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( Judith Alvarez Otero ),( Lucia Gonzalez Gonzalez ),( Jose Luis Lamas Ferreiro ),( Alexandra Arca Blanco ),( Jose Ramon Bermudez Sanjurjo ),( Maria Rodriguez Conde ),( Javier De La Fuente Aguado ) 대한내과학회 2014 대한내과학회 추계학술발표논문집 Vol.2014 No.1
Background: The aim of this study was to analyze the prevalence of carbapenem resistant Pseudomonas aeruginosa (CRPA) in urine cultures in our hospital. Moreover, we determined the mortality and risk factors associated to CRPA infection. Methods: Positive urine cultures to Pseudomonas aeruginosa between september 2012 and september 2013 were identified. We excluded repititive cultures from the same patient and episode. We created a database with demographic, clinical and laboratory items, including previous antibiotic therapy and antimicrobial resistance. Results: Forty-three cases with positive urine cultures to Pseudomonas aeruginosa were included. CRPA was observed in 12cases, with a prevalence of 27.9%. Sixty per cent were male with a median age of 73 years (range: 17-102). Sixty-seven per cent of patients were hospitalized when the culture was collected, but only 30% met criteria to nosocomial infection. Twenty-one percent of urine cultures corresponded to asymptomatic bacteriuria and 25% presented with sepsis. Mortality at 30 days was 20.7% in CRPA patients and 13.8% in the other group, without estatistical significance. Obesity (p =0.003), previous treatment with ciprofloxacin (p = 0.004) and quinolones in general (p = 0.001) and previous treatment with more than one antibiotic (p = 0.03) or with more than one family of antibiotics (p = 0.01) were risk factors to CRPA infection in the univariate analysis. Only obesity (p = 0.04) and previous treatment with ciprofloxacin (p = 0.02) showed statistically significant differences in the multivariate analysis, Conclusions: There is a high prevalence of CRPA in urine cultures in our population, wich is a potencial threat. We should assess the presence of risk factors for development of infections by such pathogen, as previous treatment with quinolones or obesity, in order to start appropiate empirical treatment in patients with severe urinary tract infections.
Litter Decomposition Process in Coffee Agroforestry Systems
Judith Petit-Aldana,Mohammed Mahabubur Rahman,Conrado Parraguirre-Lezama4,,Angel Infante-Cruz,Omar Romero-Arenas 강원대학교 산림과학연구소 2019 Journal of Forest Science Vol.35 No.2
Decomposition of litter is a function of various interrelated variables, both biotic and abiotic factors. Litter decomposition acts like a natural fertilizer play a prime role in maintaining the productivity and nutrient cycling in agroforestry systems. There are few studies of decomposition carried out in agroforestry systems with coffee; so it is necessary to perform more research work to fill the research gap, which will allow a better understanding of the management of the coffee agroforestry systems. This paper is based on the theoretical and conceptual aspects of leaf litter decomposition in agroforestry systems, emphasizing the combination with coffee cultivation and critically examined the role of the different factors involved in the decomposition. This study made a comparison of different investigations with regards to weight loss, decomposition rates (k), initial chemical composition, and release of the main nutrients. This study suggested that it is necessary to implement studies of decomposition and mineralization, and the microflora and fauna associated with these processes, so that serves as an important tool to develop a model for enabling a description of the short, medium, and long-term dynamics of soil nutrients in coffee agroforestry systems.
Predicting target data rates for dynamic spectrum allocation using Gaussian process regression
Judith Nkechinyere Njoku,유제니오,Angela Caliwag,Pei Xiao,Wansu Lim 한국통신학회 2022 ICT Express Vol.8 No.2
Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers only the performance of the weakest user. To resolve this issue, we propose a new target data rate setting algorithm for dynamic spectrum allocation. In this algorithm, a Gaussian process regression model is trained to predict the target data rate. All users that perform below the defined target rate will have their frequency band allocations changed to one that guarantees a better performance. Through simulations, we show that the maximum data rate achieved by the weakest user in our algorithm is 121.7% higher than the selfish algorithm.
Cooperating Peers for Content-Oriented XML-Retrieval
Judith Winter,Oswald Drobnik 보안공학연구지원센터 2008 International Journal of Multimedia and Ubiquitous Vol.3 No.2
Semi-structured documents formatted with the extensible markup language (XML) are gaining wide use by a whole range of applications including E-Commerce, E-Business, E-Science, Digital Libraries (DL), File Sharing, and in the last years especially by applications for Peer-to-Peer (P2P) systems. P2P architectures have been identified as an efficient means of ad-hoc collaboration and information sharing among large, diverse, and dynamic sets of user. However, current P2P search engines for XML-documents lack the use of information retrieval methods to efficiently search XML collections for relevant information. This article proposes a search engine for P2P systems that applies an extension of the vector space model and exploits structural information to compute relevance of XML-documents, and thus may significantly improve retrieval performance. We concentrate on the cooperation of peers that perform a distributed query execution through cooperated retrieval and ranking of dynamic XML documents. The interaction between the participating peers is based on a structured P2P-network and uses an adaption of the DHT-algorithm Kademlia.
Efficient Deep Learning Model for Data-Limited Modulation Recognition
Judith Nkechinyere Njoku(주디스),Angela Caliwag(안젤라),Wansu Lim(임완수) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Deep learning (DL) has been successfully applied for modulation recognition tasks. Most of the existing applications pay little attention to the volume of data used. In reality, real historic data for modulation recognition could be limited. Thus it is better to train recognition models on small amounts of data. This is however a huge challenge, since the performance of DL models depend on sufficient data. In this paper, we introduce an efficient system model based on CNN and different data augmentation methods, for the purpose of modulation recognition. Our system employs random rotation, flip, zoom, random shift and resize methods for data augmentation. The CNN model employs small filter sizes, pooling layers, and dropout layers to improve the network. We apply a small dataset consisting of 40 constellation images per modulation type, to the system. We further analyze the performance based on three data augmentation intervals. From our experiments, the model achieved an accuracy of 32% without data augmentation and 76.07%, 35.71% and 96.42% on the three data-augmentation intervals.