Congruences and ideals in pseudoeffect algebras

This paper is devoted to congruences and ideals in pseudoeffect algebras. Let I be a normal ideal in a pseudoeffect algebra E. We show that: (1) the relation ~ _I induced by I is a congruence if and only if for every a∈E, I∩ [0,a] is upper directed; (2) the relation ~ _I induced by I is a strong congruence if and only if I is a normal weak Riesz ideal in a pseudoeffect algebra E. Moreover, we introduce a stronger concept of congruence—namely Riesz strong congruence—and we prove that, if I is a normal weak Riesz ideal in a pseudoeffect algebra E, then ~ _I is a Riesz strong congruence and, conversely, if ~ is a Riesz strong congruence, then I = [0]_~ is a normal weak Riesz ideal, and ~ _I = ~. This work was supported by the National Natural Science Foundation of China (Grant No. 10271069).     

Approximate inference for dynamic Bayesian networks: sliding window approach

Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN to make inferences in uncertain conditions. Unfortunately, exact inference or even approximation in a DBN has been proved to be NP-hard and is generally computationally prohibitive. In this paper, we investigate a sliding window framework for approximate inference in DBNs to reduce the computational burden. By introducing a sliding window that moves forward as time progresses, inference at any time is restricted to a quite narrow region of the network. The main contributions to the sliding window framework include an exploration of its foundations, explication of how it operates, and the proposal of two strategies for adaptive window size selection. To make this framework available as an inference engine, the interface algorithm widely used in exact inference is then integrated with the framework for approximate inference in DBNs. After analyzing its computational complexity, further empirical work is presented to demonstrate the validity of the proposed algorithms.     

Self-Adhesive Polydimethylsiloxane Foam Materials Decorated with MXene/Cellulose Nanofiber Interconnected Network for Versatile Functionalities

Polydimethylsiloxanes (PDMS) foam as one of next-generation polymer foam materials shows poor surface adhesion and limited functionality, which greatly restricts its potential applications. Fabrication of advanced PDMS foam materials with multiple functionalities remains a critical challenge. In this study, unprecedented self-adhesive PDMS foam materials are reported with worm-like rough structure and reactive groups for fabricating multifunctional PDMS foam nanocomposites decorated with MXene/cellulose nanofiber (MXene/CNF) interconnected network by a facile silicone foaming and dip-coating strategy followed by silane surface modification. Interestingly, such self-adhesive PDMS foam produces strong interfacial adhesion with the hybrid MXene/CNF nano-coatings. Consequently, the optimized PDMS foam nanocomposites have excellent surface super-hydrophobicity (water contact angle of ≈159^o), tunable electrical conductivity (from 10⁻⁸ to 10 S m⁻¹), stable compressive cyclic reliability in both wide-temperature range (from −20 to 200 ^oC) and complex environments (acid, sodium, and alkali conditions), outstanding flame resistance (LOI value of >27% and low smoke production rate), good thermal insulating performance and reliable strain sensing in various stress modes and complex environmental conditions. It provides a new route for the rational design and development of advanced PDMS foam nanocomposites with versatile multifunctionalities for various promising applications such as intelligent healthcare monitoring and fire-safe thermal insulation.     

变结构动态贝叶斯网络的机制研究

传统的动态贝叶斯网络(Dynamic Bayesian networks, DBNs)描述的是一个稳态过程,而处理非稳态过程,变结构动态贝叶斯网络更适 用、更灵活、更有效.为了克服现有变结构离散 动态贝叶斯网络推理算法只能处理硬证据的缺陷,本文在深入分析变结构动态贝叶斯网络机制及其特 征的基础上,提出了变结构离散动态贝叶斯网络的 快速推理算法.此外,对变结构动态贝叶斯网络的特例,即数据缺失动态贝叶斯网络进行了定义并构建 了相应的模型.仿真实验验证了变结构离散动态贝 叶斯网络快速推理算法的有效性及计算效率.     

Cartesian product of compressible effect algebras

In the paper, we prove that is compatible with p}, the set of commutant of p, and , the projection commutant of a, are all normal sub-effect algebras of a compressible effect algebra E, and is a direct retraction on E} is a normal sub-effect algebra of an effect algebra E. Moreover, we answer an open question in Gudder’s (Rep Math Phys 54:93–114, 2004), Compressible effect algebras, Rep Math Phys, by showing that the cartesian product of an infinite number of E _i is a compressible effect algebra if and only if each E _i is a compressible effect algebra. This work was supported by the SF of Education Department of Shaanxi Province (Grant No. 07JK267), P. R. China.     

多层喷射沉积板坯设备的优化设计

将原始多层喷射沉积板坯设备改为由计算机控制，步进电机驱动，钢丝卷筒结构传动，介绍了该设备的设计思路，实际生产应用表明，优化后的设备操作简单平稳，工作环境良好，制备出的板坯形貌平整，厚薄均匀。     

基于近红外光谱的大黄鱼新鲜度评价模型

目的 探索定量评价大黄鱼新鲜度的方法。方法 在整鱼背部采集近红外光谱, 将原始光谱预处理后分别与挥发性盐基氮(TVB-N)、菌落总数建立偏最小二乘(PLS)模型、区间偏最小二乘(iPLS)模型、向后区间偏最小二乘(biPLS)模型和联合区间偏最小二乘(siPLS)模型。结果 biPLS模型的精度最高、预测性能最佳。TVB-N的biPLS模型的校正集和预测集相关系数分别为0.8371和0.7652; 菌落总数的biPLS模型的校正集和预测集相关系数分别为0.878和0.7009。结论 大黄鱼的近红外光谱信息与其TVB-N、菌落总数间都存在较高的相关性, 所建模型可以快速、无损地定量评价大黄鱼的新鲜度。     

卷烟主流烟气中焦油释放量的测量不确定度评定

目的评定卷烟主流烟气中焦油含量的测量不确定度,为测定主流烟气中焦油含量提供有效、可靠的测量数据。方法用吸烟机对卷烟主流烟气中的粒相成分进行捕集,应用气相色谱法对粒相成分中的烟碱和水分含量进行测定,再由总粒相物含量得出卷烟烟气中焦油含量。根据JJF(烟草)1-2007的指导方法,对上述焦油整个测量过程进行研究,系统分析得出不确定度来源和不确定度数学模型的基础上,确定焦油测量结果的置信区间,表述出了合成标准不确定和扩展不确定度。结果焦油测量不确定度的主要来源是由气相色谱测定曲线拟合及测试环境波动造成的,因此在检测过程中应尽量提高拟合曲线的线性度,并减小测试环境的波动,才能进一步的减小测量不确定度。结论该样品的焦油含量为8.98 mg/支,扩展不确定度为0.15 mg/支。     

无线移动环境中基于移动Agent的实时构件组装机制研究

由于无线网络较窄、昂贵的带宽，及移动设备自身有限的硬件能力，在无线移动环境中进行实时构件组装调用时面临着更多的技术挑战，需要一种高效、可靠的机制给予支持。本文提出一种基于移动Agent的无线网络环境构件组装调用机制。该机制利用移动Agent作为移动客户端与构件服务器之间交互的中介，提供了灵活、可配置的组装结构，从而有效避免移动客户端不必要的访问操作，节省了移动客户端的无线网络连接开销，与其他机制相比有着较高的执行效率；同时该机制可有效支持移动客户端发生故障后的状态恢复，从而确保运行过程的可靠性。最后本文通过实验分析了MAWA机制的可靠性与高效性。     

Synthesis of alpha-alumina nanoribbons: characterization and their morphological evolution

Single crystalline α-alumina nanoribbons were synthesized by reacting aluminum with silicon monoxide at high temperature. The products were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM), and high-resolution transmission electron microscope (HRTEM). The intense peaks of XRD pattern indicate that the prepared nanoribbons have a high degree of crystallinity. In the present work, silicon monoxide was used as an oxidant and precursor, which served to control the reaction rate. An interesting morphological evolution that nanoribbons resulted from nanosaws was described, and these novel nanosaws were also carefully characterized.