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.

[Field validation of a real-time PCR test for the detection of Mycoplasma hyopneumoniae in nasal swabs of live pigs]

Enzootic pneumonia (EP) of pigs, caused by Mycoplasma hyopneumoniae has been a notifiable disease in Switzerland since May 2003. The diagnosis of EP has been based on multiple methods, including clinical, bacteriological and epidemiological findings as well as pathological examination of lungs (mosaic diagnosis). With the recent development of a real-time PCR (rtPCR) assay with 2 target sequences a new detection method for M. hyopneumoniae became available. This assay was tested for its applicability to nasal swab material from live animals. Pigs from 74 herds (average 10 pigs per herd) were tested. Using the mosaic diagnosis, 22 herds were classified as EP positive and 52 as EP negative. From the 730 collected swab samples we were able to demonstrate that the rtPCR test was 100% specific. In cases of cough the sensitivity on herd level of the rtPCR is 100%. On single animal level and in herds without cough the sensitivity was lower. In such cases, only a positive result would be proof for an infection with M. hyopneumoniae. Our study shows that the rtPCR on nasal swabs from live pigs allows a fast and accurate diagnosis in cases of suspected EP.

Comparison of Various Methods of Estrus Detection in Synchronized Virgin Beef Heifers

Methods of heat detection were compared in the Mid- Crest Area Cattle Evaluation Program (MACEP) heifer development program in the 1998-breeding season. A total of 189 heifers from thirteen consignors entered the program on November 10, 1997. These heifers were condition scored, hip height measured, weighed, disposition scored, booster vaccinated, and treated for parasites at the time of arrival. Determination of the heifer’s mature weight was made and a target of 65% of their mature weight at breeding was established. The ration was designed to meet this goal. The heifers were kept in a dry lot until all heifers were AI bred once. The heifers were periodically weighed and condition scored to monitor their gains and the ration was adjusted as needed. The estrus synchronization program consisted of an oral progesterone analog for 14 days; 17 days after completion of the progesterone analog treatment a single injection of prostaglandin was given and the heifers were then estrus detected. Two concurrent methods of estrus detection were utilized: 1) Ovatec â electronic breeding probe (probe), 2) HeatWatchâ estrus detection system (HW), and 3) a combination of probe and HW. Probe readings were obtained each 12 hours and the heifers were continuously monitored for estrus activity using the HW system. The probe was used as the primary estrus detection method and the HW system was used as a back-up system. Those heifers that did not demonstrate any estrus signs prior to 96 hours post prostaglandin treatment were mass inseminated at 96 hours. Post AI breeding, 151 of the heifers were placed on pasture and ran with clean-up bulls for 60 days. The remaining heifers left the program after the AI breeding was completed. Pregnancy to the AI breeding was determined by ultrasound on June 29, 1998. Results from using both probe and HW were 60% pregnant by AI, probe alone was 32% pregnant by AI, and HW alone was 27% pregnant by AI. The result of mass insemination was 20% pregnant by AI.

Streak detection algorithm for space debris detection on optical images.

Any image processing object detection algorithm somehow tries to integrate the object light (Recognition Step) and applies statistical criteria to distinguish objects of interest from other objects or from pure background (Decision Step). There are various possibilities how these two basic steps can be realized, as can be seen in the different proposed detection methods in the literature. An ideal detection algorithm should provide high recognition sensitiv ity with high decision accuracy and require a reasonable computation effort . In reality, a gain in sensitivity is usually only possible with a loss in decision accuracy and with a higher computational effort. So, automatic detection of faint streaks is still a challenge. This paper presents a detection algorithm using spatial filters simulating the geometrical form of possible streaks on a CCD image. This is realized by image convolution. The goal of this method is to generate a more or less perfect match between a streak and a filter by varying the length and orientation of the filters. The convolution answers are accepted or rejected according to an overall threshold given by the ackground statistics. This approach yields as a first result a huge amount of accepted answers due to filters partially covering streaks or remaining stars. To avoid this, a set of additional acceptance criteria has been included in the detection method. All criteria parameters are justified by background and streak statistics and they affect the detection sensitivity only marginally. Tests on images containing simulated streaks and on real images containing satellite streaks show a very promising sensitivity, reliability and running speed for this detection method. Since all method parameters are based on statistics, the true alarm, as well as the false alarm probability, are well controllable. Moreover, the proposed method does not pose any extraordinary demands on the computer hardware and on the image acquisition process.

SPARQL-DL queries for antipattern detection

Ontology antipatterns are structures that reflect ontology modelling problems, they lead to inconsistencies, bad reasoning performance or bad formalisation of domain knowledge. Antipatterns normally appear in ontologies developed by those who are not experts in ontology engineering. Based on our experience in ontology design, we have created a catalogue of such antipatterns in the past, and in this paper we describe how we can use SPARQL-DL to detect them. We conduct some experiments to detect them in a large OWL ontology corpus obtained from the Watson ontology search portal. Our results show that each antipattern needs a specialised detection method.

Design, implementation and evaluation of an unconstrained and contactless biometric system based on hand geometry and stress detection

Esta tesis propone un sistema biométrico de geometría de mano orientado a entornos sin contacto junto con un sistema de detección de estrés capaz de decir qué grado de estrés tiene una determinada persona en base a señales fisiológicas Con respecto al sistema biométrico, esta tesis contribuye con el diseño y la implementación de un sistema biométrico de geometría de mano, donde la adquisición se realiza sin ningún tipo de contacto, y el patrón del usuario se crea considerando únicamente datos del propio individuo. Además, esta tesis propone un algoritmo de segmentación multiescala para solucionar los problemas que conlleva la adquisición de manos en entornos reales. Por otro lado, respecto a la extracción de características y su posterior comparación esta tesis tiene una contribución específica, proponiendo esquemas adecuados para llevar a cabo tales tareas con un coste computacional bajo pero con una alta precisión en el reconocimiento de personas. Por último, este sistema es evaluado acorde a la norma estándar ISO/IEC 19795 considerando seis bases de datos públicas. En relación al método de detección de estrés, esta tesis propone un sistema basado en dos señales fisiológicas, concretamente la tasa cardiaca y la conductancia de la piel, así como la creación de un innovador patrón de estrés que recoge el comportamiento de ambas señales bajo las situaciones de estrés y no-estrés. Además, este sistema está basado en lógica difusa para decidir el grado de estrés de un individuo. En general, este sistema es capaz de detectar estrés de forma precisa y en tiempo real, proporcionando una solución adecuada para sistemas biométricos actuales, donde la aplicación del sistema de detección de estrés es directa para evitar situaciónes donde los individuos sean forzados a proporcionar sus datos biométricos. Finalmente, esta tesis incluye un estudio de aceptabilidad del usuario, donde se evalúa cuál es la aceptación del usuario con respecto a la técnica biométrica propuesta por un total de 250 usuarios. Además se incluye un prototipo implementado en un dispositivo móvil y su evaluación. ABSTRACT: This thesis proposes a hand biometric system oriented to unconstrained and contactless scenarios together with a stress detection method able to elucidate to what extent an individual is under stress based on physiological signals. Concerning the biometric system, this thesis contributes with the design and implementation of a hand-based biometric system, where the acquisition is carried out without contact and the template is created only requiring information from a single individual. In addition, this thesis proposes an algorithm based on multiscale aggregation in order to tackle with the problem of segmentation in real unconstrained environments. Furthermore, feature extraction and matching are also a specific contributions of this thesis, providing adequate schemes to carry out both actions with low computational cost but with certain recognition accuracy. Finally, this system is evaluated according to international standard ISO/IEC 19795 considering six public databases. In relation to the stress detection method, this thesis proposes a system based on two physiological signals, namely heart rate and galvanic skin response, with the creation of an innovative stress detection template which gathers the behaviour of both physiological signals under both stressing and non-stressing situations. Besides, this system is based on fuzzy logic to elucidate the level of stress of an individual. As an overview, this system is able to detect stress accurately and in real-time, providing an adequate solution for current biometric systems, where the application of a stress detection system is direct to avoid situations where individuals are forced to provide the biometric data. Finally, this thesis includes a user acceptability evaluation, where the acceptance of the proposed biometric technique is assessed by a total of 250 individuals. In addition, this thesis includes a mobile implementation prototype and its evaluation.

Islanding detection in Microgrids using Harmonic Signature

In recent years, there has been a growing interest in incorporating microgrids in electrical power networks. This is due to various advantages they present, particularly the possibility of working in either autonomous mode or grid connected, which makes them highly versatile structures for incorporating intermittent generation and energy storage. However, they pose safety issues in being able to support a local island in case of utility disconnection. Thus, in the event of an unintentional island situation, they should be able to detect the loss of mains and disconnect for self-protection and safety reasons. Most of the anti-islanding schemes are implemented within control of single generation devices, such as dc-ac inverters used with solar electric systems being incompatible with the concept of microgrids due to the variety and multiplicity of sources within the microgrid. In this paper, a passive islanding detection method based on the change of the 5th harmonic voltage magnitude at the point of common coupling between grid-connected and islanded modes of operation is presented. Hardware test results from the application of this approach to a laboratory scale microgrid are shown. The experimental results demonstrate the validity of the proposed method, in meeting the requirements of IEEE 1547 standards.