•Establish formulas to compute spread and indeterminacy indices of fuzzy judgments.•Develop a novel trapezoidal fuzzy extension of Saaty’s consistency.•Propose a new framework to normalize ...trapezoidal fuzzy utility vectors.•Build a linear program to acquire trapezoidal fuzzy utility vectors from TFRPMs.•Find an analytical solution for optimal fuzzy utility vectors of consistent TFRPMs.
Fuzzy extensions of Saaty’s consistency play an important role in developing fuzzy analytic hierarchy process based decision-making methods. This paper proposes formulas to calculate ratio-based increasing and decreasing spread indices and to obtain ratio-based indeterminacy indices of modal and support intervals for fuzzy judgments of trapezoidal fuzzy reciprocal preference matrices (TFRPMs). A transitivity equation with parametric fuzzy elements is introduced and combined with the existence of parameter values to define consistency of TFRPMs. To cope with the challenge of identifying the existence, computational formulas are developed to directly obtain values of parameters from the fuzzy judgments in a TFRPM, and an equivalent consistency concept is proposed for TFRPMs. Properties of consistent TFRPMs are presented and a logarithmic deviation based formula is provided to compute consistency indices of TFRPMs. A new framework of normalized vectors with trapezoidal fuzzy elements is introduced. A logarithmic goal programming model is established and transformed into a linear program for acquiring normalized trapezoidal fuzzy utility vectors from TFRPMs. An analytical solution is found for optimized trapezoidal fuzzy utility vectors of consistent TFRPMs. The developed models are validated by three numerical illustrations possessing comparative studies.
•Illustrate deficiencies of the two recent fuzzy eigenvector methods.•Introduce two new frameworks for additively normalized fuzzy priorities (ANFPs).•Build eigenproblems from triangular fuzzy ...preference relation matrices (TFPRMs).•Develop a linear program to derive eigenvector based ANFPs of TFPRMs.•Propose a novel eigenproblem driven acceptability checking method for TFPRMs.
The eigenproblem plays a crucial role on checking acceptability of decision-makers’ pairwise comparison results and deriving priorities from preference relation matrices in the analytic hierarchy process (AHP). This paper analyzes two recent fuzzy eigenvector methods and illustrates their deficiencies. Two frameworks of additively normalized triangular fuzzy priorities (ANTFPs) are introduced to characterize a cluster of equivalent fuzzy priority vectors. Based on the approximation relationship between triangular fuzzy multiplicative preference relation matrices and their most appropriate ANTFPs, three eigenproblems with positive real matrices are established and an eigenvector based linear program is developed to obtain support interval based ANTFPs from fuzzy multiplicative preference relation matrices. The largest eigenvalue weighting consistency index and consistency ratio are defined and an acceptability checking method is then proposed by both examining acceptable consistency of fuzzy multiplicative preference relation matrices and acceptable vagueness of eigenvector based ANTFPs. Afterwards, an eigenproblem driven triangular fuzzy AHP is devised in detail. A numerical illustration including four fuzzy multiplicative preference relation matrices is provided and a comparative study is carried out to demonstrate the superiority and effectiveness of the presented models. Meanwhile, a multi-criteria graduate job selection problem is used to show the application of the proposed triangular fuzzy AHP.
Resistance to chemotherapy is a major challenge for the treatment of patients with colorectal cancer (CRC). Previous studies have found that microRNAs (miRNAs) play key roles in drug resistance; ...however, the role of miRNA‐373‐3p (miR‐375‐3p) in CRC remains unclear. The current study aimed to explore the potential function of miR‐375‐3p in 5‐fluorouracil (5‐FU) resistance. MicroRNA‐375‐3p was found to be widely downregulated in human CRC cell lines and tissues and to promote the sensitivity of CRC cells to 5‐FU by inducing colon cancer cell apoptosis and cycle arrest and by inhibiting cell growth, migration, and invasion in vitro. Thymidylate synthase (TYMS) was found to be a direct target of miR‐375‐3p, and TYMS knockdown exerted similar effects as miR‐375‐3p overexpression on the CRC cellular response to 5‐FU. Lipid‐coated calcium carbonate nanoparticles (NPs) were designed to cotransport 5‐FU and miR‐375‐3p into cells efficiently and rapidly and to release the drugs in a weakly acidic tumor microenvironment. The therapeutic effect of combined miR‐375 + 5‐FU/NPs was significantly higher than that of the individual treatments in mouse s.c. xenografts derived from HCT116 cells. Our results suggest that restoring miR‐375‐3p levels could be a future novel therapeutic strategy to enhance chemosensitivity to 5‐FU.
Resistance to chemotherapy is a major challenge for the treatment of patients with colorectal cancer (CRC). Our results suggest that the restoration of microRNA‐375‐3p levels could be a future novel therapeutic strategy to modulate and enhance chemosensitivity to 5‐fluorouracil treatment in CRC.
Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the ...quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here, we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms the state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL inferred quality index achieves an additional performance gain.
•Existing consistency notions of triangular fuzzy preference relations (TFPRs) are illustrated to be non-robust.•New consistency and acceptable consistency definitions are put forward for TFPRs.•We ...propose a notion of normalized triangular fuzzy multiplicative weights (NTFMWs).•Transformation formulae are provided to convert NTFMWs into consistent TFPRs.•A logarithmic least square model is developed to derive a NTFMW vector from a TFPR.
Triangular fuzzy preference relation (TFPR) is an effective framework to model pairwise estimations with imprecision and vagueness. In order to obtain a reliable and rational decision result, it is important to investigate consistency and priority derivation of TFPRs. The paper analyzes existing definitions and properties of consistent TFPRs, and illustrates that they have no invariance with respect to permutations of decision alternatives. A new triangular fuzzy arithmetic based transitivity equation is introduced to define consistent TFPRs. The new transitivity equation reflects multiplicative consistency of modal values and multiplicative consistency of geometric means of triangular fuzzy estimations. Some properties are presented for consistent TFPRs, and a notion of acceptable consistency is put forward for TFPRs. Geometric mean and uncertainty ratio based transformation formulae are devised to convert normalized triangular fuzzy multiplicative weights into consistent TFPRs. A logarithmic least square model is further established for deriving a normalized triangular fuzzy multiplicative weight vector from a TFPR with acceptable consistency. A geometric mean based method is developed to compare and rank triangular fuzzy multiplicative weights. Three numerical examples including a group decision making problem are examined to demonstrate validity and advantages of the proposed models.
We report new searches for solar axions and galactic axionlike dark matter particles, using the first low-background data from the PandaX-II experiment at China Jinping Underground Laboratory, ...corresponding to a total exposure of about 2.7×10^{4} kg day. No solar axion or galactic axionlike dark matter particle candidate has been identified. The upper limit on the axion-electron coupling (g_{Ae}) from the solar flux is found to be about 4.35×10^{-12} in the mass range from 10^{-5} to 1 keV/c^{2} with 90% confidence level, similar to the recent LUX result. We also report a new best limit from the ^{57}Fe deexcitation. On the other hand, the upper limit from the galactic axions is on the order of 10^{-13} in the mass range from 1 to 10 keV/c^{2} with 90% confidence level, slightly improved compared with the LUX.
•The consistency definition and the goal programming model proposed by Liu, Zhang, and Wang (2012) are technically wrong.•New transitivity conditions are put forward to correct errors in Liu et al. ...(2012).•A two-stage goal programming approach is developed to estimate missing values.
In a recently published paper by Liu et al. Liu, F., Zhang, W.G., Wang, Z.X. (2012). A goal programming model for incomplete interval multiplicative preference relations and its application in group decision-making. European Journal of Operational Research 218, 747–754, two equations are introduced to define consistency of incomplete interval multiplicative preference relations (IMPRs) and employed to develop a goal programming model for estimating missing values. This note illustrates that such consistency definition and estimation model are technically incorrect. New transitivity conditions are proposed to define consistent IMPRs, and a two-stage goal programming approach is devised to estimate missing values for incomplete IMPRs.
Building small-molecule libraries with structural and stereogenic diversity plays an important role in drug discovery. The development of switchable intermolecular cycloaddition reactions from ...identical substrates in different regioselective fashions would provide an attractive protocol. However, this also represents a challenge in organic chemistry, because it is difficult to control regioselectivity to afford the products exclusively and at the same time achieve high levels of stereoselectivity. Here, we report the diversified cycloadditions of α'-alkylidene-2-cyclopentenones catalysed by cinchona-derived primary amines. An asymmetric γ,β'-regioselective intermolecular 6+2 cycloaddition reaction with 3-olefinic (7-aza)oxindoles is realized through the in situ generation of formal 4-aminofulvenes, while a different β,γ-regioselective 2+2 cycloaddition reaction with maleimides to access fused cyclobutanes is disclosed. In contrast, an intriguing α,γ-regioselective 4+2 cycloaddition reaction is uncovered with the same set of substrates, by employing an unprecedented dual small-molecule catalysis of amines and thiols. All of the cycloaddition reactions exhibit excellent regio- and stereoselectivity, producing a broad spectrum of chiral architectures with high structural diversity and molecular complexity.
Contrast is a fundamental attribute of images that plays an important role in human visual perception of image quality. With numerous approaches proposed to enhance image contrast, much less work has ...been dedicated to automatic quality assessment of contrast changed images. Existing approaches rely on global statistics to estimate contrast quality. Here we propose a novel local patch-based objective quality assessment method using an adaptive representation of local patch structure, which allows us to decompose any image patch into its mean intensity, signal strength and signal structure components and then evaluate their perceptual distortions in different ways. A unique feature that differentiates the proposed method from previous contrast quality models is the capability to produce a local contrast quality map, which predicts local quality variations over space and may be employed to guide contrast enhancement algorithms. Validations based on four publicly available databases show that the proposed patch-based contrast quality index (PCQI) method provides accurate predictions on the human perception of contrast variations.
We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks-a distortion identification network and a quality ...prediction network-sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.
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