Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval (R) thermal analysis (PARTY(SOC)v2.0 and PARTY(SOC)v2.0 (EU))
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Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...
Redox reactions underlie several biogeochemical processes and are typically spatiotemporally heterogeneous in soils and sediments. However, redox heterogeneity has yet to be incorporated into mainstream conceptualizations and modeling of soil biogeochemist ...
Electron transfer reactions are central to the transformation of energy in the environment and play an important role in biogeochemical element cycling. In soils, one of the main drivers of carbon cycling is the activity of organisms that utilize the energ ...
This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...
Climate change is expected to alter the temporal distribution of precipitation events, leading to prolonged drought periods and an increased frequency of extreme precipitation events. Changes in precipitation pattern will directly affect soil moisture dyna ...
As air temperature and vapor pressure deficit (VPD) increase continuously, forests are losing more water through evapotranspiration, with large consequences for local and global hydrological cycles. In regions with high vegetation cover, soil warming can b ...
Forest soils harbor hyper-diverse microbial communities which fundamentally regulate carbon and nutrient cycling across the globe. Directly testing hypotheses on how microbiome diversity is linked to forest carbon storage has been difficult, due to a lack ...
Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...
Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence.Althou ...
Rainfall characteristics such as intensity, duration, and frequency are key determinants of the hydro-geomorphological response of a catchment. The presence of non-linear and threshold effects makes the relationship between rainfall variability and geomorp ...
