厌氧膜生物反应器在废水处理中的研究进展

厌氧膜生物反应器是一种处理高浓度有机废水的有效工艺.综述了厌氧膜生物反应器的特征,在工业废水处理中的应用以及低温下厌氧处理低浓度废水的效果,并展望了厌氧膜生物反应器的应用前景.     

活性炭对含铬废水的吸附处理研究

研究了活性炭吸附处理实验室模拟含铬废水。实验结果表明,当活性炭用量为1 g,废水pH=4~5,吸附接触时间大于1 h,活性炭与铬含量比值大于5 mg Cr6+/g活性炭时,处理效果可达99%以上;吸附符合Freund lich等温模式,吸附等温方程式为logq=0.7780+0.9716 logc,以物理吸附为主;吸附穿透体积为25 mL,活性炭吸附Cr6+的工作吸附容量为14.59 mg Cr6+/g活性炭,工作饱和吸附容量为803.41 mg Cr6+/g活性炭。     

Global software testing under deadline pressure: Vendor-side experiences

ContextIn the era of globally-distributed software engineering, the practice of global software testing (GST) has witnessed increasing adoption. Although there have been ethnographic studies of the development aspects of global software engineering, there have been fewer studies of GST, which, to succeed, can require dealing with unique challenges.ObjectiveTo address this limitation of existing studies, we conducted, and in this paper, report the findings of, a study of a vendor organization involved in one kind of GST practice: outsourced, offshored software testing.MethodWe conducted an ethnographically-informed study of three vendor-side testing teams over a period of 2 months. We used methods, such as interviews and participant observations, to collect the data and the thematic-analysis approach to analyze the data.FindingsOur findings describe how the participant test engineers perceive software testing and deadline pressures, the challenges that they encounter, and the strategies that they use for coping with the challenges. The findings reveal several interesting insights. First, motivation and appreciation play an important role for our participants in ensuring that high-quality testing is performed. Second, intermediate onshore teams increase the degree of pressure experienced by the participant test engineers. Third, vendor team participants perceive productivity differently from their client teams, which results in unproductive-productivity experiences. Lastly, participants encounter quality-dilemma situations for various reasons.ConclusionThe study findings suggest the need for (1) appreciating test engineers’ efforts, (2) investigating the team structure’s influence on pressure and the GST practice, (3) understanding culture’s influence on other aspects of GST, and (4) identifying and addressing quality-dilemma situations.     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.     

Aligning principal and agent’s incentives: A principal–agent perspective of social networking sites

The viability of networked communities depends on the creation and disclosure of user-generated content and the frequency of user visitation (Facebook 10-K Annual Report, 2012). However, little is known about how to align the interests of user and social networking sites. In this study, we draw upon the principal-agent perspective to extend Pavlou et al.’s uncertainty mitigation model of online exchange relationships (2007) and propose an empirically tested model for aligning the incentives of the principal (user) and the agent (service provider). As suggested by Pavlou et al., we incorporated a multi-dimensional measure of trust: trust of provider and trust of members. The proposed model is empirically tested with survey data from 305 adults aged 20-55. The results support our model, delineating how real individuals with bounded rationality actually make decision about information disclosure under uncertainty in the social networking site context. There is show little to no relationship between online privacy concerns and information disclosure on online social network sites. Perceived benefits provide the linkage between the incentives of principal (user) and agent (provider) while usage intensity demonstrated the most significant impact on information disclosure. We argue that the phenomenon may be explained through Communication Privacy Management Theory. The present study enhances our understanding of agency theory and human judgment theory in the context of social media. Practical implications for understanding and facilitating online social exchange relationships are also discussed.     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

Incrementally building frequent closed itemset lattice

A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm.     

Safety-based availability assessment at design stage

The availability of a system or equipment is one of the crucial characteristics that measures the customer satisfaction and strongly influences his final choice decision between concurrent products. The aim of this work is to provide an approach to improve the products availability assessment by taking into account the safety criteria by considering the use situations at design stage. Our work focuses on the routine design of complex products. The availability is often simply estimated considering reliability and maintainability. Basically, the intrinsic availability is the probability that it is operating satisfactorily at any point in time when used under conditions stated by design specifications. The time considered includes operating time and active repair time. Thus, intrinsic availability excludes from consideration all other times in the product lifecycle such as: accident management time, storage time, administrative time or logistic time. But many studies show that the loss of availability performance is also due to accidents that occur in different unforeseeable utilization situations. This engenders stops of the system to ensure the users safety according to standards recommendations. In this purpose, we consider the structural product architecture and the different use cases that correspond to the operational states and downtimes due to stop events that may happen during the utilization like failures, maintenance tasks and accidents. Then, we propose a product behavioral analysis including the use cases to describe interactions between the product and users or maintenance operators. We use Markov chains to model the use cases corresponding to operating time (OT), maintenance time (MT) and preparing time after accidents (RT). Then these three parameters are considered to specify a generic approach to improve the availability assessment. Such an approach provides the traceability of the product behavior along its lifecycle. In this way, the main causes of stop can be identified and this may guide the designer for improving the availability of the product future versions. To validate our approach, an application is presented considering a printing line. The comparison of our simulation considering an industrial case study shows a good agreement about the influence of safety on the availability.     

超声辅助提取-固相萃取净化-高效液相色谱法测定沉积物中的2甲4氯残留

[目的]2甲4氯(MCPA)是农业生产和市政管理中一种广泛使用的除草剂,具有使用量大、生物毒性强、易迁移累积于水体沉积物等特点。针对其在沉积物中潜在环境风险大的现状,一种简单、有效的残留测定方法迫切需要建立。[方法]建立了一种C18为分离色谱柱,甲醇-水(体积比80:20)为流动相,PDA为检测器,检测波长为280 nm的MCPA高效液相检测法,并比较了不同提取方法和净化方法对检测的影响。[结果]对于提取方法,回收率:超声萃取2次>超声萃取1次>涡旋振荡萃取;对于净化方法,回收率:固相萃取净化法>QuEChERS法>未净化法。因此,应采用2次超声辅助提取-固相萃取净化作为前处理方法。所建立的方法在0.5~40 mg/L范围内线性良好,检出限为0.02 mg/kg,定量限为0.06 mg/kg;在0.08~0.8 mg/kg的添加水平范围内,回收率为85.12%~98.66%,相对标准偏差为2.86%~4.24%。[结论]该方法准确性高、稳定性好,适用于沉积物中MCPA的测定。     

An improved ν-twin support vector machine

Twin support vector machine (TSVM) is regarded as a milestone in the development of the powerful SVM. It finds two nonparallel planes by resolving a pair of smaller-sized quadratic programming problems rather than a single large one, which makes the learning speed of TSVM approximately four times faster than that of the standard SVM. However, the empirical risk minimization principle is implemented in the TSVM, so it easily leads to the over-fitting problem and reduces the prediction accuracy of the classifier. ν-TSVM, as a variant of TSVM, also implements the empirical risk minimization principle. To enhance the generalization ability of the classifier, we propose an improved ν-TSVM by introducing a regularization term into the objective function, so there are two parts in the objective function, one of which is to maximize the margin between the two parallel hyper-planes, and the other one is to minimize the training errors of two classes of samples. Therefore the structural risk minimization principle is implemented in our improved ν-TSVM. Numerical experiments on one artificial dataset and nine benchmark datasets show that our improved ν-TSVM yields better generalization performance than SVM, ν-SVM, and ν-TSVM. Moreover, numerical experiments with different proportions of outliers demonstrate that our improved ν-TSVM is robust and stable. Finally, we apply our improved ν-TSVM to two BCI competition datasets, and also obtain better prediction accuracy.