Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view computed ...tomography (CT), tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS framework. Currently, how to choose the parameters adaptively for regularization is a major open problem. In this paper, inspired by the idea of machine learning especially deep learning, we unfold the state-of-the-art "fields of experts"-based iterative reconstruction scheme up to a number of iterations for data-driven training, construct a learned experts' assessment-based reconstruction network (LEARN) for sparse-data CT, and demonstrate the feasibility and merits of our LEARN network. The experimental results with our proposed LEARN network produces a superior performance with the well-known Mayo Clinic low-dose challenge data set relative to the several state-of-the-art methods, in terms of artifact reduction, feature preservation, and computational speed. This is consistent to our insight that because all the regularization terms and parameters used in the iterative reconstruction are now learned from the training data, our LEARN network utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms. Also, the number of layers in the LEARN network is only 50, reducing the computational complexity of typical iterative algorithms by orders of magnitude.
► We review the literature on FMEA published between 1992 and 2012. ► We use a classification framework to classify 75 papers by the approaches used. ► The most important shortcomings, the most ...popular approaches and their inadequacies are identified. ► We offer directions for future research to address the FMEA deficiencies.
Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.
Meta evaluation theory and methods were used to evaluate the review experts of science and technology projects. Dozens of meta evaluation criteria can be found within two categories: the objective ...data of the experts' review results, i.e. the coefficient of deviation, the Spearman rank correlation coefficient, the reliability coefficient of binary value; the subjective data of the experts, i.e., the degree of experts' participation, the degree of review punctuality, the qualification of experts, and the degree of review seriousness. How these criteria impact each other has few been examined, and how to integrate the objective and subjective criteria need a comprehensive model with considering the fuzzy characteristics of the subjective criteria. This study targeted these two questions. An empirical study was adopted with hundreds of experts taking part in reviewing hundreds of Sci-Tech projects. The impacting relationships among the criteria were analyzed based on the empirical study. In order to deal with the intuitionistic fuzzy data on the subjective criteria and improve the estimation efficiency, an IVIF-BWM (best worst method under interval-valued intuitionistic fuzzy environment) was proposed by combining IVIF and the classical BWM to generate the importance weight for each criterion. The MULTIMOORA (multi-objective optimization by ratio analysis plus the full multiplicative form) was used to determine how to combine these criteria. At last, the proposed meta-evaluation model based on IVIF-BWM and MULTIMOORA was applied in a real case. The case study results supported the accuracy and reliability of the proposed model.
•A meta-evaluation model was established to evaluate the technology project review experts.•Based on the empirical results, seven meta-evaluation criteria were selected and modified for the model.•BWM was improved in interval valued intuitionistic fuzzy environment, and a method called as IVIF-BWM was proposed.•A comprehensive meta-evaluation model was founded based on IVIF-BWM and MULTIMOORA.
Nowadays, failure mode and effect analysis (FMEA) is a widely used reliability analysis technique for products, processes, and services because of its relative simplicity. The conventional risk ...priority number method, however, has been criticized as having many limitations, such as in the assessment of failure modes, the weighting of risk factors, and the prioritization of failure modes. In this paper, we aim to develop a new risk priority model for FMEA by integrating hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach. The concept of hesitant 2-tuple linguistic term sets is first presented to express various uncertainties in the assessment information of FMEA team members. Borrowing the idea of grey relational analysis, a multiple objective optimization model is constructed to determine the relative weights of risk factors with incomplete weight information. The extended QUALIFLEX approach with an inclusion comparison method is then suggested to determine the risk ranking of failure modes identified in FMEA. Finally, the practicality and effectiveness of the proposed FMEA are demonstrated through a case study, and the results show that the new risk priority approach is useful and flexible for handling complicated FMEA problems and can yield a reasonable and credible priority ranking of failure modes.
Chronic kidney disease (CKD) is associated with the development of mineral bone disorder (MBD), osteoporosis, and fragility fractures. Among CKD patients, adynamic bone disease or low bone turnover ...is the most common type of renal osteodystrophy. The consequences of CKD-MBD include increased fracture risk, greater morbidity, and mortality. Thus, the goal is to prevent the occurrences of fractures by means of alleviating CKD-induced MBD and treating subsequent osteoporosis. Changes in mineral and humoral metabolism as well as bone structure develop early in the course of CKD. CKD-MBD includes abnormalities of calcium, phosphorus, PTH, and/or vitamin D; abnormalities in bone turnover, mineralization, volume, linear growth, or strength; and/or vascular or other soft tissue calcification. In patients with CKD-MBD, using either DXA or FRAX to screen fracture risk should be considered. Biomarkers such as bALP and iPTH may assist to assess bone turnover. Before initiating an antiresorptive or anabolic agent to treat osteoporosis in CKD patients, lifestyle modifications, such as exercise, calcium, and vitamin D supplementation, smoking cessation, and avoidance of excessive alcohol intake are important. Managing hyperphosphatemia and SHPT are also crucial. Understanding the complex pathogenesis of CKD-MBD is crucial in improving one's short- and long-term outcomes. Treatment strategies for CKD-associated osteoporosis should be patient-centered to determine the type of renal osteodystrophy. This review focuses on the mechanism, evaluation and management of patients with CKD-MBD. However, further studies are needed to explore more details regarding the underlying pathophysiology and to assess the safety and efficacy of agents for treating CKD-MBD.
N‐type conjugated polymers as the semiconducting component of organic electrochemical transistors (OECTs) are still undeveloped with respect to their p‐type counterparts. Herein, we report two rigid ...n‐type conjugated polymers bearing oligo(ethylene glycol) (OEG) side chains, PgNaN and PgNgN, which demonstrated an essentially torsion‐free π‐conjugated backbone. The planarity and electron‐deficient rigid structures enable the resulting polymers to achieve high electron mobility in an OECT device of up to the 10−3 cm2 V−1 s−1 range, with a deep‐lying LUMO energy level lower than −4.0 eV. Prominently, the polymers exhibited a high device performance with a maximum dimensionally normalized transconductance of 0.212 S cm−1 and the product of charge‐carrier mobility μ and volumetric capacitance C* of 0.662±0.113 F cm−1 V−1 s−1, which are among the highest in n‐type conjugated polymers reported to date. Moreover, the polymers are synthesized via a metal‐free aldol‐condensation polymerization, which is beneficial to their application in bioelectronics.
Two new n‐type semiconducting polymers, PgNaN and PgNgN, bearing oligo(ethylene glycol) (OEG) side chain are developed with a fully conformationally locked backbone and deep‐lying LUMO energy level. As a result, the polymer of PgNaN exhibits a good performance on OECT devices with a maximum dimensionally normalized transconductance of 0.212 S cm−1 and a product μC* of 0.662±0.113 F cm−1 V−1 s−1.
Rationale, aims, and objectives
Failure mode and effects analysis (FMEA) is a valuable reliability management tool that can preemptively identify the potential failures of a system and assess their ...causes and effects, thereby preventing them from occurring. The use of FMEA in the healthcare setting has become increasingly popular over the last decade, being applied to a multitude of different areas. The objective of this study is to review comprehensively the literature regarding the application of FMEA for healthcare risk analysis.
Methods
An extensive search was carried out in the scholarly databases of Scopus and PubMed, and we only chose the academic articles which used the FMEA technique to solve healthcare risk analysis problems. Furthermore, a bibliometric analysis was performed based on the number of citations, publication year, appeared journals, authors, and country of origin.
Results
A total of 158 journal papers published over the period of 1998 to 2018 were extracted and reviewed. These publications were classified into four categories (ie, healthcare process, hospital management, hospital informatization, and medical equipment and production) according to the healthcare issues to be solved, and analyzed regarding the application fields and the utilized FMEA methods.
Conclusion
FMEA has high practicality for healthcare quality improvement and error reduction and has been prevalently employed to improve healthcare processes in hospitals. This research supports academics and practitioners in effectively adopting the FMEA tool to proactively reduce healthcare risks and increase patient safety, and provides an insight into its state‐of‐the‐art.
Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a ...group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members' weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.
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