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Articles by Chenfeng Li
Sequence Types of Cryptococcus neoformans and Their Associations with Clinical Characteristics and Outcomes of AIDS Patients with Cryptococcal Meningitis in Southern China
You are already at the latest version This study investigated the molecular epidemiological characteristics of Cryptococcus neoformans (C. neoformans) isolates from AIDS patients with cryptococcal meningitis (CM) in southern China and examined their associations with clinical features and outcomes. A total of 100 clinical isolates were identified by MALDI-TOF MS and genotyped by multilocus sequence typing (MLST). Antifungal susceptibility to five agents was assessed using the FUNGUS 3 system.
Advances in Marine Mechanical and Structural Engineering-2nd Edition
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A De-Nesting Hybrid Reliability Analysis Method and Its Application in Marine Structure
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Advanced Analysis of Marine Structures-Edition II
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Advances in Marine Mechanical and Structural Engineering
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Advanced Analysis of Marine Structures
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Research on a Real-Time Prediction Method of Hull Girder Loads Based on Different Recurrent Neural Network Models
1. Introduction The hull girder loads are an important factor affecting the overall structural safety of ships, which is a key focus in the design and use of ship structures [1,2]. Real-time prediction of hull girder load data will be of great help to the captain’s navigation decision-making, especially in high-sea conditions when the hull girder loads are large and there is a high risk of failure in the ship’s structure.
Influence of Firefighting Intervention on Fire Spread Characteristics in Ship Engine Room
5.2. Effect of Particle Size of Water Mist after Cabin Sealing on Cabin Fire Temperature The influence of the water mist particle size on fire was further analyzed. A cabin fire was simulated for the case when the cabin cover is closed after timely operation, and the optimal spray speed was maintained at 20 m/s. The parameters of the nozzles were based on the “Code for Fire Protection in Building Design” (GB50016-2014) and “Code for Design of Automatic Sprinkler System” (GB50084-2001).
Pre-Layout Parasitic-Aware Design Optimizing for RF Circuits Using Graph Neural Network
2. GNN-Based Model for Interconnect RC Prediction The structure of the prediction model is shown in Figure 1. The method of data processing, training, and prediction process will be discussed in detail. Given the input netlist, each net’s parasitic resistance and capacitance parameters are predicted and attached to the corresponding entity in the structure as an output. Without physical design and layout information, the proposed model can predict certain layout and parasitic parameters.
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