Sonderlösungen bei der brandschutztechnischen Bewertung von Konstruktionsdetails

Special‐purpose solutions in the fire‐protective evaluation of construction details Very often components or construction details in existing buildings but also in new buildings cannot be evaluated with regard to its fire resistance ability considering established technical building regulations. There are no regulations for the evaluation of such components or construction details as these specific construction details have not been evaluated by a certified test authority. However, these components and construction details very often can be classified in fire resistance classes e. g. by evaluating fire tests, with regard to DIN 4102‐4, on the basis of similar proof of usability or only by pragmatic considerations. Since introducing the technical approval of the fire protection Eurocodes more often engineering methods of fire protection are used based on these Eurocodes by using temperature assessment in order to secure the evaluation results. Within this essay we show methods and ways of fire‐protective evaluation for components and construction details. The approach of proof including dimensions of possible upgrade measurements if necessary are being illustrated by examples of use out of practical experience. At the same time different materials such as steel, concrete and wood will be treated with different fire‐proof products. The essay is to explain ways of evaluation of components and construction details involving fire‐proof requirements including upgrade measurements also especially for existing buildings.     

本科生国际化培养模式探索——以北京航空航天大学土木与机场工程专业为例

北京航空航天大学土木与机场工程专业自2012年开设本科生国际化实验班以来,突出学校航空航天基础设施建设特色,综合中英文课程体系、国际暑期学校和国际交换学习完成授课内容。在人才培养上,注重参加科技竞赛,依托科研项目,辅以名师学术报告、学术会议等全方位提高学生的国际视野,激发创新思维,培养综合型高端人才。     

氯沙坦对老年慢性充血性心力衰竭患者血浆 NT-proBNP 水平的影响简

目的：探讨氯沙坦对老年慢性充血性心力衰竭（CHF）患者血浆 N 端脑钠肽前体（NT-proBNP）水平的影响。方法将60例慢性CHF患者按入院的先后顺序分为2组：观察组和对照组,每组30例。2组均采用常规治疗,包括抗心力衰竭药物、强心药和利尿剂等治疗。在此基础上,观察组研究前1周停止使用血管扩张药,采用氯沙坦治疗。使用免疫定量分析仪、采用胶体金法测定2组血浆 NT-proBNP水平。观察2组治疗前、治疗8周后的血压（SBP、DBP）、心率（HR）、心胸比、NYHA分级（NYHAⅡ级、Ⅲ级和Ⅳ级）和血浆 NT-proBNP 水平的变化情况。结果观察组治疗8周后SBP、DBP、HR、心胸比和血浆NT-proBNP水平均较对照组有所降低（均P＜0．05）, NYHAⅡ级、Ⅲ级和Ⅳ级均较治疗前、对照组明显改善（P＜0．05或P＜0．01）;对照组治疗8周后 NYHAⅡ级、Ⅲ级和Ⅳ级均较治疗前无明显改善（均P＞0．05）。结论 NT-proBNP水平可指导慢性 CHF患者药物治疗,且氯沙坦能有效地改善老年慢性CHF患者的心功能,值得临床应用。     

Molecular Dynamics Simulations of Bromodomains Reveal Binding-Site Flexibility and Multiple Binding Modes of the Natural Ligand Acetyl-Lysine

Experimental protein structures provide spatial information at the atomic level. A further dimension, time, is supplemented by molecular dynamics. Since the pioneering work on the 58-residue inhibitor of bovine pancreatic trypsin in the group of Martin Karplus in the seventies, molecular dynamics simulations have shown that the intrinsic flexibility of proteins is essential for their function. Here, we review simulation studies of bromodomains. These protein modules are involved in the recognition of acetylated lysine side chains, a post-translational modification frequently observed in histone tails. The molecular dynamics simulations have unmasked: (i) the large plasticity of the loops lining the acetyl-lysine binding site (coupled to its self-occlusion), and (ii) multiple binding modes of acetyl-lysine. These simulation results suggest that recognition of histone tails by bromodomains is modulated by their intrinsic flexibility, and further corroborate the utility of molecular dynamics in understanding (macro)molecular recognition.     

A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—over-sampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates.     

An integrated approach for aircraft turbofan engine fault detection based on data mining techniques

The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine.