Impact of water heating on disinfection byproducts concentration

Drinking water disinfection byproducts (DBP) are a group of inorganic and organic compounds formed during water disinfection. Epidemiologic studies suggest an association between rectal, and colon cancer and exposure to DBPs in chlorinated surface water. Therefore, DBPs are a growing public health concern; one that has been mitigated by multiple regulations of US Environmental Protection Agency (EPA) including the Stage 2 Disinfectants and Disinfection Byproducts Rule (Stage 2 DBPR). Tremendous efforts and cost have been spent on controlling DBPs in drinking water; however, human exposure has been poorly characterized. In addition to ingestion exposure, inhalation and dermal absorption during showering for example could also be significant exposure pathways. This dissertation focuses on investigating DBP formation and degradation in heated water (~50oC) in both lab simulated tests and field studies. The first objective of this dissertation was to investigate the temporal variability of regulated DBPs and non-regulated DBPs in cold and hot tap water at a residential home, in a water plant and in a simulated distribution system test. The results showed that the residence time of water in hot water tanks plays an important role on the formation and degradation of DBPs in the hot water plumbing. There was no obvious difference between the concentrations of TCAA (trichloroacetic acid) in long-heated hot tap water and cold tap water. The terminal DBPs for cold and hot tap water were measured and compared to the instantaneous DBP formation in cold and hot tap water. The heating of tap water in the water tank was found to increase the extent of THM formation. The second objective of this dissertation was to investigate the impact of heating scenarios on the formation and degradation of DBPs. A field study involving homes equipped with either tankless heaters or tank heaters was conducted. The concentrations of DBPs were measured for cold and hot tap water of each home. A lab-controlled heating test was later on set up to investigate the formation and degradation of DBPs in short term and long term heating to understand the difference in DBP concentrations in the hot tap water out of different types of water heaters. The results from the field study revealed that the differences in DBP levels in the hot tap water out of the two types of heaters were statistically significant for chlorine residual, total trihalomethanes (TTHMs), dichloroacetic acids (DCAA), dichloroacetonitrile (DCAN), trichloroproprane (TCP) and chloropicrin (CP). Bench scale heating tests showed that long term heating changed the concentrations of DBPs significantly. The third objective of this dissertation was to investigate the thermal formation and degradation in various conditions. Especially, the impact of water age on DBP formation and degradation in cold and heated water was investigated. The results of this study demonstrate that DBP concentration profiles in heated water were quite different from the DBP concentrations in the cold tap water. Chloroform concentrations in the heated water remained constant or even decreased slightly with increasing distribution system water age, despite the fact that its levels always increased with water age in the cold water. The final objective of this dissertation was to propose a method to model chlorine decay, not only in the distribution system, but also applicable to home heating scenarios. A robust two-site chlorine decay model of combined effects of pH, temperature in water distribution system and heating condition was proposed. A single set of readily interpretable parameters were estimated by stochastic search using differential evolution

Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics

The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification. Recently, federated averaging Langevin dynamics (FALD) was introduced as a variant of federated averaging that can efficiently implement distributed Bayesian learning in the presence of noiseless communications. In this paper, we propose wireless FALD (WFALD), a novel protocol that realizes FALD in wireless systems by integrating over-the-air computation and channel-driven sampling for Monte Carlo updates. Unlike prior work on wireless Bayesian learning, WFALD enables (\emph{i}) multiple local updates between communication rounds; and (\emph{ii}) stochastic gradients computed by mini-batch. A convergence analysis is presented in terms of the 2-Wasserstein distance between the samples produced by WFALD and the targeted global posterior distribution. Analysis and experiments show that, when the signal-to-noise ratio is sufficiently large, channel noise can be fully repurposed for Monte Carlo sampling, thus entailing no loss in performance.Comment: 6 pages, 4 figures, 26 references, submitte

Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering

We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor. To tackle the accompanying memory issue, we decompose the 4D tensor hierarchically by projecting it first into three time-aware volumes and then nine compact feature planes. In this way, spatial information over time can be simultaneously captured in a compact and memory-efficient manner. When applying Tensor4D for dynamic scene reconstruction and rendering, we further factorize the 4D fields to different scales in the sense that structural motions and dynamic detailed changes can be learned from coarse to fine. The effectiveness of our method is validated on both synthetic and real-world scenes. Extensive experiments show that our method is able to achieve high-quality dynamic reconstruction and rendering from sparse-view camera rigs or even a monocular camera. The code and dataset will be released at https://liuyebin.com/tensor4d/tensor4d.html

Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained.Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.0

Spatial variation decomposition via sparse regression

In this paper, we briefly discuss the recent development of a novel sparse regression technique that aims to accurately decompose process variation into two different components: (1) spatially correlated variation, and (2) uncorrelated random variation. Such variation decomposition is important to identify systematic variation patterns at wafer and/or chip level for process modeling, control and diagnosis. We demonstrate that the spatially correlated variation can be accurately represented by the linear combination of a small number of “templates”. Based upon this observation, an efficient algorithm is developed to accurately separate spatially correlated variation from uncorrelated random variation. Several examples based on silicon measurement data demonstrate that the aforementioned sparse regression technique can capture systematic variation patterns with high accuracy.Interconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation)Focus Center Research Program. Focus Center for Circuit & System SolutionsNational Science Foundation (U.S.) (Contract CCF-0915912

Reinforcement learning-guided long-timescale simulation of hydrogen transport in metals

Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example

Long-term outcomes of nasopharyngeal carcinoma treated with helical tomotherapy using simultaneous integrated boost technique: A 10-year result

BackgroundTo evaluate the long-term survival and treatment-related toxicities of helical tomotherapy (HT) in nasopharyngeal carcinoma (NPC) patients.MethodsOne hundred and ninety newly diagnosed non-metastatic NPC patients treated with HT from September 2007 to August 2012 were analyzed retrospectively. The dose at D95 prescribed was 70-74Gy, 60-62.7Gy and 52-56Gy delivered in 33 fractions to the primary gross tumor volume (pGTVnx) and positive lymph nodes (pGTVnd), the high risk planning target volume (PTV1), and the low risk planning target volume (PTV2), respectively, using simultaneous integrated boost technique. The statistical analyses were performed and late toxicities were evaluated and scored according to the Common Terminology Criteria for Adverse Events (version 3.0).ResultsThe median follow-up time was 145 months. The 10-year local relapse-free survival (LRFS), nodal relapse-free survival (NRFS), distant metastasis-free survival (DMFS) and overall survival (OS) were 94%, 95%, 86%, and 77.8%; respectively. Fifty (26.3%) patients had treatment-related failures at the last follow-up visit. Distant metastasis, occurred in 25 patients, was the major failure pattern. Multivariate analysis showed that age and T stage were independent predictors of DMFS and OS, Concomitant chemotherapy improved overall survival, but anti-EGFR monoclonal antibody therapy failed. The most common late toxicities were mainly graded as 1 or 2.ConclusionsHelical tomotherapy with simultaneous integrated boost technique offered excellent long-term outcomes for NPC patients, with mild late treatment-related toxicities. Age and clinical stage were independent predictors of DMFS and OS. And, concurrent chemotherapy means better OS. Further prospective study is needed to confirm the superiority of this technology and to evaluate the roles of anti-EGFR monoclonal antibody treatment

I4U System Description for NIST SRE'20 CTS Challenge

This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resulted from active collaboration among researchers across eight research teams - I$^2$R (Singapore), UEF (Finland), VALPT (Italy, Spain), NEC (Japan), THUEE (China), LIA (France), NUS (Singapore), INRIA (France) and TJU (China). The submission was based on the fusion of top performing sub-systems and sub-fusion systems contributed by individual teams. Efforts have been spent on the use of common development and validation sets, submission schedule and milestone, minimizing inconsistency in trial list and score file format across sites.Comment: SRE 2021, NIST Speaker Recognition Evaluation Workshop, CTS Speaker Recognition Challenge, 14-12 December 202