Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Dan Konsekuensinya Pada Loyalitas (Studi Pada Obyek Wisata Di Kabupaten Malang)

Studi ini meneliti kepuasan wisatawan yang mengunjungi obyek wisata yang ada di Kabupaten Malang dengan menggunakan konsep dasar Swedish Customer Satisfaction Barometer (SCSB). Tujuan penelitian untuk menganalisis pengaruh langsung kualitas layanan (service quality) terhadap kepuasan wisatawan domestik (customer satisfaction), menganalisis pengaruh langsung harapan konsumen (customer expectation) terhadap kepuasan wisatawan domestik (customer satisfaction), dan menganalisis pengaruh langsung kepuasan konsumen (customer satisfaction) terhadap loyalitas konsumen (customer loyalty) wisatawan domestik. Sampel penelitian adalah wisatawan domestik yang berkunjung ke objek wisata (Pantai Sendang Biru, Pantai Ngliyep dan Pantai Bale Kambang), yaitu sebanyak 150 responden. Teknik analisis data yang digunakan adalah Structural Equation Modelling (SEM) dengan menggunakan bantuan program AMOS. Hasil penelitian menunjukkan bahwa ada pengaruh langsung antara kualitas layanan dan kepuasan pelanggan, tidak ada pengaruh yang signifikan anatara harapan dengan kepuasan pelanggan, ada pengaruh langsung antara kepuasan pelanggan dengan loyalitas konsumen. Variabel kualitas layanan yaitu reliability dan emphaty memiliki pengaruh yang paling besar terhadap kepuasan pelanggan sedangkan responsiveness, assurance, dan tangible memilki pengaruh yang cukup signifikan

A Genetic Variant in miR-196a2 Increased Digestive System Cancer Risks: A Meta-Analysis of 15 Case-Control Studies

<div><h3>Background</h3><p>MicroRNAs (miRNAs) negatively regulate the gene expression and act as tumor suppressors or oncogenes in oncogenesis. The association between single nucleotide polymorphism (SNP) in miR-196a2 rs11614913 and the susceptibility of digestive system cancers was inconsistent in previous studies.</p> <h3>Methodology/Principal Findings</h3><p>An updated meta-analysis based on 15 independent case-control studies consisting of 4999 cancer patients and 7606 controls was performed to address this association. It was found that miR-196a2 polymorphism significantly elevated the risks of digestive system cancers (CT vs. TT, OR = 1.25, 95% CI = 1.07–1.45; CC vs. TT, OR = 1.38, 95% CI = 1.13–1.67; CC/CT vs. TT, OR = 1.29, 95% CI = 1.10–1.50; CC vs. CT/TT, OR = 1.14, 95% CI = 1.01–1.30; C vs. T, OR = 1.15, 95% CI = 1.05–1.26). We also found that variant in miR-196a2 increased the susceptibility of colorectal cancer (CRC) (CT vs. TT, OR = 1.23, 95% CI = 1.04–1.44; CC vs. TT, OR = 1.32, 95% CI = 1.08–1.61; CC/CT vs. TT, OR = 1.25, 95% CI = 1.07–1.46; C vs. T, OR = 1.15, 95% CI = 1.05–1.28), while the association in recessive model (CC vs. CT/TT, OR = 1.16, 95% CI = 0.98–1.38) showed a marginal significance. Additionally, significant association between miR-196a2 polymorphism and increased risk of hepatocellular cancer (HCC) was detected. By stratifying tumors on the basis of site of origin, source of controls, ethnicity and allele frequency in controls, elevated cancer risks were observed.</p> <h3>Conclusion/Significance</h3><p>Our findings suggest the significant association between miR-196a2 polymorphism and increased susceptibility of digestive system cancers, especially of CRC, HCC and Asians. Besides, C allele may contribute to increased digestive cancer risks.</p> </div

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic

A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China

The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA) was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI) of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC) analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole

The Smoothing Artifact of Spatially Constrained Canonical Correlation Analysis in Functional MRI

A wide range of studies show the capacity of multivariate statistical methods for fMRI to improve mapping of brain activations in a noisy environment. An advanced method uses local canonical correlation analysis (CCA) to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel; however, this is a choice of convenience and without constraints introduces artifacts, especially in regions of strong localized activation. To compensate for these deficiencies, different spatial constraints in CCA have been introduced to enforce dominance of the center voxel. However, even if the dominance condition for the center voxel is satisfied, constrained CCA can still lead to a smoothing artifact, often called the “bleeding artifact of CCA”, in fMRI activation patterns. In this paper a new method is introduced to measure and correct for the smoothing artifact for constrained CCA methods. It is shown that constrained CCA methods corrected for the smoothing artifact lead to more plausible activation patterns in fMRI as shown using data from a motor task and a memory task

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the asymmetry of the PDV and the fact that indicators are actually distributed over finite intervals; it quickly falls into premature defects. Therefore, a novel PP evaluation method based on the connection cloud model (CCM) is discussed to remedy these drawbacks. In this approach, adaptive numerical characteristics of the CCM are adopted to represent the randomness and fuzziness of the candidate PDV and evaluation indicators. Meanwhile, to avoid complex computing and to accelerate the convergence speed of the optimization procedure, an improved fruit fly optimization algorithm (FOA) is set up to find the rational PDV. Alternatively, candidate PDVs are mutated based on the mechanism “pick the best of the best” using set pair analysis (SPA) and chaos theory. Furthermore, the applicability and reliability are discussed based on an illustrative example of slope stability evaluation and comparisons with the neural network method and the PP evaluation method based on the other FOAs and the genetic algorithm. Results indicate that the proposed method with simpler code and quicker convergence speed has good global ergodicity and local searching capabilities, and can better explore the structure of high-dimensional data with multiple uncertainties and asymmetry of the PDV relative to other methods

Original and reconstructed images for the pelvis phantom data.

<p>The original MDCT image of the pelvis phantom is at top left, and reconstructed images using 60 projection views are for TV-POCS (top right), CPTV (bottom left) and FS-POCS (bottom right), respectively. The red square denotes the ROI for enlarged view in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172938#pone.0172938.g011" target="_blank">Fig 11</a>.</p

A Novel Model of Set Pair Analysis Coupled with Extenics for Evaluation of Surrounding Rock Stability

The evaluation of surrounding rock stability is a complex problem involving numerous uncertainty factors. Here, based on set pair analysis (SPA) coupled with extenics, a novel model, considering incompatibility, certainty, and uncertainty of evaluation indicators, was presented to analyze the surrounding rock stability. In this model, extension set was first utilized to describe the actual problem of surrounding rock stability. Then, the connectional membership degree of the set pair was introduced to compare the measured values with classification standards from three aspects embracing identity, discrepancy, and contrary. Also, according to identity-discrepancy-contrary (IDC) analysis in the universe of the extension set, the connection numbers were proposed to specify the connectional membership degree of an evaluation indicator to each class. Combined with the weights of evaluation indicators, integrated connectional membership degrees were calculated to determine their classes of rock stability. Finally, a case study and comparison with variable fuzzy set method, triangular fuzzy number method, and basic quality (BQ) grading method were performed to confirm the validity and reliability of the proposed model. The results show that this model can effectively and quantitatively express the differences within a group, transformation of different groups, and uncertainty of complex indicators as a whole