while actively addressing inequities in global health law, between and within countries, and across generations.