A data-driven short video international communication model based on indicator system communication network and attention BiLSTM neural network.

As one of the most popular ways to disseminate Internet content in the current era of media convergence, short videos have increasingly become part of people's daily lives. This paper takes TikTok as the research object, focusing on the study of short video communication process. The research objective of this paper is to improve the international communication effect of short videos through data-driven analysis and strategy research on the law of international communication of short videos. Firstly, the key objective indicator system about international communication of short videos is constructed, the definition, selection and design logic of key objective indicators are introduced, the construction idea of the corresponding indicator system is proposed, and then the key objective indicator system about international communication of short videos and related data set are constructed. Secondly, an international communication network model for short videos is built. The construction of this network model is divided into three steps, the construction of the short video communication network KOIS-PN based on the key objective indicators system, the construction of AT-BiLSTM neural network model to adapt to the research scenario of short video international communication, the construction of an international communication network model of short videos combining KOIS-PN and AT-BiLSTM. The international short video communication network is constructed according to the key objective index system of international short video, and the key objective index system is used as nodes in the communication network, and the short video communication network is constructed by hierarchical relationship. Thirdly, the performance of the core theoretical model proposed in this paper is verified. The experimental results show that, the KOISPN-ATBiLSTM international communication network model of short videos has certain validity and advancement.

Ocean internal tides suppress tropical cyclones in the South China Sea.

Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s-1 day-1) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present.

Spatial domain decomposition-based physics-informed neural networks for practical acoustic propagation estimation under ocean dynamics.

Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where "practical" refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset. However, collecting such a high-quality, noise-free, and dense dataset remains challenging. Under the practical hurdle posed by the above approaches, the emergence of physics-informed neural network (PINN) presents an alternative to integrate physics and data but with limited representation capacity. In this work, a framework, termed spatial domain decomposition-based physics-informed neural networks (SPINNs), is proposed to enhance the representation capacity in spatially dependent oceanic scenarios and effectively learn from incomplete and biased prior physics and noisy dataset. Experiments demonstrate SPINNs' advantages over PINN in practical acoustic propagation estimation. The learning capacity of SPINNs toward physics and dataset during training is further analyzed. This work holds promise for practical applications and future expansion.

Dynamic causes contribute to the increasing trend of red tides in the east China sea during 2020-2022.

Red tide is a marine phenomenon caused by the excessive growth of microscopic algae in the ocean. This study aims to analyze the development trends of red tides in the past 20 years and the dynamic external causes that induce red tides based on existing satellite remote sensing and numerical simulation data. And the changes in dominant species of red tides in different seasons are analyzed. The results show significant temperature fluctuations within the week before the red tide occurs, with an average increase of 1.42 °C. In contrast, the change in salinity is relatively small. Meanwhile, ocean fronts are areas in the ocean where different water masses meet and form boundaries. The average strength of ocean fronts increased by 3.7%, indicating enhanced ocean mixing over a short period of time. Under the combined influence of these factors, the probability of a red tide outbreak in the East China Sea increases rapidly. Therefore, this study has important reference value for further research on the causes of red tides and their response to ocean dynamic changes.

A Minireview of the Solid-State Electrolytes for Zinc Batteries.

Aqueous zinc-ion batteries (ZIBs) have gained significant recognition as highly promising rechargeable batteries for the future due to their exceptional safety, low operating costs, and environmental advantages. Nevertheless, the widespread utilization of ZIBs for energy storage has been hindered by inherent challenges associated with aqueous electrolytes, including water decomposition reactions, evaporation, and liquid leakage. Fortunately, recent advances in solid-state electrolyte research have demonstrated great potential in resolving these challenges. Moreover, the flexibility and new chemistry of solid-state electrolytes offer further opportunities for their applications in wearable electronic devices and multifunctional settings. Nonetheless, despite the growing popularity of solid-state electrolyte-based-ZIBs in recent years, the development of solid-state electrolytes is still in its early stages. Bridging the substantial gap that exists is crucial before solid-state ZIBs become a practical reality. This review presents the advancements in various types of solid-state electrolytes for ZIBs, including film separators, inorganic additives, and organic polymers. Furthermore, it discusses the performance and impact of solid-state electrolytes. Finally, it outlines future directions for the development of solid-state ZIBs.

Research on public opinion guidance of converging media based on AHP and transmission dynamics.

In the 5G era, media convergence and technological updates lead to tremendous changes in the dissemination of public opinion information. The guidance of public opinion in the converged media environment is a new research topic. Users participate in the discussion of different media, and flow in and out from different media, which gradually generate a new complex dynamics model of the dissemination of public opinion information. An index system for evaluating the influence of converged media communication that combines the advantages of traditional media and new media is constructed. We use AHP to determine the index weights in the evaluation system and conduct consistency tests. The final weight of each media is determined through the combination of subjective and objective methods, which provides data supports for later determining the participation of various media in the dissemination process of public opinion information. Based on the SIR model, a UCIR (unknown-contact-infected-recovered) dynamic model is established. The simulation results show that the root mean square error (RMSE) of the UCIR model is 31.6% lower than that of the SIR model. Finally, by fixing the key parameters α, ß, θ, ε, p, q in the UCIR model, and changing only one of them, we studied the effect of the transition probability between different states on the process of public opinion information transmission, and then proposed corresponding guidance. In addition, from the two perspectives of network media and government, the countermeasures and suggestions for the guidance of public opinion are proposed. We try to solve the problems of humanities and social sciences with the method of natural science. These research results can provide theoretical and methodological support for people to understand the law of public opinion information transmission and establish a guiding mechanism for public opinion information transmission.