Spherical fuzzy decision making method based on combined compromise solution for IIoT industry evaluation

The Industrial Internet of Things is crucial for enterprise and country to drive the strategic upgrade and raise the level of national intelligent manufacturing. When pondering the IIoT industry evaluation, the corresponding dominating issues involve numerous indeterminacies. Spherical fuzzy set, portrayed by memberships of positive, neutral and negative, is a more efficient methods of seizing indeterminacy. In this article, firstly, the fire-new spherical fuzzy score function is explored for solving some suspensive comparison issues. Moreover, the objective weight and combined weight are determined by Renyi entropy method and non-linear weighted comprehensive method, respectively. Later, the multi-criteria decision making method based on combined compromise solutionis developed under spherical fuzzy environment. Finally, the corresponding method is effectively validated by the issue of IIoT industry evaluation. The main characteristics of the presented algorithm are: (1) without counterintuitive phenomena; (2) no division or antilogarithm by zero problem; (3) no square root by negative number issue; (4) no violation of the original definition issue.

     

设计语用学的范式研究

目的确定设计语用学研究范畴,建立设计语用学的研究范式,进而通过设计语用学理论发展对设计方法论提供新的理论依据。方法从设计符号传播语境视角出发,对设计方、用户和用户的社交对象之间的设计符号传播模式进行论述。结论结合整合营销传播理论和设计事理学理论,得出了设计语用学的研究范畴、研究意义、研究核心及整合产品设计营销传播与定义设计符号使用场景两种设计语用学的研究路径,完成了设计语用学的理论框架。     

高效能空气净化器评价技术要求国家标准研究

本文论述了编制高效能空气净化器产品评价技术要求国家标准的意义和作用,介绍了标准研制的总体思路和原则,高效能空气净化器的定性和定量评价指标要求,测试、计算和评价方法等,通过国标的研制与实施,将引领并促进高效能空气净化器产品的生产与应用.     

快速检验技术在自制炸药爆炸事故现场勘查中的应用

针对自制炸药爆炸事故造成人员伤亡、财产损失巨大,社会影响恶劣的情况,采用红外光谱法(IR)和拉曼光谱法(Raman)等快速检验技术对爆炸现场的遗留物、残留物和抛散物等进行检验、初步定性和来源推断。通过一起自制炸药爆炸事故的现场勘查和物证临场快速检验,实现了对自制炸药主要组分的快速推断和爆炸现场重建,并对爆炸事故原因进行了分析判定,可为同类爆炸事故现场快速勘验和原因分析提供借鉴,对提升爆炸事故现场勘查效率,实现爆炸事故原因快速认定具有重要的应用价值,并对查清犯罪事实,打击犯罪和维护社会稳定有着重要的作用。     

Large capacitance and fast polarization response of thin electrolyte dielectrics by spin coating for two-dimensional MoS2 devices

Nano Research - A spin-coating method was applied to obtain thinner and smoother PEO/LiClO4 polymer electrolyte films (EFs) with a lower level of crystallization than those obtained using a...     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.