AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations by Xiaowen Zhang, Patrick Lachance, Ankita Dasgupta, Rupert A. C. Croft, Tiziana Di Matteo, Yueying Ni, Simeon Bird, Yin Li

“…Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly enhance low-resolution (LR) runs with statistically correct fine details. …”
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„Neues Format, Ungewissheit, Nervosität“ – Eine Performanzprüfung im beruflichen Lehramtsstudium aus der Perspektive von Studierenden und Prüfenden by Katharina Schwanke, Katrin Kunze, Andrej Schlobinski, Ursula Walkenhorst

“… Gesellschaftliche und digitale Transformationsprozesse beeinflussen die Kultur des Lehrens und Lernens an Hochschulen und Universitäten. …”
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Simulation of global sea surface temperature maps using Pix2Pix GAN by Deepayan Chakraborty, Adway Mitra

“…As an alternative approach, researchers are exploring if such simulated data can be generated by Generative Machine Learning models. In this work, we develop a model based on Pix2Pix conditional Generative Adversarial Network (cGAN), which can generate high-resolution spatial maps of global sea surface temperature (SST) using comparatively less computing power and time. …”
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Predicting Solar Energetic Particle Events with Time Series Shapelets by Omar Bahri, Peiyu Li, Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi

“…Our objective is to mitigate the interpretability challenges inherent to most machine learning models and to show that other methods exist that can not only yield accurate forecasts but also facilitate exploration and insight generation within the data domain. …”
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Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries by Azeez Okikiola Lawal, Tolutope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele, Oluwaloseyi Ayomipo Olorunfemi, Olalekan John Okesanya, Jerico Bautista Ogaya, Emery Manirambona, Mohamed Mustaf Ahmed, Don Eliseo Lucero-Prisno

“…The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. …”
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Tangible digital storytelling and phygital reality: benefits for inclusion and cooperation in young children by Pasqualotto Angela, Filosofi Fabio

“…Storytelling remains one of the oldest teaching methods for children: the development of technology has given impetus to the creation of tangible digital storytelling, which combines programs with physical objects, creating a single field for learning. Creating stories in which a child can interact with his/her peers has also provided new opportunities for inclusion and cooperation in young children. …”
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Differencesin Variations of Qira'at al-Sab' Tariq al-Shatibiyyah in Usul al-Qira'at based on verses 1-31 in Surah al-Baqarah by M. Jismani Yusoff, Nor Hafizi Yusof, Ismail Abd Halim

“…Learning Qira'at al-Sab' Tariq al-Shatibiyyah is the foundation of the study of the science of Qira'at. …”
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Artificial intelligence artificial muscle of dielectric elastomers by Dongyang Huang, Jiaxuan Ma, Yubing Han, Chang Xue, Mengying Zhang, Weijia Wen, Sheng Sun, Jinbo Wu

“…Establishing an AM material database is highly valuable, as seemingly minor material data can be correlated with descriptors and target values via machine learning. Through material data mining integrating materials science and data science, we can predict potential breakthroughs in AM materials. …”
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DIGITAL BADGES AS REWARDS IN SCIENCE EDUCATION: STUDENTS’ PERCEPTIONS AND EXPERIENCES by Aaron A. Funa

“…By analyzing student responses in their portfolios and interviews, four key themes of digital badges were identified: class participation, learning outcomes, time management, and social interaction. …”
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A multi-dimensional decision framework based on the XGBoost algorithm and the constrained parametric approach by Xinxin Wang, BingBing Zhang, Zeshui Xu, Ming Li, Marinko Skare

“…Under the framework, XGBoost algorithm, one of the machine learning methods, is conducted to capture the importance of attributes by using the historical data, and further calculate the attribute weights. …”
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On the implications of artificial intelligence methods for feature engineering in reliability sector with computer knowledge graph by Heling Jiang, Yongping Xia, Changjie Yu, Zhao Qu, Huaiyong Li

“…To improve operational efficiency and lower long-term maintenance costs, policy ideas include standardizing data collection techniques, investing in real-time monitoring systems, and implementing machine learning-based predictive maintenance across industries.…”
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IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced by Zhaoyang Han, Linlin Zhang, Qingyan Meng, Chongchang Wang, Wenxu Shi, Maofan Zhao

“…Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. …”
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Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data by Feng Zhihao

“…Through an analysis of these methods, the research demonstrates how applying advanced deep learning algorithms and cross-attention processes has significantly improved prediction robustness and accuracy. …”
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Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study by C. Banerjee, K. Nguyen, C. Fookes, G. Hancock, T. Coulthard

“…Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. …”
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The development trend of China’s marine economy: a predictive analysis based on industry level by Yu Chen, Huahan Zhang, Lingling Pei

“…First, the results from multi-step experiments demonstrate that the CFGM model significantly outperforms traditional statistical, machine learning models, and grey models in the context of the sectoral added value predictions, with an average accuracy improvement of 32.14%. …”
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Cryptographic Censorship by Netta Engelhardt, Åsmund Folkestad, Adam Levine, Evita Verheijden, Lisa Yang

“…Our theorem relies on an extension of a recent quantum learning no-go theorem and is proved using new techniques of pseudorandom measure concentration. …”
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The double call for joy, "Rejoice and be glad" (Matt. 5:12), as conclusion of the Matthean macarisms by F. P. Viljoen

“…How did beatitudes in general function as literary genre? What is the Sitz im Leben of Matthew’s beatitudes and what kind of response is required from the audience or readers? …”
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Technically Speaking, What Is Sturgeon Caviar? by Frank A. Chapman, Joel P. Van Eenennaam

“…Traditionally coveted by royalty and the aristocracy, sturgeon caviar today is prized by chefs and discerning food connoisseurs the world over for its delicate flavor and nutrient-rich health benefits. Learn what caviar is, find out how it’s collected, and discover more about the fascinating sturgeon fish in this 4-page fact sheet written by Frank A. …”
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O możliwości zredefiniowania niepowodzeń szkolnych. Rekonstrukcja zmiany myślenia studentek – przyszłych nauczycielek wczesnej edukacji by Grażyna Szyling

“…The results of the analyses show that the most significant modification concerns inclusion into the area of school failures also students commonly regarded as those „not having learning difficulties”, which extends the definition of this notion with the sense of “being insufficient” and leads to, inter alia, the relativisation of the term “bad grade”. …”
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CoTF-reg reveals cooperative transcription factors in oligodendrocyte gene regulation using single-cell multi-omics by Jerome J. Choi, John Svaren, Daifeng Wang

“…Next, we train a deep learning model to predict each TG expression using the co-binding TFs’ expressions. …”
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