铸铁溶液中氧的含量及形态

本文研究了铸铁溶液中全氧量的变化,并用定氧探头检测了溶解氧量。研究表明,铁水中全氧量随冲天炉送风湿度增加而明显增大。铁水含氧量增高主要是化合氧增多。而溶解氧变化很小。     

强化环保设计,促进水泥工业的可持续发展

从烟(粉)尘排放总量控制、大气污染物排放标准、清洁生产、中水回用、环境绿化和加强环境法制观念等方面,提出强化水泥建设项目环保设计和建设、促进水泥工业可持续发展的想法和建议。     

Spatial and temporal variability in lake trout diets in Lake Ontario as revealed by stomach contents and stable isotopes

Lake trout (Salvelinus namaycush) are an ecologically and economically important piscivore with reported differences in diet and feeding behaviour throughout its range. Eleven stomach content and stable isotope-based metrics were used to describe diets of 349 lake trout between two years (2013 and 2018) and among geographic zones (west, central, east, Kingston basin) in Lake Ontario. Using individual (e.g., volumetric, %V) and aggregate (e.g., index of relative importance, %IRI) diet metrics, we found an overwhelming dominance of alewife (Alosa pseudoharengus) in lake trout diets among some zones in 2013 (%V = 23.3 – 92.7; %IRI = 12.2 – 99.5) and all zones in 2018 (%V = 83.9 – 96.7; %IRI = 96.5 – 100). Round goby (Neogobius melanostomus) and rainbow smelt (Osmerus mordax) were secondary lake trout prey items with relative diet percentages only marginally reflected by spatial and temporal variation in prey abundance (round goby: %V = 1.0 – 33.3, %IRI = 0.1 – 13.2; rainbow smelt: %V = 2.5 – 54.0, %IRI = 0.1 – 54.0). Carbon (δ¹³C) and nitrogen (δ¹⁵N) isotopic niche areas and orientations were similar across all year-zone combinations reinforcing temporal and spatial consistency in lake trout diet. The findings of this study advance the time series in describing Lake Ontario lake trout diets and can be used to complement stock assessments and management decisions associated with carrying capacity for the diverse salmonid community.     

水体总磷测定方法的改进

采用过氧化氢消解水样，测定水中总磷的含量，春精密度为1.0%，回收率为96.8%-99.1%之间，此方法操作简单，稳定，准确，快速。     

Diets,population structure,and seasonal activity patterns of mudpuppies (Necturus maculosus) in an urban,Great Lakes coastal habitat

Freshwater species native to the Laurentian Great Lakes region face numerous environmental stressors, and the conservation status and ecological relationships of many remain poorly understood. One such species, the mudpuppy (Necturus maculosus), is declining, but better information on their natural history and development of more effective population monitoring techniques is needed. We assessed seasonal variation in capture success, biases in capture techniques, and feeding ecology of mudpuppies in Wolf Lake, a highly perturbed and urban former estuarine wetland complex to Lake Michigan. Trapping periods of ≥ 3 consecutive nights occurred from January to May 2015, and October 2015 to March 2016. Overall trapping success differed among trapping periods (p = 0.01) and declined precipitously at water temperatures above 14.1 °C (p < 0.001). Mudpuppies in traps (mean 26.9 ± 0.5 cm) were larger than those caught with hand nets (mean 14.7 ± 0.8 cm, p < 0.0001), suggesting that multiple methods may be needed to accurately assess demographics. Stomach contents obtained through gastric lavage included mollusks, leeches, insects, isopods, amphipods, crayfish, fish, a frog, and a juvenile conspecific. Invasive species, including rusty crayfish (Orconectes rusticus), round gobies (Neogobius melanostomus), and zebra/quagga mussels (Dreissena spp.) were present in guts, suggesting mudpuppy foraging has changed along with aquatic communities in the region. Prey community analyses revealed differences in overall diet among size classes of mudpuppies (p = 0.001), but relatively weak similarity within size classes. Results suggest that mudpuppies in lake ecosystems occupy a broad niche that changes as they grow.     

A Truncated Sum of Processing‐Times–Based Learning Model for a Two‐Machine Flowshop Scheduling Problem

Scheduling with learning effects has gained increasing attention in recent years. A well‐known learning model is called “sum‐of‐processing‐times‐based learning” in which the actual processing time of a job is a nonincreasing function of the jobs already processed. However, the actual processing time of a given job drops to zero precipitously when the normal job processing times are large. Moreover, the concept of learning process is relatively unexplored in a flowshop environment. Motivated by these observations, this article addresses a two‐machine flowshop problem with a truncated learning effect. The objective is to find an optimal schedule to minimize the total completion time. First, a branch‐and‐bound algorithm incorporating with a dominance property and four lower bounds is developed to derive the optimal solution. Then three simulated annealing algorithms are also proposed for near‐optimal solution. The experimental results indicated that the branch‐and‐bound algorithm can solve instances up to 18 jobs, and the proposed simulated annealing algorithm performs well in item of CPU time and error percentage. © 2011 Wiley Periodicals, Inc.     

Total curvature variation fairing for medial axis regularization

We present a new fairing method for planar curves, which is particularly well suited for the regularization of the medial axis of a planar domain. It is based on the concept of total variation regularization. The original boundary (given as a closed B-spline curve or several such curves for multiply connected domains) is approximated by another curve that possesses a smaller number of curvature extrema. Consequently, the modified curve leads to a smaller number of branches of the medial axis. In order to compute the medial axis, we use the state-of-the-art algorithm from [1] which is based on arc spline approximation and a domain decomposition approach. We improve this algorithm by using a different decomposition strategy that allows to reduce the number of base cases from 13 to only 5. Moreover, the algorithm reduces the number of conic arcs in the output by approx. 50%.     

一种改进的小波域图像修复算法

针对全变分小波图像修复算法在平滑区易产生阶梯效应并且噪声抑制不充分的缺陷,提出一种新颖的小波域图像修复模型。通过优化一个全变分和图像梯度的二范数的能量泛函来实现小波域的图像修复,使得在去除噪声的同时较好地保留了图像的边缘,在平滑区域削弱了阶梯效应,并利用有限差分法对所建立的扩散方程进行数值求解。实验结果表明:所提方法对于不同丢失比例的图像以及噪声图像都取得了较好的修复效果,尤其当小波系数丢失率较高时更为明显。     

基于面向分类准则的维数约简及其在人脸识别中的应用

针对高维数据导致的维数灾难问题,提出了一种基于面向分类准则的维数约简方法。所提准则使每个训练样本在特征空间中与同类样本尽可能接近,而与异类样本尽可能疏远。首先对每个训练样本定义同类样本加权平均距离和异类样本加权平均距离。然后基于上述两个概念分别定义总体同类距离和总体异类距离。以最小化总体同类距离和最大化总体异类距离为目的提出了面向分类的准则(Classification Oriented Criterion,COC)。最后,基于面向分类的准则推导出了一种新的维数约简方法。在公共人脸数据库ORL和Yale上的实验表明所提方法性能优于有代表性的维数约简方法。