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Journal articles on the topic 'Key discovery'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Kamel, Mohammed B. M., Yuping Yan, Peter Ligeti, and Christoph Reich. "Attred: Attribute Based Resource Discovery for IoT." Sensors 21, no. 14 (July 10, 2021): 4721. http://dx.doi.org/10.3390/s21144721.

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While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.

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Celis, Julio E. "Proteomics: key technology in drug discovery." Drug Discovery Today 3, no. 5 (May 1998): 193–95. http://dx.doi.org/10.1016/s1359-6446(98)01184-2.

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Zhang, Meihui, Marios Hadjieleftheriou, Beng Chin Ooi, Cecilia M. Procopiuc, and Divesh Srivastava. "On multi-column foreign key discovery." Proceedings of the VLDB Endowment 3, no. 1-2 (September 2010): 805–14. http://dx.doi.org/10.14778/1920841.1920944.

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4

Toga, Arthur W., Ian Foster, Carl Kesselman, Ravi Madduri, Kyle Chard, Eric W. Deutsch, Nathan D. Price, et al. "Big biomedical data as the key resource for discovery science." Journal of the American Medical Informatics Association 22, no. 6 (July 21, 2015): 1126–31. http://dx.doi.org/10.1093/jamia/ocv077.

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Abstract Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s.

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Brown, Helen, David Butler, and Mari Riess Jones. "Musical and Temporal Influences on Key Discovery." Music Perception 11, no. 4 (1994): 371–407. http://dx.doi.org/10.2307/40285632.

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The intervallic rivalry model of key identification is outlined and evaluated in two experiments that use a completion judgment task. Experiment 1 replicates an earlier experiment by Cuddy and Badertscher (1987), in which the rare-interval hypothesis of the intervallic rivalry model was considered. In the present study, listeners with different levels of musical training rated probe tones in the context of three different melodic patterns: arpeggiated major triads, ascending major scales, and arpeggiated diminished triads. Results of Experiment 1 indicated that in both the C major triadic and the C major scalar contexts, listeners gave higher completion ratings to all three probes that were members of the presented C major triad than to the other probes, with the exception of F, thereby producing a jagged (multipeaked) profile. For the diminished triadic context, listeners rated the single probe C, that which corresponds to the tonal center in major mode for that group of three tones, as the best completion. Experiment 2 tested the temporal-order hypothesis of the intervallic rivalry model by reordering tones in all three contexts. Again jagged tone profiles appeared with major triadic and major scalar contexts, although in the former the tone F, a perfect fifth below the root of the presented C major triad, received the best completion rating. A single-peaked function appeared with probes in the diminished triadic context, where the major-mode tonic garnered the highest rating found in all conditions of both experiments. Data are interpreted as support for both the rare-interval hypothesis and the temporal-order hypothesis derived from the intervallic rivalry model of key discovery. Complementary findings consistent with the tonal hierarchy model are also discussed.

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Cheng, Xiaoming, and Marc G. Ghany. "Key Milestones in HCV Discovery and Therapeutics." Innovation 1, no. 3 (November 2020): 100067. http://dx.doi.org/10.1016/j.xinn.2020.100067.

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7

Ezzell, C. "Gene Discovery: Key to Colon Cancer Test." Science News 140, no. 6 (August 10, 1991): 86. http://dx.doi.org/10.2307/3975961.

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8

Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.

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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.

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van Rensburg, Ilana C., and André G. Loxton. "Transcriptomics: the key to biomarker discovery during tuberculosis?" Biomarkers in Medicine 9, no. 5 (May 2015): 483–95. http://dx.doi.org/10.2217/bmm.15.16.

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10

Pernelle, Nathalie, Fatiha Saïs, and Danai Symeonidou. "An automatic key discovery approach for data linking." Journal of Web Semantics 23 (December 2013): 16–30. http://dx.doi.org/10.1016/j.websem.2013.07.001.

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11

Lawrence, Rebecca N. "Key strategies in functional genomics for drug discovery." Drug Discovery Today 5, no. 12 (December 2000): 536–38. http://dx.doi.org/10.1016/s1359-6446(00)01592-0.

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12

Clarke, Dwaine. "Hybrid certificate closure-chain discovery public key system." International Journal of Computational Science and Engineering 9, no. 4 (2014): 312. http://dx.doi.org/10.1504/ijcse.2014.060714.

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Rahman, Rezaur, and Hossen Asiful Mustafa. "Securing IPv6 Neighbor Discovery using Pre-Shared Key." Advances in Science, Technology and Engineering Systems Journal 6, no. 2 (March 2021): 722–32. http://dx.doi.org/10.25046/aj060284.

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14

KANWAL, ATTIYA, SAHAR FAZAL, SOHAIL ASGHAR, and Muhammad Naeem. "SUBGROUP DISCOVERY OF THE MODY GENES;." Professional Medical Journal 20, no. 05 (October 15, 2013): 644–52. http://dx.doi.org/10.29309/tpmj/2013.20.05.1207.

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Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to healthand economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is MaturityOnset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documentsrelated to these genes in public domain database. Data Source: The data set was obtained from PubMed. Period: September-December,2011. Materials and Methodology: APRIORI-SD subgroup discovery algorithm is used for the task of discovering subgroups. A wellknown association rule learning algorithm APRIORI is first modified into classification rule learning algorithm APRIORI-C. APRIORI-Calgorithm generates the rule from the discretized dataset with the minimum support set to 0.42% with no confidence threshold. Total 580rules are generated at the given support. APRIOIR-C is further modified by making adaptation into APRIORI-SD. Results: Experimentalresults demonstrate that APRIORI discovers the substantially smaller rule sets; each rule has higher support and significance. The rulesthat are obtained by APRIORI-C are ordered by weighted relative accuracy. Conclusion: Only first 66 rules are ordered as they cover therelation between all the 11 MODY genes with each other. These 66 rules are further organized into 11 different subgroups. The evaluationof obtained results from literature shows that APRIORI-SD is a competitive subgroup discovery algorithm. All the association amonggenes proved to be true.

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Phillips, P. A., and D. Fraser. "Discovery of selected water dispensers by newborn pigs." Canadian Journal of Animal Science 71, no. 1 (March 1, 1991): 233–36. http://dx.doi.org/10.4141/cjas91-026.

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Newborn pigs in 86 litters were offered drinking water from four types of dispenser to determine how quickly each design would be discovered. On average piglets discovered water within about 24 h from an exposed water surface (bowl or cup), whereas discovery time was delayed to more than 72 h with nipple or push-lever dispensers. A prototype dispenser with a wide bowl and continuous bubbling action reduced (P < 0.05) average discovery time to about 14 h. Key words: Piglet, water, dispenser design

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16

Qin, Qin, and Yong-qiang He. "Random Walk Based Key Nodes Discovery in Opportunistic Networks." International Journal of Online Engineering (iJOE) 12, no. 03 (March 31, 2016): 28. http://dx.doi.org/10.3991/ijoe.v12i03.5412.

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In opportunistic networks, temporary nodes choose neighbor nodes to forward messages while communicating. However, traditional forward mechanisms don’t take the importance of nodes into consideration while forwarding. In this paper, we assume that each node has a status indicating its importance, and temporary nodes choose the most important neighbors to forward messages. While discovering important neighbors, we propose a binary tree random walk based algorithm. We analyze the iteration number and communication cost of the proposed algorithm, and they are much less than related works. The simulation experiments validate the efficiency and effectiveness of the proposed algorithm.

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17

Yair, Gad. "Key educational experiences and self-discovery in higher education." Teaching and Teacher Education 24, no. 1 (January 2008): 92–103. http://dx.doi.org/10.1016/j.tate.2007.04.002.

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18

SELBY, JONATHAN. "Patterns in the crust: a key to ore discovery." Geology Today 3, no. 6 (November 1987): 160–64. http://dx.doi.org/10.1111/j.1365-2451.1987.tb00522.x.

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19

Huber, Margit A., and Norbert Kraut. "Key drivers of biomedical innovation in cancer drug discovery." EMBO Molecular Medicine 7, no. 1 (November 24, 2014): 12–16. http://dx.doi.org/10.15252/emmm.201404596.

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20

Foss-Feig, Jennifer. "3.6 Biomarker Discovery: The Key to Developing New Treatments." Journal of the American Academy of Child & Adolescent Psychiatry 56, no. 10 (October 2017): S140. http://dx.doi.org/10.1016/j.jaac.2017.07.528.

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21

Jokonowo, Bambang, Riyanarto Sarno, Siti Rochimah, and Bagus Priambodo. "Process Mining: Measuring Key Performance Indicator Container Dwell Time." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 401. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp401-411.

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<span>The issues measures duration of stay the container logistic processes at ports in developing countries is often a major problem. Therefore, a knowledge process discovery, i.e., Heuristics Miner and Fuzzy Miner, can be used to discover the insight of process by creating a process model. The container import dwell time (DT) processes can be modeled based on the event log data sources are extracted from the terminal operating system (TOS). The <em>L</em>* life-cycle model is used to perform the process behavior analysis steps. The results of analysis and verification show that the container import DT processes have a median duration of 5.5 days and a mean duration of 6.07 days.</span>

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22

Ullah, Zafar, Muhammad Uzair, and Arshad Mehmood. "Extraction of Key Motifs as a Preview from 2017 Nobel Prize Winning Novel, ‘Never Let Me Go’." Journal of Research in Social Sciences 7, no. 2 (January 18, 2021): 83–98. http://dx.doi.org/10.52015/jrss.7i2.80.

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Word clouds manifest interactive visuals along with their statistical data. Thus knowledge discovery and aesthetic data visualization interlink to produce interactive word cloud which is an interesting, textual, statistical and visual data. This study aims to generate interactive word cloud—Cirrus—on the basis of statistical data to preview text of the novel for readers. So cirrus tool is selected from Voyant open access tools to produce interactive statistical word cloud. Then the generated word cloud and statistical data are analyzed with mixed method and its analysis draws insight from Rakesh Aggrawal’s Knowledge Discovery Theory which seeks innovative and interesting knowledge patterns. This thematic word cloud verifies already known themes and discovers innovative interesting themes. Current study reveals that all mentioned key themes can be easily extracted from a voluminous novel with the help of Cirrus tool. Key motifs have been presented in the word cloud for the readers. On the other hand, unwritten themes can’t be extracted through machine learning tools, rather it is the task of human cognition. Primarily, this novel based study reveals names of chief characters, for instance “Tommy (496),” “Ruth (455)” and “I (Kathy) (355).” Furthermore, motifs of nostalgic memories with word “remember (143),” “thought (126)” about “Hailsham (203),” “carer (74),” “sex (80),” sex “lectures (8)” have been discovered as a preview. Previewing technique prepares reader’s mind and gives an epigrammatic digital view of the text. The visual themes as knowledgeable word cloud leave an indelible mark on the slate of memory.

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23

Genilloud, Olga. "Actinomycetes: still a source of novel antibiotics." Natural Product Reports 34, no. 10 (2017): 1203–32. http://dx.doi.org/10.1039/c7np00026j.

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24

Geary, Andrew. "Seismic Soundoff." Leading Edge 40, no. 4 (April 2021): 312. http://dx.doi.org/10.1190/tle40040312.1.

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In this episode, Andrew Geary speaks with Kerry Key and Chloe Gustafson about their massive freshwater discovery off the east coast of the United States. Key and Gustafson discuss how they used existing geophysical techniques in a new way to discover the fresh water. Hear the full episode at https://seg.org/podcast/post/11355 .

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25

Xinquan Chen. "A Key Node Discovery Method Based on Directed-Association Influence." International Journal of Advancements in Computing Technology 5, no. 1 (January 15, 2013): 62–69. http://dx.doi.org/10.4156/ijact.vol5.issue1.8.

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26

Ongruk, Phatsavee, Padet Siriyasatien, and Kraisak Kesorn. "New Key Factors Discovery to Enhance Dengue Fever Forecasting Model." Advanced Materials Research 931-932 (May 2014): 1457–61. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1457.

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There are several factors that can be used to predict a dengue fever outbreak. Almost all existing research approaches, however, usually exploit the use of a basic set of core attributes to forecast an outbreak, e.g. temperature, humidity, wind speed, and rainfall. In contrast, this research identifies new attributes to improve the prediction accuracy of the outbreak. The experimental results are analyzed using a correlation analysis and demonstrate that the density of dengue virus infection rate in female mosquitoes and seasons have strong correlation with a dengue fever outbreak. In addition, the research constructs a forecast model using Poisson regression analysis. The result shows the proposed model obtains significantly low forecasting error rate when compared it against the conventional model using only temperature, humidity, wind speed, and rainfall parameters.

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Boss, Christoph, Julien Hazemann, Thierry Kimmerlin, Modest von Korff, Urs Lüthi, Oliver Peter, Thomas Sander, and Romain Siegrist. "The Screening Compound Collection: A Key Asset for Drug Discovery." CHIMIA International Journal for Chemistry 71, no. 10 (October 25, 2017): 667–77. http://dx.doi.org/10.2533/chimia.2017.667.

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28

Zhang, Geng, Zejian Yuan, and Nanning Zheng. "Key Object Discovery and Tracking Based on Context-Aware Saliency." International Journal of Advanced Robotic Systems 10, no. 1 (January 2013): 15. http://dx.doi.org/10.5772/51832.

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29

Keene, Chris. "Key Issue Discovery services: next generation of searching scholarly information." Serials: The Journal for the Serials Community 24, no. 2 (July 1, 2011): 193–96. http://dx.doi.org/10.1629/24193.

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30

Chircu, Alina M., and Daniel Hae-Dong Lee. "E-government: key success factors for value discovery and realisation." Electronic Government, an International Journal 2, no. 1 (2005): 11. http://dx.doi.org/10.1504/eg.2005.006645.

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31

Luo, Xiangfeng, Lei Zhang, Yawen Yi, Ruirong Xue, and Dandan Jiang. "The key user discovery model based on user importance calculation." International Journal of Computational Science and Engineering 21, no. 2 (2020): 315. http://dx.doi.org/10.1504/ijcse.2020.10027436.

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Zhang, Lei, Dandan Jiang, Ruirong Xue, Yawen Yi, and Xiangfeng Luo. "The key user discovery model based on user importance calculation." International Journal of Computational Science and Engineering 21, no. 2 (2020): 315. http://dx.doi.org/10.1504/ijcse.2020.105744.

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33

Sefton, Peter. "Re-Discovering Repository Architecture: Adding Discovery as a Key Service." New Review of Information Networking 14, no. 2 (November 30, 2009): 84–101. http://dx.doi.org/10.1080/13614570903359407.

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34

Klok, C. J. "TRACHEAE & TRACHEOLES: A FORTUITOUS DISCOVERY REFINES A KEY DEFINITION." Journal of Experimental Biology 214, no. 19 (September 7, 2011): v. http://dx.doi.org/10.1242/jeb.049940.

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Helal, Sumyea, Jiuyong Li, Lin Liu, Esmaeil Ebrahimie, Shane Dawson, and Duncan J. Murray. "Identifying key factors of student academic performance by subgroup discovery." International Journal of Data Science and Analytics 7, no. 3 (June 21, 2018): 227–45. http://dx.doi.org/10.1007/s41060-018-0141-y.

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36

Zettler, Jennifer A., Scott C. Mateer, Melanie Link-Pérez, Jennifer Brofft Bailey, Geneva DeMars, and Traci Ness. "To Key or Not to Key: A New Key to Simplify & Improve the Accuracy of Insect Identification." American Biology Teacher 78, no. 8 (October 1, 2016): 626–33. http://dx.doi.org/10.1525/abt.2016.78.8.626.

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Insects have extraordinary species richness: over a million species have been identified, and even more await discovery and classification. Given their abundance and diversity, insects are excellent teaching tools for science classrooms. However, accurate insect identification can be especially challenging for beginning students. Accordingly, we have developed a dichotomous key that both precollege and university instructors and students can use efficiently to correctly identify 18 taxonomic orders of insects. Our key was developed to target insects most commonly encountered throughout the coastal southeastern United States, but it can easily be adapted to other regions. This key is novel in that it incorporates not only adult insects but also their immature stages. In addition, we included insects that are likely to be collected in all seasons, facilitating implementation in the classroom throughout the academic year.

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37

Campaniço, André, Shrika G. Harjivan, Digby F. Warner, Rui Moreira, and Francisca Lopes. "Addressing Latent Tuberculosis: New Advances in Mimicking the Disease, Discovering Key Targets, and Designing Hit Compounds." International Journal of Molecular Sciences 21, no. 22 (November 23, 2020): 8854. http://dx.doi.org/10.3390/ijms21228854.

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Despite being discovered and isolated more than one hundred years ago, tuberculosis (TB) remains a global public health concern arch. Our inability to eradicate this bacillus is strongly related with the growing resistance, low compliance to current drugs, and the capacity of the bacteria to coexist in a state of asymptomatic latency. This last state can be sustained for years or even decades, waiting for a breach in the immune system to become active again. Furthermore, most current therapies are not efficacious against this state, failing to completely clear the infection. Over the years, a series of experimental methods have been developed to mimic the latent state, currently used in drug discovery, both in vitro and in vivo. Most of these methods focus in one specific latency inducing factor, with only a few taking into consideration the complexity of the granuloma and the genomic and proteomic consequences of each physiological factor. A series of targets specifically involved in latency have been studied over the years with promising scaffolds being discovered and explored. Taking in account that solving the latency problem is one of the keys to eradicate the disease, herein we compile current therapies and diagnosis techniques, methods to mimic latency and new targets and compounds in the pipeline of drug discovery.

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Hu, Cong, Shi You Guan, and Da Wei Huang. "Research on Key Technologies of LXI Class C Instrument." Applied Mechanics and Materials 44-47 (December 2010): 3249–53. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3249.

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This paper briefly introduces the features and technical points of the new generation of instrument bus LXI (Lan eXtension for Instrument). Taking the deep study of LXI standard, VXI-11 protocol, RPC principles and SCPI commands as the research base, the structure of LXI Class C instrument is analysized, and the LXI Class C instrument key technologies--discovery mechanism and instrument command interpretation mechanism were especially highlighted. Moreover, the discovery mechanism and instrument command interpretation mechanism were implemented in the platform of S3C2410/Linux embedded system, and were successfully validated through Agilent IO Libraries Suite.

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Stramp, Nicholas, and John Wilkerson. "Legislative Explorer: Data-Driven Discovery of Lawmaking." PS: Political Science & Politics 48, no. 01 (December 31, 2014): 115–19. http://dx.doi.org/10.1017/s1049096514001644.

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ABSTRACTInteractive visualizations are used widely in the natural sciences to facilitate discovery in “big-data” research. This article introduces a project that applies the same data-driven discovery approach to politics. Lawmaking is a complex process that can be diﬃcult to convey in a constructive way, whether the audience is the general public, students, or researchers.Legislative Explorer(available athttp://www.legex.org) visualizes the progress of more than 250,000 congressional bills and resolutions introduced since 1973. Users of all abilities can engage with actual data to discover how bills become law. Filters designed by domain experts guide them toward key insights. It is our hope thatLegislative Explorerpromotes greater understanding of a much-maligned institution.

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Namayanja, Josephine, and Vandana P. Janeja. "Subspace Discovery for Disease Management." International Journal of Computational Models and Algorithms in Medicine 2, no. 1 (January 2011): 38–59. http://dx.doi.org/10.4018/jcmam.2011010103.

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This paper identifies key subspaces for better disease management. Disease affects individuals differently based on features such as age, race, and gender. The authors use data mining methods to discover which key factors of a disease are more relevant for particular strata of the population using bin wise clustering. The authors use a case study on Metabolic Syndrome (MetS). MetS is a combination of abnormalities that occur in the body during the processing of food and nutrients. A number of definitions have been studied to classify MetS. No clear criterion exists that can generally fit into a single satisfactory protocol. This domain encompasses a variety of demographics in society, leading to an implication that different criteria may be appropriate for different demographic strata. The authors address this issue and identify the cross section of demographic strata and the disease characteristics that are critical for understanding the disease in that subset of the population. Findings in real world NHANESIII data support this hypothesis, thus the approach can be used by clinical scientists to narrow down specific demographic pools to further study impacts of key MetS characteristics.

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SHEN, Jin-bo, Li XU, and Jian-wei CHEN. "Efficient and resilient shared-key discovery protocol in wireless sensor networks." Journal of Computer Applications 28, no. 11 (June 5, 2009): 2817–19. http://dx.doi.org/10.3724/sp.j.1087.2008.02817.

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Xiong, Xiuming, Zhijian Xu, Zhuo Yang, Yingtao Liu, Di Wang, Mei Dong, Emily J. Parker, and Weiliang Zhu. "Key Targets and Relevant Inhibitors for the Drug Discovery of Tuberculosis." Current Drug Targets 14, no. 6 (May 1, 2013): 676–99. http://dx.doi.org/10.2174/1389450111314060009.

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Xiong, Xiuming, Zhijian Xu, Zhuo Yang, Yingtao Liu, Di Wang, Mei Dong, Emily J. Parker, and Weiliang Zhu. "Key Targets and Relevant Inhibitors for the Drug Discovery of Tuberculosis." Current Drug Targets 999, no. 999 (April 1, 2013): 1–6. http://dx.doi.org/10.2174/13894501113149990155.

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Bialer, Meir, and H. Steve White. "Key factors in the discovery and development of new antiepileptic drugs." Nature Reviews Drug Discovery 9, no. 1 (January 2010): 68–82. http://dx.doi.org/10.1038/nrd2997.

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Conrad, Lettie Y. "Headlines from the discovery files: Key publications on scholarly content discoverability." Learned Publishing 30, no. 1 (December 15, 2016): 31–37. http://dx.doi.org/10.1002/leap.1080.

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Bald, Dirk. "Oxidative phosphorylation in pathogenic bacteria as key pathway for drug discovery." Biochimica et Biophysica Acta (BBA) - Bioenergetics 1857 (August 2016): e8. http://dx.doi.org/10.1016/j.bbabio.2016.04.025.

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Alias, Suraya. "Unsupervised Text Feature Extraction for Academic Chatbot using Constrained FP-Growth." ASM Science Journal 14 (April 2, 2021): 1–11. http://dx.doi.org/10.32802/asmscj.2020.576.

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In the edge where conversation merely involves online chatting and texting one another, an automated conversational agent is needed to support certain repetitive tasks such as providing FAQs, customer service and product recommendations. One of the key challenges is to identify and discover user’s intention in a social conversation where the focus of our work in the academic domain. Our unsupervised text feature extraction method for Intent Pattern Discovery is developed by applying text features constraints to the FP-Growth technique. The academic corpus was developed using a chat messages dataset where the conversation between students and academicians regarding undergraduate and postgraduate queries were extracted as text features for our model. We experimented with our new Constrained Frequent Intent Pattern (cFIP) model in contrast with the N-gram model in terms of feature-vector size reduction, descriptive intent discovery, and analysis of cFIP Rules. Our findings show significant and descriptive intent patterns was discovered with confidence rules value of 0.9 for cFIP of 3-sequence. We report an average feature-vector size reduction of 76% compared to the Bigram model using both undergraduate and postgraduate conversation datasets. The usability testing results depicted overall user satisfaction average mean score is 4.30 out of 5 in using the Academic chatbot which supported our intent discovery cFIP approach.

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McKnight, Susan, and Andrew Booth. "Identifying Customer Expectations is Key to Evidence Based Service Delivery." Evidence Based Library and Information Practice 5, no. 1 (March 17, 2010): 26. http://dx.doi.org/10.18438/b89g8d.

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Zhang, Zhen Nan, and Pei Si Zhong. "Key Issues for Cloud Manufacturing Platform." Advanced Materials Research 472-475 (February 2012): 2621–25. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.2621.

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In order to realize sharing and collaboration of manufacturing resource and manufacturing capability based on knowledge and to realize added value of manufacturing resource and manufacturing capability. In this paper, we propose an ontology-based architecture for cloud manufacturing platform. Meanwhile, several key issues for cloud manufacturing platform including semantic description of manufacturing resource and manufacturing capability, manufacturing cloud service advertisement, manufacturing cloud service discovery and manufacturing cloud service composition are studied in particular. Thus, the research provides foundation for the future research, development, implementation and application of cloud manufacturing platform.

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Panarina, Ekaterina. "University-industry Partnership as a Key Strategy for Innovative Sustainable Economic Growth." Journal of International Business Research and Marketing 1, no. 1 (2015): 25–28. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.11.3003.

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The intensified global competition for factors that drive the competitiveness of entrepreneurial ecosystems forces policymakers to seek new models of economic growth. The current Russian model, based on the exportation of natural resources, has become increasingly obsolete. Today, to achieve growth targets, Russia must move from the redistribution of mineral resources to intensify innovation activity and develop technology-intensive products. Universities and industry are two partners of the entrepreneurial ecosystem that can connect to merge the discovery-driven culture of universities with the innovation-driven environment.

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