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Partial differential equations (PDEs) are fundamental for modeling complex physical processes, often exhibiting structural features such as symmetries and conservation laws. While physics-informed neural networks (PINNs) can simulate and invert PDEs, they mainly rely on external loss functions for physical constraints, making it difficult to automatically discover and embed physically consistent network structures. We propose a physics structure-informed neural network discovery method based on physics-informed distillation, which decouples physical and parameter regularization via staged optimization in teacher and student networks. After distillation, clustering and parameter reconstruction are used to extract and embed physically meaningful structures. Numerical experiments on Laplace, Burgers, and Poisson equations, as well as fluid mechanics, show that the method can automatically extract relevant structures, improve accuracy and training efficiency, and enhance structural adaptability and transferability. This approach offers a new perspective for efficient modeling and automatic discovery of structured neural networks.

We study fluid-induced deformation and fracture of cohesive granular media, and apply photoporomechanics to uncover the underpinning grain-scale mechanics. We fabricate photoelastic spherical particles of diameter d = 2 mm, and make a monolayer granular pack with tunable intergranular cohesion in a circular Hele-Shaw cell that is initially filled with viscous silicone oil. We inject water into the oil-filled photoelastic granular pack, varying the injection flow rate, defending-fluid viscosity, and intergranular cohesion. We find two different modes of fluid invasion: viscous fingering, and fracturing with leak-off of the injection fluid. We directly visualize the evolving effective stress field through the particles' photoelastic response, and discover a hoop effective stress region behind the water invasion front, where we observe tensile force chains in the circumferential direction. Outside the invasion front, we observe compressive force chains aligning in the radial direction. We conceptualize the system's behavior by means of a two-phase poroelastic continuum model. The model captures granular pack dilation and compaction with the boundary delineated by the invasion front, which explains the observed distinct alignments of the force chains. Finally, we rationalize the crossover from viscous fingering to fracturing by comparing the competing forces behind the process: viscous force from fluid injection that drives fractures, and intergranular cohesion and friction that resist fractures.

Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the sparse invariant detector (SID), an algorithm that autodiscovers conservation laws from differential equations. Its algorithmic simplicity allows robustness and interpretability of the discovered conserved quantities. We show that SID is able to rediscover known and even discover new conservation laws in a variety of systems. For two examples in fluid mechanics and atmospheric chemistry, SID discovers 14 and 3 conserved quantities, respectively, where only 12 and 2 were previously known to domain experts.

We study the turbulent front of a current on a horizontal bed driven by a continuous buoyancy flux using a large-eddy simulation (LES) method that does not rely on the Boussinesq approximation, thereby enabling accurate simulation of flows with substantial density contrasts. The primary focus of the simulation is the frontal region, where entrained ambient fluid combines with the source fluid entering the neck of the current head to form a bulging, elevated head. The bulging current head, sustained by the flow through its neck, is significant for its role in sediment transport and its destructive force in the atmosphere and ocean. Despite numerous previous studies, the vortex model of current head proposed by Prandtl [1] remains poorly understood. Analysis of the LES results shows that the turbulence entrains ambient fluid primarily through the formation of a Kelvin-Helmholtz (K-H) vortex. The LES determine the maximum velocity and maximum flow through the neck of the current head from the formation of the frontal vortex to the attainment of the vortex's pinnacle. The vortex grows and then collapses, reaching a critical Richardson number of Ri [Formula: see text] 0.30 [Formula: see text] 0.42. More than eighty percent of the turbulent entrainment of ambient fluid into the current occurs within the current head. The pinnacle flow through the neck-a function of the source Froude number-is uniquely related to the frontal velocity and volume entrainment rate by an overall structure-similarity relationship for the coherent turbulence in the front. We evaluate the numerical accuracy and the LES model through a mesh-refinement study and a comparison with laboratory observations.

Meandering instability is familiar to everyone through river meandering or small rivulets of rain flowing down a windshield. However, its physical understanding is still premature, although it could inspire researchers in various fields, such as nonlinear science, fluid mechanics and geophysics, to resolve their long-standing problems. Here, we perform a small-scale experiment in which air flow is created in a thin granular bed to successfully find a meandering regime, together with other remarkable fluidized regimes, such as a turbulent regime. We discover that phase diagrams of the flow regimes for different types of grains can be universally presented as functions of the flow rate and the granular-bed thickness when the two quantities are properly renormalized. We further reveal that the meandering shapes are self-similar as was shown for meandering rivers. The experimental findings are explained by theory, with elucidating the physics. The theory is based on force balance, a minimum-dissipation principle, and a linear-instability analysis of a continuum equation that takes into account the fluid-solid duality, i.e., the existence of fluidized and solidified regions of grains along the meandering path. The present results provide fruitful links to related issues in various fields, including fluidized bed reactors in industry.

A unified discussion of the mechanics of the swallowing process, and its interpretation through manometric measurements of intraluminal pressure, are presented in this paper. The goals of the discussions are to provide the reader with basic knowledge of pharyngeal, esophageal, and sphincter mechanics; to relate the mechanical processes to intraluminal pressure recordings; and to clarify the relationship between intraluminal pressure and esophageal muscle contractile behavior. The esophageal phase of bolus transport, in particular, is discussed in some detail due to the relatively simple geometry and the straightforward description of peristalsis and muscle mechanics in this region. Several important issues are emphasized in the discussion. For example, pressure variation within a static bolus is fundamentally different from that within a moving bolus. Manometric recordings must be interpreted accordingly. The importance of differentiating between "hydrodynamic pressure," which is pressure measured within a fluid bolus, and "contact pressure," which is the direct squeeze of the luminal wall on the manometric port in a region devoid of bolus fluid, is discussed in some detail. We argue that pressure "amplitude" does not, in principle, give any indication of the forces required to drive the fluid bolus forward. What should be sought is the variation of intrabolus pressure relative to the contact pressure, particularly during periods in which the contractile segment fails to obliterate the esophageal lumen. Examples of intraluminal pressure recording in the esophagus, using manometry and mathematical models, are presented to demonstrate both the possibilities and the difficulties of interpreting manometric recordings in the absence of concurrent radiographic imaging. We discover that in regions of nearly complete luminal closure, the pressure signature and bolus geometry are strongly coupled during peristaltic transport, providing the possibility that in these regions quantitative measures of muscle performance might be developed without the need for radiographic imaging. On the other hand, the ambiguity in the interpretation of manometric recordings that often accompanies dysphagic conditions suggests that as more sophisticated interpretations are sought, manometry concurrent with radiography will play a more prominent role in patient evaluation.

Hydrodynamic interactions play a role in synchronized motions of coupled oscillators in fluids, and understanding the mechanism will facilitate development of applications in fluid mechanics. For example, synchronization phenomenon in two-phase flow will benefit the design of future microfluidic devices, allowing spatiotemporal control of microdroplet generation without additional integration of control elements. In this work, utilizing a characteristic oscillation of adjacent interfaces between two immiscible fluids in a microfluidic platform, we discover that the system can act as a coupled oscillator, notably showing spontaneous in-phase synchronization of droplet breakup. With this observation of in-phase synchronization, the coupled droplet generator exhibits a complete set of modes of coupled oscillators, including out-of-phase synchronization and nonsynchronous modes. We present a theoretical model to elucidate how a negative feedback mechanism, tied to the distance between the interfaces, induces the in-phase synchronization. We also identify the criterion for the transition from in-phase to out-of-phase oscillations.

Alterations in intestinal structure, mechanics and physiology underlie acute and chronic intestinal conditions, many of which are influenced by dysregulation of microbiome, peristalsis, stroma or immune responses. Studying human intestinal physiology or pathophysiology is difficult in preclinical animal models because their microbiomes and immune systems differ from those of humans. Although advances in organoid culture partially overcome this challenge, intestinal organoids still lack crucial features that are necessary to study functions central to intestinal health and disease, such as digestion or fluid flow, as well as contributions from long-term effects of living microbiome, peristalsis and immune cells. Here, we review developments in organ-on-a-chip (organ chip) microfluidic culture models of the human intestine that are lined by epithelial cells and interfaced with other tissues (such as stroma or endothelium), which can experience both fluid flow and peristalsis-like motions. Organ chips offer powerful ways to model intestinal physiology and disease states for various human populations and individual patients, and can be used to gain new insight into underlying molecular and biophysical mechanisms of disease. They can also be used as preclinical tools to discover new drugs and then validate their therapeutic efficacy and safety in the same human-relevant model.

Background: The present study aimed to explore the correlation between calcium-activated potassium channels, left atrial flow field mechanics, valvular atrial fibrillation (VAF), and thrombosis. The process of transforming mechanical signals into biological signals has been revealed, which offers new insights into the study of VAF. Methods: Computational fluid dynamics simulations use numeric analysis and algorithms to compute flow parameters, including turbulent shear stress (TSS) and wall pressure in the left atrium (LA). Real-time PCR and western blotting were used to detect the mRNA and protein expression of IKCa2.3/3.1, ATK1, and P300 in the left atrial tissue of 90 patients. Results: In the valvular disease group, the TSS and wall ressure in the LA increased, the wall pressure increased in turn in all disease groups, mainly near the mitral valve and the posterior portion of the LA, the increase in TSS was the most significant in each group near the mitral valve, and the middle and lower part of the back of the LA and the mRNA expression and protein expression levels of IKCa2.3/3.1, AKT1, and P300 increased (p < 0.05) (n = 15). The present study was preliminarily conducted to elucidate whether there might be a certain correlation between IKCa2.3 and LA hemodynamic changes. Conclusions: The TSS and wall pressure changes in the LA are correlated with the upregulation of mRNA and protein expression of IKCa2.3/3.1, AKT1, and P300.

Three-dimensional unsteady numerical simulations are performed to investigate the effects of blowing ratio [Formula: see text] ([Formula: see text]), stroke ratio [Formula: see text] ([Formula: see text]), and boundary-layer height ratio [Formula: see text] (2.1<&#x3b4; /d<8.0) on circular synthetic jet actuator (SJA) performance in crossflow. Nine cases are examined at constant free-stream velocity [Formula: see text], with systematic independent variation of averaged jet velocity [Formula: see text], actuation frequency f (200-[Formula: see text]), and boundary-layer momentum thickness Reynolds number ([Formula: see text]) to examine the influence of these parameters across varying boundary-layer conditions on a circular-nozzle SJA with fixed nozzle diameter d in crossflow. Instantaneous vortical structures exhibited tilted vortex rings with a trailing vortex pair at low actuation frequency; closely packed expelled vortical structures for higher frequency SJAs, and the largest boundary-layer height ratio induced hairpin-like vortices. Near-wall tertiary vortices, which promote downwash and increase wall shear stress, remain coherent longer and have extended spanwise coverage for low [Formula: see text]. Time-averaged boundary-layer profiles and skin-friction distributions reveal that SJAs with low to moderate [Formula: see text] have the greatest potential for separation control, maintaining increased near-wall momentum over extended streamwise distances. The online version contains supplementary material available at 10.1007/s44369-026-00011-9.

Birds, insects, bats and fish demonstrate exceptional locomotion efficiency through adaptive flapping motions, offering a wealth of inspiration for bio-inspired propulsion systems. However, traditional research often relies on simplified motion models with limited degrees of freedom, which may not fully capture the complexity, adaptability, and efficiency of natural movement. In this study, we propose an adaptive motion optimization framework based on reinforcement learning (RL), aiming to address the aforementioned challenges. By integrating high-fidelity numerical simulations with physical models of flapping wings, the framework dynamically adjusts motion patterns in real time, guided by flow field information. Departing from conventional methods that rely on pre-designed motion assumptions, this approach uncovers non-harmonic, quasi-periodic motion patterns through iterative exploration. The system refines behaviors to enhance propulsion performance, adapt to dynamic flow conditions, and reveal biologically relevant features, such as asymmetric oscillations, adaptive rhythmic formations, and progressive fine-tuning of motion strategies. These learned motions not only align with natural flapping characteristics but also surpass traditional optimization methods by expanding the search space to include more complex and effective movement patterns. This framework demonstrates the power of RL to discover sophisticated, bio-inspired motion dynamics, offering transformative potential for understanding natural flapping mechanisms and designing efficient, versatile propulsion systems for real-world applications.

Slip length describes the classical no-slip boundary condition violation of Newtonian fluid mechanics, where fluids glide on the solid surfaces. Here, we propose a new analytical model validated by experiments for characterization of the liquid slip using vibrating solid surfaces. Essentially, we use a microfluidic system integrated with quartz crystal microbalance (QCM) to investigate the relationship between the slip and the mechanical response of a vibrating solid for a moving fluid. We discover a liquid slip that emerges especially at high flow rates, which is independent of the surface wetting condition, having significant contributions to the changes in resonant frequency of the vibrating solid and energy dissipation on its surface. Overall, our work will lead to consideration of 'missing slip' in the vibrating solid-liquid systems such as the QCM-based biosensing where traditionally frequency changes are interpreted exclusively with mass change on the sensor surface, irrespective of the flow conditions.

Whether there exist finite-time blow-up solutions for the 2D Boussinesq and the 3D Euler equations are of fundamental importance to the field of fluid mechanics. We develop a new numerical framework, employing physics-informed neural networks, that discover, for the first time, a smooth self-similar blow-up profile for both equations. The solution itself could form the basis of a future computer-assisted proof of blow-up for both equations. In addition, we demonstrate physics-informed neural networks could be successfully applied to find unstable self-similar solutions to fluid equations by constructing the first example of an unstable self-similar solution to the C&#xf3;rdoba-C&#xf3;rdoba-Fontelos equation. We show that our numerical framework is both robust and adaptable to various other equations.

To solve problems in polymer fluid dynamics, one needs the equations of continuity, motion, and energy. The last two equations contain the stress tensor and the heat-flux vector for the material. There are two ways to formulate the stress tensor: (a) One can write a continuum expression for the stress tensor in terms of kinematic tensors, or (b) one can select a molecular model that represents the polymer molecule and then develop an expression for the stress tensor from kinetic theory. The advantage of the kinetic theory approach is that one gets information about the relation between the molecular structure of the polymers and the rheological properties. We restrict the discussion primarily to the simplest stress tensor expressions or constitutive equations containing from two to four adjustable parameters, although we do indicate how these formulations may be extended to give more complicated expressions. We also explore how these simplest expressions are recovered as special cases of a more general framework, the Oldroyd 8-constant model. Studying the simplest models allows us to discover which types of empiricisms or molecular models seem to be worth investigating further. We also explore equivalences between continuum and molecular approaches. We restrict the discussion to several types of simple flows, such as shearing flows and extensional flows, which are of greatest importance in industrial operations. Furthermore, if these simple flows cannot be well described by continuum or molecular models, then it is not necessary to lavish time and energy to apply them to more complex flow problems.

Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2&#xb7;5th and 97&#xb7;5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55&#x2008;761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46&#xb7;8 years (95% UI 46&#xb7;6-47&#xb7;0) in 1990 to 63&#xb7;4 years (63&#xb7;1-63&#xb7;7) in 2023. For males, mean age increased from 45&#xb7;4 years (45&#xb7;1-45&#xb7;7) to 61&#xb7;2 years (60&#xb7;7-61&#xb7;6), and for females it increased from 48&#xb7;5 years (48&#xb7;1-48&#xb7;8) to 65&#xb7;9 years (65&#xb7;5-66&#xb7;3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80&#xb7;9 years (80&#xb7;9-81&#xb7;0) and for males 74&#xb7;8 years (74&#xb7;8-74&#xb7;9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38&#xb7;0 years (37&#xb7;5-38&#xb7;4) for females and 35&#xb7;6 years (35&#xb7;2-35&#xb7;9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.

For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310&#x2008;000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120&#x2008;000 sources were used for estimation of disease and injury burden and 59&#x2008;000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100&#x2008;000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2&#xb7;5th and 97&#xb7;5th percentile ordered values from a 250-draw distribution. Total numbers of global DALYs grew 6&#xb7;1% (95% UI 4&#xb7;0-8&#xb7;1), from 2&#xb7;64 billion (2&#xb7;46-2&#xb7;86) in 2010 to 2&#xb7;80 billion (2&#xb7;57-3&#xb7;08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12&#xb7;6% (11&#xb7;0-14&#xb7;1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1&#xb7;45 billion (1&#xb7;31-1&#xb7;61) global DALYs in 2010, increasing to 1&#xb7;80 billion (1&#xb7;63-2&#xb7;03) in 2023, alongside a concurrent 4&#xb7;1% (1&#xb7;9-6&#xb7;3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90&#xb7;2 million [75&#xb7;2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62&#xb7;8% [34&#xb7;0-107&#xb7;5]), depressive disorders (26&#xb7;3% [11&#xb7;6-42&#xb7;9]), and diabetes (14&#xb7;9% [7&#xb7;5-25&#xb7;6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25&#xb7;8% (22&#xb7;6-28&#xb7;7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49&#xb7;1% (32&#xb7;7-61&#xb7;0) for diarrhoeal diseases, 42&#xb7;9% (38&#xb7;0-48&#xb7;0) for HIV/AIDS, and 42&#xb7;2% (23&#xb7;6-56&#xb7;6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16&#xb7;5% (10&#xb7;6-22&#xb7;0) and 24&#xb7;8% (7&#xb7;4-36&#xb7;7), respectively. Injury-related age-standardised DALY rates decreased by 15&#xb7;6% (10&#xb7;7-19&#xb7;8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1&#xb7;27 billion [1&#xb7;18-1&#xb7;38]) of the roughly 2&#xb7;80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8&#xb7;4% (6&#xb7;9-10&#xb7;0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30&#xb7;7% (24&#xb7;8-37&#xb7;3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6&#xb7;7% (2&#xb7;0-11&#xb7;0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54&#xb7;4% (38&#xb7;7-65&#xb7;3) for unsafe sanitation, 50&#xb7;5% (33&#xb7;3-63&#xb7;1) for unsafe water source, and 45&#xb7;2% (25&#xb7;6-72&#xb7;0) for no access to handwashing facility, and by 44&#xb7;9% (37&#xb7;3-53&#xb7;5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10&#xb7;5% [0&#xb7;1 to 20&#xb7;9]), drug use (8&#xb7;4% [2&#xb7;6 to 15&#xb7;3]), and high FPG (6&#xb7;2% [-2&#xb7;7 to 15&#xb7;6]; non-significant). Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. Gates Foundation and Bloomberg Philanthropies.

The Jarzynski relation, as an equality form of the second law of thermodynamics, represents an exact thermodynamic statement that is valid arbitrarily far away from equilibrium. This remarkable relation directly links the equilibrium free energy difference between two states and the probability distribution of the work done along a process that drives the system from one state to the other. Here, we leverage the Jarzynski equality and a local equilibrium assumption, to go beyond the calculation of free energy differences and also extract the dissipation potential from additional measurements of kinematic field variables (strain and velocity fields). The proposed strategy is exemplified over pulling experiments of mass-spring models obeying overdamped Langevin dynamics, which is a prototype for nanorods, fibrous macro-molecules and the Rouse model of polymers. Different interaction potentials, fluid viscosities and bath temperatures are studied, so as to intrinsically control how close or far away the system is from equilibrium. The data-inferred continuum models are then validated against processes governed by different pulling protocols, thereby demonstrating their predictive capability. The methods presented here represent a first step toward full material characterization from non-equilibrium data of macroscopic observables, which could potentially be obtained from experimental observations.

We study positively buoyant miscible jets through high-speed imaging and planar laser-induced fluorescence methods, and we rely on supervised machine learning techniques to predict jet characteristics. These include, in particular, predictions to the laminar length and spread angle, over a wide range of Reynolds and Archimedes numbers. To make these predictions, we use linear regression, support vector regression, random forests, K-nearest neighbour, and artificial neural network algorithms. We evaluate the performance of the aforementioned models using various standard metrics, finding that the random forest algorithm is the best for predicting our jet characteristics. We also discover that this algorithm outperforms a recent empirical correlation, resulting in a significant increase in accuracy, especially for predicting the laminar length.

We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.