A Mathematical Model for Blockchain Adoption in High-Risk Asset Management

Miftahol Arifin — 2026-05-31

This study develops and empirically validates a mathematical adoption model for blockchain-based information systems using an Extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Previous blockchain adoption studies predominantly assessed behavioral determinants through isolated hypothesis testing, with limited efforts to integrate contextual factors into a unified structural model. To address this gap, the proposed framework incorporates blockchain-specific and organizational constructs, including trust and perceived security, operational resilience expectation, technology adaptability, and regulatory compliance, along with the core UTAUT dimensions. A quantitative, explanatory, and cross-sectional design was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate model parameters from survey data collected from 132 professionals operating in high-risk power plant asset management environments. The model explains 68.9% of the variance in behavior intention and 42.0% of actual system use. Trust and perceived security emerged as the strongest predictor of behavioral intention, followed by performance expectation and operational resilience expectation. The findings demonstrate the suitability of Extended UTAUT for mathematically representing the adoption behavior of blockchain in high-risk organizational settings and provide a transferable analytical framework for future studies of the adoption of blockchain-based information systems.

Classification of H2O with HCl and H2O with NaOH Solution Images Using Otsu Segmentation and CNN

Mauliza Putri — 2026-05-31

The classification of the image of chemical solutions is crucial in laboratory automation and chemical industry applications; however, it remains challenging when solutions such as H2O with HCl and H2O with NaOH exhibit nearly identical visual characteristics under imaging conditions, particularly when their spectral fluctuation patterns are visually subtle. This study proposes an image classification framework that integrates Otsu-based segmentation in the HSV color space with convolutional neural network (CNN) models to classify High Height Fluctuation (HHF) images generated from a Multi-Scale chemical detection system (MSCS). The dataset consists of 102 HHF images, evenly distributed between the two solution classes. Transfer learning is applied using three CNN architectures, namely EfficientNetV2S, DenseNet201, and EfficientNetB0, and performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that DenseNet201 achieves the best overall performance, while EfficientNetV2S provides competitive results with computational efficiency and Efficient-NetB0 yields a lighter model with lower recall. These findings indicate that combining segmentation with modern CNN architectures can effectively improve classification robustness in chemically similar solutions. This study presents a practical framework that combines Otsu-based HSV segmentation with transfer-learning CNNs to classify chemically similar solutions, providing actionable insights for deep learning-based chemical sensing applications.

JURNAL INFOTEL

A Mathematical Model for Blockchain Adoption in High-Risk Asset Management

Classification of H2O with HCl and H2O with NaOH Solution Images Using Otsu Segmentation and CNN