Effectiveness of a Bioretention Cell Treating Stormwater Runoff in Northeastern Kansas

Stormwater runoff from paved surfaces, which contains high levels of heavy metals, suspended solids and organic contaminants, has been identified as one of the major causes of pollution in receiving waters. Recent studies have indicated that the use of ecologically-based methods for stormwater treatment, including bioretention systems, may provide increased pollutant removal and protection of downstream receiving waters. However, there is little data addressing the long-term performance of these systems in the field or the effects of contaminant accumulation over time on treatment effectiveness. In this study, we present results from a three year study of a bioretention site in northeastern Kansas. The field study was conducted in Lenexa, KS at a bioretention cell treating stormwater runoff from a 4-lane roadway. A sediment mesh trap was installed in the sewer entrance to filter large particles in the runoff. Samples were collected and analyzed after each storm event for suspended solids (TSS), heavy metals (Cu and Zn) and nutrients (nitrogen and phosphorus). Study results showed that 90% of TSS had been reduced by the bioretention system, while reductions of 50% and 70% for total Cu and total Zn, respectively, were found. Moderate removal rates were observed for total nitrogen and total phosphorus. Accumulation and fate of nutrients in the bioretention cell are analyzed to aid in the design and planning of bioretention systems for better performance under climate and soil conditions found in the Great Plains region

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

Resveratrol protects the retina from I/R injury by inhibiting RGCS apoptosis, glial activation and expression of inflammatory factors

Purpose: To study the protective effect of resveratrol against retinal injury induced by ischemiareperfusion (I/R), and the underlying mechanism  action.Methods: Retinal I/R injury was established in 72 healthy male SD rats. The rats were assigned to 3 groups: control, model and resveratrol groups, with 24 rats in each group. Pathological changes in retina were determined using H&E staining. Retinal ganglion cells (RGCs) were counted usingfluorescence gold retrograde staining. Western blotting was used to assay the expressions of caspase- 3, Bax, Bcl-2, GFAP, COX-2 and iNOS. The expressions of COX-2 and iNOS were measured by immunofluorescence.Results: The retina in the control group was dense and ordered, and its morphology was normal. In contrast, the retina in the model group was thinner, with loose cells and disordered structure. In the resveratrol group, the retina was thicker, the structure was orderly, and the cells were dense. Relative to control, the population of RGCs in model rat retina was significantly reduced, and the expressions of Bcl-2, Bax, caspase-3, GFAP, COX-2 and iNOS were significantly upregulated (p < 0.05). In the resveratrol group, the number of RGCs in the retina was markedly elevated, relative to model rats, and the expressions of Bcl-2, Bax, caspase-3, GFAP, COX-2 and iNOS were significantly decreased (p < 0.05).Conclusion: Resveratrol protects the retina from I/R injury in rats by inhibiting RGCs apoptosis, glial activation and expressions of inflammatory factors. Thus, this compound may be potentially suitable for the management of retina damage. Keywords: Resveratrol, RGCs apoptosis, Glial activation, Inflammatory factors, I/R injur

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

Knowledge Graph Transfer Network for Few-Shot Recognition

Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely specificity), which are distinct for the given few training samples but not common for the corresponding categories (see Figure 1). Fortunately, we find that transferring information of the correlated based categories can help learn the novel concepts and thus avoid the novel concept being dominated by the specificity. Besides, incorporating semantic correlations among different categories can effectively regularize this information transfer. In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing each node with the classifier weight of the corresponding category, a propagation mechanism is learned to adaptively propagate node message through the graph to explore node interaction and transfer classifier information of the base categories to those of the novel ones. Extensive experiments on the ImageNet dataset show significant performance improvement compared with current leading competitors. Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i.e, 6,000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model.Comment: accepted by AAAI 2020 as oral pape

FOCUSING ON CENTRALITY MEASURE IN EMERGENCY MEDICAL SERVICES

Emergency Medical Services (EMS) attracted many researchers because the demand of EMS was increasing over time. One of the major concerns of EMS is the response time and ambulance despatching is one of the vital factors which affects the response time. This paper focuses on the problem of ambulance despatching when many emergency calls emerge in a short time, which exists under the condition of catastrophic natural or manmade disasters. We modify a new method for ambulance despatching by centrality measure, this method constructs a nearest-neighbor coupled emergency call network and then prioritize those calls by the score of fitness, where the score of fitness considers two factors: centralized measure a call by the emergency call network and the closest policy which means despatching to the closest call site. This method is testified by a series of simulation experiments on the real topology road network of Hong Kong Island which contains 8 hospitals. These analyses demonstrate the real situation and proof the potential of centrality measure in reducing response time of EMS

The roles of familiarity and context in processing Chinese xiehouyu : an ERP study

This study conducts an ERP experiment to explore the online processing mechanism of Chinese xiehouyu, a subcategory of Chinese idiomatic expressions with a metaphorical two-part allegorical saying, regarded as a non-literal language construct. Using a 2 × 2 design, (high familiarity (HF)/low familiarity (LF)) × (literally-biasing context (LC)/metaphorically-biasing context (MC)), the researchers have obtained the following findings: (1) familiarity plays an important role in Chinese xiehouyu processing, i.e. the metaphorical meaning of a HF Chinese xiehouyu can be directly activated while that of a LF one has to be derived from its literal meaning first; (2) contextual information also weighs in the process, i.e. the metaphorical meaning of a Chinese xiehouyu can be promoted in MC condition but suppressed in LC condition; (3) the interactive effect of familiarity and contextual information can be explained by the career of metaphor hypothesis; and (4) the Standard Pragmatic Model (SPM) of non-literal languages can explain the processing of LF xiehouyu, and the Direct Access Model (DAM) may to some extent account for the mechanism of HF one but fails to explain the case of LF one, while the Graded Salience Hypothesis (GSH) can provide an acceptable explanation for the processing mechanism of Chinese xiehouyus of varied familiarity