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"Wu, Jing"
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الاقتصاد الصيني
يتناول كتاب (الاقتصاد الصيني) والذي قام بتأليفه (وو دي لي، سوي فو مين، تشنغ لي) في حوالي (127) صفحة من القطع المتوسط موضوع (اقتصاد الصيني) مستعرضا التالي : يتحدث عن الإنجازات الملحوظة التي حققها الاقتصاد الصيني منذ أكثر من ثلاثين عاما هي مدة الإصلاح والانفتاح، وارتفاع مكانته الدولية بصورة متزايدة. وعن الاتجاه المستقبلي لاقتصاد الصين وإسهامات الاقتصاد الصيني في الاقتصاد العالمي ؟ ويمكن للقارئ أن يحصل على أجوبة لكل هذه الأسئلة من ذلك الكتاب.
Book
Indirect Virus Transmission in Cluster of COVID-19 Cases, Wenzhou, China, 2020
To determine possible modes of virus transmission, we investigated a cluster of coronavirus disease cases associated with a shopping mall in Wenzhou, China. Data indicated that indirect transmission of the causative virus occurred, perhaps resulting from virus contamination of common objects, virus aerosolization in a confined space, or spread from asymptomatic infected persons.
Journal Article
إحضار الكتب المقدسة
عاش النبيل كو في بلدة تونغتايفو كان رجلا طيبا يحب الخير وعرض أن يستضيف سان تسانغ وتلاميذه لفترة طويلة، ولكنهم رفضوا رغم إلحاح عائلة النبيل؛ فلم يجدوا بدا من توديعهم. وما إن غادر سان تسانغ وتلاميذه حتى تعرض بيت النبيل للسرقة، وقتل النبيل كو، فأضمرت زوجته الضغينة لسان تسانغ وتلاميذه لأنهم لم يوافقوا على البقاء رغم الإلحاح عليهم، واتهمت الزوجة سان تسانغ وتلاميذه زورا بارتكاب الجريمة، وقدمت دعوى لمقاضاتهم. وفي طريق اللصوص للفرار، أرادوا سرقة سان تسانغ وتلاميذه أيضا، ولكن وو كونغ قبض عليهم، وحينما التقى سان تسانغ وتلاميذه بالضباط والجنود الذين جاؤوا لملاحقتهم، حدث سوء فهم أدى لاتهام سان تسانغ وتلاميذه.
Book
Enhancing academic English text reasoning via EDA-optimized BERT
At present, College English reading teaching based on computer technology has the problem of insufficient discourse reasoning ability. This study proposes a deep learning framework, which combines the simple data expansion (EDA) with the transformer (BERT) model represented by the bidirectional encoder to build a text reasoning prediction model, so as to enhance the ability of academic English text reasoning. The English Wikipedia dataset is used to evaluate the performance of the model. The results show that the prediction error of the optimized model is only 0.67%±0.01%, the reasoning time is shortened to 2.1s ± 0.2s, and the prediction error rate is only 0.8%, which is better than the traditional natural language processing model. The model has been verified in College English reading text reasoning. The average reasoning time for English literature is 1.2 min, and the similarity of text reasoning is 98.8%. The results show that this method can improve the understanding ability of academic English by improving discourse reasoning. This research not only provides a new technical solution for the teaching of academic English reading, but also can promote the application and development of deep learning model in the field of text reasoning, which is expected to significantly improve learners’ Academic English understanding ability, and have a far-reaching impact on the teaching and research in related fields.
Journal Article
Comparison of elective nodal irradiation and involved-field irradiation in esophageal squamous cell carcinoma: a meta-analysis
It remains controversial whether radical radiotherapy in patients with esophageal squamous cell carcinoma (ESCC) still requires elective nodal irradiation (ENI), or only involved-field irradiation (IFI). In this study, a meta-analysis was conducted to compare ENI and IFI in the treatment of ESCC, in order to provide guidance for clinical practice. Literature on the use of ENI and IFI in the treatment of ESCC was retrieved, and the last access date was 31 December 2017. A meta-analysis was performed to evaluate the relative advantages and disadvantages of using ENI and IFI. Ten studies, involving a total of 1348 patients, were included in this analysis; of these, 605 patients underwent radiotherapy only, and 743 underwent radiochemotherapy. There was no significant difference in the 1-, 2- or 3-year local control rates between ENI and IFI, or in the 1-, 2- or 3-year overall survival rates. However, the incidences of ≥Grade 3 acute esophagitis and pneumonia were significantly lower in the IFI group. There were no differences in the rates of ≥Grade 3 myelosuppression or of out-field recurrence or metastasis between these two groups. Thus, neither local control rates nor overall survival rates differed significantly between the ENI and IFI groups, but in the latter group, incidences of severe radiation esophagitis and pneumonia were significantly lower. IFI was not associated with an increase in out-field recurrence or metastasis.
Journal Article
Structures of human dual oxidase 1 complex in low-calcium and high-calcium states
Dual oxidases (DUOXs) produce hydrogen peroxide by transferring electrons from intracellular NADPH to extracellular oxygen. They are involved in many crucial biological processes and human diseases, especially in thyroid diseases. DUOXs are protein complexes co-assembled from the catalytic DUOX subunits and the auxiliary DUOXA subunits and their activities are regulated by intracellular calcium concentrations. Here, we report the cryo-EM structures of human DUOX1-DUOXA1 complex in both high-calcium and low-calcium states. These structures reveal the DUOX1 complex is a symmetric 2:2 hetero-tetramer stabilized by extensive inter-subunit interactions. Substrate NADPH and cofactor FAD are sandwiched between transmembrane domain and the cytosolic dehydrogenase domain of DUOX. In the presence of calcium ions, intracellular EF-hand modules might enhance the catalytic activity of DUOX by stabilizing the dehydrogenase domain in a conformation that allows electron transfer.
Dual oxidases (DUOXs), assembled from the catalytic DUOX and the auxiliary DUOXA subunits, produce hydrogen peroxide by transferring electrons from intracellular NADPH to extracellular oxygen in a calcium-activated manner. Here authors report the cryo-EM structures of human DUOX1-DUOXA1 complex in both high-calcium and low-calcium states.
Journal Article
AI-powered speech training model for business-oriented english learners
With the increasing level of internationalization, traditional foreign language training methods cannot satisfy the current requirement for compound talents’ foreign language abilities in the business environment. Based on this, this study combines artificial intelligence technology to propose an intelligent English training conversation model based on speech recognition and multi-feature parameters. The experimental results demonstrated that: first, in terms of model performance, the prediction accuracy of the training set and verification set reached 0.968 and 0.975, respectively. The word error rate in the cross dataset test was 12%-18% lower than that of the baseline method, and the processing time (4.5s/5.0s/5.7s) of single/double/multi-syllable processing of student groups was increased by more than 20%. Second, regarding speech recognition performance, the single syllable recognition rate was 96.6%, the multi-syllable recognition rate was 94.8%, and the feedback correction efficiency was as high as 98%. The difference from manual scoring was controlled within 0.5 (single syllable) and 0.25 (double/multi-syllable). Third, in the platform testing of application verification, learners’ English application ability in business scenarios improved by 28%, while the multi-syllable recognition rate of social groups (94.6%) and student groups differed by less than 2%. The research conclusion showed that the model achieved breakthroughs in speech recognition accuracy (> 94.8%), real-time response (< 5.8s), and teaching adaptability through a multi-feature dynamic feedback mechanism. The research model could significantly improve the scenario-based ability of business negotiation terminology application (such as increasing the accuracy of contract terms expression by 19%), providing quantifiable and practical intelligent training solutions for business foreign language teaching.
Journal Article