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• Data-Driven Exploration and Insights Into Temperature-Dependent Phonons in Inorganic Materials.
  Phonons, quantized vibrations of the atomic lattice, are central to thermal transport, structural stability, and phase behavior in crystalline solids. However, most large-scale materials databases rely on the harmonic approximation and neglect important temper…
  europepmc 2026 置信度 0.80
• Cluster and network formation in Fe-Si-B amorphous alloys: a machine learning molecular dynamics study.
  Local atomic arrangements in amorphous materials, particularly short- and medium-range orders (SRO and MRO, respectively), govern their metastability and distinctive physical properties. In 1979, Gaskell proposed that the structural motifs of transition metal-…
  europepmc 2026 置信度 0.80
• Towards nano-mechanical simulations of ceramics containing realistic defects <i>via</i> machine-learning potentials: the example of TiB<sub>2</sub>.
  Transition metal diboride (TMB 2 ) ceramics combine high hardness with excellent thermal and chemical stability, making them attractive for protective coating applications. TMB 2 s commonly grow as largely off-stoichiometric-containing vacancies or other simpl…
  europepmc 2026 置信度 0.80
• Atomic-Scale Molecular Dynamics Modeling of Iron Oxides: Surface Properties and Methodologies.
  Iron oxides, including hematite (α-Fe2O3), magnetite (Fe3O4), and maghemite (γ-Fe2O3), play central roles in catalysis, corrosion, environmental remediation, magnetic nanotechnology, and energy storage. Molecular dynamics simulations have become an essential t…
  europepmc 2026 置信度 0.80
• Active learning-driven global search for neutral gold clusters <i>via</i> neural network potential.
  The structural prediction of metal nanoclusters is hindered by the extremely complex potential energy surface and the prohibitive cost of first-principles calculations. Here, we develop an efficient structure-prediction framework that tightly integrates machin…
  europepmc 2026 置信度 0.80
• Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
  Abstract The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. Nevertheless, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge.I…
  europepmc Hossein Tahmasbi, Andreas Knüpfer, Thoams D. Kühne, Hossein Mirhosseini 2026 置信度 0.80
• Coupled rotational and bending dynamics of CO<sub>2</sub> near nanoporous graphene.
  Context The design of highly efficient gas separation membranes requires a fundamental understanding of molecular dynamics at the sub-nanometer scale. This study investigates the interaction of a linear CO 2 molecule with a six-pore nanoporous graphene (NPG) s…
  europepmc 2026 置信度 0.80
• Dynamic bond reconfiguration drives crack-tip carbon migration to strengthen iron grain boundaries
  Abstract Developing stronger alloys is essential for structural applications, but their reliability is limited by brittle cracking along general grain boundaries (GGBs). Solute segregation can control grain-boundary fracture, yet its effects are typically inte…
  europepmc 2026 置信度 0.80
• Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying
  Machine learning interatomic potentials (MLIPs) are typically constructed for homogeneous crystalline systems that exhibit only minimal local deviations from equilibrium configurations. However, substitutional alloying elements in multicomponent engineering al…
  europepmc Lixin Fang, Liqin Qin, Limin Zhang, Hao Zhou 等 2026 置信度 0.80
• Platonic representation of foundation machine learning interatomic potentials.
  Foundation machine learning interatomic potentials (MLIPs) have emerged as powerful tools for atomistic simulation, yet different models encode chemical environments in incompatible latent spaces, limiting direct comparison and interoperability. The platonic r…
  europepmc 2026 置信度 0.80
• CoRE 2D-HOIP DB: Computation-Ready, Experimental Database of Two-Dimensional Hybrid Organic-Inorganic Perovskites.
  In silico materials design has become a widely accepted supplement to the experimental trial-and-error approach. Computation-ready, experimental (CoRE) crystal structures are commonly the foundation for creating high-throughput screening workflows to identify …
  europepmc 2026 置信度 0.80
• A multiscale framework for developing equations of state of supercritical aqueous systems from synchrotron experiments, atomistic simulations, and machine learning
  Abstract The increase of fossil fuels use is responsible for the increased greenhouse gas emissions which have a great impact on the environment leading to global warming. Consequently, substantial efforts are devoted to decrease our dependence on fossil fuels…
  europepmc 2026 置信度 0.80
• AI-driven multiscale modeling of mechanical and corrosion properties in biodegradable Mg-Lu alloys.
  Magnesium-lutetium (Mg-Lu) alloys exhibit significant potential as biodegradable bone implant materials. However, their low alloying content necessitates the use of excessively large simulation supercells, resulting in high computational costs. Moreover, conve…
  europepmc 2026 置信度 0.80
• Mechanistic Analysis of Large Atomic Models of Molten Salt.
  Machine-learned interatomic potential (MLIP) has become a powerful tool to combine the accuracy of quantum mechanics with the efficiency of molecular dynamics in the era of artificial intelligence. However, a key open question persists: what physical mechanism…
  europepmc 2026 置信度 0.80
• How Realistic Are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces.
  Copper is a highly promising catalyst for the electrochemical CO 2 reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as ethylene and ethanol. Since the CO2RR takes place in aqueous solution, the detailed …
  europepmc 2026 置信度 0.80
• Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling.
  Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucial…
  europepmc 2026 置信度 0.80
• Probing Lattice Anharmonicity and Thermal Transport in Ultralow-κ Materials Using Machine Learning Interatomic Potentials.
  Abstract Crystalline solids with ultralow lattice thermal conductivity (κ) are highly sought after for thermoelectric energy conversion and thermal barrier coating applications. However, a comprehensive theoretical understanding of heat transport in strongly a…
  europepmc Soham Mandal, Ashutosh Srivastava, Tanmoy Das, Abhishek Kumar Singh 等 2025-12-17T20:25:48Z 置信度 0.80
• Optimizing Prediction of Chemical Bonds in Interfacial Dynamics through Local Uncertainty Estimates with Neural Network Ensembles.
  We present a framework for data-efficient training of machine-learning interatomic potentials for interfacial chemistry, especially heterogeneous catalytic systems. We establish strategies for density functional theory training data generation consisting of pr…
  europepmc 2026 置信度 0.80
• Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis.
  The design of catalysts gets its fundamental rationale from accurate and efficient modeling of reactivity on surfaces and materials. To reach this detailed atomistic understanding, density functional theory (DFT) has been the key computational technique. Howev…
  europepmc 2026 置信度 0.80
• Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120.
  Metal-organic frameworks (MOFs) are porous materials with the potential for gas adsorption and separation technologies due to their tunable structural and chemical characteristics. However, simulating gas adsorption isotherms in MOFs with DFT-level accuracy re…
  europepmc 2026 置信度 0.80
• Enabling accurate modelling of materials for a solid electrolyte interphase in lithium-ion batteries using effective machine learning interatomic potentials.
  Accurate modelling of the structural and dynamic properties of the solid electrolyte interphase (SEI) in lithium-ion batteries remains a longstanding challenge due to the high complexity of the SEI structure and the lack of structural information. Atomistic si…
  europepmc 2025 置信度 0.80
• Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials.
  Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally guided materials science, surpassing traditional force fields due to their flexible functional form and superior accuracy in reproducing physical properties of materials.…
  europepmc Igor Vorotnikov, Fedor Romashov, Nikita Rybin, Maxim Rakhuba 等 2025 置信度 0.80
• Stability constants of lanthanide-nitrate complexes in aqueous solutions: a theoretical study.
  Calculating stability constants for lanthanide-nitrate complexes in aqueous solution is challenging due to the complex free-energy landscapes of the participating species. In this work, we independently compare cluster-continuum solvation at a density function…
  europepmc 2026 置信度 0.80
• A physics-informed long-range polarizable potential based on deep learning.
  Machine-learning-based interatomic potentials are widely employed in atomistic simulations, but they struggle to capture long-range electrostatic correlations, which are ubiquitous in polar and biomolecular systems. We present a physics-informed machine-learni…
  europepmc 2026 置信度 0.80
• Sulfur-Induced Shape Compliance Modulates Fe Catalyst Agglomeration in Carbon Nanotube Growth.
  Large-scale carbon nanotube (CNT) synthesis based on floating catalyst chemical vapor deposition (FC-CVD), unlike conventional CVD, utilizes a growth promoter, commonly a sulfur-containing species, whose role in the overall growth process is still poorly under…
  europepmc 2026 置信度 0.80
• Recent advances in understanding surface reconstruction for oxygen evolution reaction electrocatalysts: insights from density functional theory.
  Surface reconstruction has emerged as a defining feature of oxygen evolution reaction (OER) electrocatalysts because many materials operate not as their as-synthesized structures but as dynamically transformed interfacial phases generated under anodic conditio…
  europepmc 2026 置信度 0.80
• A quantitatively constrained framework for defect identification in oxides: application to Cr<sup>3+</sup> centers in PbTiO<sub>3</sub>.
  Identifying paramagnetic defect centers in ferroelectric oxides is challenging due to the large configurational space of dopants and the strong sensitivity of spectroscopic signatures to local structure. In this work, we present a unified framework for assigni…
  europepmc 2026 置信度 0.80
• Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning.
  Goal: Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candi…
  europepmc 2026 置信度 0.80
• A Controlled Study of Physics-Informed Auxiliary Supervision and Scalar Triplet Attention in Equivariant Molecular Force Fields.
  Machine-learned molecular force fields require many-body geometry, but obtaining it through Clebsch-Gordan tensor products is computationally expensive. For a strong no-Clebsch-Gordan backbone such as GotenNet, we ask whether the limitation in handling three-b…
  europepmc 2026 置信度 0.80
• Coverage-Dependent Lateral Interactions Shape the Electrocatalytic Activity of High-Entropy Alloys.
  While the rich diversity of surface sites on high-entropy alloys (HEAs) is essential for tuning electrocatalytic activity, the coverage-dependent lateral interactions that shape reactive interfaces are often neglected in theoretical studies. Here, we develop a…
  europepmc 2026 置信度 0.80
• Accelerating Screening of Phase Separation Agents for Carbon Dioxide Capture: A Machine-Learning Framework with Interpretable Quantum Chemical Insights.
  Against the backdrop of addressing climate change and reducing carbon emissions, carbon dioxide capture technology has gained increasing significance as a key approach toward achieving global carbon neutrality. However, in the development of phase change absor…
  europepmc 2026 置信度 0.80
• Construction of Arbitrary-Order Internal Coordinate Transformations to Improve Studies of Large-Amplitude Motions.
  Internal coordinates and their derivatives underpin the efficient treatment of geometry optimizations, high-resolution spectroscopic simulation, and the fitting of potential surfaces in quantum chemistry. Existing descriptions of the construction of internal c…
  europepmc 2026 置信度 0.80
• Changing Data Sources in the Age of Machine Learning for Official Statistics
  Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insight…
  arxiv Cedric De Boom, Michael Reusens 2023-06-07T11:08:12Z 置信度 0.78 stat.MLcs.LG
• DOME: Recommendations for supervised machine learning validation in biology
  Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recomme…
  arxiv Ian Walsh, Dmytro Fishman, Dario Garcia-Gasulla, Tiina Titma 等 2020-06-25T12:01:39Z 置信度 0.78 q-bio.OTcs.LG
• Learning Curves for Decision Making in Supervised Machine Learning: A Survey
  Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training it…
  arxiv Felix Mohr, Jan N. van Rijn 2022-01-28T14:34:32Z 置信度 0.78 cs.LG
• Active learning for data streams: a survey
  Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recen…
  arxiv Davide Cacciarelli, Murat Kulahci 2023-02-17T14:24:13Z 置信度 0.78 stat.MLcs.LGstat.ME
• Physics-Inspired Interpretability Of Machine Learning Models
  The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understandi…
  arxiv Maximilian P Niroomand, David J Wales 2023-04-05T11:35:17Z 置信度 0.78 cs.LGcs.AI
• Privacy-preserving machine learning for healthcare: open challenges and future perspectives
  Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the en…
  arxiv Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos, Farah E. Shamout 2023-03-27T19:20:51Z 置信度 0.78 cs.LGcs.CR
• A Benchmark Study of Machine Learning Models for Online Fake News Detection
  The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a s…
  arxiv Junaed Younus Khan, Md. Tawkat Islam Khondaker, Sadia Afroz, Gias Uddin 等 2019-05-12T17:15:11Z 置信度 0.78 cs.CLcs.IRcs.LGstat.ML
• MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning
  Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We in…
  arxiv Cassandre Notton, Benjamin Stott, Philippe Schoeb, Anthony Walsh 等 2026-02-11T18:00:01Z 置信度 0.78 cs.LGcs.PLquant-ph
• Emotion in Reinforcement Learning Agents and Robots: A Survey
  This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by inf…
  arxiv Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker 2017-05-15T11:49:56Z 置信度 0.78 cs.LGcs.AIcs.HCcs.ROstat.ML
• Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
  We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, ampl…
  arxiv Mominul Rubel, Adam Meyers, Gabriel Nicolosi 2025-09-10T16:49:20Z 置信度 0.78 cs.LGmath.OC
• MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
  Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximation…
  arxiv Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Doing 等 2019-06-03T22:10:52Z 置信度 0.78 stat.MLcs.LG
• Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory
  This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information…
  arxiv Olga Cherednichenko, Dmytro Chernyshov, Dmytro Sytnikov, Polina Sytnikova 2024-04-18T21:22:42Z 置信度 0.78 cs.LG
• ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
  We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combine…
  arxiv Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein, Pietro Zanuttigh 等 2024-02-02T13:17:19Z 置信度 0.78 cs.CVcs.LGcs.NE
• Learning Representations from Dendrograms
  We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a dista…
  arxiv Morteza Haghir Chehreghani, Mostafa Haghir Chehreghani 2018-12-21T16:11:00Z 置信度 0.78 cs.LGstat.ML
• Ten simple rules for navigating AI in science.
  europepmc Aidan Crilly, Alice Malivert, Andreas Christ Sølvsten Jørgensen, Claire E. Heaney 等 2025 置信度 0.80
• Perspectives on thermodynamics for AI and AI safety
  Artificial intelligence and human intelligence share deep thermodynamic foundations: both operate as systems that convert environmental energy and entropy into ordered, predictive structures under the constraints imposed by boundary conditions. Zentropy theory…
  crossref Zi-Kui Liu, Bing Li 2026-06-17T22:49:26Z 置信度 0.70
• AI conferences use AI to assign papers to reviewers
  crossref Matthew Hutson 2021-04-02T01:10:06Z 置信度 0.70
• Can the adoption of AI technology widen the gender gap in science? postdocs' use of AI technology
  There is a known gender gap in science. Some studies suggest that this gap is decreasing, others that it is not. Although many studies have been conducted on gender equity in science, none have yet focused on the possibility that generative artificial intellig…
  crossref Monique Wangyi Dai 2026-01-28T10:15:14Z 置信度 0.70
• AI Knows Best
  crossref Stuart Mills 2022-10-27T14:26:19Z 置信度 0.70
• The AI revolution in medicine and life science
  Data is at the heart of any healthcare organization. It has the potential to do so much good—from enhancing patient experiences to empowering workforce productivity to advancing health equity for improved health outcomes. And leveraging AI is the key to unlock…
  crossref Mathias Ekman 2023-10-06T14:15:10Z 置信度 0.70
• Announcements from The Bible of AI™ Open Science
  crossref 2026-07-15T06:38:42Z 置信度 0.70
• Introduction to Robotics and AI in Animal Science
  Robotics is used in animal science to boost animal welfare, increase productivity, and support research efforts. Robots handle repetitive tasks like milking, feeding, and manure management, other activities in animal husbandry. Robots allow researchers to crea…
  crossref Sudha Sudha 2026-06-13T11:35:41Z 置信度 0.70
• Safety of AI and applying AI to Agriculture
  Nowadays, AI is a popular kind of technologies that have powerful abilities to do work that requires human intelligence. Machine learning could automatically master data of group of objectives or the environment and learn to output right answer or act right th…
  crossref Zhang Jiayi 2023-11-03T10:06:36Z 置信度 0.70
• Governing AI Bias: Semi-AI, Hexa-Algorithm, and AI Design Council
  Human cognitive limitations render algorithmic bias inevitable and persistent across AI design, training, and deployment, shifting the focus from futile eradication to principled governance.This paper proposes Semi-AI-a legislated hybrid framework that integra…
  crossref 2026-02-11T07:57:29Z 置信度 0.70
• AI and ethics
  crossref Susan Leigh Anderson, Michael Anderson 2020-09-13T23:02:28Z 置信度 0.70
• AI Nuclear Winter or AI That Saves Humanity? AI and Nuclear Deterrence
  Abstract Nuclear deterrence is an integral aspect of the current security architecture and the question has arisen whether adoption of AI will enhance the stability of this architecture or weaken it. The stakes are very high. Stable deterrence depends on a com…
  crossref Nobumasa Akiyama 2021-02-12T16:58:20Z 置信度 0.70
• China’s road slopes need long-term protection
  crossref Shenghao Ai, Zongyang Liu, Xiaoyan Ai, Yi Pan 等 2024-05-30T13:58:38Z 置信度 0.70
• Nanobodies: From Discovery to AI-Driven Design
  Nanobodies, derived from naturally occurring heavy-chain antibodies in camelids (VHHs) and sharks (VNARs), are unique single-domain antibodies that have garnered significant attention in therapeutic, diagnostic, and biotechnological applications due to their s…
  crossref Haoran Zhu, Yu Ding 2025-05-14T12:22:17Z 置信度 0.70
• How AI can help combat paper mills
  The article discusses the use of artificial intelligence (AI) to combat the problem of predatory journals. Predatory journals are journals that charge authors high fees to publish their research, often without providing any real peer review or editing services…
  crossref AI Combat-Bard 2023-06-02T08:06:16Z 置信度 0.70
• AI Goes Off the Grid: The Rise of Local AI Demands Rethinking AI Governance
  The centralized AI governance paradigm is breaking down. While policymakers focus on regulating cloud-based systems that run on massive, power-hungry data centers operated by big companies like Google and OpenAI, a revolution in the AI ecosystem unfolds. Open-…
  crossref Bahrad A. Sokhansanj 2025-06-09T20:49:27Z 置信度 0.70
• Using AI to Detect AI-Generated Research Papers
  On February 20, 2023, science fiction magazine Clarkesworld was forced to stop accepting author submissions. In the preceding weeks, magazine's editors had seen a sharp increase in submissions--approximately 70\% more than normal. The reason for this drastic i…
  crossref Scott Nicholas Pletcher 2023-05-09T01:00:47Z 置信度 0.70
• Writing With AI, About AI, and By AI
  crossref Xiao-Li Meng 2025-10-31T16:13:49Z 置信度 0.70
• AlgoVista: Journal of AI & Computer Science
  crossref 2025-02-10T07:17:18Z 置信度 0.70
• AI-Based Crypto Tokens: The Illusion of Decentralized AI?
  The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token…
  crossref Rischan Mafrur 2025-05-07T21:30:52Z 置信度 0.70
• Introduction: AI and Data Science Driving the Healthcare 5.0 Revolution
  crossref 2026-01-23T14:38:18Z 置信度 0.70
• Combustion and AI
  crossref Mingfa Yao 2023-06-18T09:28:13Z 置信度 0.70
• AI and freedom for evolution in energy science
  Artifacts such as AI, written language, science and technology are objects (devices, processes, methods) that, when added to the homo sapiens , enhance the effect of human effort.Today we are a "human and machine species " that is monumentally more powerful th…
  crossref Adrian Bejan 2020-03-23T04:48:24Z 置信度 0.70
• Developmentally aligned AI: a framework for translating the science of child development into AI design
  Abstract What would it mean to design AI not for the average user, but for the child whose fingers still miss the screen, who forgets the instructions halfway through, and who greets the voice in the box as a friend? This paper proposes Developmentally Aligned…
  crossref Nomisha Kurian 2025-08-06T07:25:39Z 置信度 0.70
• From Human-Centered AI to Culturally Situated AI
  Human-centered and hybrid artificial intelligence (AI) are usually framed around interaction, trust, explainability, usability, and responsible design. These concerns are essential, but they remain incomplete when applied to generative AI systems that produce …
  crossref Kostas Karpouzis 2026-06-08T02:07:00Z 置信度 0.70
• Can AI save endangered languages? Learning theories, language and AI
  We are now in the age of information and artificial intelligence. All the data we need is available in the palm of our hands. Mobile applications like Babbel and Duolingo lower the barriers to entry when it comes to learning a new language. So why is language …
  crossref Ahmed Zaidi 2021-07-12T15:40:23Z 置信度 0.70
• Geographies of AI
  Geography offers a method to provide certitude to how AI and robotics might manifest in societies. AI appears to defy conventional labour borders, as its proliferation in the Kenyan outsourcing centres where humans manually error-correct Google’s self-driving …
  crossref Thomas Birtchnell 2021-05-13T11:27:05Z 置信度 0.70
• Grounding AI Policy: Towards Researcher Access to AI Usage Data
  The lack of available empirical information about how people use general purpose AI systems makes it extremely challenging to develop evidence-informed policy. However, when designing new regulations, policymakers face an empirical dilemma: they must regulate …
  crossref Gabriel Nicholas 2024-09-26T20:54:05Z 置信度 0.70
• Ai Learning Platform Using Ai Quiz Generator And Ai Chatbot
  crossref 2026-04-09T07:06:57Z 置信度 0.70
• Editorial—AI, Computer Science and Robotics Technology
  intelligence, and robotics, including scientific trends in innovation.Open access and rapid dissemination will aid authors' success in an international arena.Computer-controlled intelligent machines and robots have seen explosive growth recently, accompanying …
  crossref Timothy Sands 2022-04-10T07:29:50Z 置信度 0.70
• AI and Data Science in Medical Research: An Overview
  crossref 2026-05-01T08:49:13Z 置信度 0.70
• AI‐ALOE: AI for reskilling, upskilling, and workforce development
  Abstract The National AI Institute for Adult Learning and Online Education (AI‐ALOE) develops AI learning and teaching assistants to enhance the proficiency of adult reskilling and upskilling, and thereby transform workforce development. The AI assistants both…
  crossref Ashok Goel, Chris Dede, Myk Garn, Chaohua Ou 2024-02-20T07:32:46Z 置信度 0.70
• AI and Data Science
  The transformative effects of artificial intelligence (AI) and data science on strategic decision-making and performance results in the Indian Premier League (IPL) are examined in this chapter. The use of machine learning algorithms and advanced analytics has …
  crossref G. Revathy, Sivakumar Madeshwaran, C. M. Chidambaranathan, M. Sakthivel 2025-08-08T15:43:51Z 置信度 0.70
• Interdisciplinary Journal of AI, Machine Learning & Data Science
  The Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) is a peer-reviewed, open-access journal published by Scholiora Research. The journal publishes high-quality research articles, reviews, and innovative studies in artificial intelli…
  crossref 2026-03-04T05:52:22Z 置信度 0.70
• Transforming Pharma with Data Science, AI and Machine Learning
  This chapter provides an overview of transformative changes occurring in pharma engendered by big data and artificial intelligence (AI) and discusses the importance of data strategy in realizing full potential of these technology advances in the lifecycle of d…
  crossref Harry Yang 2022-08-12T20:20:36Z 置信度 0.70
• An Introduction to AI Literacy for Science Students
  Generative AI is transforming approaches to learning, work, and creativity. This module introduces the concept of AI literacy, outlining what generative AI can and cannot do and emphasizing its responsible use in line with academic integrity. It explores pract…
  crossref Anand Rao, Stefan Bauschard 2026-08-04T16:43:27Z 置信度 0.70
• Risks posed by AI actants in human-AI health communication: AI health assistants facing HIV-related stigma inquiries
  This study conceptualizes AI health assistants as agentic non-human actants and examines how a mainstream Chinese AI health assistant responds to HIV-related stigmatizing inquiries. The findings reveal that in 22% of its responses, the AI reproduced or exacerb…
  crossref Meiyu Chen 2026-04-15T23:11:28Z 置信度 0.70
• AI in Contemporary Scientific Practice: Implications for AI-Integrated Science Education
  Over the past decade, artificial intelligence (AI) has become a familiar part of everyday life, but its role in scientific research has been evolving for much longer. Understanding how AI is used in contemporary science is increasingly important for shaping sc…
  crossref Ross H. Nehm, Marcus Kubsch 2026-05-13T23:34:19Z 置信度 0.70
• Climatic water balance for AI-Ruhba District in Najaf Governor – Iraq
  The study area is situated in the southwest of the governorate of Najaf, underwent a climatic analysis using data from Al-Najaf Meteorological Station spanning from 1980 to 2022. Findings reveal an annual rainfall of 134.2 mm, a relative humidity of 40.32%, an…
  crossref Mariam A. AI-Haider, Qusai Y. AI-Kubaisi 2025-07-01T06:52:12Z 置信度 0.70
• Evolving Regulatory Science
  crossref David Blumenthal 2025-12-24T14:00:19Z 置信度 0.70
• Reconceptualizing AI literacy to address the risks of AI agents: a citizen science approach
  crossref Reuben Sass 2025-07-30T05:52:54Z 置信度 0.70
• Backfiring AI? AI Deployment in Workplace
  Seeking value from artificial intelligence (AI) technologies, firms are rapidly deploying them to augment employees and improve business performance. The diffusion of AI into a firm’s business processes affords the tracking of task actions performed by high-pe…
  crossref Di Yuan, Manmohan Aseri, Narayan Ramasubbu 2026-05-04T16:38:41Z 置信度 0.70
• AI in Medicine versus AI in Prehospital
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