Objective: This study aims to analyze the causes of postoperative transition from euglycemic diabetic ketoacidosis (EDKA) to diabetic ketoacidosis (DKA), summarize clinical nursing experiences, enhance the recognition...Objective: This study aims to analyze the causes of postoperative transition from euglycemic diabetic ketoacidosis (EDKA) to diabetic ketoacidosis (DKA), summarize clinical nursing experiences, enhance the recognition and management of such complications, and improve patient prognosis. Methods: A detailed case analysis was conducted on a patient who developed EDKA early after breast cancer surgery, which subsequently transitioned to DKA. A multidisciplinary team (MDT) consultation was employed to formulate a personalized nursing plan. Specific methods included comprehensive clinical data collection, monitoring of blood glucose, urine ketones, and blood ketone levels;implementing dynamic insulin adjustment strategies;providing dietary education and psychological support;and guiding dietary adjustments through nutritional consultations. Results: Through personalized observation, blood glucose management, dietary management, psychological care, and wound care, the patient’s blood and urine ketone levels returned to normal, the flap healed well, and blood glucose was maintained within the normal range. The patient is currently undergoing postoperative adjuvant chemotherapy. Conclusion: For postoperative patients with unexplained nausea, vomiting, and dehydration, regardless of diabetes history, timely testing of blood glucose, blood ketones, blood urea nitrogen, creatinine, electrolytes, and blood gas analysis can facilitate early detection of EDKA. Additionally, personalized management of blood glucose, diet, psychological care, and wound care is crucial for the prevention and treatment of EDKA.展开更多
The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the...The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.展开更多
xIn this paper,we propose and investigate novel closed-form point estimators for the beta distribution.The estimators of the first type are a modified version of Pearson’s method of moments.The underlying idea is to ...xIn this paper,we propose and investigate novel closed-form point estimators for the beta distribution.The estimators of the first type are a modified version of Pearson’s method of moments.The underlying idea is to involve the sufficient statistics,i.e.,log-moments in the moment estimation equations and solve the mixed type of moment equations simultaneously.The estimators of the second type are based on an approximation to Fisher’s likelihood principle.The idea is to solve two score equations derived from the log-likelihood function of generalized beta distributions.Both two resulted estimators are in closed forms,strongly consistent and asymptotically normal.In addition,through theoretical analyses and extensive simulations,the proposed estimators are shown to perform very close to themaximumlikelihood estimators in both small and large samples,and they significantly outperform the method of moment estimators.展开更多
文摘Objective: This study aims to analyze the causes of postoperative transition from euglycemic diabetic ketoacidosis (EDKA) to diabetic ketoacidosis (DKA), summarize clinical nursing experiences, enhance the recognition and management of such complications, and improve patient prognosis. Methods: A detailed case analysis was conducted on a patient who developed EDKA early after breast cancer surgery, which subsequently transitioned to DKA. A multidisciplinary team (MDT) consultation was employed to formulate a personalized nursing plan. Specific methods included comprehensive clinical data collection, monitoring of blood glucose, urine ketones, and blood ketone levels;implementing dynamic insulin adjustment strategies;providing dietary education and psychological support;and guiding dietary adjustments through nutritional consultations. Results: Through personalized observation, blood glucose management, dietary management, psychological care, and wound care, the patient’s blood and urine ketone levels returned to normal, the flap healed well, and blood glucose was maintained within the normal range. The patient is currently undergoing postoperative adjuvant chemotherapy. Conclusion: For postoperative patients with unexplained nausea, vomiting, and dehydration, regardless of diabetes history, timely testing of blood glucose, blood ketones, blood urea nitrogen, creatinine, electrolytes, and blood gas analysis can facilitate early detection of EDKA. Additionally, personalized management of blood glucose, diet, psychological care, and wound care is crucial for the prevention and treatment of EDKA.
基金supported by the National Natural Science Foundation of China(Grant Nos.72401253,72371182,72002149,and 72271154)and the National Social Science Fund of China(23CGL018)+1 种基金the State Key Laboratory of Biobased Transportation Fuel Technology,China(Grant No.512302-X02301)a start-up grant from the ZJU-UIUC Institute at Zhejiang University(Grant No.130200-171207711).
文摘The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.
基金supported by the National Natural Science Foundation of China[project number 72401253,72271154]the State Key Laboratory of Biobased Transportation Fuel Technology[project number 512302-X02301]a start-up grant from the ZJU-UIUC Institute at Zhejiang University[project number 130200-171207711].
文摘xIn this paper,we propose and investigate novel closed-form point estimators for the beta distribution.The estimators of the first type are a modified version of Pearson’s method of moments.The underlying idea is to involve the sufficient statistics,i.e.,log-moments in the moment estimation equations and solve the mixed type of moment equations simultaneously.The estimators of the second type are based on an approximation to Fisher’s likelihood principle.The idea is to solve two score equations derived from the log-likelihood function of generalized beta distributions.Both two resulted estimators are in closed forms,strongly consistent and asymptotically normal.In addition,through theoretical analyses and extensive simulations,the proposed estimators are shown to perform very close to themaximumlikelihood estimators in both small and large samples,and they significantly outperform the method of moment estimators.
