Academic literature on the topic '004.8+025.4'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Contents
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic '004.8+025.4.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "004.8+025.4"
Burgaz, Sonia, Concepción García, María Gómez-Cañas, Alain Rolland, Eduardo Muñoz, and Javier Fernández-Ruiz. "Neuroprotection with the Cannabidiol Quinone Derivative VCE-004.8 (EHP-101) against 6-Hydroxydopamine in Cell and Murine Models of Parkinson’s Disease." Molecules 26, no. 11 (May 28, 2021): 3245. http://dx.doi.org/10.3390/molecules26113245.
Full textCaprioglio, Diego, Daiana Mattoteia, Orazio Taglialatela-Scafati, Eduardo Muñoz, and Giovanni Appendino. "Cannabinoquinones: Synthesis and Biological Profile." Biomolecules 11, no. 7 (July 5, 2021): 991. http://dx.doi.org/10.3390/biom11070991.
Full textGarcía-Martín, Adela, Martín Garrido-Rodríguez, Carmen Navarrete, Carmen del Río, María L. Bellido, Giovanni Appendino, Marco A. Calzado, and Eduardo Muñoz. "EHP-101, an oral formulation of the cannabidiol aminoquinone VCE-004.8, alleviates bleomycin-induced skin and lung fibrosis." Biochemical Pharmacology 157 (November 2018): 304–13. http://dx.doi.org/10.1016/j.bcp.2018.07.047.
Full textPalomares, Belen, Francisco Ruiz-Pino, Carmen Navarrete, Inmaculada Velasco, Miguel A. Sánchez-Garrido, Carla Jimenez-Jimenez, Carolina Pavicic, et al. "VCE-004.8, A Multitarget Cannabinoquinone, Attenuates Adipogenesis and Prevents Diet-Induced Obesity." Scientific Reports 8, no. 1 (October 31, 2018). http://dx.doi.org/10.1038/s41598-018-34259-0.
Full textNavarrete, Carmen, Francisco Carrillo-Salinas, Belén Palomares, Miriam Mecha, Carla Jiménez-Jiménez, Leyre Mestre, Ana Feliú, et al. "Hypoxia mimetic activity of VCE-004.8, a cannabidiol quinone derivative: implications for multiple sclerosis therapy." Journal of Neuroinflammation 15, no. 1 (March 1, 2018). http://dx.doi.org/10.1186/s12974-018-1103-y.
Full textdel Río, Carmen, Carmen Navarrete, Juan A. Collado, M. Luz Bellido, María Gómez-Cañas, M. Ruth Pazos, Javier Fernández-Ruiz, et al. "The cannabinoid quinol VCE-004.8 alleviates bleomycin-induced scleroderma and exerts potent antifibrotic effects through peroxisome proliferator-activated receptor-γ and CB2 pathways." Scientific Reports 6, no. 1 (February 18, 2016). http://dx.doi.org/10.1038/srep21703.
Full textDissertations / Theses on the topic "004.8+025.4"
Канівець, Дмитро Володимирович. "Математичне та програмне забезпечення класифікації наукових текстів." Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/31517.
Full textRelevance: to simplify the search for relevant information among scientific publications in Ukraine, a library classification is used. However, this system is not perfect at this time, because classification is erroneous, and in some cases it is executed for the journal as a whole, which results in partial discrepancies for some of its articles. Also, it takes a long time to perform the classification by a third party (such as a librarian or editor). The solution to this problem is to automate the classification process. By using machine learning, automatic classifier can be created, which will improve the accuracy of the classification compared to manual and accelerate the classification of new revenues. Purpose: create a classifier of scientific articles by UDC categories based on machine learning. To achieve this goal, the following tasks were formulated: - systematization of existing text data classification algorithms; - gathering sufficient training data, developing a classifier based on machine learning; - testing and analysis of the efficiency of the obtained algorithm; - determining the further direction of research. Object of study: library classification of scientific articles. Subject of study: algorithms for classification of text data. Research methods: naive Bayes classifier, neural networks, backpropagation algorithm were used to solve this problem. Scientific novelty: the most significant scientific results of a master's thesis are the study of the possibilities of automation of the classification of scientific texts; search for mistakes in already classified texts; creation of classification algorithms for distinguishing categories in texts of similar subjects. The practical value of the obtained results is determined by the fact that the proposed algorithm allows to achieve the accuracy of library classification in 86%, which allows to use it for finding and correcting errors in the classification of texts, as well as an aid in the classification of new receipts. Relationship with working with scientific programs, plans, topics: work was performed at the Department of Automated Information Processing and Management Systems of the Igor Sikorsky National Technical University of Ukraine «Kyiv Polytechnic Institute» within the topic «Mathematical Models and Technologies in DSS». State Registration Number 0117U000914 Approbation: the main provisions of the work were reported and discussed at the XIII Scientific and Practical Conference of undergraduate and graduate students «Applied Mathematics and Computing» (AMP-2019), as well as at the third all-Ukrainian scientific and practical conference of young scientists and students «Information Systems and Technologies of Management» (ISTM-2019).
Chuleekorn, Thanongsitt. "Management of information system implementation from a power perspective : case studies of organisations in Thailand." Thesis, University of Sheffield, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575715.
Full textLee, Deborah. "Modelling music : a theoretical approach to the classification of notated Western art music." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/17445/.
Full textШурук, Андрій Сергійович. "Система аналізу людської активності на основі даних з носимих пристроїв." Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/38278.
Full textThe urgency of the problem. Globalization and population growth are contributing to the development of areas related to human activity monitoring, and thus to the emergence of new tools for monitoring various human performance indicators and ways to analyze these indicators. Given these factors, it is important in today's world to properly use such volumes of data in market conditions. When it comes to, for example, caring for the elderly, it is important to properly analyze and try to recognize a particular human activity, it can help increase the life expectancy of the elderly. Relationship with working with scientific programs, plans, topics. Thesis of master's level of higher education was performed at the National Technical University of Ukraine "Kyiv Polytechnic Institute named after Igor Sikorsky" in accordance with the plans of research work of the Department of Computer Science. The purpose and objectives of the study. The aim of this work is to study the possibility of recognizing human activity based on the data of wearable devices. The aim is to develop a system built on a neural network capable of recognizing human activity and providing it to the user through a cross-platform application. Object of study. The process of recognizing human activity using elements of the neural network. Subject of study. Methods of analysis and processing of data obtained from wearable devices in real time. Novelty. A new method of recognizing human activity based on the data of wearable devices is proposed, which, due to the use of a neural network, allows to obtain real-time recognition results with high accuracy.
Бернацька, Дарина Леонідівна. "Штучний інтелект і психологія. чи може робот замінити психолога?" Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/52239.
Full textГурбанов, Т. А. "Технології порівняльного аналізу електронних текстів як засіб боротьби з плагіатом." Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/54371.
Full textЗ розвитком науки та технологій ми можемо отримати багато нових можливостей для доступу до знань, але при цьому розвиваються старі та створюються нові проблеми в галузі освіти. Технології, що дозволяють отримати блискавичний доступ до багатьох джерел інформації та наукових робіт є однією з причин гострої проблеми плагіату цих наукових робіт.
Sayah, Tarek. "Selective disclosure and inference leakage problem in the Linked Data." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1156/document.
Full textThe emergence of the Semantic Web has led to a rapid adoption of the RDF (Resource Description Framework) to describe the data and the links between them. The RDF graph model is tailored for the representation of semantic relations between Web objects that are identified by IRIs (Internationalized Resource Identifier). The applications that publish and exchange potentially sensitive RDF data are increasing in many areas: bioinformatics, e-government, open data movement. The problem of controlling access to RDF content and selective exposure to information based on privileges of the requester becomes increasingly important. Our main objective is to encourage businesses and organizations worldwide to publish their RDF data into the linked data global space. Indeed, the published data may be sensitive, and consequently, data providers may avoid to release their information, unless they are certain that the desired access rights of different accessing entities are enforced properly, to their data. Hence the issue of securing RDF content and ensuring the selective disclosure of information to different classes of users is becoming all the more important. In this thesis, we focused on the design of a relevant access control for RDF data. The problem of providing access controls to RDF data has attracted considerable attention of both the security and the database community in recent years. New issues are raised by the introduction of the deduction mechanisms for RDF data (e.g., RDF/S, OWL), including the inference leakage problem. Indeed, when an owner wishes to prohibit access to information, she/he must also ensure that the information supposed secret, can’t be inferred through inference mechanisms on RDF data. In this PhD thesis we propose a fine-grained access control model for RDF data. We illustrate the expressiveness of the access control model with several conict resolution strategies including most specific takes precedence. To tackle the inference leakage problem, we propose a static verification algorithm and show that it is possible to check in advance whether such a problem will arise. Moreover, we show how to use the answer of the algorithm for diagnosis purposes. To handle the subjects' privileges, we define the syntax and semantics of a XACML inspired language based on the subjects' attributes to allow much finer access control policies. Finally, we propose a data-annotation approach to enforce our access control model, and show that our solution incurs reasonable overhead with respect to the optimal solution which consists in materializing the user's accessible subgraph to enforce our access control model, and show that our solution incurs reasonable overhead with respect to the optimal solution which consists in materializing the user's accessible subgraph
Погорелов, Володимир Володимирович, and Volodymyr Pogorelov. "Нейромережеві моделі та методи розпізнавання комп’ютерних вірусів." Thesis, Національний авіаційний університет, 2020. https://er.nau.edu.ua/handle/NAU/44636.
Full textСмішний, Денис Миколайович. "Система прогнозування економічних показників." Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/30950.
Full textMaster's Thesis: 88 pp., 20 figs., 27 tables, 1 appendix, 33 sources. The urgency of the problem. Globalization and population growth are con-tributing to the development of the global economy and, consequently, to the emergence of new types of economic activity and new players in the labor market. When implementing your own business it is important to properly evaluate the risks of the market, analyzing and trying to predict the movement of quotations in the near future for minimal financial losses. Relationship with working with scientific programs, plans, topics. Cur-rently, it has no specific links to scientific programs or plans. The purpose and objectives of the study. The purpose of this work is re-search possibility of forecasting the economic parameters of enterprises on the ex-ample of stock prices of companies on the stock exchange. The purpose is to de-velop a system based on a neural network, capable of analyzing specified economic indicators and, based on the data obtained, to predict their dynamics. Object of study. The process of forecasting economic performance using neural network elements. Subject of study. Methods of analysis and processing of economic data for a certain period. Novelty. Obtaining a software product capable of predicting economic fluc-tuations. Investigation of the possibility of creating a universal model based on a neural network, which would not require specialization and would be able to work effectively with any set of input data without further training.
Codocedo-Henríquez, Víctor. "Contributions à l'indexation et à la recherche d'information avec l'analyse formelle de concepts." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0143/document.
Full textOne of the first models ever to be considered as an index for documents using terms as descriptors, was a lattice structure, a couple of decades before the arrival of Formal Concept Analysis (FCA) as a solid theory for data mining and knowledge discovery.While the Information Retrieval (IR) community has shifted to more advanced techniques for document retrieval, like probabilistic and statistic paradigms, the interest of the FCA community on developing techniques that would improve the state-of-the-art in IR while providing relevance feedback and semantic based features, never decayed. In this thesis we present a set of contributions on what we call FCA-based IR systems. We have divided our contributions in two sets, namely retrieval and indexing. For retrieval, we propose a novel technique that exploits semantic relations among descriptors in a document corpus and a new concept lattice navigation strategy (called cousin concepts), enabling us to support classification-based reasoning to provide better results compared with state-of-the-art retrieval techniques. The basic notion in our strategy is supporting query modification using "term replacements'' using the lattice structure and semantic similarity. For indexing, we propose a new model that allows supporting the vector space model of retrieval using concept lattices. One of the main limitations of current FCA-based IR systems is related to the binary nature of the input data required for FCA to generate a concept lattice. We propose the use of pattern structures, an extension of FCA to deal with complex object descriptions, in order to support more advanced retrieval paradigms like the vector space model. In addition, we propose an advanced model for heterogeneous indexing through which we can combine the vector space model and the Boolean retrieval model. The main advantage of this approach is the ability of supporting indexing of convex regions in an arbitrary vectorial space built from a document collection. Finally, we move forward to a mining model associated with document indexing, namely exhaustive bicluster enumeration using FCA. Biclustering is an emerging data analysis technique in which objects are related by similarity under certain attributes of the description space, instead of the whole description space like in standard clustering. By translating this problem to the framework of FCA, we are able to exploit the robust machinery associated with the computation of concept lattices to provide an algorithm for mining biclusters based on similar values. We show how our technique performs better than current exhaustive enumeration biclustering techniques