Bioaugmentation of floating treatment wetlands for the remediation of textile effluent

This pilot study evaluated the effects of bacterial augmentation on the efficiency of floating treatment wetlands (FTWs) to remediate textile wastewater. Two wetland plants, Phragmites australis and Typha domingensis, were used to develop FTWs, which were then augmented with a bacterial consortium of three strains (Acinetobacter junii, Pseudomonas indoloxydans, and Rhodococcus sp.). Results showed that both plant species removed colour, organic matter, toxicity, and heavy metals from textile wastewater and their removal efficiency was further enhanced by augmentation with bacteria. The maximum removal efficiencies of colour, COD and BOD after an 8‐day period were 97, 87 and 92%, respectively, by FTWs carrying P. australis inoculated with the bacterial consortium. Furthermore, the same combination showed 87–99% reduction of heavy metals in the textile wastewater as well. These results indicate that FTWs can be used for the treatment of textile effluent and their working efficiency can be improved by plant‐bacterial synergism.     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

A music symbols recognition method using pattern matching along with integrated projection and morphological operation techniques

Multimedia Tools and Applications - Optical Music Recognition (OMR) can be divided into three main phases: (i) staff line detection and removal. The goal of this phase is to detect and to remove...     

High-speed GPU implementation of a secret sharing scheme based on cellular automata

The Journal of Supercomputing - Parallel implementation provides a solution for the problem of accelerating cellular automata (CA)-based secret sharing schemes and make them appropriate for bulk...     

A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG

Frontiers of Information Technology & Electronic Engineering - Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need...     

An efficient and scalable plagiarism checking system using Bloom filters

With the easy access to the huge volume of articles available on the Internet, plagiarism is getting worse and worse. Most recent approaches proposed to address this problem usually focus on achieving better accuracy of similarity detection process. However, there are some real applications where plagiarized contents should be detected without revealing any information. Moreover, in such web-based applications, running time, memory consumption, communication and computational complexity should be also taken into account. In this paper, we propose a similar document detection system based on matrix Bloom filter, a new extension of standard Bloom filter. The experimental results on a real dataset show that the system can achieve 98% of accuracy. We also compare our approach with a method recently proposed for the same purpose. The results of the comparison show that the Bloom filter-based approach achieves much better performance than other in terms of the aforementioned factors.     

Vibrations of functionally graded cylindrical shells based on elastic foundations

In this paper, a study on the vibrations of functionally graded cylindrical shells based on the Winkler and Pasternak foundations is presented. The shell equations are amended by inducting the moduli of the Winkler and Pasternak foundations. The wave propagation method is employed to solve the shell dynamical equations. The method is based on the approximate eigenvalues of characteristic beam functions. The validity and accuracy of the present approach are verified by a number of comparisons.     

A new compression based approach for reconfiguration overhead reduction in virtex based RTR systems

The advantage of RTR systems usually comes with some costs. The required time to map some areas of a program to an FPGA is considerable and affects the performance of RTR systems. Several methods have been developed to speed up the configuration process in these systems. Configuration compression can reduce the total number of write operations to load a configuration and it has been proven to be an efficient technique for dealing with the configuration overhead. In this paper, we have developed a new approach for reconfiguration overhead reduction in Virtex Based RTR Systems by using a compression technique based on Lempel-Ziv (LZ) algorithm. Since the order of the sequence of configuration frames affects the compression rate, we have proposed an algorithm based on Genetic Algorithm for finding the optimal configuration sequence of frames. The proposed algorithm will be applied to the input configuration file in a batch (offline) manner, and its time complexity is tolerable considering the overhead reduction obtained by having the optimal sequence of frames in run-time configuration decompression. Also, corresponding to our approach, a hardware model has been designed for configuration decompression.     

An epidemic of scurvy in Afghanistan: assessment and response

In March 2002, there were reports of a hemorrhagic fever outbreak in western Afghanistan. It was later confirmed that the hemorrhagic symptoms and increased mortality were actually due to scurvy. Most aid workers did not include scurvy in the initial differential diagnosis because it is uncommon throughout the world and has mainly been reported in refugee populations in recent times. A rapid assessment confirmed the cases clinically, estimated a prevalence rate of 6.3% (a severe public health problem), and determined that the attack rates peaked each year in January and February (the end of the winter). Many Afghans have limited dietary diversity due to isolated locations, lengthy winters, the continuing drought of the last four years, asset depletion, and loss of livelihood. After numerous food and fortification options to prevent future outbreaks had been considered, vitamin C tablet supplementation was selected because of the relatively rapid response time as compared with other prevention methods. A three-month course of vitamin C tablets was distributed to 827 villages in at-risk areas. The tablets were acceptable and compliance was good. No cases of scurvy were reported for the winter of 2002-03. The case study from Afghanistan demonstrates that scurvy can occur in nonrefugee or nondisplaced populations; vitamin C supplementation can be an effective prevention strategy; there is an urgent need to develop field-friendly techniques to diagnose micronutrient-deficiency diseases; food-security tools should be used to assess and predict risks of nutritional deficiencies; and the humanitarian community should address prevention of scurvy in outbreak-prone areas.     

CE/electrospray ionization-MS analysis of underivatized d/l-amino acids and several small neurotransmitters at attomole levels through the use of 18-crown-6-tetracarboxylic acid as a complexation reagent/background electrolyte

A new capillary electrophoresis/mass spectrometry technique is introduced for attomole detection of primary amines (including several neurotransmitters), amino acids, and their d/l enantiomers in one run through the use of a complexation reagent while using only approximately 1 nL of sample. The technique uses underivatized amino acids in conjunction with an underivatized capillary, which significantly reduces cost and analysis time. It was found that when (+)-(18-crown-6)-2,3,11,12-tetracarboxylic acid (18-C-6-TCA, MW 440) was used as the background electrolyte/complexation reagent during the capillary electrophoresis/electrospray ionization-mass spectrometry (CE/ESI-MS) analysis of underivatized amino acids, stable complexes were formed between the amino acids and the 18-C-6-TCA molecules. These complexes, which exhibited high ionization efficiencies, were detectable at attomole levels for most amino acids. The detection limits of the AA/18-C-6-TCA complexes were on the average more than 2 orders of magnitude lower than that of the free amino acids in solution. In addition to lower detection limits under CE/ESI-MS, a solution of 18-C-6-TCA in the concentration range of 5-30 mM provided high separation efficiency for mixtures of l-amino acids as well as mixtures of d/l-amino acids. By using a solution of 18-C-6-TCA as the background electrolyte in conjunction with an underivatized, 130-cm-long, 20-microm-i.d., 150-microm-o.d. fused-silica capillary and by monitoring the m/z range of the amino acid/18-C-6-TCA complexes (m/z 515-700), most of the standard amino acids and many of their enantiomers were separated and detected with high separation efficiency and high sensitivity (nanomolar concentration detection limits) in one run. The solutions of 18-C-6-TCA also worked well as the CE/ESI-MS BGE for low-level detection of several neurotransmitters and some of their d/l enantiomers as well as for the analysis of amino acids at endogenous levels in lysed red blood cells.