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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26Cited by 0

Did Humans Really Walk on the Moon? A Thermodynamic and Bio-Physical Challenge to Apollo Missions

prashant Deshmukh

Abstract: Does current bio-physics support historical claims of human presence on the Moon? This paper raises fundamental technical doubts about the Apollo crewed lunar missions by evaluating the thermodynamic and cellular limits of human survival beyond Earth's protective matrix ($D = 8.55$). Utilizing the clinical "ICU Paradox" and the physical threshold of the Armstrong Limit, we demonstrate that localized artificial oxygen containment cannot prevent systemic cellular failure, fluid ebullism, and severe DNA degradation in an unshielded zero-pressure vacuum ($D = 2.32$). We establish that physical lunar artifacts reflect automated robotic deployment rather than crewed biological transit, presenting a decisive scientific critique of human lunar landings. Keywords: Apollo Mission Critique, Human Lunar Landing Doubts, ICU Paradox, Deep Space Survival, Biological Limits, Pressure Homeostasis, DGST Framework, Robotic Deposition.DGST,Physics · "Copyright © 2026 by Prashant Deshmukh. No part of this theory (DGST) may be reproduced or transmitted in any form without prior written permission from the author."

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openalexZenodo (CERN European Organization for Nuclear Research)2026-08-15

Deep Learning for Human Activity Recognition: A Comprehensive Review of Architectures, Performance, and Challenges Across Five Sensory Datasets

Abeer FathAllah Brery, Ascensión Gallardo-Antolín, Mahmoud Fakhry, Israel Gonzalez-Carrasco

Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

# Artificial Intelligence in Metallurgical Engineering: A Comprehensive Review of Applications, Challenges, and Future Direction

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

EGYPT AGRI-URBAN INTELLIGENCE (EAUI) : An AI-Augmented Digital Twin Framework for Agricultural Settlement Development Decision Support in Egypt

Hassanein Bahaaeldin

Egypt Agri-Urban Intelligence (EAUI) Model: Digital Twin Framework for Agricultural Settlement Development The Egypt Agri-Urban Intelligence (EAUI) model is a computational decision-support tool and digital twin framework designed specifically for agricultural settlement planning…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

phytrade

Kazi Asif

PhyTrade: Institutional Physics Library and Protocol for Global Commodity Arbitration PhyTrade is an open-source scientific software library that applies fundamental principles of physics to the analysis, optimization, and simulation of commodity transportation, logistics, and re…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

V3.Ada Phase Regulator & Extreme Stress Test: Formally Verified Ada/SPARK Framework for Bio-Electric Regulation and Safety Monitoring in Regenerative Medicine

outail benhadid

Abstract **Background** Modulating endogenously silenced regenerative pathways—such as BMP derepression through Anti-SOST and Anti-GDF8 therapies—presents significant safety challenges in bio-electric tissue engineering. Uncontrolled signaling carries severe risks of tissue hyper…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

The Value of Data in the Pre-AI Era | 前AI时代的数据价值

WU, JEFFI CHAO HUI

《前AI时代的数据价值》简介 本文作者巫朝晖(Jeffi Chao Hui Wu)基于跨越四十年的个人实证记录与多领域系统构建实践,系统性地提出了“前AI时代数据”这一核心学术概念,并将其严格界定为:2022年底生成式人工智能(Generative AI)以低成本、高仿真度大规模介入公共互联网内容生产之前,由真实人类大脑、真实的物理环境与真实的社会交互所产出的原始数字记录。作者认为,在当今海量AI生成文本、影像与逻辑推演泛滥的“数字噪音膨胀”时代,此类数据正从传统档案升格为兼具唯一性与不可复制性的稀缺基础资源,其价值遵循严格的“数据年龄”准则——即形成时…

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