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 nanometre size oxide particles using filter expansion aerosol generator

Crystalline zinc oxide particles of nanometre size (< 20 nm) particles were produced from a zinc acetate solution by using a filter expansion aerosol generator (FEAG). The FEAG is an aerosol generator that is operated at 60 torr reactor pressure and produces droplets of around 2 m. The shape of the particles produced by the FEAG were distinctively different from those produced by the ultrasonic spray source (USS). Results from scanning electron microscopy (SEM) and X-ray diffractometry (XRD) indicated that crystalline zinc oxide particles of nanometre size were produced at 600 °C in 0.02 s residence time. Weight loss of these particles, determined by thermogravimetric analysis (TGA), was 3 wt%. Based on the morphology change as a function of the reactor temperature and solute concentration, we proposed that the nanometre particles were formed by uniform precipitation at the drying stage and decomposition followed by disintegration into nanometre particles. It was also shown that exothermic decomposition of the solute was not required for the disintegration of the primary particles in the FEAG process. This result opened up an opportunity for producing carbon-free nanometre particles from nitrate salts by using the FEAG.     

嵌入式系统芯片的软硬件协同仿真环境设计

针对嵌入式系统芯片SoC开发验证阶段的需求,介绍了一种通用的SoC软硬件协同仿真平台。软件仿真由C／C＋＋和汇编语言编写,硬件仿真基于VMM验证方法学所搭建,SoC设计代码由RTL代码编写而成。将SoC设计代码中的ARM由DSM模型替代,通过VCS编译器将软硬件协同起来进行信息交互,实现一种速度快、真实性高、调试方便的...     

Effect of axial and radial velocities on solids mass flow rate measurement

In coal powder flow transportation and combustion, powder mass flow rate is a key parameter to be monitored and controlled. Electrostatics is one of the techniques used for such task with its non-intrusive, robust, and low cost natures. The passive electrostatic meters measure charge induced on the detecting electrodes. As it is known that the induced signal is not only dependent on the solids mass flow rate, but also affected by solids velocity. However, the velocity of particles usually referred to is the axial velocity. In reality, the solids velocity is a vector, its projections in both the radial and tangential directions also need to be investigated. This paper analyses the dynamic sensitivity of ring-shaped electrostatic sensors using the finite element method (FEM), and investigates the influence of the axial and radial velocities on the induced signal on the electrodes.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

Stabilization of linear systems with input delay and saturation—A parametric Lyapunov equation approach

This paper studies the problem of stabilizing a linear system with delayed and saturating feedback. It is known that the eigenstructure assignment‐based low‐gain feedback law (globally) stabilizes a linear system in the presence of arbitrarily large delay in its input, and semi‐globally stabilizes it when the input is also subject to saturation, as long as all its open‐loop poles are located in the closed left‐half plane. Based on a recently developed parametric Lyapunov equation‐based low‐gain feedback design method, this paper presents alternative, but simpler and more elegant, feedback laws that solve these problems. The advantages of this new approach include its simplicity, the capability of giving explicit conditions to guarantee the stability of the closed‐loop system, and the ease in scheduling the low‐gain parameter on line to achieve global stabilization in the presence of actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessment of working conditions in two different semiconductor manufacturing lines: Effective ergonomics interventions

This article examines two manufacturing lines producing semiconductors using different technology concepts, namely Conventional Line (CL) and Lean Production Line (LPL). Both lines manufacturing the same products were compared using various factors, including working conditions, task risks and dangers of the job, and physical body stress. Ergonomic approaches were adopted in the investigation of the two lines. Survey questionnaires were administered to 30% of the workers, and multiple statistical tests were used to determine crucial predictors and to investigate the interactions between the factors. This research has shown that improved ergonomics factors will lead to better working conditions and thus increased job satisfaction. The observed ergonomic differences of these two production lines are compared, and appropriate managerial remedial action is recommended. The interventions to both the lines should reduce accident rates, minimize waste of resources, improve work effectiveness, and provide a better working environment that enhances employees' morale and maximizes productivity and profits. © 2010 Wiley Periodicals, Inc.     

Using jump-diffusion modeling for valuing real options in infrastructure projects

In infrastructure financed projects, in order to attract private investors, host governments often provide some guarantees. This paper develops a value model of minimum revenue guarantee with multiple-exercise real options under the impact of the emergency incident. The model is applied to infrastructure financed projects using the minimum revenue guarantee under simulation. The simulation results indicate that, before quantifying the value of the minimum revenue guarantee, it is necessary to forecast the jump degree and intensity of the emergency incident, as well as prevent and control risks arising from such emergencies. Otherwise, underestimation of the guarantee value will occur and the government will have to bear huge debt in this condition. We also analyze the dependence of the guaranteed value on the minimum guaranteed revenue level, initial revenue and number of exercise rights. For various conditions, the diagrams of the guaranteed value are also presented.     

Partner selection in a virtual enterprise under uncertain information about candidates

Partner selection is a major issue in the formation of a virtual enterprise. In practice, in the partner selection process, the information about the candidates and their performances are incomplete and uncertain. Vague sets theory is one of the methods used to deal with uncertain information. In this paper, a new method based on vague sets is proposed to deal with the partner selection problem in the formation of a virtual enterprise while the factors of satisfaction degree, due date, cost and the precedence of tasks are taken into account. On the basis of the agreement index of satisfaction degree, the formulated partner selection problems are interpreted so as to maximize the minimum agreement index. To solve the problem, an improved particle swarm optimization (PSO) algorithm is proposed. Finally, the simulation of a numerical example demonstrates that the method is effective.     

A belief-rule-based inventory control method under nonstationary and uncertain demand

This paper is devoted to investigating inventory control problems under nonstationary and uncertain demand. A belief-rule-based inventory control (BRB-IC) method is developed, which can be applied in situations where demand and demand-forecast-error (DFE) do not follow certain stochastic distribution and forecasting demand is given in single-point or interval styles. The method can assist decision-making through a belief-rule structure that can be constructed, initialized and adjusted using both manager’s knowledge and operational data. An extended optimal base stock (EOBS) policy is proved for initializing the belief-rule-base (BRB), and a BRB-IC inference approach with interval inputs is proposed. A numerical example and a case study are examined to demonstrate potential applications of the BRB-IC method. These studies show that the belief-rule-based expert system is flexible and valid for inventory control. The case study also shows that the BRB-IC method can compensate DFE by training BRB using historical demand data for generating reliable ordering policy.