24 research outputs found
Effects of Aerosol Inhalation Combined with Intravenous Drip of Polymyxin B on Bacterial Clearance, Symptoms Improvement, and Serum Infection Indexes in Patients with Pneumonia Induced by Multidrug-Resistant Gram-Negative Bacteria
No full textIn recent years, the incidence of pneumonia caused by multidrug-resistant (MDR) Gram-negative bacteria (G−) has increased year by year. Polymyxin B has a good clinical effect in the treatment of MDR, but there is controversy about the administration route of this drug. In this study, we retrospectively analyzed the clinical data of 84 cases of MDR Gram-negative bacterial pneumonia, and aimed to explore the effects of aerosol inhalation combined with intravenous polymyxin B infusion on the bacterial clearance, symptom improvement, and serum infection indexes of MDR patients on the patients with Gram-negative (G−) bacterial pneumonia. The results show that aerosol inhalation combined with intravenous drip of polymyxin B can improve bacterial clearance rate, reduce levels of serum inflammatory factors, and improve clinical symptoms in patients with pneumonia induced by MDR G-bacteria
An across scale comparative morphological analysis Mapping the landscape structure of Lingnan and Jiangnan
No full textUrban landscape is a complex system. The understanding of the underlying mechanism of each subsystem and their dynamic interaction is quite crucial for human intervention regarding future development. This requires a systematic scientific approach. A cross scale analytical framework is processed which integrates the Dutch morphological school technique – reduction drawing and layer approach to systematically interpret the urban landscape. In order to exemplify the potential and the generic property of the approach, the research applies heterogeneous case study. Explorative analyses are performed on two cases in different region in China. The production landscape in Pearl River Delta and Tai Lake region are mapped on geography, landscape, settlement and architecture and public space scale respectively. Their form and formation are discussed with the maps. Comparison regarding the form of the water, pond, difference of typologies, and the scale and form of the settlements are made. Research shows the dependence and interrelation between scales. The author believes that mapping is an effective analytical and design process as well as well-presented products. The logic of the complex space and design strategies reveals itself along the process. The cross-scale mapping facilitates a comprehensive understanding of urban landscape. It is a prerequisite of design
Overseas Chinese Environmental Engineers and Scientists Association (OCEESA) Report, Regular Issue, February 2020
No full textThis OCEESA report, which is regular issue of OCEESA Journal (Overseas Chinese Environmental Engineers and Scientists Association Journal). This report is OCEESA report number: OCEESA/JL-2020/3701, February 2020, ISSN 1072 -7248. This report is also OCEESA Journal, Volume 37, Number 1, February 2020. Yung-Tse Hung, Permanent Executive Director, OCEESA, is editor of this report. This issue includes: (A) 10 OCEESA Best Papers (B) 6 OCEESA Papers; 21.Zhang-Zhi Charlie Huang 黄长志 , Implementing Compensation System for Environmental Damages: Challenges and Solutions, 22. Hanlu Yan, Kaimin Shih施凱閔 , Quantitative X-Ray Diffraction for Characterizing P Recovery Products from Wastewater, 23. Kaimin Shih 施凱閔 , Material Mineralogical Technology for Pollution Prevention and Resource Recovery材料礦物學技術於污染防治與資源回收的應用, 24. Kuo-Kunag Hsu 許國光 , Cleanup of MSW-Gasified Synthesis Gas, 25. Pao-Chiang Yuan 袁保強 , End of Useful Life Computer Recycling Program at Jackson State University, Jackson, Mississippi, USA, 26. Qin Qian钱琴, Bo Sun, Xianchang Li, Frank Sun, Che-Jen Lin, Water quality modeling with data collected by wireless sensor networks (WSNs) in an experimental pond: A case study; (C) 3 technical papers; 28. Abdulkarim Alorayfij, Yung-Tse Hung, Anaerobic digestion of agricultural waste, 29. Abdullah Alshati, Yung-Tse Hung, Methane Gas Production from Animal Waste, 30. Abdulmajeed Alshatti, Yung-Tse Hung, Treatment of Timber Industry Wastes, 31. OCEESA Constitutions By-Laws (5 November 2000 edition), 32. OCEESA Constitutions By-Laws (14 February 2006 edition), 33. OCEESA Constitutions By-Laws (27 October 2013 edition), 34. Lawrence Kong-Pu Wang letter of support Yung-Tse Hung Permanent Executive Director OCEESA 12-30-20, 35. Wen-Chi Ku letter of support Yung-Tse Hung Permanent Executive Director OCEESA 12-03-20, 36. OCEESA Member Application Form and Information, 37. Mailing Address Pag
Overseas Chinese Environmental Engineers and Scientists Association (OCEESA) Report, Regular Issue, February 2020
No full textThis OCEESA report, which is regular issue of OCEESA Journal (Overseas Chinese Environmental Engineers and Scientists Association Journal). This report is OCEESA report number: OCEESA/JL-2020/3701, February 2020, ISSN 1072 -7248. This report is also OCEESA Journal, Volume 37, Number 1, February 2020. Yung-Tse Hung, Permanent Executive Director, OCEESA, is editor of this report. This issue includes: (A) 10 OCEESA Best Papers (B) 6 OCEESA Papers; 21.Zhang-Zhi Charlie Huang 黄长志 , Implementing Compensation System for Environmental Damages: Challenges and Solutions, 22. Hanlu Yan, Kaimin Shih施凱閔 , Quantitative X-Ray Diffraction for Characterizing P Recovery Products from Wastewater, 23. Kaimin Shih 施凱閔 , Material Mineralogical Technology for Pollution Prevention and Resource Recovery材料礦物學技術於污染防治與資源回收的應用, 24. Kuo-Kunag Hsu 許國光 , Cleanup of MSW-Gasified Synthesis Gas, 25. Pao-Chiang Yuan 袁保強 , End of Useful Life Computer Recycling Program at Jackson State University, Jackson, Mississippi, USA, 26. Qin Qian钱琴, Bo Sun, Xianchang Li, Frank Sun, Che-Jen Lin, Water quality modeling with data collected by wireless sensor networks (WSNs) in an experimental pond: A case study; (C) 3 technical papers; 28. Abdulkarim Alorayfij, Yung-Tse Hung, Anaerobic digestion of agricultural waste, 29. Abdullah Alshati, Yung-Tse Hung, Methane Gas Production from Animal Waste, 30. Abdulmajeed Alshatti, Yung-Tse Hung, Treatment of Timber Industry Wastes, 31. OCEESA Constitutions By-Laws (5 November 2000 edition), 32. OCEESA Constitutions By-Laws (14 February 2006 edition), 33. OCEESA Constitutions By-Laws (27 October 2013 edition), 34. Lawrence Kong-Pu Wang letter of support Yung-Tse Hung Permanent Executive Director OCEESA 12-30-20, 35. Wen-Chi Ku letter of support Yung-Tse Hung Permanent Executive Director OCEESA 12-03-20, 36. OCEESA Member Application Form and Information, 37. Mailing Address Pag
Large-Scale Independent Vector Analysis (IVA-G) via Coresets
No full textJoint blind source separation (JBSS) involves the factorization of multiple matrices, i.e. “datasets”, into “sources” that are statistically dependent across datasets and independent within datasets. Despite this usefulness for analyzing multiple datasets, JBSS methods suffer from considerable computational costs and are typically intractable for hundreds or thousands of datasets. To address this issue, we present a methodology for how a subset of the datasets can be used to perform efficient JBSS over the full set. We motivate two such methods: a numerical extension of independent vector analysis (IVA) with the multivariate Gaussian model (IVA-G), and a recently proposed analytic method resembling generalized joint diagonalization (GJD). We derive nonidentifiability conditions for both methods, and then demonstrate how one can significantly improve these methods’ generalizability by an efficient representative subset selection method. This involves selecting a coreset (a weighted subset) that minimizes a measure of discrepancy between the statistics of the coreset and the full set. Using simulated and real functional magnetic resonance imaging (fMRI) data, we demonstrate significant scalability and source separation advantages of our “coreIVA-G” method vs. other JBSS methods.https://ieeexplore.ieee.org/document/10798966/authors#author
Mode Coresets for Efficient, Interpretable Tensor Decompositions: An Application to Feature Selection in fMRI Analysis
No full textGeneralizations of matrix decompositions to multidimensional arrays, called tensor decompositions, are simple yet powerful methods for analyzing datasets in the form of tensors. These decompositions model a data tensor as a sum of rank-1 tensors, whose factors provide uses for a myriad of applications. Given the massive sizes of modern datasets, an important challenge is how well computational complexity scales with the data, balanced with how well decompositions approximate the data. Many efficient methods exploit a small subset of the tensor’s elements, representing most of the tensor’s variation via a basis over the subset. These methods’ efficiencies are often due to their randomized natures; however, deterministic methods can provide better approximations, and can perform feature selection, highlighting a meaningful subset that well-represents the entire tensor. In this paper, we introduce an efficient subset-based form of the Tucker decomposition, by selecting coresets from the tensor modes such that the resulting core tensor can well-approximate the full tensor. Furthermore, our method enables a novel feature selection scheme unlike other methods for tensor data. We introduce methods for random and deterministic coresets, minimizing error via a measure of discrepancy between the coreset and full tensor. We perform the decompositions on simulated data, and perform on real-world fMRI data to demonstrate our method’s feature selection ability. We demonstrate that compared with other similar decomposition methods, our methods can typically better approximate the tensor with comparably low computational complexities.This work was supported in part by NSF under Grant 2316420; in part by NIH under Grant R01MH118695, Grant R01MH123610, and Grant R01AG073949; in part by the Computational Hardware used is part of the University of Maryland, Baltimore County (UMBC) High Performance Computing Facility (HPCF) funded by the U.S. NSF through the MRI and SCREMS Programs under Grant CNS-0821258, Grant CNS-1228778, Grant OAC-1726023, and Grant DMS-0821311; and in part by UMBC.https://ieeexplore.ieee.org/document/10798430/authors#author
