The Impact of Data Agglomeration on Export Structure Upgrading in Cities: A Factor Mobility Perspective

The digital economy and export upgrading are important topics of common concern for policymakers and academics during the period of high-quality economic development. From the perspective of factor mobility, this paper constructs the two-way fixed effect, mediation effect, and spatial Durbin models to analyze the impacts of data agglomeration on urban export structure upgrading. Using panel data of 280 cities at the prefecture level and above in China from 2005 to 2018, the empirical analysis reveals a positive impact of data agglomeration on urban export structure upgrading, with capital transfer and technology diffusion further reinforcing this impact. Additionally, data agglomeration promotes urban export structure upgrading by optimizing innovation resource allocation and exhibits a spatial spillover effect on urban export structure upgrading. Finally, the facilitating effect on urban export structure upgrading is heterogeneous. Consequently, it is imperative to expedite the construction of new digital infrastructure, foster the integration and symbiotic evolution of data and traditional production factors, and implement distinct innovation development pathways based on regional comparative advantages

Probing the thermodynamics of charged Gauss Bonnet AdS black holes with the Lyapunov exponent

In this paper, we investigate the thermodynamic properties of charged AdS Gauss-Bonnet black holes and the associations with the Lyapunov exponent. The chaotic features of the black holes and the isobaric heat capacity characterized by Lyapunov exponent are studied to reveal the stability of black hole phases. With the consideration of both timelike and null geodesic, we find the relationship between Lyapunov exponent and Hawking temperature can fully embody the feature of the Small/Large phase transition and the triple point even further. Then we briefly reveal the properties of Lyapunov exponent as an order parameter and explore the black hole shadow with it

Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model

traffic control (ATC) performance is important to ensure flight safety and the sustainability of aviation growth. To better evaluate the performance of ATC, this paper introduces the HFACS-BN model (HFACS: Human factors analysis and classification system BN: Bayesian network), which can be combined with the subjective information of relevant experts and the objective data of accident reports to obtain more accurate evaluation results. The human factors of ATC in this paper are derived from screening and analysis of 142 civil and general aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, among which the most important 25 HFs are selected to construct the evaluation model. The authors designed and implemented a questionnaire survey based on the HFACS framework and collected valid data from 26 frontline air traffic controllers (ATCO) and experts related to ATC in 2019. Combining the responses with objective data, the noisy MAX model is used to calculate the conditional probability table. The results showed that, among the four levels of human factors, unsafe acts had the greatest influence on ATC Performance (79.4%), while preconditions for safe acts contributed the least (40.3%). The sensitivity analysis indicates the order of major human factors influencing the performance of ATC. Finally, this study contributes to the literature in terms of methodological development and expert empirical analysis, providing data support for human error management intervention of ATC in aviation safety. Document type: Articl

The influence of graphical user interface on motion onset brain-computer interface performance and the effect of data augmentation on motor imagery brain-computer interface

Motor Imagery Brain Computer Interface (MI BCI) is one of the most frequently used BCI modalities, due to the versatility of its applications. However, it still has unresolved issues like time-consuming calibration, low information transfer rate, and inconsistent performance across individuals. Combining MI BCI with Motion Onset Visual Evoked Potential (mVEP) BCI in a hybrid structure may solve some of these problems. Combining MI BCI with more robust mVEP BCI, would increase the degrees of freedom thereby increasing the information transfer rate, and would also indirectly improve intrasubject consistency in performance by replacing some MI-based tasks with mVEP. Unfortunately, due to Covid -19 pandemic experimental research on hybrid BCI was not possible, therefore this thesis focuses on two BCI separately. Chapter 1 provides an overview of different BCIs modalities and the underlying neurophysiological principles, followed by the objectives of the thesis. The research contributions are also highlighted. Finally, the thesis outlines are presented at the end of this chapter. Chapter 2 presents a comprehensive state of the art to the thesis, drawing on a wide range of literature in relevant fields. Specifically, it delves into MI BCI, mVEP BCI, Deep Learning, Transfer Learning (TL), Data Augmentation (DA) and Generative Adversarial Networks (GANs). Chapter 3 investigates the effect of graphical elements, in online and offline experiments. In the offline experiment, graphical elements such as the color, size, position, and layout were explored. Replacing a default red moving bar with a green and blue bar, changing the background color from white to gray, and using smaller visual angles did not lead to statistically significant improvement in accuracy. However, the effect size of η2 (0.085) indicated a moderate effect for these changes of graphical factors. Similarly, no statistically significant difference was found for the two different layouts in online experiments. Overall, the mVEP BCI has achieved a classification accuracy of approximately 80%, and it is relatively impervious to changes in graphical interface parameters. This suggests that mVEP is a promising candidate for a hybrid BCI system combined with MI, that requires dynamic, versatile graphical design features. In Chapter 4, various DA methods are explored, including Segmentation and Recombination in Time Domain, Segmentation and Recombination in Time-Frequency Domain, and Spatial Analogy. These methods are evaluated based on three feature extraction approaches: Common Spatial Patterns, Time Domain Parameters (TDP), and Band Power. The evaluation was conducted using a validated BCI set, namely the BCI Competition IV dataset 2a, as well as a dataset obtained from our research group. The methods are effective when a small dataset of single subject are available. All three DA methods significantly affect the performance of the TDP feature extraction method. Chapter 5 explored the use of GANs for DA in combination with TL and cropped training strategies using ShallowFBCSP classifier. It also used the same validated dataset (BCI competition IV dataset 2a) as in Chapter 4. In contrast to DA method explored in Chapter 4, this DA is suitable for larger datasets and for generalizing training based on other people’s data. Applying GAN-based DA to the dataset resulted on average in a 2% improvement in average accuracy (from 68.2% to 70.7%). This study provides a novel method to enable MI GAN training with only 40 trials per participant with the rest 8 people’s data for TL, addressing the data insufficiency issue for GANs. The evaluation of generated artificial trials revealed the importance of inter-class differences in MI patterns, which can be easily identified by GANs. Overall the thesis addressed the main practical issues of both mVEP and MI BCI paving the way for their successful combination in future experiments

Self-interacting neutrinos: Solution to Hubble tension versus experimental constraints

Exotic self-interactions among the Standard Model neutrinos have been proposed as a potential reason behind the tension in the expansion rate, $H_0$, of the universe inferred from different observations. We constrain this proposal using electroweak precision observables, rare meson decays, and neutrinoless double-$β$ decay. In contrast to previous works, we emphasize the importance of carrying out this study in a framework with full Standard Model gauge invariance. We implement this first by working with a relevant set of Standard Model effective field theory operators and subsequently by considering a UV completion in the inverse seesaw model. We find that the scenario in which all flavors of neutrinos self-interact universally is strongly constrained, disfavoring a potential solution to the $H_0$ problem in this case. The scenario with self-interactions only among tau neutrinos is the least constrained and can potentially be consistent with a solution to the $H_0$ problem

A PML method for signal-propagation problems in axon

This work is focused on the modelling of signal propagations in myelinated axons to characterize the functions of the myelin sheath in the neural structure. Based on reasonable assumptions on the medium properties, we derive a two-dimensional neural-signaling model in cylindrical coordinates from the time-harmonic Maxwell's equations. The well-posedness of model is established upon Dirichlet boundary conditions at the two ends of the neural structure and the radiative condition in the radial direction of the structure. Using the perfectly matched layer (PML) method, we truncate the unbounded background medium and propose an approximate problem on the truncated domain. The well-posedness of the PML problem and the exponential convergence of the approximate solution to the exact solution are established. Numerical experiments based on finite element discretization are presented to demonstrate the theoretical results and the efficiency of our methods to simulate the signal propagation in axons

Determining hyperelastic properties of the constituents of the mussel byssus system

The mussel byssus system, comprising of the adhesive plaque, distal thread, and proximal thread, plays a crucial role in the survival of marine mussels amongst ocean waves. Whilst recent research has explored the stress-strain behaviour of the distal thread and proximal thread through experimental approaches, little attention has been paid to the potential analytical or modelling methods within the current literature. In this work, analytical and finite element (FE) inverse methods were employed for the first time to identify the hyperelastic mechanical properties of both the plaque portion and the proximal thread. The results have demonstrated the feasibility of applied inverse methods in determining the mechanical properties of the constituents of the mussel byssus system, with the residual sum of squares of 0.0004 ($N^2$) and 0.01 ($mm^2$) for the proximal thread and the plaque portion, respectively. By leveraging mechanical and optical tests, this inverse methodology offers a simple and powerful means to anticipate the material properties for different portions of the mussel byssus system, thus providing insights for mimetic applications in engineering and materials design