Science

144 stories · page 2 of 10

ScienceEvidence-backed problem

Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large-Scale, International Cross-Sectional Survey

Background Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM res…

Journal of Evidence-Based Medicine

Evidence-backed gain

Code-multiplexed multi-frequency impedance cytometry with a unified deep-unfolding network

Evidence-backed problem

Applications of artificial intelligence in occupational noise assessment and noise‑related health outcomes: a scoping review

Evidence-backed problem

Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk

Evidence-backed gain

Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction

Background Decoding the genetic code to unveil its genome functionality is a monumental task which would greatly advance the understanding of disease mechanisms and development of targeted treatments. Although large language models (LLMs) have transformed natural language processing across diverse domains, translating the complex language of DNA into human-readable form remains challenging due to genomic data complexity and unexplored regions of the human genome. Current language models either are capable of pro…

Evidence-backed gain

Rethinking catalysis: interpretable AI and description of real-world conditions <i>via</i> materials genes

Descriptors link basic physicochemical parameters that characterize the materials and the environment to the catalytic performance. Traditionally, descriptors are rooted in mechanistic understanding of elementary surface reactions gained from surface science and atomistic simulations on well-defined surfaces and under vacuum. However, real-world catalysis operates under elevated pressures and temperatures, where an intricate interplay of multiple physical processes, including significant materials' restructuring…

Evidence-backed gain

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologica…

Evidence-backed gain

Carbon dioxide hydrogenation on copper and nickel catalysts <i>via</i> a conformal sampling approach

We present a computational workflow, the Conformal Sampling of Catalytic Processes (CSCP) approach, and its application to the case of heterogeneous hydrogenation/reduction of carbon dioxide (CO 2 ) on copper and nickel catalysts. CO 2 activation is of critical importance for a sustainable global future and one of the major reactions for which sustainable routes must be found urgently. We use the fcc(100) facet of Cu as a worked-out case, and exhaustively derive at the DFT level its reaction mechanisms. We then…

Science

Can AI Predict Publication? Multimodal Large Language Models and the Structural Determinants of Surgical Scholarship

BackgroundWhether artificial intelligence can identify publishable scientific work is untested. We evaluated whether a multimodal large language model (MLLM) could predict, from poster content alone, which abstracts at the American Association for the Surgery of Trauma (AAST) Annual Meetings reached publication, and characterized the investigator, institutional, and domain level determinants situating model performance.MethodsWe retrospectively analyzed 260 abstracts from the 2021-2022 AAST Annual Meetings. Bibl…

Science

Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum

The semi-quantitative assessment of brain [18F]FDG PET provides a more objective interpretation and improved accuracy in differentiating across neurodegenerative diseases. However, the correct identification of anatomical regions of interest without a structural MRI is challenging. Thus, this study aims to develop a deep-learning-based (DL-based) model for the automatic segmentation of 52 anatomical regions in brain [18F]FDG PET images and validate it across different metabolic profiles. 1628 brain [18F]FDG PET…

Science

Leveraging generative artificial intelligence for simulation-based physics experiments: A new approach to virtual learning about the real world

This study investigates the impact of a novel application of generative artificial intelligence (AI) in physics instruction: engaging students in prompting, refining, and validating AI-constructed simulations of physical phenomena. In a second-semester physics course for life science majors, we conducted a comparative study of three instructional approaches in a laboratory focused on electric p...

Climate

Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning

Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Compared to single-chemical indicators, biomarkers more sensitively reflect biological effects and early ecological risks. To address these issues, a method for constructing a comprehensive index integrating biological and non-biological multi-indexes was proposed in this study. First, the Criteria Importance Through Intercriteria Correlation (CRITIC)-Fuzzy Biomarker Response Index (CFBRI) is developed by i…

Climate

‘Tidal wave’ of Pfas being launched to satisfy AI industry, campaigners warn

Pfas companies are launching a “tidal wave” of forever chemicals production to meet demand from the AI industry, a campaign organisation has warned. Despite alarm over the chemicals, which have been linked to cancer, a survey of the 10 biggest manufacturers found most had plans to increase production. Many frame their investments around the “AI revolution”, because of the role Pfas play in semiconductor production and next-generation datacentre cooling systems. But opponents warn that the chemicals, which never…

Climate

Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework

Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and safety of solid-fuel boilers and gasifiers. However, conventional machine learning methods typically treat each characteristic temperature as an independent prediction target, resulting in temperature inversions that violate the required physical ordering of AFTs. This study aimed to develop a Categorical Chain Differential framework for the coupled and physically consistent prediction of the four AFTs ac…

Climate

Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across va…