Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach by Zhonghui Lv, Karinna Nunez, Ethan Brewer, Dan Runfola

“…This study leverages the combined capabilities of Sentinel-2 and National Agriculture Imagery Program (NAIP) imagery and a UNet architecture to accurately classify tidal marsh communities. …”
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Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks. by Ramzan Kh Umarov, Victor V Solovyev

“…Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. …”
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The use of machine learning methods in the development of nasal dosage forms with cerebroprotective action by B. S. Burlaka, I. F. Bielenichev

“…The methods employed were a set of methods for binary classification of machine learning (pycaret.org) using the programming language Python 3.8 (python.org) in the package management environment Miniconda (conda.io). …”
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Leveraging historical datasets to quantify the recovery of an impaired stream entering Yellowstone National Park by Andrew M. Ray, Alysa M. Yoder, Brian D. Ertel, Tom H. Henderson, Christopher A. Mebane

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Deep learning-based detection of indicator species for monitoring biodiversity in semi-natural grasslands by Deepak H. Basavegowda, Inga Schleip, Paul Mosebach, Cornelia Weltzien

“…Deep learning (DL) has huge potential to provide valuable insights into biodiversity changes in species-rich agricultural ecosystems such as semi-natural grasslands, helping to prioritize and plan conservation efforts. …”
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Understanding Primary School Students’ Knowledge and Attitudes Towards Water Management: Insights from Environmental Education by Oana Irimia, Claudia Tomozei, Mirela Panainte-Lehadus, Dana Chitimus, Florin Nedeff, Narcis Barsan, Emilian Mosnegutu, Diana Mirila

“…Interactive and practical educational programs, along with the involvement of parents and the community, enhance learning and create connections between school and daily life. …”
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Assessing the UNESCO community capacity building initiative: a case study from the historic city of Vigan, Philippines by Wanling Jian

“…The findings and lessons learned provide guidelines for future capacity-building program designs targeting local communities at both World Heritage sites and places of cultural significance.…”
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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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