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"Sun, Zhixin"
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Age of empires : art of the Qin and Han dynasties
\"The first in-depth exploration of the artistic and cultural achievements of China's \"classical\" era Age of Empires presents the art and culture of China during one of the most critical periods of its history - the four centuries from 221 B.C. to A.D. 200-- when, for the first time, people of diverse backgrounds were brought together under centralized imperial rule that fostered a new and unified identity. The Qin and Han empires represent the \"classical\" era of Chinese civilization, coinciding in both importance and timing with the Greco-Roman period in the West. Under the short-lived Qin and centuries-long Han, warring principalities were united under a common emperor, creating not only political and intellectual institutions but also the foundation for a Chinese art, culture, and national identity that lasted over two millennia. Over 150 works from across the full breadth of Chinese artistic and decorative media-- including ceramics, metalwork, textiles, armor, sculpture, and jewelry - are featured in this book and attest to the unprecedented role of art in ancient Chinese culture. These stunning objects, among them soldiers from the renowned terracotta army of Qin Shihuang, China's first emperor, are drawn from institutions and collections in China and appear here together for the first time. Essays by leading scholars, accompanied by dazzling new photography of the objects, address the sweeping societal changes underway, and trace a progression from the early, formative years through unprecedented sophistication and technical accomplishment--embodied in an artistic legacy that reverberates in China's national identity to this day\"-- Provided by publisher.
Book
A Review of Research Progress in Selective Laser Melting (SLM)
SLM (Selective Laser Melting) is a unique additive manufacturing technology which plays an irreplaceable role in the modern industrial revolution. 3D printers can directly process metal powder quickly to obtain the necessary parts faster. Shortly, it will be possible to manufacture products at unparalleled speeds. Advanced manufacturing technology is used to produce durable and efficient parts with different metals that have good metal structure performance and excellent metal thermal performance, to lead the way for laser powder printing technology. Traditional creative ways are usually limited by time, and cannot respond to customers’ needs fast enough; for some parts with high precision and complexity, conventional manufacturing methods are inadequate. Contrary to this, SLM technology offers some advantages, such as requiring no molds this decreases production time and helps to reduce costs. In addition, SLM technology has strong comprehensive functions, which can reduce assembly time and improve material utilization. Parts with complex structures, such as cavities and three-dimensional grids, can be made without restricting the shape of products. Products or parts can be printed quickly without the use of expensive production equipment. The product quality is better, and the mechanical load performance is comparable to traditional production technologies (such as forging). This paper introduces in detail the process parameters that affect SLM technology and how they affect SLM, commonly used metal materials and non-metallic materials, and summarizes the current research. Finally, the problems faced by SLM are prospected.
Journal Article
A Method for Evaluating the Competitiveness of Human Resources in High-tech Enterprises Based on Self-organized Data Mining Algorithms
The level of human resources competitiveness of high-tech companies affects the efficiency and effectiveness of enterprises to a particular extent. To achieve sustainable development of high-tech enterprises, an evaluation method of human resource competitiveness of high-tech enterprises based on a self-organized data mining algorithm is proposed. The fuzzy clustering algorithm is used to select five first-level indexes for the evaluation of HR competitiveness of high-tech companies, including human capital power, human resources policy incentive power, and human resources performance manifestation power, and to construct the initial evaluation indicator setting. The self-organized data mining algorithm is used to identify the key attributes related to the human resource competitiveness of high-tech companies within the initial assessment indicator setup, reduce the complexity of the indexes and construct the final rating index system. The multi-level fuzzy evaluation method is applied to calculate the evaluation index weights and fuzzy evaluation matrix to obtain the assessment results of HR competitivity of high-tech enterprises. The experimental results show that the information contribution rate of the evaluation index system constructed by this method is higher than 95%, which can accurately evaluate the human resource competitiveness of high-tech enterprises.
Journal Article
A novel twin time series network for building energy consumption predicting
Energy consumption prediction in buildings is crucial for optimizing energy management. The latest research faces three critical challenges: (1) Insufficient temporal correlation extraction and prediction accuracy, hindering widespread adoption and application; (2) The positive impact of timestamp embedding in time series prediction under multi-mode decomposition; and (3) The issue of adaptive coupling with multi-source data. To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. The model was evaluated on datasets from university dormitories, office buildings, and school classrooms, showing significant improvements over the optimal baseline method. For instance, on the university classroom dataset, T2SNET reduced MAE by 4.56%, RMSE by 9.45%, and MAPE by 3.16% compared to the CEEMDAN-RF-LSTM model. These results highlight T2SNET’s effectiveness in predicting building energy consumption, providing a robust solution for energy management systems. The proposed method, along with baseline model code and data, has been updated and is available at https://github.com/HaileiYuan/T2SNET-Pro.git .
Journal Article
First occurrence of the Cambrian arthropod Sidneyia Walcott, 1911 outside of Laurentia
The arthropod Sidneyia Walcott, 1911 is a remarkable animal of the Burgess Shale biota (Cambrian Miaolingian, Wuliuan; British Columbia, Canada), which has not been confidently reported from other Cambrian Konservat-Lagerstätten. Here we report the discovery of Sidneyia cf. inexpectans from the Wuliuan Mantou Formation of North China, which substantially expands the known palaeogeographical distribution of this genus. Our discovery suggests that Sidneyia had much greater dispersal ability than hitherto thought. It also confirms the presence of exceptionally preserved fossils in the Wuliuan Mantou Formation, one of the rare Burgess Shale-type deposits of North China.
Journal Article
Discussion on the Key Links in the Process of Building Material Quality Inspection and Testing
With the development of the construction industry, the applied building materials are becoming more and more diversified. As we all know, the performance and quality of the building materials themselves will have a direct impact on the quality of the construction project, which is related to the usability and safety of the construction project. If the quality of building materials cannot be inspected effectively, the probability of safety accidents in construction projects will be greatly increased, which will seriously threaten people’s lives and property. This article analyzes the development process of building material testing and testing, and on the basis of summarizing the importance of building material testing and testing, it analyzes several key aspects of building material testing and testing.
Journal Article
Research on Personalized Course Resource Recommendation Method Based on GEMRec
With the rapid growth of online educational resources, existing personalized course recommendation systems face challenges in multimodal feature integration and limited recommendation interpretability when dealing with complex and diverse instructional content. This paper proposes a graph-enhanced multimodal recommendation method (GEMRec), which effectively integrates text, video, and audio features through a graph attention network and differentiable pooling. Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. Additionally, it combines SHAP (SHapley Additive exPlanations) value computation with large language models to generate reliable and personalized recommendation explanations. Experiments on the MOOCCubeX dataset demonstrate that the GEMRec model exhibits strong convergence and generalization during training. Compared with existing methods, GEMRec achieves 0.267, 0.265, and 0.297 on the Precision@10, Recall@10, and NDCG@10 metrics, respectively, significantly outperforming traditional collaborative filtering and other deep learning models. These results validate the effectiveness of multimodal feature integration and knowledge graph enhancement in improving recommendation performance.
Journal Article
A Multi-Agent Reinforcement Learning-Based Task-Offloading Strategy in a Blockchain-Enabled Edge Computing Network
In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme’s comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.
Journal Article
Neighbor discovery latency in bluetooth low energy networks
Bluetooth Low Energy (BLE) networks have shown great promise as the Internet of Things (IoT) takes center stage. BLE devices featuring low-power are suitable for IoT applications. Meanwhile, the standard neighbor discovery protocol provides a wide range settings of parameters, which chould meet the variety of IoT applications. Whereas the different settings also have a great influence on neighbor discovery latency. This paper presents a theoretical model based on the Chinese Remainder Theorem (CRT) for analyzing the neighbor discovery latency in BLE networks, where the scanner and the advertiser are modeled in 3-distributed channels. The neighbor discovery latency in BLE is derived by applying the CRT to each specific channel. According to the simulations of the proposed model, we found some interesting results, which offers a better understanding of the relationship between parameters and latency performance. Meanwhile, the results provide a valuable clue to optimize the neighbor discovery latency.
Journal Article
Research on multi decision making security performance of IoT identity resolution server based on AHP
The application scenarios of IoT (Internet of Things) are complex and diverse. Failure of security defense in any part of IoT can lead to huge information leakage and incalculable losses. IoT security issues are affecting and limiting its application prospects, and have become one of the hotspots in the field of IoT. Identity resolution security of IoT has become a core issue in solving the security problem of IoT. The aim of this paper is to apply AHP, a well-known decision making method, to IoT identity resolution security. Selecting 6 indicators, several pairwise comparison matrices are constructed based on scores from experts and lab researchers. The AHP method is used to calculate malicious resolution value as a quantitative basis for judging the security performance of each resolution server. An experimental case is used to verify the validity and correctness of the AHP-based IoT identity resolution security evaluation model.
Journal Article