Text2Bundle: Towards Personalized Query-based Bundle Generation

Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms. Existing bundle generation methods mainly utilized user's preference from historical interactions in common recommendation paradigm, and ignored the potential textual query which is user's current explicit intention. There can be a scenario in which a user proactively queries a bundle with some natural language description, the system should be able to generate a bundle that exactly matches the user's intention through the user's query and preferences. In this work, we define this user-friendly scenario as Query-based Bundle Generation task and propose a novel framework Text2Bundle that leverages both the user's short-term interests from the query and the user's long-term preferences from the historical interactions. Our framework consists of three modules: (1) a query interest extractor that mines the user's fine-grained interests from the query; (2) a unified state encoder that learns the current bundle context state and the user's preferences based on historical interaction and current query; and (3) a bundle generator that generates personalized and complementary bundles using a reinforcement learning with specifically designed rewards. We conduct extensive experiments on three real-world datasets and demonstrate the effectiveness of our framework compared with several state-of-the-art methods

Structural Modeling And Optimization for REBCO Ring Magnet Using Coupled Electromagnetic-Thermal-Mechanical T-A Formulation

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Integrated Multi-Project Scheduling and Hierarchical Workforce Allocation in the ETO Assembly Process

The engineer-to-order (ETO) production strategy plays an important role in today’s manufacturing industry. This paper studies integrated multi-project scheduling and hierarchical workforce allocation in the assembly process of ETO products. The multi-project scheduling problem involves the scheduling of tasks of different projects under many constraints, and the workforce allocation problem involves assigning hierarchical workers to each task. These two problems are interrelated. The task duration depends on the number of hierarchical workers assigned to the task. We developed a mathematical model to represent the problem. In order to solve this issue with the minimization of the makespan as the objective, we propose a hybrid algorithm combining particle swarm optimization (PSO) and Tabu search (TS). The improved PSO is designed as the global search process and the Tabu search is introduced to improve the local searching ability. The proposed algorithm is tested on different scales of benchmark instances and a case that uses industrial data from a collaborating steam turbine company. The results show that the solution quality of the hybrid algorithm outperforms the other three algorithms proposed in the literature and the experienced project manager

β-Si3N4 Microcrystals Prepared by Carbothermal Reduction-Nitridation of Quartz

Single phase β-Si3N4 with microcrystals was synthesized via carbothermal reduction-nitridation (CRN) of quartz and carbon coke powder as starting materials. The effects of reaction parameters, i.e., heating temperature, holding time, C/SiO2 ratio, Fe2O3 additive and β-Si3N4 seeds on the phase transformation and morphology of products were investigated and discussed. Rather than receiving a mixture of both α- and β- phases of Si3N4 in the products, we synthesized powders of β-Si3N4 single polymorph in this work. The mechanism for the CRN synthesis of β-Si3N4 from quartz and the formation mechanism of Fe3Si droplets were discussed. We also firstly reported the formation of Fe3Si Archimedean solids from a CRN process where Fe2O3 was introduced as additive. Comparing to the gear-like short columnar morphology observed in samples without β-Si3N4 seeding, the addition of β-Si3N4 seeds led to an elongated morphology of final products and much finer widths. In addition, the β-Si3N4 microcrystals exhibited a violet‒blue spectral emission range, which could be highly valuable for their future potential optoelectronic applications

Comparative Analysis of the Gut Microbiota Composition between Captive and Wild Forest Musk Deer

The large and complex gut microbiota in animals has profound effects on feed utilization and metabolism. Currently, gastrointestinal diseases due to dysregulated gut microbiota are considered important factors that limit growth of the captive forest musk deer population. Compared with captive forest musk deer, wild forest musk deer have a wider feeding range with no dietary limitations, and their gut microbiota are in a relatively natural state. However, no reports have compared the gut microbiota between wild and captive forest musk deer. To gain insight into the composition of gut microbiota in forest musk deer under different food-source conditions, we employed high-throughput 16S rRNA sequencing technology to investigate differences in the gut microbiota occurring between captive and wild forest musk deer. Both captive and wild forest musk deer showed similar microbiota at the phylum level, which consisted mainly of Firmicutes and Bacteroidetes, although significant differences were found in their relative abundances between both groups. α-Diversity results showed that no significant differences occurred in the microbiota between both groups, while β-diversity results showed that significant differences did occur in their microbiota compositions. In summary, our results provide important information for improving feed preparation for captive forest musk deer and implementing projects where captive forest musk deer are released into the wild