Results 71 to 80 of about 3,492,245 (278)

What Really Drives Agri‐Environment‐Climate Measures' Adoption? Mandatory Requirements, Behavioral Traits, and Structural Factors in the CAP Green Architecture

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT To increase farmer adoption of green practices, the EU Common Agricultural Policy includes both mandatory (conditionality) and voluntary instruments (eco‐schemes–ECS– and more demanding, multi‐annual agri‐environment‐climate measures –AECM–). Building on the experiment of Barreiro‐Hurle et al.
L. Sanchez‐Mata   +4 more
wiley   +1 more source

A novel multivariate decomposition-based hybrid model for interpretable multi-step-ahead daily reference evapotranspiration forecasting

open access: yesJournal of Hydrology: Regional Studies
Study region: The Yazd and Ramsar stations are located in hybrid arid and humid climates in Iran, respectively. Study focus: This research study develops a complementary expert system for accurately forecasting reference evapotranspiration (ET0) over one,
Ali Matoog Obaid Lebawi   +3 more
doaj   +1 more source

Channel Identification with Improved Variational Mode Decomposition

open access: yes, 2022
The identification of the wireless channel characteristics is an important function in the wireless communication design and deployment especially in rich propagation environments.
BALDINI Gianmarco, BONAVITACOLA Fausto
core   +1 more source

The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry

open access: yesAgribusiness, EarlyView.
ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley   +1 more source

Using Empirical Mode Decomposition to Estimate Amplitudes in Noisy Data. [PDF]

open access: yes
Empirical Mode Decomposition, an adaptive data-driven technique which can be used to extract non-stationary signals buried in noise, seldom admits theoretical calculation of the statistical properties of the extracted signals.
Claire Blackman
core  

Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

open access: yes, 2019
Soil moisture forecasts are vital for environmental monitoring, the health of ecological systems, hydrology, agriculture and understanding the soil characteristics. In this study, we design a new multivariate sequential predictive model that utilizes the
Deo, Ravinesh C.   +3 more
core   +1 more source

Cost Pass‐Through in Crisis: Evidence From the German Malt‐Beer Supply Chain

open access: yesAgribusiness, EarlyView.
Abstract Global agri‐food supply chains are increasingly exposed to geopolitical shocks, climate volatility, and market consolidation, factors that disrupt traditional price relationships and reshape market power dynamics. Nowhere is this more visible than in the brewing sector, where agricultural raw materials meet complex industrial processing and ...
Nikolas Bublik, Lukáš Čechura
wiley   +1 more source

Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer

open access: yesEnergies
Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy management.
Haoda Ye, Qiuyu Zhu, Xuefan Zhang
doaj   +1 more source

Variational Methods for Nonsmooth Mechanics [PDF]

open access: yes, 2003
In this thesis we investigate nonsmooth classical and continuum mechanics and its discretizations by means of variational numerical and geometric methods.
Fetecau, Razvan Constantin
core   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia   +1 more
wiley   +1 more source

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