Synthesis of colloidal nanosilica from waste glass powder as a low cost precursor

Waste glass powder was used as a low cost precursor for production of colloidal nanosilica for the first time. The process includes production of wet silica gel and thermal peptization of the wet gel. Purification of the glass powder and wet gel production were initiated by acid washing. The obtained powder was reacted with sodium hydroxide to produce wet silica gel. Type of the applied acid was examined in one factor at a time route. Temperature of the alkaline step and concentrations of the applied acid and base were investigated using Taguchi design of experiments. After finding the best combination of the investigated factor levels in production of the wet gel, time of the stabilization in thermal peptization was studied. Characterizations of the wet gel and colloidal silica were performed by XRF, DLS, FESEM, TEM, FTIR and N₂ sorption evaluation. Accordingly pure and stable colloidal nanosilica (98.50%) with average particle size of 21.9?nm was produced from the glass powder successfully. Specific surface area of the dried porous optimum sample was 83.63?m²/g.     

Patient involvement in Danish hospital design

This paper examines patient-involvement in the process used to design new hospitals. Denmark is in the midst of a phase of hospital building. This recent experience is studied by means of expert interviews with senior practitioners involved in three projects. Examined thematically, the data covers the structure of the design process, identification and ranking of stakeholders, the methods of user-involvement and approaches to accessibility. The paper adds new insight on an under-studied area, design for hospitals and makes recommendations for a change of approach to user-participation in their design. It highlights the difficulties in applying the methods of user-centred design and Design for All in large projects.     

Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting

The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly to an approximate solution, but can take a long time to converge to a solution of high-accuracy. Previously, Anderson acceleration has been applied to ADMM, by treating it as a fixed-point iteration for the concatenation of the dual variables and a subset of the primal variables. In this paper, we note that the equivalence between ADMM and Douglas-Rachford splitting reveals that ADMM is in fact a fixed-point iteration in a lower-dimensional space. By applying Anderson acceleration to such lower-dimensional fixed-point iteration, we obtain a more effective approach for accelerating ADMM. We analyze the convergence of the proposed acceleration method on nonconvex problems, and verify its effectiveness on a variety of computer graphics including geometry processing and physical simulation.     

深水水下管汇的结构设计概述

以南海某油气田的深水水下管汇为例,结合管汇的功能需求、结构组成、结构计算等,对深水水下管汇的结构设计进行了概述。     

基于CFD-DEM方法的净化器流场模拟与结构优化

针对空气净化器能耗高的问题，使用离散元方法(DEM)在吸附滤网中建立随机堆积柱形活性炭模型，采用计算流体力学(CFD)方法对空气净化器内部流场进行数值模拟，在模拟与实验验证的基础上，考察了压降最小、流场最均匀的吸附滤网结构。结果表明，空气净化器压降主要发生在轴向，活性炭吸附滤网中回流、沟流现象严重，流体阻力是其他两种滤网的3倍。边数对多边形填充孔结构吸附滤网内压降与流场均匀性无影响，当孔结构改为圆形时，压降减小约52 Pa，节能18.4%(49 W)；当孔直径由8 mm增至12 mm，压降减小约48 Pa，节能19.4%(45 W)；滤网间距对空气净化器压降无影响，圆形、小孔径的吸附滤网内流场最均匀。     

S6-11综放工作面回风巷支护设计优化

针对常村矿回采巷道变形严重,影响正常生产的问题,通过对矿井赋存条件、巷道现有支护形式和围岩变形过程分析,采用新型联合支护技术对S6-11回风巷道支护进行优化。现场实测巷道顶底板相对移近量最大120mm,两帮相对移近量最大140mm,均在允许正常范围之内。     

Optimal design of Raman fibre amplifier based on terminal value optimization strategy and shuffled frog leaping algorithm

This paper introduces an evolutionary algorithm, Shuffled Frog Leaping Algorithm (SFLA), to solve the optimization problem in designing the multi-pumped Raman Fibre Amplifier (RFA). SFLA is a powerful optimizer tool because of its efficient mathematical expressions and global search capability. We utilize SFLA to determine the optimal pump wavelengths and pump powers by minimizing the gain ripple of RFA. To accelerate calculations, a terminal value optimization strategy (TVOS) is incorporated into the evolution of SFLA. This proposed strategy takes the terminal power values of pumps as the decision variables in optimization. Then, the optimal original power values of the pumps are obtained by solving the Power Coupled Equations once, without using the traditional method of repetitive guesses.The combination of SFLA and TVOS enhances the efficiency of optimization and accelerates calculation, while satisfying the design requirements of RFA.The simulation results show that nearly 65% of computational time has been saved compared with the traditional average power analysis. The 4-pumped C+L band of backward multi-pumped RFA with the average net gain of 0 dB, 1 dB and 2 dB are designed individually, where the gain ripple is less than 0.64 dB. The combination of SFLA and TVOS enhance the optimization efficiency and improve the performance of RFA with good gain profile.     

利用调度优化模型研究原油调合方案的可行性

针对某沿海炼油企业拟投用原油调合系统以稳定混炼原油的性质,设计方案要求将部分码头罐的操作模式由混储改为单储的情况,采用约束规划与数学规划复合建模,实现了码头至厂区大容积长输线模拟,建立了该企业的原油调度优化模型并研究了设计方案的可行性。针对240h调度周期、10种可加工原油、3艘到港油轮的复杂工况进行了优化计算。结果表明:该设计方案具备可行性,且油轮仍可实现到港即卸油,蒸馏装置混炼油种也可长时间保持稳定;模型的优化计算时间较短(小于15min),且对长输线收付及存油情况模拟准确,复合建模方法满足工业应用要求。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.