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Total number of articles in this issue: 7 Total number of pages in this issue: 61
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Year
2026
Volume
4
Number
1
Status
Archived
Published
2026 February 4
Articles
Open Access Research ArticlePublished 4 February 2026
This paper focuses on the development of intelligent quality and operational control systems for industrial electrical appliance companies, examining whether these systems integrate ISO 14001 (Environmental Management System) and ISO 9001 (Quality Management System) standards. By utilizing these standards, the paper elucidates how the dual-standard framework can improve overall enterprise operational efficiency, quality control, and environmental sustainability. Furthermore, the proposed algorithm combines traditional management strategies with new technologies such as Artificial Intelligence (AI）, the Internet of Things（IoT）, and big data to demonstrate that the system can reduce operating costs, improve product quality, and control effectiveness, ultimately achieving effective and long-term sustainable development for the enterprise.
This paper analyzes a non-experimental declarative framework for interpreting changes in the U.S. automotive market, including chip shortages, accelerated car adoption, and the continued dominance of SUVs and trucks. It proposes an in-depth analysis of a four-layered demand hierarchy, focusing on the following layers: basic needs, functional/performance needs, experience/service needs, and identity/value needs. Furthermore, through a complementary emotional-functional ontology, it covers factors related to safety/ADAS, powertrain, and charging, and infotainment/human-machine interaction. It outlines measurement blueprints (co-occurrence enhancement, conditional share, journey slicing) and management tools (importance matrix, demand hierarchy scorecard), and verifies that electric vehicle anxiety is more strongly influenced by charging reliability than by rated range; trust depends on service transparency and OTA stability. Finally, it prioritizes related services and user experience, while establishing a clear path for future empirical verification.
Because chips exhibit large differences in speed, power consumption, and cost across tasks, performance, energy use, and time often involve intricate trade-offs under heterogeneous workloads. As frontier models continue to scale, compute is increasingly becoming a binding constraint, yet the economic meaning of “one additional unit of compute” remains insufficiently well defined. This paper links chip specialization to cost functions, market structure, and industrial policy, and explicitly incorporates the system-level division of labor between training and inference. It further emphasizes that leading-edge process capacity is scarce, so compute tends to concentrate in a small number of firms and countries, raising entry barriers in the compute–chip ecosystem and strengthening the extent to which industrial policy instruments such as subsidies, tax incentives, and export controls can generate salient shocks to the supply of compute.
Currently, semiconductor data analysis requires processing massive amounts of real-time data, and traditional data warehouses face challenges in meeting the demands for low latency and high-concurrency queries. Therefore, this paper proposes a cloud-native Lakehouse architecture specifically designed for real-time semiconductor analysis. By introducing an innovative query routing mechanism and data lineage tracing framework, a dynamic multi-tiered storage system is designed. This system can tier data based on access frequency to achieve efficient storage and faster query performance. This research provides a practical solution to overcome the limitations of existing architectures and offers valuable insights for the future development of cloud-native platforms for real-time industrial analysis.
Metal components, due to their high prefabrication rate and discrete manufacturing characteristics, have become an ideal carrier for verifying intelligent construction technologies. However, traditional modeling methods face bottlenecks such as low efficiency, poor robustness, and semantic gaps. This study systematically reviews the research progress of Building Information Modeling (BIM) and Artificial Intelligence (AI) in the automated modeling and life-cycle management of metal components. First, it elucidates the semantic support for metal structures in the IFC 4.3 standard and the theoretical basis of BIM-AI integration. Then, from a forward design perspective, it reviews parametric modeling, deep learning topology optimization, and generative adversarial networks (GANs), while from a reverse reconstruction perspective, it outlines point cloud semantic segmentation and Scan-to-BIM automation technologies. Further, it explores intelligent management methods such as knowledge graph compliance checks, digital twin frameworks, and visual defect detection. Finally, it points out key challenges and future development directions, including data heterogeneity, scarce annotations, and interpretability.
High-QPS (Queries Per Second) services, such as large language model (LLM) inference and real-time recommendation systems, are increasingly pervasive in AI-driven applications, but their energy consumption has become a critical challenge—accounting for up to 40% of data center operational costs. Existing optimization efforts primarily focus on latency reduction and throughput improvement, overlooking the intricate interplay between KV cache management (a core component of transformer-based model inference) and inference scheduling in energy efficiency. Traditional energy modeling methods (e.g., linear regression, hardware-centric power meters) fail to capture dynamic dependencies between cache behavior, scheduling policies, and workload volatility, leading to inaccurate energy predictions and suboptimal resource allocation. To address these gaps, this study proposes a hybrid energy modeling framework tailored for high-QPS services, integrating KV cache characteristics and inference scheduling dynamics. First, we construct a multi-dimensional energy factor system encompassing four core dimensions: KV Cache Configuration (e.g., cache size, eviction policy, hit ratio), Inference Scheduling Strategy (e.g., batching size, task prioritization, resource partitioning), System Environment (e.g., CPU/GPU utilization, memory bandwidth, power capping), and Workload Traits (e.g., QPS volatility, request complexity, sequence length distribution). Second, we design a two-stage modeling approach: a data-driven component (Gradient Boosting Tree, GBT) to capture non-linear relationships between factor interactions and energy consumption, and an analytical component (Queueing Theory-based latency-energy tradeoff model) to ensure QPS and latency constraints are satisfied. Third, we validate the framework using a real-world dataset from an LLM inference service (2022–2024) with QPS ranging from 5k to 30k, comparing it against three baseline methods. Experimental results show that the proposed framework outperforms traditional models: it achieves an energy prediction accuracy of 92.7% (vs. 78.3% for linear regression and 83.5% for hardware-centric modeling), reduces energy consumption by 18.9%–25.3% while maintaining target QPS and latency SLAs (Service Level Agreements), and identifies key optimization levers (e.g., adaptive KV cache resizing based on QPS fluctuations reduces energy by 14.2%). This study provides a practical tool for system administrators to balance performance and energy efficiency, supporting the development of sustainable high-QPS AI services.
Data centers have become a core contributor to global digital carbon emissions, with their carbon footprint growing 19% annually alongside the expansion of AI and cloud services. Traditional carbon accounting methods are either trapped in macro-level rough calculation based on Power Usage Effectiveness (PUE) or limited to micro-level hardware power consumption measurement, failing to establish a traceable correlation between chip-level energy behavior and datacenter-wide carbon emissions. To address this gap, this study proposes a Hierarchical Coupling Carbon Emission Estimation Model (HCCEEM) that integrates physical modeling and graph neural network (GNN)-based statistical aggregation. The model constructs a four-level traceability chain spanning board (chip), server node, rack cluster, and campus datacenter, and introduces a real-time load adaptation module to capture dynamic workload impacts. Validated on a 14-month dataset from a heterogeneous cloud datacenter, HCCEEM achieves an estimation accuracy of 95.7%, reducing mean absolute error (MAE) by 27.1% and 19.3% compared to PUE-based models and single-level machine learning models respectively. Moreover, the model realizes fine-grained attribution of carbon contributions across levels, revealing that chip-level dynamic power consumption drives 65.2% of server emissions, and rack-level cooling losses account for 33.8% of datacenter emissions. This research provides an interpretable, scalable tool for targeted carbon reduction, bridging the gap between hardware-level optimization and datacenter-wide carbon management. Specifically, HCCEEM exhibits remarkable applicability in high-load scenarios such as large language model (LLM) training and inference, where it can reduce carbon accounting errors by over 30% compared to conventional methods. For small and medium-sized datacenters with limited monitoring resources, the model’s modular design allows lightweight deployment by simplifying partial hierarchical modules without significant accuracy loss. Additionally, the hierarchical contribution quantification function of HCCEEM can directly support enterprises’ carbon disclosure and compliance reporting, aligning with the carbon neutrality requirements of the digital industry in various regions.
