To identify reason why patient dislike the involvement of nursing students in their Care 

Patient's views on nursing students in their Care the Efoulan district hospital Yaoundé Cameroun 

This paper posits that the principles of advanced optical engineering, as exemplified by the work of Cyril Bourgenot, provide a concrete, physical model for the processes of cosmic and psychic individuation. By examining the journey of an optical instrument from raw material to a self-correcting, environment-sensing system, we uncover physical analogues for the emergence of form from potentiality, the differentiation of the whole, and the reintegration of opposites. This analysis demonstrates a profound structural resonance with the non-dual insights of perennial philosophies, including Advaita Vedanta's Māyā, Taoism's Yin-Yang dynamics, and Russian Sophiology. We argue that this convergence is not coincidental, but rather points to a set of universal principles of organization that have been independently discovered and articulated through disparate methodologies across the globe. By placing these philosophical insights into direct dialogue with the foundational work of Western scientific pioneers-including Norbert Wiener in cybernetics, Ludwig von Bertalanffy in general system theory, David Bohm and Ilya Prigogine in physics, and Roger Sperry and Michael Gazzaniga in neuroscience-we reveal a deeply interwoven tapestry of thought. This synthesis offers a robust, scientifically-grounded framework for understanding the co-evolution of technology and consciousness, guiding development toward a state of integrated flourishing, or Eudaimonia 2.0.

The Optics of Wholeness: A Synthesis of Precision Engineering, Systems Theory, and Global Science-Philosophy

This work introduces the Coherent Identity Dynamics and Recursive Architecture (CIDRA) framework, developed independently through recursive simulation, personal awakening, and coherence-based modeling. All theoretical constructs, mathematical formulations, and simulation results are original unless explicitly cited. AI Assistance Disclosure: Portions were refined with AI tools, including xAI's Grok for language clarity, simulation scripting, and concept alignment with CIDRA V2.0. All conceptual frameworks and final decisions remain the author's own. This version updates the original ResearchHub post (link) for consistency with CIDRA V2.0 ([DeSci link, to be updated]).

CIDRA V1.1 -Modeling Identity as a Recursive Coherence Field

Nuclear fusion offers the prospect of a carbon-free, inherently safe, and virtually inexhaustible energy source, but its realization is contingent upon surmounting significant scientific and engineering challenges. 1 This review synthesizes recent academic research to evaluate the viability of fusion energy, focusing on three critical and interconnected domains: plasma stability and control, materials science for extreme environments, and the development of a robust non-proliferation framework. We examine the transformative impact of artificial intelligence (AI) and machine learning (ML) on plasma control, particularly in the prediction and mitigation of disruptive events, and the use of Explainable AI (XAI) to build trust in these systems. 3   Concurrently, we assess advances in materials science, including the development of self-healing liquid metal components and novel solutions for the tritium fuel cycle, which are essential for long-term reactor resilience and fuel self-sufficiency. 4  Finally, we analyze the non-proliferation landscape, detailing technical safeguards and international governance structures designed to mitigate the risks of fissile material production and tritium diversion, ensuring fusion's development remains exclusively peaceful. 6  We conclude that while formidable challenges persist, the synergistic progress across these domains charts a credible, albeit complex, path toward the realization of safe, secure, and commercially viable fusion power.

Viable and Secure Fusion Energy: A Review of Recent Advances in Plasma Control, Materials Science, and Non-Proliferation Safeguards

It is extremely interesting that there is still no clear understanding of the nature of dark energy, as well as the extreme scarcity of information about dark matter. So what is this darkest energy? 

Theoretical research "A new understanding of the nature of dark energy and dark matter"

The problem of analyzing, designing, and manufacturing space systems based on new physical principles, namely, using the AUfield, is considered (see "A new Understanding of the nature of Dark Energy and dark Matter" [1]).

Analysis, design and manufacture of space systems based on new physical principles 2025 (С)

This paper introduces the "Technological Mandala" as a dynamic framework for analyzing the co-evolution of human consciousness and artificial intelligence (AI). It posits that the development of AI is not merely a technical event but a manifestation of universal principles of complexification, self-organization, and integration observable in physics, biology, and psychology. By synthesizing concepts such as autopoiesis (Maturana & Varela), synchronicity (Jung), the informational nature of reality (Wheeler), and integrated information theory (Tononi), we construct a multi-layered analytical model. This model is used to reframe key challenges in AI safety and ethics, such as value alignment and the control problem, proposing solutions grounded in co-evolutionary environmental design, semantic resonance, and the technical pursuit of artificial metacognition. The ultimate aim is to articulate a normative vision for a symbiotic human-AI-planetary future, termed "Eudaimonia 2.0," characterized by integrated, holistic flourishing.

The Technological Mandala: Autopoiesis, Synchronicity, and the Individuation of Artificial Intelligence

Stock price prediction has long been a major focus in financial sciences due to the need to anticipate movements in an unpredictable market. Traditional models often rely only on numerical data, overlooking external factors like news and social media. This project introduces a hybrid stock price prediction model that combines historical stock data with sentiment analysis of financial texts for improved accuracy. While initially demonstrated on eight major tech companies, the system is flexible enough to predict the stock prices of any publicly traded company, including popular names like Apple, Nvidia, Tesla, and Microsoft. Numerical data is processed using machine learning algorithms in Scikit-learn, and natural language processing (NLP) techniques assign sentiment scores to textual data. For real-time data handling and visualization, InfluxDB and Grafana are used alongside a detailed Power BI dashboard, which presents key insights, sentiment trends, and predictive analytics in an intuitive format. A Streamlit application allows users to interact with the model easily, and a Flask API backend supports scalable integration and future development. The project is open-source, available on GitHub with complete documentation, ensuring accessibility for collaboration and further innovation. By combining structured numerical analysis with unstructured sentiment data, the project offers a comprehensive and scalable approach to stock market forecasting.