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NH_(3)laser THz emission under optical pumping by“long”(~100μs)CO_(2)laser pulses

Chinese Optics Letters 2023年第2期21卷 133-137页

作者： Andrey Alekseevich Ionin Igor Olegovich Kinyaevskiy Yuri Mikhailovich Klimachev Andrey Yurievich Kozlov Darya Ivanovna Kormashova Andrey Alexandrovich Kotkov Yury Alekseevich Mityagin Sergey Alexandrovich Savinov adilya maratovna sagitova Dmitry Vasilievich Sinitsyn Maxim Vladimirovich Ionin P.N.Lebedev Physical Institute of the Russian Academy of Sciences Moscow 119991Russia

Terahertz(THz)NH_(3) lasing with optical pumping by electron-beam-sustained discharge“long”(~100μs)CO_(2) laser pulses was *** NH3laser emission pulses and the“long”pulses of the CO_(2) pump laser were simultaneously measured with nanosecond response *** NH3 lasing duration and its delay with respect to the pump pulse were measured for various CO_(2) laser pulse *** the CO_(2) laser pump line 9R(30),three wavelengths of 67.2,83.8,and 88.9μm were *** the CO_(2) laser pump line 9R(16),only a single NH3 laser line with a wavelength of 90.4μm was detected.

关键词： NH3 laser CO_(2)laser terahertz emission nanosecond response time

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吉国大学生网购影响因素及企业营销策略研究

吉国大学生网购影响因素及企业营销策略研究

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作者：娜卡（Maratova Nargiza maratovna）长安大学

学位级别：硕士

吉尔吉斯斯坦经济体量小,国内市场小,基础设施建设不够完整,在电子商务上远远落后于中国,但是吉尔吉斯斯坦的电子商务也正在快速发展,吉国的网络普及率和电子支付比例在快速上升,消费者对电子商务的兴趣日益浓厚,电子商务是吉国重要的... 详细信息

吉尔吉斯斯坦经济体量小,国内市场小,基础设施建设不够完整,在电子商务上远远落后于中国,但是吉尔吉斯斯坦的电子商务也正在快速发展,吉国的网络普及率和电子支付比例在快速上升,消费者对电子商务的兴趣日益浓厚,电子商务是吉国重要的经济增长点,我们以吉国大学生为研究对象,通过问卷调查获取吉国大学生参与网购的基本行为数据,并使用回归分析验证现阶段吉国大学生网购的影响因素,探讨吉国大学生网购行为特征,并为相关网络企业提供营销政策依据。研究表明:网页设计与吉国大学生网购感知价值无显著关系;网站安全等其它六个自变量与吉国大学生网购感知价值均呈现显著正相关关系;本文选取的所有自变量与吉国大学生网购的心流体验都呈现显著相关关系;消费者心流体验与吉国大学生网购感知价值呈现显著的正相关关系;本文的中介变量对吉国大学生的计划性网购行为都没有显著影响,但对吉国大学生冲动性网购行为有显著影响。立足上述研究结论,文章从产品、价格、营销、渠道、服务以及广告等多个维度提出了网络企业完善网络营销的措施。

关键词：吉国大学生网上购物影响因素

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Artificial intelligence-Developments in medicine in the last two years

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Chronic Diseases and Translational Medicine 2019年第1期5卷 64-68页

作者： Rezida maratovna Galimova Igor Vyacheslavovich Buzaev Kireev Ayvar Ramilevich Lev Khadyevich Yuldybaev Aigul Fazirovna Shaykhulova Department of Neurology GOU VPO Bashkir State Medical University Ufa 450077 Russia Department of Interventional Cardiology GBUZ Republic Heart Centre GOU VPO Bashkir State Medical University Ufa 450077 Russia Ufa State Aviation Technical University Ufa 450077 Russia Mathematic Department Ufa State Oil Technical University Ufa 450077 Russia Ufa State Aviation Technical University Institute of Aviation Technological Systems Ufa 450077 Russia

Dear Editor , Artificial intelligence (AI) is the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. There are some knowledge and thinking tasks that humans cannot perform as perfectly as they wish to or should be able to. These tasks are closely related to security and responsibility. A multitude of cognitive distortions have been well explored1 and present opportunities to use AI for powerful assistance in thinking tasks. The core of the Industrial Revolution 4.0 is the adoption of AI methods. This revolution has affected all aspects of human activities and medicine is one example. AI systems can usually include formal algorithms for subtasks that can be solved using logic, for example, a decision tree. The task solution process moves from logic point to logic point similar to a train on a railway. These algorithms are fast and have the ability to explain.

关键词： Artificial intelligence Clinical decision Machine learning Scientific tools Healthcare Deep learning

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Artificial intelligence:Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes

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Chronic Diseases and Translational Medicine 2016年第3期2卷 -页

作者： Igor Vyacheslavovich Buzaev Vladimir Vyacheslavovich Plechev Irina Evgenievna Nikolaeva Rezida maratovna Galimova Interventional Cardiology 1 Department GBUZ Republic Heart Centre Ufa Russia Bashkir State Medical University Ufa Russia c GBUZ Republic Heart Centre Ufa Russia

Objective: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. Method: aLYNX system includes:registry with significant factors, decisions and results;machine learning process based on this registry data;the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. Results: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team (16.8%(787/4679) vs. 20.3%(87/428), P ? 0.065)) Conclusion: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina.

关键词： Coronary artery bypass grafting Percutaneous coronary intervention Artificial intelligence Decision making

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