A novel pre-dilution,swirling jet diffuser to enhance effluent mixing: Hydrodynamics and dilution performance

Diffusers are widely-used to quickly dilute effluents in receiving water bodies. This study proposed a novel diffuser that pre-mixes effluent with ambient water before discharging and that uses the swirling jet to further enhance near-field dilution. The nozzle of the diffuser was examined in two ambient flow conditions: co-flow and counter-flow that are commonly-met in the environment such as oceans due to tidal effect. Physical experiments were first conducted in co-flow on its dilution performance and hydrodynamics, using heated water as the effluent. A 3-D CFD model was developed and calibrated the co-flow scenarios, and then used to investigate the diffuser in counter-flow. The results showed that the nozzle can effectively reduce the maximum temperature rise of the effluent by about 50 % before discharging. The swirling jet from the outlet has a larger shear area, half-width and entrainment rate, enabling the effluent to be rapidly diluted to a minimum of around 10 times at x/D = 6 in co-flow, whereas the dilution for conventional nozzles is about 1 because of the potential core. The flow amplification ratio (α) decreases gradually with increasing velocity ratio in co-flow but increases with increasing velocity ratio in counter-flow. The counter-flow reduces the water drawn into the device; however, the pre-dilution effect at the outlet remains stable. The near-field dilution in counter-flow was significantly enhanced than that in co-flow. Environmental regulations at outfalls and mixing zones can be more easily met using this novel diffuser.     

以新发展理念引领高校专业基础课教学改革

以“创新、协调、绿色、开放、共享”为内核的新发展理念，是对马克思主义发展理念的继承和发扬，极具时代精神，富含问题意识，为高校思想政治教育发展、教育教学改革实践提供了强大的理论支撑。本文以“通信原理”为例，阐述了新发展理念在课程改革中的思路和方法，实现了思想政治教育与专业基础课程有机融合，为深化高校教学改革、创新人才培养模式提供了思路。     

Passive micromixer with baffles distributed on both sides of microchannels based on the Koch fractal principle

Since the beginning of the 21st Century, the development of microfluidic chip technology has been very rapid and has attracted the attention of more and more scholars. As an important part of the microfluidic chip, the performance of the micromixer is critical. The fractal structure in the microchannels helps to improve the mixing performance of the micromixer and improve the mixing efficiency of the micromixer. The research results of other scholars are of great significance to the research of the present paper, which mainly studies the effect of changing the baffle state on the mixing efficiency of the micromixer based on the Koch fractal principle. Through simulation analysis, it was found that the mixing efficiency of the baffles distributed on both sides of the microchannel was higher than the mixing efficiency of the baffles distributed on the microchannel side. When the distance between adjacent baffles was divided into 0.15, 0.25 and 0.35 mm, simulated data suggested that the baffle distance of 0.15 mm was best. Increasing the number of baffles from six to eight groups increased the mixing path of the fluid in the microchannel and improved mixing efficiency. A comparison of mixing efficiencies of the 0°, 15° and 30° baffle angles revealed that very significant improvement in mixing efficiency was obtained at 30°. © 2019 Society of Chemical Industry     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

“The innovation governance dilemma: Alternatives to the precautionary principle”

This article reviews the four innovation governance approaches (the precautionary principle, responsible innovation, permissionless innovation, and the innovation principle), including definitions, important attributes, and weaknesses found in each approach, and when utilizing an affinity diagram as a tool of analysis, identifies their distinctive characteristics and common relationships. A discussion section summarizes the paper’s findings and offers insights into where there is common relationships for further possible convergence between two innovation governance approaches – responsible innovation and permissionless innovation – that conceptually share substantially more in common than they contrast with each other. For addressing this challenge, the study recommends the following policy proposals: embrace artificial intelligence/machine learning/data analytics for risk management and regulatory adaptability; consider “soft law”as an option to public regulation; and substitute corporate citizenship for corporate social responsibility.     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

An iterative method for suboptimal control of linear time-delayed systems

This article presents a new approach for solving the Optimal Control Problem (OCP) of linear time-delay systems with a quadratic cost functional. The proposed method can also be used for designing optimal control time-delay systems with disturbance. In this study, the Variational Iteration Method (VIM) is employed to convert the original Time-Delay Optimal Control Problem (TDOCP) into a sequence of nonhomogeneous linear two-point boundary value problems (TPBVPs). The optimal control law obtained consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence. The feedback term is determined by solving Riccati matrix differential equation. By using the finite-step iteration of a nonlinear compensation sequence, we can obtain a suboptimal control law. Finally, Illustrative examples are included to demonstrate the validity and applicability of the technique.     

最大塑性功原理在烧结体上限分析中的应用

最大塑性功原理又称第二塑性变分原理，在致密体塑性变形分析叶l是运用能量法进行变形力能计算的基础。本文从分析Drucker公设的亢要条件出发，证明了烧结体塑性变形最大塑性功原理的存在，片在该原理的荩础卜，推导出塑性变形的上限功率表达式，并以平面挤压条件下的上限法应用作为实例加以说明。