Evidence of successful river spawning by lake trout (Salvelinus namaycush) in the Lower Niagara River,Lake Ontario

Restoration of a wild-produced lake trout Salvelinus namaycush population in Lake Ontario has not been successful despite the adult population often meeting or exceeding restoration targets. Lack of high-quality spawning habitat in Lake Ontario is suggested as one impediment to recruitment of wild lake trout, although the quantity and location of spawning habitat is poorly understood. If high-quality spawning habitat is limited in Lake Ontario, lake trout may be using uncommon spawning locations such as rivers. Anecdotal angler accounts point to the Niagara River as a lake trout spawning location. To better understand the potential of the Niagara River as a spawning location, egg and juvenile fish collections were conducted 12–14 river kilometers from the mouth of the Niagara River from 2010 to 2012; and mature female lake trout with surgically implanted acoustic tags were monitored from 2015 to 2019. Genetic analyses confirmed 60% of collected eggs and 93% of collected post-hatch juvenile fish in the Niagara River were lake trout. Tagged female lake trout returned to the Niagara River over consecutive years during the spawning season. The short duration of lake trout presence in the river (mean = 56 days/year) suggests female lake trout use the Niagara River primarily for spawning. Diversity in spawning locations may provide lake trout population’s resilience against environmental variability through a portfolio effect. Improved identification of riverine spawning locations, including their overall contribution to wild recruitment, may be a useful tool for managers to restore a wild-produced population of lake trout in Lake Ontario.     

Monthly Streamflow Forecasting Using Convolutional Neural Network

Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

     

Public–Private Partnerships in the Water Sector: The Case of Desalination

Water Resources Management - Public–private partnerships (PPPs) have grown in popularity as a method to leverage private sector actors in the production of government services. With the...     

海域双排钢板桩围堰与明挖基坑变形特性分析

以深圳海滨大道一期Ａ段海底隧道工程为背景,针对海域环境双排钢板桩围堰与明挖基坑在施工过程中的变形控制难题,利用ＰＬＡＸＩＳ数值模拟软件建立有限元模型,通过建立有限元模型,系统的研究了海域双排钢板桩围堰与明挖基坑变形特性及相互影响规律,分析了围堰与基坑距离、基坑内支撑形式及基坑开挖步序等因素对钢板桩围堰变形的影响作用。研究结果表明:基坑开挖将造成围堰较大的水平位移;地连墙最大变形出现在墙体中下部;减小施工平台宽度和增大基坑内支撑刚度等方式可以有效减小钢板桩围堰变形。研究成果可以为类似工程的钢板桩围堰与明挖基坑设计提供参考依据。     

嘉祥县南水北调干线灌排影响处理探讨

南水北调东线一期工程南四湖—东平湖段工程运行后,嘉祥县赵王河以北区域受梁济运河输水影响产生了灌溉排水的限制.文章通过对影响区域原灌排情况的研究,分析了南水北调影响灌排的情况;根据影响情况提出了调引梁济运河南四湖湖水和调引洙水河南四湖湖水两大方案,提出了相应的工程布置设计,测算了相应工程规模.工程实施完成后,效果明显,基本解决了影响区的灌溉影响问题.     

Simple systolic arrays for discrete cosine transform

in this paper, simple 1-D and 2-D systolic array for realizing the discrete cosine transform (DCT) based on the discrete Fourier transform (DFT) fo an input sequence are presented. The proposed arrays are obtained by a simple modified DFT (MDFT) and an inverse DFT (IDFT) version of the Goertzel algorithm combined with Kung's approach. The 1-D array requiresN cells, one multiplier and takesN clock cycles to produce a completeN-point DCT. The 2-D array takes N clock cycles, faster than the 1-D array, but the area complexity is larger. A continuous flow of input data is allowed and no idle time is required between the input sequences.     

Microstructural characterization of rapidly solidified nickel-base superalloys

Several Ni-Al-Mo-based eutectic superalloys were rapidly solidified using a chilled block melt spinning process. The effects of rapid solidification on the microstructure were studied using optical microscopy, transmission electron microscopy (TEM), and scanning transmission electron microscopy (STEM). Results showed, except for the alloy containing chromium, that the microstructure varied as a function of ribbon thickness from segregationless solidification at the wheel side of the ribbon to dendritic solidification at the free side. In addition, alloys with the same solidification rate showed a large variation in microstructure depending upon the solid state cooling rate. The rapidly solidified eutectic Ni-Al-Mo alloy with a small amount of rhenium and vanadium did not show any improvement on delaying or prohibiting the formation of the embrittling-NiMo phase on ageing at 1000 C. This was determined from microstructural as well as chemical analysis using STEM. Differential thermal analysis was used to obtain melting temperature,-Ni₃Al solvus, and heat of formation for the alloys.     

The parameterized level set method for structural topology optimization with shape sensitivity constraint factor

In recent years, the parameterized level set method (PLSM) has attracted widespread attention for its good stability, high efficiency and the smooth result of topology optimization compared with the conventional level set method. In the PLSM, the radial basis functions (RBFs) are often used to perform interpolation fitting for the conventional level set equation, thereby transforming the iteratively updating partial differential equation (PDE) into ordinary differential equations (ODEs). Hence, the RBFs play a key role in improving efficiency, accuracy and stability of the numerical computation in the PLSM for structural topology optimization, which can describe the structural topology and its change in the optimization process. In particular, the compactly supported radial basis function (CS-RBF) has been widely used in the PLSM for structural topology optimization because it enjoys considerable advantages. In this work, based on the CS-RBF, we propose a PLSM for structural topology optimization by adding the shape sensitivity constraint factor to control the step length in the iterations while updating the design variables with the method of moving asymptote (MMA). With the shape sensitivity constraint factor, the updating step length is changeable and controllable in the iterative process of MMA algorithm so as to increase the optimization speed. Therefore, the efficiency and stability of structural topology optimization can be improved by this method. The feasibility and effectiveness of this method are demonstrated by several typical numerical examples involving topology optimization of single-material and multi-material structures.

     

Combining teacher-Led and technology-assisted instruction to improve phoneme blending to read words for elementary students with disabilities

Technology-assisted instruction has potential for helping students improve their reading skills. In the current studies, PowerPoint software was used to supplement teacher-led reading instruction for elementary-aged students with disabilities who struggled with phoneme blending to read words. The effectiveness of the intervention was assessed using two multiple probe design studies across sets of words based on an onset-rime (word family) strategy. In Study 1, teacher-led instruction was paired with technology-assisted instruction for all intervention sessions for three students in first and second grade. In Study 2, teacher-led instruction occurred between baseline and technology-assisted intervention sessions for three students in third and fourth grade. During the initial instruction sessions, participants recorded their own voices onto PowerPoint slides so that their technology-assisted instruction included self-modelling. Results of both studies indicated that technology-assisted instruction can be effective for helping students learn how to blend phonemes to read words; however, performance varied and there were limitations in each study that should be addressed in future research. Relevant implications for classroom instruction are emphasized.     

Support NNEF execution model for NNAPI

The Journal of Supercomputing - With growing applications such as image recognition, speech recognition, ADAS, and AIoT, artificial intelligence (AI) frameworks are becoming popular in various...