Journal of Hunan University Natural Sciences

The digitization of medical records in the healthcare sector demands robust mechanisms to ensure data confidentiality, integrity, and privacy. This paper proposes an innovative multi-factor authentication (MFA) mechanism that leverages smart contracts and blockchain technology to secure the tracking of medical records. The proposed system, named Blockchain Authentication with Zero-Knowledge Proof (BAZKP), provides a tamper-proof environment for storing and accessing records while preserving users’ personally identifiable information (PII). A key novelty of BAZKP lies in storing only the character count structure of passwords rather than the actual credentials, combined with zero-knowledge proofs (ZKP) to verify identity without exposing sensitive data. This hybrid blockchain/ZKP approach addresses limitations of centralized and hardware-based solutions, reducing vulnerabilities while avoiding the cost and usability constraints of dedicated hardware systems.
The system was implemented and tested on a private Ethereum testnet, with a proof-of-concept application developed using Solidity, Web3.js, and MetaMask. Performance evaluation over 100 transactions for core operations (registration, login, and password reset) demonstrated practical viability: registration incurred the highest latency (≈4500 ms) and gas consumption (≈120,000 gas), while login and reset operations were more efficient (≈4000 ms/80,000 gas and ≈3500 ms/60,000 gas, respectively). Comparative security analysis against existing MFA methods—including 2FA, hardware tokens, and biometrics—confirms that BAZKP provides superior privacy protection through decentralization and ZKP, without the cost and usability drawbacks of hardware-based solutions. Overall, this approach enhances trust in digital health systems by offering a secure, transparent, and privacy-preserving authentication framework for medical data, representing a significant advancement in digital healthcare security.

Keywords: Blockchain; Multi-Factor Authentication; Smart Contracts; Zero-Knowledge Proof; Medical Record Security.

DOI https://doi.org/10.55463/issn.1674-2974.52.12.10

Elementary students’ mathematical thinking is frequently constrained by persistent misconceptions and an overreliance on procedural instruction. International assessments of mathematical literacy consistently report lower levels of achievement among students in many developing countries, underscoring the need for instructional approaches that promote conceptual understanding rather than rote learning. Scaffolding, understood as temporary and adaptive instructional support, has been widely acknowledged as an effective means of facilitating students’ conceptual development. Nevertheless, its classroom enactment—particularly the processes through which support is responsively adjusted and gradually withdrawn—remains insufficiently documented and systematically analyzed in empirical research.
This study aims to examine the forms of scaffolding employed by teachers, their responsive strategies in addressing student errors, and the observable indicators of scaffolding reduction (fading) in mathematics instruction grounded in visual pattern recognition and comparative reasoning. A descriptive qualitative methodology was adopted, using a case study design involving three upper elementary school students. Data were collected through analyses of students’ written work, classroom interaction observations, and semi-structured interviews. The data were analyzed thematically within the framework of contingent scaffolding.
The findings indicate differentiated learning trajectories among the participants. Student MA demonstrated a shift from intuitive verbal descriptions to symbolic comparative reasoning following interactive scaffolding. Student RFM exhibited independent formal reasoning from the outset, requiring minimal instructional support. In contrast, student IAM experienced a substantial conceptual transition after receiving explicit instructional intervention. Notably, all three students were ultimately able to generalize that the number of blue triangles was consistently less than half of the total number of triangles.
These results highlight the critical role of adaptive and contingent scaffolding in fostering conceptual understanding and learning autonomy in elementary mathematics. By documenting the forms, timing, and transitions of instructional support, this study contributes to the empirical literature on scaffolding practices in primary education. Importantly, the findings provide a novel account of how scaffolding is dynamically enacted and strategically faded in response to students’ errors, enabling a progression from descriptive to symbolic proportional reasoning. The identification of concrete indicators of scaffolding reduction aligned with students’ emerging autonomy offers theoretically grounded and practice-oriented implications for the design of adaptive instructional support in elementary mathematics classrooms.

Keywords: instructional scaffolding; mathematical reasoning; mathematics education; proportional reasoning; gradual fading of support.

DOI https://doi.org/10.55463/issn.1674-2974.52.12.9

Sustainable water resource management has become a critical challenge amid rising demand, climate variability, and accelerating environmental degradation. Conventional evaluation approaches often fail to adequately capture the complex, dynamic, and multi-dimensional characteristics of water systems. This study proposes an integrated framework that combines System Dynamics (SD) modeling with Multi-Criteria Decision Analysis (MCDA) to assess water resource sustainability in a holistic and adaptive manner. The objective is to evaluate key sustainability indicators—namely water availability, water quality, allocation efficiency, and ecosystem health—under multiple development and climate scenarios.
An SD model was developed to simulate interactions among hydrological, socio-economic, and environmental variables over a 30-year time horizon. Model outputs were subsequently assessed using MCDA, incorporating stakeholder preferences and expert judgments to rank alternative water management strategies. The study focuses on a semi-arid watershed experiencing increasing water stress due to agricultural expansion and urban growth.
The results indicate that integrated water-saving strategies, combined with effective demand-side management and ecosystem conservation measures, substantially enhance long-term sustainability performance across most scenarios. The proposed SD–MCDA framework facilitates the identification of robust strategies that balance environmental protection, economic viability, and social equity. Overall, this research highlights the value of integrating dynamic simulation with participatory decision-support tools to inform resilient and adaptive water governance in complex socio-environmental systems.

Keywords: water sustainability; system dynamics; multi-criteria decision analysis; integrated water resource management; scenario analysis.

DOI https://doi.org/10.55463/issn.1674-2974.52.12.8

This study evaluates the implementation of Indonesia’s carbon tax policy and its role in promoting the transformation toward environmentally friendly industries. Using a qualitative literature review approach, the research synthesizes evidence from academic publications, policy reports, and institutional documents to assess policy effectiveness, institutional readiness, and industry responses. The findings indicate that the current carbon tax—characterized by low pricing levels, limited sectoral coverage, and weak enforcement—has not yet functioned as a significant driver of green innovation or sustainable investment. The analysis shows that many firms perceive the tax primarily as a regulatory formality rather than a strategic market signal, largely due to policy ambiguity, insufficient integration with green finance mechanisms, and weak inter-institutional coordination. The study underscores the importance of aligning fiscal instruments with complementary policies and robust institutional frameworks to enhance their effectiveness in developing country contexts. It also highlights the need for future empirical research focusing on firm-level behavioral responses and the long-term effects of carbon pricing on industrial sustainability.

Keywords: carbon tax; environmental fiscal policy; green industrial transformation; Indonesia; climate policy implementation.

DOI https://doi.org/10.55463/issn.1674-2974.52.12.7

In this paper, we propose a modeling framework to analyze the spread of the dengue virus in human populations by explicitly incorporating noise terms to simulate variability in transmission. A system of differential equations is employed to represent the transmission dynamics of the virus, and the effects of stochastic perturbations on these dynamics are systematically investigated. The effectiveness and robustness of the proposed model are demonstrated through numerical simulations that examine how the inclusion of noise influences the reliability and accuracy of transmission predictions.
The results highlight the importance of accounting for variability in transmission rates and provide valuable insights into the behavior of dengue virus spread under realistic conditions. In particular, this study addresses a key limitation of traditional deterministic dengue models by introducing noise terms to represent realistic fluctuations in transmission dynamics. The central research question focuses on how stochastic variability affects infection trajectories and the stability of dengue outbreaks.
The novelty of this work lies in the integration of noise-modulated differential equations with the Adomian Decomposition Method, which enables a semi-analytical characterization of uncertainty in disease transmission. Our findings demonstrate that random perturbations can either amplify or attenuate outbreak intensity, depending on system conditions, thereby offering a more robust framework for epidemiological forecasting. Overall, this study provides an original contribution by bridging deterministic and stochastic modeling approaches to enhance the predictive capability of vector-borne disease simulations.

Keywords: Adomian method, noise terms, Dengue virus, Semi-numerical method, Iterative method.

DOI https://doi.org/10.55463/issn.1674-2974.52.11.16