AI-Driven Research Methodologies

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 97 - 122

Published: Feb. 21, 2025

This chapter examines how artificial intelligence (AI) is changing engineering and physical science researchers do their work. It demonstrates (AI)-driven technologies—like machine learning deep predictive analytics—are transforming conventional approaches by making it possible to process analyse enormous datasets at previously unheard-of speeds precision. In fields where sophisticated simulations data patterns have produced ground-breaking discoveries such as materials renewable energy aerospace manufacturing the explores integration of AI in these fields. also discusses can stimulate interdisciplinary collaboration increase power improve research efficiency. The covers obstacles requirement for transparent algorithms ethical issues biases. usefulness developments demonstrated through case studies effective applications scientific research.

Language: Английский

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Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome

Medicina, Journal Year: 2025, Volume and Issue: 61(2), P. 343 - 343

Published: Feb. 14, 2025

Background and Objectives: Glaucoma is a major cause of irreversible blindness, with primary open-angle glaucoma (POAG) being the most prevalent form. While elevated intraocular pressure (IOP) well-known risk factor for POAG, emerging evidence suggests that human gut microbiome may also play role in disease. This review synthesizes current findings on relationship between glaucoma, focus mathematical modeling artificial intelligence (AI) approaches to uncover key insights. Materials Methods: A comprehensive literature search was conducted using PubMed Google Scholar, covering studies from its inception 1 August 2024. Selected included basic science, observational research, those incorporating mathematical-related models. Results: Traditional statistical machine learning approaches, such as random forest regression Mendelian randomization, have identified associations specific microbiota POAG features. These highlight potential AI explore complex, nonlinear interactions gut-eye axis. However, limitations include variability study designs lack integrative, mechanistic Conclusions: Preliminary supports existence axis influencing Combining data-driven mechanism-driven models could identify therapeutic targets novel biomarkers. Future research should prioritize longitudinal diverse populations integrate physiological data improve model accuracy clinical relevance. Furthermore, physics-based deepen our understanding advancing beyond associative actionable

Language: Английский

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Perceived Benefits and Challenges of Virtual Laboratory Implementation in Chemistry Education: A Mixed-Methods Study (Preprint)

Published: Feb. 26, 2025

Chemistry education relies heavily on experimentation to bridge theoretical concepts with practical applications. However, universities often face challenges in providing real laboratory experiences due resource limitations, equipment shortages, and logistical constraints. Virtual laboratories have emerged as a promising alternative, offering interactive, computer-based simulations that replicate lab experiments enhance learning. This study investigates the perceived benefits of implementing virtual chemistry at selected Southern Ethiopia, assessing their effectiveness teaching learning tool. An explanatory sequential mixed-method design was employed provide comprehensive analysis. Quantitative data were collected from 63 instructors 143 undergraduate students using structured questionnaires, while qualitative insights obtained through interviews. Descriptive statistics used analyze numerical data, thematic coding applied categorize responses. The findings indicate significantly by improving academic achievement conceptual understanding, particularly grasping key complex topics (average mean score: 3.9). They also contribute development essential scientific skills, such hypothesis formulation, problem-solving abilities, effective report writing 3.8). Additionally, labs offer flexibility supporting self-paced serving viable alternatives when access is limited despite these advantages, several identified. Limited technical expertise (kappa = 0.63), high software costs 0.61), difficulties understanding specific required for absence engaging 0.51) among primary obstacles. Furthermore, lack preparedness address 0.23) infrastructural insufficient computer facilities 0.25), further hinder implementation laboratories. underscores transformative potential education, traditional instruction. successful requires addressing existing challenges, digital infrastructure, instructor training, enhancing accessibility. Universities should consider integrating alongside optimize outcomes foster technologically advanced educational environments.

Language: Английский

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Natural language access point to digital metal–organic polyhedra chemistry in The World Avatar

Data-Centric Engineering, Journal Year: 2025, Volume and Issue: 6

Published: Jan. 1, 2025

Abstract Metal–organic polyhedra (MOPs) are discrete, porous metal–organic assemblies known for their wide-ranging applications in separation, drug delivery, and catalysis. As part of The World Avatar (TWA) project—a universal interoperable knowledge model—we have previously systematized MOPs expanded the explorable MOP space with novel targets. Although these data available via a complex query language, more user-friendly interface is desirable to enhance accessibility. To address similar challenge other chemistry domains, natural language question-answering system “Marie” has been developed; however, its scalability limited due reliance on supervised fine-tuning, which hinders adaptability new domains. In this article, we introduce an enhanced database first-of-its-kind tailored chemistry. By augmenting TWA’s geometry data, enable visualization not just empirically verified structures but also machine-predicted ones. addition, renovated Marie’s semantic parser adopt in-context few-shot learning, allowing seamless interaction extensive repository. These advancements significantly improve accessibility versatility TWA, marking important step toward accelerating automating development reticular materials aid digital assistants.

Language: Английский

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Estimating expert prior knowledge from optimization trajectories

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130219 - 130219

Published: April 1, 2025

Language: Английский

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Large Language Models as Spectrographic Assistants: Opportunities and Challenges in Laboratory Data Analysis

Published: April 1, 2025

Language: Английский

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