Suning (China)

The digital transformation of grant management in higher education is essential to enhance transparency, efficiency, and accountability in research and community service programs. However, many institutions still depend on frag mented monitoring mechanisms, limited real-time information access, and systems that are not fully user oriented, leading to in effective supervision and delayed decision making. This study aims to design and develop a higher education grant monitoring platform based on Human-Centric Orange Technology to support real-time tracking, structured reporting, and process transparency across the grant lifecycle. The research adopts a system development approach combined with qualitative analysis, including requirement analysis, system design, implementation, and user evaluation through interviews and direct observations involving academic and administrative stakeholders. This approach enables a comprehensive understanding of user needs, usability considerations, and system relevance within the institutional grant management context. The findings indicate that the proposed platform improves monitoring effectiveness through real-time grant status visualization, transparent information access, and integrated reporting features, while enhancing user experience through an intuitive, role based, and human centric interface. The study concludes that implementing a Human-Centric Orange Technology based monitoring platform can strengthen grant governance, improve transparency, and optimize administrative efficiency in higher education institutions in a user oriented manner.

Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. The pretrain-and-finetune paradigm offers a promising approach for efficiently adapting to new robot entities and tasks. Inspired by the idea that acquired knowledge can accelerate learning new tasks with the same robot and help a new robot master a trained task, we propose a latent training framework where a transferable latent-to-latent locomotion policy is pretrained alongside diverse task-specific observation encoders and action decoders. This policy in latent space processes encoded latent observations to generate latent actions to be decoded, with the potential to learn general abstract motion skills. To retain essential information for decision-making and control, we introduce a diffusion recovery module that minimizes information reconstruction loss during pretrain stage. During fine-tune stage, the pretrained latent-to-latent locomotion policy remains fixed, while only the lightweight task-specific encoder and decoder are optimized for efficient adaptation. Our method allows a robot to leverage its own prior experience across different tasks as well as the experience of other morphologically diverse robots to accelerate adaptation. We validate our approach through extensive simulations and real-world experiments, demonstrating that the pretrained latent-to-latent locomotion policy effectively generalizes to new robot entities and tasks with improved efficiency.

In the era of digital intelligence, this paper studies the task allocation algorithm of distributed large data stream group computing, and reasonably allocates the task of group computing to meet the needs of massive computing and analysis of distributed large data stream. According to the idea of swarm intelligence perception and crowdsourcing platform, the task allocation model of distributed large data stream group computing is constructed to realize the task allocation of group computing. A distributed large data stream group computing task model and a user model are constructed, user attributes are initialized by using the accuracy of the answers submitted by users, the possibility that users can participate in the group computing task is predicted by a logistic regression algorithm, so that user candidate sequences participating in the computing task can be obtained, and the accuracy of the user’s real topics and corresponding topics can be grasped by capturing the candidate users’ real topics and evaluating the accuracy algorithm. Select the users who meet the subject area, update the candidate user sequence, and filter the users again on the basis of fully considering the factors such as information gain, user integrity and cost, so as to get the final user sequence and complete the task allocation of group computing. Experiments show that this method can solve the problem of distributed large data flow group computing task allocation, achieve high accuracy, reduce the cost, and effectively improve the information gain.

Abstract In the construction stage, due to construction errors and longitudinal differential settlement during tunnel operation, the amount of dislocation and opening at the segment joint increases, increasing the likelihood of water leakage. Therefore, it is necessary to conduct an in-depth study on the influence of the dislocation and opening amount at the segment joint on the contact stress of the section. Firstly, through theoretical analysis, this paper deduces that the waterproof performance of the gasket depends not only on its own contact area, linear compression stiffness, and Poisson’s ratio but also on the height of the segment joint specimen and the amount of joint opening caused by the sinking offset angle. Then, the effects of different openings and dislocations at the segment joints on the contact stress of the segment gasket section were compared using numerical simulation and model experiments. The results show that the influence of the opening amount on the waterproof performance of the tunnel segment joint’s sealing gasket is greater than that of the dislocation, and the waterproof rate of the joint of the segment sealing gasket section is greater than 40% under the modified condition.