Understanding the local sustainable economic development from new “3D” perspective: Case of Hainan Island

This paper proposes a new T-3D measurement framework for calculating tourism economic space and as a tool able to assist in determining the sustainability of tourism development. The T-3D framework is described as a tourism-specific version of the Density, Distance, Division (3D) framework. Tourism economic concentration, level of integration and the degree of specialization are used to provide a touristic interpretation of density, distance and division. Taking Hainan Province as an example, this paper outlines the T-3D characteristics of tourism economic space. The results show that Hainan Province has large differences in the distribution of the tourism economy. In addition to the spatial division of the tourism economy, the spatial density and distance of the tourism economy are basically consistent in value. Further, the spatial density and division of the tourism economy exhibits a dual-core based on the cities of Sanya and Haikou, and the spatial distance of the tourism economy exhibits “core-peripheral” characteristics. The tourism economic space shows that the highest agglomeration based on T-3D characteristics occurs in the east followed by the west with the lowest agglomeration in the middle of the province. Using empirical analysis, the validity of the T-3D analysis system of the tourism economic space is verified and this is more conducive to improving the competitiveness of the tourism industry and promoting sustainable tourism development

State-space model for the regulatory gene network responding to silica stimulation in cultured human fibroblasts.

<p>The numbers next to the edges are the coefficients in the state-space equations for the normal (black color) and SSc (red color) fibroblasts, respectively. The numbers in the boxes denote the mean expression values of the genes in normal (black color) and SSc (red color) fibroblasts.</p

Rhodium-Catalyzed Relay Carbenoid Functionalization of Aromatic C–H Bonds toward Fused Heteroarenes

A rhodium-catalyzed annulation between ethyl benzimidates and α- aroyl sulfur ylides was developed, affording a series of pyrano­[4,3,2-<i>ij</i>]­isoquinoline derivatives in moderate to good yields with good functional group compatibility. The procedure featured dual <i>ortho</i>-C–H functionalization and dual cyclization in one pot. The optoelectronic properties of those fused heteroarenes were tested by UV/vis and fluorescence spectrometers

Eigenvalues of the transition matrix A of the state-space model for the genes in a regulatory network responding to silica in cultured human normal and SSc fibroblasts.

<p>Eigenvalues of the transition matrix A of the state-space model for the genes in a regulatory network responding to silica in cultured human normal and SSc fibroblasts.</p

Protein solubility data.

<p>Protein solubility data accuracies for default and tuned parameters settings, as well as for reported and eumerative methods. Reported values are from Magnan <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Magnan1" target="_blank">[5]</a>. The DMFS pipeline results are stable with small standard deviations as determined by 20 runs with random data partitioning: (a) default parameter settings: 0.006 (SVM) and 0.0048 (RF), and (b) tuned parameter settings: 0.0052 (SVM) and 0.0049 (RF).</p

Unit step signal and impulse signal.

<p>Unit step signal and impulse signal.</p

Root-locus of gene expression in normal fibroblasts.

<p>Root-locus of gene expression in normal fibroblasts.</p

Illustrative diagram of data flow through the pipeline.

<p>Data is initially partitioned into discovery and classification sets. The classification set is further partitioned into training and validation sets. After WordSpy elicits motifs using the discovery set, fuzznuc or fuzzpro counts corresponding motif occurrences in the remaining data. The training data counts are used to train a classifier, while the validation data counts are used to determine performance (e.g. AUC) of the learned classifier.</p

Nucleosome occupancy data.

<p>Mean AUCs for the nucleosome occupancy datasets and approaches as described in the text. Reported values are from Gupta <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Gupta1" target="_blank">[4]</a>. The DMFS pipeline results are stable with small standard deviations as determined by 40 runs with random data partitioning: Dennis data with (a) default parameter settings: 0.0055 (SVM) and 0.0036 (RF), and (b) tuned parameter settings: 0.0048 (SVM) and 0.0041 (RF); Ozsolak data with (a) default parameter settings: 0.0084 (SVM) and 0.0078 (RF), and (b) tuned parameter settings: 0.011 (SVM) and 0.0086 (RF).</p

ROC curves from DMFS and enumerative methods for the nucleosome occupancy datasets.

<p>The red and green curves are from Gupta <i>et al.. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Gupta1" target="_blank">[4]</a> for the Dennis and Ozsolak data respectively. The black and blue curves are from the DMFS method for the Dennis and Ozsolak data respectively. For both datasets, the DMFS ROC curve is approximately equal to the ROC curve using enumerative feature generation. This figure was created by manipulating <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone-0027382-g001" target="_blank">Figure 1</a> of Gupta <i>et al.. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0027382#pone.0027382-Gupta1" target="_blank">[4]</a> in GIMP. The DMFS ROC curves are relative stable. As the false positive rate ranges from 10% to 90% the true positive rate standard deviations have range to for the Dennis data and to for the Ozsolak data.</p