How key features of early development shape deep convective systems by Sophie Abramian, Caroline Muller, Camille Risi, Thomas Fiolleau, Rémy Roca

“…Understanding the complex factors that control how these systems grow and how they will behave in a warming climate remain fundamental challenges. Research efforts have been directed, on one hand, towards understanding the environmental control on these systems, and on the other hand, towards exploring the internal potential of systems to develop and self-aggregate in idealized simulations. …”
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Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation by Amna Faisal, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Muhammad Ali Tahir, N. Z. Jhanjhi, Mehedi Masud, Mohammad Shorfuzzaman

“…This paper proposes a novel technique using unsupervised machine learning and remotely sensed satellite data to assess reforestation potential across vast and diverse landscapes. …”
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Assessment of the influence of gap wall mobility factor at calculation of leakage in working section of a scroll compressor. Part 1 by V. A. Pronin, A. V. Kovanov, V. A. Tsvetkov, E. N. Mikhailova, E. A. Kalashnikova

“…Today there is an active process of changing from hydrofluorocarbons to natural refrigerants with low global warming potential. CO2, with its low critical point and correspondingly high operating pressures, is gaining in importance. …”
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Toward skillful forecasting of super El Niño events using a diffusion-based westerly wind burst parameterization by Chaopeng Ji, Mu Mu, Bo Qin, Tao Lian, Shijin Yuan, Jie Feng, Xunshu Song, Yuntao Wei, Guokun Dai, Jinyu Wang, Xianghui Fang

“…Consequently, DDPM produces more realistic eastern Pacific sea surface temperature anomaly warming patterns. These findings underscore WWB's accuracy as key to super El Niño prediction and demonstrate machine learning’s potential for WWB's parameterization.…”
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Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy by C. G. Jones, F. Adloff, B. B. B. Booth, P. M. Cox, V. Eyring, V. Eyring, P. Friedlingstein, P. Friedlingstein, K. Frieler, H. T. Hewitt, H. A. Jeffery, S. Joussaume, T. Koenigk, T. Koenigk, B. N. Lawrence, E. O'Rourke, M. J. Roberts, B. M. Sanderson, R. Séférian, S. Somot, P. L. Vidale, D. van Vuuren, D. van Vuuren, M. Acosta, M. Bentsen, M. Bentsen, R. Bernardello, R. Betts, R. Betts, E. Blockley, J. Boé, T. Bracegirdle, P. Braconnot, V. Brovkin, C. Buontempo, F. Doblas-Reyes, F. Doblas-Reyes, M. Donat, I. Epicoco, I. Epicoco, P. Falloon, P. Falloon, S. Fiore, T. Frölicher, T. Frölicher, N. S. Fučkar, N. S. Fučkar, M. J. Gidden, H. F. Goessling, R. G. Graversen, S. Gualdi, J. M. Gutiérrez, T. Ilyina, T. Ilyina, T. Ilyina, D. Jacob, C. D. Jones, C. D. Jones, M. Juckes, M. Juckes, E. Kendon, E. Kendon, E. Kjellström, R. Knutti, J. Lowe, J. Lowe, M. Mizielinski, P. Nassisi, M. Obersteiner, P. Regnier, R. Roehrig, D. Salas y Mélia, C.-F. Schleussner, M. Schulz, E. Scoccimarro, L. Terray, H. Thiemann, R. A. Wood, S. Yang, S. Zaehle

“…We recommend an increased focus on high-impact–low-likelihood (HILL) outcomes – in particular, the risk and consequences of exceeding critical tipping points during a warming overshoot and the potential impacts arising from this. …”
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glenglat: a database of global englacial temperatures by M. Jacquemart, M. Jacquemart, E. Welty, M. Gastaldello, G. Carcanade, G. Carcanade

“…We hope that glenglat will improve our understanding of glacier thermal regimes, refine glacier thermodynamic models, and give insight into hazardous glacier instabilities in a warming world.</p>…”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…With the intensification of global warming, climate change has impacted every aspect of the occurrence, transmission, and variation of infectious diseases. …”
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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…Wetlands are essential carbon sinks in the global ecosystem, absorbing CO₂ in their biomass and soils and mitigating global warming. Accurate aboveground biomass (AGB) and organic carbon (Corg) estimation are crucial for wetland carbon sink research. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…These rapid ecological shifts necessitate fine-scale monitoring of forest type to detect potential type conversions and guide management interventions. …”
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Drivers of the spatiotemporal distribution of dissolved nitrous oxide and air–sea exchange in a coastal Mediterranean area by S. Flecha, M. de la Paz, F. F. Pérez, N. Marbà, C. Morell, E. Alou-Font, J. Tintoré, J. Tintoré, I. E. Hendriks

“…<p>Among the well-known greenhouse gases (GHGs), nitrous oxide (<span class="inline-formula">N₂O</span>) is the third most impactful, possessing a global warming potential approximately 300 times greater than that of carbon dioxide (<span class="inline-formula">CO₂</span>) over a century. …”
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Numerical Polynomial Homotopy Continuation Method and String Vacua by Dhagash Mehta

“…We first solve a simple toy model as a warm-up example to demonstrate the NPHC method at work. …”
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Soybeans tempeh image dataset for tempeh maturity detectionGitHub by Riccosan, Soni Yora, Nur Azmi Karim, Ezrela Fidelyn Surya Wibisana

“…The observation of tempeh creation in the soya bean’s fermentation phase has automation potential by implementing computer image detection with a Machine Learning method that can increase the working time efficiency. …”
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Editorial by Bulent Cavas

“…The eighth article, “Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms,” by Mustafaoğlu and Alkan (Türkiye), applies machine learning techniques to identify factors predicting students&#39; recycling behavior. …”
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