BackgroundRheumatoid arthritis (RA) patients are at risk of acquiring drug-related problems (DRPs). However, there has been a lack of studies on DRPs in patients with RA up to now.MethodThis retrospective study was conducted in a tertiary hospital in Malaysia from January 2012 to December 2017 with the purpose of assessing DRPs in RA patients and factors associated with its occurrence. A total of 200 patients who had received pharmacological treatment for RA were enrolled in this study. Assessment of DRPs was based on the Pharmaceutical Network Care Europe tool version 5.01.ResultsA total of 289 DRPs with an average of 1.5±1.0 problems per patient were identified, in which 78.5% of the population had at least one DRP. The most common DRPs encountered were adverse reactions (38.8%), drug interactions (33.6%), and drug-choice problems (14.5%). Factors that had significant association with the occurrence of DRPs were polypharmacy (P=0.003), multiple comorbidities (P=0.001), hyperlipidemia (P=0.009), osteo (P=0.040), and renal impairment (P=0.044). These data indicated that the prevalence of DRPs was high among RA patients.ConclusionEarly identification of types of DRPs and associated factors may enhance the prevention and management of RA.

A neural network was used to relate color and texture features of wheat samples to damage caused by Fusarium scab infection. A total of 55 color and texture features were extracted from images captured by a machine vision system. Random errors were reduced by using average values of features from multiple images of individual samples. A four‐layer backpropagation neural network was used. The percentage of visual scabby kernels (%VSK) estimated by the trained network followed the actual percentage with a correlation coefficient of 0.97; maximum and mean absolute errors were 5.14 and 1.93%, respectively. A comparison between the results by the machine vision‐neural network technique and the human expert panel led to the conclusion that the machine vision‐neural network technique produced more accurate determination of %VSK than the human expert panel.

No abstract

MODerate-resolution Imaging Spectroradiometer (MODIS) is a new generation remote sensing (RS) sensor and its applications in hydrology and water resources have attracted much attention. To overcome the problems of slow response in flood disaster monitoring based on traditional RS techniques in China, the Flood Disaster Monitoring and Assessing System (FDMAS), based on MODIS and a Geographic Information System (GIS), was designed and applied to Dongting Lake, Hunan Province, China. The storage curve of Dongting Lake for 1995 was obtained using 1:10 000 topographic map data and then a relationship between water level at the Chenglingji hydrological station and lake area was derived. A new relationship between water level and lake area was obtained by processing MODIS images of Dongting Lake from April 2002 to April 2003 and the influence of lake area variation on water level was analysed with the 1996 flood data. It was found that the water level reduction reached 0.64 m for the 1996 flood if the original lake area curve was replaced with the area curve of 2002. This illustrates that the flood water level has been considerably reduced as a result of the increased area of Dongting Lake since the Chinese Central Government's "return land to lake" policy took effect in 1998. Key words

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise &remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.