A5020438240- Data Mining Algorithms and Applications
- AI-based Problem Solving and Planning
- Advanced Database Systems and Queries
- Data Stream Mining Techniques
- Smart Agriculture and AI
- Data Management and Algorithms
- Remote Sensing in Agriculture
- Delphi Technique in Research
- Cerebral Palsy and Movement Disorders
- Long-Term Effects of COVID-19
- Advanced Control Systems Optimization
- Agriculture Sustainability and Environmental Impact
- Metaheuristic Optimization Algorithms Research
- Advanced Manufacturing and Logistics Optimization
- Global Energy and Sustainability Research
- Scheduling and Optimization Algorithms
- Remote-Sensing Image Classification
- Water-Energy-Food Nexus Studies
- Gene expression and cancer classification
- Process Optimization and Integration
- Simulation Techniques and Applications
- Safety Warnings and Signage
- Spectroscopy and Chemometric Analyses
- Gene Regulatory Network Analysis
- Environmental Impact and Sustainability
International Institute for Applied Systems Analysis
2019-2025
Universitat Politècnica de Catalunya
2008-2016
Laboratoire d'Informatique de Paris-Nord
2016
Instituto Tecnológico Superior de Xalapa
2007-2013
<title>Abstract</title> The global food and land-use system significantly impacts planetary boundaries, contributing one-third of anthropogenic greenhouse gas (GHG) emissions driving biodiversity loss. Trade, as a driver economic development, has the potential to enhance sustainability through integration environmental social standards. This study uses FABLE-C model evaluate impact increased commodity exports on agriculture-related GHG under various scenarios. Our analysis highlights that...
Abstract There is an urgent need for countries to transition their national food and land-use systems toward nutritional security, climate stability, environmental integrity. How can satisfy demands while jointly delivering the required transformative change achieve global sustainability targets? Here, we present a collaborative approach developed with FABLE—Food, Agriculture, Biodiversity, Land, Energy—Consortium reconcile both elements developing system pathways. This includes three key...
Abstract The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards food land systems in all countries. We have built a flexible, collaborative modeling framework to foster national pathways by local research teams their integration up global scale. Local researchers independently customize models explore mid-century use system transformation collaboration with stakeholders. An online platform connects models, iteratively...
The creation of crop-type maps from satellite data has proven challenging, often impeded by a lack accurate in-situ data. This paper aims to demonstrate method for (ie. Maize, Wheat and Other) recognition based on Convolutional Neural Networks using bottom-up approach. We trained the model with highly dataset crowdsourced labelled street-level imagery. Classification results achieved an AUC 0.87 wheat, 0.85 maize 0.73 other. Given that wheat are two most common food crops globally, combined...
Reinforcement Learning (RL) systems are trial-and-error learners. This feature altogether with delayed reward, makes RL flexible, powerful and widely accepted. However, could not be suitable for control of critical where the learning actions by trial error is an option. In literature, use simulated experience generated a model called planning. this paper, planningByInstruction planningByExploration techniques introduced, implemented compared to coordinate, heterogeneous multi-agent...
The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack accurate in situ data. Street-level imagery represents new potential source that may aid mapping, but it requires automated algorithms to recognize the features interest. This paper aims demonstrate method for (i.e., maize, wheat others) recognition street-level based on convolutional neural network using bottom-up approach. We trained model with highly dataset crowdsourced labelled...
This work shows how a Linker agent coordinates cooperative MAS environment to seek global optimum. The approach is applied the Barcelona Drinking Water Network (DWN) administrated by AGBAR where main problem was coordinate control of three different sectors network. Each part has local controller (local agent) solve water demands, but it also cooperate with other agents satisfy demands whole implemented, learns using Reinforcement Learning algorithm, called PlanningByExploration Behaviour...
&lt;p&gt;Part of ESA&amp;#8217;s Digital Twin Earth Precursor projects, our project focuses on supporting ESA in the definition concept a Earth, and establishing solid scientific technical basis to realise this. The project, run by CGI close collaboration with Oxford University Innovation, Trillium &amp; IIASA, has focus developing Food Systems Twin, taking board interdisciplinary systems through biosphere, atmosphere, hydrosphere systems. These turn would allow for new...