An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

Early Warning of Cultivated Land Quality Based on Multiple Indicators

At present,the quality protection and early warning of cultivated land in China are actually based on the increase and decrease of the quantity of cultivated land and the change of its quality.The early warning of cultivated land quality based on this is easy to cause the problem of missing and wrong alarm.Based on the quality and productivity change of regional cultivated land,early warning on the regional cultivated land quality is conducted in this study respectively,and the results of the two early warning are compared.Moreover,a multi-indicator cultivated land quality early warning based on the quality change,productivity change and auxiliary indicators is proposed.The results show that:early warning of cultivated land quality based on quality and productivity change of cultivated land has certain limitations.Based on multi-indicator early warning,the number of counties with heavy,medium and light warnings in Henan Province is 52,8 and 11 respectively.The multi-indicator early warning result of cultivated land quality is more accurate,which can provide a basis for cultivated land protection and management assessment,and has great application value.

The effect of the quality of vital sign recording on clinical decision making in a regional acute care trauma ward

Purpose： Recording vital signs is important in the hospital setting and the quality of this documentation influences clinical decision making. The Modified Early Warning Score （MEWS） uses vital signs to categorise the severity of a patient＇s physiological derangement and illustrates the clinical impact of vital signs in detecting patient deterioration and making management decisions. This descriptive study measured the quality of vital sign recordings in an acute care trauma setting, and used the MEWS to determine the impact the documentation quality had on the detection of physiological derangements and thus, clinical decision making. Methods： Vital signs recorded by the nursing staff of all trauma patients in the acute care trauma wards at a regional hospital in South Africa were collected from January 2013 to February 2013. Investigator- measured values taken within 2 hours of the routine observations and baseline patient information were also recorded. A MEWS for each patient was calculated from the routine and investigator-measured observations. Basic descriptive statistics were performed using EXCEL Results： The details of lgl newly admitted patients were collected. Completion of recordings was 81% for heart rate, 88~; for respiratory rate, 98~; for blood pressure, 92% for temperature and 41~ for GCS. The recorded heart rate was positively correlated with the investigator＇s measurement （Pearson＇s correlation coefficient of 0.76）; while the respiratory rate did not correlate （Pearson＇s correlation coefficient of 0.02）. In 59~ of patients the recorded respiratory rate （RR） was exactly 20 breaths per minute and 27~ had a recorded RR of exactly 15. Seven percent of patients had aberrant Glasgow Coma Scale readings above the maximum value of 15. The average MEWS was 2 for both the recorded （MEWS（R）） and investigator （MEWS（1）） vitals, with the range of MEWS（R） 0-7 and MEWS（1） 0-9. Analysis showed 59% of the MEWS（R） underestimated the physiological derangement （scores were lower than the MEWS（1））; 80%; of patients had a MEWS（R） requiring 4 hourly checks which was only completed in 2%;; 86% of patients had a MEWS（R） of less than three （i.e. not necessitating escalation of care）, but 33% of these showed a MEWS（1） greater than three （i.e. actually necessitating escalation of care）. Conclusion： Documentation of vital signs aids management decisions, indicating the physiological derangement of a patient and dictating treatment. This study showed that there was a poor quality of vital sign recording in this acute care trauma setting, which led to underestimation of patients＇ physi- ological derangement and an inability to detect deteriorating patients. The MEWS could be a powerful tool to empower nurses to become involved in the diagnosis and detection of deteriorating patients, as well as providing a framework to communicate the severity of derangement between health workers. However, it requires a number of strategies to improve the quality of vital sign recording, including continuing education, increasing the numbers of competent staff and administrative changes in vital sign charts.