Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive so...Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point(IBP)and end boiling point(EBP)in crude distillation unit.In this work,adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning(JITL)approach.The different types of local models designed are locally weighted regression(LWR),multiple linear regression(MLR),partial least squares regression(PLS)and support vector regression(SVR).In addition to model development,the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated.Results show that the JITL model based on support vector regression with iterative single data algorithm optimization(ISDA)local model(JITL-SVR:ISDA)yielded best prediction accuracy in reasonable computation time.展开更多
Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during ho...Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during hospitalization often display clinical decline for several hours before the event is observed. Non-critical care Nurses’ inconsistent recognition and response to patient deterioration lead to an increase in the length of hospital stay, unexpected admissions to the ICU, and increased morbidity and mortality. Aim: The study aimed to assess the factors that facilitate or impede the detection of early warning signs among adult patients hospitalized in tertiary care settings. Training should be provided to improve nurses’ knowledge, practice and attitude toward early warning signs of deteriorating patients leading to enhanced clinical judgment, skills and decision-making in addressing alerts. Methodology: A literature search was carried out in various databases;these were Cumulative Index to Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, Science Direct, and Sage. The search area was narrowed from 2017 to 2022. The keywords used were “prevalence” AND “unplanned ICU admission”, “the importance of early warning signs” “outcome failure in rescue” “patient deterioration, communication” “improvement in early detection” AND “patient outcome admission” AND “early warning signs” AND “Pakistan”. After the analysis process, around 33 articles that met the inclusion criteria and were most relevant to the scope and context of the current study were considered. Conclusion: Most of the studies had reviewed literature in a qualitative retrospective observational study, content analysis, mixed method, and quasi-experimental study. The literature review identified that long hours of shift, nurse staffing levels, missed vital signs, lack of nursing training and education, and communication impact nurses’ ability to recognize and respond to early warning signs.展开更多
文摘Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point(IBP)and end boiling point(EBP)in crude distillation unit.In this work,adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning(JITL)approach.The different types of local models designed are locally weighted regression(LWR),multiple linear regression(MLR),partial least squares regression(PLS)and support vector regression(SVR).In addition to model development,the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated.Results show that the JITL model based on support vector regression with iterative single data algorithm optimization(ISDA)local model(JITL-SVR:ISDA)yielded best prediction accuracy in reasonable computation time.
文摘Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during hospitalization often display clinical decline for several hours before the event is observed. Non-critical care Nurses’ inconsistent recognition and response to patient deterioration lead to an increase in the length of hospital stay, unexpected admissions to the ICU, and increased morbidity and mortality. Aim: The study aimed to assess the factors that facilitate or impede the detection of early warning signs among adult patients hospitalized in tertiary care settings. Training should be provided to improve nurses’ knowledge, practice and attitude toward early warning signs of deteriorating patients leading to enhanced clinical judgment, skills and decision-making in addressing alerts. Methodology: A literature search was carried out in various databases;these were Cumulative Index to Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, Science Direct, and Sage. The search area was narrowed from 2017 to 2022. The keywords used were “prevalence” AND “unplanned ICU admission”, “the importance of early warning signs” “outcome failure in rescue” “patient deterioration, communication” “improvement in early detection” AND “patient outcome admission” AND “early warning signs” AND “Pakistan”. After the analysis process, around 33 articles that met the inclusion criteria and were most relevant to the scope and context of the current study were considered. Conclusion: Most of the studies had reviewed literature in a qualitative retrospective observational study, content analysis, mixed method, and quasi-experimental study. The literature review identified that long hours of shift, nurse staffing levels, missed vital signs, lack of nursing training and education, and communication impact nurses’ ability to recognize and respond to early warning signs.
