Forthcoming Articles

International Journal of Student Project Reporting

International Journal of Student Project Reporting (IJSPR)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Student Project Reporting (3 papers in press)

Regular Issues

  • Review of Cavity Design and Laser Control Strategies for Improved Laser Isotope Enrichment Efficiency   Order a copy of this article
    by Lillian Carncross, Alyssa Jin, Charles Randall 
    Abstract: Laser isotope separation (LIS) techniques provide high isotope selectivity and efficiency, but practical limitations such as Doppler broadening, photon utilisation inefficiencies, and process control restrict scalability and throughput. This review examines recent advancements in cavity design, including thermal management and multipass optical configurations, and automated laser control strategies, such as real-time Doppler spectrum monitoring and adaptive laser pulse shaping. These innovations improve enrichment yield and operational efficiency, with direct implications for high-assay low-enriched uranium (HALEU) fuel production and medical isotope synthesis. By integrating these approaches, LIS techniques can achieve enhanced efficiency and reliability, helping to bridge the gap between laboratory demonstrations and industrial applications.
    Keywords: Laser isotope separation (LIS); molecular laser isotope separation (MLIS); cavity enhancements; laser bandwidth control; HALEU fuel.
    DOI: 10.1504/IJSPR.2025.10076230
     
  • BactoBot: A Low-Cost, Bacteria-Inspired Soft Underwater Robot for Marine Exploration   Order a copy of this article
    by Rubaiyat Tasnim Chowdhury, Nayan Bala, Ronojoy Roy, Tarek Mahmud 
    Abstract: Traditional rigid underwater vehicles pose risks to delicate marine ecosystems due to high-speed propellers and rigid hulls. This paper presents BactoBot, a low-cost, soft underwater robot designed for safe and gentle marine exploration. Inspired by the efficient flagellar propulsion of bacteria, BactoBot features 12 flexible, silicone-based arms arranged on a dodecahedral frame. Unlike high-cost research platforms, this prototype was fabricated using accessible DIY methods, including food-grade silicone molding, FDM 3D printing, and off-the-shelf DC motors. A novel multi-stage waterproofing protocol was developed to seal rotating shafts using a grease-filled chamber system, ensuring reliability at low cost. The robot was successfully tested in a controlled aquatic environment, demonstrating stable forward propulsion and turning maneuvers. With a total fabrication cost of approximately $355 USD, this project validates the feasibility of democratizing soft robotics for marine science in resource-constrained settings.
    Keywords: Soft robotics; bio-inspired design; flagellar propulsion; low-cost robotics; underwater exploration; open-source.
    DOI: 10.1504/IJSPR.2025.10079880
     
  • Document Analysis Using Text Embeddings and Machine Learning   Order a copy of this article
    by Klara Bagić Zagajski, Marina Babac 
    Abstract: The rapid growth of unstructured textual data requires automated systems capable of analysing and organising large document collections. This study presents an end-to-end framework for document classification and similarity analysis that integrates text extraction, pre-processing, embedding generation, and supervised machine learning. Four embedding techniques (TF-IDF, Word2Vec, Doc2Vec, and GloVe) are combined with three classifiers (linear SVM, KNN, and multinomial Naive Bayes) and evaluated on a resume dataset of 2,485 documents across 22 professional categories using accuracy, precision, recall, and F1-score. The best performance is achieved by the TF-IDF and linear SVM combination, reaching an accuracy of 63% and F1-score of 0.63. While overall performance is moderate, the results indicate that classical embedding-based approaches remain effective for small and medium-sized datasets, whereas dense semantic embeddings show lower performance under these conditions. The proposed framework provides a modular baseline for organising unstructured text and supports future extensions toward transformer-based document analysis.
    Keywords: Document Analysis; Information Extraction; Document Classification; Python; Text Embeddings; Machine Learning.
    DOI: 10.1504/IJSPR.2026.10079926
     

 

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