Water analysis for emerging environmental contaminants

The presence of emerging environmental contaminants in water bodies used either as drinking water or for recreational purpose has received considerable attention in the recent years. The emerging environmental contaminants can be defined as a wide range of chemicals that have been determined in the environment which may present serious health risks for humans. The occurrence of these contaminants indicate that both household and industrial chemicals have been introduced to water resources, a wide variety of chemicals, such as disinfection byproducts, pharmaceutical and personal care products and so on, have been detected at cetiain levels in either water bodies or treatment plants in worldwide. Although developments in new regulations and detection methods have taken place in the past decades that impact water analysis, there is currently no validated EPA or consensus organization methods for many of the listed emerging environmental contaminants. This body of work developed LC/MS/MS or ICP-MS based techniques for water analysis of several classes of emerging environmental contaminants, including herbicides degradation byproducts; cyanotoxins; N-nitrosamines and heavy metal leaching from plastic bottles. In addition, the developed methods were used to conduct high throughput screening of these emerging contaminants in water samples of various types, and to investigate the removal efficiency of these contaminants by using various oxidants and physical treatment with emphasis on analysis and treatment --Abstract, page iv

Kahler submanifolds and the Umehara algebra

We show that an indefinite Euclidean complex space is not a relative of an indefinite non-flat complex space form. We further study whether two compact Fubini-Study spaces are relatives or not

A Parameter Matching Method of the Parallel Hydraulic Hybrid Excavator Optimized with Genetic Algorithm

This paper proposed a parameter matching method based on the energy storage unit, the accumulator in the parallel hydraulic hybrid excavator (PHHE). The working condition, system structure, and control strategy of the excavator were all considered. It took the 20-ton series PHHE as the example and displayed the parameter matching course of the main components: engine, accumulator, and hydraulic secondary regulatory pump. Their installed powers were reduced after the matching course. Furthermore, the parameters of the PHHE system were optimized with the genetic algorithm to get the most suitable values for system initialization. By analyzing the simulation results, it could be concluded that the parameter matching method had an impressive improvement of the energy saving under the same working condition and brought obscure influence to the mechanism dynamics

Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

Unmanned Aerial Vehicles (UAVs), have intrigued different people from all walks of life, because of their pervasive computing capabilities. UAV equipped with vision techniques, could be leveraged to establish navigation autonomous control for UAV itself. Also, object detection from UAV could be used to broaden the utilization of drone to provide ubiquitous surveillance and monitoring services towards military operation, urban administration and agriculture management. As the data-driven technologies evolved, machine learning algorithm, especially the deep learning approach has been intensively utilized to solve different traditional computer vision research problems. Modern Convolutional Neural Networks based object detectors could be divided into two major categories: one-stage object detector and two-stage object detector. In this study, we utilize some representative CNN based object detectors to execute the computer vision task over Stanford Drone Dataset (SDD). State-of-the-art performance has been achieved in utilizing focal loss dense detector RetinaNet based approach for object detection from UAV in a fast and accurate manner.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0111

GMAN: A Graph Multi-Attention Network for Traffic Prediction

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.Comment: AAAI 2020 pape

Kahler submanifolds and the Umehara algebra

We show that indefinite Euclidean spaces and indefinite non-flat complex space forms are relatives. We also study when two Fubini-Study spaces are relatives