THE TRISECTION OF AN ANGLE USING < A RULER AND A COMPASS > & THE QUANTUM, SIX WAVE - MIXING, MECHANISM USED BY THE DUALITY - PHOTONS

Markos Georgallides — 2026-07-23

a.. The Present Trisection Method is based on an - Directional Triangle - [Eφ O Aφ] on Diameter [Eφ Aφ = 2.O Aφ] of Circle {O, O Aφ}, which Vector OAφ, Rotates through center O, Forming ANY-Angle {BÔAφ=0–180°} On This Triangle-Circle-Mechanism are Drawn with <A Ruler & a Compass> 6-Conjugate Circles of Radius OAφ on which is Defined at Point H, the Angle {BĤAφ} such That Angle ⇒ {BÔAφ} = 3.{BĤAφ}. The Point H, is That which was Indicated by Archimedes Trisection Method. On This SIX-Wave Circles Mechanism, The Photons Quantized Energy E₃ = [4πtr²/3]·f₁ = 3.E₁, And is → The Mechanical SPDC - Trisection Method ←

b.. The Unsolvability of the Three famous Ancient Greek Problems—Trisecting the Angle, Doubling the Cube, and Squaring the Circle -- Stands on the Base justification of the Algebraic Field Theory (specifically Galois Theory) and the Theory of Constructible Numbers, which were developed in the 19th century.

c.. The Greeks often used other Techniques (like Conic sections or Mechanical tools) to Solve these Problems, But their self-imposed "Euclidean" constraint, with a Ruler and a Compass, (straightedge and compass) made these Specific Problems impossible to solve.

d.. In the Published Articles [123],[124],[126], it is clearly evident that the Solution of the Ancient, Unsolved Greek Problems, using a Ruler and a Compass, as the constraint has been set by Euclid], Has Become Possible, and is in the Critique of Both, Human Logic Thinking and, The Artificial Intelligence when it uses The Path of Knowledge to the Truths of Nature, and which is the Dialectic Logic of Euclidean Geometry.

In article [123], are given on the 2-Vectors, 3-Poles Rotation Squares Mechanism, where Ē = M̄, such the Geometrical as the Mechanical Proof, by using the Conjugate circles of Polhode and Herpolhode which consist the Spin of Photons and which is their motion. The frequency needed for the velocity vector c̄ to Rotate, is used from Kepler's Unit meter of Time k = fₑ² a³, where, a = λ / 2, and which is the clock measuring the changes of motions. In article [124], It was Noted that When in Photons, Ē = M̄, these acquire the common Plane-meter the Square CMNH = [CM]² = π.[EC]², where EC is the circle's Radius, Using the Bellow-Motion Method by creating the Square CMNH = [CM]² from E, M, and from [E/√2]² = π.[M/√2]², π = [E/√2]² / [M/√2]², and Thus Photon is Squared to an UNID SQUARE.

In article [126], It is important to Note that When in Photons, E ≠ M, these acquire the common Space-meter ³√2, Using Quantum-Cloning Method for creating a Perfect-Copy of E, M, as are when E = 2.M and, [E ≠ M]³ = 2.[Ē = 2.M̄]³. With this Way Photon is Dublicated to a New-One. The simplified But a Rigorous Proof of the Problem, The Squaring of the Circle using a Ruler and a Compass, as it was first Posed by the Ancient Greeks, is followed by The Photons which Square their, Energy circle ≡ the Herpolhode = SPIN, to equal an Energy Unit Square, which UNIT-Square they Promote, common Plane-meter, either as the Speed of the Photon which is their Electric Field, or they store it Perpendicularly to the motion in an equal area, The Anti-Square which is their Magnetic Field. Promotion is done at the Birefringence angle of 45°, and thus The Bellow-motion is their Torsional motion. At Phase Angle where Ē = 2.M̄, Energy Cube [M / 2]³ is Dublicated and enters INTO. [E]³ = [2.M]³ = 8.M³ = Tetrahedron-Cube-Sphere-Mechanism. [102]

From [40]--The Special Problems of Euclidean Geometry [47] consist the, Moulds of Quantization, of E-Geometry in it, to become → Monad, through mould of Space–Anti-Space in itself, which is the material Dipole in inner monad Structure and which is identical with the Electromagnetic cycloidal field → Linearly through the mould of the Parallel Theorem [44-45], which are the equal distances between Points of Parallel and line → In Plane, through mould of Squaring the circle [46], where the Two Equal and Perpendicular Monad-Vectors, E = M, consist a Plane acquiring The common Plane-meter, π, and in Space (volume) through mould of Duplication of the Cube [46], where any Two Unequal Perpendicular monads, E ≠ M, acquire the common Space-meter ³√2, Using Quantum-Cloning Method to be Twice each other as analytically Proved-explained. The Unification of → Space and Energy ← Becomes through [STPL] Geometrical Mould Mechanism of Elements, the minimum Energy-Quanta, In monads → Particles, Anti-Particles, Bosons, Gravity–Force, Gravity–Field, Photons, Dark Matter, and Dark-Energy, consisting the Material Dipoles in inner monad Structures, i.e. → the innate Electromagnetic Cycloidal Field of monads ← [39-41]

Euclid’s Elements consist of assuming a small set of intuitively appealing Axioms, Proving many other Propositions. Because No One until [9] succeeded to Prove the Parallel Postulate By means of Pure Geometric Logic, many self consistent Non-Euclidean Geometries have been discovered, Based on Definitions, Axioms or Postulates, in order that Non of them contradicts any of the other Postulates. It was Proved [39], that the only Space-Energy Geometry is Euclidean, agreeing with the Physical Reality on Unit AB ≡ Segment ≡ Vector which is The Electromagnetic field of the Quantized on AB̄ Energy Space Vector of Angular Momentum = Spin, on the contrary to the General Relativity of Space-time which is based on the Rays of the Non-Euclidean Geometries to the limited velocity of light in Planck's cavity. Euclidean Geometry elucidated the Definitions of its geometry-content, i.e. { [ for Point, Segment, Straight Line, Plane, Volume, Space [S], Anti-space [AS], Sub-space [SS],

Cave, The Space-Anti-Space Mechanism of the Six-Triple-Points-Line, that Produces and transfers Points of Spaces, Anti-Spaces and Sub-Spaces in a Common Inertial Sub-Space, and a cylinder, in Gravity field [MFMF] Particles} and describes the Space-Energy vacuum beyond Plank's length level [Gravity's Length 3,969.10⁻⁶² m], reaching the absolute Point
Lᵥ = e^(i·(Nπ/2))₍b=10₎ = N = −∞, m = 0 m, which is Nothing, and the Absolute Primary Neutral Space [PNS] = cave [r = 10⁻³⁵ ∼∼ 10⁻⁶² m [43-46].

In Physics, there is No single Process called "Duplication of Energy" for Photons Because Energy must always be conserved, or when Two Photons are "merged" into a single Photon.

In Mechanics, The Gravity-cave Energy Volume quantity |c̄| ≡ [wr] is Doubled, and is Quantized in Planck's-cave Space quantity (h/2π) = The Spin = 2.[wr]³ → i.e. Energy Space quantity [wr] is Quantized, doubled, and becomes the Space quantity h/π following Euclidean Space-moulds of Duplication of the cube, in Sphere volume V = (4π/3).[wr]³ and follows the Squaring of the circle π, and in Sub-Space-Sphere volume ³√2, as Trisection.

A High-Performance Scalable Architecture for Cloud-Based Deep Learning and Data-Intensive Applications

GBOR, Grace Dooshima — 2026-07-23

The rapid growth of big data and the increasing complexity of deep learning applications have created significant challenges for traditional data processing infrastructures, particularly in terms of scalability, performance, and resource efficiency. This study presents a high-performance, scalable architecture for cloud-based deep learning and data-intensive applications, designed to address these challenges through the integration of cloud computing, machine learning, and efficient system design principles. The research adopted the Design Science Research (DSR) methodology to develop and evaluate a functional system that supports end-to-end data processing, including data ingestion, preprocessing, model training, deployment, and real-time inference. The proposed architecture was implemented as a modular Django-based web application, incorporating machine learning libraries such as Scikit-learn, and deployed on a Linux-based Virtual Private Server with PostgreSQL as the data storage backend. The system was designed to handle heterogeneous data sources, including structured, semi-structured, and streaming data, while ensuring data quality through preprocessing techniques such as normalization, imputation, and outlier detection. Multiple supervised learning models were trained and evaluated using standard validation techniques, achieving reliable classification performance. A key feature of the system is its scalability, achieved through efficient resource utilization and cloud-based infrastructure, enabling the system to adapt dynamically to varying workloads. Performance evaluation demonstrated that the architecture maintained low latency, high throughput, and optimal resource usage under increasing demand. In addition, robust security mechanisms including encryption, authentication, and access control were integrated to ensure data protection and compliance. The results indicate that the proposed architecture provides a flexible, cost-effective, and efficient solution for deploying deep learning and data-intensive applications in cloud environments. This study contributes to the field by offering a practical and comprehensive framework that bridges the gap between machine learning theory and real-world scalable system deployment, with potential for further enhancement through distributed computing and advanced deep learning integration.

GPH-International Journal of Applied Science

THE TRISECTION OF AN ANGLE USING < A RULER AND A COMPASS > & THE QUANTUM, SIX WAVE - MIXING, MECHANISM USED BY THE DUALITY - PHOTONS

A High-Performance Scalable Architecture for Cloud-Based Deep Learning and Data-Intensive Applications