While semantic ID-based generative retrieval enables efficient end-to-end modeling in industrial applications, these methods face a persistent trade-off. On one hand, data-rich head items often suffer from ID collisions, which blur their distinct features and degrade downstream tasks. On the other hand, data-sparse tail items especially cold-start items are prone to semantic fragmentation during quantization; they are often mapped as isolated discrete points, which severely hinders their ability to generalize. To address this issue, we propose the Anchored Curriculum with Sequential Adaptive Quantization (SA2CRQ) framework. The framework introduces Sequential Adaptive Residual Quantization (SARQ) to dynamically allocate code lengths based on item path entropy, assigning longer, discriminative IDs to head items and shorter, generalizable IDs to tail items. To mitigate data sparsity, the Anchored Curriculum Residual Quantization (ACRQ) component utilizes a frozen semantic manifold learned from head items to regularize and accelerate the representation learning of tail items. Experimental results from a large-scale industrial search system and multiple public datasets indicate that SA2CRQ yields consistent improvements over existing baselines, particularly in cold-start retrieval scenarios.

Gait recognition is one of the key technologies for exoskeleton robot control. The existing gait recognition methods cannot meet the needs of real-time control well in terms of recognition accuracy and robustness. In this paper, a gait recognition method based on the recurrent neural network and fully convolutional network (RNN-FCN) algorithm is proposed. In this paper, a human lower limb gait information acquisition device is developed, ten types of human lower limb gait data are collected, and a human gait recognition model is constructed using the RNN-FCN algorithm. The experimental results demonstrate that the RNN-FCN algorithm achieves an average recognition classification accuracy of 94.43% in the experiments, outperforming the other four algorithms.

An electromagnetic vibrational energy harvester (EVEH) capable of harvesting low-frequency and high-amplitude vibration energy is designed, fabricated and characterized in this paper. The high impact/shock-resistance of the proposed EVEH is enabled by a pair of specially designed dampers. The dampers, combined with a pair of nonlinear magnetic springs, also expands the effective bandwidth of the EVEH device. The fabricated EVEH, with a size of Φ2 cm X 10 cm, is capable of generating an open-circuit peak-to-peak voltage of 4.35 V and an average output power of 55.3 mW under a sinusoidal excitation of ±10g at 48 Hz. The designed shock/impact-resistance of the EVEH is verified by a sinusoidal excitation of ±20g at 18 Hz, and the fabricated EVEH is capable of generating an output voltage of 4.21V steadily.

The paper first discusses the characteristics of the risks within project financing. It is possible for us to predict the risks from their special roots, and the risks are transferable, separable and usable. In addition, some of the risks are mutually exclusive and their impact can be neutralized. These characteristics are somewhat different to those of the common funding methods and also provide the possibility to control and prevent them. Project financing always has several participants and complex structure. According to its complexity, from the project company's point of view, this paper analyzes the functions and conflicts of main participants, which are given out in detail in a table. The major participants discussed include government, project sponsor, project company, creditors, suppliers of equipment/raw materials/energy, contractors, and purchasers of the products or users of the project. Based upon this, we put forward the principle of risk allocation, which emphasizes the combination of the three: risks, expectation and capability, opposite to the previous rule proposed in most of the other literature: risk should be allocated to the partner, who is most capable of controlling it.