36th PATTAYA World Conference on Latest Computational Technologies (WCLCT-27) scheduled on Jan. 20-22, 2027 Pattaya (Thailand) is for the engineers, practitioners, scientists, researchers, scholars, and students from all around the world and it also includes the industry people to present ongoing research activities, and hence to foster research relations between Academia and industry. The conference is being organized by Emirates Research Publication (ERPUB) operting under Pilares D Elegancia LDA (Portugal). This conference provides opportunities for the delegates to share new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration. All the submitted conference papers will be peer reviewed by the program/technical committees of the Conference.

Call for papers/Topics

All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:

Part 1: Independent Computational Technologies

These are distinct paradigm shifts in how data is processed, stored, or secured, driven by unique physical principles or architectural designs.

Quantum Computing

• Superconducting Qubits and Ion-Trap Systems: The competing hardware architectures used to build quantum processors.

• Quantum Error Correction (QEC): Methods to mitigate "noise" and decoherence in Noisy Intermediate-Scale Quantum (NISQ) devices.

• Post-Quantum Cryptography (PQC): Developing mathematical algorithms that cannot be cracked by future quantum computers.

Neuromorphic and Brain-Inspired Computing

• Spiking Neural Networks (SNNs): Hardware mimicking the physical, time-dependent spikes of human brain neurons.

• Memristor-Based Architectures: Non-volatile memory components that process data directly where it is stored to eliminate transfer delays.

• Ultra-Low-Power Edge Intelligence: Designing chips that can process complex environmental data on milliwatts of power.

Biocomputing and DNA Data Storage

• DNA Digital Data Architecture: Encoding binary data (0s and 1s) into synthesized biological nucleotide sequences (A, T, C, G).

• Synthetic Biology Logic Gates: Programming living cells or synthetic proteins to act as organic processing units.

• Enzymatic Reading and Writing: Scale-up technologies for high-throughput, room-temperature molecular data retrieval.

Photonic and Optical Computing

• Silicon Photonics Interconnects: Replacing traditional copper wiring with laser-driven data channels to achieve light-speed data transfers.

• All-Optical Neural Networks (ONNs): Processing machine learning matrix math natively through optical lenses and beams without converting back to electricity.

• Laser-Driven Coherent Ising Machines: Specialized optical processors optimized to solve complex combinatorial problems instantly.

Part 2: Interrelated Computational Technologies

These technologies do not exist in a vacuum; they overlap, feed into one another, and create interdependent ecosystems.

Frontier Artificial Intelligence and High-Performance Compute (HPC)

• How they interrelate: Advanced AI models require hardware built specifically to handle trillions of mathematical operations simultaneously, transforming server architecture.

• Next-Generation Tensor and Graphics Processing Units: Hardware tailored for massive parallelization and mixed-precision arithmetic.

• Liquid-Cooling and Hyperscale Data Infrastructure: Thermal management techniques designed to survive the intense heat generated by modern AI training clusters.

• Automated Model Co-Design: Using AI algorithms to actively design and optimize the physical layout of the next generation of microchips.

Edge Computing and Distributed Internet of Things (IoT)

• How they interrelate: Centralized data centers cannot handle the sheer volume of global device data without lag, pushing computation to the absolute "edge" of the network.

• 5G Advanced and 6G Terahertz Communication: Ultra-low latency data pipelines acting as the nervous system for decentralized networks.

• Federated Learning on the Edge: Training AI models locally across thousands of consumer devices without uploading private user data to a central cloud.

• Real-Time Mesh Networks: Dynamic, self-healing communication webs formed by autonomous cars, drones, or smart infrastructure.

Decentralized Trust Systems and Advanced Cryptography

• How they interrelate: As computation becomes globally distributed and AI becomes more pervasive, verifying data integrity and maintaining privacy requires advanced cryptographic layers.

• Zero-Knowledge Proofs (ZKPs): Allowing a system to verify that a piece of information is true without revealing the actual data itself.

• Fully Homomorphic Encryption (FHE): Enabling cloud servers to run calculations on fully encrypted data without ever decrypting it first.

• Decentralized Autonomous Compute Networks: Crowdsourcing global, idle GPU and CPU power via blockchain-based coordination protocols.

Autonomous Robotics and Spatial Computing

• How they interrelate: Physical robots and mixed-reality headsets require a massive synthesis of edge AI, real-time spatial processing, and extreme low-latency tracking.

• Simultaneous Localization and Mapping (SLAM): Computational geometry algorithms that build 3D virtual maps of environments in real-time.

• Kinematic Edge AI: Embedding localized machine learning models into physical joints and limbs for real-world reflex actions.

• Neural Radiance Fields (NeRFs) and Gaussian Splatting: Transforming standard 2D camera feeds into highly detailed, interactive digital 3D scenes on the fly.