This is a test abstract  for checking special-character support in the OJS submission system.

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Volume 2, Issue 2 - 2026 (July - December 2026)

Issue Details:

Volume 2 Issue 2

Published:Sep 29, 2026

Editorial: July - December 2026

Welcome to the 2026 issue of Demo Journal from ScholarJMS. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

ScholarJMS
Editor in Chief
Demo Journal from ScholarJMS

Articles in This Issue

Showing 2 of 2 articles

Research PaperID: demo220052Pages 1-5

Open Access

This is a test abstract for checking special-character support in the OJS submission system.

This is a test abstract for checking special-character support in the OJS submission system. The text intentionally includes different characters and Unicode symbols: χ² ≥ ≤ = à á â ä ã å æ ç è é ê ë ì í î ï ñ ò ó ô ö õ ø ù ú û ü ý ÿ, À Á Â Ä Ã Å Æ Ç È É Ê Ë Ì Í Î Ï Ñ Ò Ó Ô Ö Õ Ø Ù Ú Û Ü Ý. It also includes Greek characters such as α β γ δ ε θ λ μ π σ φ Ω, mathematical symbols such as ± × ÷ ≠ ≤ ≥ ≈ ∑ √ ∞ ∂ ∆ ∇, and arrows such as ← → ↑ ↓ ↔ ⇒ ⇔. Other symbols include © ® ™ € £ ¥ ₹ $ ¢ ₩, § ¶ † ‡ • … ‰, and fractions such as ½ ¼ ¾. The test also includes punctuation and quotation marks: “double quotes”, ‘single quotes’, «French quotes», „German quotes“, — em dash, – en dash, hyphen - underscore _, slash /, backslash , parentheses ( ), brackets [ ], braces { }, angle brackets < >, colon :, semicolon ;, comma ,, period ., question mark ?, exclamation mark !, apostrophe ', and quotation marks " ". Unicode characters from different writing systems are also included: हिंदी, ગુજરાતી, বাংলা, தமிழ், తెలుగు, मराठी, العربية, فارسی, 中文, 日本語, 한국어, Ελληνικά, Русский, and Español. Emoji and miscellaneous symbols are included for testing: 😀 😃 🚀 🔬 📚 ✓ ✔ ✗ ⚠ ★ ☆ ♥. The purpose of this test is to determine whether the OJS submission system correctly accepts, stores, displays, and retrieves special characters without producing encoding errors, replacing characters with question marks (?), displaying corrupted text, or removing unsupported characters. The same content should be checked after submission, during editorial review, on the article metadata page, and after publication.

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Contributors:

Research PaperID: demo220800Pages 1-1

Open Access

demo title by scholarjms

test user

Large language models (LLMs) such as GPT and BERT have revolutionized natural language processing but impose enormous computational and energy costs due to their massive parameter sizes and quadratic attention complexity. This study introduces LoRA-S, a unified framework that combines Low-Rank Adaptation (LoRA) with sparse attention mechanisms to achieve energy-efficient and scaljournalable training of transformer-based LLMs. By freezing pretrained weights and injecting low-rank trainable matrices into attention and feed-forward layers, LoRA reduces the number of trainable parameters by over 90%, significantly lowering gradient computation and memory overhead. Simultaneously, sparse attention restricts token interactions to structured subsets, cutting attention-related FLOPs from 100 G to 7 G in WikiText-2 experiments. Comparative analysis across Full Fine-Tuning, LoRA, and LoRA-S demonstrates that LoRA-S achieves the lowest energy consumption of 22,380 J (6.22 Wh) while maintaining competitive task performance, with perplexity of 115.26 on WikiText-2 and sentiment classification accuracy of 73.90% on IMDB. Pareto frontier analysis confirms LoRA-S as an optimal trade-off between computational efficiency and predictive capability, enabling resource-constrained and eco-friendly model deployment. These results establish LoRA-S as a practical step toward Green AI, providing a novel, integrated approach to minimize FLOPs and parameter updates without substantially compromising LLM performance.

Large Language ModelsLow-Rank Adaptation (LoRA)Sparse AttentionEnergy-Efficient TrainingGreenReceived:+3 more

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1,114 downloads