Displaying all 2 publications

Abstract:
Sort:
  1. Anam SK, Leonardy J, Suwardi S, Baldi A, Nugroho FAA, Darmadi I
    F1000Res, 2026;15:1187.
    PMID: 42935482 DOI: 10.12688/f1000research.180635.1
    BACKGROUND: UV-visible spectroscopy is widely used to monitor real-time transformations in material systems. However, accessible and open-source tools for real-time tracking and analysis of spectral features-including peak position, width, and intensity-remain limited. Existing workflows are often multi-step and/or reliant on proprietary software.

    METHODS: We developed PySpecTrace, an open-source, Python-based graphical user interface (GUI) for real-time spectroscopy analysis. The platform integrates spectral data acquisition, preprocessing, visualization, and feature extraction within a unified and modular framework. Core functionalities include real-time tracking of peak/dip wavelength, spectral width, and intensity, as well as configurable data processing workflows. The software is designed for flexibility, enabling adaptation to generic experimental setups and time-resolved measurements.

    RESULTS: PySpecTrace enables continuous monitoring and analysis of spectral evolution with minimal user intervention. Demonstration cases show its capability to track dynamic spectral changes and extract key parameters in real time, improving analysis efficiency and consistency compared to conventional post-processing approaches. In addition to real-time analysis, the platform supports post-processing of pre-recorded spectral data including feature extraction and quantitative analysis.

    CONCLUSIONS: PySpecTrace provides an accessible and extensible solution for real-time spectroscopy analysis, bridging experimental measurement and data processing. By offering open-source availability and reproducible workflows, it facilitates more efficient and transparent analysis of time-resolved spectroscopic data.

  2. Syahruddin S, Mohd Yaakob MF, Rasyad A, Widodo AW, Sukendro S, Suwardi S, et al.
    Heliyon, 2021 Sep;7(9):e08043.
    PMID: 34611566 DOI: 10.1016/j.heliyon.2021.e08043
    This study was conducted to investigate the perspectives of sports science students on factors affecting distance learning in the setting of Indonesian higher education institutions (HEIs). This study proposed an extended technology acceptance model (TAM) with eight variables; experience, enjoyment, self-efficacy, perceived ease of use, perceived usefulness, attitude, intention to use, and actual use. An online survey was used to collect data from 1291 respondents. The structural model was examined through the partial least square structural equation modeling (PLS-SEM). The multi-group analysis (MGA) was conducted to understand the role of geographical areas in moderating all hypothetical relationships. The findings show that the respondents were not excited about online learning due to weak means (below 3) for most items of five variables; enjoyment, perceived ease of use, perceived usefulness, attitude, and intention to use. All relationships were supported except the relationship between experience and perceived usefulness. The strongest significant relationship emerged between intention to use and actual use. Meanwhile, the least significant relationship was found between self-efficacy and perceived usefulness. Three out of 12 hypotheses were confirmed regarding the differences of geographical areas (rural and urban) regarding all relationship paths. The findings add to a deeper understanding of the acceptability of distance learning during pandemics like COVID-19.
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links