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: 46
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.
The education industry is being transformed by Artificial Intelligence through hyper-personalized learning, real-time feedback, and scalable, modular content delivery. This paper focuses on key AI methodologies—machine learning, natural language processing, computer vision, and speech recognition—and examines how they enable pedagogical innovation, improve operational efficiency, and reshape business models in education. It explores future scenarios such as AI-assisted teaching, lifelong learning ecosystems, and adaptive curriculum design, using real-world case analyses. The study also addresses critical infrastructure challenges, including data quality, system integration, faculty preparedness, and cultural alignment. By combining strategic and technological perspectives, this research provides a comprehensive view of AI’s long-term impact on educational services.
The integration of large language models (LLMs) into mobile development workflows has been fundamentally constrained by three competing requirements: computational efficiency, contextual awareness, and real-time responsiveness. While cloud-based LLMs offer unparalleled reasoning capabilities, their reliance on remote infrastructure introduces prohibitive latency, privacy risks, and energy inefficiencies for mobile environments. Conversely, on-device models, though responsive and privacy-preserving, often lack the contextual depth required for complex code understanding and automation tasks. To address these challenges, we present SolidGPT, a hybrid edge-cloud framework that achieves an optimal balance between these competing demands through three key architectural innovations.
First, we introduce a Markov Decision Process (MDP)-based dynamic routing system that intelligently allocates tasks between on-device lightweight models (DistilGPT, TinyLLaMA) and cloud-based LLMs (GPT-4). This system evaluates real-time parameters—including contextual complexity, hardware constraints, and network conditions—to minimize energy consumption (28.6% reduction) while maintaining high accuracy (91% diagnostic accuracy). Second, our deep integration with Android’s Model-View-ViewModel (MVVM) architecture enables semantic-aware analysis across UI layouts, business logic, and runtime logs, bridging the gap between static code analysis and dynamic mobile runtime environments. Third, a novel prompt engineering pipeline preserves codebase-specific context across execution boundaries, ensuring continuity between local and cloud processing.
To validate our framework, we conducted a 12-week deployment with United Airlines’ Android application (128,500 LOC), involving 43 developers across six feature teams. The results demonstrate significant improvements: bug resolution time decreased by 64.1% (*p*<0.001), cloud API calls were reduced by 56.3%, and 87% of developer queries were resolved with sub-second latency. Notably, the system maintained a median energy consumption of 0.81mJ/token for on-device operations, outperforming cloud-only alternatives. These advancements highlight the framework’s ability to harmonize the strengths of edge and cloud computing while addressing critical challenges in energy efficiency, privacy preservation, and toolchain integration.
Beyond mobile development, SolidGPT establishes a template for deploying LLM-powered assistants in resource-constrained edge environments, such as IoT devices and embedded systems. By combining adaptive task allocation, platform-aware semantic analysis, and context-preserving prompt design, our work paves the way for next-generation AI tools that are both powerful and pragmatic—capable of scaling across domains without compromising responsiveness or user trust.
The paper is an extensive review looking at the convergence of Building Information Model (BIM) with deep learning (DL) in the digital transformation of architecture. As BIM evolves from a 3D model-centric design tool to a knowledge-based decision support system, the fusion with deep learning will provide strong support for intelligent automation, predictive analytics, and semantic understanding. We systematically evaluate the hybrid approaches of deep learning for design optimization, semantic segmentation, anomaly detection, energy modeling, construction scheduling, and lifecycle management in this paper. A concluding section of the article is devoted to trendy aspects such as multimodal data fusion, generative models, model-based interoperability, and digital twin alignment. With this integration, BIM transforms into a self-adapting, real-time system that assists decision-makers in making informed choices during design and construction, and also in operating and maintaining the building over its long life, thereby changing architectural processes and enabling more sustainable, efficient, and resilient buildings.
This research presents an intelligent optimization algorithm framework for chain restaurant spatial layout generation based on Generative Adversarial Networks (GANs). Contemporary restaurant design methodologies rely on subjective expertise and static planning approaches that inadequately address dynamic operational requirements and evolving consumer preferences. The proposed GAN-based architecture incorporates a dual-generator framework with progressive upsampling modules and multi-head attention mechanisms specifically designed for restaurant spatial optimization. The multi-objective optimization function integrates operational efficiency metrics, spatial utilization coefficients, and aesthetic quality assessments through weighted objective aggregation, achieving balanced performance across competing design criteria. Experimental validation utilizing 3,892 restaurant layouts across 47 chain brands demonstrates substantial improvements in spatial layout quality metrics. Generated layouts achieve average efficiency scores of 87.3% compared to traditional baseline measurements of 72.8%, representing a 19.9% performance enhancement. The algorithm reduces average customer movement distances by 23.4% while maintaining 92.6% regulatory compliance rates. Implementation case studies across three distinct restaurant chains validate practical deployment feasibility with measurable improvements in operational efficiency ranging from 12.9% to 24.6%. The research establishes foundational technologies for next-generation intelligent restaurant design systems that enable data-driven optimization while reducing traditional design development timelines by approximately 65%. Commercial deployment analysis indicates potential cost savings of $12,000-$18,000 per location through reduced architectural consultation requirements.
As data increasingly becomes a key factor of production for artificial intelligence (AI), this paper proposes a blockchain-enabled, decentralized AI data-market framework. To address the long-standing problems of low transparency, high privacy risk, and misaligned incentives in traditional data trading, we design a layered hybrid consensus that combines Proof of Stake (PoS) with Practical Byzantine Fault Tolerance (PBFT), balancing economic security with sub-second finality. A token-based incentive model that weights data quality, volume, and staking risk is introduced to couple value discovery with the suppression of low-quality data. By combining symmetric encryption with proxy re-encryption, the framework allows data to be “usable yet invisible” while exposing a compliance interface for regulated auditability. A prototype deployed on 18 nodes achieves 4,750 tx · s⁻¹ throughput and 148 ms latency, with energy consumption far below Proof-of-Work (PoW) schemes—demonstrating performance, privacy, and ESG friendliness. This work provides a reproducible technical path and theoretical foundation for sustainable innovation in data-factor circulation and AI applications.
