Computational Simulation of Mass Transport and Energy Transfer in the Microbial Fuel Cell System

This doctoral dissertation introduces the research in the computational modeling and simulation for the microbial fuel cell (MFC) system which is a bio-electrochemical system that drives a current by using bacteria and mimicking bacterial interactions found in nature. The numerical methods, research approaches and simulation comparison with the experiments in the microbial fuel cells are described; the analysis and evaluation for the model methods and results that I have achieved are presented in this dissertation. The development of the renewable energy has been a hot topic, and scientists have been focusing on the microbial fuel cell, which is an environmentally-friendly and promising technology. The MFC full cell is a complex system which has different reactions, coupled with mass and electrons transport in bulk liquid. Therefore, this research contains interdisciplinary fields. The methods will be adopted includes: (1) numerical methods (finite volume method/finite difference method/ parallel computation/ multiple step times etc.); (2) computational fluid dynamics method (diffusion equation, Nernst-Planck equation etc.); (3) experimental electrochemical analysis methods; (4) the biological treatment process (biofilm growth, anaerobic/aerobic bacteria etc.). The uniqueness of this work: (1) a comprehensive computational bioelectrochemical fuel cell models was firstly constructed in the research; (2) the primary physical phenomena have been systematically analyzed in both steady and transient states; (3) The simulation evaluated the MFC system which are hardly obtained directly in the experiments. The computational work in MFC modeling achieved four goals: (1) Characterized the primary factors which affect the MFC performance and used them to describe a complex microbial fuel cell model; (2) Derived a series of appropriate electrochemical /biological /chemical reactions equations for the analysis of the mechanics in MFC; (3) Applied computational methods in the model construction and built a series of sub-models for the MFC system; (4) Simulated the models and compared with the experimental results, gave the analysis for the MFC phenomena which are used for optimizing the design of the MFC system

Optimization of Hybrid Electric Bus Driving System's Control Strategy

AbstractThe popularity of hybrid electric bus (HEB) is a most realistic way to solve emission and energy problem currently, so it's important to improve the HEB's fuel economy and efficiency. This paper optimizes the HEB's driving system to satisfy the conditions of this city. We applied the fuzzy logic control of modern control theory to the driving system's control of parallel-HEB, and optimized the driving system's control strategy of this city's hybrid bus based on this theory. We adopted the ADVISOR2002 for HEB's driving system's re-development, namely established the driving system's simulation model for this city's hybrid bus, then we tested the simulation model on the HEB urban driving cycle which had been developed in our preparatory work. The simulation results of our new control strategy and the simulation model proposed in this paper can further enhance the fuel economy and improve the driving system's efficiency, thus the results provided important reference for the upgrading of this type HEB's driving system

Exposure to Aroclor 1254 differentially affects the survival of pancreatic β-cells and α-cells in the male mice and the potential reason

Abstract(#br)Previous works showed that chronic exposure to Aroclor 1254 disrupted glucose homeostasis and induced insulin resistance in male mice. To further observe the different effects of Aroclor 1254 exposure on the pancreatic α-cells and β-cells, male mice were exposed to Aroclor 1254 (0, 0.5, 5, 50, 500 μg/kg) for 60 days, the pancreas was performed a histological examination. The results showed that the percentage of apoptosis cell (indicated by TUNEL assay) was increased in both α-cells and β-cells, as the Aroclor 1254 dose was increased; the proliferation (indicated by PCNA expression) rate of β-cells was elevated while that of α-cells was not affected, resulting in an increased β-cell mass and a decreased α-cell mass in a dose-depend manner. The number of Pdx-1 positive β-cells was significantly increased whereas that of Arx positive α-cells was markedly decreased, indicating an enhanced β-cell neogenesis and a weakened α-cell neogenesis. The drastically reduction of serum testosterone levels in all the treatments suggested an anti-androgenic potency of Aroclor 1254. The up-regulation of estrogen receptors (ERα and ERβ) and androgen receptor in β-cells might be responsible for the increased β-cell mass and neogenesis

Global Epidemiology of Dengue Outbreaks in 1990–2015: A Systematic Review and Meta-Analysis

Dengue is an arthropod-borne infectious disease caused by dengue virus (DENV) infection and transmitted by Aedes mosquitoes. Approximately 50–100 million people are infected with DENV each year, resulting in a high economic burden on both governments and individuals. Here, we conducted a systematic review and meta-analysis to summarize information regarding the epidemiology, clinical characteristics, and serotype distribution and risk factors for global dengue outbreaks occurring from 1990 to 2015. We searched the PubMed, Embase and Web of Science databases through December 2016 using the term “dengue outbreak.” In total, 3,853 studies were identified, of which 243 studies describing 262 dengue outbreaks met our inclusion criteria. The majority of outbreak-associated dengue cases were reported in the Western Pacific Region, particularly after the year 2010; these cases were primarily identified in China, Singapore and Malaysia. The pooled mean age of dengue-infected individuals was 30.1 years; of the included patients, 54.5% were male, 23.2% had DHF, 62.0% had secondary infections, and 1.3% died. The mean age of dengue patients reported after 2010 was older than that of patients reported before 2010 (34.0 vs. 27.2 years); however, the proportions of patients who had DHF, had secondary infections and died significantly decreased after 2010. Fever, malaise, headache, and asthenia were the most frequently reported clinical symptoms and signs among dengue patients. In addition, among the identified clinical symptoms and signs, positive tourniquet test (OR = 4.86), ascites (OR = 13.91) and shock (OR = 308.09) were identified as the best predictors of dengue infection, DHF and mortality, respectively (both P < 0.05). The main risk factors for dengue infection, DHF and mortality were living with uncovered water container (OR = 1.65), suffering from hypotension (OR = 6.18) and suffering from diabetes mellitus (OR = 2.53), respectively (all P < 0.05). The serotype distribution varied with time and across WHO regions. Overall, co-infections were reported in 47.7% of the evaluated outbreaks, and the highest pooled mortality rate (2.0%) was identified in DENV-2 dominated outbreaks. Our study emphasizes the necessity of implementing programs focused on targeted prevention, early identification, and effective treatment

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe