On the first Hochschild cohomology of admissible algebras

Our aim in this paper is to investigate the first Hochschild cohomology of {\em admissible algebras} which can be seen as a generalization of basic algebras. For this purpose, we study differential operators on an admissible algebra. Firstly, differential operators from a path algebra to its quotient algebra as an admissible algebra are discussed. Based on this discussion, the first cohomology with admissible algebras as coefficient modules is characterized, including their dimension formula. Besides, for planar quivers, the $k$-linear bases of the first cohomology of acyclic complete monomial algebras and acyclic truncated quiver algebras are constructed over the field $k$ of characteristic $0$.Comment: 16 page

Experimental investigation on thermal comfort model between local thermal sensation and overall thermal sensation

To study the human local and overall thermal sensations, a series of experiments under various conditions were carried out in a climate control chamber. The adopted analysis method considered the effect of the weight coefficient of local average skin temperature and density of the cold receptors’ distribution in different local body areas. The results demonstrated that the thermal sensation of head, chest, back and hands is warmer than overall thermal sensation. The mean thermal sensation votes of those local areas were more densely distributed. In addition, the thermal sensation of arms, tight and calf was colder than the overall thermal sensation, which pronounced that thermal sensation votes were more dispersed. The thermal sensation of chest and back had a strong linear correlation with overall thermal sensation. Considering the actual scope of air-conditioning regulation, the human body was classified into three local parts: a) head, b) upper part of body and c) lower part of body. The prediction model of both the three-part thermal sensation and overall thermal sensation was developed. Weight coefficients were 0.21, 0.60 and 0.19 respectively. The model provides scientist basis for guiding the sage installation place of the personal ventilation system to achieve efficient energy use

Leveraging Reaction-aware Substructures for Retrosynthesis Analysis

Retrosynthesis analysis is a critical task in organic chemistry central to many important industries. Previously, various machine learning approaches have achieved promising results on this task by representing output molecules as strings and autoregressively decoded token-by-token with generative models. Text generation or machine translation models in natural language processing were frequently utilized approaches. The token-by-token decoding approach is not intuitive from a chemistry perspective because some substructures are relatively stable and remain unchanged during reactions. In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach. Our approach achieved improvement over previously reported models, and we find that the performance can be further boosted if the accuracy of substructure extraction is improved. The substructures extracted by our approach can provide users with better insights for decision-making compared to existing methods. We hope this work will generate interest in this fast growing and highly interdisciplinary area on retrosynthesis prediction and other related topics.Comment: Work in progres

Shape-programmable Adaptive Multi-material Microrobots for Biomedical Applications

Flagellated microorganisms can swim at low Reynolds numbers and adapt to changes in their environment. Specifically, the flagella can switch their shapes or modes through gene expression. In the past decade, efforts have been made to fabricate and investigate rigid types of microrobots without any adaptation to the environments. More recently, obtaining adaptive microrobots mimicking real microorganisms is getting more attention. However, even though some adaptive microrobots achieved by hydrogels have emerged, the swimming behaviors of the microrobots before and after the environment-induced deformations are not predicted in a systematic standardized way. In this work, experiments, finite element analysis, and dynamic modeling are presented together to realize a complete understanding of these adaptive microrobots. The above three parts are cross-verified proving the success of using such methods, facilitating the bio-applications with shape-programmable and even swimming performance-programmable microrobots. Moreover, an application of targeted object delivery using the proposed microrobot has been successfully demonstrated. Finally, cytotoxicity tests are performed to prove the potential for using the proposed microrobot for biomedical applications