A quantum-like text representation based on syntax tree for fuzzy semantic analysis

To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words.

Optimizing the supply of essential goods under closed-off management: A case study of COVID-19

The COVID-19 pandemic is threatening people's health and economic development around the world. Many areas and cities are opting for actions such as lockdown or closed-off management to prevent rapid spread of the virus. Under these actions, it is necessary and important to guarantee the supply of essential goods. In this paper, we develop a multi-objective optimization model to optimize the supply of essential goods under closed-off management. A mixed binary integer programming model is built to help identify the optimal safety stock level as well as the supply network so that the demands for essential goods could be guaranteed. The effectiveness of our method is demonstrated by an example. We also perform sensitivity analysis for key parameters. Our work provides a reasonable solution for the supply of essential goods under closed-off management.

Vehicle Routing Optimization for Pandemic Containment: A Systematic Review on Applications and Solution Approaches

The global spread of the novel coronavirus (COVID-19) has accounted for many deaths. The effective containment of the current COVID-19 epidemic calls for a fast and sustainable delivery strategy to minimize the impact of this crisis. As such, this study aimed to conduct a comprehensive review of research on the vehicle routing problem (VRP) from a sustainable viewpoint during the pandemic and explore viable delivery solutions that may aid in the containment of the COVID-19 pandemic. Through a systematic review of the selected articles, four broad themes of pandemic containment measures from the delivery aspect were identified: efficient pharmaceutical delivery strategy, contactless distribution, sustainable waste transportation strategy, and isolated and quarantine vehicle scheduling. Following that, the methodology utilized to execute the containment measures were analyzed, research gaps were hightlighted, and possibilities for future studies were suggested. In summary, the goal of this research is to provide an overview of the literature on the application of VRPs in pandemic control and to assist academics and practitioners in learning more about the performance metrics, models, and solution techniques utilized in pandemic control delivery operations.