Готові списки джерел за темами / Odor vector

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Добірка наукової літератури з теми "Odor vector"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Odor vector"

Yan, Luchun, Chuandong Wu, and Jiemin Liu. "Visual Analysis of Odor Interaction Based on Support Vector Regression Method." Sensors 20, no. 6 (March 19, 2020): 1707. http://dx.doi.org/10.3390/s20061707.

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The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components’ mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.

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Aleixandre, Manuel, Kaoru Nakazawa, and Takamichi Nakamoto. "Optimization of Modulation Methods for Solenoid Valves to Realize an Odor Generation System." Sensors 19, no. 18 (September 17, 2019): 4009. http://dx.doi.org/10.3390/s19184009.

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An artificial olfactory system coupled with an odor generation system is herein reported. The artificial olfactory system is composed of four chemical sensors consisting of quartz crystal microbalances (QCMs) coated with room temperature ionic liquids (RTILs). The sensors are interrogated by four vector network analyzers, which are used to measure the series resonant frequency and motional resistance. The odor generation system can generate eight different odors and mix them in any composition. Solenoid valves are used to switch the path and control the concentration of the different odors before blending. Two algorithms to control the solenoid valves, delta-sigma modulator, and simple pulse width modulation (PWM) are studied, optimized, and compared. Finally, the uncertainty of the odor generating system is calculated.

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Yamanaka, Takao, Nitikarn Nimsuk, and Takamichi Nakamoto. "Concurrent Recording and Regeneration of Visual and Olfactory Information Using Odor Sensor." Presence: Teleoperators and Virtual Environments 16, no. 3 (June 1, 2007): 307–17. http://dx.doi.org/10.1162/pres.16.3.307.

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In this paper, a method of concurrent recording and regeneration of visual and olfactory information is presented using electronic nose technology. To accomplish this objective, the sensor response patterns of odors in the atmosphere were measured using QCM (quartz crystal microbalance) odor sensors with partially overlapping specificities. Then the odors were identified from the response patterns using LVQ (learning vector quantization), a pattern classification algorithm of neural networks with supervised learning. Visual information, presented as a movie, was captured using a digital video camera; concurrently, odors using odor sensor responses were paired to the video. The recorded visual and olfactory information was evaluated by sensory tests to investigate the effectiveness of the proposed system. As a result, it was found that the olfactory information recorded using the proposed method was appropriate for odor regeneration associated with the movie.

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Chai, Hwa Chia, and Kek Heng Chua. "The Potential Use of Volatile Biomarkers for Malaria Diagnosis." Diagnostics 11, no. 12 (November 30, 2021): 2244. http://dx.doi.org/10.3390/diagnostics11122244.

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Pathogens may change the odor and odor-related biting behavior of the vector and host to enhance pathogen transmission. In recent years, volatile biomarker investigations have emerged to identify odors that are differentially and specifically released by pathogens and plants, or the pathogen-infected or even cancer patients. Several studies have reported odors or volatile biomarkers specifically detected from the breath and skin of malaria-infected individuals. This review will discuss the potential use of these odors or volatile biomarkers for the diagnosis of malaria. This approach not only allows for the non-invasive mean of sample collection but also opens up the opportunity to develop a biosensor for malaria diagnosis in low-resource settings.

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Robinson, Ailie, Annette O. Busula, Mirjam A. Voets, Khalid B. Beshir, John C. Caulfield, Stephen J. Powers, Niels O. Verhulst, et al. "Plasmodium-associated changes in human odor attract mosquitoes." Proceedings of the National Academy of Sciences 115, no. 18 (April 16, 2018): E4209—E4218. http://dx.doi.org/10.1073/pnas.1721610115.

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Malaria parasites (Plasmodium) can change the attractiveness of their vertebrate hosts to Anopheles vectors, leading to a greater number of vector–host contacts and increased transmission. Indeed, naturally Plasmodium-infected children have been shown to attract more mosquitoes than parasite-free children. Here, we demonstrate Plasmodium-induced increases in the attractiveness of skin odor in Kenyan children and reveal quantitative differences in the production of specific odor components in infected vs. parasite-free individuals. We found the aldehydes heptanal, octanal, and nonanal to be produced in greater amounts by infected individuals and detected by mosquito antennae. In behavioral experiments, we demonstrated that these, and other, Plasmodium-induced aldehydes enhanced the attractiveness of a synthetic odor blend mimicking “healthy” human odor. Heptanal alone increased the attractiveness of “parasite-free” natural human odor. Should the increased production of these aldehydes by Plasmodium-infected humans lead to increased mosquito biting in a natural setting, this would likely affect the transmission of malaria.

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Ogawa, Keishiro, Katsufumi Inoue, Michifumi Yoshioka, and Hidekazu Yanagimoto. "Odor Detecting Algorithm with Boundary Compensation Support Vector Machine." IEEJ Transactions on Electronics, Information and Systems 135, no. 7 (2015): 920–26. http://dx.doi.org/10.1541/ieejeiss.135.920.

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Husni, Nyayu Latifah, Siti Nurmaini, Irsyadi Yani, and Ade Silvia. "Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4133. http://dx.doi.org/10.11591/ijece.v8i6.pp4133-4147.

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Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work.

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Mweresa, Collins K., W. R. Mukabana, J. J. A. van Loon, M. Dicke, and W. Takken. "Use of semiochemicals for surveillance and control of hematophagous insects." Chemoecology 30, no. 6 (June 23, 2020): 277–86. http://dx.doi.org/10.1007/s00049-020-00317-1.

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Abstract Reliance on broad-spectrum insecticides and chemotherapeutic agents to control hematophagous insect vectors, and their related diseases is threatened by increasing insecticide and drug resistance, respectively. Thus, development of novel, alternative, complementary and effective technologies for surveillance and control of such insects is strongly encouraged. Semiochemicals are increasingly developed for monitoring and intervention of insect crop pests, but this has not been adequately addressed for hematophagous insects of medical and veterinary importance. This review provides an insight in the application of semiochemicals for control of hematophagous insects. Here, we provide specific information regarding the isolation and identification of semiochemical compounds, optimization approaches, detection, perception and discrimination by the insect olfactory system. Navigation of insects along wind-borne odor plumes is discussed and methods of odor application in field situations are reviewed. Finally, we discuss prospects and future challenges for the application of semiochemical-based tools with emphasis on mosquitoes. The acquired knowledge can guide development of more effective components of integrated vector management, safeguard against emerging resistance of insects to existing insecticides and reduce the burden of vector-borne diseases.

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Mayer, Christoph J., Andreas Vilcinskas, and Jürgen Gross. "Phytopathogen Lures Its Insect Vector by Altering Host Plant Odor." Journal of Chemical Ecology 34, no. 8 (July 4, 2008): 1045–49. http://dx.doi.org/10.1007/s10886-008-9516-1.

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Choi, Sang-Il, and Gu-Min Jeong. "A Discriminant Distance Based Composite Vector Selection Method for Odor Classification." Sensors 14, no. 4 (April 17, 2014): 6938–51. http://dx.doi.org/10.3390/s140406938.

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Більше джерел

Дисертації з теми "Odor vector"

Galan, Roberto Fernandez. "Odor coding and memory traces in the antennal lobe of honeybee." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2003. http://dx.doi.org/10.18452/14997.

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In dieser Arbeit werden zwei wesentliche neue Ergebnisse vorgestellt. Das erste bezieht sich auf die olfaktorische Kodierung und das zweite auf das sensorische Gedaechtnis. Beide Phaenomene werden am Beispiel des Gehirns der Honigbiene untersucht. In Bezug auf die olfaktorische Kodierung zeige ich, dass die neuronale Dynamik waehrend der Stimulation im Antennallobus duftspezifische Trajektorien beschreibt, die in duftspezifischen Attraktoren enden. Das Zeitinterval, in dem diese Attraktoren erreicht werden, betraegt unabhaengig von der Identitaet und der Konzentration des Duftes ungefaehr 800 ms. Darueber hinaus zeige ich, dass Support-Vektor Maschinen, und insbesondere Perzeptronen, ein realistisches und biologisches Model der Wechselwirkung zwischen dem Antennallobus (dem kodierenden Netwerk) und dem Pilzkoerper (dem dekodierenden Netzwerk) darstellen. Dieses Model kann sowohl Reaktionszeiten von ca. 300 ms als auch die Invarianz der Duftwahrnehmung gegenueber der Duftkonzentration erklaeren. In Bezug auf das sensorische Gedaechtnis zeige ich, dass eine einzige Stimulation ohne Belohnung dem Hebbschen Postulat folgend Veraenderungen der paarweisen Korrelationen zwischen Glomeruli induziert. Ich zeige, dass diese Veranderungen der Korrelationen bei 2/3 der Bienen ausreichen, um den letzten Stimulus zu bestimmen. In der zweiten Minute nach der Stimulation ist eine erfolgreiche Bestimmung des Stimulus nur bei 1/3 der Bienen moeglich. Eine Hauptkomponentenanalyse der spontanen Aktivitaet laesst erkennen, dass das dominante Muster des Netzwerks waehrend der spontanen Aktivitaet nach, aber nicht vor der Stimulation das duftinduzierte Aktivitaetsmuster bei 2/3 der Bienen nachbildet. Man kann deshalb die duftinduzierten (Veraenderungen der) Korrelationen als Spuren eines Kurzzeitgedaechtnisses bzw. als Hebbsche "Reverberationen" betrachtet werden.
Two major novel results are reported in this work. The first concerns olfactory coding and the second concerns sensory memory. Both phenomena are investigated in the brain of the honeybee as a model system. Considering olfactory coding I demonstrate that the neural dynamics in the antennal lobe describe odor-specific trajectories during stimulation that converge to odor-specific attractors. The time interval to reach these attractors is, regardless of odor identity and concentration, approximately 800 ms. I show that support-vector machines and, in particular perceptrons provide a realistic and biological model of the interaction between the antennal lobe (coding network) and the mushroom body (decoding network). This model can also account for reaction-times of about 300 ms and for concentration invariance of odor perception. Regarding sensory memory I show that a single stimulation without reward induces changes of pairwise correlation between glomeruli in a Hebbian-like manner. I demonstrate that those changes of correlation suffice to retrieve the last stimulus presented in 2/3 of the bees studied. Succesful retrieval decays to 1/3 of the bees within the second minute after stimulation. In addition, a principal-component analysis of the spontaneous activity reveals that the dominant pattern of the network during the spontaneous activity after, but not before stimulation, reproduces the odor-induced activity pattern in 2/3 of the bees studied. One can therefore consider the odor-induced (changes of) correlation as traces of a short-term memory or as Hebbian reverberations.

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Носова, Яна Віталіївна. "Методи та засоби визначення респіраторно-ольфакторних порушень". Thesis, Харківський національний університет радіоелектроніки, 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/39479.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.11.17 – біологічні та медичні прилади і системи. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2019. За відсутності сучасних доказових методів ольфактометрії доцільно розробляти методи та засоби респіраторно-ольфакторних порушень. У дисертаційній роботі подано розв'язання конкретного наукового завдання – розробка методів та засобів об'єктивного визначення респіраторно-ольфакторних порушень. На основі дослідження аеродинаміки носа на мікрорівні розроблено метод визначення ламінарного пограничного шару повітряного потоку у верхніх дихальних шляхах, що дозволяє за рахунок дослідження товщини пристінкової течії повітря відносно нерівномірності оболонки визначати патологічні ділянки носової порожнини за різних режимів дихання. Розроблено метод та засіб об'єктивної діагностики респіраторно-ольфакторних порушень, які дозволяють за рахунок визначення енергетичних характеристик носового дихання під час дії різних одорівекторів визначати на доказовому рівні відповідні пороги ольфакторної чутливості. Удосконалено метод визначення порогу ольфакторної чутливості, що дозволяє за рахунок аналізу форми циклограми носового дихання підвищити об'єктивність діагностики порушень нюхової чутливості або респіраторно-ольфакторних порушень.
The thesis of competition for the scientific degree of Candidate of Technical Sciences on specialty 05.11.17 – Biological and Medical Devices and Systems. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2019. In the absence of modern evidence-based methods of olfactometry, it is advisable to develop methods and means of respiratory and olfactory disorders. The dissertation is devoted to the solution of a specific scientific problem - the development of methods and means for the objective determination of respiratory and olfactory disorders. Based on the study of the aerodynamics of the nose at the micro level a method has been developed for determining the laminar boundary layer of the air flow in the upper respiratory tract, which makes it possible, by studying the thickness of the near-wall air flow relative to the unevenness of the mucous membrane, to determine the pathological regions of the nasal cavity in different breathing patterns. A method and means for objective diagnosis of respiratory and olfactory disorders have been developed, which allow, by determining the energy characteristics of nasal breathing under the action of various odorivectors, to determine the corresponding thresholds of olfactory sensitivity at an evidence-based level. The improved method for determining the threshold of olfactory sensitivity allows to increase the objectivity of diagnosing olfactory sensitivity disorders or respiratory olfactory disorders by analyzing the shape of the cycloramas of nasal breathing.

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Носова, Яна Віталіївна. "Методи та засоби визначення респіраторно-ольфакторних порушень". Thesis, Національний технічний університет "Харківський політехнічний інститут", 2019. http://repository.kpi.kharkov.ua/handle/KhPI-Press/39477.

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Matowo, Nancy Stephen. "Attracting and killing outdoor-biting malaria vectors using odour-baited mosquito landing boxes (MLB) equipped with low-cost electrocuting grids." Thesis, 2015. http://hdl.handle.net/10539/18436.

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This research report has been submitted to the Faculty of Health Sciences, University of the Witwatersrand , in partial fulfillment of the requirements for the award of the Masters of Science in Medicine (Biology and Control of African Disease Vectors)degree. May 2015
Background: Ongoing residual malaria transmission is increasingly mediated by outdoor-biting mosquito populations, especially in communities where insecticidal interventions like indoor residual insecticides (IRS) and long-lasting insecticide treated nets (LLINs), are used. Often, the vectors are also physiologically resistant to the insecticides, making this a major against malaria elimination.

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Частини книг з теми "Odor vector"

Zhang, Lei, Fengchun Tian, and David Zhang. "Discriminative Support Vector Machine-Based Odor Classification." In Electronic Nose: Algorithmic Challenges, 79–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2167-2_6.

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Kusumoputro, B. "Development of Fuzzy Learning Vector Quantization Neural Network for Artificial Odor Discrimination System." In Artificial Neural Nets and Genetic Algorithms, 312–16. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6384-9_52.

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Goletsis, Y., C. Papaloukas, Th Exarhos, and C. D. Katsis. "Bankruptcy Prediction through Artificial Intelligence." In Machine Learning, 684–93. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch320.

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Bankruptcy prediction or corporate failure is considered a classic issue in both, academic and business communities. Bankruptcy risk is one of the most important factors (if not the most important one) to be considered when credit requests are screened or even existing debtors are evaluated. On the other hand, all potential stakeholders (shareholders, suppliers, customers, employees, creditors, auditors, etc.) have potential interest to identify if a company is on a trajectory that is tending towards failure. Commercial banks, public accounting firms and other institutional entities (e.g., bond rating agencies) appear to be the primary beneficiaries of accurate bankruptcy prediction, since they can use research results to minimize exposure to potential client failures. In addition to avoiding potentially troubled obligors, the research can also benefit in other ways. It can help in accurately assessing the credit risk of bank loan portfolios. Credit risk has been the subject of much research activity, since the regulators are acknowledging the need and are urging the banks to assess the credit risk in their portfolios. Measuring the credit risk accurately also allows banks to engineer future lending transactions, so as to achieve targeted return/risk characteristics. The other benefit of the prediction of bankruptcies is for accounting firms. If an accounting firm audits a potentially troubled firm, and misses giving a warning signal then it faces costly lawsuits (Atiya, 2001). A series of techniques have been applied in literature. Econometric / statistical methods have first appeared in literature: In late 1960’s (multiple) discriminant analysis (DA) was the dominant method; during the 1980’s logistic analysis. In the 1990’s artificial intelligence starts appearing in financial literature with neural networks (Odom & Sharda 1990) serving as an alternative to statistical methods demonstrating promising results. The goal of this chapter is therefore two-fold: First, it intends to give an overview of the artificial intelligence techniques successfully applied to the problem, ranging from the first neural network applications to recent applications of biologically inspired algorithms, such as genetic algorithms. Then, two kernel based methods, namely the Radial Basis Function Neural Networks and the Support Vector Machines are applied to the bankruptcy problem.

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Тези доповідей конференцій з теми "Odor vector"

Husni, Nyayu Latifah, Ade Silvia Handayani, Siti Nurmaini, and Irsyadi Yani. "Odor classification using Support Vector Machine." In 2017 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2017. http://dx.doi.org/10.1109/icecos.2017.8167170.

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ul Hasan, Najam, Naveed Ejaz, Waleed Ejaz, and Hyung Seok Kim. "Malicious odor item identification using an electronic nose based on support vector machine classification." In 2012 IEEE 1st Global Conference on Consumer Electronics (GCCE). IEEE, 2012. http://dx.doi.org/10.1109/gcce.2012.6379638.

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Kusumoputro, Benyamin, Hary Budiarto, and Wisnu Jatmiko. "Fuzzy learning vector quantization neural network and its application for artificial odor recognition system." In AeroSense 2000, edited by Kevin L. Priddy, Paul E. Keller, and David B. Fogel. SPIE, 2000. http://dx.doi.org/10.1117/12.380590.

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Jatmiko, W., Rochmatullah, B. Kusumoputro, H. R. Sanabila, K. Sekiyama, and T. Fukuda. "Visualization and statistical analysis of fuzzy-neuro learning vector quantization based on particle swarm optimization for recognizing mixture odors." In 2009 International Symposium on Micro-NanoMechatronics and Human Science (MHS). IEEE, 2009. http://dx.doi.org/10.1109/mhs.2009.5352022.

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