•Propose some formulae to calculate the gain and loss for unbalanced HFLTSs.•Extend the TODIM method to deal with MCGDM problems with unbalanced HFLTSs.•Provide three applications to demonstrate the ...proposed TODIM method.
Uncertainty and impreciseness usually exist widely in decision making problems nowadays. When eliciting assessments over alternatives, decision makers tend to have some hesitancy and thus provide hesitant fuzzy linguistic term sets (HFLTSs). Moreover, the unbalanced linguistic term set sometimes has advantages over the balanced one for dealing with practical linguistic decision making problems. The purpose of this paper is to develop a new method to deal with multi-criteria group decision making (MCGDM) problems with unbalanced HFLTSs by considering the psychological behavior of decision makers. To achieve this goal, some formulae are first proposed to calculate the gain and loss for an unbalanced HFLTS over another. As a special case of the unbalanced HFLTS, the formulae of gain and loss for a balanced HFLTS are also provided. Afterwards, the classical TODIM method is extended to develop a new MCGDM method based on unbalanced HFLTSs. Eventually, the proposed method is demonstrated by using three practical applications, including a personnel selection process, an investment alternative selection process and a telecommunication service provider selection process.
Display omitted
•Nile tilapia skin ASC is similar to PSC in the physicochemical properties.•Nile tilapia skin collagens are higher thermal stability comparison to other fish.•Both are characterized ...by type I collagens with intact triple helical structures.•Fibrous and porous structures indicate their further use in biomaterial areas.•Nile tilapia skin collagen is an alternative source of mammalian collagen.
Acid-soluble (ASC) and pepsin-soluble (PSC) collagen were extracted from the skin of Nile tilapia (Oreochromis niloticus), purified and physicochemically examined. Amino acid content analyses revealed that glycine accounted for approximately one-third of the total amino acid residues. The proline and hydroxyproline contents of Nile tilapia ASC and PSC were 189 residues and 205 residues/1000 residues, respectively, and the rate of proline hydroxylation was found to be 41.8% and 42.0%, respectively. Denaturation temperatures (Td), as measured by an Ubbelohde viscometer, were 35.2°C and 34.5°C, respectively, 6°C lower than that of the type I collagen found in calf skin. In this study, we measured the intrinsic viscosity, circular dichroism (CD) and, X-ray diffraction (XRD), and employed Fourier transform infrared spectroscopy (FTIR) analyses to confirm that the ASC and PSC samples from Nile tilapia skin were native and undenatured, and therefore, maintained their original, intact triple helical structure. Our SDS-PAGE results showed that the extracted ASC and PSC peptides were in their native molecular form; (α1)2α2 (type I collagen). Furthermore, the loose, fibrous, and porous structures, shown in the cross-sections of ASC and PSC, indicate that Nile tilapia skin collagen represents a powerful physical foundation for further use in biomaterial applications.
Affinity Propagation (AP) clustering has been successfully used in a lot of clustering problems. However, most of the applications deal with static data. This paper considers how to apply AP in ...incremental clustering problems. First, we point out the difficulties in Incremental Affinity Propagation (IAP) clustering, and then propose two strategies to solve them. Correspondingly, two IAP clustering algorithms are proposed. They are IAP clustering based on K-Medoids (IAPKM) and IAP clustering based on Nearest Neighbor Assignment (IAPNA). Five popular labeled data sets, real world time series and a video are used to test the performance of IAPKM and IAPNA. Traditional AP clustering is also implemented to provide benchmark performance. Experimental results show that IAPKM and IAPNA can achieve comparable clustering performance with traditional AP clustering on all the data sets. Meanwhile, the time cost is dramatically reduced in IAPKM and IAPNA. Both the effectiveness and the efficiency make IAPKM and IAPNA able to be well used in incremental clustering tasks.
Traffic accidents usually lead to severe human casualties and huge economic losses in real-world scenarios. Timely accurate prediction of traffic accidents has great potential to protect public ...safety and reduce economic losses. However, it is challenging to predict traffic accidents due to the complex causality of traffic accidents with multiple factors, including spatial correlations, temporal dynamic interactions and external influences in traffic-relevant heterogeneous data. To overcome the above issues, this paper proposes a novel Deep Spatio-Temporal Graph Convolutional Network, namely DSTGCN, to predict traffic accidents. The proposed model is composed of three components: the first component is the spatial learning layer which performs graph convolutional operations on spatial information to learn the correlations in space. The second component is the spatio-temporal learning layer which utilizes graph and standard convolutions to capture the dynamic variations in both spatial and temporal perspective. The third component is the embedding layer which aims to obtain meaningful and semantic representations of external information. To evaluate the proposed model, we collect large-scale real-world data, including accident records, citi-wide vehicle speeds, road networks, meteorological conditions, and Point-of-Interest distributions. Experimental results on real-world datasets demonstrate that DSTGCN outperforms both classical and state-of-the-art methods.
The visual characteristics of landslide susceptibility have not yet been fully explored. Professional or trained technicians have to take much time and effort to interpret remote sensing images and ...locate landslides accordingly. Although conventional machine learning methods based on hand-crafted features for landslide susceptibility prediction (LSP) have acquired remarkable performance, they have certain requirements for prior knowledge. Aiming to learn complex and inherent visual patterns of landslides through minimal manual intervention and achieve fine-grained prediction, in this paper, we define LSP as a semantic segmentation problem on optical remote sensing images. Six widely used semantic segmentation models including Fully Convolutional Network, U-Net, Pyramid Scene Parsing Network, Global Convolutional Network (GCN), DeepLab v3 and DeepLab v3+ are introduced and evaluated for LSP. As the lack of landslide datasets, an open labeled landslide dataset of remote sensing imagery is created for research. The results show that GCN and DeepLab v3 are more applicable for this problem scenario, and the best Mean Intersection-over-Union and Pixel Accuracy of models are 54.2% and 74.0% respectively, which could be further improved by more targeted network architectures. In conclusion, semantic segmentation methods are demonstrated to be effctive for predicting new potential landslides based on remote sensing images.
•Landslide susceptibility prediction is formulated as a semantic segmentation problem.•Six popular semantic segmentation methods are applied in landslide detection.•Extensive experiments are conducted to evaluate the performance of models.•An open labeled remote sensing landslide dataset is created for research.
The aim of this study was to investigate whether exogenous hydrogen sulfide (H
S) could mitigate NLRP3 inflammasome-mediated inflammation through promoting autophagy via the AMPK-mTOR pathway in L02 ...cells. L02 cells were stimulated with different concentrations of oleic acid (OA), then cell viability and the protein expression of NLRP3 and pro-caspase-1 were detected by MTT and western blot, respectively, to determine appropriate OA concentration in this study. The cells were divided into four groups: the cells in the control group were cultured with RPMI-1640 for 24.5 h; the cells in the OA group were cultured with RPMI-1640 for 0.5 h, then were stimulated with 1.2 mmol/l OA for 24 h; the cells in the NaHS+OA group were pretreated with sodium hydrogen sulfide (NaHS, a donor of H
S) for 0.5 h before exposure to OA for 24 h; and the cells in the NaHS group were treated with NaHS 0.5 h, then were cultured with RPMI-1640 for 24 h. Subsequently, the cells in every group were collected and the protein expression of NLRP3, procaspase-1, cleaved caspase-1, P62, LC3, Beclin1, T-AMPK, P-AMPK, T-mTOR, P-mTOR and the level of IL-1β were detected by western blot and ElISA, respectively. Exogenous H
S reduced the level of NLRP3, caspase-1, P62, IL-1β and the ratio of P-mTOR/T-mTOR induced by OA and increased the ratio of LC3 II/I and the protein expression of Beclin1 suppressed by OA. This study demonstrates for the first time that H
S might suppress NLRP3 inflammasome-mediated inflammation induced by OA through promoting autophagy via the AMPK-mTOR pathway. It provides a theoretical basis for the further study of the anti-inflammatory mechanism of H
S.
In the highly competitive social environment, people are facing psychological problems of anxiety, tension, and depression. This paper mainly examines the impact of tourism on negative emotions. This ...article uses the 2016 cross-sectional data of the Chinese Family Panel Studies (CFPS) to observe the relationship between household tourism expenditures and the emotions of family members. We used the Probit model and instrumental variables and selected two types of negative emotions as indicators to measure the mental health to establish a model together with economic indicators such as family tourism expenditure and other economic activity expenditures. The empirical results show that families spending on tourism have low probability of negative emotions or mental problems P<0.01. There are positive effects between family tourism activities and members' mental health. It can be seen that tourism is an effective way to alleviate national mental health problems.
Affinity propagation (AP) is a classic clustering algo-rithm. To improve the classical AP algorithms, we propose a clustering algorithm namely, adaptive spectral affinity propagation (AdaSAP). In ...particular, we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine fam-ous arbitrary-shaped clustering algorithms. We propose a strategy of extending AP in non-spherical clustering by constructing cate-gory similarity of objects. Leveraging the monotonicity that the clusters' number increases with the self-similarity in AP, we pro-pose a model selection procedure that can determine the number of clusters adaptively. For the parameters introduced by extending AP in non-spherical clustering, we provide a grid-evolving stra-tegy to optimize them automatically. The effectiveness of Ada-SAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks. Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.
In order to determine the mechanisms by which fungi weather potassium-bearing minerals on a molecular level, we prepared differential expression cDNA libraries of Aspergillus fumigatus using ...suppression subtractive hybridization (SSH) technology. The specimens were cultured with and without potassium-bearing minerals. Among genes that were upregulated by the presence of these minerals, 24% were found to be involved in carbon source consumption. Of downregulated genes, 54% were found to code for membrane proteins. This study showed that the factors that could accelerate weathering of K-bearing minerals involved organic acids, carbonic acid, and redox participant molecules. K-bearing minerals were found to upregulate the expression of carbonic anhydrase (CA), implying that A. fumigatus was capable of converting CO2 into carbonate to accelerate the weathering of potassium-bearing minerals, which fixed CO2. During mineral weathering, the fungus changed its metabolism, produced more metal-binding proteins, and reduced membrane metal transporter expression, which can modulate ion absorption and disposal and promote acid production. The results of this study may improve our understanding of the mechanisms by which microorganisms weather silicate minerals. Because silicate weathering consumes CO2, the current study provides molecular evidence for the participation of microorganisms in silicate weathering and carbonate formation.
► We discuss a project that monitored gene expression in fungal weathering. ► Factors found to accelerate weathering of K-bearing minerals are shown. ► These minerals induce upregulation of the expression of carbonic anhydrase (CA). ► Results may increase understanding of fungal weathering of silicate minerals.
PDF
