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Aim: To determine the effects of collaborative health management of congestive heart failure through the rigorous evaluation and extraction of evidence. Background: Over the past two decades, cardiovascular disease has been the leading cause of death worldwide. Multidisciplinary team intervention for congestive heart failure has increased with population ageing and congestive heart failure incidence rate as well as cost of care. However, the effectiveness and feasibility of collaborative health management need to be explored. Design: Systematic review and meta‐analysis. Methods: We conducted systematic literature searches in the Cochrane Library, PubMed, CINAHL and Medline for articles published between 2002 and 2022. After screening based on the inclusion and exclusion criteria, 13 articles were included in a rigorous review and evidence extraction process, evaluated methodological quality using the Jadad Quality Scale. Statistical heterogeneity was evaluated using Review Manager (RevMan Version 5.4) for the meta‐analysis. Results: In this study, a systematic review and meta‐analysis were performed on 13 studies regarding the collaborative health management of people with congestive heart failure. The common result is that the collaborative health management model enables the enhancement of self‐care and monitoring abilities, the strengthening of cardiac function, the alleviation of physiological and psychological symptoms and the improvement of readmission rates, mortality rate and quality of life. Conclusion: The congestive heart failure collaborative health management model could decrease the hospitalization rate related to congestive heart failure, all‐cause mortality rate, and all‐cause hospitalization rate, and improve the quality of life. Implications for Practice: The collaborative health management model could effectively coordinate interdisciplinary team cooperation and provide information, which decreases hospitalization and mortality risks and improves their quality of life. No patient or Public Contribution: Our paper is a systematic review and meta‐analysis, and such details do not apply to our work. What does this paper contribute to the wider global clinical community?: The Collaborative Health Management Model provides in‐depth insights, aiding in the design tailored to the specific circumstances of each country. Highlighting its critical role in the context of a global shortage of nursing staff, the model emphasizes the integration of multidisciplinary professional roles and the strengthening of collaboration as essential elements in addressing challenges posed by workforce shortages.Implementation of the Collaborative Health Management Model not only enhances patient care outcomes but also relieves pressure on healthcare systems, lowers medical costs, and addresses challenges arising from the shortage of nursing staff. Consequently, this model not only contributes to individual patient care improvement but also holds broader implications for enhancing the efficiency and sustainability of global healthcare systems. Trial and Protocol Registration: The detailed study protocol can be found on the PROSPERO website. [ABSTRACT FROM AUTHOR]

We proposed a new architecture with multiple pick and place heads to suck inspected chips and to be moved synchronously for acquiring the images of different surfaces on different chips at their corresponding acquisition stations. The computation loading of defect inspection for each chip also can be distributed because of sequential image acquisition. Considering the different computing loading of 6 surfaces of a chip, image acquisition for the front and back sides of chips is allocated at the first and second stations, and then 4 edge sides of chips. The modification and calibration of position and orientation for each opto-mechanical device will be simpler and easier when the characteristics of inspected chips changed. Thus system can accomplish the inspection for 6 surfaces of each chip and determine its quality in real-time before that chip is put onto target wafer. Developed inspection system has advantages and contributions of real-time inspection with high throughput during chip sorting process, and easily adjustment and calibration for all opto-mechanical devices to inspectdifferent types of chips. The first phase of development was accomplished for an AOI system to inspect defects on 5 surfaces of chips. System has been integrated in customer’s chip sorter for testing. The inspecting accuracy for chip backside is better than 95%.

Background: To collect medical datasets, it is usually the case that a number of data samples contain some missing values. Performing the data mining task over the incomplete datasets is a difficult problem. In general, missing value imputation can be approached, which aims at providing estimations for missing values by reasoning from the observed data. Consequently, the effectiveness of missing value imputation is heavily dependent on the observed data (or complete data) in the incomplete datasets.Objective: In this paper, the research objective is to perform instance selection to filter out some noisy data (or outliers) from a given (complete) dataset to see its effect on the final imputation result. Specifically, four different processes of combining instance selection and missing value imputation are proposed and compared in terms of data classification.Methods: Experiments are conducted based on 11 medical related datasets containing categorical, numerical, and mixed attribute types of data. In addition, missing values for each dataset are introduced into all attributes (the missing data rates are 10%, 20%, 30%, 40%, and 50%). For instance selection and missing value imputation, the DROP3 and k-nearest neighbor imputation methods are employed. On the other hand, the support vector machine (SVM) classifier is used to assess the final classification accuracy of the four different processes.Results: The experimental results show that the second process by performing instance selection first and imputation second allows the SVM classifiers to outperform the other processes.Conclusions: For incomplete medical datasets containing some missing values, it is necessary to perform missing value imputation. In this paper, we demonstrate that instance selection can be used to filter out some noisy data or outliers before the imputation process. In other words, the observed data for missing value imputation may contain some noisy information, which can degrade the quality of the imputation result as well as the classification performance. [ABSTRACT FROM AUTHOR]

Biotechnology and medicine enterprises (BMEs) integrate the intellectual capital of bioengineering and medical treatment as well as value-added R&D capacity. The financial performance of this capital influences whether a BME can operate perpetually. This paper applies grey relational analysis to the case of a BME, Abnova, in order to identify its key performance indicators (KPIs) in finance. The research results show that the KPIs of this BME are the turnover rate of cash, the percentage of patent numbers and the growth rate of sales. The results verify the application of grey relation analysis in KPI identification and provide implications for BME managers' decision efficiency. [ABSTRACT FROM AUTHOR]

In this paper we used a rigorous optical model to investigate the viewing angle properties of patterned retarder (PR) stereoscopic 3D liquid crystal display (LCD). By analyzing the simulation and experiment of passive glasses and PR structure, we suggested several new design structures to enlarge the viewing angle at oblique and horizontal direction. Compare to conventional structure and several new structures [1] [2], our new designs had several advanced features, such as simple structure, low cost, easy to realize and of course wide viewing angle. [ABSTRACT FROM AUTHOR]