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Science Get Journal
Published by CV. Get Press Indonesia
ISSN : -     EISSN : 30626595     DOI :  http://doi.org/10.69855/science
Core Subject : Science, Education,
Biochemistry, Genetics & Molecular Biology Chemistry Mathematics Physics
A Peer Reviewed Research Science Get Journal e-ISSN: 3062-6595 Science Get Journal is an Open Access and Anonymous Reviewer/Anonymous Author journal. The field of Science is a vehicle for scientific communication in the field of Science which covers the cross-fields of Mathematics, Physics, Chemistry, Biology, Geography and Mathematics,  Natural Sciences Education and Social Sciences. Science Get Journal is published by Get Press Indonesia. Science Get Journal is used to publish research published every month January, April, July, and October. The Science Get Journal template can be downloaded here (Click). Information about article submission: Articles sent by the author (author) will be seen and read by the editor, if there are still discrepancies with the applicable template and do not comply with the scope of Science Get Journal then the article will be returned to the author. If it is appropriate, the article will be forwarded to the Science Get Journal reviewer for a review process carried out by the Science Get Journal reviewer. A total of two reviewers within a two week period of evaluating the article.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol 1 No 1 (2024): January, 2024" : 2 Documents clear
Marine-Derived Biodegradable Polymers for Cold-Water Marine Pollution Eka Cahya Muliawati; Titis Istiqomah
Science Journal Get Press Vol 1 No 1 (2024): January, 2024
Publisher : CV. Get Press Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69855/science.v1i1.448

Abstract

Marine plastic pollution persists as a global environmental crisis, particularly in cold-water marine environments where low temperatures significantly inhibit the degradation of most commercial biodegradable plastics. Conventional polymers such as polyethylene (PE) and polypropylene (PP) accumulate for decades, fragmenting into microplastics that permeate marine ecosystems from coastal zones to polar and deep-sea regions. Although biodegradable polymers have been proposed as an alternative, many widely used materials, including polylactic acid (PLA), exhibit negligible degradation in cold seawater. This study experimentally evaluates the degradation and solubility behavior of selected marine-derived biodegradable polymers under cold-water marine conditions, with a specific focus on temperature-dependent mechanisms. Emphasis is placed on comparative performance among candidate polymers, including marine-derived polysaccharides and microbially produced polyhydroxyalkanoates, as well as emerging supramolecular systems designed for rapid dissolution in seawater. By integrating experimental observations with insights from marine microbiology, polymer chemistry, and material design, this study identifies key pathways and design principles for developing polymers that remain effective in cold marine environments, contributing to the development of environmentally benign plastic alternatives
Number-Theoretic Cryptographic Framework for Securing Generative Artificial Intelligence Against Adversarial Attacks Eka Cahya Muliawati
Science Journal Get Press Vol 1 No 1 (2024): January, 2024
Publisher : CV. Get Press Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69855/science.v1i1.472

Abstract

The rapid adoption of Generative Artificial Intelligence (GenAI) has intensified concerns regarding security, privacy, and robustness against adversarial attacks. Most existing defense mechanisms rely on adversarial training, differential privacy, or cryptographic techniques applied as external protection layers, which often lack formal mathematical guarantees and are weakly coupled with the internal generative process.This study proposes a novel Number-Theoretic Cryptographic Framework that embeds cryptographic primitives directly into the GenAI lifecycle, including latent-space representations and model parameter handling. Unlike prior approaches, the proposed framework integrates number-theoretic hardness assumptions specifically lattice-based and elliptic-curve cryptography into the core generative mechanism, enabling mathematically grounded and provably secure protection against adversarial exploitation.A comprehensive synthetic dataset is constructed by jointly modeling cryptographic parameters, generative model specifications, and adversarial attack scenarios to systematically evaluate the framework. Experimental results demonstrate that number-theoretic cryptographic integration significantly reduces privacy leakage and model extraction vulnerability while preserving generative utility. Lattice-based schemes provide the strongest privacy protection, while elliptic-curve cryptography achieves a balanced trade-off between security and computational efficiency. This work introduces a new paradigm for securing GenAI by unifying generative modeling with formal number-theoretic cryptographic security, offering a robust and future-proof solution against both classical and post-quantum adversarial threats.

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