Air quality data clustering using EPLS method

[EN] Nowadays air quality data can be easily accumulated by sensors around the world. Analysis on air quality data is very useful for society decision. Among five major air pollutants which are calculated for AQI (Air Quality Index), PM2.5 data is the most concerned by the people. PM2.5 data is also cross-impacted with the other factors in the air and which has properties of non-linear non-stationary including high noise level and outlier. Traditional methods cannot solve the problem of PM2.5 data clustering very well because of their inherent characteristics. In this paper, a novel model-based feature extraction method is proposed to address this issue. The EPLS model includes: (1) Mode Decomposition, in which EEMD algorithm is applied to the aggregation dataset; (2) Dimension Reduction, which is carried out for a more significant set of vectors; (3) Least Squares Projection, in which all testing data are projected to the obtained vectors. Synthetic dataset and air quality dataset are applied to different clustering methods and similarity measures. Experimental results demonstrate that EPLS is efficient in dealing with high noise level and outlier air quality clustering problems, and which can also be adapted to various clustering techniques and distance measures. (C) 2016 Elsevier B.V. All rights reserved.This work was supported in part by the National Natural Science Foundation of China (Nos. 61440018, 61501411), the Hubei Natural Science Foundation (No. 2014CFB904), China Scholarship Council Funding.Chen, Y.; Wang, L.; Li, F.; Du, B.; Choo, KR.; Hassan Mohamed, H.; Qin, W. (2017). Air quality data clustering using EPLS method. Information Fusion. 36:225-232. https://doi.org/10.1016/j.inffus.2016.11.015S2252323

第45回『中国・朝鮮の旧日本租界』研究会 中国・朝鮮の旧日本租界

研究会報

Effects of fertilizer application schemes and soil environmental factors on nitrous oxide emission fluxes in a rice-wheat cropping system, east China

Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with agricultural soils representing its largest anthropogenic source. However, the mechanisms involved in the N2O emission and factors affecting N2O emission fluxes in response to various nitrogenous fertilizer applications remain uncertain. We conducted a four-year (2012–2015) field experiment to assess how fertilization scheme impacts N2O emissions from a rice-wheat cropping system in eastern China. The fertilizer treatments included Control (CK), Conventional fertilizer (CF), CF with shallow-irrigation (CF+SI), CF with deep-irrigation system (CF+DI), Optimized fertilizer (OF), OF with Urease inhibitor (OF+UI), OF with conservation tillage (OF+CT) and Slow-release fertilizer (SRF). N2O emissions were measured by a closed static chamber method. N2O emission fluxes ranged from 0.61 μg m-2 h-1 to 1707 μg m-2 h-1, indicating a significant impact of nitrogen fertilizer and cropping type on N2O emissions. The highest crop yields for wheat (3515–3667 kg ha-1) and rice (8633–8990 kg ha-1) were observed under the SRF and OF+UI treatments with significant reduction in N2O emissions by 16.94–21.20% and 5.55–7.93%, respectively. Our findings suggest that the SRF and OF+UI treatments can be effective in achieving maximum crop yield and lowering N2O emissions for the rice-wheat cropping system in eastern China

Analysis and Approximation of Optimal Co-Scheduling on Chip Multiprocessors ABSTRACT

Cache sharing among processors is important for Chip Multiprocessors to reduce inter-thread latency, but also brings cache contention, degrading program performance considerably. Recent studies have shown that job co-scheduling can effectively alleviate the contention, but it remains an open question how to efficiently find optimal co-schedules. Solving the question is critical for determining the potential of a co-scheduling system. This paper presents a theoretical analysis of the complexity of co-scheduling, proving its NPcompleteness. Furthermore, for a special case when there are two sharers per chip, we propose an algorithm that finds the optimal co-schedules in polynomial time. For more complex cases, we design and evaluate a sequence of approximation algorithms, among which, the hierarchical matching algorithm produces near-optimal schedules and shows good scalability. This study facilitates the evaluation of co-scheduling systems, as well as offers some techniques directly usable in proactive job co-scheduling

The Clinical Characteristics and Predictors of Refractory Mycoplasma pneumoniae Pneumonia in Children.

OBJECTIVE:To analyze the clinical characteristics of refracory Mycoplasma pneumoniae pneumonia (RMPP), and explore the related factors predicting RMPP. METHODS:Retrospective analysis was performed on 634 children with Mycoplasma pneumoniae pneumonia (MPP) hospitalized in our hospital between January 1, 2011 and December 31, 2014. The clinical features, laboratory data, radiological findings between the RMPP group and the general Mycoplasma pneumoniae pneumonia (GMPP) group were compared and the predictive values of related factors were analyzed. RESULTS:The median age of the RMPP patients (n = 145) was much older than that of the GMPP patients (n = 489) (P<0.01). We also found more severe presentations, higher incidence of extra-pulmonary complications and more serious radiological findings in RMPP group, which needed oxygen more often, longer antibiotics administration and intensive care (P<0.05). Meanwhile, the levels of C-reactive protein (CRP), lactic dehydrogenase (LDH), immunoglobulin A (IgM), interleukin (IL)-6, IL-10, interferon gamma (IFN-γ) and the percentage of neutrophils, CD8+ in RMPP group were significantly higher than those in GMPP group (P<0.05); while the levels of prealbumin (PAB) were lower than that in GMPP group (P<0.01). In ROC curve analysis, the percentage of neutrophil, CRP, LDH, PAB, IL-6, IL-10 and IFN-γ were useful for differentiating patients with RMPP from those with GMPP. Multiple logistic regression analysis showed that the CRP≥16.5mg/L, LDH ≥417IU/L and IL-6 ≥14.75pg/ml were significant predictors regarding to RMPP. CONCLUSIONS:CRP≥16.5mg/L, LDH ≥417IU/L and IL-6 ≥14.75pg/ml might be the significant predictors of RMPP in children, which can aid in early recognition of RMPP