The growing energy footprint of artificial intelligence

Why Are There Still So Many Jobs? The History and Future of Workplace Automation

Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Patterson, D., Gonzalez, J., Holzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., & Dean, J. (2022) · Computer · doi:10.1109/mc.2022.3148714 · Cited by 400

Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2022) · arXiv · doi:10.48550/arxiv.2211.02001 · Cited by 378

A systematic review of Green AI

Verdecchia, R., Sallou, J., & Cruz, L. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1507 · Cited by 375

Cryptocurrencies on the road to sustainability: Ethereum paving the way for Bitcoin

de Vries, A. (2022) · Patterns · doi:10.1016/j.patter.2022.100633 · Cited by 110

Proceedings of the Workshop on Edge and Mobile Foundation Models

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FArSS: Fast and Efficient Semantic Question Similarity in Arabic

Alkaoud, M. (2025) · IEEE Access · doi:10.1109/access.2025.3529527 · Cited by 4

Are humans still necessary? Expanding the discussion

See, J. E., Gilpin-Mcminn, J. K., Dorton, S. L., Cooke, N. J., Lee, J. D., Lau, N. K. C., & Li, R. (2025) · Ergonomics · doi:10.1080/00140139.2025.2496949 · Cited by 3

PowerSensor3: A Fast and Accurate Open Source Power Measurement Tool

van der Vlugt, S., Oostrum, L. C., Schoonderbeek, G., van Werkhoven, B., Veenboer, B., Doekemeijer, K., & Romein, J. W. (2025) · IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) · doi:10.1109/ispass64960.2025.00028 · Cited by 10

Current AI-Based Software Engineering, Strengths and Weaknesses - Results from a MLR

Walshe, J., Maloney, R. C., Grant, E., Conde, C. A. N., Marks, G., Yılmaz, M., Messnarz, R., Clarke, P. M., & McCarren, A. L. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-04288-0_6 · Cited by 3

A Global Perspective on Supercomputer Power Provisioning: Case Studies from United States and Europe

Patki, T., Rountree, B., Wilde, T., Bartolini, A., Brink, S., Heiskanen, E., Idgunji, S., Maiterth, M., Rogers, J., Rrapaj, E., et al. (2025) · International Conference on Supercomputing · doi:10.1145/3721145.3734532 · Cited by 3

SysLLMatic: Large language models are software system optimizers

Peng, H., Gupte, A., Hasler, R., Eliopoulos, N., Ho, C.-C., Mantri, R., Deng, L., Läufer, K., Thiruvathukal, G. K., & Davis, J. C. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112929 · Cited by 5

Is Quantization a Deal-Breaker? Empirical Insights From Large Code Models

Afrin, S., Xu, B., & Mastropaolo, A. (2025) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme64153.2025.00049 · Cited by 3

Sustainable Development Integration in Higher Education Institutions: Challenges and Opportunities in the Age of AI and Big Data

Low, D. Y. S., Shaw, K. M., & Balan, P. (2025) · Sustainable Development · doi:10.1002/sd.70446 · Cited by 5

Laser ablation of high-loading Li-ion battery electrodes improves accessible capacity and cycle life for Behind-the-Meter Storage

Pereira, D. J., Tancin, R. J., Trask, S. E., Fink, K. E., Stiles, J. W., Martin, T. R., Ha, Y., Finegan, D. P., Burrell, A. K., de Villers, B. J. T., et al. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238969 · Cited by 3

Building Accurate Energy-Use Statistics for Data Centers

Wang, Y.-Z., Han, T., & Wei, Y.-M. (2025) · Engineering · doi:10.1016/j.eng.2025.12.014 · Cited by 4

The environmental administrative penalties and firm digital transformation: A reexamination of Porter Hypothesis

Xiao, Y., & Liu, X. (2026) · Economic Modelling · doi:10.1016/j.econmod.2026.107481 · Cited by 3

Artificial intelligence and the energy transition: Towards ethical and equitable future power systems

Zhu, R. R., & Ma, J. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.104564 · Cited by 3

Brain-inspired energy efficient technologies for next-generation artificial intelligence

Chiel, H. J., Coggan, J. S., Datta, G., Fellous, J., Nourse, W. R. P., Quinn, R. D., & Thomas, P. J. (2026) · Biological Cybernetics · doi:10.1007/s00422-026-01038-4 · Cited by 5

A theory of inference compute scaling: reasoning through directed stochastic skill search

Ellis-Mohr, A. R., Nayak, A. K., & Varshney, L. R. (2026) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0510 · Cited by 3

Techno-Economic Optimization of an Integrated Renewable-Hydrogen-Data Center Hub for Yanbu Industrial City in Saudi Arabia

Alturki, A. A. (2026) · Energies · doi:10.3390/en19061482 · Cited by 3

Energy-Aware Multilingual Evaluation of Large Language Models

de Zarzà, I., Liz, M., de Curtò, J., & Calafate, C. T. (2026) · Electronics · doi:10.3390/electronics15071395 · Cited by 7

Threat of China’s nuclear dominance boosts Americans’ support for nuclear energy

Clark, R., Magistro, B., & Alvarez, R. M. (2026) · Energy Policy · doi:10.1016/j.enpol.2026.115276 · Cited by 3

Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing

Hu, D., Yang, R., Ye, Z., & Lu, J. (2026) · Nano-Micro Letters · doi:10.1007/s40820-026-02171-2 · Cited by 7

Decoupling economic growth from water use intensification

D’Odorico, P., & Rulli, M. C. (2026) · Nature Water · doi:10.1038/s44221-026-00611-2 · Cited by 3

Automated building heritage assessment using street-level imagery

Dabrock, K., Johansson, T. D., Donarelli, A., Mangold, M., Pflugradt, N., Weinand, J. M., & Linssen, J. G. A. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114654 · Cited by 3

The nonlinear link between AI and energy poverty: Empirical evidence from global data

Dai, J. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.148590 · Cited by 3

Deuterium–tritium levitated dipole fusion power plants

Simpson, T., Badcock, R. A., Berry, T., Chisholm, C. S., Fimognari, P. J., Fisher, P. H., Garnier, D., Lenagh-Glue, K., Leuw, B., Mataira, R. C., et al. (2026) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2026.115864 · Cited by 3

The impact of artificial intelligence on carbon emission intensity: evidence for an early-stage inverted U-shaped relationship

Wang, S. (2026) · Frontiers in Environmental Science · doi:10.3389/fenvs.2026.1756431 · Cited by 3

A 1.1-nJ/Classification True Analog Current Computing on Multilayer Neural Network With Crystalline-IGZO/Si-CMOS Monolithic Stack Technology

Tsuda, K., Furutani, K., Yakubo, Y., Godo, H., Ando, Y., Kosuge, A., Nakura, T., & Yamazaki, S. (2024) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2024.3439712 · Cited by 3

Probabilistic prediction system based on quantile deep learning model and multi-level information recognition

Zhang, L., Wang, J., Li, J., Qian, Y., & Li, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126734 · Cited by 3

Integrating GenAI into communication education for ‘generation prompt’: an exploration of academics' perspectives on its benefits, challenges, and future prospects in Turkiye

Şenyüz, B., Özgen, E., & Oğuzcan, A. U. (2025) · Encontros Bibli Revista Eletrônica de Biblioteconomia e Ciência da Informação · doi:10.5007/1518-2924.2025.e103465 · Cited by 5

Carbon Topography Representation: Improving Impacts of Data Center Lifecycle

Weppe, O., Bekri, D., Marty, T., Guibert, L., Aubet, L., Prévotet, J.-C., Pelcat, M., & Rumley, S. (2025) · ACM SIGEnergy Energy Informatics Review · doi:10.1145/3757892.3757898 · Cited by 3

Energy use and carbon emissions in high-performance computing: A case study for universities and reduction strategies

Hölbling, S., Kirchengast, G., Christian Briese, A., & Thüminger, H. (2025) · Cleaner Environmental Systems · doi:10.1016/j.cesys.2025.100332 · Cited by 5

Towards responsible software engineering: combining value-based processes, agile practices, and green metering

Kapferer, S., Stocker, M., & Zimmermann, O. (2024) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas61960.2024.10732097 · Cited by 3

Automated biodiversity research critically requires multidisciplinary expertise

Cardoso, P., Branco, V. V., Correia, L., Emerson, B. C., Laine, V. N., & Lopes, M. C. (2025) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2025.05.003 · Cited by 3

The Social Structure of Scientific Evaluation: AI, Benchmarking, and the Deep Learning MonoculturePreprint

Koch, B., & Peterson, D. (2026) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/s4qdy_v2 · Cited by 17

Ai-Driven Cooling Technologies for High-Performance Data Centers: State-of-The-Art Review and Future DirectionsPreprint

Kez, D. A., Foley, A. M., Wong, F. W. B. M. H., Dolfi, A., & Srinivasan, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5148380 · Cited by 3

Long hours and low wages: the human labour powering AI’s development

Taylor, B. (2023) · doi:10.64628/aam.5wxvexj3n · Cited by 3

AI in Education: Towards a Pedagogically Grounded and Interdisciplinary Field

Chatzichristofis, S. A. (2025) · AI in Education · doi:10.3390/aieduc1010001 · Cited by 3

How (not) to communicate about the environmental implications and impacts of AI technologies

Molek‐Kozakowska, K., & Radziej, R. (2024) · Media Biznes Kultura · doi:10.4467/25442554.mbk.24.017.20891 · Cited by 2

Integration of Sustainable Energy Sources Into Data Centre Electrical Systems

Sahin, C., Andiç, C., Aydın, E., & Türkay, B. E. (2024) · Global Energy Conference (GEC) · doi:10.1109/gec61857.2024.10881114 · Cited by 2

Animals and AI: Using Environmental Thinking to Understand Media Labor

Ostroski, C. F. (2025) · Environmental Communication · doi:10.1080/17524032.2025.2521331 · Cited by 2

Modeling AI-Driven IoT Energy Consumption: From Device-Level Forecasts to System-Level Dynamics

Medojević, M., Marković, N., & Rikalović, A. (2025) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech65624.2025.11091761 · Cited by 2

Switched Affine System-Based Current Sharing Control in Interleaved VRM: A Lyapunov-LMI Approach for Compute Chip Power Delivery

Su, X., Hu, W., Zeng, Y., Zhang, G., & Meng, X. (2025) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2025.3595202 · Cited by 2

Navigating GenAI in Malaysian Universities: Use, Problems, and Challenges

Al-kumaim, N. H., Hassan, S. H., Mohammed, F., & Saleh, A. (2025) · 2025 5th International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta66764.2025.11132252 · Cited by 4

Estimating the Carbon Footprint of Cyberattacks: The Ransomware Case

Jaafar, F., & Decian, J. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-05832-4_3 · Cited by 2

Towards Efficient LLM Inference via Collective and Adaptive Speculative Decoding

Wang, S., Yang, H., Wang, X., Liu, T., Wang, P., Xu, Y., Liang, X., Ma, K., Feng, T., You, X., et al. (2025) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3712285.3759834 · Cited by 2

A Role for Artificial Intelligence (AI) in Qualitative Research? An Exploratory Analysis Examining New York City Residents’ Perceptions on Climate Change

Sprague, N. L., Meltzer, G., Dandeneau, M., Santos, D. D. L., O’Neil, D., Kim, A. K., Parisi, A., Araujo, S., Ekenga, C. C., Siegel, E., et al. (2025) · Sustainability · doi:10.3390/su172310459 · Cited by 2

A Deployment-Aware Framework for Carbon- and Water- Efficient LLM Serving

Hoxha, J., Thanasi-Boçe, M., & Khalifa, T. (2025) · Sustainability · doi:10.3390/su172310473 · Cited by 3

Antecedents, enablers and outcomes of twin transformation in high reliability organizations: a systematic literature review

Kleinschmidt, N., Weiß, L., Stubner, S., Gast, J., & Steinhoff, M. M. (2025) · Review of Managerial Science · doi:10.1007/s11846-025-00955-1 · Cited by 3

Realizing rapid energy storage and efficient release in a tesla valve integrated cold energy storage unit for data center cooling

Ling, H., Wu, Y., Gu, Y., Zhao, Y., Zhu, X. H., Jiang, F., Ding, Y., Ling, X., & Fang, D. (2026) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.129683 · Cited by 2

The Role of Hydrogen in Energy Communities: Current Status, Challenges, and Future Developments

Velaz-Acera, N., Blázquez, C. S., Casado-Lorenzo, V., & Lagüela, S. (2026) · Hydrogen · doi:10.3390/hydrogen7010014 · Cited by 2

Long-run electricity consumption in computing: Exponential growth followed by stabilization due to efficiency gains

Pinto, R. C., Brockway, P. E., Domingos, T., & Sousa, T. (2026) · iScience · doi:10.1016/j.isci.2026.114876 · Cited by 2

Nonlinear optical extreme learner via data reverberation with incoherent light

Liu, B., Mei, X., Shafi, S., Xia, T., Khoo, I. C., Liu, Z., & Ni, X. (2026) · Science Advances · doi:10.1126/sciadv.aeb4237 · Cited by 2

Integrating gamification and artificial intelligence in sustainability education: a conceptual framework toward the 2030 Agenda

Pinzón-Salazar, S., Martínez-Gil, F., Gil, C. R., & Alcayde, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1727938 · Cited by 2

Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks

Shang, C., Yu, J., & Hoang, D. T. (2025) · Globecom · doi:10.1109/globecom59602.2025.11431955 · Cited by 2

From Awareness to Action? The Impact of CO2 Emission Feedback on Student LLM Usage

Andersen, L. B., Herklotz, M., Liu, A., Goeke, M., Jülich, M., Kern, C. A., & Kreuter, F. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798840 · Cited by 2

Vampire Slop

Crabapple, M. (2026) · Critical AI · doi:10.1215/2834703x-12347627 · Cited by 2

The Ethical Landscape of Generative AI in Education: A Narrative Literature Review Through the Lens of Consequentialism (2022–2026)

Creely, E. (2026) · AI in Education · doi:10.3390/aieduc2020020 · Cited by 6

Nanolaser potential in communications and data handling

Puccioni, G. P., Wang, T., & Lippi, G. L. (2024) · Semiconductor Lasers and Laser Dynamics · doi:10.1117/12.3024495 · Cited by 2

Using quantum transport networks for classification: A path toward quantum computing for machine learning

Lorber, S., Zimron, O., Zak, I. L., Milo, A., & Dubi, Y. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.014041 · Cited by 2

Spin Wave Threshold Majority Gate

Zegbroeck, A. V., Ciubotaru, F., Anagnostou, P., Meng, F., Adelmann, C., Hamdioui, S., & Cotöfană, S. (2024) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano61778.2024.10628869 · Cited by 2

Programmable Optical Synaptic Linking of Neuromorphic Photonic-Electronic RTD Spiking Circuits

Hejda, M., Zhang, W., Al-Taai, Q. R. A., Malysheva, E., Owen-Newns, D., Figueiredo, J. M. L., Romeira, B., Robertson, J., Dolores-Calzadilla, V., Wasige, E., et al. (2024) · ACS Photonics · doi:10.1021/acsphotonics.4c01199 · Cited by 2

Risk Evaluation of AI Systems in the Energy Sector - Three Case Studies from TSO Business

Heymann, F., Marot, A., Subramanian, M., & Galus, M. D. (2024) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi61910.2024.10761769 · Cited by 3

Inteligencia artificial transformación, retos y prospectiva social

de Ensenada, C. D. I. C. Y. D. E. S., García‐Macías, J. A., Rojo, I. D. J. P., California, U. A. D. B., Rivas, D., & California, U. A. D. B. (2024) · doi:10.61728/ae20240998 · Cited by 3

Environmental Sustainability of Machine Learning Systems: Reducing the Carbon Impact of Their Lifecycle Process

Martínez‐Fernández, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78386-9_1 · Cited by 2

Epistemological Dilemma

Mohmmed, A. O., El-Mardi, A., Saleem, M. W., & Ganawa, E. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-5748-4.ch005 · Cited by 2

A Hybrid Auto-Scaling Method for Energy Efficiency of Network Services

Lee, D., Shin, S., Lee, C.-S., Na, T., & Kim, T. (2024) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc62082.2024.10827077 · Cited by 2

1.2 From Chips to Thoughts: Building Physical Intelligence into Robotic Systems

Rus, D. L. (2025) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc49661.2025.10904576 · Cited by 2

Fused-MemBrain: a spiking processor combining CMOS and self-assembled memristive networks

Cipollini, D., Greatorex, H., Mastella, M., Chicca, E., & Schomaker, L. (2025) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/adc6b5 · Cited by 2

Low-cost algorithms for clinical notes phenotype classification to enhance epidemiological surveillance: A case study

Petri, J., Barbeira, P. B., Pesce, M., Xhardez, V., Laje, R., & Cotik, V. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104795 · Cited by 2

Data retrieval from local heritage books—Is artificial intelligence the solution?

Stelter, R., & Biehler, R. (2025) · Historical Methods A Journal of Quantitative and Interdisciplinary History · doi:10.1080/01615440.2025.2512744 · Cited by 2

ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back Propagation

Zhong, L., Huang, S., & Shi, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-8912-5_9 · Cited by 3

Dimensionless group-driven ensemble neural networks for robust design optimization in engineering

Massoudi, S., & Schiffmann, J. (2025) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwaf056 · Cited by 2

Can Artificial Intelligence Pave a Greener Path for China? Exploring the Synergistic Effects of Intelligentization and Industrialization on Carbon Emission Efficiency

Zhang, L., Chen, B., Jiao, Y., & Dong, Z. (2025) · Emerging Markets Finance and Trade · doi:10.1080/1540496x.2025.2559936 · Cited by 4

Low-Carbon Economic Dispatch of Data Center Microgrids via Heat-Determined Computing and Tiered Carbon Trading

Ma, L., Shi, H., Liu, G., Lu, W., & Gu, N. (2026) · Energies · doi:10.3390/en19030699 · Cited by 2

Artificial Intelligence, Like Cryptocurrency, Eats Energy—Lots of It

Leslie, M. (2023) · Engineering · doi:10.1016/j.eng.2023.11.006 · Cited by 2

Use of Visibility Graphs for the Early Detection of DoS Attacks

Lopes, J., Pinto, P., Partida, A., & Pinto, A. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679430 · Cited by 2

The process is the product: a review of Co-Intelligence by Ethan Mollick

Bartucz, J. (2024) · AI and Ethics · doi:10.1007/s43681-024-00633-0 · Cited by 2

On-Demand Internationalization for Learning Management System Moodle

John, T., & Möller, A. L. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-61953-3_11 · Cited by 1

A Call for Bildung

Skaar, Ø. O. (2024) · Nordic Journal of Digital Literacy · doi:10.18261/njdl.19.2.4 · Cited by 1

Computility-Power Coupled Scheduling of Data Centers Toward Grid Resilience Enhancement

Wu, Y., Wang, J., An, Q., Ding, Z., & Dai, J. (2024) · IEEE International Conference on Power Science and Technology (ICPST) · doi:10.1109/icpst61417.2024.10602063 · Cited by 1

Achieving net-zero for artificial intelligence: Big-tech's sustainability challenges from 2030 to 2050

Hankendi, C., Coskun, A. K., & Sovacool, B. K. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.149009 · Cited by 1

Sustainable AI: Experiences, Challenges & Recommendations

Broadway, E., Lee, J. K. L., & Weiland, M. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00227 · Cited by 1

A replacement for the scaling axiomatic approach to scheduling dependent tasks on DVFS computing platforms

Hagras, T., & El-Sayed, G. A. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07637-5 · Cited by 1

Overcoming computational bottlenecks in large language models through analog in-memory computing

Lin, Y., & Tang, J. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00860-3 · Cited by 1

AI and generational amnesia: An ecological approach to ‘new memory’ regimes

Morehouse, H. (2025) · Memory Mind & Media · doi:10.1017/mem.2025.10017 · Cited by 1

Automating the static and seismic design of 2-D multistorey reinforced concrete structures by using Monte Carlo Tree Search and Genetic Algorithm

Rossi, L., & Winands, M. H. M. (2025) · AI in Civil Engineering · doi:10.1007/s43503-025-00073-7 · Cited by 1

Catastrophic Computation. On the Impossibility of Sustainable Artificial Intelligence

Rehak, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-11108-1_8 · Cited by 1

What Is Required for AI to Improve the Assessment and Treatment of Patients With Lower Urinary Tract Dysfunction? ICI‐RS 2025

Werneburg, G. T., Wyndaele, M., Speich, J. E., Agrò, E. F., Belal, M., Malde, S., Kheir, G. B., Hurі, E., Nambiar, A., Mosiello, G., et al. (2025) · Neurourology and Urodynamics · doi:10.1002/nau.70186 · Cited by 1

Third-Factor Aware Neuromorphic Architectures Based on Gated Ferroelectric Tunnel Junction Devices

Srinivasan, R., Barve, S., Sieber, R., Socolik, C., Gogi, V. K., & Jha, R. (2025) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas53549.2025.11244531 · Cited by 1

Industrial Metaverse challenges, opportunities, and the impact on sustainability

Tsakalos, D., Krimpas, G. A., & Mourtzis, D. (2025) · Digital Twin · doi:10.1080/27525783.2025.2600190 · Cited by 1

Back to functional hydrides: Effects of neutron-irradiated microstructure on hydrogen retention in yttrium hydride

Cinbiz, M. N., Lin, Y.-R., Huang, Y., & Eapen, J. (2025) · Journal of Nuclear Materials · doi:10.1016/j.jnucmat.2025.156312 · Cited by 1

Towards an Energy-Efficient and Sustainable IIoT using Embedded Neuromorphic AI

Azadi, B., Anzengruber-Tanase, B., Sopidis, G., Haslgrübler, M., & Ferscha, A. (2025) · International Conference on the Internet of Things (IoT) · doi:10.1145/3770501.3770529 · Cited by 1

The Use of Artificial Intelligence in Dentistry: Current Applications and Future Perspective

Kwan, J. C., Fritz, P. C., & Nguyen, T. (2026) · Springer eBooks · doi:10.1007/978-981-96-7448-0_13-1 · Cited by 1

A vision for responsible AI integration in citizen science

Lotfian, M., Claramunt, C., Suman, A. B., Pérez-Uribe, A., Chavent, N., Ertz, O., & Ingensand, J. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06594-5 · Cited by 1

La réglementation de la soutenabilité environnementale de l’I.A. : reformulation critique au prisme du courant Law & Technology

Ducuing, C., & Garsia, S. (2026) · Archives de philosophie du droit · doi:10.3917/apd.661.0432 · Cited by 1

Critical review of lithium recovery from geothermal brines with implications for Smackover Formation, USA

Huang, X., Neil, C. W., Ahmmed, B., Mehana, M. Z., Bhattacharya, S., Nicot, J., & Elliott, B. A. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.101671 · Cited by 2

From PhysioNet to foundation models—a history and potential futures

Clifford, G. D. (2026) · Physiological Measurement · doi:10.1088/1361-6579/ae92e7 · Cited by 1

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