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Disinfection of simulated and real winery wastewater using sulphate radicals: Peroxymonosulphate/transition metal/UV-A LED oxidation
Get PDFThe inactivation of four different microorganisms, Escherichia coli, Bacillus mycoides, Staphylococcus aureus and Candida albicans, inoculated in simulated (SWW) and real winery wastewaters (RWW), was assessed by the first time using free sulphate and hydroxyl radicals from photolytic (UV-A LED radiation; 370 nm) and metal [Fe(II) or Co(II)] activation of peroxymonosulphate (PMS). The experimental conditions tested were [PMS] = 0.1 mM and [Fe(II) or Co(II)] = 0.1 mM and pH 5.0 for the inactivation of microorganisms in SWW. However, due to the complexity of the water matrix, not unexpectedly, a fivefold concentration of reagents was required to inactivate the same organisms in RWW. In addition, compared to the bacteria, the fungus C. albicans presented a higher oxidative stress resistance to the treatments, and different experimental conditions were necessary to inactivate these cells. After 90 min, the photolytic activation of PMS through UV-A LED radiation achieved complete inactivation of E. coli, followed by S. aureus (approximate to 4 log) and B. mycoides (approximate to 3 log). Total inactivation of C. albicans was also achieved, but with higher dosages of PMS (10 mM). The metal activation of PMS through the use of a transition metal [Fe(II) or Co(II)] accelerated the inactivation rate, particularly in the first minutes of exposure time. These treatments reached between 1 and 3 log inactivation of microorganisms in the first minute of the experiment. In addition, the use of Co(II) as promoter in the activation of PMS, was more effective in the inactivation of S. aureus and C. albicans than activation with Fe(II). Since linear mathematical models do not adjust satisfactorily to inactivation results in all cases, different mathematical models were tested to fit the experimental inactivation data. In general, the Hom model correctly fits the inactivation results of the four microorganisms in all applied treatments. However, other models such as Biphasic and Double Weibull fit acceptably as well
A data mining based system for credit-card fraud detection in e-tail
No full textCredit-card fraud leads to billions of dollars in losses for online merchants. With the development of machine learning algorithms, researchers have been finding increasingly sophisticated ways to detect fraud, but practical implementations are rarely reported. We describe the development and deployment of a fraud detection system in a large e-tail merchant. The paper explores the combination of manual and automatic classification, gives insights into the complete development process and compares different machine learning methods. The paper can thus help researchers and practitioners to design and implement data mining based systems for fraud detection or similar problems. This project has contributed not only with an automatic system, but also with insights to the fraud analysts for improving their manual revision process, which resulted in an overall superior performance
DESIGN AND CONTROL OF PARALLEL THREE PHASE VOLTAGE SOURCE INVERTERS IN LOW VOLTAGE AC MICROGRID
Get PDFDesign and hierarchical control of three phase parallel Voltage Source Inverters are developed in this paper. The control scheme is based on synchronous reference frame and consists of primary and secondary control levels. The primary control consists of the droop control and the virtual output impedance loops. This control level is designed to share the active and reactive power correctly between the connected VSIs in order to avoid the undesired circulating current and overload of the connected VSIs. The secondary control is designed to clear the magnitude and the frequency deviations caused by the primary control. The control structure is validated through dynamics simulations. The obtained results demonstrate the effectiveness of the control structure
Dynamic Model, Control and Stability Analysis of MMC in HVDC Transmission Systems
No full textA control technique is proposed in this paper for control of modular multilevel converters (MMC) in high-voltage direct current (HVDC) transmission systems. Six independent dynamical state variables are considered in the proposed control technique, including two ac currents, three circulating currents, and the dc-link voltage, for effectively attaining the switching state functions of MMCs, as well as for an accurate control of the circulating currents. Several analytical expressions are derived based on the reference values of the state variables for obtaining the MMC switching functions under steady state operating conditions. In addition, dynamic parts of the switching functions are accomplished by the direct Lyapunov method to guarantee stable operation of the proposed technique for control of MMCs in HVDC systems. Moreover, the capability curve of MMC is developed to validate maximum power injection from MMCs into the power grid and/or loads. The impacts of the variations of MMC output and dc-link currents on the stability of dc-link voltage are also evaluated in detail by small-signal analysis
Raman imaging studies on the adsorption of methylene blue species onto silver modified linen fibers
Get PDFWe demonstrate in this research that surface-enhanced resonance Raman scattering combined with Raman imaging can be effectively used for analysis of distinct forms of organic dyes in antimicrobial Ag-loaded textile fibers. The potential of this approach, as a non-destructive characterization method of fabrics, was evaluated with Raman studies performed on the molecular forms of methylene blue (MB), used here as the organic dye model. On the basis of the surface-enhanced Raman scattering spectra of MB monomers and dimers, the Raman imaging of Ag-loaded linen fibers previously treated with MB solution was performed and then used for identification of the adsorbate species in distinct regions of the substrates. A semi-quantitative analysis is then performed by considering the area of the Raman bands ascribed to the MB molecular forms and image analysis applied to Raman images. Copyright (c) 2017 John Wiley & Sons, Ltd
Evaluation of tissue displacement and regional strain in the Achilles tendon using quantitative high-frequency ultrasound
Get PDFThe Achilles tendon has a unique structure-function relationship thanks to its innate hierarchical architecture in combination with the rotational anatomy of the sub-tendons from the triceps surae muscles. Previous research has provided valuable insight in global Achilles tendon mechanics, but limitations with the technique used remain. Furthermore, given the global approach evaluating muscle-tendon junction to insertion, regional differences in tendon mechanical properties might be overlooked. However, recent advancements in the field of ultrasound imaging in combination with speckle tracking have made an intratendinous evaluation possible. This study uses high-frequency ultrasound to allow for quantification of regional tendon deformation. Also, an interactive application was developed to improve clinical applicability. A dynamic ultrasound of both Achilles tendons of ten asymptomatic subjects was taken. The displacement and regional strain in the superficial, middle and deep layer were evaluated during passive elongation and isometric contraction. Building on previous research, results showed that the Achilles tendon displaces non-uniformly with a higher displacement found in the deep layer of the tendon. Adding to this, a non-uniform regional strain behavior was found in the Achilles tendon during passive elongation, with the highest strain in the superficial layer. Further exploration of tendon mechanics will improve the knowledge on etiology of tendinopathy and provide options to optimize existing therapeutic loading programs
A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level
No full textForest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.</jats:p
Classification of breast cancer histology images using Convolutional Neural Networks
Get PDFBreast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%
Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models
No full textTo address the static voltage stability issue and suppress the voltage fluctuation caused by the increasing integration of wind farms and solar photovoltaic (PV) power plants, a two-tier reactive power and voltage control strategy based on ARMA power forecasting models for wind and solar plants is proposed in this paper. Firstly, ARMA models are established to forecast the output of wind farms and solar PV plants. Secondly, the discrete equipment is pre-regulated based on the single-step prediction information from ARMA forecasting models according to the optimization result. Thirdly, a multi-objective optimization model is presented and solved by particle swarm optimization (PSO) according to the measured data and the proposed static voltage stability index. Finally, the IEEE14 bus system including a wind farm and solar PV plant is utilized to test the effectiveness of the proposed strategy. The results show that the proposed strategy can suppress voltage fluctuation and improve the static voltage stability under the condition of high penetration of renewables including wind and solar power