The quorum-sensing effect of aerobic granules on bacterial adhesion, biofilm formation, and sludge granulation

Quorum sensing (QS) through signal chemical molecules is known to be essential to bacterial adhesion and biofilm formation. In this study, the QS ability of aerobic granules-a special form of biofilms used for biological wastewater treatment-was investigated and compared with that of conventional activated sludge flocs. A novel sectional membrane bioreactor was used together with a flow-cell to evaluate the possible influence of signal chemicals produced by the source sludge on the growth mode of bacterial cells. The results demonstrate the apparent production of QS chemicals from granules and its impact on initial cell attachment and granule formation. When granules were used as the signal-producing biomass, the attached-growth mode was dominant for the free cells, and the biofilm formation rate in the flow-cell was about ten times faster than in cases which used activated sludge as the signal source biomass. In addition, the intracellular extract from mature granules significantly accelerated the sludge granulation process. It is argued that the production and expression of QS signal chemicals from granules and granule precursors might have induced the gene expression of bacteria in suspension for attached growth rather than suspended growth, leading to granule formation and its stable structure. © Springer-Verlag 2010.postprin

Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

A circular polarization hybrid-integrated rectangular ring antenna for RFID reader

In this paper, a compact hybrid-integrated microstrip rectangular ring antenna with circular polarization is proposed for RFID reader. By integrating a cross-slotted patch hybrid with a rectangular ring antenna in the same plane, the proposed antenna has the features of circular polarization, compact size, low cost, light weight and unique flexibility for RFID applications. A prototype operating at 915 MHz is designed and fabricated. The measured results agree well with the simulated results. © 2012 IEEE.published_or_final_versio

Compact metallic RFID tag antennas with a loop-fed method

Several compact, low profile and metal-attachable RFID tag antennas with a loop-fed method are proposed for UHF RFID systems. The structure of the proposed antennas comprise of two parts: (1) The radiator part consists of two shorted patches, which can be treated as two quarter-wave patch antennas or a cavity. (2) A small loop printed on the paper serves as the feeding structure. The small loop provides the needed inductance for the tag and is connected to the RFID chip. The input impedance of the antenna can be easily adjusted by changing loop dimensions. The antenna has the compact size of 80 mm × 25 mm × 3.5 mm, and the realized gain about -3.6 dB. The measured results show that these antennas have good performance when attached onto metallic surfaces. © 2011 IEEE.published_or_final_versio

A new sponge tray bioreactor in primary treated sewage effluent treatment

The new attached growth sponge tray bioreactor (STB) was evaluated at different operating conditions for removing organics and nutrients from primary treated sewage effluent. This STB was also assessed when using as a pre-treatment prior to micro-filtration (MF) for reducing membrane fouling. At a short hydraulic retention time (HRT) of 40min, the STB could remove up to 92% of DOC and 40-56% of T-N and T-P at an organic loading rate (OLR) of 2.4kg COD/m3 spongeday. This OLR is the best for the STB as compared to the OLRs of 0.6, 1.2 and 3.6kg COD/m3 spongeday. At 28mL/min of flow velocity (FV), STB achieved the highest efficiencies with 92% of DOC, 87.4% of T-P, and 54.8% of T-N removal. Finally, at the optimal OLR and FV, the STB could remove almost 90% of organic and nutrient, significantly reduce membrane fouling with HRT of only 120min. © 2010 Elsevier Ltd

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Multi-population methods in unconstrained continuous dynamic environments: The challenges

Themulti-populationmethod has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing peaks simultaneously. However, to make this approach efficient, several crucial challenging issues need to be addressed, e.g., how to determine the moment to react to changes, how to adapt the number of populations to changing environments, and how to determine the search area of each population. In addition, several other issues, e.g., communication between populations, overlapping search, the way to create multiple populations, detection of changes, and local search operators, should be also addressed. The lack of attention on these challenging issues within multi-population methods hinders the development of multi-population based algorithms in dynamic environments. In this paper, these challenging issues are comprehensively analyzed by a set of experimental studies from the algorithm design point of view. Experimental studies based on a set of popular algorithms show that the performance of algorithms is significantly affected by these challenging issues on the moving peaks benchmark. Keywords: Multi-population methods, dynamic optimization problems, evolutionary computatio

Chaotic motions in the real fuzzy electronic circuits

Fuzzy electronic circuit (FEC) is firstly introduced, which is implementing Takagi-Sugeno (T-S) fuzzy chaotic systems on electronic circuit. In the research field of secure communications, the original source should be blended with other complex signals. Chaotic signals are one of the good sources to be applied to encrypt high confidential signals, because of its high complexity, sensitiveness of initial conditions, and unpredictability. Consequently, generating chaotic signals on electronic circuit to produce real electrical signals applied to secure communications is an exceedingly important issue. However, nonlinear systems are always composed of many complex equations and are hard to realize on electronic circuits. Takagi-Sugeno (T-S) fuzzy model is a powerful tool, which is described by fuzzy IF-THEN rules to express the local dynamics of each fuzzy rule by a linear system model. Accordingly, in this paper, we produce the chaotic signals via electronic circuits through T-S fuzzy model and the numerical simulation results provided by MATLAB are also proposed for comparison. T-S fuzzy chaotic Lorenz and Chen-Lee systems are used for examples and are given to demonstrate the effectiveness of the proposed electronic circuit. © 2013 Shih-Yu Li et al

3D embedded freeform electrical circuitry in metal componentry

A novel embedding process combining the use an ultrasonic solid-state welding technique coupled with a material direct writing technique to embed electronic devices and to print conductive features inside a three dimensional metallic housing container is proposed. This process has the advantages of (i) inexpensive fabrication, (ii) flexibility in sizes of embedded devices, and (iii) allowing for arrangement of embedded components in 3D. A fully functional demonstrator of a complete 3D LED circuitry has been successfully fabricated as the results of our fundamental researches gathered

Investigation of the shape change of bio-flocs and its influence on mass transport using particle image velocimetry

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