Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.

The heterogeneous populations of exosomes with distinct nanosize have impeded our understanding of their corresponding function as intercellular communication agents. Profiling signaling proteins packaged in each size-dependent subtype can disclose this heterogeneity of exosomes. Herein, new strategy was developed for deconstructing heterogeneity of distinct-size urine exosome subpopulations by profiling N-glycoproteomics and phosphoproteomics simultaneously. Two-dimension size exclusion liquid chromatography (SEC) was utilized to isolate large exosomes (L-Exo), medium exosomes (M-Exo), and small exosomes (S-Exo) from human urine samples. Then, hydrophilic carbonyl-functionalized magnetic zirconium-organic framework (CFMZOF) was developed as probe for capturing the two kinds of post-translational modification (PTM) peptides simultaneously. Finally, liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with database search was used to characterize PTM protein contents. We identified 144 glycoproteins and 44 phosphoproteins from L-Exo, 156 glycoproteins, and 46 phosphoproteins from M-Exo and 134 glycoproteins and 10 phosphoproteins from S-Exo. The ratio of the proteins with simultaneous glycosylation and phosphorylation is 11%, 9%, and 3% in L-Exo, M-Exo, and S-Exo, respectively. Based on label-free quantification intensity results, both principal component analysis and Pearson’s correlation coefficients indicate that distinct-size exosome subpopulations exist significant differences in PTM protein contents. Analysis of high abundance PTM proteins in each exosome subset reveals that the preferentially packaged PTM proteins in L-Exo, M-Exo, and S-Exo are associated with immune response, biological metabolism, and molecule transport processes, respectively. Our PTM proteomics study based on size-dependent exosome subtypes opens a new avenue for deconstructing the heterogeneity of exosomes.

Many relations in the real world can be described by mathematical language. Fuzzy set theory can transform human language into mathematical language and use membership degree function to describe relations between events. Dempster–Shafer evidence theory provides basic probability assignment (BPA), which can describe the occurrence rate of attributes in basic events. Based on the known membership degree function and BPA distribution, a new evaluation method is proposed in this paper to analyze decision making. Given the relations among relevant events, which are expressed by BPA distribution and membership degree function, the relations among basic events and top event can be obtained. The Dempster's combination rule and pignistic probability transformation are used to transform BPA distribution into probability distribution. The belief measure is applied to deal with these fuzzy relations. Some numerical examples are given in this paper to illustrate the proposed evaluation methodology.

A Zr-based metal–organic framework (Zr-MOF) which has free carbonyl groups is synthesized successfully through mix-ligand strategy. Subsequently, Tb 3+ is encapsulated into a Zr-MOF by postcoordinated modification. The Tb 3+ @Zr-MOF exhibits the characteristic emission of Tb 3+ because of efficient sensitization through antenna effects. The Tb 3+ @Zr-MOF is further developed as a novel “turn-on” fluorescent probe to detect fluoride ions in aqueous solution. The results show that Tb 3+ @Zr-MOF exhibits excellent selectivity, high stability, low detection limits, and good anti-interference for sensitizing fluoride ions. In addition, the possible sensing mechanism that the induced luminescence properties may be attributed to Lewis acid–base interactions is discussed.

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A new class of Ln-MOFs (Ln = Eu, Tb, Eu/Tb, Sm) are synthesized through a post-synthetic modification of the parent MOF, UMCM-NH2. The luminescence spectra of Eu-MOF, Tb-MOF and Eu/Tb-MOF exhibit the characteristic emission bands of ligands and corresponding Ln3+, particularly Eu3+. Multi-color luminescence could be modulated by adjusting the excitation wavelength of the Eu-MOF and Tb-MOF. What's more, white-light emission may be realized by co-doping the Eu3+ and Tb3+ due to the synergistic contribution of the three luminescence centers. In addition, the Eu-MOF is selected as a luminescence sensor to explore the potential of sensing organic small molecules. Most interestingly, it exhibits a highly selective, sensitive and rapid response to THF, which could be distinguished easily by the naked eye under UV-light irradiation. Consequently, Eu-MOF is a promising candidate as the "turn on" fluorescent probe for detecting THF that has been rarely reported.

Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently based on deep learning fingerprints of plasma exosomes. Sequential sizeexclusion chromatography (SSEC) was developed efficiently for separating exosomes from human plasma. SSEC isolated plasma exosomes, taking as less as 2 h for a single sample with high purity such that the discard rates of high-density lipoproteins and low/ very low-density lipoproteins were 93 and 85%, respectively. Benefitting from the rapid and high-purity isolation, the contents encapsulated in exosomes, covered by plasma proteins, were well profiled by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS). We further analyzed 220 clinical samples, including 79 breast cancer patients, 57 pancreatic cancer patients, and 84 healthy controls. After MS data preprocessing and feature selection, the extracted MS feature peaks were utilized as inputs for constructing a multi-classifier artificial neural network (denoted as Exo-ANN) model. The optimized model avoided overfitting and performed well in both training cohorts and test cohorts. For the samples in the independent test cohort, it realized a diagnosed accuracy of 80.0% with an area under the curve of 0.91 for the whole group. These results suggest that our integrated pipeline may become a generic tool for liquid biopsy based on the analysis of plasma exosomes in clinics.

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