Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

在ASP.NET 2.0中自动化构建数据访问层

使用ASP.NET2.0所提供的BuildProvider类,将自动化构建的数据访问层源代码编译到程序集中,使得开发者利用Ob-jectDataSource控件,可以简单、快速地实现三层架构的Web应用。     

Evaluation of different disinfection calculation methods using CFD

Computational Fluid Dynamics combined with a particle tracking technique provides valuable information concerning residence times and contact times in chemical reactors. In drinking water treatment, for example an accurate estimation of the disinfection is important to predict the microbial safety. Ozone contactors are widely used for disinfection, but the complex geometry of the system causes suboptimal hydraulics and requires optimizations of the flow. This results in a lower ozone dosage, which may reduce the formation of unwanted disinfection-by-products and the consumption of energy. To that end disinfection needs to be calculated precisely, accounting for the complex hydraulics. Several calculation methods estimating the disinfection performance of ozone contactors were evaluated using Computational Fluid Dynamics. For an accurate disinfection prediction, the full distribution of ozone exposures (CT values) is needed, only a mean CT value or residence time distribution provides insufficient information for an accurate disinfection prediction. Adjustments to the geometry of the ozone contactor that reduce the short-circuit flows resulted in an increase in disinfection capacity, whereas the mean CT value remained the same. A sensitivity analysis with respect to the kinetics was conducted. The gain in disinfection capacity obtained by optimizing the hydraulics was significant for typical values used in practice.     

Distinct element analysis of the effect of temperature on the bulk crushing of α-lactose monohydrate

Cryogenic milling could reduce the ductility in the milling operations of semi-brittle and relatively ductile pharmaceutical particles. However, to achieve a better application of this technology, it is necessary to establish the relationship between the influence of temperature on the mechanical properties and breakage characteristics of the single particle and the bulk crushing behavior of these types of material. The focus of this paper is on the analysis of bulk crushing behavior of α-lactose monohydrate particles in response to temperature variations, based on single particle mechanical properties and side crushing strength at different temperatures and the use of distinct element analysis. The effect of temperature on the side crushing strength of the particles has been quantified by quasistatic side crushing tests. The experimental results show a significant increase in the strength of the single particles by decreasing the temperature. These results are used in the distinct element analysis to simulate the bulk crushing behavior of pharmaceutical particles as affected by the temperature. The predictions are compared with the experimental results, for which a reasonable agreement is found for the ambient temperature case. There are some differences for the case of −20°C, due to lack of reliable data for Young's modulus.     

Application of a dynamic numerical solution to high speed fracture experiments—I. analysis of experimental geometries

The application of a dynamic, generation mode, finite element program to the analysis of experimental geometries is reported. Particular attention is given to the DCB specimen, which is widely used in high speed fracture studies despite strong inertia effects, which are described. Finite strip and infinite plate results are also considered. Here, idealised cases are discussed, while in Part II the application of the analysis to experimental measurements, to derive propagating crack fracture resistance data, is reported.