Helps tourists to customize travel plans easily with local experience: Dotrips app

Traveling with group tours can no longer meet the needs of tourists. In-depth tourism has become a new choice for Chinese tourists, especially middle- and high-income groups. Outbound travel, tourists need to find far away from the hustle and bustle of the city and to deeply understand the cultural heritage and folklore origins. Pursue a personal experience of natural beauty and artistic style or a bold exploration of the way of life and the essence of life. The rapid development of the tourism industry and the proliferation of user-shared content have made the problem of information overload in the field of tourism increasingly prominent. How to help tourists quickly develop personalized tour routes while improving the travel experience has become the key to the current research on tourism route planning. The fear of visiting a new, unfamiliar place could lead to stress of travel, making travel plans can also make travel anxiety. Searching for an efficient and local travel experience is very important

Diffusion Models for Reinforcement Learning: A Survey

Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement learning (RL) solutions. This survey aims to provide an overview of this emerging field and hopes to inspire new avenues of research. First, we examine several challenges encountered by RL algorithms. Then, we present a taxonomy of existing methods based on the roles of diffusion models in RL and explore how the preceding challenges are addressed. We further outline successful applications of diffusion models in various RL-related tasks. Finally, we conclude the survey and offer insights into future research directions. We are actively maintaining a GitHub repository for papers and other related resources in utilizing diffusion models in RL: https://github.com/apexrl/Diff4RLSurvey.Comment: Fixed typo

Machine learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China

Climate change influences the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. Although machine learning is increasingly employed for hydrological simulations, few studies have used it to project hydrological droughts, not to mention bivariate risks (referring to drought duration and severity) as well as their socioeconomic effects under climate change. We developed a cascade modeling chain to project future bivariate hydrological drought characteristics in 179 catchments over China, using five bias-corrected global climate model (GCM) outputs under three shared socioeconomic pathways (SSPs), five hydrological models, and a deep-learning model. We quantified the contribution of various meteorological variables to daily streamflow by using a random forest model, and then we employed terrestrial water storage anomalies and a standardized runoff index to evaluate recent changes in hydrological drought. Subsequently, we constructed a bivariate framework to jointly model drought duration and severity by using copula functions and the most likely realization method. Finally, we used this framework to project future risks of hydrological droughts as well as the associated exposure of gross domestic product (GDP) and population. Results showed that our hybrid hydrological–deep-learning model achieved > 0.8 Kling–Gupta efficiency in 161 out of the 179 catchments. By the late 21st century, bivariate drought risk is projected to double over 60 % of the catchments mainly located in southwestern China under SSP5-85, which shows the increase in drought duration and severity. Our hybrid model also projected substantial GDP and population exposure by increasing bivariate drought risks, suggesting an urgent need to design climate mitigation strategies for a sustainable development pathway

Photoacoustic Identification of Laser-induced Microbubbles as Light Scattering Centers for Optical Limiting in Liquid Suspension of Graphene Nanosheets

Liquid suspensions of carbon nanotubes, graphene and transition metal dichalcogenides have exhibited excellent performance in optical limiting. However, the underlying mechanism has remained elusive and is generally ascribed to their superior nonlinear optical properties such as nonlinear absorption or nonlinear scattering. Using graphene as an example, we show that photo-thermal microbubbles are responsible for the optical limiting as strong light scattering centers: graphene sheets absorb incident light and become heated up above the boiling point of water, resulting in vapor and microbubble generation. This conclusion is based on direct observation of bubbles above the laser beam as well as a strong correlation between laser-induced ultrasound and optical limiting. In-situ Raman scattering of graphene further confirms that the temperature of graphene under laser pulses rises above the boiling point of water but still remains too low to vaporize graphene and create graphene plasma bubbles. Photo-thermal bubble scattering is not a nonlinear optical process and requires very low laser intensity. This understanding helps us to design more efficient optical limiting materials and understand the intrinsic nonlinear optical properties of nanomaterials

Circumstellar Material Ejected Violently by A Massive Star Immediately before its Death

Type II supernovae represent the most common stellar explosions in the Universe, for which the final stage evolution of their hydrogen-rich massive progenitors towards core-collapse explosion are elusive. The recent explosion of SN 2023ixf in a very nearby galaxy, Messier 101, provides a rare opportunity to explore this longstanding issue. With the timely high-cadence flash spectra taken within 1-5 days after the explosion, we can put stringent constraints on the properties of the surrounding circumstellar material around this supernova. Based on the rapid fading of the narrow emission lines and luminosity/profile of $\rm H\alpha$ emission at very early times, we estimate that the progenitor of SN 2023ixf lost material at a mass-loss rate $\dot{\rm M} \approx 6 \times 10^{-4}\, \rm M_{\odot}\,yr^{-1}$ over the last 2-3 years before explosion. This close-by material, moving at a velocity $v_{\rm w} \approx 55\rm \, km\,s^{-1}$, accumulates a compact CSM shell at the radius smaller than $7 \times 10^{14}$ cm from the progenitor. Given the high mass-loss rate and relatively large wind velocity presented here, together with the pre-explosion observations made about two decades ago, the progenitor of SN 2023ixf could be a short-lived yellow hypergiant that evolved from a red supergiant shortly before the explosion.Comment: 10 pages, 6 figures in main body, accepted for publication in Science Bulleti

RITA: Boost Autonomous Driving Simulators with Realistic Interactive Traffic Flow

High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating traffic patterns that accurately reflect the various features of real-world data while also simulating human-like reactive responses to the tested autopilot driving strategies. Taking one step forward to addressing such a problem, we propose Realistic Interactive TrAffic flow (RITA) as an integrated component of existing driving simulators to provide high-quality traffic flow for the evaluation and optimization of the tested driving strategies. RITA is developed with consideration of three key features, i.e., fidelity, diversity, and controllability, and consists of two core modules called RITABackend and RITAKit. RITABackend is built to support vehicle-wise control and provide traffic generation models from real-world datasets, while RITAKit is developed with easy-to-use interfaces for controllable traffic generation via RITABackend. We demonstrate RITA's capacity to create diversified and high-fidelity traffic simulations in several highly interactive highway scenarios. The experimental findings demonstrate that our produced RITA traffic flows exhibit all three key features, hence enhancing the completeness of driving strategy evaluation. Moreover, we showcase the possibility for further improvement of baseline strategies through online fine-tuning with RITA traffic flows.Comment: 8 pages, 5 figures, 3 table

APOC1 predicts a worse prognosis for esophageal squamous cell carcinoma and is associated with tumor immune infiltration during tumorigenesis

Background: Esophageal carcinoma (ESCA), a common malignant tumor of the digestive tract with insidious onset, is a serious threat to human health. Despite multiple treatment modalities for patients with ESCA, the overall prognosis remains poor. Apolipoprotein C1 (APOC1) is involved in tumorigenesis as an inflammation-related molecule, and its role in esophageal cancer is still unknown.Methods: We downloaded documents and clinical data using The Cancer Genome Atlas (TCGA)and Gene Expression Omnibus (GEO) databases. We also conducted bioinformatics studies on the diagnostic value, prognostic value, and correlation between APOC1 and immune infiltrating cells in ESCA through STRING (https://cn.string-db.org/), the TISIDB (http://cis.hku.hk/TISIDB/) website, and various other analysis tools.Results: In patients with ESCA, APOC1 was significantly more highly expressed in tumor tissues than in normal tissues (p < 0.001). APOC1 could diagnose ESCA more accurately and determine the TNM stage and disease classification with high accuracy (area under the curve, AUC≥0.807). The results of the Kaplan–Meier curve analysis showed that APOC1 has prognostic value for esophageal squamous carcinoma (ESCC) (p = 0.043). Univariate analysis showed that high APOC1 expression in ESCC was significantly associated with worse overall survival (OS) (p = 0.043), and multivariate analysis shows that high APOC1 expression was an independent risk factor for the OS of patients with ESCC (p = 0.030). In addition, the GO (gene ontology)/KEGG (Kyoto encyclopedia of genes and genomes) analysis showed a concentration of gene enrichment in the regulation of T-cell activation, cornification, cytolysis, external side of the plasma membrane, MHC protein complex, MHC class II protein complex, serine-type peptidase activity, serine-type endopeptidase activity, Staphylococcus aureus infection, antigen processing and presentation, and graft-versus-host disease (all p < 0.001). GSEA (gene set enrichment analysis) showed that enrichment pathways such as immunoregulatory-interactions between a lymphoid and non-lymphoid cell (NES = 1.493, p. adj = 0.023, FDR = 0.017) and FCERI-mediated NF-KB activation (NES = 1.437, p. adj = 0.023, FDR = 0.017) were significantly enriched in APOC1-related phenotypes. In addition, APOC1 was significantly associated with tumor immune infiltrating cells and immune chemokines.Conclusion: APOC1 can be used as a prognostic biomarker for esophageal cancer. Furthermore, as a novel prognostic marker for patients with ESCC, it may have potential value for further investigation regarding the diagnosis and treatment of this group of patients

Progress and hotspot of diet or exercise therapy in the treatment of non-alcoholic fatty liver disease

IntroductionThe primary treatment for non-alcoholic fatty liver disease (NAFLD) is modifying lifestyle through dietary or exercise interventions. In recent decades, it has received increasing attention. However, the lack of bibliometric analysis has posed a challenge for researchers seeking to understand the overall trends in this field.MethodsAs of February 3rd, 2024, 876 articles on treating NAFLD through diet or exercise therapy from 2013 to 2023 had been retrieved. Two software tools, VOSviewer and CiteSpace, were utilized to analyze the growth of publications, countries, institutions, authors, journals, citations, and keywords. Additionally, the keywords with strong citation burstiness were identified to determine the changes and future trends of research hotspots in this field.ResultsChina had the highest number of articles, followed by the United States and South Korea. Yonsei University and Nutrients were the institutions and journals with the most significant contributions. Professor Younossi Zobair M, from the United States, is the most prolific author in this field. Through analyzing the keywords, three research hotspots were identified: research on the pathogenesis of NAFLD, research on the treatment modalities of NAFLD, and research on the risk factors and diagnosis methods of NAFLD. In recent years, the research emphasis in this field has changed, suggesting that future research will focus on two frontier keywords: “oxidative stress” and “aerobic capacity.”ConclusionIn the past eleven years, the attention in this field was still rising, and the authors, journals, countries and so on had formed a considerable cooperative relationship. There were also many highly influential and productive researchers in this field. It is speculated that new research will continue around “aerobic exercise” and “oxidative stress” in the future