Results 181 to 190 of about 1,630,954 (334)

Shade matters: heat stress alleviation in Gyr and Girolando cows through silvopastoral management in tropical conditions. [PDF]

open access: yesInt J Biometeorol
McManus C   +7 more
europepmc   +1 more source

Probability prediction of true‐triaxial compressive strength of intact rocks based on the improved PSO‐RVM model

open access: yesDeep Underground Science and Engineering, EarlyView.
In this work, we propose an improved particle swarm optimization (PSO) algorithm and develop an improved PSO‐relevance vector machine (RVM) model as a substitute for traditional true‐triaxial testing. The model's high prediction accuracy was validated through comparisons with two other machine learning methods and five three‐dimensional Hoek–Brown type
Qi Zhang   +4 more
wiley   +1 more source

Alpine ungulates adjust diel activity to the natural return of wolves amid anthropogenic pressures

open access: yesEcography, EarlyView.
As wolves recolonise their historical range across Europe, ungulates face predation once more – but in landscapes profoundly altered by human activity. This shift raises crucial questions about their capacity to express adaptive antipredator behaviours.
Charlotte Vanderlocht   +20 more
wiley   +1 more source

Precipitation and tree biomass correlate with the diversity and functional composition of tropical rainforest cricket assemblages across climate and disturbance gradients

open access: yesEcography, EarlyView.
Disturbance‐driven changes in rainforest structure and environmental conditions can alter ecosystem functioning, yet the consequences for invertebrate communities – key contributors to decomposition, herbivory, and trophic interactions – are not fully understood, particularly in relation to structural changes in vegetation.
Charlotte E. Raven   +5 more
wiley   +1 more source

Performance Monitoring of Photovoltaic Modules Using Machine‐Learning‐Based Solutions: A Survey of Current Trends

open access: yesEnergy Science &Engineering, EarlyView.
The graphical abstract presents the concept of applying machine‐learning algorithms to assess the performance of photovoltaic modules. Data from solar panels are fed to surrogates of intelligent models, to assess the following performance metrics: identifying faults, quantifying energy production and trend degradation over time. The combination of data
Nangamso Nathaniel Nyangiwe   +3 more
wiley   +1 more source

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