一种基于BP神经网络方法的HY-2A散射计反演风场偏差订正方案

针对HY-2A散射计风矢量场数据,利用BP神经网络方法,引入NDBC浮标的降水海温等环境要素,对HY-2A散射计风场进行偏差订正。实验结果表明:BP神经网络方法对HY-2A散射计的风速风向均有较好的订正效果,能有效修正HY-2A的风速高估现象,风速平均偏差由2.32 m/s改善至0.25 m/s;同时通过敏感性试验,发现了各样本输入量以及各环境要素对实验结果的敏感性。国家重点研发计划(2016YFC1401704

Research of Corporate Governance in Chinese Family Business

改革开放20年来，家族企业迅速崛起，并成为推动中国经济蓬勃发展的新的生力军。在市场经济秩序逐步得以确立的今天，企业的制度建设已经成为企业能否在市场竞争中生存与发展的决定因素。家族企业如能在制度建设方面有实质性的进展，必将释放出巨大的能量。 构筑有效的公司治理结构是家族企业规范化的制度基础。本文把公司治理纳入家族企业系统中进行研究，从家族企业独有的特性和发展的角度来探讨我国家族企业治理结构。分析我国家族企业治理结构的现状与特征，并有针对性地提出了进行家族企业治理改革和完善的措施。全文共分三章。 第一章：本章回顾了家族企业的定义，以及公司治理的相关理论。根据相关法规，界定本文所讨论的对象为按公...The family business rushes in recent 20 years with China’s reform and open policy. If the family business, playing an important role in Chinese economic, can reform its corporate governance, great progress will be made since the corporate governance becomes a crucial factor that determines the corporate fate in current period that market-oriented economy systems having been established step by ste...学位：工商管理硕士院系专业：管理学院工商管理教育中心_工商管理硕士(MBA)学号：20001508

基于聯邦學習的推薦系統綜述

隨著互聯網和移動計算等技術的發展,人們的在線行為產生了越來越多的數據,想要從海量數據中挑選出用戶可能喜歡的物品,推薦系統不可或缺.然而傳統的推薦算法需要將用戶數據收集到服務端才能構建模型,這會泄露用戶隱私.最近,谷歌針對機器學習任務中需要收集用戶數據才能進行建模的問題,提出了一種新的學習范式——聯邦學習.聯邦學習與推薦系統相結合,使得聯邦推薦算法能夠在模型構建過程中,始終將用戶數據保留在客戶端本地,從而保護了用戶隱私.本文主要對聯邦學習與推薦系統相結合的研究工作進行綜述,并從架構設計、系統的聯邦化和隱私保護技術的應用3個角度重點分析聯邦推薦算法的研究進展.最后,對基于聯邦學習的推薦系統可研究的方向進行展望. With the development of the Internet and mobile computing, people’s online behaviors have generated increasing amounts of data. In order to select items that users may like from massive data, recommender systems are indispensable. However, traditional recommendation algorithms need to collect user data to the server to build the model, which will leak user privacy. Recently, Google has proposed a new learning paradigm called federated learning for machine learning problems that require user data to be collected for modeling. The combination of federated learning and recommender systems enables federated recommendation algorithms to always keep user data in clients during the modeling process, so as to protect user privacy. In this study, the research works on the combination of federated learning with recommendation algorithms are surveyed. Then, the research development on federated recommendation algorithms is analyzed from three perspectives, namely, design of architectures, federalization of models, and application of privacy-preserving technology. Finally, some research directions and prospects for recommender systems based on federated learning are discussed

基于联邦学习的推荐系统综述

随着互联网和移动计算等技术的发展, 人们的在线行为产生了越来越多的数据, 想要从海量数据中挑选出用户可能喜欢的物品, 推荐系统不可或缺. 然而传统的推荐算法需要将用户数据收集到服务端才能构建模型, 这会泄露用户隐私. 最近, 谷歌针对机器学习任务中需要收集用户数据才能进行建模的问题, 提出了一种新的学习范式 —— 联邦学习. 联邦学习与推荐系统相结合, 使得联邦推荐算法能够在模型构建过程中, 始终将用户数据保留在客户端本地, 从而保护了用户隐私. 本文主要对联邦学习与推荐系统相结合的研究工作进行综述, 并从架构设计、系统的联邦化和隐私保护技术的应用 3 个角度重点分析联邦推荐算法的研究进展. 最后, 对基于联邦学习的推荐系统可研的方向进行展望 With the development of the Internet and mobile computing, people's online behaviors have generated increasing amounts of data. In order to select items that users may like from massive data, recommender systems are indispensable. However, traditional recommendation algorithms need to collect user data to the server to build the model, which will leak user privacy. Recently, Google has proposed a new learning paradigm called federated learning for machine learning problems that require user data to be collected for modeling. The combination of federated learning and recommender systems enables federated recommendation algorithms to always keep user data in clients during the modeling process, so as to protect user privacy. In this study, the research works on the combination of federated learning with recommendation algorithms are surveyed. Then, the research development on federated recommendation algorithms is analyzed from three perspectives, namely, design of architectures, federalization of models, and application of privacy-preserving technology. Finally, some research directions and prospects for recommender systems based on federated learning are discussed. © 2022, Science China Press. All right reserved

纤维二糖发酵生产丙酮丁醇

分别考察C．acetobutylicum810705、810706以不同浓度的麸皮和玉米粉添加物作为营养元素，纤维二糖直接进行丙酮丁醇（ABE）发酵的结果，发现2株菌对于玉米粉和麸皮的浓度变化趋势一致，C．acetobutylicum810706转化率较高。纤维二糖ABE发酵工艺条件表明：玉米粉添加量为总糖含量的30％、底物糖质量浓度60g/L，pH6．5、温度35℃时，C．acetobutylicum810706转化率达到37．38％，总溶剂质量浓度22．43g/L，比葡萄糖、木糖ABE发酵转化率高。模拟纤维素酶水解产物配制混合糖培养基，其溶剂转化率较单独的葡萄糖、木糖发酵的转化率高，为34．95％。对比纤维素酶水解条件，c．acetobutylicum810706具有优良的纤维素酶水解同步糖化ABE发酵能力

玉米浆干粉在葡萄糖和木糖混合丙酮丁醇发酵中的应用

为降低丙酮丁醇梭菌发酵生产丁醇的成本,研究了以玉米浆干粉为氮、磷源等营养因子的单糖培养基发酵条件。结果表明：只需添加适量的玉米浆干粉,无需添加其它营养物质,以葡萄糖或葡萄糖与木糖混合糖为原料的培养基可以满足丙酮丁醇梭菌的生长及溶剂产生所需要的营养元素,50 g/L葡萄糖-2.5 g/L玉米浆干粉培养基溶剂转化率可以达到35.14%,总溶剂产量17.57 g/L,丁醇产量11.21 g/L。30 g/L葡萄糖＋20 g/L木糖-2.5 g/L玉米浆干粉溶剂转化率可以达到33.66%,总溶剂16.83 g/L,丁醇11.10 g/L,为进一步研究木质纤维素原料水解液生产丁醇提供理论指导

在Ni沉积的石墨表面氧吸附和还原的反应机理第一性密度泛函理论研究

Nitrogen doped graphene （N-graphene）has been reproted possessing significant oxygen reduction reaction （ORP）activity in recent years. However, how the activity depended on the distribution condiguration of nitrogen and doping concentration are still ambigugous, and the ORP mechanism on N-graphene and the rate-determaing steps are figured out. The adsorption energy of Oads species on the surface in contrast with that on Pt can be used to approximately describe the ORP activity.Using density functional calculationas (VASP),We find that on N-graphene with 9%~20% doping concentration the dissociation adsorption energy of dioxygen is comparable with that on Pt. The change atom and the adsorption energy in variation with N concentration is dominated by the electrostatic force between the oxygen atom and the adsorption-site carbon atom. Too low (20%)nitrogen concentration will depress the electrostatic force of C-O adsorption bond and weaken the adsorption because the oxygen atom will withdraw fewer electrons from the surface