Journal of Hunan University Natural Sciences

Global supply chains are increasingly exposed to transparency challenges, information asymmetry, and vulnerabilities to fraud and counterfeiting. Blockchain technology can play a pivotal role by providing a decentralized, tamper-resistant, and verifiable infrastructure that enhances trust and transparency across supply chain networks. This paper presents a systematic literature review (SLR) of 33 peer-reviewed articles published between 2020 and 2025, following the PRISMA guidelines, to examine how blockchain improves transparency in supply chains, identify key adoption barriers, and highlight emerging research trends and opportunities. The results indicate that blockchain enhances transparency through data immutability, smart contracts, and real-time data sharing enabled by IoT technologies. However, its adoption remains constrained by challenges related to scalability, interoperability, regulatory uncertainty, implementation costs, and organizational culture, particularly among small and medium-sized enterprises (SMEs). Emerging trends include the integration of blockchain with IoT and artificial intelligence (AI), the development of consortium and hybrid blockchain models, sustainability-oriented applications, and privacy-preserving mechanisms. Overall, the review highlights the transformative potential of blockchain to reshape secure and ethical supply chains while emphasizing the importance of industry collaboration, regulatory clarity, and organizational readiness for large-scale adoption.

Keywords: Blockchain; transparency; global supply chains; traceability; logistics.

DOI https://doi.org/10.55463/issn.1674-2974.53.2.7

Dandruff and fungal-associated alopecia result from microbial dysbiosis, including Malassezia furfur, M. globosa, Trichophyton rubrum, and Staphylococcus epidermidis, highlighting the need for alternative therapeutics with improved safety. This study evaluates the antimicrobial activity of Albizia saponaria stem bark against dandruff-associated microorganisms and identifies the key bioactive metabolites responsible. The work combines in vitro bioassays and GC-MS profiling. Pharmacokinetic screening and molecular docking provide mechanistic insights. Ethanolic extracts were fractionated using hexane, ethyl acetate, butanol, and water, followed by antimicrobial evaluation. The hexane fraction showed the most potent antifungal activity, with inhibition zones of 18.25±1.53 mm (M. furfur), 21.64±1.80 mm (M. globosa), and 22.00±2.35 mm (T. rubrum), significantly higher than other fractions (p<0.05). Its activity against T. rubrum was comparable to ketoconazole (p>0.05). GC-MS identified 21 predominantly lipophilic metabolites, including fatty acid esters, terpenoids, phenolics, and sterol derivatives. Docking analysis revealed notable binding affinities, ranging from -4.3 to -9.5 kcal/mol, with 4-campestene-3-one and norambreinolide exhibiting interactions comparable to ketoconazole at CYP51 and TcaR. Collectively, the results demonstrate that the hexane fraction contains multiple bioactive metabolites with strong antimicrobial potency, mechanistic relevance to ergosterol disruption, biofilm inhibition, and metabolic interference. These findings highlight the hexane fraction as a credible natural antidandruff candidate, warranting further isolation studies and in vivo evaluation.

Keywords: Albizia saponaria; Antimicrobial; GC-MS; Molecular docking.

DOI https://doi.org/10.55463/issn.1674-2974.53.3.3

Overturning accidents involving heavy construction vehicles may occur due to soft ground conditions caused by subsurface water accumulation or sinkholes. Although such accidents may appear unexpected during visual inspection, subsurface failure often precedes these events. Therefore, real-time assessment of ground stability is essential for improving operational safety.
Electrical Resistivity Tomography (ERT) is a widely used geophysical method for analyzing subsurface conditions through deep ground investigation. However, ERT data interpretation typically involves complex nonlinear inversion processes that are computationally intensive and highly dependent on initial parameter estimates.
In this study, an inference-based deep learning approach is proposed to efficiently identify areas susceptible to overturning accidents. The method converts data obtained using a Wenner array configuration into a high-resolution dipole–dipole configuration, thereby eliminating the need for traditional inversion procedures.
To evaluate the performance of the proposed framework, three deep learning models—Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—were trained and tested using regression-based loss functions. Among these models, the MLP demonstrated the best performance, achieving an average R² value of 0.9935 ± 0.0014 and a root mean square error (RMSE) of 3.44%.
The proposed approach leverages the inherent stability of the Wenner array and the high spatial resolution of the dipole–dipole configuration. By employing forward inference, the method significantly reduces the computational complexity associated with conventional inversion techniques. As a result, the efficiency and practical applicability of ERT-based ground inspection are substantially improved.
Overall, the proposed framework provides an effective tool for identifying hazardous ground conditions and assessing the susceptibility of construction sites to overturning accidents involving heavy vehicles.

Keywords: Deep learning; electrical resistivity tomography; ground stability assessment; construction equipment safety; overturning accident prediction; machine learning models; subsurface hazard detection.

DOI https://doi.org/10.55463/issn.1674-2974.53.3.2

Rockbursts are critical geohazards in deep underground mining and tunneling and can cause catastrophic structural failures, severe safety risks, and substantial economic losses. Owing to their nonlinear and spatiotemporally variable behavior, conventional microseismic monitoring approaches often provide limited predictive capability.
This study proposes a data-driven machine learning (ML) framework to model rockburst fragility surfaces and assess seismic resilience under complex geological conditions. The workflow begins with preprocessing of in-situ monitoring data, including dimensionality reduction, feature engineering, and normalization to enhance data quality and relevance. Multiple ML algorithms, including XGBoost, Random Forest, LightGBM, and CatBoost, are trained using labeled datasets representing moderate-to-severe rockburst events.
To improve robustness and generalization, an ensemble voting classifier is developed, with hyperparameters optimized using grid search and evolutionary optimization. The framework maintains strong predictive consistency even for imbalanced datasets. Feature importance analysis identifies seismic energy release and ground stress as the dominant predictors of rockburst occurrence.
Using these insights, the model generates three-dimensional fragility surfaces that characterize structural vulnerability under varying seismic loads, along with probabilistic fragility curves that delineate risk zones to support early-warning systems and real-time operational decision-making. By integrating physics-informed assessment with explainable ML, the proposed approach improves interpretability while meeting practical engineering requirements.
Overall, this study provides a scalable, high-performance framework for seismic resilience analysis, enabling improved hazard prediction, risk management, and the design of safer underground systems in complex geomechanical environments.

Keywords: Rockburst; Seismic resilience; Machine learning; Fragility curve; Hazard prediction; Seismic risk assessment; Ensemble learning; 3D fragility surfaces; Explainable AI.

DOI https://doi.org/10.55463/issn.1674-2974.53.3.1

This study examines the institutional design of Indonesia’s representative bodies - the House of Representatives of the Republic of Indonesia (DPR RI) and the Regional Representative Council of the Republic of Indonesia (DPD RI) - with particular attention to their constitutional status, functions, and competences within Indonesia’s democratic constitutional framework. Using a doctrinal (normative juridical) legal research method, the study analyzes relevant constitutional provisions, statutory regulations, judicial decisions, and constitutional doctrines governing Indonesia’s bicameral legislature.
The findings reveal structural and functional deficiencies within Indonesia’s representative system that potentially undermine legislative effectiveness and equitable national development. These deficiencies include the asymmetrical distribution of legislative authority between the two chambers, overlapping competences, and procedural constraints that significantly restrict the DPD RI’s substantive participation in the lawmaking process. Such institutional imbalances weaken the system of checks and balances envisioned in a democratic constitutional state and limit the effectiveness of bicameralism as a mechanism for territorial representation.
In response, this study proposes a strategic restructuring of Indonesia’s representative institutions aimed at strengthening democratic accountability and institutional equilibrium. The proposed reforms consist of two principal measures: (1) permitting independent (non-party-affiliated) candidacy for membership in the DPR RI in order to broaden political inclusion and enhance representational diversity; and (2) establishing functional and authority parity between the DPR RI and the DPD RI to reinforce genuine bicameralism and improve legislative coherence. The study concludes that such reforms are essential to enhancing democratic representation, consolidating checks and balances, and fostering inclusive and sustainable national development in Indonesia.

Keywords: Bicameralism; Constitutional Reform; Legislative Institutional Design; Democratic Representation; Checks and Balances; Territorial Representation; National Development; Indonesia.

DOI https://doi.org/10.55463/issn.1674-2974.53.2.6