Validation of a method for the analysis of volatile organic compounds in water

Dissertação de mestrado, Qualidade em Análises, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Detection of MegaTAL-induced HIV pol Mutation By Droplet Digital PCR

Thesis (Master's)--University of Washington, 2015

Field experiments for marine casualty detection with autonomous surface vehicles

In this paper we present a set of field tests for detection of human in the water with an unmanned surface vehicle using infrared and color cameras. These experiments aimed to contribute in the development of victim target tracking and obstacle avoidance for unmanned surface vehicles operating in marine search and rescue missions. This research is integrated in the work conducted in the European FP7 research project Icarus aiming to develop robotic tools for large scale rescue operations. The tests consisted in the use of the ROAZ unmanned surface vehicle equipped with a precision GPS system for localization and both visible spectrum and IR cameras to detect the target. In the experimental setup, the test human target was deployed in the water wearing a life vest and a diver suit (thus having lower temperature signature in the body except hands and head) and was equipped with a GPS logger. Multiple target approaches were performed in order to test the system with different sun incidence relative angles. The experimental setup, detection method and preliminary results from the field trials performed in the summer of 2013 in Sesimbra, Portugal and in La Spezia, Italy are also presented in this work.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

Evaluation of probabilistic occupancy map people detection for surveillance systems

In this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

Two-colour photocurrent detection technique for coherent control of a single InGaAs/GaAs quantum dot

We present a two-colour photocurrent detection method for coherent control of a single InGaAs/GaAs self-assembled quantum dot. A pulse shaping technique provides a high degree of control over picosecond optical pulses. Rabi rotations on the exciton to biexciton transition are presented, and fine structure beating is detected via time-resolved measurements. (c) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Label-free DNA detection based on modified conducting polypyrrole films at microelectrodes

A label-free electrochemical detection method for DNA hybridization based on electrostatic modulation of the ion-exchange kinetics of a polypyrrole film deposited at microelectrodes is reported. Synthetic single-stranded 27-mer oligonucleotides (probe) have been immobilized at 2,5-bis(2-thienyl)-N-(3-phosphorylpropyl)pyrrole film formed by electropolymerization on the previously formed polypyrrole layer. The 27- or 18-mer target oligonucleotides were monitored via the electrochemically driven anion exchange of the inner polypyrrole film. The performance of the miniaturized DNA biosensor system was studied in respect to selectivity, sensitivity, reproducibility, and regeneration of the sensor. Control experiments were performed with a noncomplementary target of 27-mer DNA and 12 base-pair mismatched 18-mer sequences, respectively, and did not show any unspecific binding. Under optimized experimental conditions, the label-free electrochemical biosensor enabled the detection limits of 0.16 and 3.5 fmol for the 18- and 2 7-mer DNA strand, respectively. Furthermore, we demonstrate reusability of the electrochemical DNA biosensor after successful recovery of up to 100% of the original signal by regenerating the DNA label-free electrode with 50 mM HCl at room temperature.

Determination of buprofezin, pyridaben, and tebufenpyrad residues by gas chromatography mass-selective detection in clementine citrus

A gas chromatography-mass-selective (GC-MS) detection method to determine buprofezin, pyridaben, and tebufenpyrad on the pulp, peel, and whole fruit of clementines is described. The extraction/partition procedure was performed in one step and no cleanup was necessary with the GC-MS in the SIM-mode pesticide determination. Recovery ranged from 75 to 124% with coefficients of variance ranging between 1 and 13%. The limit of determination was 0.01 mg/kg for all pesticides. The field trials showed a similar degradative behavior for all active ingredients (AI), with a great residue decrease during the first week and stability in the second. Just after treatment buprofezin and tebufenpyrad showed lower residues than the maximum residue limit (MRL) fixed in Italy, while pyridaben was below the MRL after a week.

PCR-based detection of Babesia bovis and Babesia bigemina in their natural host Boophilus microplus and cattle

PCR and nested-PCR methods were used to assess the frequency of Babesia bovis and Babesia bigemina infection in Boophilus microplus engorged females and eggs and in cattle reared in an area with endemic babesiosis. Blood and the engorged female ticks were from 27 naturally infested calves and 25 crossbred cows. The frequency of both Babesia species was similar in calves and cows (P > 0.05). Babesia bovis was detected in 23 (85.2%) calves and in 25 (100%) cows and B. bigemina was detected in 25 (92.6%) calves and in 21 (84%) cows. Mixed infections with the both Babesia species were identified in 42 animals, 21 in each age category. Of female ticks engorged on calves, 34.9% were negative and single species infection with B. bigemina (56.2%) was significantly more frequent (P < 0.01) than with B. bovis (4.7%). Most of the females (60.8%) engorged on cows did not show Babesia spp. infection and the frequency of single B. bovis infection (17.6%) was similar (P > 0.05) to the frequency of single B. bigemina infection (15.9%). Mixed Babesia infection was lower (P < 0.01) than single species infection in female ticks engorged either in cows (5.7%) or in calves (4.3%). An egg sample from each female was analysed for the presence of Babesia species. Of the egg samples from female ticks infected with B. bovis, 26 (47.3%) were infected while from those from female ticks infected with B. bigemina 141 (76.6%) were infected (P < 0.01). The results showed that although the frequency of both species of Babesia was similar in calves and cows, the infectivity of B. bigemina was higher to ticks fed on calves while to those ticks fed on cows the infectivity of both Babesia species was similar. © 2004 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Faecal contamination (viral and bacteria) detection in groundwater used for drinking purposes in São Paulo, Brazil

This project has been developed to evaluate the possible relationship between the cesspit (pit latrine) in as far as it degrades the quality of underground water. Its importance is due to the fact that in the rural communities in the State of São Paulo (Brazil) this type of cesspit is very common as a means of sewage disposal and these communities use underground water for their supply of drinking water. Rural properties distributed over the rural area in the municipality of São José do Rio Preto were selected. A preliminary study was then set up to determine the social situation and health of the households as well as qualitative evaluations on the type of water supply and sewage disposal of these communities. Campaigns of water sampling then followed and laboratory analyses of water taken from wells were carried out. Parameters were set up to evaluate the potability according to Brazilian legislation (2004) paying attention to microbiologic (coliforms, Crytosporidium sp., and adenovirus). The analyses showed evidence of possible interaction between the wells and the sewage effluents and drainage in these communities. A PCR reaction to detect adenovirus showed a presence in 53.3% of the samples. The tests for the detection of Cryotosporidium sp all showed a negative result.

Laboratorial training of examiners for using a visual caries detection system in epidemiological surveys

Background: In epidemiological surveys, a good reliability among the examiners regarding the caries detection method is essential. However, training and calibrating those examiners is an arduous task because it involves several patients who are examined many times. To facilitate this step, we aimed to propose a laboratory methodology to simulate the examinations performed to detect caries lesions using the International Caries Detection and Assessment System (ICDAS) in epidemiological surveys. Methods: A benchmark examiner conducted all training sessions. A total of 67 exfoliated primary teeth, varying from sound to extensive cavitated, were set in seven arch models to simulate complete mouths in primary dentition. Sixteen examiners (graduate students) evaluated all surfaces of the teeth under illumination using buccal mirrors and ball-ended probe in two occasions, using only coronal primary caries scores of the ICDAS. As reference standard, two different examiners assessed the proximal surfaces by direct visual inspection, classifying them in sound, with non-cavitated or with cavitated lesions. After, teeth were sectioned in the bucco-lingual direction, and the examiners assessed the sections in stereomicroscope, classifying the occlusal and smooth surfaces according to lesion depth. Inter-examiner reproducibility was evaluated using weighted kappa. Sensitivities and specificities were calculated at two thresholds: all lesions and advanced lesions (cavitated lesions in proximal surfaces and lesions reaching the dentine in occlusal and smooth surfaces). Conclusion: The methodology purposed for training and calibration of several examiners designated for epidemiological surveys of dental caries in preschool children using the ICDAS is feasible, permitting the assessment of reliability and accuracy of the examiners previously to the survey´s development.

Immunofluorescence versus xTAG multiplex PCR for the detection of respiratory picornavirus infections in children

BACKGROUND: Polymerase chain reaction (PCR) is a sensitive tool for detection of respiratory picornaviruses. However, the clinical relevance of picornavirus detection by PCR is unclear. Immunofluorescence (IF), widely used to detect other respiratory viruses, has recently been introduced as a promising detection method for respiratory picornaviruses. OBJECTIVES: To compare the clinical manifestations of respiratory picornavirus infections detected by IF with those of respiratory picornavirus infections detected by xTAG multiplex PCR in hospitalized children. STUDY DESIGN: During a 1-year period, nasopharyngeal aspirates (NPA) from all children hospitalized due to an acute respiratory infection were prospectively analyzed by IF. All respiratory picornavirus positive IF samples and 100 IF negative samples were further tested with xTAG multiplex PCR. After exclusion of children with co-morbidities and viral co-infections, monoinfections with respiratory picornaviruses were detected in 108 NPA of 108 otherwise healthy children by IF and/or PCR. We compared group 1 children (IF and PCR positive, n=84) with group 2 children (IF negative and PCR positive, n=24) with regard to clinical manifestations of the infection. RESULTS: Wheezy bronchitis was diagnosed more often in group 1 than in group 2 (71% vs. 46%, p=0.028). In contrast, group 2 patients were diagnosed more frequently with pneumonia (17% vs. 6%, p=0.014) accompanied by higher levels of C-reactive protein (46mg/l vs. 11mg/l, p=0.009). CONCLUSIONS: Picornavirus detection by IF in children with acute respiratory infection is associated with the clinical presentation of wheezy bronchitis. The finding of a more frequent diagnosis of pneumonia in picornavirus PCR positive but IF negative children warrants further investigation.

Stochastic knock detection model for spark ignited engines

This report presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. Knock Signal Simulator (KSS) was developed as the plant model for the engine. The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies. The SKD method is implemented in Knock Detection Module (KDM) which processes the knock intensities generated by KSS with a stochastic distribution estimation algorithm and outputs estimates of high and low knock intensity levels which characterize knock and reference level respectively. These estimates are then used to determine a knock factor which provides quantitative measure of knock level and can be used as a feedback signal to control engine knock. The knock factor is analyzed and compared with a traditional knock detection method to detect engine knock under various engine operating conditions. To verify the effectiveness of the SKD method, a knock controller was also developed and tested in a model-in-loop (MIL) system. The objective of the knock controller is to allow the engine to operate as close as possible to its border-line spark-timing without significant engine knock. The controller parameters were tuned to minimize the cycle-to-cycle variation in spark timing and the settling time of the controller in responding to step increase in spark advance resulting in the onset of engine knock. The simulation results showed that the combined system can be used adequately to model engine knock and evaluated knock control strategies for a wide range of engine operating conditions.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.