Renewable energy has been considered as an effective way to solve environmental pollution and energy crisis in China. However, the development of renewable energy sector depends on economic growth ...and financial issues. Thus, it is significant to study the relationships of these three indicators in China. Using ARDL-PMG model, this paper explores the long-run and short-run impacts of economic growth and financial development on renewable energy consumption based on the panel data during period 1997–2017 at national and regional levels of China. The long-run relationships indicate that for China as a whole and western China, economic growth stimulates renewable energy consumption while financial development impacts it negatively. However, the short-run relationships show that economic growth and financial development influence renewable energy consumption negatively and positively, respectively. Moreover, we employ the Granger causality test to study the existence and direction of causality among variables. The causality test results display that the unidirectional causality relationships exist from financial development to renewable energy consumption for China as a whole and eastern China. Meanwhile, economic growth unidirectionally causes renewable energy consumption in China as a whole, eastern and western China. Our empirical results provide some policies to promote the development of renewable energy sector.
•Dynamic impacts of financial development and economic growth on renewable energy use are studied.•The causalities among financial development and economic growth and renewable energy use are explored.•Our study was conducted at both national and regional levels using panel data models.•Financial development impacted renewable energy use negatively in the long run.•A unidirectional causality ran from renewable energy use to financial development.
In order to make use of the advantages of optical computing and promote the application and popularization of Ternary Optical Computer (TOC), this paper proposes a new computing-data model, presents ...the corresponding computing-data file in detail, and gives the implementation method of programming application with the model files. It tries to set up a bridge between users and TOC. Experimental results show that the model presented in this paper is correct and the implementation method is feasible. The model can better simplify TOC’s application process, and allow users to apply TOC to carry out various computational applications without needing to understand any details of optical implementation.
Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, ...it is necessary to establish a feasible and effective method to accurately monitor and estimate PWD infection. In this study, we used hyperspectral imagery (HI) collected by an unmanned airship with a hyperspectral imaging spectrometer to detect PWD in healthy, early, middle and serious infection stages. To avoid massive calculations on the full spectral dimensions of the HI, 16 spectral features were extracted from the HI, and a genetic algorithm (GA) was implemented to identify the optimal ones with the least fitness. Simultaneously, a support vector machine (SVM) classifier was established to predict the PWD infection stage for an individual pine tree. The following results were obtained: (1) the spectral characteristics for pine trees in different PWD infection stages were distinctive in the green region (510–580 nm), red edge (680–760 nm) and near-infrared (780–1000 nm) spectra; (2) the six optimal spectral features (Dgreen, SDgreen, Dred, DRE, DNIR, SDNIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests.
Hericium erinaceus is a precious edible and medicinal fungus with high nutritional value. It has many functions, such as enhancing immunity, antitumor antioxidation, antihyperglycemic, ...antihyperlipidemic, and protecting gastric mucosa. However, there are few researches about the H. erinaceus compounds. In this paper, ultraperformance liquid chromatography tandem high-resolution mass spectrometry (UPLC-Q-exactive-MS/MS) was used to isolate and identify the compounds in H. erinaceus. 102 compounds were identified in H. erinaceus by comparing with standard databases such as MZVault, MZCloud, and BGI Library (self-built standard Library by BGI Co., Ltd), including flavonoids, terpenoids, phenolic acids, phenylpropanoids, steroids, organic acids, and amino acids.
Robust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these ...three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.
Compelling evidence has indicated the vital role of lysine-specific demethylase 4 A (KDM4A), hypoxia-inducible factor-1α (HIF1α) and the mechanistic target of rapamycin (mTOR) signaling pathway in ...nasopharyngeal carcinoma (NPC). Therefore, we aimed to investigate whether KDM4A affects NPC progression by regulating the HIF1α/DDIT4/mTOR signaling pathway. First, NPC and adjacent tissue samples were collected, and KDM4A protein expression was examined by immunohistochemistry. Then, the interactions among KDM4A, HIF1α and DDIT4 were assessed. Gain- and loss-of-function approaches were used to alter KDM4A, HIF1α and DDIT4 expression in NPC cells. The mechanism of KDM4A in NPC was evaluated both in vivo and in vitro via RT-qPCR, Western blot analysis, MTT assay, Transwell assay, flow cytometry and tumor formation experiments. KDM4A, HIF1α, and DDIT4 were highly expressed in NPC tissues and cells. Mechanistically, KDM4A inhibited the enrichment of histone H3 lysine 9 trimethylation (H3K9me3) in the HIF1α promoter region and thus inhibited the methylation of HIF1α to promote HIF1α expression, thus upregulating DDIT4 and activating the mTOR signaling pathway. Overexpression of KDM4A, HIF1α, or DDIT4 or activation of the mTOR signaling pathway promoted SUNE1 cell proliferation, migration, and invasion but inhibited apoptosis. KDM4A silencing blocked the mTOR signaling pathway by inhibiting the HIF1α/DDIT4 axis to inhibit the growth of SUNE1 cells in vivo. Collectively, KDM4A silencing could inhibit NPC progression by blocking the activation of the HIF1α/DDIT4/mTOR signaling pathway by increasing H3K9me3, highlighting a promising therapeutic target for NPC.
In big data era, the special data with rare characteristics may be of great significations. However, it is very difficult to automatically search these samples from the massive and high-dimensional ...datasets and systematically evaluate them. The DoPS, our previous work <xref ref-type="bibr" rid="ref1">1 , provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey. The identification of the results is mainly depended on visually inspection by astronomers. In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors. The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories. A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets. Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed. Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods.
A novel clustering algorithm by fast search and find of density peaks (DP) was proposed in Science, 2014. It has attracted much attention from researchers. It can easily select clusters centers with ...decision graph. However, it cannot be used to cluster manifold data sets as the existing distance measurement is not suitable to evaluate the dissimilarity between objects on manifold structure. Some researchers use graph-based distance to measure the dissimilarity between objects on manifold clusters, but computing the graph-based distance on the original data set is time consuming. An improved density peaks clustering algorithm based on shared-neighbors between local cores, SLORE-DP, is proposed in this paper. First, it finds local cores to represent the data set and redefines the graph-based distance between local cores with shared-neighbors-based distance. Then natural neighbor-based density and the new defined graph-based distance are used to construct decision graph on local cores and DP algorithm is employed to cluster local cores. Finally, the remaining points are assigned to the same cluster as their local cores belong to. Since we use the new defined graph-based distance to estimate the dissimilarity between local cores, SLORE-DP can be used to cluster manifold data sets and at the same time it only calculates the shortest path between local cores, which greatly reduces the running time of the algorithm. We do experiments on several synthetic data sets containing manifold clusters and several real data sets from UCI. The results show that SLORE-DP is more effective and efficient than other algorithms when clustering manifold data sets.
Clustering analysis has been widely used in pattern recognition, image processing, machine learning, and so on. It is a great challenge for most existing clustering algorithms to discover clusters ...with complex manifolds or great density variation. Most of the existing clustering needs manually set neighborhood parameter <inline-formula> <tex-math notation="LaTeX">{K} </tex-math></inline-formula> to search the neighbor of each object. In this paper, we use natural neighbor to adaptively get the value of <inline-formula> <tex-math notation="LaTeX">{K} </tex-math></inline-formula> and natural density of each object. Then, we define two novel concepts, natural core point and the distance between clusters to solve the complex manifold problem. On the basis of above-proposed concept, we propose a novel hybrid clustering algorithm that only needs one parameter <inline-formula> <tex-math notation="LaTeX">{M} </tex-math></inline-formula> (the number of final clusters) based on minimum spanning tree of natural core points, called NCP. The experimental results on the synthetic dataset and real dataset show that the proposed algorithm is competitive with the state-of-the-art methods when discovering with the complex manifold or great density variation.
In order to enhance the efficiency of regression testing, test case prioritization (TCP) has been widely implemented, wherein a higher priority test case is executed earlier. Traditional TCP methods ...focus on improving the prioritization algorithm's efficacy. However, the majority of TCP approaches are characterized by a predetermined sequence of test cases prior to execution. Once established, this sequence remains consistent throughout the entire test execution process. As a result, any execution information generated during current test execution (such as fault-detected information) is unavailable for use in current round of test case prioritization and can only be utilized in subsequent regression testing. To address the issue of lagging utilization of fault-detected information, a cluster-based adaptive test case prioritization approach is proposed, which adds the new adaptive adjustment content in pre-prioritization. First, a new clustering criterion is defined and designed, by which produces test-case clusters in advance. Second, an adaptive TCP algorithm is proposed, which utilizes fault-detected information to adaptively adjust the order of test cases during the execution process based on the test-case clusters. Finally, one open-source Java program and three industrial-grade Java programs were selected for empirical evaluation. The experimental results demonstrate that the proposed technique not only serves as an enhanced version of pre-prioritization to improve the performance of the corresponding pre-prioritization technique, but also functions as an independent approach that outperforms other TCP techniques, including cluster-based TCPs, and another adaptive TCP. Specifically, when step=2 is applied using our cluster-based adaptive TCP approach, the results are significantly better than those obtained with step=1. For instance, in CT-14, the median APFD improvement rate for step=2 reaches 17.08 %, which is substantially higher than that achieved with step=1 (5.48 %).
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