基于概率密度演化的输电塔-线体系抗风可靠性分析

以三线两塔直线段输电塔-线体系为工程对象,应用有限元数值模拟,建立了基于概率密度演化的输电塔-线体系抗风可靠性分析方法。首先,应用谱表示-降维方法模拟结构脉动风场,生成风荷载的代表性样本集合。然后,结合概率密度演化理论,分析了输电塔-线体系考虑气弹效应的随机动力反应。最后,应用等价极值思想构建了风荷载作用下输电塔-线体系失效准则,进而对输电塔-线体系的抗风可靠性进行精细化分析。本文结合谱表示-降维方法与概率密度演化理论,实现了仅用较少数量的代表性样本来精细地分析结构的抗风可靠性,为工程实践提供参考。     

分光光度法同时测定铝(Ⅲ)和铁(Ⅱ)

以甲基百里酚蓝(MTB)为显色剂、CTMAB为增溶增敏剂,利用系数补偿和双峰双波长的两次分光光度法同时测定铝(Ⅲ)和铁(Ⅱ)的结果令人满意.     

双热源空调-热水器一体机冬季制热的实验研究

冬季室外温度低时,空气源热泵系统的蒸发器会结霜,使系统COP降低。所设计的空调-热水器一体机可以制冷、制热、一年四季提供生活用热水。冬季室外温度低时,用太阳能加热后的水作为热泵的低温热源,可以提高热泵的效率。分别用空气源蒸发器和水源蒸发器独立工作使系统给房间制热,实验结果发现水源蒸发器工作时系统的COP平均值为3.56,空气源蒸发器工作时的COP平均值为2.51。     

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.     

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.     

草一光可湿性粉剂活性成分的反相高效液相色谱分析

采用反相高效液相色谱法同时测定15.5％草一光可湿性粉剂中的异丙甲草胶和苄嘧黄隆两种活性成分,以V（甲醇）：V（酸性水）=70：30作为流动相,采用ZORBAXSB-C18色谱柱。方法回收率达98.1％～102.6％,变异系数为0.4％～0.8％。     

Dual recognition strategy for ultra-sensitive fluorescent detection of Hg2+ at femto-molar level based on aptamer functionalized sulfur quantum dots

In the present study, a dual recognition strategy for ultrasensitive detection of Hg²⁺ was successfully developed for the first time based on aptamer functionalized sulfur quantum dots (Apt-SQDs). The developed Apt-SQDs not only retained the good fluorescence properties of quantum dots but also overcame the problem of poor selectivity of SQDs for heavy metal ions. This system used the dual recognition strategy, including the combination of S_x^2? and Hg²⁺ and T-Hg²⁺-T structures to excellently identify and capture Hg²⁺, and an ultrahigh sensitivity fluorescent aptasensor was fabricated. The fluorescent aptasensor had a good response to Hg²⁺ at concentrations ranging of 10^?15 to 10^?7 M with an ultralow limit of detection of 0.3 fM, and the response to other metal ions was far less than that to Hg²⁺. It was successfully applied to detect Hg²⁺ in nearby environmental water samples (tap water, lake water and river water) with a good recovery rate. Moreover, portable test papers that would be useful for Hg²⁺ monitoring in environmental water were designed. The dual recognition strategy not only achieves ultrasensitive fluorescent detection of Hg²⁺ but also provides a new insight into the further expansion of the application of SQDs.     

利用地球上丰富的炭黑和NiS_2双助催化剂修饰提高CdS纳米片体系的可见光产氢活性(英文)

光催化产氢技术是目前解决能源和环境问题的最有潜力的方法之一,因此制备安全高效的光催化剂已成为目前的研究热点.在目前研究的各种光催化剂中,CdS光催化剂因为具有较窄的带隙(2.4 eV)和合适的导带位置,所以在可见光催化产氢领域受到广泛关注.然而,光生电子/空穴对易复合和光腐蚀作用极大地限制了CdS光催化剂的放大应用.因此,人们采用众多改性策略以提高CdS光催化剂的可见光产氢活性,其中构建CdS纳米结构和负载助催化剂被认为是最有效的方式.构建CdS纳米结构既可以缩短载流子的迁移路径,也可以减少CdS晶体中的缺陷.很多不同纳米结构的CdS光催化剂已经被开发,例如纳米线、纳米颗粒和纳米棒等.因为制备过程极为复杂繁琐,所以CdS纳米片的研究鲜见报道.本文采用乙酸鎘和硫脲为原材料,通过简单的溶剂热法合成了CdS纳米片.在CdS的各类助催化剂中,由于常用的Pt,Ag和Au等贵金属的高成本和低储量等问题严重限制了它们的实际应用,所以近年来众多非贵金属助催化剂(例如MoS_2,WS2,NiS,NiO和WC等)得到了广泛关注.由于非贵金属助催化剂存在弱电导率和低功函数等问题,影响了对光生电子的收集和利用.纳米碳材料具有极高的电导率、强可见光吸收、有效的载流子分离和较多的反应位点等优点,因此组合纳米碳材料和非贵金属助催化剂被认为是一种有效的解决方案.本文首次采用炭黑和NiS_2作为双助催化剂改性CdS纳米片,通过简单的溶剂热/沉淀两步法成功合成了廉价高效的CdS/CB/NiS_2三元光催化体系.光催化产氢性能测试表明,CdS-0.5%CB-1%NiS_2展现出最高的光催化效率(166.7μmol h~(-1)),分别是CdS NSs和CdS-1.0%NiS_2的5.16和1.87倍.X射线衍射、高分辨电子显微镜和X射线光电子能谱结果证实了CdS催化剂的片状结构,且炭黑和NiS_2成功负载在CdS纳米片表面.紫外-可见漫反射结果表明,随着炭黑和NiS_2的负载,复合催化剂的吸收边缘产生明显的红移,且对可见光的吸收增强.荧光光谱、阻抗和瞬态光电流曲线测试结果证明,炭黑和NiS_2的加入可以有效地促进光生电子/空穴对分离.极化曲线结果表明,加入炭黑和NiS_2可以降低CdS的产氢过电势,因此加速表面产氢动力学.总之,炭黑和NiS_2之间显著的协同效应极大地提高了可见光吸收,促进光生电子/空穴对分离,加速表面产氢动力学,最终得到了三元光催化体系极高的光催化产氢活性.     

一种新型毛细管电泳双圆盘电极电导检测池

研制厂一种新型双凼盘电极毛细管电泳电导检测池。从理论和实验上分析验证了电导池参数、电导池位置对检测器性能的影响该检测池不仅结构简单、易于制作，而且极大地减小了柱外区带展宽，消除了高电压对检测器的影响，获得了较高的分离效率和信噪比采用Li^＋、Na^＋、K^＋的混合物对该检测池的性能进行了测试在最佳实验条件下，其俭出限分别为5．1、3．9和2．3μmol／L。     

桥联茂金属催化剂用于双功能催化体系制备LLDPE的研究

以四种取代基不同的桥联茂金属作为乙烯共聚催化剂 ,以Ti(OR) 4为二聚催化剂组成双功能原位聚合催化剂体系 ,在同一反应釜中 ,乙烯为唯一聚合单体 ,以阳离子助剂B(C6 F5 ) 3为唯一助催化剂 ,原位制备LLDPE .该聚合体系催化剂活性高、单体插入率高、得到的聚合物为熔点低、分子量可调的超低密聚乙烯