4,457 research outputs found
Introduction to the Three- County Study
A number of the articles in this volume are based on case studies in three counties: Donglan in Guangxi province,' Shibing in Guizhou province and Xunyi in Shaanxi province. Fieldwork and data analysis were carried out in collaboration with a research institution in each province: Guangxi Health Management College, Guiyang Medical College and Xi'an Medical University The health bureaux of each
province have also participated in the programme.
Routine reports were collected from the county statistical bureaux, and the county governments provided routine financial data. The county health bureaux provided financial and health service utilisation data for county-level health facilities and township health centres. Structured interviews were held with key members of the county governments, and county and township health facilities. Focus group discussions were held with health workers and users of health services
A Hybrid Decision Model for Improving Warehouse Efficiency in a Process-oriented View
published_fina
Motor Vehicle Fatalities During Memorial Day Weekends, 1981-2016
OBJECTIVE: Motor vehicle crashes are a leading cause of injury death in the United States, and Memorial Day weekend is one of six holiday periods with an increased number of motor vehicle fatalities in the United States. However, few motor vehicle fatality comparisons were made between Memorial Day weekend and non-holiday periods. Our aims were to determine which day(s) during the holiday had highest motor vehicle fatality risk compared to non-holiday travel and to identify potential risk factors.
RESULTS: Of 43,457 traffic fatalities studied, 15,292 (35%) occurred during the holiday, with Saturday being deadliest but Monday having highest odds of traffic fatality. Both sexes, all years, age \u3c 65, drivers and passengers, rural and urban, and all regions in the United States were at increased risk during the holiday versus non-holiday periods
Location Time of Power Quality Disturbances and Noise Using Morphology Gradient and Skeletonization in 3D
In power system, there are some disturbances such as, voltage dip, momentary interruption, voltage swell, or oscillatory transients that may result in mal-function or failure in operation of some devices. Knowing the location where the disturbances occur in the system is an essential part in selecting the appropriate method on improving power quality issues in order to get effective and efficient results. One of methods in locating power quality disturbances is using Mathematical Morphology (MM). In this paper, signals with disturbances were filtered using morphology gradient. Top-hat transform is applied for the filtered signal using flat structuring element. The simulation results show that the location of disturbances can be detected accurately. Skeletonization is used to identify the location time of the noises in the system. By plotting the results in 3D, it makes easier to identify the location of disturbances with or without noises from their different color and shape as pattern recognition
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