Effects of Drinking Water Treatment Processes on Removal of Algal Matter and Subsequent Water Quality

Seasonal algal blooms in drinking water sources have increased significantly over the recent past as a result of increased temperature and nutrient loading in surface water due to agricultural and surface runoff. More than 95% of algal cells can be removed by coagulation and flocculation processes. However, algal organic matter (AOM) is not removed well during coagulation, thus causes several operational challenges in drinking water treatment. This research was conducted to investigate the effectiveness of coagulation, granular activated carbon adsorption, and filtration processes on AOM removal and to evaluate disinfection by-products formation potential with/without UV irradiation. Initially, coagulation performance for the treatment of algae-laden raw water was investigated systematically by central composite design using response surface methodology. The main mechanism of algae and AOM removal was charge neutralization at an optimum pH of around 6.0. Thereafter, the optimum coagulation conditions using alum for AOM of six different algal and cyanobacterial species were determined. The AOM removal by coagulation correlated well with the hydrophobicity of the AOM solution. The disinfection by-product formation potential of the AOM due to chlorination was determined after coagulation. The efficiency and mechanism of AOM removal by granular activated carbon (GAC) adsorption were determined by batch adsorption experiments. The adsorption equilibrium data followed both Langmuir and Freundlich models. The adsorption process followed a pseudo-second-order kinetic model, and the calculated thermodynamic parameters indicated that GAC adsorption for AOM removal was spontaneous and endothermic in nature. The fouling behavior of the microfiltration membranes after GAC adsorption pre-treatment was investigated and the filtration resistance and AOM removal efficiency were determined. The GAC adsorption increased the removal of AOM, decreased membrane fouling, and identified intermediate blocking as the major fouling mechanism of the membrane. The effects of combined low-pressure ultraviolet (LPUV) irradiation and chlorination on the disinfection byproducts (DBPs) formation from AOM was investigated for common algae existed in surface water, AOM degradation was likely promoted by photodegradation of aromatics, and chlorine oxidation/substitution. Insights obtained of this work will help in properly designing and operating the AOM removal and reducing DBPs formation during water treatment of algae-laden source water

Towards Baselines for Shoulder Surfing on Mobile Authentication

Given the nature of mobile devices and unlock procedures, unlock authentication is a prime target for credential leaking via shoulder surfing, a form of an observation attack. While the research community has investigated solutions to minimize or prevent the threat of shoulder surfing, our understanding of how the attack performs on current systems is less well studied. In this paper, we describe a large online experiment (n=1173) that works towards establishing a baseline of shoulder surfing vulnerability for current unlock authentication systems. Using controlled video recordings of a victim entering in a set of 4- and 6-length PINs and Android unlock patterns on different phones from different angles, we asked participants to act as attackers, trying to determine the authentication input based on the observation. We find that 6-digit PINs are the most elusive attacking surface where a single observation leads to just 10.8% successful attacks, improving to 26.5\% with multiple observations. As a comparison, 6-length Android patterns, with one observation, suffered 64.2% attack rate and 79.9% with multiple observations. Removing feedback lines for patterns improves security from 35.3\% and 52.1\% for single and multiple observations, respectively. This evidence, as well as other results related to hand position, phone size, and observation angle, suggests the best and worst case scenarios related to shoulder surfing vulnerability which can both help inform users to improve their security choices, as well as establish baselines for researchers.Comment: Will appear in Annual Computer Security Applications Conference (ACSAC

Auto-Encoding Adversarial Imitation Learning

Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imitation Learning (AIL) sheds light on automatic policy acquisition without access to the reward signal from the environment. In this work, we propose Auto-Encoding Adversarial Imitation Learning (AEAIL), a robust and scalable AIL framework. To induce expert policies from demonstrations, AEAIL utilizes the reconstruction error of an auto-encoder as a reward signal, which provides more information for optimizing policies than the prior discriminator-based ones. Subsequently, we use the derived objective functions to train the auto-encoder and the agent policy. Experiments show that our AEAIL performs superior compared to state-of-the-art methods on both state and image based environments. More importantly, AEAIL shows much better robustness when the expert demonstrations are noisy.Comment: 15 page

Drill String Mechanics and Extension Capacity of Extended-Reach Well

For the design and construction of the extended-reach well, the ultimate elongation capacity of the extended-reach well was studied. Given the drilling practice of extended-reach well, the extension limit prediction criterion of extended-reach well is established. In this paper, based on the drilling practice of extended-reach well, by the finite element method, the gap element method and the dynamic finite element method of the whole drill string, static analysis model of the extended-reach well and the mechanicsꞌ analysis model of drill string vibration are established. The frictional resistance condition and strength condition of the limit extension of the extended-reach well are solved respectively, and the extended limit prediction criterion of the extended-reach well is established. The software was prepared based on the model and theory, the model and software were validated with the example of drilling, and the average error of the calculated value is 6.94% when compared with the measured values in the field. It can meet the needs of drilling engineering and study of the extension capability of the extended-reach wells

Validation of a guideline-based decision support system for the diagnosis of primary headache disorders based on ICHD-3 beta

BACKGROUND: China may have the largest population of headache sufferers and therefore the most serious burden of disease worldwide. However, the rate of diagnosis for headache disorders is extremely low, possibly due to the relative complexity of headache subtypes and diagnostic criteria. The use of computerized clinical decision support systems (CDSS) seems to be a better choice to solve this problem. METHODS: We developed a headache CDSS based on ICHD-3 beta and validated it in a prospective study that included 543 headache patients from the International Headache Center at the Chinese PLA General hospital, Beijing, China. RESULTS: We found that the CDSS correctly recognized 159/160 (99.4%) of migraine without aura, 36/36 (100%) of migraine with aura, 20/21 (95.2%) of chronic migraine, and 37/59 (62.7%) of probable migraine. This system also correctly identified 157/180 (87.2%) of patients with tension-type headache (TTH), of which infrequent episodic TTH was diagnosed in 12/13 (92.3%), frequent episodic TTH was diagnosed in 99/101 (98.0%), chronic TTH in 18/20 (90.0%), and probable TTH in 28/46 (60.9%). The correct diagnostic rates of cluster headache and new daily persistent headache (NDPH) were 90.0% and 100%, respectively. In addition, the system recognized 32/32 (100%) of patients with medication overuse headache. CONCLUSIONS: With high diagnostic accuracy for most of the primary and some types of secondary headaches, this system can be expected to help general practitioners at primary hospitals improve diagnostic accuracy and thereby reduce the burden of headache in China

Analysis of Contact Surface Wear Performance of O-Ring Dynamic Seal Based on Archard Model

With the development of hydraulic system to high pressure gradually, the leakage risk of sealing system increases, and it is necessary to confirm the performance parameters of sealing structure through analysis and calculation. The traditional analysis of the friction and wear performance of the seal ring is limited to the amount of wear, and cannot describe the surface wear characteristics of the O-ring in detail. Based on the Archard model, this paper constructs a model to analyse and calculate the friction and wear performance of the dynamic seal structure through the material characteristics and operating parameters, analyses the friction and wear characteristics of the O-ring seal structure under different compression ratio, medium pressure, relative slip velocity and temperature, and summarizes the influence of each single variable on the wear characteristics of the dynamic seal structure. According to the analysis in this paper, the increase of medium pressure of 5 MPa will cause the wear concentration area of the contact surface to move to the back pressure side, and the overall wear will be reduced, but the increase of contact area will lead to the weakening of sealing effect. By the action of 15 MPa, when the compression ratio is between 5% and 10%, the change of cumulative wear rate and the wear rate of each node is small

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied to large-scale heterogeneous graphs. To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning. Unlike traditional heterogeneous graph neural networks, we adopt the contrastive learning mechanism to deal with the complex heterogeneity of large-scale heterogeneous graphs. We first learn relation-aware node embeddings under the network schema view. Then we propose a novel positive sample selection strategy to choose meaningful positive samples. After learning node embeddings under the positive sample graph view, we perform a cross-view contrastive learning to obtain the final node representations. Moreover, we adopt the label smoothing technique to boost the performance of RHCO. Extensive experiments on three large-scale academic heterogeneous graph datasets show that RHCO achieves best performance over the state-of-the-art models