Rethinking scenario building for sustainable futures: mobilizing conscientização, social learning and knowledge co-production by Danilo Borja, Jan Daněk, Julia C. Assis, Antonella Gorosábel, Carolyn Lundquist, Isabel Rosa, Fabio Rubio Scarano, Nino Tavares Amazonas, Silas C. Principe, Rob Alkemade, Adnan Arshad, Sandra Benavides-Gordillo, Rafael Cavalcanti Lembi, Tássia Rayane Ferreira Chagas, Diana Isabel Clavijo Rojas, Lara Cornejo-Denman, Viviane Dib, Simon Ferrier, Edberto Moura Lima, Luis Filipe Lopes, Matheus Camargo Silva Mancini, Juan Andrés Martínez-Lanfranco, Wladimir Moya, Julia Niemeyer, Unai Pascual, Alice Ramos de Moraes, Mercy Funke Salami, Carla Rivera Rebella, Fábio H. C. Sanches, Priyanka Sarkar, Juliana Siqueira-Gay, Raísa Romênia Silva Vieira, Catalina Zuluaga Rodríguez, Carlos Alfredo Joly, Rafael C. Borges

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European stakeholders’ perspectives on achieving more sustainable wheat cultivation across different pedoclimatic zones by Alicia Morugán-Coronado, Javier Calatrava, Laura Meno, Laura Meno, Flora Alonso-Vega, Flora Alonso-Vega, Eija Pouta, Annika Tienhaara, Stefan Schrader, David-Alexander Bind, Hilde Wustenberghs, Sander Fleerakkers, David Fernández-Calviño, David Fernández-Calviño, María Dolores Gómez-López

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Harnessing machine learning for transmembrane pressure prediction in MBR systems during textile wastewater treatment by Onaira Zahoor, Sher Jamal Khan, Muhammad Usama, Henry J. Tanudjaja, Noreddine Ghaffour, Muhammad Saqib Nawaz

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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…In the context of the Santos project, dedicated to comprehending the spatio-temporal dynamics of benthic, pelagic, and physical systems for the facilitation of conservation and monitoring programs, the application of machine learning's random forest (RF) technique for modeling univariate data offers notable advantages. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…In the context of the Santos project, dedicated to comprehending the spatio-temporal dynamics of benthic, pelagic, and physical systems for the facilitation of conservation and monitoring programs, the application of machine learning's random forest (RF) technique for modeling univariate data offers notable advantages. …”
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Federated Learning for Real-Time Demand Response by Data Centers Toward Energy Efficiency and Privacy Preservation by Mohannad Alhazmi, Alexis Pengfei Zhao, Wanzi Li, Chenlu Yang

“…Our novel method utilizes real-time data to generate effective and efficient actions in response to operational demands, in compliance with energy conservation and stringent privacy standards. We evaluate various demand response programs by the proposed method in different scenarios and case studies to demonstrate their efficacy in adapting to energy consumption patterns for optimal energy usage and cost reduction. …”
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Jesús A. Bonilla De Gracia by Jesús A. Bonilla De Gracia, Jorge Alarcón- Novoa, Erick Gallegos-Romero, Jorge Villegas-Rugel

“…The literature review indicates that Education on NCR has been used in three areas of research to explain or address a particular construct: (1) promotion of formal and informal programs for use of the intangible environmental services, (2) development of an ICEE that promotes biotrade of intangible services from the Romerillos forests, and (3) short- and long-term conservation of the Romerillos forests. …”
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How experiential service‐learning affects student perceptions of education in their careers and as a wildlife management activity by Kathryn T. Stevenson, M. Nils Peterson

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Coastal Areas as Learning Resources to Enhance Regional Potential Understanding: A Study in Pesisir Selatan District by Muhammad Arif, Enok Maryani, Mamat Ruhimat, Dede Sugandi

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Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios by Rodrigo N. Vasconcelos, Mariana M. M. de Santana, Diego P. Costa, Soltan G. Duverger, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa, Carlos Leandro Cordeiro, Washington J. S. Franca Rocha

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Integrating Genetic Algorithm and Geographically Weighted Approaches into Machine Learning Improves Soil pH Prediction in China by Wantao Zhang, Jingyi Ji, Binbin Li, Xiao Deng, Mingxiang Xu

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The impact of ecological civilization construction on the resilience of green technology innovation: evidence based on double dual machine learning by Cunlin Li, Cunlin Li, Shiyu Zhang, Shiyu Zhang, Baojun Yang, Baojun Yang

“…The findings of this study provide crucial insights and valuable guidance for developing national environmental conservation policies and programs.…”
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Unveiling processing–property relationships in laser powder bed fusion: The synergy of machine learning and high-throughput experiments by Mahsa Amiri, Zahra Zanjani Foumani, Penghui Cao, Lorenzo Valdevit, Ramin Bostanabad

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Conceptualizing Coproduced Climate Research as Care: Practical Lessons Learned With Women Farmland‐Owners in the Central Midwest United States by Linda Shenk, Jean Eells, Jr. William J. Gutowski, Kristie Franz, Danielle Robinson

“…We outline the creation and evolution of a storytelling‐based conservation program that allowed our diverse group to discover how the language of care could integrate climate analysis, conservation, and relationship‐building to foster tangible solutions. …”
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Genetic diversity insights from population genomics and machine learning tools for Nordic Arctic charr (Salvelinus alpinus) populations by Christos Palaiokostas, Khrystyna Kurta, Fotis Pappas, Henrik Jeuthe, Ørjan Hagen, José Beirão, Matti Janhunen, Antti Kause

“…Furthermore, in the case of the Finnish population, the sampled animals originated from the only active conservation program for Arctic charr in the country with a potential interest in farming. …”
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Integration of Google Earth Engine, Sentinel-2 images, and machine learning for temporal mapping of total dissolved solids in river systems by Eric Ariel L. Salas, Sakthi S. Kumaran, Robert Bennett, Eric B. Partee, Jason Brownknight, Kellsie Schrack, Bryant Willis

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Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records by Shumit Saha, Heather Ross, Pedro Elkind Velmovitsky, Chloe X. Wang, Julie K. K. Vishram-Nielsen, Cedric Manlhiot, Bo Wang, Joseph A. Cafazzo

“…This study aimed to enhance Medly’s performance with machine learning and include a richer set of data, including EHR, for predicting decompensated HF episodes. …”
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Dual pathways of forest landscape dynamics in China: Integrating ecological land evolution Index (ELEI) and machine learning to decipher fragmentation and restoration by Yifan Xie, Sui Liu, Bowen Pang, Nannan Wang, Jiwei Xu, He Huang, Yao Cui, Jiaming Yang, Yaolin Liu, Yanfang Liu

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Multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and stochastic processing time via reinforcement learning-based evolu... by Yaping Fu, Fuquan Wang, Zhengyuan Li, Guangdong Tian, Duc Truong Pham, Hao Sun

“…First, a stochastic programming model is developed to minimize maximum completion time and total tardiness. …”
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Editorial: Computational bioacoustics and automated recognition of bird vocalizations: new tools, applications and methods for bird monitoring by Yizhar Lavner, Cristian Pérez-Granados, Cristian Pérez-Granados

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