恶性黑色素瘤合并嗜肺军团菌感染性肺炎病例报告

Legionella pneumonia is mainly in community-acquired and nosocomial pneumonias caused by the L. pneumophila. The paper reported a case of Legionella pneumonia caused by Legionella pneumophila Sg1 in a man with malignant melanoma. The method for diagnosing Legionella pneumonia by standard culture method，serotyping，PCR-enzymatic digestion analysis and gene sequencing was elaborate. To confirm the diagnosis result of this rapid diagnostic method, sequencing of the bacteria in patient’s sputum partial gene was also carried out. The diagnosis result of this rapid diagnostic method was consistent with the culture method which indicated that it was effective in diagnosing L. pneumophila infection.军团菌肺炎主要是由嗜肺军团菌感染引起的一种社区获得性或医院内感染性肺炎。本文报告了1例临床上极为罕见的恶性黑色素瘤合并嗜肺军团菌血清1型感染引起的军团菌肺炎，并对其实验室诊断作了系统描述，包括病人痰液标本的细菌分离培养、血清学分型、PCR-酶切分型和基因测序鉴定等分子生物学诊断技术，结果表明PCR-酶切分型对于诊断军团菌病是一种快速、准确可靠的试验方法

Isospin dependence of nucleon effective mass in Dirac Brueckner-Hartree-Fock approach

The isospin dependence of the nucleon effective mass is investigated in the framework of the Dirac Brueckner-Hartree-Fock (DBHF) approach. The definition of nucleon scalar and vector effective masses in the relativistic approach is clarified. Only the vector effective mass is the quantity related to the empirical value extracted from the analysis in the nonrelatiistic shell and optical potentials. In the relativistic mean field theory, where the nucleon scalar and vector potentials are both energy independent, the neutron vector potential is stronger than that of proton in the neutron rich nuclear matter, which produces a smaller neutron vector effective mass than that of proton. It is pointed out that the energy dependence of nucleon potentials has to be considered in the analysis of the isospin dependence of the nucleon effective mass. In the DBHF the neutron vector effective mass is larger than that of proton once the energy dependence of nucleon potentials is considered. The results are consistent with the analysis of phenomenological isospin dependent optical potentials.Comment: 4 pages, 3 Postscript figure

An Investigation of Darwiche and Pearl's Postulates for Iterated Belief Update

Belief revision and update, two significant types of belief change, both focus on how an agent modify her beliefs in presence of new information. The most striking difference between them is that the former studies the change of beliefs in a static world while the latter concentrates on a dynamically-changing world. The famous AGM and KM postulates were proposed to capture rational belief revision and update, respectively. However, both of them are too permissive to exclude some unreasonable changes in the iteration. In response to this weakness, the DP postulates and its extensions for iterated belief revision were presented. Furthermore, Rodrigues integrated these postulates in belief update. Unfortunately, his approach does not meet the basic requirement of iterated belief update. This paper is intended to solve this problem of Rodrigues's approach. Firstly, we present a modification of the original KM postulates based on belief states. Subsequently, we migrate several well-known postulates for iterated belief revision to iterated belief update. Moreover, we provide the exact semantic characterizations based on partial preorders for each of the proposed postulates. Finally, we analyze the compatibility between the above iterated postulates and the KM postulates for belief update

Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning

Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (ASR). However, the robustness impact of combining the two pre-training tasks and constructing different negative samples for contrastive learning still remains unclear. In this paper, we propose a noise-robust data2vec for self-supervised speech representation learning by jointly optimizing the contrastive learning and regression tasks in the pre-training stage. Furthermore, we present two improved methods to facilitate contrastive learning. More specifically, we first propose to construct patch-based non-semantic negative samples to boost the noise robustness of the pre-training model, which is achieved by dividing the features into patches at different sizes (i.e., so-called negative samples). Second, by analyzing the distribution of positive and negative samples, we propose to remove the easily distinguishable negative samples to improve the discriminative capacity for pre-training models. Experimental results on the CHiME-4 dataset show that our method is able to improve the performance of the pre-trained model in noisy scenarios. We find that joint training of the contrastive learning and regression tasks can avoid the model collapse to some extent compared to only training the regression task.Comment: Submitted to ICASSP 202

Stationary Response of a Class of Nonlinear Stochastic Systems Undergoing Markovian Jumps

Systems whose specifications change abruptly and statistically, referred to as Markovianjump systems, are considered in this paper. An approximate method is presented to assess the stationary response of multidegree, nonlinear, Markovian-jump, quasi-nonintegrable Hamiltonian systems subjected to stochastic excitation. Using stochastic averaging, the quasi-nonintegrable Hamiltonian equations are first reduced to a one-dimensional Itô equation governing the energy envelope. The associated Fokker-Planck-Kolmogorov equation is then set up, from which approximate stationary probabilities of the original system are obtained for different jump rules. The validity of this technique is demonstrated by using a nonlinear two-degree oscillator that is stochastically driven and capable of Markovian jumps

Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication

With the rapid development of the Internet of vehicles,the emerging new types of in-vehicle tasks put forward higher requirements for communication and computing capabilities.The development of satellite communication technology and the large-scale deployment of 5G millimeter-wave base stations provide safer and more reliable services for highway vehicle users.At the same time,mobile edge computing technology deploys mobile edge computing(MEC) servers with computing and storage capabi-lities around user terminals to provide computing services for on-board tasks while reducing transmission delays.Aiming at the problem of offloading decision-making and communication resource allocation of vehicle tasks in highway scenarios,the joint optimization problem of computing and communication resources is modeled as a 0-1 mixed integer linear programming problem.Firstly,the original optimization problem is decoupled into the resource block allocation sub-problem and the offloading decision sub-problem.Secondly,the sub-problems are solved by using the water injection algorithm and the particle swarm algorithm.Finally,the sub-problems are iteratively solved based on the heuristic algorithm to obtain the optimal resource block allocation scheme and offload decision vector.Simulation results show that the algorithm minimizes the average system delay while meeting the requirements of all on-board missions

2,4-Dihydr­oxy-N′-(2-hydr­oxy-4-methoxy­benzyl­idene)benzohydrazide

In the title compound, C15H14N2O5, the dihedral angle between the two benzene rings is 4.3 (3)° and the mol­ecule adopts an E configuration with respect to the C=N bond. Intra­molecular O—H⋯N and N—H⋯O hydrogen bonds are observed. In the crystal structure, the mol­ecules are linked through inter­molecular N—H⋯O and O—H⋯O hydrogen bonds to form layers parallel to the ac plane