The appealing theoretical measure of irreversibility in a stochastic process, as the ratio of the probabilities of a trajectory and its time reversal, cannot be accessed directly in experiments since the probability of a single trajectory is zero. We regularize this definition by considering, instead, the limiting ratio of probabilities for trajectories to remain in the tubular neighborhood of a smooth path and its time reversal. The resulting pathwise medium entropy production agrees with the formal expression from stochastic thermodynamics and can be obtained from measurable tube probabilities. Estimating the latter from numerically sampled trajectories for Langevin dynamics yields excellent agreement with theory. By combining our measurement of pathwise entropy production with a Markov chain Monte Carlo algorithm, we infer the entropy-production distribution for a transition path ensemble directly from short recorded trajectories. Our work enables the measurement of irreversibility along individual paths and path ensembles in a model-free manner.
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http://dx.doi.org/10.1103/PhysRevE.105.044107 | DOI Listing |
PLoS One
January 2025
Wroclaw University of Economics and Business, Wrocław, Poland.
The paper analyzes the problem of entropy in the moments of transition from a normal economic situation (2015-2019) to the Pandemic period (2020-2021) and the period of Russia's attack on Ukraine (2022-2023). The research in the article is based on the analysis of electricity, oil, coal, and gas prices in 27 countries of the European Union and Norway. The daily data cover the period from January 1, 2015, to March 30, 2023, and were analyzed using two-dimensional sets of electricity and commodity prices.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
The Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, China.
Electrocatalytic nitrate reduction to ammonia (eNRA) is a promising route toward environmental sustainability and clean energy. However, its efficiency is often limited by the slow conversion of intermediates due to spin-forbidden processes. Here, we introduce a novel A-site high-entropy strategy to develop a new perovskite oxide (LaPrNdBaSr)CoO (LPNBSC) for eNRA.
View Article and Find Full Text PDFNano Lett
January 2025
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China.
Efficient oxygen evolution reaction (OER) catalysts with fast kinetics, high efficiency, and stability are essential for scalable green production of hydrogen. The rational design and fabrication of catalysts play a decisive role in their catalytic behavior. This work presents a high-entropy catalyst, FeCoNiCuMo-O, synthesized via carbothermal shock.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
School of Nursing, University of Washington, Seattle, WA, USA.
Background: The prevalence of diabetes is escalating globally, underscoring the need for comprehensive evidence to inform health systems in effectively addressing this epidemic. The purpose of this study was to examine the patterns of countries' capacity to manage diabetes using latent class analysis (LCA) and to determine whether the patterns are associated with diabetes-related deaths and healthcare costs.
Methods: Eight indicators of country-level capacity were drawn from the World Health Organization Global Health Observatory dataset: the widespread availability of hemoglobin A1C (HbA1c) testing, existence of diabetes registry, national diabetes management guidelines, national strategy for diabetes care, blood glucose testing, diabetic retinopathy screening, sulfonylureas, and metformin in the public health sector.
PLoS One
January 2025
Centro Ricerche Enrico Fermi, Rome, Italy.
The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines.
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