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Best Educational Apps for Children by Age Group to Enhance Learning

Educational apps have become indispensable tools for children's learning. These apps cater to various age groups, offering tailored content that enhances cognitive skills and creativity. For tech enthusiasts in India, understanding the best educational apps by age group can help in




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Educational Tech Apps: Enhancing Learning for Special Needs Children Through Personalised Experiences

In today's digital age, technology is transforming education, especially for children with special needs. Educational tech apps offer tailored learning experiences that cater to individual requirements. These apps can significantly enhance learning outcomes and provide a supportive environment for children with




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Canon Launches EOS R1 and EOS R5 Mark II With High-Speed Burst Shooting, Deep Learning AF, & More

Canon, a known face in digital imaging solutions, recently announced the introduction of two highly anticipated cameras to its EOS R series: the EOS R1 and EOS R5 Mark II. These cameras boast next-generation intelligent features, superior quality, impressive speed, and




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Machine learning-driven investigation of the structure and dynamics of the BMIM-BF4 room temperature ionic liquid

Faraday Discuss., 2024, 253,129-145
DOI: 10.1039/D4FD00025K, Paper
Open Access
  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
Fabian Zills, Moritz René Schäfer, Samuel Tovey, Johannes Kästner, Christian Holm
We demonstrate a learning-on-the-fly procedure to train machine-learned potentials from single-point density functional theory calculations before performing production molecular dynamics simulations.
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The Museum as Experience : Learning, Connection, and Shared Space

[S.l.] : ARC HUMANITIES PR, 2023.




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Social networks in language learning and language teaching [Electronic book] / edited by Avary Carhill-Poza, Naomi Kurata.

New York, NY : Bloomsbury Academic, 2020.




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Republican learning : John Toland and the crisis of Christian culture, 1696-1722 [Electronic book] / Justin Champion.

Manchester : Manchester University Press, [2018]




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A million pictures : magic lantern slides in the history of learning [Electronic book] / editors, Sarah Dellmann and Frank Kessler.

New Barnet, Herts, United Kingdom : John Libbey Publishing Ltd, [2020]




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Learning online : what research tells us about whether, when and how [Electronic book] / Barbara Means, Marianne Bakia, Robert Murphy.

New York ; Abingdon, Oxon : Routledge, Taylor & Francis Group, 2014.




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Applied text analysis with Python : enabling language-aware data products with machine learning [Electronic book] / Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda.

Sebastopol : O'Reilly Media, 2018.




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Screening for Urothelial Carcinoma Cells in Urine Based on Digital Holographic Flow Cytometry through Machine Learning and Deep Learning Method

Lab Chip, 2024, Accepted Manuscript
DOI: 10.1039/D3LC00854A, Paper
Lu Xin, Xi Xiao, Wen Xiao, Ran Peng, Hao Wang, Feng Pan
The incidence of urothelial carcinoma continue to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a...
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Deep learning-enabled detection of rare circulating tumor cell clusters in whole blood using label-free, flow cytometry

Lab Chip, 2024, 24,2237-2252
DOI: 10.1039/D3LC00694H, Paper
Open Access
  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
Nilay Vora, Prashant Shekar, Taras Hanulia, Michael Esmail, Abani Patra, Irene Georgakoudi
We present a deep-learning enabled, label-free flow cytometry platform for identifying circulating tumor cell clusters in whole blood based on the endogenous scattering detected at three wavelengths. The method has potential for in vivo translation.
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A prediction model for CO2/CO adsorption performance on binary alloys based on machine learning

RSC Adv., 2024, 14,12235-12246
DOI: 10.1039/D4RA00710G, Paper
Open Access
Xiaofeng Cao, Wenjia Luo, Huimin Liu
Machine-learning models were constructed to accurately predict CO2 and CO adsorption affinity on a wide range of binary alloying.
The content of this RSS Feed (c) The Royal Society of Chemistry




learning

Correction: Photocatalytic degradation of drugs and dyes using a machine learning approach

RSC Adv., 2024, 14,12983-12983
DOI: 10.1039/D4RA90045F, Correction
Open Access
  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
Ganesan Anandhi, M. Iyapparaja
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ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction

RSC Adv., 2024, 14,13083-13094
DOI: 10.1039/D4RA00748D, Paper
Open Access
  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
Jiahui Huang, Tanja Osthushenrich, Aidan MacNamara, Anders Mälarstig, Silvia Brocchetti, Samuel Bradberry, Lia Scarabottolo, Evandro Ferrada, Sergey Sosnin, Daniela Digles, Giulio Superti-Furga, Gerhard F. Ecker
Predict SLC6 mutation clinical pathogenicity by calculating the amino acid descriptors in different ranges with rationalization analysis of the prediction.
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Interpretable machining learning assisted insights into bifunctional squaramide catalyzed ring-opening polymerization of lactide

Polym. Chem., 2024, 15,4562-4569
DOI: 10.1039/D4PY00866A, Paper
Shaoju Cao, Mengting Hong, Junyuan Hu, Zhenjiang Li, Jin Huang, Kai Guo
A bifunctional squaramide catalyst with tunable activity enables the controlled ring-opening polymerization of lactide. Combining bench experiments and interpretable machine learning offer insights into catalyst structures, promoting catalyst design.
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Machine Learning-Assisted Pattern Recognition and Imaging of Multiplexed Cancer Cells via Porphyrin-Embedded Dendrimer Array

J. Mater. Chem. B, 2024, Accepted Manuscript
DOI: 10.1039/D4TB01861C, Paper
Jiabao Hu, Weiwei Ni, Mengting Han, Yunzhen Zhan, Fei Li, Hui Huang, Jinsong Han
Early cancer detection plays a vital role in improving the survival rate of cancer patients, underscoring the importance of developing cancer detecting methods. However, it is of great challenge to...
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learning

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time

Analyst, 2024, 149,5535-5545
DOI: 10.1039/D4AN00729H, Paper
Open Access
Katherine J. I. Ember, Nassim Ksantini, Frédérick Dallaire, Guillaume Sheehy, Trang Tran, Mathieu Dehaes, Madeleine Durand, Dominique Trudel, Frédéric Leblond
Raman spectroscopy and machine learning is used in combination to detect COVID-19 positive saliva in liquid form.
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Machine-learning-accelerated structure prediction of PtSnO nanoclusters under working conditions

Phys. Chem. Chem. Phys., 2024, 26,27624-27632
DOI: 10.1039/D4CP03769C, Paper
Fanke Zeng, Wanglai Cen
Credible property calculations based on the structure prediction of multi-component catalyst clusters under working conditions via a machine-learning-accelerated genetic algorithm and ab initio thermodynamics approach.
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learning

Hydrogen bond network structures of protonated 2,2,2-trifluoroethanol/ethanol mixed clusters probed by infrared spectroscopy combined with a deep-learning structure sampling approach: the origin of the linear type network preference in protonated fluoroal

Phys. Chem. Chem. Phys., 2024, 26,27751-27762
DOI: 10.1039/D4CP03534H, Paper
Po-Jen Hsu, Atsuya Mizuide, Jer-Lai Kuo, Asuka Fujii
Infrared spectroscopy combined with a deep-learning structure sampling approach reveals the origin of the unusual structure preference in protonated fluorinated alcohol clusters.
The content of this RSS Feed (c) The Royal Society of Chemistry




learning

Deciphering nonlinear optical properties in functionalized hexaphyrins via explainable machine learning

Phys. Chem. Chem. Phys., 2024, Advance Article
DOI: 10.1039/D4CP03303E, Paper
Eline Desmedt, Michiel Jacobs, Mercedes Alonso, Freija De Vleeschouwer
The NLO response of hexaphyrins is traced back to its driving forces using kernel ridge regression and explainable machine learning. Orbital and charge-transfer based features play a key role, as opposed to aromaticity and geometrical descriptors.
To cite this article before page numbers are assigned, use the DOI form of citation above.
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In the age of e-learning

With advancing technology, the process of teaching and learning is also undergoing radical change.




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Some learning, some fun




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Machine Learning Directed Discovery and Optimisation of a Platinum-Catalysed Amide Reduction

Chem. Commun., 2024, Accepted Manuscript
DOI: 10.1039/D4CC05273K, Communication
Open Access
Eleonora Casillo, Benon Maliszewski, Cesar Blanco, Thomas Scattolin, Catherine Cazin, Steven P Nolan
The discovery and optimisation of reaction conditions leading to the reduction of amides, a fundamental large-scale industrial reaction, is achieved using a machine learning (ML) platform and a platinum catalyst....
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learning

Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach

Org. Biomol. Chem., 2024, Advance Article
DOI: 10.1039/D4OB00408F, Paper
Open Access
Yuya Tsutsui, Issei Yanaka, Kazuhiro Takeda, Masaru Kondo, Shinobu Takizawa, Ryosuke Kojima, Akihito Konishi, Makoto Yasuda
Exploration of a Lewis acid with high selectivity for hydrocarbon moieties is assisted by a machine learning approach. Molecular polarizability is an essential factor, leading to design guidelines for Lewis acid catalysts with dispersion forces.
To cite this article before page numbers are assigned, use the DOI form of citation above.
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Learning to live with competition

One does not have to run the rat race; there are other ways to handle corporate life




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Risk Management: Learning to tackle operational surprises

Knowing what can go wrong is key to getting things done right




learning

Rethinking writing instruction in the age of AI [electronic resource]: a universal design for learning approach Randy Laist ; with contributions from Nicole Brewer, Cynthia J. Murphy, and Dana Sheehan

Lynnfield, Massachusetts CAST [2024]




learning

Decolonial underground pedagogy [electronic resource] : unschooling and subcultural learning for peace and human rights / Noah Romero.

London : Bloomsbury Academic, 2024.




learning

Outdoor learning in higher education [electronic resource] : educating beyond the seminar room / edited by Wendy Garnham and Paolo Oprandi.

Abingdon, Oxon ; New York, NY : Routledge , 2025.




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Postdigital learning spaces [electronic resource] : towards convivial, equitable, and sustainable spaces for learning / James Lamb, Lucila Carvalho, editors.

Cham, Switzerland : Springer Nature, 2024.




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Lenovo launches ‘Lenovo Aware’ smart learning solution for consumers

Will come pre-bundled with IdeaPad Slim 3i and IdeaPad Slim 5i laptops




learning

The biomimicry revolution : learning from nature how to inhabit the earth / Henry Dicks.

New York : Columbia University Press, [2023]




learning

Learning support : a guide for mature students / Elizabeth Hoult.

London : SAGE Publications, 2006.




learning

Teaching study skills and supporting learning / Stella Cottrell.

Basingstoke : Palgrave, 2001.




learning

Marcus du Sautoy's The Creativity Code: How AI is learning to write, paint and think

Simonyi professor and mathematician Marcus du Sautoy’s new book, The Creativity Code, explores the future of creativity and how machine learning will disrupt, enrich, and transform our understanding of what it means to be human. In this engaging video, Marcus talks about using a definition from cognitive scientist Margaret Boden, claiming that creative acts must be novel, surprising and with value. According to du Sautoy, all three of these traits were present when AlphaGo – an AI trained to play the ancient game of Go – made a particularly unique and important move against world champion Lee Sedol in the second game of their match. ABOUT WIRED PULSE: AI AT THE BARBICAN 450 business executives, technologists and enthusiasts gathered at The Barbican Centre’s Concert Hall in London, for WIRED Pulse: AI at the Barbican on June 15, 2019. Discover some of the fascinating insights from speakers here: http://wired.uk/ai-event ABOUT WIRED PULSE AND WIRED EVENTS The WIRED Pulse series offers an engaging, top-level perspective on how disruptive technology and fast-changing industries - such as artificial intelligence, deep tech and health - are impacting the human experience. The aim is to distill the most pertinent strands of themes within each complex topic and to share it with the wider public as a thought-provoking conversation-starter. WIRED events shine a spotlight on the innovators, inventors and entrepreneurs who are changing our world for the better. Explore this channel for videos showing on-stage talks, behind-the-scenes action, exclusive interviews and performances from our roster of events. Join us as we uncover the most relevant, up-and-coming trends and meet the people building the future. ABOUT WIRED WIRED brings you the future as it happens - the people, the trends, the big ideas that will change our lives. An award-winning printed monthly and online publication. WIRED is an agenda-setting magazine offering brain food on a wide range of topics, from science, technology and business to pop-culture and politics. CONNECT WITH WIRED Events: http://wired.uk/events Web: http://po.st/WiredVideo Twitter: http://po.st/TwitterWired Facebook: http://po.st/FacebookWired Google+: http://po.st/GoogleWired Instagram: http://po.st/InstagramWired Magazine: http://po.st/MagazineWired Newsletter: http://po.st/NewslettersWired




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Computer Scientist Explains Machine Learning in 5 Levels of Difficulty

WIRED has challenged computer scientist and Hidden Door cofounder and CEO Hilary Mason to explain machine learning to 5 different people; a child, teen, a college student, a grad student and an expert.




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Dilwale digital le jayenge – the smart future of online learning

Welcome to the fast and furious world of online learning in India, where education meets the digital age with a dash of chutney.




learning

Machine learning for deconstructing contributions of atomic characterizations to achieve hybridization-determined electron transfer in a perovskite catalyst

J. Mater. Chem. A, 2024, 12,30722-30728
DOI: 10.1039/D4TA05018E, Paper
Jun Zhu, Mengdan Song, Qiling Qian, Yang Yue, Guangren Qian, Jia Zhang
Machine learning deconstructed the atomic contribution of a perovskite to catalytic toluene decomposition and found that wider hybridization resulted in smaller impedance, faster electron transfer speed, and enhanced catalytic activity.
The content of this RSS Feed (c) The Royal Society of Chemistry




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Development and application of Few-shot learning methods in materials science under data scarcity

J. Mater. Chem. A, 2024, 12,30249-30268
DOI: 10.1039/D4TA06452F, Review Article
Yongxing Chen, Peng Long, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji
Machine learning, as a significant branch of artificial intelligence, shortens the cycle of material discovery and synthesis by exploring the characteristics of data.
The content of this RSS Feed (c) The Royal Society of Chemistry




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Machine learning aided design of high performance copper-based sulfide photocathodes

J. Mater. Chem. A, 2024, Advance Article
DOI: 10.1039/D4TA06128D, Paper
Yuxi Cao, Kaijie Shen, Yuanfei Li, Fumei Lan, Zeyu Guo, Kelu Zhang, Kang Wang, Feng Jiang
With the help of machine learning algorithms, we developed software that can predict the performance of copper-based sulfide photocathodes and this system shows excellent accuracy of up to 96%.
To cite this article before page numbers are assigned, use the DOI form of citation above.
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Director Sai Rajesh: ‘Baby’ has been a learning experience; henceforth I will be more cautious in my writing

Sai Rajesh, the writer-director of the Telugu romantic drama ‘Baby’ that has been eliciting extreme responses, says he did not intend to make a toxic film




learning

Online and offline learning using fading memory functions in HfSiOx-based ferroelectric tunnel junctions

J. Mater. Chem. C, 2024, 12,17362-17376
DOI: 10.1039/D4TC03397C, Communication
Jungwoo Lee, Chaewon Youn, Jungang Heo, Sungjun Kim
We demonstrate online and offline learning as well as associative learning such as in Pavlov's dog experiments using the non-volatile and volatile properties of HfSiOx-based FTJs.
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“It is not just the shape, there is more”: students’ learning of enzyme–substrate interactions with immersive Virtual Reality

Chem. Educ. Res. Pract., 2025, Advance Article
DOI: 10.1039/D4RP00210E, Paper
Henry Matovu, Mihye Won, Roy Tasker, Mauro Mocerino, David Franklin Treagust, Dewi Ayu Kencana Ungu, Chin-Chung Tsai
To cite this article before page numbers are assigned, use the DOI form of citation above.
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Self-regulated learning strategies for success in an online first-year chemistry course

Chem. Educ. Res. Pract., 2025, Accepted Manuscript
DOI: 10.1039/D4RP00159A, Paper
Langanani Rakhunwana, Angelique Kritzinger, Lynne Alison Pilcher
During their first year of study at university, many students encounter challenges in developing learning strategies that align with success in the courses in which they are enrolled. The emergence...
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Elucidating the impact of oxygen functional groups on the catalytic activity of M–N4–C catalysts for the oxygen reduction reaction: a density functional theory and machine learning approach

Mater. Horiz., 2024, 11,1719-1731
DOI: 10.1039/D3MH02115G, Communication
Liang Xie, Wei Zhou, Yuming Huang, Zhibin Qu, Longhao Li, Chaowei Yang, Yani Ding, Junfeng Li, Xiaoxiao Meng, Fei Sun, Jihui Gao, Guangbo Zhao, Yukun Qin
While current experimental and computational studies often concentrate on introducing external structures or idealized MN4 models, we emphasize the often-overlooked impact of inherent OGs within the carbon structure of MN4 materials, presenting a new perspective on their catalytic activity origin.
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Exploring negative thermal expansion materials with bulk framework structures and their relevant scaling relationships through multi-step machine learning

Mater. Horiz., 2024, Advance Article
DOI: 10.1039/D3MH01509B, Communication
Yu Cai, Chunyan Wang, Huanli Yuan, Yuan Guo, Jun-Hyung Cho, Xianran Xing, Yu Jia
We uses the multi-step ML method to mine 1000 potential NTE materials from ICSD, MPD and COD databases, and the presented phase diagram can serve as a preliminary criterion for judging and designing new NTE materials.
To cite this article before page numbers are assigned, use the DOI form of citation above.
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Union Budget 2024-25 — no signs of learning

The mismatch between the problem at hand and what the Budget offers is stark be it welfare or even taking care of key political allies




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When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by deep learning [electronic journal].




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Tax Simplicity and Heterogeneous Learning [electronic journal].

National Bureau of Economic Research