Current Issue

All articles published in this issue have undergone a thorough peer review process, and stringent checks for repetition rates have been implemented to ensure the integrity of the content.

Total number of articles in this issue: 5
Total number of pages in this issue: 69

For inquiries regarding the content of specific articles, please feel free to contact the respective authors via their provided email addresses. For questions related to the journal itself, please reach out directly to SUAS Press.

Year

2026

Volume

1

Number

1

Status

Archived

Published

2026 February 5

Articles

Open Access Research ArticlePublished 5 February 2026

Multi-Chain DAO Treasury Management: a Risk and Compliance Optimization Framework for the U.S. Ecosystem

Allen Lin

Multi-chain deployment has become a mainstream strategy for U.S.-based DAOs, yet treasury management faces three core bottlenecks: cross-chain liquidity fragmentation, inadequate compliance with U.S. regulations (including OFAC sanctions screening and SEC transparency requirements), and inefficient revenue distribution. Leveraging the incubation practices of over 12 U.S. DAOs (via daos.world) and expertise in multi-chain smart contract development, this study proposes a three-dimensional risk and compliance optimization framework (cross-chain risk hedging + real-time regulatory screening + hierarchical revenue distribution). Empirical testing on 8 U.S. DAOs (operating on Base/Ethereum/Solana, covering AI-focused, meme coin-focused, and investment-focused types) over a 6-month period (September 2025 - February 2026) demonstrates that the framework reduces cross-chain compliance risks by 82.3% (OFAC violation rate drops from 18.0% to 3.2%), increases the annualized treasury return rate by 17.6% (from 4.2% to 5.04%), lowers cross-chain transaction costs by 28.5% (average Gas fee decreases from $12.8 to $9.1), and shortens liquidity adjustment response time from 48 hours to 6 hours. Integrating U.S. regulatory requirements with cross-chain technical logic, this research addresses the theoretical gap in multi-chain DAO treasury management, provides a replicable paradigm for U.S. DAOs to balance compliance, security, and profitability, aligns with the standardization strategy of the U.S. Web3 ecosystem, and is expected to unlock $15-20 billion in potential investment value.

Disciplines:Computer ScienceSubjects:Risk and Compliance Optimization FrameworkReferences:29

DOI10.70393/6a696574.333834ARKark:/40704/JIET.v1n1a02
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 5 February 2026

Constructing a Multi-Objective Decision-Making Model for Investment Valuation of Technological Innovation Projects: a Blockchain–Big Data Fusion Perspective

Yuanchu Liu

Technological innovation projects are notoriously difficult to value due to their high uncertainty and the coupling of multiple value dimensions. Traditional valuation approaches suffer from several critical limitations, including insufficient data credibility (tampering risk rate exceeding 30%), overly single-dimensional objective design (financial orientation accounting for 75%), and subjective weight assignment (reliance on expert judgment above 60%). To address these issues, this study develops a five-dimensional, blockchain–big data fusion-driven multi-objective decision-making valuation model integrating technology, finance, market, risk, and ecosystem dimensions. Blockchain-based decentralized evidence preservation enables trusted provenance and traceability of multi-source data, while big data analytics—using an integrated LSTM + Random Forest framework—supports objective quantification and dynamic optimization. An improved CRITIC–TOPSIS method is further employed to solve the multi-objective collaborative decision-making problem. Empirical validation based on 426 global innovation projects (covering six domains such as AI and medical devices, including 158 cross-border projects) demonstrates that the proposed model controls valuation deviation to 7.2% ± 1.3%, representing reductions of 74.8% and 68.4% compared with the traditional DCF method (28.6% ± 4.2%) and relative valuation (23.1% ± 3.8%), respectively. Improvements are more pronounced for technology-intensive projects (deviation 5.9%), and the risk quantification error for cross-border technology transfer projects decreases by 62.7%. Overall, the model overcomes the conventional “single-objective, static, experience-driven” limitations and provides a rigorous yet practical methodological framework for investment decision-making in innovation projects [1–3].

Disciplines:Computer ScienceSubjects:Big DataReferences:27

DOI10.70393/6a696574.333835ARKark:/40704/JIET.v1n1a01
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 5 February 2026

Uniswap V4 Concentrated Liquidity Pricing: a Machine Learning Model for U.S. Institutional Liquidity Providers

Allen Lin

Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.

Disciplines:Computer ScienceSubjects:Machine Learning ModelReferences:25

DOI10.70393/6a696574.333836ARKark:/40704/JIET.v1n1a03
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 5 February 2026

Integrating Real-Time KPI Dashboards with Monte Carlo Simulation for Optimizing Semiconductor Manufacturing Processes

Min Yin

Currently, wafer fabs in semiconductor applications rely heavily on experience. Errors in production scheduling strategies, maintenance plans, and resource allocation can lead to downtime, scrap, and delivery delays. This research proposes an integrated decision-making intelligent dashboard and hypothetical scenario simulation method to improve real-time decision-making capabilities in semiconductor manufacturing by optimizing key performance indicators such as overall equipment efficiency, yield, and cost. By combining digital twin technology with historical production data and simulation models, a system capable of simulating various production scenarios is constructed to assess the impact of changes in production planning, resource allocation, and equipment utilization on OEE, yield, and cost. This research contributes to improving manufacturing capacity utilization and supply chain reliability, shortening process change verification cycles, reducing downtime and scrap risks in the manufacturing industry, and adapting to multi-factory, multi-node manufacturing expansion.

Disciplines:Computer ScienceSubjects:AIReferences:25

DOI10.70393/6a696574.333837ARKark:/40704/JIET.v1n1a04
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 5 February 2026

Risk Transmission Mechanisms and Risk Mitigation Pathways in Cross-border Technology Investment: Evidence From the China–U.S. Market

Yuanchu Liu

Against the backdrop of accelerating cross-border mobility of global technological factors, the scale of China–U.S. cross-border technology investment has continued to expand, yet the investment failure rate reaches 37.2%, significantly higher than that of domestic technology investment (18.5%). Addressing key challenges such as multidimensional risk complexity, covert transmission pathways, and high difficulty of prevention and control, this study draws on risk transmission theory, technology diffusion theory, and institutional distance theory to develop a four-dimensional risk identification framework covering technology, market, institution, and finance. Using a dataset of 486 China–U.S. cross-border technology investment projects from 2018 to 2023, we empirically reveal a three-stage risk transmission mechanism characterized by “chain triggering–network diffusion–compound amplification.” The results show that institutional risk is the core initial risk (contribution rate 38.6%), transmitting through a chain of “institutional differences → impeded technological adaptation → market entry barriers → tightened funding chains.” Cross-transmission between technology risk and market risk increases the probability of investment failure by 2.3 times. Furthermore, the strength of intellectual property protection and the signing status of bilateral investment treaties exert significant moderating effects on risk transmission, with moderation coefficients of −0.27 and −0.31, respectively. Based on the empirical findings, this paper proposes targeted solutions from three perspectives—risk early warning, pathway blocking, and collaborative governance—providing theoretical support and practical guidance for reducing risk losses in China–U.S. cross-border technology investment.

Disciplines:Computer ScienceSubjects:Risk Transmission MechanismReferences:25

DOI10.70393/6a696574.333838ARKark:/40704/JIET.v1n1a05
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 20 March 2026

Structure-Aware Deep Reinforcement Learning for Latency-Minimal Scheduling of Edge AI Inference on Heterogeneous Cores

Zihe Hao

The exponential surge in latency-sensitive deep learning workloads at the network edge demands the implementation of highly stringent resource scheduling mechanisms to mitigate this situation. Yet, achieving this is impeded by the intrinsic computational asymmetry of contemporary edge processors, which imposes severe constraints on task scheduling.While heuristic algorithms have traditionally been employed to manage these tasks which are intrinsically modeled as Directed Acyclic Graphs they frequently stagnate at local optima due to an inability to dynamically adapt to complex, non-linear topological dependencies. Furthermore, existing learning-based solutions often fail to generalize across diverse neural network architectures, as they typically rely on scalar feature inputs that cannot capture the rich structural priors of the computation graph. To bridge this gap, this paper proposes a structure-aware scheduling framework that synergizes Graph Neural Networks with Deep Reinforcement Learning. By leveraging a Graph Isomorphism Network to extract high-dimensional topological embeddings of task dependencies, our agent learns to map computational layers to heterogeneous cores with the primary objective of minimizing end-to-end inference latency. Extensive experiments on real-world Deep Neural Network workloads, including ResNet and Inception architectures, demonstrate that the proposed approach not only surpasses classic heuristics such as HEFT in terms of makespan reduction but also achieves more robust convergence compared to structure-agnostic learning baselines. These findings suggest that explicitly encoding structural priors into learning algorithms offers a promising approach to effectively address the NP-hard scheduling problems inherent in next-generation edge artificial intelligence systems.

Disciplines:Intelligent SystemsSubjects:OtherReferences:25

DOI10.70393/6a696574.343033ARKark:/40704/JIET.v1n1a06
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 20 March 2026

Dynamic Task Prioritization for Edge AI in Smart Cities: Balancing Latency and Energy Efficiency

Zihe Hao

The growth of latency sensitive smart city applications is rapid nowadays. Therefore deploying microservice architecture in the heterogeneous edge cloud continuum has become a mainstream choice. However a structural challenge arises in orchestrating these coupled services modeled as Directed Acyclic Graphs. Exact algorithms like Branch and Bound struggle with computation in high concurrency scenarios. Meanwhile Deep Reinforcement Learning methods face the challenges of excessive training overhead and a lack of zero shot adaptation capability for topology changes. Recently popular quantum inspired algorithms often fail to satisfy strict predecessor constraints. This makes them unsuitable for use in dependent workflows. To alleviate this dilemma this paper proposes a Dependency Aware Quantum Inspired Scheduler. This framework utilizes a topological quantum coding scheme and dynamic dependency masks to integrate DAG constraints into the quantum search process. It also introduces an entropy weighted evolutionary rotation mechanism to accelerate the convergence of critical paths. After conducting extensive simulation experiments in city level environments we found that the scheduling success rate of DAQ Scheduler is 100% while the success rate of standard quantum inspired algorithms is only 58.1%. Compared with leading multi objective DRL baselines this method reduces the average makespan by 9.9%. This method provides near optimal scheduling solutions with millisecond level inference latency. It builds an efficient and lightweight paradigm for real time edge intelligence and balances theoretical optimality with engineering feasibility.

Disciplines:Intelligent SystemsSubjects:OtherReferences:22

DOI10.70393/6a696574.343034ARKark:/40704/JIET.v1n1a07
View ArticleDownload PDFScholar

Open Access Research ArticlePublished 20 March 2026

Research on Innovative Paths for Regional Logistics and Supply Chain Integration: A Perspective on the Scalable Operations of Micro and Small Logistics Enterprises

Yanfang Chen

The complex integration of Industry 4.0 technologies into micro and small logistics enterprises presents profound adoption barriers stemming from severe resource constraints. Navigating the inherent difficulties of fragmented regional networks, this study constructs a dual driven framework combining lightweight digital empowerment with industry standardization to facilitate scalable and high resilience operations. Rather than assuming linear technological diffusion, we formulate advanced mathematical models including mixed integer programming for cost optimization and continuous time Markov chains for risk evolution, revealing the underlying mechanisms of supply chain resilience. Empirical analyses utilizing operational datasets indicate that algorithmic synergy and standardized data protocols substantially improve order processing efficiency while reducing comprehensive logistics costs. However, considering the potential sample biases inherent in regional data, these sustainability outcomes might also reflect variations in external policy support to some extent. This leads us to further thinking regarding the scalability of such models across divergent economic contexts. Ultimately, the successful technology transfer of these lightweight systems to numerous enterprises demonstrates profound practical significance. Further research is needed to explore the multidimensional variables influencing longitudinal technological assimilation in sustainable supply chain management.

Disciplines:Intelligent SystemsSubjects:OtherReferences:26

DOI10.70393/6a6374616d.343038ARKark:/40704/JIET.v1n1a08
View ArticleDownload PDFScholar

Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Journal of Intelligence and Engineering Technology (JIET)
Published by Southern United Academy of Sciences Limited ISNI: 0000000512776460, operated by the Publications Division.