Identification and location of ship pipeline leakage based on VMD

Pipeline plays an important role in various systems of the ship. However, due to the harsh environment, leakage often occurs in ship pipeline. This paper proposes a method to identity and locate the pipeline leakage. Using the variational mode decomposition (VMD) algorithm, the vibration signal is decomposed into band-limited intrinsic mode functions (BIMFs). The effective BIMFs are then selected by the correlation coefficient. Center frequency and energy value of the effective BIMFs are extracted as feature vector. Radial Basis Function (RBF) neural network is then used as a tool to identify and locate the leakage. The proposed method is finally verified by experiments

Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway

A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access the Internet through a home gateway (HGW). In this paper, we propose a software-defined network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart home networks

Sustained increase in suspended sediments near global river deltas over the past two decades

River sediments play a critical role in sustaining deltaic wetlands. Therefore, concerns are raised about wetlands’ fate due to the decline of river sediment supply to many deltas. However, the dynamics and drivers of suspended sediment near deltaic coasts are not comprehensively assessed, and its response to river sediment supply changes remains unclear. Here we examine patterns of coastal suspended sediment concentration (SSC) and river sediment plume area (RPA) for 349 deltas worldwide using satellite images from 2000 to 2020. We find a global increase in SSC and RPA, averaging +0.46% and +0.48% yr−1, respectively, with over 59.0% of deltas exhibiting an increase in both SSC and RPA. SSC and RPA increases are prevalent across all continents, except for Asia. The relationship between river sediment supply and coastal SSCs varies between deltas, with as much as 45.2% of the deltas showing opposing trends between river sediments and coastal SSCs. This is likely because of the impacts of tides, waves, salinity, and delta morphology. Our observed increase in SSCs near river delta paints a rare promising picture for wetland resilience against sea-level rise, yet whether this increase will persist remains uncertain

The relationship between habitat factors and the nutrient contents of wild Allium victorialis L. in the Changbai Mountains

Allium victorialis L. (Family: Liliaceae) is an herb with nutritional and medicinal properties. In Jilin Province, China, A. victorialis is mainly distributed in the Changbai Mountains and grows in various habitat conditions. However, the relationship between habitat factors and the nutritional quality of A. victorialis in the Changbai Mountains has not yet been examined. We assessed the nutritional quality of five A. victorialis populations growing in five different habitats in the Changbai Mountains and analyzed the relationship between nutritional quality and habitat factors. Allium victorialis populations in this region were primarily found in the undergrowth at elevations above 500 m and within specific ranges of air temperature, air relative humidity, soil temperature, and soil water content. Among the habitat factors investigated, canopy density significantly affected several nutritional components of A. victorialis; however, elevation had a significant effect only on the total flavonoid content, and the vitamin C content was not strongly associated with the main habitat factors in this study. During germplasm selection and artificial cultivation, it is important to simulate the growth conditions of the original habitat. Our results provide useful information for site selection and environmental condition optimization for the artificial cultivation of A. victorialis

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique

Benefits and risks of antiplatelet therapy for moyamoya disease: a systematic review and meta-analysis

BackgroundMoyamoya disease (MMD) is a leading cause of stroke in children and young adults, whereas no specific drugs are available. Antiplatelet therapy (APT) has been considered a promising treatment option, but its effectiveness remains controversial. Therefore, we aimed to comprehensively evaluate the benefits and risks of APT for MMD.MethodsWe systematically searched PubMed, Embase, and Cochrane Library electronic databases from their inception to 30 June 2022 and conducted a systematic review. All-cause mortality was taken as the primary outcome.ResultsNine studies that enrolled 16,186 patients with MMD were included. The results from a single study showed that APT was associated with lower mortality [hazard ratio (HR) = 0.60; 95% confidence interval (CI) (0.50–0.71); p < 0.01] and improved bypass patency after surgical revascularization [HR = 1.57; 95% CI (1.106–2.235); p < 0.05]. The results of the meta-analysis showed that APT reduced the risk of hemorrhagic stroke [HR = 0.47; 95% CI (0.24–0.94); p < 0.05] but neither reduced the risk of ischemic stroke [HR = 0.80; 95% CI (0.33–1.94); p = 0.63] nor increased the proportion of independent patients [RR = 1.02; 95% CI (0.97–1.06); p = 0.47].ConclusionCurrent evidence showed that APT was associated with a reduced risk of hemorrhagic stroke in MMD patients but did not reduce the risk of ischemic stroke or increase the proportion of independent patients. There was insufficient evidence about the benefit of APT on survival and postoperative bypass patency after surgical revascularization. However, the results should be interpreted cautiously because of the limited number of studies.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/