Passenger perception of service quality of Chinese Airlines and the impact on intention to recommend

Mestrado em MarketingA qualidade do serviço é uma parte crucial para a satisfação e para intenção de recompra. Como a concorrência aumentou fortemente para as companhias aéreas chinesas, é importante compreender as avaliações feita pelos passageiros da qualidade do serviço, a fim de satisfazer as necessidade dos clientes e desenvolver lealdade. O principal objetivo desta dissertação é analisar a percepção de qualidade do serviço na ótica dos passageiros com recurso a classificações e análises on-line e identificar os atributos determinantes da avaliação global. Os dados foram coletados do site de Skytrax, 2035 itens de avaliações da quatro maiores companhias foram selecionados, além das classificações dos atributos de serviço (conforto do assento, alimentos e bebidas , serviço da tripulação de cabine, serviço em terra, entretenimento a bordo, valor do dinheiro despendido) foram incluídos nas análises. Os resultados revelaram que, os seis atributos de serviço são estatisticamente significativos em termos de percepção geral da qualidade dos viajantes individuais e casais em lazer. Contudo, o entretenimento a bordo não é significativo para os passageiros em lazer e negócios. Para passageiros em lazer em família, a qualidade do serviço de alimentação e bebidas também não é significativa. "Valor ao dinheiro" é o mais importante para todos os tipos de viajantes nos ambos aspectos, enquanto o "entretenimento a bordo" tem o menor impacto. Terceiro, os determinantes significativos da recomendação variam de acordo com o tipo de viajante. A análise de conteúdo revelou onze temas mentais relacionadas à com a sua experiência geral.Service quality is crucial for satisfaction and repurchase intention. As the competition has increased strongly in the Chinese airline market, it is important for Chinese airlines to understand passenger perception of service quality, in order to satisfy their needs and to develop loyalty. The main objective of this dissertation is to analyse passenger perception of service quality through online ratings and reviews, identifying the determinant service attributes of overall rating and of the intention to recommendation. The data were gathered from the website of Skytrax, 2035 passenger evaluations of four Chinese airlines. The ratings of service attributes (seat comfort, food and beverages, cabin crew service, ground service, inflight entertainment, value for money) were included in the multiple linear regression model and logistic regression analyses, for the purpose of investigating their impact on passengers' overall perception and intention to recommend. The results of this investigation revealed that, in first place, the six service attributes are all statistically significant in terms of solo and couple leisure travellers' overall quality perception, "?Inflight entertainment" is non-significant for family leisure passengers and business passengers. For family leisure passengers, "food and beverages" service quality is also not significant. Secondly, "value for money" is the most important attribute for all types of travellers in both overall service quality perception and passengers' intention to recommend, while, "inflight entertainment" has the least impact. Third, the significant determinants of recommendation vary with traveller type. Finally, content analysis revealed eleven mind themes of passengers' reviews related to their overall experience.info:eu-repo/semantics/publishedVersio

STATISTICAL METHODS FOR EXPLORING NEURONAL INTERACTIONS

Generalized linear models (GLMs) offer a platform for analyzing multi-electroderecordings of neuronal spiking. We suggest an L1-regularized logistic regressionmodel to detect short-term interactions under certain experimental setups. Weestimate parameters of this model using a coordinate descent algorithm; we determinethe optimal tuning parameter using BIC, and prove its asymptotic validity. Simulationstudies of the method's performance show that this model can detect excitatoryinteractions with high sensitivity and specificity with reasonably large recordings,even when the magnitude of the interactions is small; similar results hold forinhibition for sufficiently high baseline firing rates. The method is somewhat robustto network complexity and partial observation of networks. We apply our method tomulti-electrode recording data from monkey dorsal premotor cortex (PMd). Our resultspoint to certain features of short-term interactions when a monkey plans a reach.Next, we propose a variable coefficients GLM model to assess the temporal variationof interactions across trials. We treat the parameters of interest as functions overtrials, and fit them by penalized splines. There are also nuisance parameters assumedconstant, which are mildly penalized to guarantee the finite maximum of thelog-likelihood. We choose tuning parameters for smoothness by generalized crossvalidation, and provide simultaneous confidence bands and hypothesis tests fornull models. To achieve efficient computation, some modifications are also made. Weapply our method to a subset of the monkey PMd data. Before the implementation to thereal data, simulations are done to assess the performance of the proposed model.Finally, for the logistic and Poisson models, one possible difficulty is that iterativealgorithms for estimation may not converge because of certain data configurations(called complete and quasicomplete separation for the logistic). We show that thesefeatures are likely to occur because of refractory periods of neurons, and show howstandard software deals with this difficulty. For the Poisson model, we show that suchdifficulties arise possibly due to bursting or specifics of the binning. Wecharacterize the nonconvergent configurations for both models, show that they can bedetected by linear programming methods, and propose remedies

RESURF Ga2_{2}O3_{3}-on-SiC Field Effect Transistors for Enhanced Breakdown Voltage

Heterosubstrates have been extensively studied as a method to improve the heat dissipation of Ga$_{2}$O$_{3}$ devices. In this simulation work, we propose a novel role for $p$-type available heterosubstrates, as a component of a reduced surface field (RESURF) structure in Ga$_{2}$O$_{3}$ lateral field-effect transistors (FETs). The RESURF structure can eliminate the electric field crowding and contribute to higher breakdown voltage. Using SiC as an example, the designing strategy for doping concentration and dimensions of the $p$-type region is systematically studied using TCAD modeling. To mimic realistic devices, the impacts of interface charge and binding interlayer at the Ga$_{2}$O$_{3}$/SiC interface are also explored. Additionally, the feasibility of the RESURF structure for high-frequency switching operation is supported by the short time constant ($\sim$0.5 ns) of charging/discharging the $p$-SiC depletion region. This study demonstrates the great potential of utilizing the electrical properties of heat-dissipating heterosubstrates to achieve a uniform electric field distribution in Ga$_{2}$O$_{3}$ FETs.Comment: 6 pages, 7 figures, under revie

NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation

Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots. This paper presents NUV-DoA algorithm, that augments Bayesian sparse reconstruction with spatial filtering for super-resolution DoA estimation. By modeling each direction on the azimuth's grid with the sparsity-promoting normal with unknown variance (NUV) prior, the non-convex optimization problem is reduced to iteratively reweighted least-squares under Gaussian distribution, where the mean of the snapshots is a sufficient statistic. This approach not only simplifies our solution but also accurately detects the DoAs. We utilize a hierarchical approach for interference cancellation in multi-source scenarios. Empirical evaluations show the superiority of NUV-DoA, especially in low SNRs, compared to alternative DoA estimators.Comment: 5 pages include reference, 11 figures, submitted to ICASSP 2024, on Sep 6 202

Orientation-Dependent Atomic-Scale Mechanism of β\beta-Ga2O3\mathrm{Ga}_{2}\mathrm{O}_{3} Thin Film Epitaxial Growth

$\beta$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ has gained intensive interests of research and application as an ultrawide bandgap semiconductor. Epitaxial growth technique of the $\beta$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ thin film possesses a fundamental and vital role in the $\mathrm{Ga}_{2}\mathrm{O}_{3}$-based device fabrication. In this work, epitaxial growth mechanisms of $\beta$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ with four low Miller-index facets, namely (100), (010), (001), and ($\overline{2}$01), are systematically explored using large-scale machine-learning molecular dynamics simulations at the atomic scale. The simulations reveal that the migration of the face-centered cubic stacking O sublattice plays a predominant role in rationalizing the different growth mechanisms between (100)/(010)/(001) and ($\overline{2}$01) orientations. The resultant complex combinations of the stacking faults and twin boundaries are carefully identified, and shows a good agreement with the experimental observation and ab initio calculation. Our results provide useful insights into the gas-phase epitaxial growth of the $\beta$-$\mathrm{Ga}_{2}\mathrm{O}_{3}$ thin films and suggest possible ways to tailor its properties for specific applications.Comment: 6 pages, 5 figures, under peer revie

Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models

Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains. Leveraging the bidirectional Markov chains, diffusion probabilistic models generate samples by inferring the reversed Markov chain based on the learned distribution mapping at the forward diffusion process. In this work, we propose Modiff, a conditional paradigm that benefits from the denoising diffusion probabilistic model (DDPM) to tackle the problem of realistic and diverse action-conditioned 3D skeleton-based motion generation. We are a pioneering attempt that uses DDPM to synthesize a variable number of motion sequences conditioned on a categorical action. We evaluate our approach on the large-scale NTU RGB+D dataset and show improvements over state-of-the-art motion generation methods

COVIDanno, COVID-19 Annotation in Human

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected in vitro model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 in vitro models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim of this resource is to provide a reference resource of intensive functional annotations of differentially expressed genes (DEGs) among different time points of COVID-19 infection in human in vitro models. To do this, we performed differential expression analysis for 136 individual datasets across 13 tissue types. In total, we identified 4,935 DEGs. We performed multiple bioinformatics/computational biology studies for these DEGs. Furthermore, we developed a novel tool to help users predict the status of SARS-CoV-2 infection for a given sample. COVIDanno will be a valuable resource for identifying SARS-CoV-2-related genes and understanding their potential functional roles in different time points and multiple tissue types

Complex Ga2O3\mathrm{Ga}_{2}\mathrm{O}_{3} Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials

$\mathrm{Ga}_{2}\mathrm{O}_{3}$ is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting $\mathrm{Ga}_{2}\mathrm{O}_{3}$ polymorphs and low-symmetry disordered structures is missing. In this work, we develop two types of kernel-based machine-learning Gaussian approximation potentials (ML-GAPs) for $\mathrm{Ga}_{2}\mathrm{O}_{3}$ with high accuracy for $\beta$/$\kappa$/$\alpha$/$\delta$/$\gamma$ polymorphs and generality for disordered stoichiometric structures. We release two versions of interatomic potentials in parallel, namely soapGAP and tabGAP, for excellent accuracy and exceeding speedup, respectively. We systematically show that both the soapGAP and tabGAP can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results, meanwhile boost the computational efficiency with $5\times10^{2}$ and $2\times10^{5}$ computing speed increases compared to density functional theory, respectively. The results show that the liquid-solid phase transition proceeds in three different stages, a "slow transition", "fast transition" and "only Ga migration". We show that this complex dynamics can be understood in terms of different behavior of O and Ga sublattices in the interfacial layer.Comment: 13 pages, 7 figure

Method Comparison for Simulating Non-Gaussian Beams and Diffraction for Precision Interferometry

In the context of simulating precision laser interferometers, we use several examples to compare two wavefront decomposition methods—the Mode Expansion Method (MEM) and the Gaussian Beam Decomposition (GBD) method—for their precision and applicability. To assess the performance of these methods, we define different types of errors and study their properties. We specify how the two methods can be fairly compared and based on that, compare the quality of the MEM and GBD through several examples. Here, we test cases for which analytic results are available, i.e., non-clipped circular and general astigmatic Gaussian beams, as well as clipped circular Gaussian beams, in the near, far, and extremely far fields of millions of kilometers occurring in space-gravitational wave detectors. Additionally, we compare the methods for aberrated wavefronts and their interaction with optical components by testing reflections from differently curved mirrors. We find that both methods can generally be used for decomposing non-Gaussian beams. However, which method is more accurate depends on the optical system and simulation settings. In the given examples, the MEM more accurately describes non-clipped Gaussian beams, whereas for clipped Gaussian beams and the interaction with surfaces, the GBD is more precise