ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society

Proceedings of EWGT2012 - 15th Meeting of the EURO Working Group on Transportation Paris, France, 10-13 septembre - Procedia Social and Behavioral Sciences, Vol. 54, 4 October

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Multidimensional Models for Methodological Validation in Multifractal Analysis

Multifractal analysis is known as a useful tool in signal analysis. However, the methods are often used without methodological validation. In this study, we present multidimensional models in order to validate multifractal analysis methods

Footstep energy harvesting using heel strike-induced airflow for human activity sensing

Body sensor networks are increasingly popular in healthcare, sports, military and security. However, the power supply from conventional batteries is a key bottleneck for the development of body condition monitoring. Energy harvesting from human motion to power wearable or implantable devices is a promising alternative. This paper presents an airflow energy harvester to harness human motion energy from footsteps. An air bladder-turbine energy harvester is designed to convert the footstep motion into electrical energy. The bladders are embedded in shoes to induce airflow from foot-strikes. The turbine is employed to generate electrical energy from airflow. The design parameters of the turbine rotor, including the blade number and the inner diameter of the blades (the diameter of the turbine shaft), were optimized using the computational fluid dynamics (CFD) method. A prototype was developed and tested with footsteps from a 65 kg person. The peak output power of the harvester was first measured for different resistive loads and showed a maximum value of 90.6 mW with a 30.4 Ω load. The harvested energy was then regulated and stored in a power management circuit. 14.8 mJ was stored in the circuit from 165 footsteps, which means 90 μJ was obtained per footstep. The regulated energy was finally used to fully power a fitness tracker which consists of a pedometer and a Bluetooth module. 7.38 mJ was consumed by the tracker per Bluetooth configuration and data transmission. The tracker operated normally with the harvester working continuously

Antioxidant and free radical-scavenging properties of three flavonoids isolated from the leaves of Rhamnus alaternus L.(Rhamnaceae):A structure-activity relationship study

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