Abrasion-Resistant and Enhanced Super-Slippery Flush Toilets Fabricated by a Selective Laser Sintering 3D Printing Technology

Flush toilets waste a significant amount of water every day due to the unavoidable adhesions between human waste and the toilet surfaces. Super-slippery surfaces can repel complex fluids and various viscoelastic solids, however, are easily broken by mechanical abrasions. Herein, the fabrication of an abrasion-resistant super-slippery flush toilet (ARSFT) is reported using a selective laser sintering 3D printing technology. Unlike traditional super-slippery surfaces with limited thicknesses which can be easily worn away, the powder-sintered strategy endows the ARSFT not only with a self-supporting 3D complex shape but also with a porous structure that can accommodate considerable lubricants for an abrasion-resistant super-slippery property. As a result, the as-prepared ARSFT remains clean after contacting with various liquids such as milk, yogurt, highly sticky honey, and starch gel mixed congee, demonstrating excellent repellence to complex fluids. Besides liquids, the ARSFT exhibits a high resistance to sticky synthetic feces. Notably, even after being abraded to 1,000 cycles of abrasion using sandpaper, the ARSFT maintains its record-breaking super-slippery capability. The concept of the 3D-printed object with a superior abrasion-resistant slippery ability will improve the development of super-slippery materials and further save water consumption in the human society.     

Preparation of Porous Silver Nanowires-Melamine Formaldehyde Hybrid Composite Materials and Their Electromagnetic Shielding and Flame-Retardant Properties

Along with the booming development of communication technology and electronic equipment, higher requirements of flame-retardant and EMI shielding performances for electromagnetic interference (EMI) shielding materials are put forward. Herein, the ultralight and porous silver nanowires (AgNWs)-melamine formaldehyde (MF) hybrid composite with unique micro-/nanostructure is developed by a facile dip-coating method, which uses the AgNWs as 1D conductive coating and MF foam (MF foam) as 3D skeleton template. Benefiting from the unique porous micro-/nanostructure, the resultant hybrid composite displays low density, excellent EMI shielding performances, and superior flame-retardant property. The EMI shielding effectiveness (SE) and specific EMI SE (SSEt) of the hybrid composite in X-band (8.2–12.4 GHz) can be up to 77 dB and 26971.4 dB cm⁻² g⁻¹, respectively. At the same time, the hybrid composite also passes the vertical burning test and shows an increased LOI value of 40.6%. The combination of flame-retardant and EMI shielding performances for EMI shielding materials makes the AgNWs-MF hybrid composite great application potential in civil and military fields. This work provides a new guide for the design of multifunctional high-performance EMI shielding materials.     

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.     

Stability and boundedness in difference systems with finite delay

In this paper, stability and boundedness theorems for delay difference systems with the condition

δV(n,x(n))≤-W₂(|x(n)|)+σ

are given, where Δ is the backward difference operator. Some known results are generalized.     

TMLNN: triple-valued or multiple-valued logic neural network

We discuss the problem of representing and processing triple-valued or multiple-valued logic knowledge using neural network. A novel neuron model, triple-valued or multiple-valued logic neuron (TMLN), is presented. Each TMLN can represent a triple-valued or multiple-valued logic rule by itself. We will show that there are two TMLNs: TMLN-AND (triple-valued or multiple-valued "logic AND") neuron and TMLN-OR (triple-valued or multiple-valued "logic OR") neuron. Two simplified TMLN models are also presented, and show that a multilayer neural network made up of triple-valued or multiple-valued logic neurons (TMLNN) can implement a triple-valued or multiple-valued logic inference system. The training algorithm for TMLNN is presented and can be shown to converge. In our model, triple-valued or multiple-valued logic rules can be extracted from TMLNN with ease. TMLNN can thus form a base for representing logic knowledge using neural network.     

Amino acid sequence of a Ca(2+)-transporting ATPase from the sarcoplasmic reticulum of the cross-striated part of the adductor muscle of the deep sea scallop: comparison to serca enzymes of other animals

Emotion regulation has been conceptualized as the extrinsic and intrinsic processes responsible for monitoring, facilitating, and inhibiting heightened levels of positive and negative affect. Regulation of distress is related to the use of certain behavioral strategies. Our study examined whether putative regulatory behaviors widely assumed to be conceptually associated with these strategies are actually empirically associated with the changes in fearful and angry distress in 6-, 12-, and 18-month-old infants. Our key finding was that the use of some putative regulatory behaviors (e.g., distraction and approach) reduced the observable intensity of anger but were less effective in reducing the intensity of fear. The results suggest (1) caution in assuming that postulated regulatory behaviors actually have general distress-reducing effects and (2) the likelihood that "distress" is too global a construct for research on emotion regulation.     

汽车铸件生产工时数理统计计算方法

采用多元非线性回归分析方法 ,对我国汽车铸造生产管理数据进行微机辅助分析 ,得出了铸件生产工时计算公式 ,编制了计算机辅助管理程序。     

新一代动三轴仪计算机数据采集与处理系统

在８０年代研制的动三轴仪基础上,采用现代计算机技术,实现了对动三轴试验全过程动应力、动应变和动孔压发展变化的实时采集、显示和数据文件在线存盘。不仅使数据处理过程全部自动化,且成功地解决了滞回圈等非规则曲线面积及莫尔圆公切线求解等一系列难题。     

双枝模糊集(Ⅲ)

提出双枝模糊集Ｓ的理论,本课题是［Ⅰ,Ⅱ］研究的继续．在单枝模糊集理论（Ｌ．Ａ．ＺａｄｅｈＦｕｚｚｙＳｅｔＴｈｅｏｒｙ）研究中,由于引进λ－截集Ｓλ的概念,便产生了单枝模糊集的并－普通分解定理［３］,交－普通分解定理［４］．［Ⅰ,Ⅱ］提出了双枝模糊集Ｓ,由于引进了λ－截集Ｓλ的概念,便产生了双枝模糊集Ｓ的并－普通分解定理,交－普通分解定理．人们自然提出这样的问题：双枝模糊集可以分解成若干个普通集Ｓλ;双枝模糊集Ｓ是否能分解成若干个模糊集Ｓα？本文的研究说明：双枝模糊集Ｓ可以分解成若干个模糊集Ｓα．（一个单枝模糊集Ａ也可以分解成若干个模糊集Ａα,但是在单枝模糊集理论中没有被人们去研究．）本文给出Ｓ的α－嵌入集Ｓα的概念,提出Ｓ的α－嵌入定理,Ｓ的α－嵌入模糊分解定理．     

双枝模糊集(Ⅱ)

提出双枝模糊集Ｓ的理论,是（Ｉ）研究的继续．提出双枝模糊集Ｓ的并－普通分解定理,交－普通分解定理．这些结论建立了双枝模糊集Ｓ与普通集Ｓλ之间的关系,这些结论为建立双枝模糊推理,双枝模糊控制作了理论准备．结论指出：（１）双枝模糊集Ｓ同时存在并－普通分解形式：Ｓ＝∪λ∈［－１,１］λＳλ和交－普通分解形式：Ｓ＝∩λ∈［－１,１］λＳλ;这两种分解形式是建立双枝模糊推理理论,双枝模糊控制理论的理论基础．（２）单枝模糊集Ａ（Ｌ．Ａ．ＺａｄｅｈＦｕｚｚｙＳｅｔＡ）的并－普通分解定理,交－普通分解定理是双枝模糊集Ｓ的并－普通分解定理,交－普通分解定理的特例．