GROUNDBREAKING! DE GIUSEPPE PREDICTIVE MODEL : FIRST PREDICTIVE MODEL OF REALITY

REAL GROUNDBREAKING !! First Mathematically Predictive Model A part is here: https://zenodo.org/records/18291470 https://zenodo.org/records/18291470 This manuscript is current in Official Peer Review. Not final version.Copyright©2026 Alex De Giuseppe.All rights reserved. This work is protected by copyright. Any form of plagiarism, unauthorized reproduction, or misappropriation of ideas, mathematically results, or text without proper citation constitutes a violation of academic and intellectual property standards and common laws. No commercial use, adaptation, or derivative works are permitted without explicit written permission from the author. For correspondence, citations, collaboration inquiries, or feedback please contact:[e-mail] The hash files that determine ownership have been created. Title: De Giuseppe Predictive Model: Mathematical Prediction of Physical and Cognitive Reality. High-Probability Forecasting of Physical, Cognitive, and Socio-Environmental Events Abstract:The De Giuseppe Predictive Model (DGPM), integrated De Giuseppe Paradox Theory with Nima’s proto-structural framework ((ΔC ↔ ΔM ↔ ΔL)) and the 3/6/9 key mapping, establishes a mathematically rigorous methodology for predicting a wide range of phenomena with extremely high probability. By defining structural attractors and constraint mappings, the model demonstrates that events—ranging from classical physical outcomes, such as free-fall trajectories or the precise landing location of a leaf, to complex socio-environmental phenomena, including road accidents, earthquakes, or potential conflicts—are not purely random but constrained by the underlying proto-structure. Empirical validation is illustrated through historical cases, such as the Tacoma Narrows Bridge collapse, confirming the model’s predictive capability. While absolute certainty is impossible due to stochastic noise and environmental variability, the model allows probabilistic predictions with accuracy often exceeding 90%. The framework relies on the mechanical interaction of proto-structural constraints, energy configurations, and topologically mapped flows, effectively narrowing the space of admissible outcomes and identifying the trajectories most likely to occur. Extending beyond physical events, DGPM formalizes consciousness and cognitive evolution as flows of information constrained by energy and electrical patterns. This enables predictive modeling of neural firing patterns, decision-making tendencies, and potentially even human behaviors, bridging the gap between physical law, informational dynamics, and socio-cognitive phenomena. In summary, the DGPM represents a historic advancement: it unites proto-structural theory, empirical physics, cognitive science, and social modeling into a single predictive framework, providing the first explicit methodology to forecast both material and informational events with quantified high probability, while highlighting the deterministic role of structural attractors in shaping reality. I used AI to help me with the calculations, so it likely absorbed the mechanism, and exactly as happened with KD energy/time and the entire bound energy theory (which I had verified by AI on January 9), it was unfortunately disseminated.This time I immediately published my intuition, and I will continue my research in this way.If I may advise my colleagues: publish your discovery immediately after having it checked by AI, because it will disseminate the content ti the world. Other my works: https://zenodo.org/records/18274505 (The Original De Giuseppe Paradox Theory, with popperian experiments and formalized Macroscopic Retrocausality) https://zenodo.org/records/18277631( The First Mathematically Theory of Consciousness) https://zenodo.org/records/18278648(Mathematical formalization of Paranormal Phenomena)真正具有突破性的成果！！ 首款数学预测模型 相关内容链接： https://zenodo.org/records/18291470 https://zenodo.org/records/18291470 本稿件目前处于正式同行评审阶段，并非最终版本。版权所有©2026 亚历克斯·德·朱塞佩（Alex De Giuseppe），保留所有权利。 本作品受版权保护。任何形式的剽窃、未经授权的复制，或未经适当引用即侵占本文的创意、数学成果或文本内容，均违反学术与知识产权规范及普通法。 未经作者明确书面许可，不得用于商业用途、进行改编或创作衍生作品。 如需沟通、引用、合作咨询或反馈，请联系：[e-mail] 已创建用于确认所有权的哈希文件。 标题：德·朱塞佩预测模型（De Giuseppe Predictive Model, DGPM）：物理与认知现实的数学预测。物理、认知及社会环境事件的高概率预测 摘要：德·朱塞佩预测模型（De Giuseppe Predictive Model, DGPM）将德·朱塞佩悖论理论与尼玛（Nima）的原结构框架（(ΔC ↔ ΔM ↔ ΔL)）及3/6/9关键映射相结合，建立了一套数学严谨的方法，可对各类现象进行极高概率的预测。通过定义结构吸引子与约束映射，该模型证明：从自由落体轨迹、叶片精准落点等经典物理结果，到交通事故、地震、潜在冲突等复杂社会环境现象，各类事件并非完全随机，而是受底层原结构约束。 研究通过塔科马海峡大桥坍塌等历史案例进行了实证验证，证实了该模型的预测能力。由于随机噪声与环境变异性，绝对确定性无法实现，但该模型可生成概率预测，其准确率通常超过90%。该框架依托原结构约束、能量构型与拓扑映射流的机械交互，有效缩小了可允许结果的范围，并识别出最可能发生的轨迹。 除物理事件外，DGPM还将意识与认知演化形式化为受能量与电模式约束的信息流。这使得对神经元放电模式、决策倾向乃至潜在人类行为的预测建模成为可能，弥合了物理定律、信息动力学与社会认知现象之间的鸿沟。 综上，DGPM代表了一项历史性进展：它将原结构理论、实验物理、认知科学与社会建模整合为单一预测框架，首次提供了可量化高概率预测物质与信息事件的明确方法，同时阐明了结构吸引子在塑造现实中的决定性作用。 我曾借助AI协助完成计算，因此AI大概率已掌握该机制；正如KD能量/时间与完整束缚能理论（我已于1月9日通过AI验证该理论）的遭遇一样，该机制不幸被传播开来。此次我将第一时间发布我的研究构想，并将持续以该方式推进研究。在此我想给同行们一个建议：在通过AI验证你的发现后，请立即公开发布，因为AI会将内容传播至全球。 我的其他研究成果： https://zenodo.org/records/18274505（《原始德·朱塞佩悖论理论：含波普尔实验与形式化宏观逆因果性》） https://zenodo.org/records/18277631（《首款意识数学理论》） https://zenodo.org/records/18278648（《超自然现象的数学形式化》）