Whipple's disease of the respiratory system: A case report.

Whipple's disease (WD) is a multiple-system chronic disease caused by Tropheryma whipplei (T. whipplei) infection. The present study describes 3 cases of WD with clinical manifestations of cough, chest pain, headache, dyspnea, sputum, joint pain, abdominal pain, diarrhea and weight loss. Chest computed tomography (CT) showed signs of plaques, nodules and pleural thickening; and bronchoscopic alveolar lavage fluid metagenomic-sequencing indicated that it was T. whipplei. One patient was treated with meropenem as the starting regimen and two patients were treated with ceftriaxone as the starting regimen. Furthermore, two patients were provided with a maintenance regimen of cotrimoxazole and one was given a maintenance regimen of minocycline, which was combined with meropenem and ceftriaxone in order to improve their cough, chest pain, headache and dyspnea symptoms. To the best of our knowledge, there are few reports on WD of the respiratory system caused by T. whipplei, and differential diagnosis is the key to clinical diagnosis. When WD of the respiratory system is difficult to diagnose, metagenomic second-generation sequencing (mNGS) may be a better choice, which can achieve early diagnosis and early treatment. However, its clinical value is still limited; therefore, more research needs to be conducted in the future.

The effects of musical feedback training on metacognition and self-directed learning.

Introduction: Metacognition and self-directed learning are key components in educational research, recognized for their potential to enhance learning efficiency and problem-solving skills. This study explores the effects of musical feedback training on these competencies. Methods: The study involved 84 preservice teachers aged 18 to 21. Participants were randomly assigned to either an experimental group, which received musical feedback training, or a control group. Results: The findings indicate that musical feedback training effectively improved metacognitive abilities. However, its impact on the readiness for self-directed learning was inconclusive. A notable difference in metacognition and self-directed learning readiness was observed between the experimental and control groups during the session, indicating a significant interaction effect. Furthermore, a positive correlation was identified between metacognition and self-directed learning. Discussion: These results contribute to educational discourse by providing empirical evidence on the utility of musical feedback training in fostering metacognition. They also highlight the importance of consistent and long-term engagement in self-directed learning practices. The significance of these findings advocates for incorporating music feedback training into music education curricula to enhance metacognition and improve overall learning efficiency.

Underwater Acoustic Target Recognition Based on Attention Residual Network.

Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method.

A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs.

Because of the specific characteristics, like high vehicular mobility, unstable topology, and interruption of inter-vehicle wireless communication, it is hard to make a perfect decision on packet forwarding in highly dynamic topology VANETs. Especially in a sparse urban environment, the poor connectivity of nodes will mostly cause problems such as data packet loss and routing redundancy. Therefore, how to choose the best relay node becomes a key challenge in the design of fast and reliable routing protocols. This paper presents real-time effective information traffic routing (RTEIT), which provides an optimal route for forwarding the data packets toward their destination when choosing the relay node. RTEIT introduces a new network parameter named effective information traffic which can estimate the connectivity of nodes by the path that has been successfully created. Moreover, for avoiding unexpected communication interruption, we propose a new formula to evaluate the status of the links via considering the speed, direction, and location information. Finally, the node utility, as the criterion of routing decision, is calculated by effective information traffic and link status. We use the simulator of SUMO and NS-3 platform to evaluate RTEIT, and the results are compared with GPSR MOPR, and MM-GPSR. The evaluation results demonstrate that RTEIT outperforms in terms of packet loss rate, end-to-end delay, and network yield.

Optimal experimental conditions for Welan gum production by support vector regression and adaptive genetic algorithm.

Welan gum is a kind of novel microbial polysaccharide, which is widely produced during the process of microbial growth and metabolism in different external conditions. Welan gum can be used as the thickener, suspending agent, emulsifier, stabilizer, lubricant, film-forming agent and adhesive usage in agriculture. In recent years, finding optimal experimental conditions to maximize the production is paid growing attentions. In this work, a hybrid computational method is proposed to optimize experimental conditions for producing Welan gum with data collected from experiments records. Support Vector Regression (SVR) is used to model the relationship between Welan gum production and experimental conditions, and then adaptive Genetic Algorithm (AGA, for short) is applied to search optimized experimental conditions. As results, a mathematic model of predicting production of Welan gum from experimental conditions is obtained, which achieves accuracy rate 88.36%. As well, a class of optimized experimental conditions is predicted for producing Welan gum 31.65g/L. Comparing the best result in chemical experiment 30.63g/L, the predicted production improves it by 3.3%. The results provide potential optimal experimental conditions to improve the production of Welan gum.

Optimization to the Phellinus experimental environment based on classification forecasting method.

Phellinus is a kind of fungus and known as one of the elemental components in drugs to avoid cancer. With the purpose of finding optimized culture conditions for Phellinus production in the lab, plenty of experiments focusing on single factor were operated and large scale of experimental data was generated. In previous work, we used regression analysis and GA Gene-set based Genetic Algorithm (GA) to predict the production, but the data we used depended on experimental experience and only little part of the data was used. In this work we use the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time and rotation speed, to establish a high yield and a low yield classification model. Subsequently, a prediction model of BP neural network is established for high yield data set. GA is used to find the best culture conditions. The forecast accuracy rate more than 90% and the yield we got have a slight increase than the real yield.

A Cooperative Downloading Method for VANET Using Distributed Fountain Code.

Cooperative downloading is one of the effective methods to improve the amount of downloaded data in vehicular ad hoc networking (VANET). However, the poor channel quality and short encounter time bring about a high packet loss rate, which decreases transmission efficiency and fails to satisfy the requirement of high quality of service (QoS) for some applications. Digital fountain code (DFC) can be utilized in the field of wireless communication to increase transmission efficiency. For cooperative forwarding, however, processing delay from frequent coding and decoding as well as single feedback mechanism using DFC cannot adapt to the environment of VANET. In this paper, a cooperative downloading method for VANET using concatenated DFC is proposed to solve the problems above. The source vehicle and cooperative vehicles encodes the raw data using hierarchical fountain code before they send to the client directly or indirectly. Although some packets may be lost, the client can recover the raw data, so long as it receives enough encoded packets. The method avoids data retransmission due to packet loss. Furthermore, the concatenated feedback mechanism in the method reduces the transmission delay effectively. Simulation results indicate the benefits of the proposed scheme in terms of increasing amount of downloaded data and data receiving rate.

A Timing Estimation Method Based-on Skewness Analysis in Vehicular Wireless Networks.

Vehicle positioning technology has drawn more and more attention in vehicular wireless networks to reduce transportation time and traffic accidents. Nowadays, global navigation satellite systems (GNSS) are widely used in land vehicle positioning, but most of them are lack precision and reliability in situations where their signals are blocked. Positioning systems base-on short range wireless communication are another effective way that can be used in vehicle positioning or vehicle ranging. IEEE 802.11p is a new real-time short range wireless communication standard for vehicles, so a new method is proposed to estimate the time delay or ranges between vehicles based on the IEEE 802.11p standard which includes three main steps: cross-correlation between the received signal and the short preamble, summing up the correlated results in groups, and finding the maximum peak using a dynamic threshold based on the skewness analysis. With the range between each vehicle or road-side infrastructure, the position of neighboring vehicles can be estimated correctly. Simulation results were presented in the International Telecommunications Union (ITU) vehicular multipath channel, which show that the proposed method provides better precision than some well-known timing estimation techniques, especially in low signal to noise ratio (SNR) environments.