Готові списки джерел за темами / CO₂ detection

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Добірка наукової літератури з теми "CO₂ detection"

Автор: Grafiati
Опубліковано: 21 грудня 2024

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Статті в журналах з теми "CO₂ detection"

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Langenfeld-Heyser, R., Bruno Schella, Kirsten Buschmann, and Frieder Speck. "Microautoradiographic detection of CO." Trees 10, no. 4 (1996): 255. http://dx.doi.org/10.1007/s004680050031.

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Fu, Huazhu, Xiaochun Cao, and Zhuowen Tu. "Cluster-Based Co-Saliency Detection." IEEE Transactions on Image Processing 22, no. 10 (October 2013): 3766–78. http://dx.doi.org/10.1109/tip.2013.2260166.

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Pardo Pedraza, Diana. "Sensory Co-laboring." Environmental Humanities 15, no. 3 (November 1, 2023): 30–51. http://dx.doi.org/10.1215/22011919-10745968.

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Abstract Demining has not been an exclusively human affair. Mine detection dogs have been indispensable in the work of detection and in the slow but essential effort to regain trust in mine-suspected landscapes. Famously renowned for their extraordinary sensory perception, physical strength, and mental traits, they are part of human-nonhuman units training and working together to perceive explosives’ odors. This article considers the role of these units, known in Colombia as binomios caninos, in the strenuous task of mine clearance. Drawing on eighteen months of ethnographic engagement with global and local humanitarian demining efforts in Colombia, it examines detection choreographies and daily interactions, proposing to think of their joint work in terms of sensory co-laboring. Bringing anthropological work on collaboration between worlds, sensory labor, and animal work into dialogue, this composite term foregrounds detection as labor and as a result of human-nonhuman cooperation. It also highlights the asymmetrical field in which these collaborators converge and the divergent desires, affects, and attachments that mobilize their participation in demining. Mine detection is conceptualized as a sensory task through which dogs and humans intra-act, both together and apart. Recognizing this partial connection allows us to rethink how humans and other creatures are ontologically reconstituted and how overlapping histories of warfare and humanitarianism, legacies of animal behavioral practice, and instrumental-affective interactions shape these reconstitutions.
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Lu, Xiaofei, Jingjing Jia, Zonghua Wang, and Wenjing Wang. "MXene/Carbon Dots Nanozyme Composites for Glutathione Detection and Tumor Therapy." Nanomaterials 14, no. 13 (June 25, 2024): 1090. http://dx.doi.org/10.3390/nano14131090.

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Co-N-CDs-based MXene nanocomposites (MXene@PDA/Co-N-CDs) were constructed by decorating Co-N-CDs on polydopamine-functionalized MXene nanosheets. Both Co-N-CDs and MXene nanosheets have peroxidase-like activity; when the two materials are combined to form MXene@PDA/Co-N-CDs nanocomposites, the peroxide-like activity can be further enhanced. MXene@PDA/Co-N-CDs could oxidize the substrate 3,3′5,5′-tetramethylbenziline (TMB) to form ox-TMB, as confirmed by detecting the absorption of the blue products. A highly selective colorimetric biosensor was developed for the determination of glutathione (GSH) in the concentration range of 0.3 to 20 µM with a lower detection limit (LOD) of 0.12 µM, which realized the accurate detection of GSH in human serum and urine samples. Moreover, in the tumor microenvironment, MXene@PDA/Co-N-CDs could catalyze hydrogen peroxide to produce hydroxyl free radicals and produce a photothermal effect under the exposure of NIR-I irradiation. The catalytic activity of MXene@PDA/Co-N-CD nanocomposites was fully achieved for the death of cancer cells through photothermal/photodynamic synergistic therapy. The MXene@PDA/Co-N-CDs nanozyme offers multiple applications in GSH detection and tumor therapy.
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YANG Ming-yu, 杨名宇. "Detecting of photoelectric peeping devices based on active laser detection." Chinese Optics 8, no. 2 (2015): 255–62. http://dx.doi.org/10.3788/co.20150802.0255.

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Dananché, Cédric, Gláucia Paranhos-Baccalà, Mélina Messaoudi, Mariam Sylla, Shally Awasthi, Ashish Bavdekar, Jean-William Pape, et al. "Nasopharyngeal Viral and Bacterial Co-Detection among Children from Low- and Middle-Income Countries with and without Pneumonia." American Journal of Tropical Medicine and Hygiene 106, no. 4 (April 6, 2022): 1086–93. http://dx.doi.org/10.4269/ajtmh.21-0980.

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ABSTRACT. The role of microbial coinfection in the pathogenesis of pneumonia in children is not well known. The aim of this work was to describe the prevalence of microorganism co-detection in nasopharyngeal samples (NPS) of pneumonia cases and control subjects and to study the potential association between nasopharyngeal microorganism co-detection and pneumonia. A case-control study was carried out from 2010 to 2014 in nine study sites located in low- or middle-income countries. The data from 888 children under 5 years of age with pneumonia (cases) and 870 children under 5 without pneumonia (controls) were analyzed. Nasopharyngeal samples were collected; reverse transcription polymerase chain reaction (RT-PCR) enabled the detection of five bacteria and 19 viruses. Multiple, mixed-effects logistic regression modeling was undertaken to evaluate the association between microorganism co-detection and pneumonia. A single Streptococcus pneumoniae colonization was observed in 15.2% of the controls and 10.1% of the cases (P = 0.001), whereas S. pneumoniae and a single virus co-detection was observed in 33.3% of the cases and in 14.6% of the controls (P < 0.001). Co-detections with rhinovirus, respiratory syncytial virus, parainfluenza virus, human metapneumovirus, and influenza virus were more frequent in the cases compared with the controls (P < 0.001) and were significantly associated with pneumonia in multiple regression analysis. The proportion of single virus detection without bacterial co-detection was not different between cases and controls (13.6% versus 11.3%, P = 0.13). This study suggests that coinfection of S. pneumoniae and certain viruses may play a role in the pathophysiology of pneumonia in children.
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Ye, Linwei, Zhi Liu, Junhao Li, Wan-Lei Zhao, and Liquan Shen. "Co-Saliency Detection via Co-Salient Object Discovery and Recovery." IEEE Signal Processing Letters 22, no. 11 (November 2015): 2073–77. http://dx.doi.org/10.1109/lsp.2015.2458434.

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Probst, Varvara, Bhinnata Piya, Laura Stewart, Susan Gerber, Brian Rha, Joana Yu, Suman Das, Angela P. Campbell, John V. Williams, and Natasha B. Halasa. "741. Impact of Adenovirus Co-detections on Illness Severity." Open Forum Infectious Diseases 5, suppl_1 (November 2018): S266. http://dx.doi.org/10.1093/ofid/ofy210.748.

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Abstract Background Human adenovirus (AdV) is a common pathogen among children with acute respiratory illnesses (ARI) and is often associated with co-detection with other respiratory viral pathogens. We sought to compare demographic and clinical characteristics in children with ARI who had single-AdV vs. AdV-co-detection with other viruses. Methods Children &lt;18 years with fever and/or ARI were enrolled in Vanderbilt Children’s Hospital inpatient setting from 2015 to 2018 and emergency department from 2016 to 2018. Interviews were conducted using standardized case report forms. Nose and throat swab specimens were collected and tested by RT-qPCR for common respiratory pathogens (AdV, RSV, HRV, hMPV, PIV1–4 and Influenza). Results Of 2,740 ARI cases, 174 were positive for AdV [88 (51%) single detection], with 53% male, 47% White, 36% Black, 30% Hispanic and median age of 17.2 months. Co-detected pathogens in AdV-positive specimens were RSV(15%), HRV(14%), influenza(5%), PIV1(1%), PIV2(0.6%), PIV3(1.7%), and PIV4(0.6%), hMPV(3%), &gt;1 co-pathogens(9%). Subjects with single-AdV detection were more likely to have an underlying medical condition (42% vs. 24%, P &lt; 0.05). Table 1 compares clinical presentation and severity of single-AdV and AdV-co-detection cases. Conclusion Patients with single-AdV detection were less likely to present with ARI symptoms and require oxygen, but were more likely to have underlying medical conditions compared with AdV-co-detection. Further studies to type AdV isolates will help elucidate the role of specific adenovirus types associated with co-detections and illness severity and inform epidemiological information for future vaccine initiatives. Disclosures J. V. Williams, Quidel: Board Member, Consulting fee. GlaxoSmithKline: Consultant, Consulting fee. N. B. Halasa, sanofi pasteur: Investigator, Research support. GSK: Consultant, Consulting fee. Moderna: Consultant, Consulting fee.
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Encrenaz, Th, E. Lellouch, P. Drossart, H. Feuchtgruber, G. S. Orton, and S. K. Atreya. "First detection of CO in Uranus." Astronomy & Astrophysics 413, no. 2 (December 18, 2003): L5—L9. http://dx.doi.org/10.1051/0004-6361:20034637.

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Beheshtian, Javad, Zargham Bagheri, Mohammad Kamfiroozi, and Ali Ahmadi. "Toxic CO detection by B12N12 nanocluster." Microelectronics Journal 42, no. 12 (December 2011): 1400–1403. http://dx.doi.org/10.1016/j.mejo.2011.10.010.

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Дисертації з теми "CO₂ detection"

1

Li, Yuan. "Multiuser Detection for Co-channel Interference Cancellation." 京都大学 (Kyoto University), 2000. http://hdl.handle.net/2433/180902.

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Hogan, Justin Allan. "Multi-spectral imaging of vegetation for COâ‚‚ leak detection." Thesis, Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/hogan/HoganJ0511.pdf.

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Though its status as a crisis situation remains the subject of much debate [1,2] there does exist evidence that global warming is a real phenomenon [3] and that its processes are to some degree enhanced by anthropogenically introduced greenhouse gases, perhaps most notably carbon dioxide (COâ‚‚) [3]. This claim is backed by observations of increasing atmospheric COâ‚‚ concentrations from nearly 280-ppm around 1750 to 360 ppm in 1995 [4]. By the end of 2010, this number was up to approximately 390 ppm [5]. To reduce human influence on the global environment, carbon capture and sequestration (CCS) is proposed as a means of collecting COâ‚‚ generated through industrial and consumer processes and sequestering it so as not to release it into the atmosphere, thereby reducing atmospheric concentrations of the gas. Suggested methods of sequestration include direct deep-sea injection [6], soil sequestration through improved land use and management practices [7], and geological carbon sequestration in which captured carbon is injected into underground geological features. This research focuses primarily on development and testing of a leak detection technology for deployment to geological sequestration sites. A diverse technology portfolio will be required to implement safe and efficient sequestration solutions [8]. Included in this portfolio is technology capable of monitoring sequestration site integrity; detecting and signaling leakage, should it occur. Early leak detection is paramount to ensuring on-site safety and to minimize, or at least understand, potentially harmful environmental leak effects.
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Zavala, Martin. "Autonomous detection and characterization of nuclear materials using co-robots." Thesis, Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55052.

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Radiation safety is the biggest concern of the nuclear industry, and co-robots are a crucial component to insuring that safety. Currently, radiation mapping data is typically gathered using hand held detectors or other detection systems requiring constant human interaction. This results in direct exposure to radiation of the individual performing the survey. Co-robots can coordinate computer algorithms and human input to determine the most efficient and accurate methods of surveying these same regions while eliminating health hazards. These surveying methods can then be adapted for multiple uses in the industry including nonproliferation, maintenance, and accident response scenarios. This work describes the process by which two vehicles were modified to detect radiation with minimal human interaction. An algorithm was developed to control the robot and to navigate the area of interest while ensuring that all sources are found. A compact detector system was used to keep the vehicles as small and light as possible. The vehicles were constructed to satisfy the requirements of the detector system and relay the necessary information back to the control station. The process, which is nearly fully autonomous, can map an area of interest and proceed to characterize the radiation materials that are found using neutron and gamma spectroscopy. The vehicles were tested in several scenarios which included obstacles, multiple sources, and shielding of the sources to determine the practicality of these co-robots. The evaluation of these co-robots was critical, as the future of radiation safety lies in the research and construction of small autonomous radiation detection systems to minimize the risk that radiation exposure poses to humans.
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Chen, Yi-Ching. "Co-design of Fault-Tolerant Systems with Imperfect Fault Detection." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104942.

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In recent decades, transient faults have become a critical issue in modernelectronic devices. Therefore, many fault-tolerant techniques have been proposedto increase system reliability, such as active redundancy, which can beimplemented in both space and time dimensions. The main challenge of activeredundancy is to introduce the minimal overhead of redundancy and to schedulethe tasks. In many pervious works, perfect fault detectors are assumed to simplifythe problem. However, the induced resource and time overheads of suchfault detectors make them impractical to be implemented. In order to tacklethe problem, an alternative approach was proposed based on imperfect faultdetectors. So far, only software implementation is studied for the proposed imperfectfault detection approach. In this thesis, we take hardware-acceleration intoconsideration. Field-programmable gate array (FPGA) is used to accommodatetasks in hardware. In order to utilize the FPGA resources efficiently, themapping and the selection of fault detectors for each task replica have to be carefullydecided. In this work, we present two optimization approaches consideringtwo FPGA technologies, namely, statically reconfigurable FPGA and dynamicallyreconfigurable FPGA respectively. Both approaches are evaluated andcompared with the proposed software-only approach by extensive experiments.
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Sahin, Mustafa. "Baseband receiver algorithms for 4G co-channel femtocells." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003283.

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Rosso, Kevin M. "Detection limits of CO₂in fluid inclusions using microthermometry and Raman spectroscopy and the spectroscopic characterization of CO₂." Thesis, Virginia Tech, 1994. http://hdl.handle.net/10919/40534.

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In many geologic environments, dominant1y aqueous solutions contain low concentrations of CO2. At ambient temperature, in fluid inclusions which trap these solutions, the typical phase assemblage consists of a CO2-rich vapor (where Pco2 = PintemaI) and an aqueous phase containing dissolved salts and CO2. In this study, the CO2 minimum detection limits (MDLs) using microthermometry and laser Raman spectroscopy are established in terms of Pco2 using synthetic H20-C02 inclusions. The purpose of the microthermometric experiments was to examine the diagnostic CO2 phase changes and determine the quantity of CO2 necessary to result in observable solid CO2 melting. The results of these experiments show that an observable solid CO2 melting event requires Pco2 ~ 45 bar at 25°C. The Raman spectroscopic detection limits were investigated using a multichannel Raman spectrometer. Because the Raman spectroscopic MDLs are a function of counts, the CO2 MDLs were determined by collecting signal-to-noise ratios for both the upper and lower V1-2V2 bands as a function of CO2 pressure (5-60 bars) and over a range of integration times and incident laser power to predict the optimal instrument settings. The resulting CO2 MDLs are on the 'order of 1 bar for our instrument. The band splitting of the v 1-2v2 diad as a function of CO2 pressure was measured up to 500 bar at ambient temperature. The CO2 pressures were converted to pc02 and the results are given in terms of the frequency separation between the upper and lower bands. These results are compared to those of previous studies. An analysis of the estimated errors indicates that the technique can be used to determine CO2 densities in fluid inclusions containing a homogenous, free CO2 phase to a precision of approximately ± 0.02 g/cm3• The temperature dependence of the intensity ratio of the hot bands to the v1-2v2 diad was measured from 270 to 315 K. The close agreement between the calculated and observed results indicate that laser induced sample heating is not significant. The intensity ratio can be used to estimate the CO2 temperature and, combined with the Raman density determination, allows calculation of the CO2 pressure.
Master of Science
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Rosso, Kevin Michael. "Detection limits of CO₂ in fluid inclusions using microthermometry and Raman spectroscopy and the spectroscopic characterization of CO₂ /." This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-01052009-091123/.

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Raisi, Elaheh. "Weakly Supervised Machine Learning for Cyberbullying Detection." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89100.

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The advent of social media has revolutionized human communication, significantly improving individuals' lives. It makes people closer to each other, provides access to enormous real-time information, and eases marketing and business. Despite its uncountable benefits, however, we must consider some of its negative implications such as online harassment and cyberbullying. Cyberbullying is becoming a serious, large-scale problem damaging people's online lives. This phenomenon is creating a need for automated, data-driven techniques for analyzing and detecting such behaviors. In this research, we aim to address the computational challenges associated with harassment-based cyberbullying detection in social media by developing machine-learning framework that only requires weak supervision. We propose a general framework that trains an ensemble of two learners in which each learner looks at the problem from a different perspective. One learner identifies bullying incidents by examining the language content in the message; another learner considers the social structure to discover bullying. Each learner is using different body of information, and the individual learner co-train one another to come to an agreement about the bullying concept. The models estimate whether each social interaction is bullying by optimizing an objective function that maximizes the consistency between these detectors. We first developed a model we referred to as participant-vocabulary consistency, which is an ensemble of two linear language-based and user-based models. The model is trained by providing a set of seed key-phrases that are indicative of bullying language. The results were promising, demonstrating its effectiveness and usefulness in recovering known bullying words, recognizing new bullying words, and discovering users involved in cyberbullying. We have extended this co-trained ensemble approach with two complementary goals: (1) using nonlinear embeddings as model families, (2) building a fair language-based detector. For the first goal, we incorporated the efficacy of distributed representations of words and nodes such as deep, nonlinear models. We represent words and users as low-dimensional vectors of real numbers as the input to language-based and user-based classifiers, respectively. The models are trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. Our experiments on Twitter, Ask.fm, and Instagram data show that deep ensembles outperform non-deep methods for weakly supervised harassment detection. For the second goal, we geared this research toward a very important topic in any online automated harassment detection: fairness against particular targeted groups including race, gender, religion, and sexual orientations. Our goal is to decrease the sensitivity of models to language describing particular social groups. We encourage the learning algorithm to avoid discrimination in the predictions by adding an unfairness penalty term to the objective function. We quantitatively and qualitatively evaluate the effectiveness of our proposed general framework on synthetic data and data from Twitter using post-hoc, crowdsourced annotation. In summary, this dissertation introduces a weakly supervised machine learning framework for harassment-based cyberbullying detection using both messages and user roles in social media.
Doctor of Philosophy
Social media has become an inevitable part of individuals social and business lives. Its benefits, however, come with various negative consequences such as online harassment, cyberbullying, hate speech, and online trolling especially among the younger population. According to the American Academy of Child and Adolescent Psychiatry,1 victims of bullying can suffer interference to social and emotional development and even be drawn to extreme behavior such as attempted suicide. Any widespread bullying enabled by technology represents a serious social health threat. In this research, we develop automated, data-driven methods for harassment-based cyberbullying detection. The availability of tools such as these can enable technologies that reduce the harm and toxicity created by these detrimental behaviors. Our general framework is based on consistency of two detectors that co-train one another. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. When designing the general framework, we address three tasks: First, we use machine learning with weak supervision, which significantly alleviates the need for human experts to perform tedious data annotation. Second, we incorporate the efficacy of distributed representations of words and nodes such as deep, nonlinear models in the framework to improve the predictive power of models. Finally, we decrease the sensitivity of the framework to language describing particular social groups including race, gender, religion, and sexual orientation. This research represents important steps toward improving technological capability for automatic cyberbullying detection.
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Haddad, Lema. "New approaches to co-segregation studies and mutation detection in familial hypercholesterolaemia." Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286402.

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Doblas, Jiménez David. "Exploration and detection of ultra-traces of explosives by chip calorimetry." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAE017/document.

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La détection de très faibles quantités de Matériaux Energétiques (ME) est un challenge important dans la lutte contre le terrorisme. En plus des méthodes de détection des ME par affinité chimique, il est aussi intéressant d'utiliser les variations enthalpiques dues à la décomposition des ME pour les détecter par analyse thermique. Cependant, la sensibilité des methodes classiques est insuffisante pour la détection des particules dont la masse se situe dans le domaine des nanogrammes. En revanche, la nanocalorimétrie est parfaitement adaptée pour la caractérisation de très faibles quantités d'échantillons et est de ce fait adaptée aux exigences de la détection. Afin d'explorer la possibilité de détecter et d'identifier des micro-particules solides de ME à l'aide de l'analyse thermique, nous avons élaboré des protocols optimisés pour la détection et l'identification de particules pures unitaires de quelques nanogrammes de ME ainsi que leurs mélanges. Les résultats montrent que la limite de détection se situe environ à quelques centaines de picrogrammes. Les expériences ont été complétées par de l'analyse structurale in-situ en utilisant sa combinaison avec de la DRX par faisceau nanofocus synchrotron
Being able to sense the minuscule amounts of energetic materials is crucial in the context of the fight against terrorism. Apart from the methods of detection of EM, which are specific to the chemical structure, one could use the enthalpy variations of the EM decomposition process for their detection by means of thermal analysis. However, the sensitivity of classical methods would be still insufficient to sense particles in the nanogram range. By contrast, the recently developed technique of chip calorimetry is perfectly suited for characterizing small amounts of samples and is therefore fully adequate for this task.In order to explore the possibilities of detection and identification of solid micro-particles of EM with thermal analysis, we discuss on the protocols optimized for the detection and identification of nanogram-size particles of EM and its mixtures with the chip calorimeter accessory. The results obtained on pure EM and its mixtures show that the detection threshold can be put at approximately several hundred picograms. The experiments were completed by the in-situ structural analysis using a combination with nanofocus synchrotron XRD
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Книги з теми "CO₂ detection"

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Mulligan, Carol Heather. The detection and toxicological evaluation of polluting inputs to the lower river Faughan Co. Londonderry. [s.l: The Author], 1993.

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2

United States. National Aeronautics and Space Administration., ed. Comparison of 2 [micron]m Ho and 10 [micron]m CO lidar for atmospheric backscatter and Doppler windshear detection: Progress report. Tampa, Fla: Dept. of Physics, University of South Florida, 1991.

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3

Octavia, Camps, and United States. National Aeronautics and Space Administration., eds. Detection of obstacles in monocular image sequences: Final technical report for NASA co-operative research agreement number NCC 2-916, "A vision-based obstacle detection system for aircraft navigation," period of grant--August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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4

Great Britain. Department of the Environment, Transport and the Regions., ed. A low-cost on-line steam leak detection sysytem: Holroyd Instruments Ltd, University of Sunderland, Allen Consultant Engineering Co Ltd, National Power plc, Cleveland Potash Ltd. Garston: BRECSU, 1999.

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Research Co-ordination Meeting on the Use of Novel DNA Fingerprinting Techniques for the Detection and Characterization of Genetic Variation in Vegetatively Propagated Crops (3rd 1997 Mumbai, India). Use of novel DNA fingerprinting techniques for the detection and characterization of genetic variation in vegetatively propagated crops: Proceedings of a final Research Co-ordination Meeting organized by the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture and held in Mumbai, India, 24-28 February 1997. Vienna, Austria: International Atomic Energy Agency, 1998.

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6

Spenneberg, Ralf. Intrusion Detection und Prevention mit Snort 2 & Co. Addison Wesley Verlag, 2004.

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7

Haroon, Muhammad. Co-morbidities. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198737582.003.0015.

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Psoriatic arthritis (PsA) is a chronic, multisystem inflammatory condition where patients are at a higher risk for a number of major systemic comorbidities including cardiovascular disease, obesity, depression, uveitis, and cancer. These comorbidities which are frequently unrecognized or undertreated contribute significantly to the morbidity and mortality associated with PsA. There is emerging data further supporting the link between inflammation and cardiovascular disease. The detection and management of PsA patients with comorbidities requires a coordinated approach which is not yet clearly defined. Meanwhile, it is important to conduct periodic comprehensive assessments in our PsA patients in order to identify and monitor comorbidities. The importance of multispecialty cooperation and multidisciplinary assessment cannot be over-stated.
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Non Co-Operative Detection of LPI/LPD Signals Via Cyclic Spectral Analysis. Storming Media, 1999.

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Detection of obstacles in monocular image sequences: Final technical report for NASA co-operative research agreement number NCC 2-916, "A vision-based obstacle detection system for aircraft navigation," period of grant--August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Probability of Symbol Error for Coherent and Non-Coherent Detection of M-ary Frequency-Shift Keyed (MFSK) Signals Affected by Co-Channel Interference and Additive White Gaussian Noise (AWGN) in a. Storming Media, 2000.

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Частини книг з теми "CO₂ detection"

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Bao, Sid Yingze, Yu Xiang, and Silvio Savarese. "Object Co-detection." In Computer Vision – ECCV 2012, 86–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33718-5_7.

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Zhang, Zhao, Wenda Jin, Jun Xu, and Ming-Ming Cheng. "Gradient-Induced Co-Saliency Detection." In Computer Vision – ECCV 2020, 455–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58610-2_27.

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İnci, Mehmet Sinan, Berk Gulmezoglu, Thomas Eisenbarth, and Berk Sunar. "Co-location Detection on the Cloud." In Constructive Side-Channel Analysis and Secure Design, 19–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43283-0_2.

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Papalexakis, Evangelos E., Alex Beutel, and Peter Steenkiste. "Network Anomaly Detection Using Co-clustering." In Encyclopedia of Social Network Analysis and Mining, 1–17. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_354-1.

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Papalexakis, Evangelos E., Alex Beutel, and Peter Steenkiste. "Network Anomaly Detection Using Co-clustering." In Encyclopedia of Social Network Analysis and Mining, 1054–68. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_354.

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Papalexakis, Evangelos E., Alex Beutel, and Peter Steenkiste. "Network Anomaly Detection Using Co-clustering." In Encyclopedia of Social Network Analysis and Mining, 1501–16. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_354.

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Li, Bo, Zhengxing Sun, Jiagao Hu, and Junfeng Xu. "Co-saliency Detection via Sparse Reconstruction and Co-salient Object Discovery." In Advances in Multimedia Information Processing – PCM 2017, 222–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_22.

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Ghidoni, Stefano, Arrigo Guizzo, and Emanuele Menegatti. "Crowd Detection Based on Co-occurrence Matrix." In Biologically Inspired Cognitive Architectures 2012, 145–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34274-5_28.

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Lei, Zhengchao, Weiyan Chai, Sanyuan Zhao, Hongmei Song, and Fengxia Li. "Co-saliency Detection Based on Siamese Network." In Communications in Computer and Information Science, 99–109. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8890-2_8.

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Metin, Senem Kumova. "Standard Co-training in Multiword Expression Detection." In Intelligent Human Computer Interaction, 178–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72038-8_14.

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Тези доповідей конференцій з теми "CO₂ detection"

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Ding, Chuang, Yang Wu, Huihui Song, Kaihua Zhang, Xu Zhang, and Zhenhua Guo. "Language-Guided Semantic Alignment for Co-saliency Detection." In 2024 IEEE International Conference on Multimedia and Expo (ICME), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687964.

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Lebedeva, E. D., A. M. Buryakov, P. Yu Avdeev, and A. V. Gorbatova. "INVESTIGATION OF THZ RADIATION PARAMETERS IN CO/WSE2 AND CO/IRMN3 STRUCTURES." In Terahertz and Microwave Radiation: Generation, Detection and Applications (ТЕRА-2023). Moscow: Our Style, 2023. http://dx.doi.org/10.59043/9785604953914_106_1.

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Guo, Xin, Dong Liu, Brendan Jou, Mojun Zhu, Anni Cai, and Shih-Fu Chang. "Robust Object Co-detection." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.412.

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Chen, Hwann-Tzong. "Preattentive co-saliency detection." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650014.

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Xie, Yufeng, Linwei Ye, Zhi Liu, and Xuemei Zou. "Video co-saliency detection." In Eighth International Conference on Digital Image Processing (ICDIP 2016), edited by Charles M. Falco and Xudong Jiang. SPIE, 2016. http://dx.doi.org/10.1117/12.2245113.

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Gai, Tianyang, Tong Qu, Xiaojing Su, Shuhan Wang, Lisong Dong, Libin Zhang, Rui Chen, Yajuan Su, Yayi Wei, and Tianchun Ye. "Multi-level layout hotspot detection based on multi-classification with deep learning." In Design-Technology Co-optimization XV, edited by Chi-Min Yuan and Ryoung-Han Kim. SPIE, 2021. http://dx.doi.org/10.1117/12.2583726.

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Yang, Haoyu, Piyush Pathak, Frank E. Gennari, Ya-Chieh Lai, and Bei Yu. "Hotspot detection using squish-net." In Design-Process-Technology Co-optimization for Manufacturability XIII, edited by Jason P. Cain and Chi-Min Yuan. SPIE, 2019. http://dx.doi.org/10.1117/12.2515172.

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Hsu, Kuang-Jui, Yen-Yu Lin, and Yung-Yu Chuang. "DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00905.

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Yu, Heng, Ying Lu, Cong Yu, Hongya Zhao, and Lei Wang. "Co-Occurrence Morphological Edge Detection." In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2019. http://dx.doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00086.

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Wei, Lina, Shanshan Zhao, Omar El Farouk Bourahla, Xi Li, and Fei Wu. "Group-wise Deep Co-saliency Detection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/424.

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In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation, and model this in a collaborative learning framework. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.
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Звіти організацій з теми "CO₂ detection"

1

Wigley, T. M. L., and P. D. Jones. Detection of CO sub 2 -Induced climatic change. Office of Scientific and Technical Information (OSTI), August 1989. http://dx.doi.org/10.2172/5721338.

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2

Wigley, T., and P. Jones. Detection of CO sub 2 -induced climatic change. Office of Scientific and Technical Information (OSTI), July 1990. http://dx.doi.org/10.2172/6371968.

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Alonso, Jesus. Intrinsic Fiber Optic Chemical Sensors for Subsurface Detection of CO2. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1245137.

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Hanson, Ronald K. Novel Extended-Wavelength Diode Lasers to Enable Sensitive Detection of CO and NOx. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada383138.

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5

Sinclair, Michael B., Jeb Hunter Flemming, Raymond Blair, and Kent Bryant Pfeifer. Detection of carbon monoxide (CO) as a furnace byproduct using a rotating mask spectrometer. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/903153.

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Sun, Alexander. Pressure-Based Inversion and Data Assimilation System (PIDAS) for CO2 Leakage Detection. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1494374.

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Yang, X., T. A. Buscheck, K. Mansoor, and S. A. Carroll. Likelihood of Brine and CO2 Leak Detection using Magnetotellurics and Electrical Resistivity Tomography Methods. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1393348.

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Haider, Huma. Malaria, HIV and TB in Nigeria: Epidemiology and Disease Control Challenges. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/k4d.2022.040.

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Анотація:
Nigeria has the world’s highest number of people affected by malaria and the world’s second largest human immunodeficiency virus (HIV)/AIDS burden. There is a high occurrence of co-infection of malaria in HIV patients (Gumel et al., 2021). Nigeria is also ranked as one of the thirty high tuberculosis (TB) and TB-HIV co-infection burden countries in the world (Odume et al., 2020, 8). Co-infection can make each disease more severe and potentially more infectious (Gumel et al., 2021; Jemikalajah et al., 2021; Chukwuocha et al., 2019). This rapid literature review highlights key aspects of the epidemiology of malaria, HIV and TB in Nigeria, in addition to challenges in controlling the three diseases, in terms of prevention, detection and treatment. This is part of a series of reports looking into Epidemiology of Malaria, human immune deficiency virus (HIV) and tuberculosis (TB) across a set of African Nations.
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Deschamps, Henschel, and Robert. PR-420-123712-R01 Lateral Ground Movement Detection Capabilities Derived from Synthetic Aperture Radar. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 2014. http://dx.doi.org/10.55274/r0010831.

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The objective of this research was to quantify long-term ground deformation at the Belridge Oil Field, in the San Joaquin Valley (SJV), California using operational Interferometric Synthetic Aperture Radar (InSAR) monitoring techniques. A high spatial and temporal resolution, millimeter-precision time-series of ground deformation measurements was produced for the entire oil field from 2000 to 2012 using imagery from multiple satellites and beam modes. Trihedral Corner Reflectors (CRs) with co-located Global Navigation Satellite System (GNSS) units were used to validate the wide-area measurements along a section of Southern California Gas Company (SoCalGas) Line 7056. The GNSS measurements were also used to validate the precision of the InSAR measurements, and to determine what component of the overall motion was lateral motion. Deformation profiles over Lines 1203 were analyzed to identify periods of rapid deformation related to known pipeline incidents. Finally, we also investigated the use Multiple Aperture Interferometry (MAI) for measuring horizontal motion in the alongtrack (north-south) direction. The result is a detailed, seamless, long-term, validated time-series of ground change observations that could prove useful for further analysis of reservoir changes. Combined with injection and production data, the results may be used to extend an understanding of the geomechanics of Enhanced Oil Recovery (EOR) fields. This work reinforces the operational capability of InSAR for monitoring both EOR reservoir dynamics and deformation over buried pipelines.
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Kamrath, Matthew, Vladimir Ostashev, D. Wilson, Michael White, Carl Hart, and Anthony Finn. Vertical and slanted sound propagation in the near-ground atmosphere : amplitude and phase fluctuations. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40680.

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Sound propagation along vertical and slanted paths through the near-ground atmosphere impacts detection and localization of low-altitude sound sources, such as small unmanned aerial vehicles, from ground-based microphone arrays. This article experimentally investigates the amplitude and phase fluctuations of acoustic signals propagating along such paths. The experiment involved nine microphones on three horizontal booms mounted at different heights to a 135-m meteorological tower at the National Wind Technology Center (Boulder, CO). A ground-based loudspeaker was placed at the base of the tower for vertical propagation or 56m from the base of the tower for slanted propagation. Phasor scatterplots qualitatively characterize the amplitude and phase fluctuations of the received signals during different meteorological regimes. The measurements are also compared to a theory describing the log-amplitude and phase variances based on the spectrum of shear and buoyancy driven turbulence near the ground. Generally, the theory correctly predicts the measured log-amplitude variances, which are affected primarily by small-scale, isotropic turbulent eddies. However, the theory overpredicts the measured phase variances, which are affected primarily by large-scale, anisotropic, buoyantly driven eddies. Ground blocking of these large eddies likely explains the overprediction.
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