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Objectives This paper aims to expand the prediction method of a power increase in waves for ships with podded propulsion, and to predict the power increase of a luxury cruise ship in actual seas through model experimentations.Methods A self-propulsion test for a cruise ship with podded propulsion in waves is performed in the towing tank. A tailor-made dynamometer is installed inside the model scale pod unit for torque and revolution measurement, and the traditional self-propulsion test method in calm water is further developed and applied to the test in waves. Additionally, the experiment data is post-processed according to ITTC recommended procedures, and the error sources was analyzed.Results According to the ITTC torque and revolution method, the obtained peak power increase of the target cruise ship occurs at wave periods close to 12 s; when the significant wave height is 2 m, the power increase in waves is 554 kW, corresponding to 18.53% of the power consumption in calm water.Conclusions The experimental method presented in this paper is applicable for ships with podded propulsion and can be used for the prediction of a power increase in waves. This provides a reference for a more detailed investigation into the ship's propulsion characteristics in waves and can help expand the design optimization approaches towards more energy-efficient ships.

Digital subtraction angiography (DSA) is considered the gold standard for cerebral vasculature observation and is increasingly applied among the elderly population. The aim of this study is to determine whether the use of a video-based education system can improve the image quality of percutaneous cerebral angiography.

Chemical reduction in oxides plays a crucial role in engineering the material properties through structural transformation and electron filling. Controlling the reduction at nanoscale forms a promising pathway to harvest functionalities, which however is of great challenge for conventional methods (e.g., thermal treatment and chemical reaction). Here, we demonstrate a convenient pathway to achieve nanoscale chemical reduction for vanadium dioxide through the electron-beam illumination. The electron beam induces both surface oxygen desorption through radiolytic process and positively charged background through secondary electrons, which contribute cooperatively to facilitate the vacancy migration from the surface toward the sample bulk. Consequently, the VO2 transforms into a reduced V2O3 phase, which is associated with a distinct insulator to metal transition at room temperature. Furthermore, this process shows an interesting facet-dependence with the pronounced transformation observed for the c-facet VO2 as compared with the a-facet, which is attributed to the intrinsically different oxygen vacancy formation energy between these facets. Remarkably, we readily achieve a lateral resolution of tens nanometer for the controlled structural transformation with a commercial scanning electron microscope. This work provides a feasible strategy to manipulate the nanoscale chemical reduction in complex oxides for exploiting functionalities. The authors demonstrate a nanoscale chemical reduction for VO2 into V2O3 through electron-beam illumination, showcasing potential for nanoscale manipulation of oxygen ionic evolution for advanced harvesting functionalities. [ABSTRACT FROM AUTHOR]

This paper mainly analyzes the main external load types of offshore platforms and the current status of offshore platform health monitoring technology. On this basis, a platform health monitoring system which is consist of a wireless sensor network and a satellite communication system is an typical application of Internet of Things Technology in the field of offshore platform monitoring. This paper has a detailed discussion of the overall system and key equipment. It has certain reference significance to engineers and technicians in related fields.

This article was to explore the adoption value of deep learning combined with computed tomography (CT) imaging omics in the prediction of metastatic lymph nodes of nasopharyngeal carcinoma (NPC). An end‐to‐end neural network architecture was designed based on the fully convolutional neural network (FCNN), which was applied to the CT image analysis of 52 patients with lymphatic metastasis and 36 patients without lymphatic metastasis. Patient's lymph node volume (V), the largest cross‐sectional shortest diameter (d‐value), and other macro characteristics were recorded. The microscopic features of its CT imaging omics were extracted. Moreover, receiver operating characteristic (ROC) curve was utilized to analyse the prediction performance (accuracy, area under the curve [AUC], and Youden index) of each feature for lymphatic metastasis. The results showed that the lymph node volume (4.37 ± 0.67) and the shortest diameter of the largest cross section (12.35 ± 2.31) of patients with lymph node metastasis were greatly larger than those without lymph node metastasis (1.84 ± 0.65, 7.98 ± 2.04) (P < 0.05). There were five features that met the conditions of AUC > 0.7 and Yoden index>0.5, including lymph node volume (AUC area 0.945, Youden index 0.597), the shortest diameter of the largest cross section (AUC area 0.746, Youden index 0.539), Surface Area Density (AUC area 0.809, Youden index 0.552), Compactness1 (AUC area 0.751, Youden index 0.537), and Convex Hull Volume (AUC area 0.751, Youden index 0.537). The AUC of V+ Surface Area Density + Compactness1 + Convex Hull Volume was 0.876, and the prediction accuracy was 92.11%. In short, the prediction model composed of the macroscopic features of CT images and some imaging omics features based on deep learning showed high accuracy and AUC for the prediction of NPC metastatic lymph nodes. Moreover, V + Surface Area Density + Compactness1 + Convex Hull Volume can be used as the optimal feature combination model for predicting NPC lymphatic metastasis. [ABSTRACT FROM AUTHOR]

Chemical reduction in oxides plays a crucial role in engineering the material properties through structural transformation and electron filling. Controlling the reduction at nanoscale forms a promising pathway to harvest functionalities, which however is of great challenge for conventional methods (e.g., thermal treatment and chemical reaction). Here, we demonstrate a convenient pathway to achieve nanoscale chemical reduction for vanadium dioxide through the electron-beam illumination. The electron beam induces both surface oxygen desorption through radiolytic process and positively charged background through secondary electrons, which contribute cooperatively to facilitate the vacancy migration from the surface toward the sample bulk. Consequently, the VO 2 transforms into a reduced V 2 O 3 phase, which is associated with a distinct insulator to metal transition at room temperature. Furthermore, this process shows an interesting facet-dependence with the pronounced transformation observed for the c-facet VO 2 as compared with the a-facet, which is attributed to the intrinsically different oxygen vacancy formation energy between these facets. Remarkably, we readily achieve a lateral resolution of tens nanometer for the controlled structural transformation with a commercial scanning electron microscope. This work provides a feasible strategy to manipulate the nanoscale chemical reduction in complex oxides for exploiting functionalities., (© 2023. The Author(s).)