Effect of coral sand powders and seawater salinity on the impact mechanical properties of cemented coral sand

Coral sand is one kind of the important building materials in coral reef engineering practice. The use of cement as a stabilizing agent can significantly improve the mechanical properties of coral sands and is widely applied in the subbase engineering construction in coral reef islands. Cement-stabilized coral sand structures may contain high contents of fine coral particles and salinity because of the high crushability of coral sands and the existence of seawater surrounding them. In this study, the effects of coral sand powders and seawater salinity on the dynamic mechanical properties of cemented coral sand (CCS) were investigated through the split Hopkinson pressure bar (SHPB) tests and Scanning Electron Microscope (SEM) analysis. It was found that the strength (i.e., the peak stress) of CCS specimens increased firstly and then decreased with the increase of powder content. The specimens reached the maximum peak stress when 3% powder content was included. The initial improvement of CCS strength was attributed to the pore-filling effect of coral powders, namely, the micro pores of the CCS specimens could be more effectively filled with higher percentages of coral powders being used in the experiments. However, excessive coral powders resulted in the reduction of specimen strength because these powders could easily be cemented into agglomerates by absorbing water from the specimens. These agglomerates could reduce the cementation strength between the coarse coral particles and the cement. Meanwhile, the peak stress of CCS specimens was found to be negatively correlated with the average strain rate and the ultimate strain. The degree of specimen fracture was found to be correlated with the amount of specific energy absorption during the tests. Furthermore, the “sulfate attack” caused by the inclusion of salinity of water had different influences on the CCS specimens with different coral powder contents. The ettringite and gypsum produced in “sulfate attack” could fill the pores and lead to cracking of the specimens, significantly affecting the specimen strength.     

Generation,validation and application of dynamic load profiles in flow measurement using cavitating Herschel–Venturi nozzles

Today, utility meters for water are tested for measurement behavior at stable operating conditions at specified flow rates as part of the approval process. The measurement error that occurs during start and stop or when changing between flow rates may not be taken into account. In addition, there are new technologies whose measuring behavior under real-world conditions is only known to a limited extend. To take these facts into account, a new method has been developed and tested to determine the measurement behavior of water meters under dynamic load profiles as they occur in the real application. For this purpose, a test rig for flow rate measurement was extended by a cavitation nozzle apparatus and the generation of dynamic load profiles was validated. For the cavitation nozzles used, possible factors influencing the flow rate, such as temperature and purity of the water as well as the upstream pressure were investigated. Using different types of domestic water meters, the applicability of the dynamic test procedure was demonstrated and the measurement behavior of the meters was characterised.     

Microenvironment-Sensitive Fluorescent Ligand Binds Ascaris Telomere Antiparallel G-Quadruplex DNA with Blue-Shift and Enhanced Emission

The development of small molecules that can selectively target G-quadruplex (G4) DNAs has drawn considerable attention due to their unique physiological and pathological functions. However, only a few molecules have been found to selectively bind a particular G4 DNA structure. We have developed a fluorescence ligand Q1 , a molecular scaffold with a carbazole–pyridine core bridged by a phenylboronic acid side chain, that acts as a selective ascaris telomere antiparallel G4 DNA ASC20 ligand with about 18 nm blue-shifted and enhanced fluorescence intensity. Photophysical properties revealed that Q1 was sensitive to the microenvironment and gave the best selectivity to ASC20 with an equilibrium binding constant K_a=6.04×10⁵ M⁻¹. Time-resolved fluorescence studies also demonstrated that Q1 showed a longer fluorescence lifetime in the presence of ASC20. The binding characteristics of Q1 with ASC20 were shown in detail in a fluorescent intercalator displacement (FID) assay, a 2-Ap titration experiment and by molecular docking. Ligand Q1 could adopt an appropriate pose at terminal G-quartets of ASC20 through multiple interactions including π–π stacking between aromatic rings; this led to strong fluorescence enhancement. In addition, a co-staining image showed that Q1 is mainly distributed in the cytoplasm. Accordingly, this work provides insights for the development of ligands that selectively targeting a specific G4 DNA structure.     

基于旋翼负压混合吸附的爬壁清洗机器人系统动力学性能研究

针对爬壁清洗机器人越障时,负压装置的吸附力下降导致吸附不稳定的问题,设计了一种旋翼负压混合吸附工作的多边形履带清洗机器人,并对爬行稳定性及越障性能进行了动力学分析。首先根据机器人爬壁的运动原理,建立机器人沿玻璃幕墙上的动力学模型,计算出电机理论驱动力矩;其次分析机器人在跨越障碍过程中的运动模型,结合与壁面接触的实际受力状态,对越障过程中关键阶段的本体倾翻、滑移两种失效形式进行运动学和动力学分析;然后根据玻璃幕墙实际的障碍高度,确定多边形履带的参数和理论驱动力矩的大小,并研制了实验样机进行爬行和越障试验,结果表明所设计的机器人具有良好的越障性能。     

多机器人体间动态碰撞检测方法仿真研究

当前多结合包围求碰撞检测法、Average-Case法、K-DOPs法等实现多机器人体间动态碰撞的检测,均存在寻优性能较差、检测效率较低的问题。为此提出一种基于动态粒子群的多机器人体间动态碰撞检测方法。采用OBB层次包围盒方法,缩小多机器人之间需要动态碰撞检测的区域,同时把动态碰撞检测问题转换为物体特征对间距离机制的非线性优化问题,进而构建层次拓扑框架进行局部碰撞检测,将机器人体引入到粒子群算法中建立混合进化算法,找到动态碰撞检测的最优解,实现多机器人体间动态碰撞检测。仿真结果证明,所提方法的检测效率高达96%,且具有较高的寻优性能。     

Quality assurance and quality control of subgrade compaction using the dynamic cone penetrometer

The Dynamic Cone Penetrometer (DCP) is a device that is used in the construction industry for the assessment of in situ soil compaction quality. Over the past few decades, numerous correlations have been developed between the DCP test results and soil strength and stiffness parameters. This paper proposes a comprehensive set of criteria and recommendations for quality control (QC) of compacted subgrade that take into account the inherent statistical variability of DCP test results. For the development of the QC criteria, a new statistical methodology is used to extract representative test values from the raw field DCP test data. In order to use the proposed QC criteria, soils are first classified into two broad categories (fine-grained and coarse-grained) depending on their fabric and response to compaction efforts. Test results indicate that (i) for fine-grained soils, the DCP test values have good correlation with the plasticity index (PI), which is indicative of the type and amount of clay content of the soil and (ii) for coarse-grained soils, the DCP test values have good correlation with the optimum water content of the soil, which is directly related to its in situ density. DCP blow count correlation equations are presented for both soil categories. Recommendations for field DCP testing and data analysis are also provided to highlight the significance of the statistical distribution of the DCP test results in QC testing of compacted subgrade.     

Computing approximate blocking probabilities for transparent waveband switching based WDM networks using hierarchical cross-connects

An analytical model is presented to study the dynamics of wavelength division multiplexing (WDM) networks with waveband switching (WBS). The reduced load approximation method is considered to compute approximated network blocking probabilities in WBS-based WDM networks. The analytical model considers the link blocking probability due to insufficient link capacity and an impact of the waveband granularity (G). The analytical model also considers the node blocking probability due to unavailability of a switch port at the wavelength cross connect (WXC) layer of an Hierarchical cross connect (HXC) switch node. The set of nonlinear equations is obtained with the link independence assumption and solved using repeated substitutions. The accuracy of the analytical model is examined by comparing with simulation results considering the random-fit algorithm for waveband and wavelength assignments in different network scenarios. Lightpaths are routed between source and destination (s-d) HXC switch nodes using shortest path first (SPF) routing. An impact of the switch parameter to limit the input and the output WXC switch ports of an HXC switching node is also being investigated using the analytical model as well as through simulation results.     

Practices,projects and portfolios: Current research trends and new directions

Project portfolio management (PPM) bridges strategy and project management. Traditional research in PPM has primarily investigated the rational, top-down and structural aspects of strategizing. By doing so, it has failed to focus on the underlying practices that are triggered by the strategy and how these practices frame strategy implementation. Practice-based research provides a methodological lens to explore the reality of strategic enactment through the project portfolio. Practice-based perspectives are under-represented in PPM research; therefore the aim of this paper is to provide an agenda for further practice-based research in PPM. Central to this agenda is a concern with various aspects of practice, including its discursivity, representation, dynamic capabilities, leadership and materiality.     

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.