Studies of the time structure of extended air showers for direction reconstruction with the HAWC outrigger array

The High Altitude Water Cherenkov (HAWC) gamma-ray observatory is a ground-based air shower array designed to detect Cherenkov light produced in water by secondary particles from atmospheric air showers. In order to improve the sensitivity at the highest energies, especially for the shower cores falling outside the main array, 345 smaller Water Cherenkov Detectors (WCDs) were installed around the main array, the outrigger array. This extension increased the instrumented area of HAWC by a factor of four. With the increased size of the array, and the ability to detect shower particles further away from the core, understanding of the time structure of the shower front is crucial for accurate direction reconstruction and mandates proper modeling. In this contribution, we present a model of the shower front as expected to be observed by the outriggers obtained from Monte-Carlo simulations. Applying this model to shower reconstruction, the improvements on air shower parameter are studied

Tracing the Most Powerful Galactic Cosmic-ray Accelerators with the HAWC Observatory

Since Victor Hess\u27s groundbreaking detection of cosmic rays in the Earth\u27s atmosphere in 1912, the origins of these charged particles have remained an enduring mystery. Recent studies suggest that these cosmic rays are accelerated beyond Peta electronvolts by powerful astrophysical sources within our own galaxy. While the cosmic rays themselves are being deflected in all directions by magnetic fields, the gamma rays produced by them, being electrically neutral, travel to the observer in a straight line. They carry crucial information, allowing us to trace cosmic-ray accelerators within our galaxy. The High Altitude Water Chrenkov (HAWC) Observatory, located on the slopes of the Sierra Negra volcano near Puebla, surveys the gamma-ray sky with a duty cycle of over 95\%. A sensitivity to gamma rays ranging from about 100 GeV to beyond 100 TeV, coupled with a 2-steradian instantaneous field of view, makes HAWC one of the premier observatories for studying the most energetic galactic gamma-ray sources. The gamma-ray source eHWC J1825-134, passing the field of view of HAWC at a zenith angle of $32^{\circ}$, is located in the brightest region above 50~TeV in the HAWC data set. This region contains several astrophysical objects, including three pulsar wind nebulae powered by fast-spinning pulsars, a young star cluster, a gamma-ray binary system, and four supernova remnant shells. All these objects are capable of accelerating charged particles and contributing to the cosmic rays detected at Earth. This dissertation focuses on in-depth morphological and spectral studies within the eHWC J1825-134 region. Through a multi-source maximum likelihood analysis, we are able to separate gamma-ray emissions from different sources: the binary system LS 5039, PSR J1826-1254 and its associated pulsar wind nebulae, and the emission source HAWC J1825-134 which is either associated with PSR J1826-1334 or a young star cluster. The gamma-ray sources LS 5039 and HAWC J1825-134 are PeVatron candidates emitting to about 200 hundred TeV at least. Additionally, two TeV halo candidates surround PSR J1826-1334 and PSR J1813-1246

Factors affecting sustainability of smart city services in China:From the perspective of citizens’ sense of gain

The citizen-centric smart city has become an essential paradigm for dealing with the problems caused by rapid urbanization. The Chinese government proposed enhancing citizens' sense of gain to achieve the citizen-centric development goal. To develop a more realistic improving path for the sustainability of smart city services (SCS), it is necessary to clarify the factors that affect citizens' sense of gain of smart city services (CSGSCS). To achieve this objective, 9 hypotheses were developed based on the modified expectation confirmation theory. Hypothesis testing, mediating effect testing, and heterogeneity analysis was conducted based on data collected from Nanjing citizens. The results indicate that: 1) Expectation-Perception Performance, including Content of SCS, Channel of SCS, and Support of SCS, all have positive direct effects on CSGSCS; 2) Expectation Confirmation directly affects CSGSCS and mediates the positive effect of the Expectation-Perception Performance on CSGSCS; 3) Heterogeneity of age and usage frequency have significant effects on CSGSCS. Finally, three policy implications were proposed, including encouraging citizens to participate in SCS supply, bridging the digital divide created by SCS, and improving the policy and legal system on SCS. This research enriches the academic framework and provides guidance for sustainable supply of SCS in similar cities around the world.</p

Planar Cell Polarity Signaling Pathway in Congenital Heart Diseases

Congenital heart disease (CHD) is a common cardiac disorder in humans. Despite many advances in the understanding of CHD and the identification of many associated genes, the fundamental etiology for the majority of cases remains unclear. The planar cell polarity (PCP) signaling pathway, responsible for tissue polarity in Drosophila and gastrulation movements and cardiogenesis in vertebrates, has been shown to play multiple roles during cardiac differentiation and development. The disrupted function of PCP signaling is connected to some CHDs. Here, we summarize our current understanding of how PCP factors affect the pathogenesis of CHD

Adaptive Command-Filtered Backstepping Control for Linear Induction Motor via Projection Algorithm

A theoretical framework of the position control for linear induction motors (LIM) has been proposed. First, indirect field-oriented control of LIM is described. Then, the backstepping approach is used to ensure the convergence and robustness of the proposed control scheme against the external time-varying disturbances via Lyapunov stability theory. At the same time, in order to solve the differential expansion and the control saturation problems in the traditional backstepping, command filter is designed in the control and compensating signals are presented to eliminate the influence of the errors caused by command filters. Next, unknown total mass of the mover, viscous friction, and load disturbances are estimated by the projection-based adaptive law which bounds the estimated function and simultaneously guarantees the robustness of the proposed controller against the parameter uncertainties. Finally, simulation results are given to illustrate the validity and potential of the designed control scheme

ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer

In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-based framework. While previous studies have shown the crucial importance of the intrinsic synergy between text detection and recognition, recent advances in Transformer-based methods usually adopt an implicit synergy strategy with shared query, which can not fully realize the potential of these two interactive tasks. In this paper, we argue that the explicit synergy considering distinct characteristics of text detection and recognition can significantly improve the performance text spotting. To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Specifically, we decompose the conventional shared query into task-aware queries for text polygon and content, respectively. Through the decoder with the proposed vision-language communication module, the queries interact with each other in an explicit manner while preserving discriminative patterns of text detection and recognition, thus improving performance significantly. Additionally, we propose a task-aware query initialization scheme to ensure stable training. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods. Code is available at https://github.com/mxin262/ESTextSpotter.Comment: Accepted to ICCV 202

Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation~(ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication~(ISAC) devices to sense the targets and offload the data to the base station~(BS) for further processing. However, as the number of ISAC devices growing, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which model the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.Comment: 13 pages,9 figure

Multi-omics analysis reveals the mechanism underlying the edaphic adaptation in wild barley at Evolution Slope (Tabigha)

At the microsite “Evolution Slope”, Tabigha, Israel, wild barley (Hordeum spontaneum) populations adapted to dry Terra Rossa soil, and its derivative abutting wild barley population adapted to moist and fungi-rich Basalt soil. However, the mechanisms underlying the edaphic adaptation remain elusive. Accordingly, whole genome bisulfite sequencing, RNA-sequencing, and metabolome analysis are performed on ten wild barley accessions inhabiting Terra Rossa and Basalt soil. A total of 121 433 differentially methylated regions (DMRs) and 10 478 DMR-genes are identified between the two wild barley populations. DMR-genes in CG context (CG-DMR-genes) are enriched in the pathways related with the fundamental processes, and DMR-genes in CHH context (CHH-DMR-genes) are mainly associated with defense response. Transcriptome and metabolome analysis reveal that the primary and secondary metabolisms are more active in Terra Rossa and Basalt wild barley populations, respectively. Multi-omics analysis indicate that sugar metabolism facilitates the adaptation of wild barley to dry Terra Rossa soil, whereas the enhancement of phenylpropanoid/phenolamide biosynthesis is beneficial for wild barley to inhabit moist and fungi pathogen-rich Basalt soil. The current results make a deep insight into edaphic adaptation of wild barley and provide elite genetic and epigenetic resources for developing barley with high abiotic stress tolerance

Topics and sentiment surrounding vaping on Twitter and Reddit during the 2019 e-cigarette and vaping use-associated lung injury outbreak: Comparative study

BACKGROUND: Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use-associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. OBJECTIVE: This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. METHODS: Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). RESULTS: Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P\u3c.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P\u3c.001, 95% CI -0.4289 to -0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. CONCLUSIONS: Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms