Numerical Study on Shear Performance of a New Perfobond Connector with Controllable Stiffness

To improve the shear behavior and design applicability of rubber ring perfobond connectors (RPBLs), a new rubber ring that aims to make the shear stiffness of RPBLs controllable was proposed. Firstly, the conceptual design and configuration of the new rubber rings were presented and discussed. Subsequently, finite element (FE) models for modified push-out tests of new RPBLs were established based on the validated modeling method. The initial shear stiffness is dominated by the horizontal projected contact area between hole walls and concrete dowels. γ is defined as the ratio of the horizontal projected length of hollows to the diameter of holes. The shear stiffness of new RPBLs is about 35%, 60%, and 82% of the shear stiffness of PBLs when γ equals 0.25, 0.5, and 0.75, respectively. Employing the new rubber rings with varying central angles on conventional PBLs is feasible to obtain the required stiffness for RPBLs. Further, the effects of the number of sectors, the size of side wings, the central angle of hollows, the offset angle, and the thickness of rubber rings were analysed. Based on the numerical results, the proper thickness of side wings is no larger than 2 mm. The thicker side wing could reduce the confinement effects provided by surrounding concrete on concrete dowels, resulting in a drop of the yield load of new RPBLs. The number of sectors is suggested to be no less than 6 so that the shear behavior of new RPBLs is irrelevant to the offset angle. Besides, the shear stiffness is not related to the thickness of rubber rings. To improve the yield load of RPBLs and obtain the moderate recovered stiffness, the thickness of rubber rings is recommended as 2 mm. Finally, the expression for the shear stiffness of new RPBLs was proposed.Steel & Composite Structure

Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility

Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities.Sanitary Engineerin

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on decision-making styles and further emphasis on incentives and punishments. Additionally, we model a traffic scene via graph model to better represent the interaction between vehicles, and adopt the graph convolutional network (GCN) to extract the features of the graph structure to help the connected autonomous vehicles perform decision-making directly. Furthermore, we combine GCN with deep Q-learning and multi-step double deep Q-learning to train four decision-making modes, which are named the graph convolutional deep Q-network (GQN) and the multi-step double graph convolutional deep Q-network (MDGQN). In the simulation, the superiority of the reward function matrix is proved by comparing it with the baseline, and evaluation metrics are proposed to verify the performance differences among decision-making modes. Results show that the trained decision-making modes can satisfy various driving requirements, including task completion rate, safety requirements, comfort level, and completion efficiency, by adjusting the weight values in the reward function matrix. Finally, the decision-making modes trained by MDGQN had better performance in an uncertain highway exit scene than those trained by GQN.Transport and Plannin

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This article presents an efficient learning-based method to solve the <italic>inverse kinematic</italic> (IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and ManufacturingMechatronic Desig

Fe powder catalyzed highly efficient synthesis of alkenyl halides via direct coupling of alcohols and alkynes with aqueous HX as exogenous halide sources

A simple and efficient catalytic method for the synthesis of alkenyl halides via direct coupling of alcohols and alkynes using aqueous HX (X=Cl, Br) as halide sources has been developed under mild conditions in the presence of Fe powder (1 mol %). In comparison with the high loading of FeX3 in previously reported protocols, the present approach provides a remarkable attractive methodology to a diverse range of alkenyl halides due to the advantages of simple operation and low-level metal contamination. (C) 2015 Elsevier Ltd. All rights reserved

Phosphomonoesterase Activities, Kinetics and Thermodynamics in a Paddy Soil After Receiving Swine Manure for Six Years

Soil phosphomonoesterase plays a critical role in controlling phosphorus (P) cycling for crop nutrition, especially in P-deficient soils. A 6-year field experiment was conducted to evaluate soil phosphomonoesterase activities, kinetics and thermodynamics during rice growth stages after consistent swine manure application, to understand the impacts of swine manure amendment rates on soil chemical and enzymatic properties, and to investigate the correlations between soil enzymatic and chemical variables. The experiment was set out in a randomized complete block design with three replicates and five treatments including three swine manure rates (26, 39, and 52 kg P ha(-1), representing low, middle, and high application rates, respectively) and two controls (no-fertilizer and superphosphate at 26 kg P ha(-1)). The results indicated that the grain yield and soil chemical properties were significantly improved with the application of P-based swine manure from 0 to 39 kg P ha(-1); however, the differences between the 39 (M-39) and 52 kg P ha(-1) treatments (M-52) were not significant. The enzymatic property analysis indicated that acid phosphomonoesterase was the predominant phosphomonoesterase in the tested soil. The M-39 and M-52 treatments had relatively high initial velocity (V-0), maximal velocity (V-max), and activation grade (lgN(a)) but low Michaelis constant (K-m), temperature coefficient (Q(10)), activation energy (E-a), and activation enthalpy (Delta H), implying that the M-39 and M-52 treatments could stimulate the enzyme-catalyzed reactions more easily than all other treatments. The correlation analysis showed that the distribution of soil phosphomonoesterase activities mainly followed the distributions of total C and total N. Based on these results, 39 kg P ha(-1) could be recommended as the most appropriate rate of swine manure amendment.Soil phosphomonoesterase plays a critical role in controlling phosphorus (P) cycling for crop nutrition, especially in P-deficient soils. A 6-year field experiment was conducted to evaluate soil phosphomonoesterase activities, kinetics and thermodynamics during rice growth stages after consistent swine manure application, to understand the impacts of swine manure amendment rates on soil chemical and enzymatic properties, and to investigate the correlations between soil enzymatic and chemical variables. The experiment was set out in a randomized complete block design with three replicates and five treatments including three swine manure rates (26, 39, and 52 kg P ha(-1), representing low, middle, and high application rates, respectively) and two controls (no-fertilizer and superphosphate at 26 kg P ha(-1)). The results indicated that the grain yield and soil chemical properties were significantly improved with the application of P-based swine manure from 0 to 39 kg P ha(-1); however, the differences between the 39 (M-39) and 52 kg P ha(-1) treatments (M-52) were not significant. The enzymatic property analysis indicated that acid phosphomonoesterase was the predominant phosphomonoesterase in the tested soil. The M-39 and M-52 treatments had relatively high initial velocity (V-0), maximal velocity (V-max), and activation grade (lgN(a)) but low Michaelis constant (K-m), temperature coefficient (Q(10)), activation energy (E-a), and activation enthalpy (Delta H), implying that the M-39 and M-52 treatments could stimulate the enzyme-catalyzed reactions more easily than all other treatments. The correlation analysis showed that the distribution of soil phosphomonoesterase activities mainly followed the distributions of total C and total N. Based on these results, 39 kg P ha(-1) could be recommended as the most appropriate rate of swine manure amendment

The on-orbit calibration of DArk Matter Particle Explorer

The DArk Matter Particle Explorer (DAMPE), a satellite-based cosmic ray and gamma-ray detector, was launched on December 17, 2015, and began its on-orbit operation on December 24, 2015. In this work we document the on-orbit calibration procedures used by DAMPE and report the calibration results of the Plastic Scintillator strip Detector (PSD), the Silicon-Tungsten tracKer-converter (STK), the BGO imaging calorimeter (BGO), and the Neutron Detector (NUD). The results are obtained using Galactic cosmic rays, bright known GeV gamma-ray sources, and charge injection into the front-end electronics of each sub-detector. The determination of the boundary of the South Atlantic Anomaly (SAA), the measurement of the live time, and the alignments of the detectors are also introduced. The calibration results demonstrate the stability of the detectors in almost two years of the on-orbit operation

The on-orbit calibration of DArk Matter Particle Explorer

The DArk Matter Particle Explorer (DAMPE), a satellite-based cosmic ray and gamma-ray detector, was launched on December 17, 2015, and began its on-orbit operation on December 24, 2015. In this work we document the on-orbit calibration procedures used by DAMPE and report the calibration results of the Plastic Scintillator strip Detector (PSD), the Silicon-Tungsten tracKer-converter (STK), the BGO imaging calorimeter (BGO), and the Neutron Detector (NUD). The results are obtained using Galactic cosmic rays, bright known GeV gamma-ray sources, and charge injection into the front-end electronics of each sub-detector. The determination of the boundary of the South Atlantic Anomaly (SAA), the measurement of the live time, and the alignments of the detectors are also introduced. The calibration results demonstrate the stability of the detectors in almost two years of the on-orbit operation

The on-orbit calibration of DArk Matter Particle Explorer

The DArk Matter Particle Explorer (DAMPE), a satellite-based cosmic ray and gamma-ray detector, was launched on December 17, 2015, and began its on-orbit operation on December 24, 2015. In this work we document the on-orbit calibration procedures used by DAMPE and report the calibration results of the Plastic Scintillator strip Detector (PSD), the Silicon-Tungsten tracKer-converter (STK), the BGO imaging calorimeter (BGO), and the Neutron Detector (NUD). The results are obtained using Galactic cosmic rays, bright known GeV gamma-ray sources, and charge injection into the front-end electronics of each sub-detector. The determination of the boundary of the South Atlantic Anomaly (SAA), the measurement of the live time, and the alignments of the detectors are also introduced. The calibration results demonstrate the stability of the detectors in almost two years of the on-orbit operation