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In recent years, the influence of global tropical cyclones (TCs) has a tendency to expand towards the polar direction, and the proportion of high-intensity TCs has also increased greatly. The Northwest Pacific is a high incidence area of TCs, the northward shift of landfalling TCs in China and the increase of high-intensity TCs will lead to an increased risk of storm surge in the East China Sea (ECS). However, due to the lack of long-term observations in the ECS, previous studies have focused on the analysis of individual TC, and the response mechanism of storm surge to long-term trends of TCs is not clear. Based on the long-term observations of TCs from 1949 to 2022 and the numerical experiments of storm surge, the trend of landfalling TCs in China and the response characteristics of storm surge in the ECS were studied in this paper. The observations showed that the probability of TCs entering ECS became greater in the latter 37 years compared with the former 37 years. The average intensity of landfalling TCs (maximum wind speed) increased by 36% to 30 m/s, and the average speed of landfalling TCs increased by 100% to 8 m/s. The model results showed that the intensity of the storm surge is closely related to the intensity and translation speed of TCs. Under the condition of the same translation speed of TCs, the storm surge enhanced with the intensity of TCs. For the TCs with high intensity (strong typhoon and super typhoon), the intensity of the storm surge continues to increase as the translation speed of the TCs increases. However, for the TCs with low or medium intensity (severe tropical storms and typhoons), the intensity of the storm surge reaches a peak with the increase of the translation speed of TCs. The intensity of the storm surge is determined by both the wind speed and the duration of strong wind. The TCs with faster translation speed can produce a stronger wind field but shorter duration of strong wind. Therefore, the intensity of the storm surge decreases when the translation speed of TCs exceeds the threshold value.

Objective. The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of a CT volume and the context relationship across multiple channels. Approach. We proposed a mutually enhanced multi-view information model (MEMI) to propagate and fuse the spatial correlations and the context relationship and then apply it to lung tumor CT segmentation. First, a feature map was obtained from segmentation backbone encoder, which contained many image region nodes. An attention mechanism from the region node perspective was presented to determine the impact of all the other nodes on a specific node and enhance the node attribute embedding. A gated convolution-based strategy was also designed to integrate the enhanced attributes and the original node features. Second, transformer across multiple channels was constructed to integrate the channel context relationship. Finally, since the encoded node attributes from the gated convolution view and those from the channel transformer view were complementary, an interaction attention mechanism was proposed to propagate the mutual information among the multiple views. Main results. The segmentation performance was evaluated on both public lung tumor dataset and private dataset collected from a hospital. The experimental results demonstrated that MEMI was superior to other compared segmentation methods. Ablation studies showed the contributions of node correlation learning, channel context relationship learning, and mutual information interaction across multiple views to the improved segmentation performance. Utilizing MEMI on multiple segmentation backbones also demonstrated MEMI's generalization ability. Significance. Our model improved the lung tumor segmentation performance by learning the correlations among multiple region nodes, integrating the channel context relationship, and mutual information enhancement from multiple views. [ABSTRACT FROM AUTHOR]

Liquid droplets hanging from solid surfaces are commonplace, but their physics is complex. Examples include dew or raindrops hanging onto wires or droplets accumulating onto a cover placed over warm food or windshields. In these scenarios, determining the force of detachment is crucial to rationally design technologies. Despite much research, a quantitative theoretical framework for detachment force remains elusive. In response, we interrogated the elemental droplet–surface system via comprehensive laboratory and computational experiments. The results reveal that the Young–Laplace equation can be utilized to accurately predict the droplet detachment force. When challenged against experiments with liquids of varying properties and droplet sizes, detaching from smooth and microtextured surfaces of wetting and non-wetting chemical make-ups, the predictions were in an excellent quantitative agreement. This study advances the current understanding of droplet physics and will contribute to the rational development of technologies. Understanding the physics that govern the dynamics of liquid droplets and their interaction with solid surfaces is crucial for a range of industrial applications, from pesticides to solar cells. Here, the authors develop a framework based on the Young-Laplace equation, to predict the force required to detach a drop from flat and microstructured solid surfaces. [ABSTRACT FROM AUTHOR]

This work investigates a new semi-online scheduling problem with lookahead. We focus on job scheduling on two identical parallel machines, where deterministic online algorithms only know the information of k initial jobs (i.e., the initial-lookahead information), while the following jobs still arrive one-by-one in an over-list fashion. We consider makespan minimization as the objective. The study aims at revealing the value of knowing k initial jobs, which are used to improve the competitive performance of those online algorithms without such initial-lookahead information. We provide the following findings: (1) For the scenario where the k initial jobs are all the largest jobs with length Δ , we prove that the classical LIST algorithm is optimal with competitive ratio (k + 3) / (k + 2) ; (2) For the scenario where the total length of these k   (> 1) jobs is at least Δ , we show that any online algorithm has a competitive ratio at least 3/2, implying that the initial-lookahead knowledge is powerless since there exists a 3/2-competitive online algorithm without such information; (3) For the scenario where the total length of these k   (> α) jobs is at least α Δ (α ≥ 2), we propose an online algorithm, named as LPT-LIST, with competitive ratio of (α + 2) / (α + 1) , implying that the initial-lookahead information indeed helps to improve the competitiveness of those online algorithms lacking such information. [ABSTRACT FROM AUTHOR]