A Multi-methods Approach in Communication Studies: autoethnography meets qualitative interviews and netnography

This article developed a multi-methods approach in the particular branch of Communication Studies (fandom studies) to understand transcultural fans' engagement. It has creatively deployed three research methods to unpack the Chinese fans of a British TV series- Sherlock. Research methods of autoethnography, netnography and qualitative interviews have been largely employed to test the loyalty of the author as an acafan (an academic/scholar who is also a fan), Chinese fans' online activity and fans' self-disclosure as a self-claimed fan. The multi-methods worked effectively to help draw the conclusion that a significant percentage of self-claimed fans do not qualify as fans in the sense defined by the relevant existing literature. The creative combination of three methods greatly facilitated the future research of fan studies to identify fans as a focal point of the research

Does Digital Transformation Promote Breakthrough Green Innovation? Empirical Evidence from Listed Chinese Manufacturing Companies

In the current era of digital economy, green innovation has gradually become an important symbol of the green development of enterprises. This paper accelerates the coordinated development of digitalization and “greenization” by using panel data from China’s A-share-listed manufacturing companies from 2007 to 2019 to study the relationship between digital transformation and breakthrough green innovation. The empirical results reveal that the source of the increase in the number of green patents promoted by digital transformation is not the breakthrough innovation reflecting quality and effect, but demonstrates technical similarity. Further analysis demonstrates that enterprises in technology-intensive industries and strong market competition environment will be more inclined toward breakthrough green innovation after a digital transformation. This study empirically supports green transformations of manufacturing enterprises while providing new ideas for cultivating enterprises to choose high-quality green innovation modes

A Factor Graph Based Indoor Localization Approach for Healthcare

In healthcare facilities, indoor localization technology has a broad range of applications. Traditional Pedestrian Dead Reckoning (PDR) and WiFi fingerprint-based methods each have their limitations. To address these challenges, this study introduces a multi-source fusion indoor localization system that uses a Factor Graph to integrate inertial positioning algorithms with WiFi fingerprint-based localization. The system processes accelerometer and gyroscope data using a data-driven PDR algorithm. For WiFi localization, considering that the extensive data collection required is a significant barrier to the deployment of WiFi-based localization methods, the proposed approach applies Gaussian process regression techniques to limited WiFi fingerprint data, significantly reducing initial deployment costs and enhancing accuracy. Finally, the entire system employs a Factor Graph for the integration of the data-driven PDR and WiFi fingerprint localization results. Experimental results show that, compared to using only inertial or WiFi data for localization, this method significantly improves localization accuracy. The findings suggest that this approach could prompt the utilization of indoor localization technology in healthcare facilities.<br/

A novel Toxoplasma gondii TGGT1_316290 mRNA-LNP vaccine elicits protective immune response against toxoplasmosis in mice

Toxoplasma gondii (T. gondii) can infect almost all warm-blooded animals and is a major threat to global public health. Currently, there is no effective drug or vaccine for T. gondii. In this study, bioinformatics analysis on B and T cell epitopes revealed that TGGT1_316290 (TG290) had superior effects compared with the surface antigen 1 (SAG1). TG290 mRNA-LNP was constructed through the Lipid Nanoparticle (LNP) technology and intramuscularly injected into the BALB/c mice, and its immunogenicity and efficacy were explored. Analysis of antibodies, cytokines (IFN-γ, IL-12, IL-4, and IL-10), lymphocytes proliferation, cytotoxic T lymphocyte activity, dendritic cell (DC) maturation, as well as CD4+ and CD8+ T lymphocytes revealed that TG290 mRNA-LNP induced humoral and cellular immune responses in vaccinated mice. Furthermore, T-Box 21 (T-bet), nuclear factor kappa B (NF-kB) p65, and interferon regulatory factor 8 (IRF8) subunit were over-expressed in the TG290 mRNA-LNP-immunized group. The survival time of mice injected with TG290 mRNA-LNP was significantly longer (18.7 ± 3 days) compared with the survival of mice of the control groups (p &lt; 0.0001). In addition, adoptive immunization using 300 μl serum and lymphocytes (5*107) of mice immunized with TG290 mRNA-LNP significantly prolonged the survival time of these mice. This study demonstrates that TG290 mRNA-LNP induces specific immune response against T. gondii and may be a potential toxoplasmosis vaccine candidate for this infection

Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation

Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The conventional approaches for bearing-only cooperative estimation are mainly based on the framework distributed Kalman filtering (DKF). In this paper, we propose a new optimal bearing-only cooperative estimation algorithm, named spatial-temporal triangulation, based on the method of distributed recursive least squares, which provides a more flexible framework for designing distributed estimators than DKF. The design of the algorithm fully incorporates all the available information and the specific triangulation geometric constraint. As a result, the algorithm has superior estimation performance than the state-of-the-art DKF algorithms in terms of both accuracy and convergence speed as verified by numerical simulation. We rigorously prove the exponential convergence of the proposed algorithm. Moreover, to verify the effectiveness of the proposed algorithm under practical challenging conditions, we develop a vision-based cooperative aerial target pursuit system, which is the first of such fully autonomous systems so far to the best of our knowledge

The dynamic finite element model calibration method of concrete dams based on strong-motion records and multivariate relevant vector machines

In this work, a new finite element (FE) model calibration method of concrete dams based on strong-motion records and multivariate relevant vector machines (MRVM) is proposed. The modal features of a dam are extracted using second order blind identification (SOBI) based method at first. For some selected combinations of uncertain parameters of the FE model using the Latin hypercube design, the corresponding structural modal features are calculated using the finite element method (FEM). With these data, a procedure to calibrate the uncertain parameters of a dam’s dynamic FE model is developed. By taking the uncertain parameters as inputs and the calculated structural modal features using FEM as outputs, the MRVM model is trained to record the complex relationship between them. Then, the genetic algorithm (GA) is adopted to solve the optimization problem corresponding to the dynamic FE model calibration problem, and the trained MRVM model, instead of FEM, is used to obtain the modal parameters of a dam for different feasible solutions during the optimization search process to improve the computational efficiency. Using the simulated seismic response records of a numerical example the accuracy, robustness and computation efficiency of the proposed dynamic FE model calibration method is verified. The analysis result using the strong-motion records of a realistic concrete dam indicates that the proposed dynamic FE model calibration method has good performance

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method

Inocellia (Amurinocellia) calida (Raphidioptera, Inocelliidae) was first observed as a predator of Monochamus saltuarius (Coleoptera, Cerambycidae) in China, the vector of Bursaphelenchus xylophilus (Aphelenchida, Aphelenchoididae)

Monochamus saltuarius Gebler (Coleoptera, Cerambycidae) serves as the primary carrier of Bursaphelenchus xylophilus (Steiner &amp;amp; Buhrer) (Aphelenchida, Aphelenchoididae) in the middle-temperate zone of China. Pine wilt disease caused by B. xylophilus leads to serious losses to pine forestry around the world. It is necessary to study the biological control of M. saltuarius to effectively prevent the further spread of B. xylophilus. To explore the insect resources that act as natural enemies of M. saltuarius, investigations were conducted on natural enemy insects by splitting Pinus koraiensis Siebold &amp;amp; Zucc (Pinales, Pinaceae) damaged by M. saltuarius and dissecting their trunks in Yingpan Village, Fushun County, Fushun City, Liaoning Province, China, in 2023. A larva of Inocellia (Amurinocellia) calida (H. Aspöck &amp;amp; U. Aspöck) (Raphidioptera, Inocelliidae) was discovered in the trunk of an infested P. koraiensis. Additionally, the feeding habits of I. calida were preliminarily examined under indoor conditions and a description of its morphological characteristics was provided. When placed in an indoor environment, the I. calida larva began pupating after a period of 21 days, during which time it consumed and attacked a total of 23 M. saltuarius larvae. Ultimately, after a pupal period of ten days, the I. calida larva emerged successfully as an adult. This discovery marks the first recorded presence of I. calida in Liaoning Province and the first documentation of I. calida in China, serving as a natural predatory enemy of M. saltuarius