Application of microcrystalline cellulose as an efficient and cheap sorbent for the extraction of metoprolol from plasma and wastewater before HPLC–MS/MS determination

A dispersive solid-phase extraction method based on a new sorbent has been performed on plasma and wastewater samples to determine metoprolol by high-performance liquid chromatography–tandem mass spectrometry. In this study, the analyte was adsorbed from the samples onto microcrystalline cellulose as a green and efficient sorbent and then eluted for use in the determination step. In the mass spectrometer, the analyte was detected in the positive mode and selectivity of the analysis was increased by sequential mass analysis through multiple reaction monitoring. All of the effective parameters in the extraction of metoprolol from plasma and wastewater were optimized. Under optimal conditions the method was linear in the ranges of 1–1,000 and 0.1–1,000 ng/ml in plasma and wastewater samples, respectively. The detection limits of the method were 0.30 and 0.03 ng/ml in plasma and wastewater samples, respectively. The data showed that the method provides low detection limit, wide linear range, good precision and high extraction recovery. Finally several plasma and wastewater samples were successfully analyzed using the method. The use of a small amount of a green and inexpensive sorbent and a low volume of plasma without the need for further pretreatment steps are the main advantages of the method.     

Trends in the detection of pharmaceuticals and endocrine-disrupting compounds by Field-Effect Transistors (FETs)

Field-effect transistors (FETs) are one of the most widely-used electronic sensors for continuous monitoring and detection of contaminants such as pharmaceuticals and endocrine-disrupting compounds at low concentrations. FETs have been successfully utilized for the rapid analysis of these environmental pollutants due to their advantageous material properties like the disposability, rapid responses and simplicity. This paper presented an up-to-date overview of applied strategies with different bio-based materials in order to enhance the analytical performances of the designed sensors. Comparison and discussion were made between characteristics of recently engineered FET bio-sensors used for the detection of famous and selected pharmaceutical compounds in the literature. The recent progress in environmental research applications, comments on interesting trends, current challenge for future research in endocrine-disrupting chemicals’ (EDCs) detection using FETs biosensors were highlighted.     

从废水到新能源:光催化燃料电池的优化与应用

随着经济的飞速发展,社会对能源的需求日益扩大,对工业废水的无害化处理也提出了更高的要求。光催化燃料电池 (photocatalytic fuel cell, PFC) 在燃料电池中引入半导体光催化材料作为电极,实现了有机污染物高效降解和同步对外产电的双重功能,在废水无害化与资源化利用方面具有潜在的应用价值。半导体光催化电极是PFC系统高效运行的核心组件,增强其可见光响应和光生载流子分离是提高PFC性能的关键策略。反应器结构设计和运行参数优化也有利于改善PFC性能。本文从PFC基本原理和应用入手,综述了PFC在环境污染物资源化处理中的研究进展,并详细阐述了提高PFC的污染控制性能和产电效率的优化手段,为进一步设计高效稳定的PFC系统并实现其在水污染控制和清洁能源生产中的应用提供理论指导。     

Simulated annealing with noisy or imprecise energy measurements

The annealing algorithm (Ref. 1) is modified to allow for noisy or imprecise measurements of the energy cost function. This is important when the energy cannot be measured exactly or when it is computationally expensive to do so. Under suitable conditions on the noise/imprecision, it is shown that the modified algorithm exhibits the same convergence in probability to the globally minimum energy states as the annealing algorithm (Ref. 2). Since the annealing algorithm will typically enter and exit the minimum energy states infinitely often with probability one, the minimum energy state visited by the annealing algorithm is usually tracked. The effect of using noisy or imprecise energy measurements on tracking the minimum energy state visited by the modified algorithms is examined.The research reported here has been supported under Contracts AFOSR-85-0227, DAAG-29-84-K-0005, and DAAL-03-86-K-0171 and a Purdue Research Initiation Grant.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

环面上具有间断梯度的势函数的模拟退火

本文证明了环面上具有间断梯度的势函数的模拟退火过程：dXt=-VU(Xt)dt √2dWt概率收敛到势函数的全局极小集附近。     

着生刚毛藻处理富营养化湖泊水

分别研究了室内和露天条件下在鹅卵石上着生的刚毛藻Cladophora oligoclona对富营养化湖水中氮(N)和磷(P)的净化效果及其对水华藻类生长的抑制能力,同时对处理后湖水的藻类生长潜力进行了测试.结果显示,刚毛藻在原始浓度总氮(TN)10.512 mg/L和总磷(TP)0.856 mg/L的富营养化湖水中能维持正常的生长代谢,并能有效去除水体中的N、P养分.在室内12 d培养期间,刚毛藻对TN、氨氮(NH4-N)、TP和无机磷(PO4-P)的平均去除率分别达53.13%,44.40%,35.71%和30.53%.在室外6 d培养期间,刚毛藻对TN、NH4-N、TP和PO4-P的日均减少量分别为1.643 5±0.413 9,1.350 3±0.352 4,0.113 7±0.041 1,0.074 2±0.033 0 mg/L,总去除率分别高达93.81%,94.62%,79.67%和77.66%.刚毛藻对水华微囊藻生长的抑制率达99.63%,处理后湖水的藻类生长潜力较原湖水下降了40.17%.据此认为,刚毛藻在净化污染水体、修复受损湖泊及防治水体富营养化等方面具有潜在的应用前景.     

甜菜糖蜜酒精废液生产菌体蛋白饲料初探

甜菜糖蜜酒精废液含有大量的有机物，pH值低，BOD、CDO值高，直接排和会造成环境的严重污染，又损失了一些有机物，在此废液中可直接接入酵母菌，使其大量繁殖，生产出的菌体粗蛋白含量达41．7％，CDO值降低了20％以上，减轻了环境污染。     

活性污泥法处理城市污水中鼓风和纯氧两种曝气方法的比较

对活性污泥法处理城市污水中鼓风曝气和纯氧（或富氧）曝气这两种方法，从曝气设备结构、特点、工艺技术指标、运行管理及经济性等方面进行了分析和比较。     

两性田菁胶在处理生活污水中的助凝作用

研究了以聚合硫酸铁为主凝剂、两性田菁胶(ASG)为助凝剂处理城市生活废水的各种影响因素.实验结果表明:聚合硫酸铁和两性田菁胶分别以200mg/L和10mg/L的复配浓度、pH值接近7时混凝效果最佳,CODCr去除率达到近80%,处理后废水CODCr达到国家排放标准.