Factors Impacting Satisfaction and Loyalty Towards Network Teaching Platform Among Students Majoring in Humanities and Social Sciences in Chengdu, China

This research aims to evaluate influencing factors of satisfaction and loyalty towards network teaching platform among students majoring in humanities and social sciences in Chengdu, China. The conceptual framework was built based on trust, experience, service quality, perceived value, privacy, satisfaction, and loyalty. Quantitative method was applied for this research by using probability and nonprobability methods, The sampling techniques are judgmental, stratified random and convenience sampling. 517 samples were collected from two target universities, namely Xihua University (XHU) and Jincheng College of Chengdu (JCCCD). Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were utilized to determine the relationships of the variables.  The results show that service quality, perceived value, privacy and trust have a significant impact on satisfaction. Additionally, satisfaction and trust significantly impact loyalty. Conversely, experience has no significant impact on satisfaction. online teaching platform developers, educators, and universities must continuously improve service quality, make users perceive the platform’s value, protect users’ privacy, and make them believe the platforms are trustworthy

Next generation sequencing in early diagnosis of pneumocystis jirovecii pneumonia after chemotherapy: a case report

The incidence of Pneumocystis pneumonia is increasing in immunosuppressive patients. How to diagnose and treat Pneumocystis pneumonia in the early stage has become an important issue for clinicians. The development of Next-generation Sequencing (NGS) provides technical support for the diagnosis of Pneumocystis pneumonia. Case report: A 14-year-old male patient was diagnosed with T lymphoblastoma and treated with chemotherapy. After chemotherapy, the patient developed bone marrow suppression and was complicated with severe pneumonia. He was given endotracheal intubation and ventilator assisted respiration. Samples of patients' alveolar lavage fluid were obtained, and Next-generation Sequencing (NGS) was used for diagnosis, confirming the pathogen as Pneumocystis jiroveci, which was treated by TMP/SMX. The patient's condition gradually improved, and was finally removed from ventilator and endotracheal tube. Pneumocystis jiroveci is a common opportunistic pathogen in immunosuppressive patients, and Next-generation Sequencing (NGS) can be used for rapid diagnosis of Pneumocystis pneumonia, thus improving the clinical therapeutic effect.

H∞ filtering for non-linear systems with stochastic sensor saturations and Markov time delays: The asymptotic stability in probability

This study is concerned with the filtering problem for a class of non-linear systems with stochastic sensor saturations and Markovian measurement transmission delays, where the asymptotic stability in probability is considered. The sensors are subject to random saturations characterised by a Bernoulli distributed sequence. The transmission time delays are governed by a discrete-time Markov chain with finite states. In the presence of the non-linearities, stochastic sensor saturations and Markovian time delays, sufficient conditions are established to guarantee that the filtering process is asymptotically stable in probability without disturbances and also satisfies the H∞ criterion with respect to non-zero exogenous disturbances under the zero-initial condition. Moreover, it is illustrated that the results can be specialised to linear filters. Two simulation examples are presented to show the effectiveness of the proposed algorithms.This work was supported by National Natural Science Foundation of China under Grants 61490701, 61290324, 61273156, 61473163, and 61210012, and Research Fund for the Taishan Scholar Project of Shandong Province of China, and Jiangsu Provincial Key Laboratory of E-business at Nanjing University of Finance and Economics of China under Grant JSEB201301, and Tsinghua University Initiative Scientific Research Program

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe