The Inquiry of Storytelling Narrative in the Museum Display Design

This article starts from space story construction, display scene design and the “people-oriented†experience design direction, combines with quality cases that leading the world museum display design, discusses how to use the storytelling narrative into museum display design. The paper will help better promote the plot development between space and exhibits, exhibits, people and exhibits, optimize thesocial education function of traditional museum, and spread exhibition information more effectively

Enhanced cancer therapy with cold-controlled drug release and photothermal warming enabled by one nanoplatform

Stimuli-responsive nanoparticles hold great promise for drug delivery to improve the safety and efficacy of cancer therapy. One of the most investigated stimuli-responsive strategies is to induce drug release by heating with laser, ultrasound, or electromagnetic field. More recently, cryosurgery (also called cryotherapy and cryoablation), destruction of diseased tissues by first cooling/freezing and then warming back, has been used to treat various diseases including cancer in the clinic. Here we developed a cold-responsive nanoparticle for controlled drug release as a result of the irreversible disassembly of the nanoparticle when cooled to below ∼10 °C. Furthermore, this nanoparticle can be used to generate localized heating under near infrared (NIR) laser irradiation, which can facilitate the warming process after cooling/freezing during cryosurgery. Indeed, the combination of this cold-responsive nanoparticle with ice cooling and NIR laser irradiation can greatly augment cancer destruction both in vitro and in vivo with no evident systemic toxicity

Drug Resistance of Ocular Bacteria Considering Biofilm Mechanism

In order to further analyze the relationship between the coating mechanism of microorganisms and their drug resistance, a study of ocular bacterial drug resistance considering the coating mechanism of microorganisms was proposed. Firstly, the mechanism of drug resistance was analyzed, and on this basis, the experimental study was carried out. Staphylococcus aureus DH5 with RP4 was used as the control α( R) Objective to investigate the relationship between drug-resistant bacteria and coating mechanism in the cross genus conjugation system of Pseudomonas aeruginosa PAOi and donor bacteria. The conclusion is that: under the condition that the horizontal transfer of drug-resistant genes between transgeneric bacteria in biofilm is inhibited, the frequency of drug-resistant gene conjugation and transfer gradually decreases, and the inhibition of the formation of drug-resistant bacterial biofilm will directly lead to the decrease of bacterial drug resistance

UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding

This paper explores the development of UniFolding, a sample-efficient, scalable, and generalizable robotic system for unfolding and folding various garments. UniFolding employs the proposed UFONet neural network to integrate unfolding and folding decisions into a single policy model that is adaptable to different garment types and states. The design of UniFolding is based on a garment's partial point cloud, which aids in generalization and reduces sensitivity to variations in texture and shape. The training pipeline prioritizes low-cost, sample-efficient data collection. Training data is collected via a human-centric process with offline and online stages. The offline stage involves human unfolding and folding actions via Virtual Reality, while the online stage utilizes human-in-the-loop learning to fine-tune the model in a real-world setting. The system is tested on two garment types: long-sleeve and short-sleeve shirts. Performance is evaluated on 20 shirts with significant variations in textures, shapes, and materials. More experiments and videos can be found in the supplementary materials and on the website: https://unifolding.robotflow.aiComment: CoRL 202

Statistical analysis of micro-variability properties of the blazar S5 0716+714

The typical blazar S5 0716$+$714 is very interesting due to its rapid and large amplitude variability and high duty cycle of micro-variability in optical band. We analyze the observations in I, R and V bands obtained with the $1.0m$ telescope at Weihai observatory of Shandong University from 2011 to 2018. The model of synchrotron radiation from turbulent cells in a jet has been proposed as a mechanism for explaining micro-variability seen in blazar light curves. Parameters such as the sizes of turbulent cells, the enhanced particle densities, and the location of the turbulent cells in the jet can be studied using this model. The model predicts a time lag between variations as observed in different frequency bands. Automatic model fitting method for micro-variability is developed, and the fitting results of our multi-frequency micro-variability observations support the model. The results show that both the amplitude and duration of flares decomposed from the micro-variability light curves confirm to the log-normal distribution. The turbulent cell size is within the range of about 5 to 55 AU, and the time lags of the micro-variability flares between the I-R and R-V bands should be several minutes. The time lags obtained from the turbulence model are consistent with the fitting statistical results, and the time lags of flares are correlated with the time lags of the whole light curve.Comment: 12 pages, 11 figures, Accepted by Ap

General multi-agent reinforcement learning integrating heuristic-based delay priority strategy for demand and capacity balancing

Reinforcement learning (RL) techniques have been studied for solving the demand and capacity balancing (DCB) problem in air traffic management to exploit their full computational potential. Due to the lack of generalisation and the seemingly reduced optimisation performance affected by the training scenarios, it is challenging for existing RL-based DCB methods to be effectively applied in practice. This paper proposes a general multi-agent reinforcement learning (MARL) method that integrates a heuristic-based delay priority strategy to improve the efficiency of the solution and the generalisation of the model. The delay priority strategy is used to reduce the potential learning task and thus training difficulty. This study explores what features of the delay priority strategy are better suited to the MARL method. A long short-term memory (LSTM) network is integrated into a deep q-learning network (DQN) to ensure the model compatible with arbitrary DCB instances and to facilitate agents to identify key sectors. This study is conducted as a part of a large-scale European DCB research project, where real data from French and Spanish airspace are used for experimentation. Results suggest that the proposed method has advantages in generalisation, optimisation performance and computational performance over state-of-the-art RL-based DCB methods