On the detection and attribution of gravity waves generated by the 20 March 2015 solar eclipse

Internal gravity waves are generated as adjustment radiation whenever a sudden change in forcing causes the atmosphere to depart from its large-scale balanced state. Such a forcing anomaly occurs during a solar eclipse, when the Moon’s shadow cools part of the Earth’s surface. The resulting atmospheric gravity waves are associated with pressure and temperature perturbations, which in principle are detectable both at the surface and aloft. In this study, surface pressure and temperature data from two UK sites at Reading and Lerwick are analysed for eclipse-driven gravity-wave perturbations during the 20 March 2015 solar eclipse over north-west Europe. Radiosonde wind data from the same two sites are also analysed using a moving parcel analysis method, to determine the periodicities of the waves aloft. On this occasion, the perturbations both at the surface and aloft are found not to be confidently attributable to eclipse-driven gravity waves. We conclude that the complex synoptic weather conditions over the UK at the time of this particular eclipse helped to mask any eclipse-driven gravity waves.

Ground-based detection of sprites and their parent lightning flashes over Africa during the 2006 AMMA campaign

Sprites have been detected in video camera observations from Niger over mesoscale convective systems in Nigeria during the 2006 AMMA (African Monsoon Multidisciplinary Analysis) campaign The parent lightning flashes have been detected by multiple Extremely Low Frequency (ELF) receiving stations worldwide The recorded charge moments of the patent lightning flashes are often in excellent agreement between different receiving sites, and are furthermore consistent with conventional dielectric breakdown in the mesosphere as the origin of the sprites Analysis of the polarization of the horizontal magnetic field at the distant receivers provides evidence that the departure from linear magnetic polarization at ELF is caused primarily by the clay night asymmetry of the Earth-ionosphere cavity Copyright (C) 2009 Royal Meteorological Society

Biolabeling with nanoparticles based on Y(2)O(3):Nd(3+) and luminescence detection in the near-infrared

Neodymium based fluorescence presents several advantages in comparison to conventional rare earth or enzyme-substrate based fluorescence emitting sources (e.g.Tb, HRP). Based on this fact we have herein explored a Nd-based fluoroimmunoassay. We efficiently detected the presence of an oxidized low-density lipoprotein (oxLDL) in human plasma a well-known marker for cardiovascular diseases, which causes around 30% of deaths worldwide. Conventional fluoroimmunoassay uses time-resolved luminescence techniques, with detection in the visible range, to eliminate the fluorescence background from the biological specimens. By using an immunoassay based on functionalized Y(2)O(3):Nd(3+) nanoparticles, where the excitation and emission processes in the Nd(3+) ion occur in the near-infrared (NIR) region, we have succeeded in eliminating the interferences from the biological fluorescence background, avoiding the use of time-resolved techniques. This yields higher emission intensity from the Nd(3+)-nanolabels and efficient detection of anti-oxidized low-density lipoproteins (anti-oxLDL) by Y(2)O(3):Nd(3+)-antibody-antigen conjugation, leading to a novel biolabeling method. (C) 2010 Elsevier B.V. All rights reserved.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

Leakage Detection And Localization Method Based On A Hybrid Inverse/Direct Modelling Approach Suitable For Handling Multiple-Leak Scenarios

When an accurate hydraulic network model is available, direct modeling techniques are very straightforward and reliable for on-line leakage detection and localization applied to large class of water distribution networks. In general, this type of techniques based on analytical models can be seen as an application of the well-known fault detection and isolation theory for complex industrial systems. Nonetheless, the assumption of single leak scenarios is usually made considering a certain leak size pattern which may not hold in real applications. Upgrading a leak detection and localization method based on a direct modeling approach to handle multiple-leak scenarios can be, on one hand, quite straightforward but, on the other hand, highly computational demanding for large class of water distribution networks given the huge number of potential water loss hotspots. This paper presents a leakage detection and localization method suitable for multiple-leak scenarios and large class of water distribution networks. This method can be seen as an upgrade of the above mentioned method based on a direct modeling approach in which a global search method based on genetic algorithms has been integrated in order to estimate those network water loss hotspots and the size of the leaks. This is an inverse / direct modeling method which tries to take benefit from both approaches: on one hand, the exploration capability of genetic algorithms to estimate network water loss hotspots and the size of the leaks and on the other hand, the straightforwardness and reliability offered by the availability of an accurate hydraulic model to assess those close network areas around the estimated hotspots. The application of the resulting method in a DMA of the Barcelona water distribution network is provided and discussed. The obtained results show that leakage detection and localization under multiple-leak scenarios may be performed efficiently following an easy procedure.

Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers: a powerful strategy for breast cancer diagnosis

A sensitive assay to identify volatile organic metabolites (VOMs) as biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. Therefore the aim of this study was to establish the urinary metabolomic profile of breast cancer patients and healthy individuals (control group) and to explore the VOMs as potential biomarkers in breast cancer diagnosis at early stage. Solid-phase microextraction (SPME) using CAR/PDMS sorbent combined with gas chromatography–mass spectrometry was applied to obtain metabolomic information patterns of 26 breast cancer patients and 21 healthy individuals (controls). A total of seventy-nine VOMs, belonging to distinct chemical classes, were detected and identified in control and breast cancer groups. Ketones and sulfur compounds were the chemical classes with highest contribution for both groups. Results showed that excretion values of 6 VOMs among the total of 79 detected were found to be statistically different (p < 0.05). A significant increase in the peak area of (−)-4-carene, 3-heptanone, 1,2,4-trimethylbenzene, 2-methoxythiophene and phenol, in VOMs of cancer patients relatively to controls was observed. Statiscally significant lower abundances of dimethyl disulfide were found in cancer patients. Bioanalytical data were submitted to multivariate statistics [principal component analysis (PCA)], in order to visualize clusters of cases and to detect the VOMs that are able to differentiate cancer patients from healthy individuals. Very good discrimination within breast cancer and control groups was achieved. Nevertheless, a deep study using a larger number of patients must be carried out to confirm the results.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Identification of lubricant contamination by biodiesel using vibration analysis and neural network

Purpose - The purpose of this paper is to provide information on lubricant contamination by biodiesel using vibration and neural network.Design/methodology/approach - The possible contamination of lubricants is verified by analyzing the vibration and neural network of a bench test under determinated conditions.Findings - Results have shown that classical signal analysis methods could not reveal any correlation between the signal and the presence of contamination, or contamination grade. on other hand, the use of probabilistic neural network (PNN) was very successful in the identification and classification of contamination and its grade.Research limitations/implications - This study was done for some specific kinds of biodiesel. Other types of biodiesel could be analyzed.Practical implications Contamination information is presented in the vibration signal, even if it is not evident by classical vibration analysis. In addition, the use of PNN gives a relatively simple and easy-to-use detection tool with good confidence. The training process is fast, and allows implementation of an adaptive training algorithm.Originality/value - This research could be extended to an internal combustion engine in order to verify a possible contamination by biodiesel.

First Description of a Cluster of Acute/Subacute Paracoccidioidomycosis Cases and Its Association with a Climatic Anomaly

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

N-methyl-D-aspartate channel and consciousness: From signal coincidence detection to quantum computing

Research on Blindsight, Neglect/Extinction and Phantom limb syndromes, as well as electrical measurements of mammalian brain activity, have suggested the dependence of vivid perception on both incoming sensory information at primary sensory cortex and reentrant information from associative cortex. Coherence between incoming and reentrant signals seems to be a necessary condition for (conscious) perception. General reticular activating system and local electrical synchronization are some of the tools used by the brain to establish coarse coherence at the sensory cortex, upon which biochemical processes are coordinated. Besides electrical synchrony and chemical modulation at the synapse, a central mechanism supporting such a coherence is the N-methyl-D-aspartate channel, working as a 'coincidence detector' for an incoming signal causing the depolarization necessary to remove Mg 2+, and reentrant information releasing the glutamate that finally prompts Ca 2+ entry. We propose that a signal transduction pathway activated by Ca 2+ entry into cortical neurons is in charge of triggering a quantum computational process that accelerates inter-neuronal communication, thus solving systemic conflict and supporting the unity of consciousness. © 2001 Elsevier Science Ltd.

An attack signature model to computer security intrusion detection

Internal and external computer network attacks or security threats occur according to standards and follow a set of subsequent steps, allowing to establish profiles or patterns. This well-known behavior is the basis of signature analysis intrusion detection systems. This work presents a new attack signature model to be applied on network-based intrusion detection systems engines. The AISF (ACME! Intrusion Signature Format) model is built upon XML technology and works on intrusion signatures handling and analysis, from storage to manipulation. Using this new model, the process of storing and analyzing information about intrusion signatures for further use by an IDS become a less difficult and standardized process.

HPLC-EICD: An Useful Tool for the Pursuit of Novel Analytical Strategies for the Detection of Antioxidant Secondary Metabolites

Living cells are continuously exposed to a variety of challenges that exert oxidative stress and are directly related with senescence and the onset of various pathological conditions such as coronary heart disease, rheumatoid arthritis and cancer. Nevertheless, living organisms have developed a complex antioxidant network to counteract reactive species that are detrimental to life. With the aim of bio-prospecting plant species from the Brazilian Cerrado and Atlantic Forest, we have established a methodology to detect secondary antioxidant metabolites in crude extracts and fractions obtained from plant species. Combining HPLC with an electrochemical detector allowed us to detect micromolecules that showed antioxidant activities in Chimarrhis turbinata (DC) leaf extracts. Comparison with purified flavonoid standards led us to identify the compounds in their natural matrices giving valuable information on their antioxidant capacity.

Adaptative techniques for the human faces detection

This paper presents results from an efficient approach to an automatic detection and extraction of human faces from images with any color, texture or objects in background, that consist in find isosceles triangles formed by the eyes and mouth.

Delay time detection and accommodation for a networked DC motor control

This paper presents a control method that is effective to reduce the degenerative effects of delay time caused by a treacherous network. In present application a controlled DC motor is part of an inverted pendulum and provides the equilibrium of this system. The control of DC motor is accomplished at the distance through a treacherous network, which causes delay time in the control signal. A predictive technique is used so that it turns the system free of delay. A robust digital sliding mode controller is proposed to control the free-delay system. Due to the random conditions of the network operation, a delay time detection and accommodation strategy is also proposed. A computer simulation is shown to illustrate the design procedures and the effectiveness of the proposed method. © 2011 IEEE.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.