Hydrate-based technologies have excellent application potential in gas separation, gas storage, transportation, and seawater desalination, etc. However, the long induction time and the slow formation rate are critical factors affecting the application of hydrate-based technologies. Micro-nano bubbles (MNBs) can dramatically increase the formation rate of hydrates owing to their advantages of providing more nucleation sites, enhancing mass transfer, and increasing the gas–liquid interface and gas solubility. Initially, the review examines key performance MNBs on hydrate formation and dissociation processes. Specifically, a qualitative and quantitative assembly of the formation and residence characteristics of MNBs during hydrate dissociation is conducted. A review of the MNB characterization techniques to identify bubble size, rising velocity, and bubble stability is also included. Moreover, the advantages of MNBs in reinforcing hydrate formation and their internal relationship with the memory effect are summarized. Finally, combining with the current MNBs to reinforce hydrate formation technology, a new technology of gas hydrate formation by MNBs combined with ultrasound is proposed. It is anticipated that the use of MNBs could be a promising sustainable and low-cost hydrate-based technology.

Object detection powered by deep learning is extensively utilized across diverse sectors, yielding substantial outcomes. However, adverse weather conditions such as rain, snow, and haze interfere with images, leading to a decline in quality and making it extremely challenging for existing methods to detect images captured in such environments. In response to the problem, our research put forth a detection approach grounded in the YOLOv8 model, which we named YOLOv8-STE. Specifically, we introduced a new detection module, ST, on the basis of YOLOv8, which integrates global information step-by-step through window movement while capturing local details. This is particularly important in adverse weather conditions and effectively enhances detection accuracy. Additionally, an EMA mechanism was incorporated into the neck network, which reduced computational burdens through streamlined operations and enriched the original features, making them more hierarchical, thus improving detection stability and generalization. Finally, soft-NMS was used to replace the traditional non-maximum suppression method. Experimental results indicate that our proposed YOLOv8-STE demonstrates excellent performance under adverse weather conditions. Compared to the baseline model YOLOv8, it exhibits superior results on the RTTS dataset, providing a more efficient method for object detection in adverse weather.

The Joint Video Exploration Team (JVET) has created the Versatile Video Coding Standard (VVC/H.266), the most up-to-date video coding standard, offering a broad selection of coding tools. The maturity of commercial VVC codecs can significantly reduce costs and improve coding efficiency. However, the latest video coding standards have introduced binomial and trinomial tree partitioning methods, which cause the coding units (CUs) to have various shapes, increasing the complexity of coding. This article proposes a technique to simplify VVC intra prediction through the use of gradient analysis and a multi-feature fusion CNN. The gradient of CUs is computed by employing the Sobel operator, the calculation results are used for predecision-making. Further decisions can be made by CNN for coding units that cannot be judged whether they should be segmented or not. We calculate the standard deviation (SD) and the initial depth as the input features of the CNN. To implement this method, the initial depth can be determined by constructing a segmented depth prediction dictionary. For the initial segmentation depth of the coding unit, regardless of its shape, it can also be determined by consulting the dictionary. The algorithm can determine whether to split CUs of varying sizes, decreasing the complexity of the CU division process and making VVC more practical. Experimental results demonstrate that the proposed algorithm can reduce encoding time by 36.56% with a minimal increase of 1.06% Bjøntegaard delta bit rate (BD-BR) compared to the original algorithm.

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.

The H.266/Versatile Video Coding (VVC) standard poses a great challenge for encoder design due to its high computational complexity and long encoding time. In this paper, the fast partitioning decision of coding blocks is investigated to reduce the computational complexity and save the coding time of VVC intra-frame predictive coding. A fast partitioning algorithm of VVC intra-frame coding blocks based on gradient structure similarity and directional features is proposed. First, the average gradient structure similarity of four sub-coding blocks under the current coding block is calculated, and two thresholds are set to determine whether the current coding block terminates the partitioning early or performs quadtree partitioning. Then, for the coding blocks that do not satisfy the above thresholds, the standard deviation of the vertical and horizontal directions of the current coding block is calculated to determine the texture direction and skip unnecessary partitioning to reduce computational complexity. Based on the VTM10.0 platform, this paper evaluates the performance of the designed fast algorithm for partitioning within the VVC coding unit. Compared with VTM10.0, the encoding rate is improved by 1.38% on average, and the encoder execution time is reduced by 49.32%. The overall algorithm achieves a better optimization of the existing VVC intra-frame coding technique.

With the rapid development of edge computing in the Industrial Internet, data sharing schemes among edge users require reliable cross-domain authentication and key agreement mechanisms to guarantee the security and reliability of inter-device communication. To tackle the deficiencies of existing group key agreement schemes, including dependence on trusted third parties, high computational overhead, and the difficulty of achieving both privacy preservation and attack resistance, this paper presents a cross-domain authenticated key agreement protocol designed for edge computing environments. This protocol supports anonymous identity authentication between cross-domain users, and innovatively constructs a multi-dimensional virtual iterative cyberspace model to generate massive secure keys via the collaborative iteration of multi-user key sequences. The proposed protocol is decentralized, lightweight, and resistant to replay attacks and man-in-the-middle attacks, while satisfying forward and backward secrecy. Security analysis and performance comparison experiments illustrate that the protocol significantly reduces computational and communication overhead, matches the resource-constrained characteristics of edge devices, and can be widely deployed in large-scale data encryption and sharing scenarios under edge computing environments.