For fuzzy counterparts of hull operators and interval operators, two types of fuzzy hull operators and one type of fuzzy interval operators are proposed. Firstly, the concept of L-hull operators is ...introduced and the resulting category is shown to be isomorphic to that of L-convex spaces. Secondly, considering the graded inclusions of L-subsets, the notion of L-ordered hull operators is presented, which is shown to be categorically isomorphic to strong L-convex structures. Finally, the concept of L-interval operators is introduced and it is shown that there is a Galois correspondence between the category of L-interval spaces and that of L-convex spaces. In particular, the category of arity 2 L-convex spaces can be reflectively embedded into that of L-interval spaces.
In this paper, several types of L-convex spaces are introduced, including stratified L-convex spaces, convex-generated L-convex spaces, weakly induced L-convex spaces and induced L-convex spaces. ...Their relations are discussed category-theoretically. Firstly, it is shown that there is a Galois correspondence between the category SL-CS of stratified L-convex spaces (resp. the category WIL-CS of weakly induced L-convex spaces) and the category L-CS of L-convex spaces. In particular, SL-CS and WIL-CS are both coreflective subcategories of L-CS. Secondly, it is proved that there is a Galois correspondence between the category CS of convex spaces and the category SL-CS (resp. WIL-CS). Specially, CS can be embedded into SL-CS and WIL-CS as a coreflective subcategory. Finally, it is shown that the category CGL-CS of convex-generated L-convex spaces, the category IL-CS of induced L-convex spaces and CS are isomorphic.
The nonradiative recombination of electrons and holes has been identified as the main cause of energy loss in hybrid organic–inorganic perovskite solar cells (PSCs). Sufficient built‐in field and ...defect passivation can facilitate effective separation of electron–hole pairs to address the crucial issues. For the first time, we introduce a homochiral molecular ferroelectric into a PSC to enlarge the built‐in electric field of the perovskite film, thereby facilitating effective charge separation and transportation. As a consequence of similarities in ionic structure, the molecular ferroelectric component of the PSC passivates the defects in the active perovskite layers, thereby inducing an approximately eightfold enhancement in photoluminescence intensity and reducing electron trap‐state density. The photovoltaic molecular ferroelectric PSCs achieve a power conversion efficiency as high as 21.78 %.
A homochiral molecular ferroelectric was incorporated into a perovskite film to enlarge the built‐in electric field of the perovskite solar cell (PSC), thereby facilitating charge separation and transportation. The molecular ferroelectric component of the PSC passivates the defects in the perovskite active layers to induce an approximately eightfold enhancement in photoluminescence intensity and a reduction in electron trap‐state density.
We provide some new characterizations of L-convex spaces by using the way-below relation in domain theory. For this purpose, we first study the notion of domain finiteness in the lattice-valued case, ...and then introduce three kinds of spaces: algebraic L-closure spaces, restricted L-hull spaces, and L-entailment spaces. These three spaces are shown to be categorically isomorphic to L-convex spaces. Additionally, we introduce the notion of L-polytopes and prove that a subcollection of a dense L-convex structure is a base if and only if it contains all L-polytopes. These results indicate that in the study of the theory of (fuzzy) convex spaces, (fuzzy) domain theory has important applications.
Bacterial infections represent a significant health threat globally, and are responsible for the majority of hospital-acquired infections, leading to extensive mortality and burden on global ...healthcare systems. The second generation fluoroquinolone ciprofloxacin which exhibits excellent antimicrobial activity and pharmacokinetic properties as well as few side effects is introduced into clinical practice for the treatment of various bacterial infections for around 3 decades. The emergency and widely spread of drug-resistant pathogens making ciprofloxacin more and more ineffective, so it's imperative to develop novel antibacterials. Numerous of ciprofloxacin derivatives have been synthesized for seeking for new antibacterials, and some of them exhibited promising potency. This review aims to summarize the recent advances made towards the discovery of ciprofloxacin derivatives as antibacterial agents and the structure-activity relationship of these derivatives was also discussed.
This review aims to outline the antibacterial activity of ciprofloxacin derivatives, and discuss their structure-activity relationship to pave the way for the further rational development. Display omitted
•The recent advances in ciprofloxacin derivatives as antibacterial agents were summarized.•Some ciprofloxacin derivatives exhibited promising in vitro and in vivo antibacterial potency.•The structure-activity relationship was also discussed.
Recently, with the assumption that samples can be reconstructed by themselves, subspace clustering (SC) methods have achieved great success. Generally, SC methods contain some parameters to be tuned, ...and different affinity matrices can obtain with different parameter values. In this paper, for the first time, we study a method for fusing these different affinity matrices to promote clustering performance and provide the corresponding solution from a multi-view clustering (MVC) perspective. That is, we argue that the different affinity matrices are consistent and complementary, which is similar to the fundamental assumption of MVC methods. Based on this observation, in this paper, we use least squares regression (LSR), which is a typical SC method, as an example since it can be efficiently optimized and has shown good clustering performance and we propose a novel robust least squares regression method from an MVC perspective (RLSR/MVCP). Specifically, we first utilize LSR with different parameter values to obtain different affinity matrices. Then, to fully explore the information contained in these different affinity matrices and to remove noise, we further fuse these affinity matrices into a tensor, which is constrained by the tensor low-rank constraint, i.e., the tensor nuclear norm (TNN). The two steps are combined into a framework that is solved by the augmented Lagrange multiplier (ALM) method. The experimental results on several datasets indicate that RLSR/MVCP has very encouraging clustering performance and is superior to state-of-the-art SC methods.
In recent years, researchers have proposed many graph-based multi-view clustering (GMC) algorithms to solve the multi-view clustering (MVC) problem. However, there are still some limitations in the ...existing GMC algorithm. In these algorithms, a graph is usually constructed to represent the relationship between the samples in a view; however, it cannot represent the relationship very well since it is often preconstructed. In addition, these algorithms ignore the robustness problem of each graph and high-level information between different graphs. Then, in the paper, we propose a novel MVC method, i.e., robust and optimal neighborhood graph learning for MVC (RONGL/MVC). Specifically, we first build an initial graph for each view. However, these initial graphs cannot represent the relationship between the samples in each view well, so we look for the optimal graph with k connected components in the neighborhood of each initial graph, where k is the number of clusters. Then, to improve the robustness of RONGL/MVC, we reconstruct the optimal graph with the self-representation matrix. Furthermore, we stack all the self-representation matrices into a tensor and impose the tensor low-rank constraint, which can maximize consistent features and explore the high-order relationship between optimal graphs. In addition, we provide an optimization strategy by utilizing the Augmented Lagrange Multiplier (ALM) method. Experimental results on several datasets indicate that RONGL/MVC outperforms state-of-the-art methods.
Starvation not only affects the nutritional and health status of the animals, but also the microbial composition in the host's intestine. Next-generation sequencing provides a unique opportunity to ...explore gut microbial communities and their interactions with hosts. However, studies on gut microbiomes have been conducted predominantly in humans and land animals. Not much is known on gut microbiomes of aquatic animals and their changes under changing environmental conditions. To address this shortcoming, we determined the microbial gene catalogue, and investigated changes in the microbial composition and host-microbe interactions in the intestine of Asian seabass in response to starvation.
We found 33 phyla, 66 classes, 130 orders and 278 families in the intestinal microbiome. Proteobacteria (48.8%), Firmicutes (15.3%) and Bacteroidetes (8.2%) were the three most abundant bacteria taxa. Comparative analyses of the microbiome revealed shifts in bacteria communities, with dramatic enrichment of Bacteroidetes, but significant depletion of Betaproteobacteria in starved intestines. In addition, significant differences in clusters of orthologous groups (COG) functional categories and orthologous groups were observed. Genes related to antibiotic activity in the microbiome were significantly enriched in response to starvation, and host genes related to the immune response were generally up-regulated.
This study provides the first insights into the fish intestinal microbiome and its changes under starvation. Further detailed study on interactions between intestinal microbiomes and hosts under dynamic conditions will shed new light on how the hosts and microbes respond to the changing environment.
Dealing with partial occlusion or illumination is one of the most challenging problems in image representation and classification. In this problem, the characterization of the representation error ...plays a crucial role. In most current approaches, the error matrix needs to be stretched into a vector and each element is assumed to be independently corrupted. This ignores the dependence between the elements of error. In this paper, it is assumed that the error image caused by partial occlusion or illumination changes is a random matrix variate and follows the extended matrix variate power exponential distribution. This has the heavy tailed regions and can be used to describe a matrix pattern of l × m dimensional observations that are not independent. This paper reveals the essence of the proposed distribution: it actually alleviates the correlations between pixels in an error matrix E and makes E approximately Gaussian. On the basis of this distribution, we derive a Schatten p-norm-based matrix regression model with L q regularization. Alternating direction method of multipliers is applied to solve this model. To get a closed-form solution in each step of the algorithm, two singular value function thresholding operators are introduced. In addition, the extended Schatten p-norm is utilized to characterize the distance between the test samples and classes in the design of the classifier. Extensive experimental results for image reconstruction and classification with structural noise demonstrate that the proposed algorithm works much more robustly than some existing regression-based methods.
In this article, a natural L-topology is constructed on the set of L-fuzzy numbers, which is called the standard L-topology. It is proved that the standard L-topology can be induced by an L-metric. ...The space of trapezoidal fuzzy numbers can be regarded as a subspace of the space of L-fuzzy numbers. An L-metric on the set of trapezoidal fuzzy numbers is presented.
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