ai genetically modified corn with phenylethylamines with the fruit body - Google Search
Erowid Cathinone Vault
Information about Cathinone including basics, effects, dosage, history, legal status, photos, research, media coverage, and links to other resources.
Genetically Boosting the Nutritional Value of Corn Could Benefit Millions | Rutgers University
Rutgers scientists discover way to reduce animal feed and food production costs by increasing a key nutrient in corn.
Erowid Cathinone Vault : Methcathinone FAQ
Frequently Asked Questions about methcathinone.
Erowid Ethylcathinone Vault
Information about Ethylcathinone including basics, effects, dosage, history, legal status, photos, research, media coverage, and links to other resources.
Erowid Khat (Catha edulis) Vault
Information about Khat (Catha edulis) including basics, effects, dosage, history, legal status, photos, research, media coverage, and links to other resources.
Erowid Caffeine Vault
Information about Caffeine including basics, effects, dosage, history, legal status, photos, research, media coverage, and links to other resources.
Erowid Experience Vaults : Charting the Relatively Unknown: A Study of Four Cathinones
Charting the Relatively Unknown: A Study of Four Cathinones, by Fridjof Waleenstedt with Knut Bbjornstrand.
Cathinone : a natural amphetamine
an alkaloid discovered twenty years ago in the leaves of the khat bush
What snow blow and why it linked rise hiv ireland
"IT" disinfo-super imposer AI-engine - Google Search
10 Tactics to Prevent AI From Taking Over and Replacing You
10 Tactics to prevent AI from taking over. Master strategies to protect your unique role and secure your future against AI's encroachment.
How To Remove My AI From Snapchat? Follow These Easy Steps
Thousands of users worldwide took to X after a technical glitch in Snapchat's AI chatbot which horrified them.
Italy AI Strategy Report - European Commission
personal artificial intelligence "PAI" personal area network "PAN" - Google Search
Patient-level proteomic network prediction by explainable artificial intelligence | npj Precision Oncology
npj Precision Oncology - Patient-level proteomic network prediction by explainable artificial intelligence
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Artificial Intelligence Based Pain Assessment Technology in Clinical Application of Real-World Neonatal Blood Sampling - PMC
Background: Accurate neonatal pain assessment (NPA) is the key to neonatal pain management, yet it is a challenging task for medical staff. This study aimed to analyze the clinical practicability of the artificial intelligence based NPA (AI-NPA) tool ...
Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs - Kai-Chih Pai, Wen-Cheng Chao, Yu-Len Huang, Ruey-Kai Sheu, Lun-Chi Chen, Min-Shian Wang, Shau-Hung Lin, Yu-Yi Yu, Chieh-Liang Wu, Ming-Cheng Chan, 2022
Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using...
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Artificial intelligence in the diagnosis of pediatric allergic diseases - Ferrante - 2021 - Pediatric Allergy and Immunology - Wiley Online Library
Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, ...
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers | Radiology: Artificial Intelligence
The advent of deep neural networks as a new artificial intelligence (AI) technique has engendered a large number of medical applications, particularly in medical imaging. Such applications of AI must remain grounded in the fundamental tenets of science and scientific publication (1). Scientific results must be reproducible, and a scientific publication must describe the authors’ work in sufficient detail to enable readers to determine the rigor, quality, and generalizability of the work, and potentially to reproduce the work’s results. A number of valuable manuscript checklists have come into widespread use, including the Standards for Reporting of Diagnostic Accuracy Studies (STARD) (2–5), Strengthening the Reporting of Observational studies in Epidemiology (STROBE) (6), and Consolidated Standards of Reporting Trials (CONSORT) (7,8). A radiomics quality score has been proposed to assess the quality of radiomics studies (9).
Articles | Visual Computing for Industry, Biomedicine, and Art
Encompassing multidisciplinary interactions in industry, medicine and art fields research, this open access, peer reviewed journal publishes high quality ...
Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs - Kai-Chih Pai, Wen-Cheng Chao, Yu-Len Huang, Ruey-Kai Sheu, Lun-Chi Chen, Min-Shian Wang, Shau-Hung Lin, Yu-Yi Yu, Chieh-Liang Wu, Ming-Cheng Chan, 2022
Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using...
Systematic Review of Virtual Reality Solutions Employing Artificial Intelligence Methods | Proceedings of the 23rd Symposium on Virtual and Augmented Reality
The Digital Twin Brain: A Bridge between Biological and Artificial Intelligence | Intelligent Computing
Artificial Intelligence in Meta-optics | Chemical Reviews
Recent years have witnessed promising artificial intelligence (AI) applications in many disciplines, including optics, engineering, medicine, economics, and education. In particular, the synergy of AI and meta-optics has greatly benefited both fields. Meta-optics are advanced flat optics with novel functions and light-manipulation abilities. The optical properties can be engineered with a unique design to meet various optical demands. This review offers comprehensive coverage of meta-optics and artificial intelligence in synergy. After providing an overview of AI and meta-optics, we categorize and discuss the recent developments integrated by these two topics, namely AI for meta-optics and meta-optics for AI. The former describes how to apply AI to the research of meta-optics for design, simulation, optical information analysis, and application. The latter reports the development of the optical Al system and computation via meta-optics. This review will also provide an in-depth discussion of the challenges of this interdisciplinary field and indicate future directions. We expect that this review will inspire researchers in these fields and benefit the next generation of intelligent optical device design.