Relevant bibliographies by topics / KNOWLEDGE DISCOVERY BASED TECHNIQUE

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'KNOWLEDGE DISCOVERY BASED TECHNIQUE'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

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Chen, Po-Chi, Ru-Fang Hsueh, and Shu-Yuen Hwang. "An ILP Based Knowledge Discovery System." International Journal on Artificial Intelligence Tools 06, no. 01 (March 1997): 63–95. http://dx.doi.org/10.1142/s0218213097000050.

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Interest in research into knowledge discovery in databases (KDD) has been growing continuously because of the rapid increase in the amount of information embedded in real-world data. Several systems have been proposed for studying the KDD process. One main task in a KDD system is to learn important and user-interesting knowledge from a set of collected data. Most proposed systems use simple machine learning methods to learn the pattern. This may result in efficient performance but the discovery quality is less useful. In this paper, we propose a method to integrated a new and complicated machine learning method called inductive logic programming (ILP) to improve the KDD quality. Such integration shows how this new learning technique can be easily applied to a KDD system and how it can improve the representation of the discovery. In our system, we use user's queries to indicate the importance and interestingness of the target knowledge. The system has been implemented on a SUN workstation using the Sybase database system. Detailed examples are also provided to illustrate the benefit of integrating the ILP technique with the KDD system.
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JONYER, ISTVAN, LAWRENCE B. HOLDER, and DIANE J. COOK. "GRAPH-BASED HIERARCHICAL CONCEPTUAL CLUSTERING." International Journal on Artificial Intelligence Tools 10, no. 01n02 (March 2001): 107–35. http://dx.doi.org/10.1142/s0218213001000441.

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Hierarchical conceptual clustering has proven to be a useful, although greatly under-explored data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides the advantages of both approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains, as well as compare SUBDUE to earlier clustering algorithms. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data.
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Weng, Cheng-Hsiung. "Knowledge discovery of digital library subscription by RFC itemsets." Electronic Library 34, no. 5 (2016): 772–88. http://dx.doi.org/10.1108/el-06-2015-0086.

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Purpose The paper aims to understand the book subscription characteristics of the students at each college and help the library administrators to conduct efficient library management plans for books in the library. Unlike the traditional association rule mining (ARM) techniques which mine patterns from a single data set, this paper proposes a model, recency-frequency-college (RFC) model, to analyse book subscription characteristics of library users and then discovers interesting association rules from equivalence-class RFC segments. Design/methodology/approach A framework which integrates the RFC model and ARM technique is proposed to analyse book subscription characteristics of library users. First, the author applies the RFC model to determine library users’ RFC values. After that, the author clusters library users’ transactions into several RFC segments by their RFC values. Finally, the author discovers RFC association rules and analyses book subscription characteristics of RFC segments (library users). Findings The paper provides experimental results from the survey data. It shows that the precision of the frequent itemsets discovered by the proposed RFC model outperforms the traditional approach in predicting library user subscription itemsets in the following time periods. Besides, the proposed approach can discover interesting and valuable patterns from library book circulation transactions. Research limitations/implications Because RFC thresholds were assigned based on expert opinion in this paper, it is an acquisition bottleneck. Therefore, researchers are encouraged to automatically infer the RFC thresholds from the library book circulation transactions. Practical implications The paper includes implications for the library administrators in conducting library book management plans for different library users. Originality/value This paper proposes a model, the RFC model, to analyse book subscription characteristics of library users.
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Zhang, Guan Zhu, and Yu Ye Zhu. "Research of After-Sales Management System of Enterprises Based on J2EE and Data Mining Technology." Applied Mechanics and Materials 608-609 (October 2014): 375–81. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.375.

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With the globalization of market and economy, more and more enterprises realize the importance of after-sales service system. However, traditonal after-sales service system only focuses on the business process of system, and ignores important information of after-sales service data. It is data mining technique that solves the problem as a knowledge discovery technique. Data mining technique only can discover potential and valuable information and knowledge in lots of data for decision support. The paper analyzes the business process of after-sales service of enterprises, uses the idea of J2EE design mode, and expounds the development design of the system including the design of J2EE frame, functional module, system component and database.
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Giustolisi, Orazio, and Dragan A. Savic. "A symbolic data-driven technique based on evolutionary polynomial regression." Journal of Hydroinformatics 8, no. 3 (July 1, 2006): 207–22. http://dx.doi.org/10.2166/hydro.2006.020b.

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This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulæ with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data.
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Guan, Qing, and Jian He Guan. "Knowledge Acquisition of Interval Set-Valued Based on Granular Computing." Applied Mechanics and Materials 543-547 (March 2014): 2017–23. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2017.

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The technique of a new extension of fuzzy rough theory using partition of interval set-valued is proposed for granular computing during knowledge discovery in this paper. The natural intervals of attribute values in decision system to be transformed into multiple sub-interval of [0,1]are given by normalization. And some characteristics of interval set-valued of decision systems in fuzzy rough set theory are discussed. The correctness and effectiveness of the approach are shown in experiments. The approach presented in this paper can also be used as a data preprocessing step for other symbolic knowledge discovery or machine learning methods other than rough set theory.
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Li, Jian, and Jun Deng. "A Theoretical Study on Knowledge Discovery Technique for Structural Health Monitoring." Applied Mechanics and Materials 166-169 (May 2012): 1250–53. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1250.

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Based on the similarity between knowledge discovery from data bases (KDD) and Structural health monitoring (SHM), and considered the particularity of SHM problems, a four-step framework of SHM is proposed. The framework extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is consisted by second order structural parameter identification as feature extraction and statistical control chart analysis of identified stiffness for feature analysis is then presented. Through clarifying the goal and hierarchy of extracting useful knowledge of SHM problems, the framework has potential to facilitate the further development of SHM.
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Usai, Antonio, Marco Pironti, Monika Mital, and Chiraz Aouina Mejri. "Knowledge discovery out of text data: a systematic review via text mining." Journal of Knowledge Management 22, no. 7 (October 8, 2018): 1471–88. http://dx.doi.org/10.1108/jkm-11-2017-0517.

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Purpose The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics. Design/methodology/approach This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1). Findings The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment. Originality/value This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.
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Mahoto, Naeem Ahmed, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon, and Adel Sulaiman. "Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment." Sustainability 13, no. 16 (August 9, 2021): 8900. http://dx.doi.org/10.3390/su13168900.

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The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.
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UYSAL, İLHAN, and H. ALTAY GÜVENIR. "An overview of regression techniques for knowledge discovery." Knowledge Engineering Review 14, no. 4 (December 1999): 319–40. http://dx.doi.org/10.1017/s026988899900404x.

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Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5).
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Dissertations / Theses on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

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Mohd, Saudi Madihah. "A new model for worm detection and response : development and evaluation of a new model based on knowledge discovery and data mining techniques to detect and respond to worm infection by integrating incident response, security metrics and apoptosis." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5410.

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Worms have been improved and a range of sophisticated techniques have been integrated, which make the detection and response processes much harder and longer than in the past. Therefore, in this thesis, a STAKCERT (Starter Kit for Computer Emergency Response Team) model is built to detect worms attack in order to respond to worms more efficiently. The novelty and the strengths of the STAKCERT model lies in the method implemented which consists of STAKCERT KDD processes and the development of STAKCERT worm classification, STAKCERT relational model and STAKCERT worm apoptosis algorithm. The new concept introduced in this model which is named apoptosis, is borrowed from the human immunology system has been mapped in terms of a security perspective. Furthermore, the encouraging results achieved by this research are validated by applying the security metrics for assigning the weight and severity values to trigger the apoptosis. In order to optimise the performance result, the standard operating procedures (SOP) for worm incident response which involve static and dynamic analyses, the knowledge discovery techniques (KDD) in modeling the STAKCERT model and the data mining algorithms were used. This STAKCERT model has produced encouraging results and outperformed comparative existing work for worm detection. It produces an overall accuracy rate of 98.75% with 0.2% for false positive rate and 1.45% is false negative rate. Worm response has resulted in an accuracy rate of 98.08% which later can be used by other researchers as a comparison with their works in future.
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Radovanovic, Aleksandar. "Concept Based Knowledge Discovery from Biomedical Literature." Thesis, Online access, 2009. http://etd.uwc.ac.za/usrfiles/modules/etd/docs/etd_gen8Srv25Nme4_9861_1272229462.pdf.

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Aamot, Elias. "Literature-based knowledge discovery in climate science." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27047.

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Climate change caused by anthropogenic activity is one of the biggest challenges of our time. Researchers are striving to understand the effects of global warming on the ecological systems of the oceans, and how these ecological systems influence the global climate, a line of research that is crucial in order to counteract or adapt to the effects of global warming. A major challenge that researchers in this area are facing, is the huge amount of potentially relevant literature, as insights from widely different fields such as biology, chemistry, climatology and oceanography can prove crucial in understanding the effects of global warming on the oceans. To alleviate some of the work load from researchers, information extraction tools can be used to extract relevant information from the scientific literature automatically, and discovery support tools can be developed to assist researchers in their efforts. This master thesis conducts fundamental research into the development of discovery support tools for oceanographic climate science, focusing primarily on the information extraction component.
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Shelke, Yuri Rajendra. "Knowledge Based Topology Discovery and Geo-localization." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276877783.

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Yildiz, Meliha Yetisgen. "Using statistical and knowledge-based approaches for literature-based discovery /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7178.

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Vermilyer, Robert. "Knowledge Discovery in Content-Based Image Retrieval Systems." NSUWorks, 2005. http://nsuworks.nova.edu/gscis_etd/898.

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The advent of the World Wide Web and digital photography has led to a phenomenal increase in the number and complexity of stored images. Accordingly, the ability to browse and retrieve images based upon image content is of rapidly growing importance. The goals of this research project are to develop a Content-Based Image Retrieval (CBIR) system that combines dynamic, user-driven search capabilities with artificial intelligence techniques and to examine the system's effectiveness. The experimental method will be used to test the specific hypotheses and various research questions proposed in this research project. All of the experiments will be conducted using a CBIR prototype system that incorporates intelligent User Interface Agents (UIA). The UlAs will use both neural networks and an expert reasoning system. The actual experiments will be conducted using a task-oriented approach, with both descriptive and analytical statistics used to assess the results. In addition, a new evaluation CBIR metric will be proposed and applied. It is expected that this research will benefit CBIR research and CBIR system development by: 1) demonstrating the effectiveness of providing users with an interface that allows them to sketch an image, provides a relevance feedback mechanism that is based on providing similar images, and offers query refinement suggestions; 2) presenting a reusable modular design approach that can be used to create CBIR systems; 3) showing how AI techniques, particularly intelligent User Interface Agents, can be used effectively in CBIR systems; 4) proposing a "standard" CBIR user interface; and 5) proposing a new CBIR evaluation metric. The results of this research project should advance the current state of CBIR in that it designs, implements and evaluates an interactive CBIR system that uses image input and incorporates both the user's interactive guidance and artificial intelligence techniques to access images.
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Ajala, Adebunmi Elizabeth. "Acquiring and filtering knowledge : discovery & case-based reasoning." Thesis, University of Surrey, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433304.

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Phan, John H. "Biomarker discovery and clinical outcome prediction using knowledge based-bioinformatics." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33855.

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Advances in high-throughput genomic and proteomic technology have led to a growing interest in cancer biomarkers. These biomarkers can potentially improve the accuracy of cancer subtype prediction and subsequently, the success of therapy. However, identification of statistically and biologically relevant biomarkers from high-throughput data can be unreliable due to the nature of the data--e.g., high technical variability, small sample size, and high dimension size. Due to the lack of available training samples, data-driven machine learning methods are often insufficient without the support of knowledge-based algorithms. We research and investigate the benefits of using knowledge-based algorithms to solve clinical prediction problems. Because we are interested in identifying biomarkers that are also feasible in clinical prediction models, we focus on two analytical components: feature selection and predictive model selection. In addition to data variance, we must also consider the variance of analytical methods. There are many existing feature selection algorithms, each of which may produce different results. Moreover, it is not trivial to identify model parameters that maximize the sensitivity and specificity of clinical prediction. Thus, we introduce a method that uses independently validated biological knowledge to reduce the space of relevant feature selection algorithms and to improve the reliability of clinical predictors. Finally, we implement several functions of this knowledge-based method as a web-based, user-friendly, and standards-compatible software application.
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Yu, Zhiguo. "Cooperative Semantic Information Processing for Literature-Based Biomedical Knowledge Discovery." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/33.

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Given that data is increasing exponentially everyday, extracting and understanding the information, themes and relationships from large collections of documents is more and more important to researchers in many areas. In this paper, we present a cooperative semantic information processing system to help biomedical researchers understand and discover knowledge in large numbers of titles and abstracts from PubMed query results. Our system is based on a prevalent technique, topic modeling, which is an unsupervised machine learning approach for discovering the set of semantic themes in a large set of documents. In addition, we apply a natural language processing technique to transform the “bag-of-words” assumption of topic models to the “bag-of-important-phrases” assumption and build an interactive visualization tool using a modified, open-source, Topic Browser. In the end, we conduct two experiments to evaluate the approach. The first, evaluates whether the “bag-of-important-phrases” approach is better at identifying semantic themes than the standard “bag-of-words” approach. This is an empirical study in which human subjects evaluate the quality of the resulting topics using a standard “word intrusion test” to determine whether subjects can identify a word (or phrase) that does not belong in the topic. The second is a qualitative empirical study to evaluate how well the system helps biomedical researchers explore a set of documents to discover previously hidden semantic themes and connections. The methodology for this study has been successfully used to evaluate other knowledge-discovery tools in biomedicine.
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Siochi, Fernando C. "Building a knowledge based simulation optimization system with discovery learning." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-155425/.

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Books on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

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Edgar, Jacoby, ed. Chemogenomics: Knowledge-based approaches to drug discovery. London: Imperial College Press, 2006.

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Triantaphyllou, Evangelos. Data Mining and Knowledge Discovery via Logic-Based Methods. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1630-3.

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Hong, Jia-Fei. Verb Sense Discovery in Mandarin Chinese—A Corpus based Knowledge-Intensive Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44556-3.

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Data mining and knowledge discovery via logic-based methods: Theory, algorithms, and applications. New York: Springer, 2010.

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Vathy-Fogarassy, Ágnes. Graph-Based Clustering and Data Visualization Algorithms. London: Springer London, 2013.

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Optimization based data mining: Theory and applications. London: Springer-Verlag, 2011.

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S, Gero John, and Maher Mary Lou, eds. Modeling creativity and knowledge-based creative design: Edited by John S. Gero and Mary Lou Maher. Hillsdale, N.J: Lawrence Erlbaum Associates, Publishers, 1993.

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1943-, Gero John S., and Maher Mary Lou, eds. Modeling creativity and knowledge-based creative design: Edited by John S. Gero and Mary Lou Maher. Hillsdale, NJ: L. Erlbaum, 1992.

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Ji yu nei zai ren zhi ji li de zhi shi fa xina li lun: Knowledge discovery theory based on inner cognitive mechanism. Beijing Shi: Guo fang gong ye chu ban she, 2009.

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Nirmalie, Wiratunga, and SpringerLink (Online service), eds. Case-Based Reasoning Research and Development: 19th International Conference on Case-Based Reasoning, ICCBR 2011, London, UK, September 12-15, 2011. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Book chapters on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

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Zhang, Wenyin. "Content-Based Information Security Technique for Chinese Text." In Fuzzy Systems and Knowledge Discovery, 650–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_78.

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Massa, S., M. Paolucci, and P. P. Puliafito. "A New Modeling Technique Based on Markov Chains to Mine Behavioral Patterns in Event Based Time Series." In DataWarehousing and Knowledge Discovery, 331–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48298-9_35.

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Nalbantov, Georgi, Patrick Groenen, and Evgueni Smirnov. "A Comparative Analysis of Instance-based Penalization Techniques for Classification." In Reliable Knowledge Discovery, 227–38. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1903-7_13.

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Liao, Zhining, Hui Wang, David Glass, and Gongde Guo. "KNN Based Evolutionary Techniques for Updating Query Cost Models." In Fuzzy Systems and Knowledge Discovery, 797–800. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11540007_99.

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Sarmah, Sauravjyoti, Rosy Das Sarmah, and Dhruba Kumar Bhattacharyya. "An Effective Density-Based Hierarchical Clustering Technique to Identify Coherent Patterns from Gene Expression Data." In Advances in Knowledge Discovery and Data Mining, 225–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20841-6_19.

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Liu, Haishan, Gwen Frishkoff, Robert Frank, and Dejing Dou. "Ontology-Based Mining of Brainwaves: A Sequence Similarity Technique for Mapping Alternative Features in Event-Related Potentials (ERP) Data." In Advances in Knowledge Discovery and Data Mining, 43–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13672-6_5.

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Riondato, Matteo. "Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies." In Machine Learning and Knowledge Discovery in Databases, 516–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44845-8_48.

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Mollá, Diego, and Christopher Jones. "Classification Betters Regression in Query-Based Multi-document Summarisation Techniques for Question Answering." In Machine Learning and Knowledge Discovery in Databases, 624–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_56.

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TeCho, Jakkrit, Cholwich Nattee, and Thanaruk Theeramunkong. "A Corpus-Based Approach for Automatic Thai Unknown Word Recognition using Ensemble Learning Techniques." In Advances in Knowledge Discovery and Data Mining, 533–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_50.

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Chen, Chaomei, and Min Song. "Literature-Based Discovery." In Representing Scientific Knowledge, 263–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62543-0_7.

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Conference papers on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

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Sun, Hao-jun, and Lang-huan Xiong. "Genetic Algorithm-Based High-dimensional Data Clustering Technique." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.215.

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"A NOVEL QUERY EXPANSION TECHNIQUE BASED ON A MIXED GRAPH OF TERMS." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003660500840093.

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Mohd, Masnizah, Jaffar Atwan, and Kiyoaki Shirai. "Pseudo Relevance Feedback Technique and Semantic Similarity for Corpus-based Expansion." In 7th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005626904450450.

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Liu, Jing, Yancong Liu, and Chengxin Yan. "Feature Extraction Technique Based on the Perceptive Invariability." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.232.

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F. L. Schmitt, Murilo, and Eduardo J. Spinosa. "Forgetting on Evolving Graphs for Accurate and Diverse Stream-Based Recommendation." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/kdmile.2022.227804.

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Stream-based recommender systems are an active research field, relying on incremental algorithms to update models by incorporating new data on a single pass, discarding such data after processing. A limitation of solely including new data is the accumulation of obsolete concepts, which eventually raises accuracy and scalability concerns. In this work, we propose a gradual forgetting technique for incremental neighborhood-based methods that locally forgets items based on recency and popularity, by decreasing importance of neighborhood of items for every incoming observation to emphasize more recent and reinforced ones. The technique includes parameters to increase diversity, by retaining less popular yet relevant items, and scalability, by pruning obsolete connections not reinforced by new data. Experiments conducted by extending a recent incremental graph-based approach highlight the effectiveness of the proposed technique, as its application improved scalability and diversity, outperforming baselines.
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Liu Ri-Xian and Yuan Li-Yong. "A bias routing tree avoiding technique based on population-based incremental learning algorithm." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382258.

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Li, Cuiping, Zhiguo Cao, Zhongxue Li, Yiqing Zhao, and Zhenming Sun. "An AutoCAD based GIS integrated technique for open-pit mine." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6233739.

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Lv, Shijun, Minghu Zhang, Youfeng Li, and Xiaojuan Yu. "Design on the Fault Diagnostic System Based on Virtual Instrument Technique." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.138.

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Sackey, Samson Hansen, Junfeng Chen, James Adu Ansere, Godwin Kobby Gapko, and Mohsin Kamal. "A Bio-Inspired Technique based on Knowledge Discovery for Routing in IoT Networks." In 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE, 2020. http://dx.doi.org/10.1109/inmic50486.2020.9318105.

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Zhong, Shaohong, Huajun Huang, and Lili Pan. "An effective spam filtering technique based on active feedback and Maximum entropy." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569301.

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Reports on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, August 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business,and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committe members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its success.
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Salter, R., Natàlia Garcia-Reyero, Alicia Ruvinsky, Maria Seale, and Edward Perkins. Adverse outcome pathways for engineered systems. Engineer Research and Development Center (U.S.), July 2023. http://dx.doi.org/10.21079/11681/47336.

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Companies and organizations around the world spend massive amounts of money each year to discover, predict, and remediate failures within engineered systems. These tasks require individuals with specialized knowledge in a variety of topics related to failure. This knowledge is often acquired through years of academic and on-the-job training centered around the review of scientific documentation such as books, reports, manuals, and peer-reviewed publications. The loss of this knowledge through employee attrition can be detrimental to a group as knowledge is often difficult to reacquire. The aggregation and representation of known failure mechanisms for engineered materials could aid in the sharing of knowledge, the acquisition of knowledge, and the discovery of failure causes, reducing the risk of failure. Thus, the current work proposes the Adverse Outcome Pathway for Engineered Systems (AOP-ES) framework, an extension of the Adverse Outcome Pathway used in toxicology. The AOP-ES is designed to document failure knowledge, enabling knowledge transfer and the prediction of failures of novel engineered materials based on the performance of similar materials. This paper introduces the AOP-ES framework and its key elements alongside the principles that govern the framework. An application of the framework is presented, and additional benefits are explored.
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Altstein, Miriam, and Ronald Nachman. Rationally designed insect neuropeptide agonists and antagonists: application for the characterization of the pyrokinin/Pban mechanisms of action in insects. United States Department of Agriculture, October 2006. http://dx.doi.org/10.32747/2006.7587235.bard.

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The general objective of this BARD project focused on rationally designed insect neuropeptide (NP) agonists and antagonists, their application for the characterization of the mechanisms of action of the pyrokinin/PBAN (PK-PBAN) family and the development of biostable, bioavailable versions that can provide the basis for development of novel, environmentally-friendly pest insect control agents. The specific objectives of the study, as originally proposed, were to: (i) Test stimulatory potencies of rationally designed backbone cyclic (BBC) peptides on pheromonotropic, melanotropic, myotropic and pupariation activities; (ii) Test the inhibitory potencies of the BBC compounds on the above activities evoked either by synthetic peptides (PBAN, LPK, myotropin and pheromonotropin) or by the natural endogenous mechanism; (iii) Determine the bioavailability of the most potent BBC compounds that will be found in (ii); (iv) Design, synthesize and examine novel PK/PBAN analogs with enhanced bioavailability and receptor binding; (v) Design and synthesize ‘magic bullet’ analogs and examine their ability to selectively kill cells expressing the PK/PBAN receptor. To achieve these goals the agonistic and antagonistic activities/properties of rationally designed linear and BBC neuropeptide (NP) were thoroughly studied and the information obtained was further used for the design and synthesis of improved compounds toward the design of an insecticide prototype. The study revealed important information on the structure activity relationship (SAR) of agonistic/antagonistic peptides, including definitive identification of the orientation of the Pro residue as trans for agonist activity in 4 PK/PBANbioassays (pheromonotropic, pupariation, melanotropic, & hindgut contractile) and a PK-related CAP₂b bioassay (diuretic); indications that led to the identification of a novel scaffold to develop biostbiostable, bioavailable peptidomimetic PK/PBANagonists/antagonists. The work led to the development of an arsenal of PK/PBAN antagonists with a variety of selectivity profiles; whether between different PKbioassays, or within the same bioassay between different natural elicitors. Examples include selective and non-selective BBC and novel amphiphilic PK pheromonotropic and melanotropic antagonists some of which are capable of penetrating the moth cuticle in efficacious quantities. One of the latter analog group demonstrated unprecedented versatility in its ability to antagonize a broad spectrum of pheromonotropic elicitors. A novel, transPro mimetic motif was proposed & used to develop a strong, selective PK agonist of the melanotropic bioassay in moths. The first antagonist (pure) of PK-related CAP₂b diuresis in flies was developed using a cisPro mimetic motif; an indication that while a transPro orientation is associated with receptor agonism, a cisPro orientation is linked with an antagonist interaction. A novel, biostablePK analog, incorporating β-amino acids at key peptidase-susceptible sites, exhibited in vivo pheromonotropic activity that by far exceeded that of PBAN when applied topically. Direct analysis of neural tissue by state-of-the-art MALDI-TOF/TOF mass spectrometry was used to identify specific PK/PK-related peptides native to eight arthropod pest species [house (M. domestica), stable (S. calcitrans), horn (H. irritans) & flesh (N. bullata) flies; Southern cattle fever tick (B. microplus), European tick (I. ricinus), yellow fever mosquito (A. aegypti), & Southern Green Stink Bug (N. viridula)]; including the unprecedented identification of mass-identical Leu/Ile residues and the first identification of NPs from a tick or the CNS of Hemiptera. Evidence was obtained for the selection of Neb-PK-2 as the primary pupariation factor of the flesh fly (N. bullata) among native PK/PK-related candidates. The peptidomic techniques were also used to map the location of PK/PK-related NP in the nervous system of the model fly D. melanogaster. Knowledge of specific PK sequences can aid in the future design of species specific (or non-specific) NP agonists/antagonists. In addition, the study led to the first cloning of a PK/PBAN receptor from insect larvae (S. littoralis), providing the basis for SAR analysis for the future design of 2ⁿᵈgeneration selective and/or nonselective agonists/antagonists. Development of a microplate ligand binding assay using the PK/PBAN pheromone gland receptor was also carried out. The assay will enable screening, including high throughput, of various libraries (chemical, molecular & natural product) for the discovery of receptor specific agonists/antagonists. In summary, the body of work achieves several key milestones and brings us significantly closer to the development of novel, environmentally friendly pest insect management agents based on insect PK/PBANNPs capable of disrupting critical NP-regulated functions.
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Lagus, P. L., and R. A. Grot. PR-221-9215-R01 Manufacture Pre-Production Gas Flow Measurement System. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 1995. http://dx.doi.org/10.55274/r0011966.

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Measurement of the throughput of a natural gas compressor can be undertaken using a constant flow tracer dilution technique. In this technique, a constant flow of tracer is introduced into the piping upstream of the compressor while samples of diluted tracer are measured downstream of the compressor. The samples are analyzed using a specially configured analyzer. Knowledge of the injection rate and injection concentration coupled with the downstream measurement of tracer concentration allows the throughput through the compressor to be calculated. Under this contract, three systems were fabricated and were to be provided to operating pipeline companies for use and evaluation. Each system consisted of an SF6 analyzer, a high-pressure SF6 tracer injection module, and a gas sampling and analyzer support module. The design was modified based on the findings from the field evaluation and a final inspection.
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Borgwardt, Stefan, and Rafael Peñaloza. Infinitely Valued Gödel Semantics for Expressive Description Logics. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.217.

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Fuzzy Description Logics (FDLs) combine classical Description Logics with the semantics of Fuzzy Logics in order to represent and reason with vague knowledge. Most FDLs using truth values from the interval [0; 1] have been shown to be undecidable in the presence of a negation constructor and general concept inclusions. One exception are those FDLs whose semantics is based on the infinitely valued Gödel t-norm (G). We extend previous decidability results for the FDL G-ALC to deal with complex role inclusions, nominals, inverse roles, and qualified number restrictions. Our novel approach is based on a combination of the known crispification technique for finitely valued FDLs and an automata-based procedure for reasoning in G-ALC.
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Droby, Samir, Joseph W. Eckert, Shulamit Manulis, and Rajesh K. Mehra. Ecology, Population Dynamics and Genetic Diversity of Epiphytic Yeast Antagonists of Postharvest Diseases of Fruits. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7568777.bard.

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One of the emerging technologies is the use of microbial agents for the control of postharvest diseases of fruits and vegetables. A number of antagonistic microorganisms have been discovered which have the potential to effectively control postharvest diseases. Some of this technology has been patented and commercial products such as AspireTM (Ecogen Corporatin, Langhorne, PA, USA), Biosave 10TM and Biosave 11TM (Ecoscience Inc., Worchester, MA, USA) have been registered for commercial use. The principal investigator of this project was involved in developing the yeast-based biofungicide-AspireTM and testing its efficacy under commercial conditions. This research project was initiated to fill the gap between the knowledge available on development and commercial implementation of yeast biocontrol agents and basic understanding of various aspects related to introducing yeast antagonists to fruit surfaces, along with verification of population genetics. The main objectives of this study were: Study ecology, population dynamics and genetic diversity of the yeast antagonists Candida guilliermondii, C. oleophila, and Debaryomyces hansenii, and study the effect of preharvest application of the yeast antagonist C. oleophila naturally occurring epiphytic microbial population and on the development of postharvest diseases of citrus fruit during storage. Our findings, which were detailed in several publications, have shown that an epiphytic yeast population of grapefruit able to grow under high osmotic conditions and a wide range of temperatures was isolated and characterized for its biocontrol activity against green mold decay caused by Penicillium digitatum. Techniques based on random amplified polymorphic DNA (RAPD) and arbitrary primed polymerase chain reaction (ap-PCR), as well as homologies between sequences of the rDNA internal transcribed spacers (ITS) and 5.8S gene, were used to characterize the composition of the yeast population and to determine the genetic relationship among predominant yeast species. Epiphytic yeasts exhibiting the highest biocontrol activity against P. digitatum on grapefruit were identified as Candida guilliermondii, C. oleophila, C. sake, and Debaryomyces hansenii, while C. guilliermondii was the most predominant species. RAPD and ap-PCR analysis of the osmotolerant yeast population showed two different, major groups. The sequences of the ITS regions and the 5.8S gene of the yeast isolates, previously identified as belonging to different species, were found to be identical. Following the need to develop a genetically marked strain of the yeast C. oleophila, to be used in population dynamics studies, a transformation system for the yeast was developed. Histidine auxotrophy of C. oloephila produced using ethyl methanesulfonate were transformed with plasmids containing HIS3, HIS4 and HIS5 genes from Saccharomyces cerevisiae. In one mutant histidin auxotrophy was complemented by the HIS5 gene of S. cerevisiae is functionally homologous to the HIS5 gene in V. oleophila. Southern blot analysis showed that the plasmid containing the S. cerevisiae HIS5 gene was integrated at a different location every C. oleophila HIS+ transformant. There were no detectable physiological differences between C. oleophila strain I-182 and the transformants. The biological control ability of C. oleophila was not affected by the transformation. A genetically marked (with b-glucuronidase gene) transformant of C. oleophila colonized wounds on orange fruits and its population increased under field conditions. Effect of preharvest application of the yeast C. oleophila on population dynamics of epiphytic microbial population on wounded and unwounded grapefruit surface in the orchard and after harvest was also studied. In addition, the effect of preharvest application of the yeast C. oleophila on the development of postharvest decay was evaluated. Population studies conducted in the orchard showed that in control, non-treated fruit, colonization of wounded and unwounded grapefruit surface by naturally occurring filamentous fungi did not vary throughout the incubation period on the tree. On the other hand, colonization of intact and wounded fruit surface by naturally occurring yeasts was different. Yeasts colonized wounded surface rapidly and increased in numbers to about two orders of magnitude as compared to unwounded surface. On fruit treated with the yeast and kept on the tree, a different picture of fungal and yeast population had emerged. The detected fungal population on the yeast-treated intact surface was dramatically reduced and in treated wounds no fungi was detected. Yeast population on intact surface was relatively high immediately after the application of AspireTM and decreased to than 70% of that detected initially. In wounds, yeast population increased from 2.5 x 104 to about 4x106 after 72 hours of incubation at 20oC. Results of tests conducted to evaluate the effect of preharvest application of AspireTM on the development of postharvest decay indicated the validity of the approach.
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Corriveau, L., J. F. Montreuil, O. Blein, E. Potter, M. Ansari, J. Craven, R. Enkin, et al. Metasomatic iron and alkali calcic (MIAC) system frameworks: a TGI-6 task force to help de-risk exploration for IOCG, IOA and affiliated primary critical metal deposits. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329093.

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Australia's and China's resources (e.g. Olympic Dam Cu-U-Au-Ag and Bayan Obo REE deposits) highlight how discovery and mining of iron oxide copper-gold (IOCG), iron oxide±apatite (IOA) and affiliated primary critical metal deposits in metasomatic iron and alkali-calcic (MIAC) mineral systems can secure a long-term supply of critical metals for Canada and its partners. In Canada, MIAC systems comprise a wide range of undeveloped primary critical metal deposits (e.g. NWT NICO Au-Co-Bi-Cu and Québec HREE-rich Josette deposits). Underexplored settings are parts of metallogenic belts that extend into Australia and the USA. Some settings, such as the Camsell River district explored by the Dene First Nations in the NWT, have infrastructures and 100s of km of historic drill cores. Yet vocabularies for mapping MIAC systems are scanty. Ability to identify metasomatic vectors to ore is fledging. Deposit models based on host rock types, structural controls or metal associations underpin the identification of MIAC-affinities, assessment of systems' full mineral potential and development of robust mineral exploration strategies. This workshop presentation reviews public geoscience research and tools developed by the Targeted Geoscience Initiative to establish the MIAC frameworks of prospective Canadian settings and global mining districts and help de-risk exploration for IOCG, IOA and affiliated primary critical metal deposits. The knowledge also supports fundamental research, environmental baseline assessment and societal decisions. It fulfills objectives of the Canadian Mineral and Metal Plan and the Critical Mineral Mapping Initiative among others. The GSC-led MIAC research team comprises members of the academic, private and public sectors from Canada, Australia, Europe, USA, China and Dene First Nations. The team's novel alteration mapping protocols, geological, mineralogical, geochemical and geophysical framework tools, and holistic mineral systems and petrophysics models mitigate and solve some of the exploration and geosciences challenges posed by the intricacies of MIAC systems. The group pioneers the use of discriminant alteration diagrams and barcodes, the assembly of a vocab for mapping and core logging, and the provision of field short courses, atlas, photo collections and system-scale field, geochemical, rock physical properties and geophysical datasets are in progress to synthesize shared signatures of Canadian settings and global MIAC mining districts. Research on a metamorphosed MIAC system and metamorphic phase equilibria modelling of alteration facies will provide a foundation for framework mapping and exploration of high-grade metamorphic terranes where surface and near surface resources are still to be discovered and mined as are those of non-metamorphosed MIAC systems.
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Yogev, David, Ricardo Rosenbusch, Sharon Levisohn, and Eitan Rapoport. Molecular Pathogenesis of Mycoplasma bovis and Mycoplasma agalactiae and its Application in Diagnosis and Control. United States Department of Agriculture, April 2000. http://dx.doi.org/10.32747/2000.7573073.bard.

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Mycoplasma bovis and M. agalactiae are two phylogenetically related mycoplasmas which cause economically significant diseases in their respective bovine or small ruminant hosts. These organisms cause persistent asymptomatic infections that can result in severe outbreaks upon introduction of carrier animals into susceptible herds. Little is known about the mechanisms underlying mycoplasma-host interaction, variation in virulence, or of the factors enabling avoidance of the host immune system. In recent years it has become apparent that the ability of pathogenic microorganisms to rapidly alter surface antigenic structures and to fine tune their antigenicity, a phenomena called antigenic variation, is one of the most effective strategies used to escape immune destruction and to establish chronic infections. Our discovery of a novel genetic system, mediating antigenic variation in M. bovis (vsp) as well as in M. agalactiae (avg) served as a starting point for our proposal which included the following objectives: (i) Molecular and functional characterization of the variable surface lipoproteins (Vsp) system of M. bovis and comparison with the Vsp-counterpart in M. agalactiae (ii) Determination of the role of Vsp proteins in the survival of M. bovis when confronted by host defense factors, (iii) Assessment of Vsp-based genetic and antigenic typing of M. bovis and M. agalactiae for epidemiology of infection and (iv) Improvement of diagnostic tests for M. bovis and M. agalactiae based on the vsp-and vsp-analogous systems. We have carried out an extensive molecular characterization of the vsp system and unravelled the precise molecular mechanism responsible for the generation of surface antigenic variation in M. bovis. Our data clearly demonstrated that the two pathogenic mycoplasma species possess large gene families encoding variable lipoprotein antigens that apparently play an important role in immune evasion and in pathogen-host interaction during infection. Phase variable production of these antigens was found to be mediated by a novel molecular mechanism utilizing double site-specific DNA inversions via an intermediate vsp configuration. Studies in model systems indicate that phase variation of VspA is relevant in interaction between M. bovis and macrophages or monocytes, a crucial stage in pathogenesis. Using an ELISA test with captured VspA as an antigen, phase variation was shown to occur in vivo and under field conditions. Genomic rearrangements in the avg gene family of M. agalactiae were shown to occur in vivo and may well have a role in evasion of host defences and establishment of chronic infection. An epidemiological study indicated that patterns of vsp-related antigenic variation diverge rapidly in an M. bovis infected herd. Marked divergence was also found with avg-based genomic typing of M. agalactiae in chronically infected sheep. However, avg-genomic fingerprints were found to be relatively homogeneous in different animals during acute stages of an outbreak of Contagious Agalactiae, and differ between unrelated outbreaks. These data support the concept of vsp-based genomic typing but indicate the necessity for further refinement of the methodology. The molecular knowledge on these surface antigens and their encoding genes provides the basis for generating specific recombinant tools and serological methods for serodiagnosis and epidemiological purposes. Utilization of these methods in the field may allow differentiating acutely infected herds from chronic herds and disease-free herds. In addition the highly immunogenic nature of these lipoproteins may facilitate the design of protective vaccine against mycoplasma infections.
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Dudareva, Natalia, Alexander Vainstein, Eran Pichersky, and David Weiss. Integrating biochemical and genomic approaches to elucidate C6-C2 volatile production: improvement of floral scent and fruit aroma. United States Department of Agriculture, September 2007. http://dx.doi.org/10.32747/2007.7696514.bard.

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The specific objectives of approved proposal include to: 1. Elucidate the C6-C2 biochemical pathways leading to the biosynthesis of phenylacetaldehyde, phenylethyl alcohol and phenylethyl acetate in floral tissues of ornamentally important plants, pefunia and roses. 2. Isolate and characterrze genes responsible for the production of these C6-C2 compounds and those involved in the regulation of the pathway using genomic and transcriptomic tools. 3. Determine whether altering the expression of key genes of this pathway can result in changing the aroma characteristics of flowers. Aldehydes are intermediates in a variety of biochemical pathways including those involved in the metabolism of carbohydrates, vitamins, steroids, amino acids, benzylisoquinoline alkaloids, hormones, and lipids. In plants they are also synthesized in response to environmental stresses such as salinity, cold, and heat shock or as flavors and aromas in fruits and flowers. Phenylacetaldehyde along with 2-phenylethanol and its acetate ester, are important scent compounds in numerous flowers, including petunias and roses. However, little is known about the biosynthesis of these volatile compounds in plants. We have shown that the formation PHA and 2-phenylethanol from Phe does not occur via trans-cinnamic acid and instead competes with the key enzyme of phenypropanoid metabolism Pheammonia-lyase (PAL) for Phe utilization. Using functional genomic approach and comparative gene expression profiling, we have isolated and characterized a novel enzyme from petunia and rose flowers that catalyzes the formation of the Ca-Czcompound phenylacetaldehyde (PHA) from L-phenylalanine (Phe) by the removal of both the carboxyl and amino groups. This enzyme, designated as phenylacetaldehyde synthases (PAAS), is a bifunctional enzyme that catalyzes the unprecedented efficient coupling of phenylalanine decarboxylation to oxidation, generating phenylacetaldehyde, CO2, ammonia, and hydrogen peroxide in stoichiometric amounts. Down-regulation of PAAS expression via RNA interference-based (RNAi) technology in petunia resulted in no PHA emission when compared with controls. These plants also produced no 2-phenylethanol, supporting our conclusion that PHA is a precursor of 2-phenylethanol. To understand the regulation of scent formation in plants we have also generated transgenic petunia and tobacco plants expressing the rose alcohol acetyltransferase (RhAAT) gene under the control of a CaMV-35S promoter. Although the preferred substrate of RhAAT in vitro is geraniol, in transgenic petunia flowers, it used phenylethyl alcohol and benzyl alcohol to produce the corresponding acetate esters, not generated by control flowers. These results strongly point to the dependence of volatile production on substrate availability. Analysis of the diurnal regulation of scent production in rose flowers revealed that although the daily emission of most scent compounds is synchronized, various independently evolved mechanisms control the production, accumulation and release of different volatiles. This research resulted in a fundamental discovery of biochemical pathway, enzymes and genes involved in biosynthesis of C6-C2s compounds, and provided the knowledge for future engineering plants for improved scent quality.
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Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concept of modeling and technological integration of various organizational forms and teaching methods based on the concept of mobility. Within this approach, the leading learning tools of bachelors of electromechanics are mobile Internet devices (MID) – a multimedia mobile devices that provide wireless access to information and communication Internet services for collecting, organizing, storing, processing, transmitting, presenting all kinds of messages and data. The authors reveals the main possibilities of using MID in learning to ensure equal access to education, personalized learning, instant feedback and evaluating learning outcomes, mobile learning, productive use of time spent in classrooms, creating mobile learning communities, support situated learning, development of continuous seamless learning, ensuring the gap between formal and informal learning, minimize educational disruption in conflict and disaster areas, assist learners with disabilities, improve the quality of the communication and the management of institution, and maximize the cost-efficiency. Bachelor of electromechanics competency in modeling of technical objects is a personal and vocational ability, which includes a system of knowledge, skills, experience in learning and research activities on modeling mechatronic systems and a positive value attitude towards it; bachelor of electromechanics should be ready and able to use methods and software/hardware modeling tools for processes analyzes, systems synthesis, evaluating their reliability and effectiveness for solving practical problems in professional field. The competency structure of the bachelor of electromechanics in the modeling of technical objects is reflected in three groups of competencies: general scientific, general professional and specialized professional. The implementation of the technique of using MID in learning bachelors of electromechanics in modeling of technical objects is the appropriate methodic of using, the component of which is partial methods for using MID in the formation of the general scientific component of the bachelor of electromechanics competency in modeling of technical objects, are disclosed by example academic disciplines “Higher mathematics”, “Computers and programming”, “Engineering mechanics”, “Electrical machines”. The leading tools of formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects are augmented reality mobile tools (to visualize the objects’ structure and modeling results), mobile computer mathematical systems (universal tools used at all stages of modeling learning), cloud based spreadsheets (as modeling tools) and text editors (to make the program description of model), mobile computer-aided design systems (to create and view the physical properties of models of technical objects) and mobile communication tools (to organize a joint activity in modeling).
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