Indication of long-range correlations governing city size.

City systems are characterized by the functional organization of cities on a regional or country scale. While there is a relatively good empirical and theoretical understanding of city size distributions, insights about their spatial organization remain on a conceptual level. Here, we analyze empirically the correlations between the sizes of cities (in terms of area) across long distances.

Noise-induced stochastic switching of microcargoes transport in artificial microtubule.

Synchronization plays an important role in propelling microrobots, especially for those driven by an external magnetic field. Here, we substantially contribute to the understanding of a novel out-of-sync phenomenon called "slip-out", which has been recently discovered in experiments of an artificial microtubule (AMT). In a deterministic situation, we interpret and quantitatively characterize the switching in such a system between the stick and slip modes, whose different combinations over time define four long-term states.

Climate policies that achieved major emission reductions: Global evidence from two decades.

Meeting the Paris Agreement's climate targets necessitates better knowledge about which climate policies work in reducing emissions at the necessary scale. We provide a global, systematic ex post evaluation to identify policy combinations that have led to large emission reductions out of 1500 climate policies implemented between 1998 and 2022 across 41 countries from six continents. Our approach integrates a comprehensive climate policy database with a machine learning-based extension of the common difference-in-differences approach.

Guiding principles for the next generation of health-care sustainability metrics.

Metrics for health-care sustainability are crucial for tracking progress and understanding the advantages of different operations or systems as the health-care sector addresses the climate crisis and other environmental challenges. Measurement of the key metrics of absolute energy use and greenhouse gas emissions now has substantial momentum, but our overall measurement framework generally has serious deficiencies. Because existing metrics are often borrowed from other sectors, many are unconnected to the specifics of health-care provision or existing health system performance indicators, the potential negative effects of health care on public health are largely absent, a consistent and standardised set of health-care sustainability measurement concepts does not yet exist, and current dynamics in health systems such as privatisation are largely ignored.

Uncertainties too large to predict tipping times of major Earth system components from historical data.

One way to warn of forthcoming critical transitions in Earth system components is using observations to detect declining system stability. It has also been suggested to extrapolate such stability changes into the future and predict tipping times. Here, we argue that the involved uncertainties are too high to robustly predict tipping times.

Reconciling the EU forest, biodiversity, and climate strategies.

Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non-wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy.

Achieving net zero greenhouse gas emissions critical to limit climate tipping risks.

Under current emission trajectories, temporarily overshooting the Paris global warming limit of 1.5 °C is a distinct possibility. Permanently exceeding this limit would substantially increase the probability of triggering climate tipping elements.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence.

Cross-disciplinary mathematical modelling to benefit healthcare - could clinical pharmacology play an enabling role?

Clinical pharmacology is often the nexus in any cross-disciplinary team that is seeking solutions for human healthcare issues. The use and application of real-world data and artificial intelligence to better understand our ecosystem has influenced our view at the world and how we do things. This has resulted in remarkable advancements in the healthcare space and development of personalized medicines with great attributes from the application of models and simulations, contributing to a more efficient healthcare development process.

AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria.

Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<10), much smaller than public polymer datasets (>10), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning.

Recurrence microstates for machine learning classification.

Recurrence microstates are obtained from the cross recurrence of two sequences of values embedded in a time series, being the generalization of the concept of recurrence of a given state in phase space. The probability of occurrence of each microstate constitutes a recurrence quantifier. The set of probabilities of all microstates are capable of detecting even small changes in the data pattern.

Classification of soft cliff dynamics using remote sensing and data mining techniques.

Coastal soft cliffs are subject to changes related to both marine and subaerial processes. It is imperative to comprehend the processes governing cliff erosion and develop predictive models for effective coastal protection. The primary objective of this study was to bridge the existing knowledge gap by elucidating the intricate relationship between changes in cliff system morphology and the driving forces behind these changes, all within the context of ongoing climate change.

Model adaptive phase space reconstruction.

Phase space reconstruction (PSR) methods allow for the analysis of low-dimensional data with methods from dynamical systems theory, but their application to prediction models, such as those from machine learning (ML), is limited. Therefore, we here present a model adaptive phase space reconstruction (MAPSR) method that unifies the process of PSR with the modeling of the dynamical system. MAPSR is a differentiable PSR based on time-delay embedding and enables ML methods for modeling.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters.

Quality of nutritional status assessment and its relationship with the effect of rainfall on childhood stunting: a cross-sectional study in rural Burkina Faso.

Objectives: In Burkina Faso, one in every four children under 5 years is stunted. Climate change will exacerbate childhood stunting. Strengthening the health system, particularly the quality of nutrition care at primary health facilities, can minimise the adverse climate effect on stunting.

The South Atlantic Dipole via multichannel singular spectrum analysis.

This study analyzes coupled atmosphere-ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959-2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations.

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model.

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero.

Temperature-related neonatal deaths attributable to climate change in 29 low- and middle-income countries.

Exposure to high and low ambient temperatures increases the risk of neonatal mortality, but the contribution of climate change to temperature-related neonatal deaths is unknown. We use Demographic and Health Survey (DHS) data (n = 40,073) from 29 low- and middle-income countries to estimate the temperature-related burden of neonatal deaths between 2001 and 2019 that is attributable to climate change. We find that across all countries, 4.

In-ice light measurements during the MOSAiC expedition.

We present light measurements in Arctic sea ice obtained during the year-long MOSAiC drift through the central Arctic Ocean in 2019-2020. Such measurements are important as sea ice plays a fundamental role in the Arctic climate and ecosystem. The partitioning of solar irradiance determines the availability of radiation energy for thermodynamic processes and primary productivity.

Recurrent chaotic clustering and slow chaos in adaptive networks.

Adaptive dynamical networks are network systems in which the structure co-evolves and interacts with the dynamical state of the nodes. We study an adaptive dynamical network in which the structure changes on a slower time scale relative to the fast dynamics of the nodes. We identify a phenomenon we refer to as recurrent adaptive chaotic clustering (RACC), in which chaos is observed on a slow time scale, while the fast time scale exhibits regular dynamics.