Relevant bibliographies by topics / Random selection

Academic literature on the topic 'Random selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Random selection"

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Cortines, Aser, and Bastien Mallein. "A N-branching random walk with random selection." Latin American Journal of Probability and Mathematical Statistics 14, no. 1 (2017): 117. http://dx.doi.org/10.30757/alea.v14-07.

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ARLOTTO, ALESSANDRO, and J. MICHAEL STEELE. "Optimal Sequential Selection of a Unimodal Subsequence of a Random Sequence." Combinatorics, Probability and Computing 20, no. 6 (October 5, 2011): 799–814. http://dx.doi.org/10.1017/s0963548311000411.

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We consider the problem of selecting sequentially a unimodal subsequence from a sequence of independent identically distributed random variables, and we find that a person doing optimal sequential selection does so within a factor of the square root of two as well as a prophet who knows all of the random observations in advance of any selections. Our analysis applies in fact to selections of subsequences that have d+1 monotone blocks, and, by including the case d=0, our analysis also covers monotone subsequences.
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Stone, Peter. "A Renaissance for Random Selection?" Redescriptions: Political Thought, Conceptual History and Feminist Theory 16, no. 1 (January 1, 2013): 148. http://dx.doi.org/10.7227/r.16.1.8.

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Genuer, Robin, Jean-Michel Poggi, and Christine Tuleau-Malot. "Variable selection using random forests." Pattern Recognition Letters 31, no. 14 (October 2010): 2225–36. http://dx.doi.org/10.1016/j.patrec.2010.03.014.

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Morse, Janice M. "What's Wrong with Random Selection?" Qualitative Health Research 8, no. 6 (November 1998): 733–35. http://dx.doi.org/10.1177/104973239800800601.

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Bissell, A. F. "Ordered Random Selection Without Replacement." Applied Statistics 35, no. 1 (1986): 73. http://dx.doi.org/10.2307/2347867.

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Boland, Philip J., and Kevin Hutchinson. "Student Selection of Random Digits." Journal of the Royal Statistical Society: Series D (The Statistician) 49, no. 4 (December 2000): 519–29. http://dx.doi.org/10.1111/1467-9884.00250.

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Stone, Peter. "The Logic of Random Selection." Political Theory 37, no. 3 (February 11, 2009): 375–97. http://dx.doi.org/10.1177/0090591709332329.

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Arlotto, Alessandro, Robert W. Chen, Lawrence A. Shepp, and J. Michael Steele. "Online Selection of Alternating Subsequences from a Random Sample." Journal of Applied Probability 48, no. 04 (December 2011): 1114–32. http://dx.doi.org/10.1017/s0021900200008652.

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We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.
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Arlotto, Alessandro, Robert W. Chen, Lawrence A. Shepp, and J. Michael Steele. "Online Selection of Alternating Subsequences from a Random Sample." Journal of Applied Probability 48, no. 4 (December 2011): 1114–32. http://dx.doi.org/10.1239/jap/1324046022.

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We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.
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Dissertations / Theses on the topic "Random selection"

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Tyrrell, Simon. "Random and rational methods for compound selection." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370002.

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Stringer, Harold. "BEHAVIOR OF VARIABLE-LENGTH GENETIC ALGORITHMS UNDER RANDOM SELECTION." Master's thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2657.

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In this work, we show how a variable-length genetic algorithm naturally evolves populations whose mean chromosome length grows shorter over time. A reduction in chromosome length occurs when selection is absent from the GA. Specifically, we divide the mating space into five distinct areas and provide a probabilistic and empirical analysis of the ability of matings in each area to produce children whose size is shorter than the parent generation's average size. Diversity of size within a GA's population is shown to be a necessary condition for a reduction in mean chromosome length to take place. We show how a finite variable-length GA under random selection pressure uses 1) diversity of size within the population, 2) over-production of shorter than average individuals, and 3) the imperfect nature of random sampling during selection to naturally reduce the average size of individuals within a population from one generation to the next. In addition to our findings, this work provides GA researchers and practitioners with 1) a number of mathematical tools for analyzing possible size reductions for various matings and 2) new ideas to explore in the area of bloat control.
M.S.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science MS
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Choukri, Sam. "Selection of malaria-specific epitopes from random peptide libraries /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9962513.

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Frondana, Iara Moreira. "Model selection for discrete Markov random fields on graphs." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-02022018-151123/.

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In this thesis we propose to use a penalized maximum conditional likelihood criterion to estimate the graph of a general discrete Markov random field. We prove the almost sure convergence of the estimator of the graph in the case of a finite or countable infinite set of variables. Our method requires minimal assumptions on the probability distribution and contrary to other approaches in the literature, the usual positivity condition is not needed. We present several examples with a finite set of vertices and study the performance of the estimator on simulated data from theses examples. We also introduce an empirical procedure based on k-fold cross validation to select the best value of the constant in the estimators definition and show the application of this method in two real datasets.
Nesta tese propomos um critério de máxima verossimilhança penalizada para estimar o grafo de dependência condicional de um campo aleatório Markoviano discreto. Provamos a convergência quase certa do estimador do grafo no caso de um conjunto finito ou infinito enumerável de variáveis. Nosso método requer condições mínimas na distribuição de probabilidade e contrariamente a outras abordagens da literatura, a condição usual de positividade não é necessária. Introduzimos alguns exemplos com um conjunto finito de vértices e estudamos o desempenho do estimador em dados simulados desses exemplos. Também propomos um procedimento empírico baseado no método de validação cruzada para selecionar o melhor valor da constante na definição do estimador, e mostramos a aplicação deste procedimento em dois conjuntos de dados reais.
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Ushan, Wardah. "Portfolio selection using Random Matrix theory and L-Moments." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/16921.

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Markowitz's (1952) seminal work on Modern Portfolio Theory (MPT) describes a methodology to construct an optimal portfolio of risky stocks. The constructed portfolio is based on a trade-off between risk and reward, and will depend on the risk- return preferences of the investor. Implementation of MPT requires estimation of the expected returns and variances of each of the stocks, and the associated covariances between them. Historically, the sample mean vector and variance-covariance matrix have been used for this purpose. However, estimation errors result in the optimised portfolios performing poorly out-of-sample. This dissertation considers two approaches to obtaining a more robust estimate of the variance-covariance matrix. The first is Random Matrix Theory (RMT), which compares the eigenvalues of an empirical correlation matrix to those generated from a correlation matrix of purely random returns. Eigenvalues of the random correlation matrix follow the Marcenko-Pastur density, and lie within an upper and lower bound. This range is referred to as the "noise band". Eigenvalues of the empirical correlation matrix falling within the "noise band" are considered to provide no useful information. Thus, RMT proposes that they be filtered out to obtain a cleaned, robust estimate of the correlation and covariance matrices. The second approach uses L-moments, rather than conventional sample moments, to estimate the covariance and correlation matrices. L-moment estimates are more robust to outliers than conventional sample moments, in particular, when sample sizes are small. We use L-moments in conjunction with Random Matrix Theory to construct the minimum variance portfolio. In particular, we consider four strategies corresponding to the four different estimates of the covariance matrix: the L-moments estimate and sample moments estimate, each with and without the incorporation of RMT. We then analyse the performance of each of these strategies in terms of their risk-return characteristics, their performance and their diversification.
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Wonkye, Yaa Tawiah. "Innovations of random forests for longitudinal data." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1563054152739397.

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Tran, The Truyen. "On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling." Curtin University of Technology, Dept. of Computing, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=18614.

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There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis focuses on a special type of graphical models known as Conditional Random Fields (CRFs) (Lafferty et al., 2001), in which the output state spaces, when conditioned on some observational input data, are represented by undirected graphical models. The contributions of thesis involve both (a) broadening the current applicability of CRFs in the real world and (b) deepening the understanding of theoretical aspects of CRFs. On the application side, we empirically investigate the applications of CRFs in two real world settings. The first application is on a novel domain of Vietnamese accent restoration, in which we need to restore accents of an accent-less Vietnamese sentence. Experiments on half a million sentences of news articles show that the CRF-based approach is highly accurate. In the second application, we develop a new CRF-based movie recommendation system called Preference Network (PN). The PN jointly integrates various sources of domain knowledge into a large and densely connected Markov network. We obtained competitive results against well-established methods in the recommendation field.
On the theory side, the thesis addresses three important theoretical issues of CRFs: feature selection, parameter estimation and modelling recursive sequential data. These issues are all addressed under a general setting of partial supervision in that training labels are not fully available. For feature selection, we introduce a novel learning algorithm called AdaBoost.CRF that incrementally selects features out of a large feature pool as learning proceeds. AdaBoost.CRF is an extension of the standard boosting methodology to structured and partially observed data. We demonstrate that the AdaBoost.CRF is able to eliminate irrelevant features and as a result, returns a very compact feature set without significant loss of accuracy. Parameter estimation of CRFs is generally intractable in arbitrary network structures. This thesis contributes to this area by proposing a learning method called AdaBoost.MRF (which stands for AdaBoosted Markov Random Forests). As learning proceeds AdaBoost.MRF incrementally builds a tree ensemble (a forest) that cover the original network by selecting the best spanning tree at a time. As a result, we can approximately learn many rich classes of CRFs in linear time. The third theoretical work is on modelling recursive, sequential data in that each level of resolution is a Markov sequence, where each state in the sequence is also a Markov sequence at the finer grain. One of the key contributions of this thesis is Hierarchical Conditional Random Fields (HCRF), which is an extension to the currently popular sequential CRF and the recent semi-Markov CRF (Sarawagi and Cohen, 2004). Unlike previous CRF work, the HCRF does not assume any fixed graphical structures.
Rather, it treats structure as an uncertain aspect and it can estimate the structure automatically from the data. The HCRF is motivated by Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998). Importantly, the thesis shows that the HHMM is a special case of HCRF with slight modification, and the semi-Markov CRF is essentially a flat version of the HCRF. Central to our contribution in HCRF is a polynomial-time algorithm based on the Asymmetric Inside Outside (AIO) family developed in (Bui et al., 2004) for learning and inference. Another important contribution is to extend the AIO family to address learning with missing data and inference under partially observed labels. We also derive methods to deal with practical concerns associated with the AIO family, including numerical overflow and cubic-time complexity. Finally, we demonstrate good performance of HCRF against rivals on two applications: indoor video surveillance and noun-phrase chunking.
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Linusson, Henrik, Robin Rudenwall, and Andreas Olausson. "Random forest och glesa datarespresentationer." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16672.

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In silico experimentation is the process of using computational and statistical models to predict medicinal properties in chemicals; as a means of reducing lab work and increasing success rate this process has become an important part of modern drug development. There are various ways of representing molecules - the problem that motivated this paper derives from collecting substructures of the chemical into what is known as fractional representations. Assembling large sets of molecules represented in this way will result in sparse data, where a large portion of the set is null values. This consumes an excessive amount of computer memory which inhibits the size of data sets that can be used when constructing predictive models.In this study, we suggest a set of criteria for evaluation of random forest implementations to be used for in silico predictive modeling on sparse data sets, with regard to computer memory usage, model construction time and predictive accuracy.A novel random forest system was implemented to meet the suggested criteria, and experiments were made to compare our implementation to existing machine learning algorithms to establish our implementation‟s correctness. Experimental results show that our random forest implementation can create accurate prediction models on sparse datasets, with lower memory usage overhead than implementations using a common matrix representation, and in less time than existing random forest implementations evaluated against. We highlight design choices made to accommodate for sparse data structures and data sets in the random forest ensemble technique, and therein present potential improvements to feature selection in sparse data sets.
Program: Systemarkitekturutbildningen
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Patel, Richa. "Random mutagenesis and selection for RubisCO function in the photosynthetic bacterium rhodobacter capsulatus." Connect to resource, 2008. http://hdl.handle.net/1811/32176.

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Peng, Xiaoling. "Methods of variable selection and their applications in quantitative structure-property relationship (QSPR)." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/594.

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Books on the topic "Random selection"

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Dunson, David B., ed. Random Effect and Latent Variable Model Selection. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5.

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Dougherty, Dick. Dougherty revisited: A random selection of columns. Rochester, N.Y: Dougherty Editions, 2004.

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T. S. U. De Zylva. Images of birds: A random selection of the birds of Sri Lanka. Kurunegala: Victor Hasselblad Wildlife Trust (Sri Lanka), 2000.

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Schreiber, Sebastian J. Urn models, replicator process and random genetic drift. [Philadelphia, Pa.]: Society for Industrial and Applied Mathematics, 2001.

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Aiyer, Ajay Subramanian. Optimal portfolio selection with fixed transaction costs in the presence of jumps and random drift. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.

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Scott, J. C. Computerized stratified random site-selection approaches for design of a ground-water-quality sampling network. Oklahoma City, Okla: Dept. of the Interior, U.S. Geological Survey, 1990.

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Feldman, Roger D. Suitability of non-random designs for PACE evaluation: Final report. [Minneapolis, Minnesota?]: University of Minnesota School of Public Health, 1990.

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Beth, Allen. Continuous random selections from the equilibrium correspondence. Louvain-la-Neuve: CORE, 1985.

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Paratiyar. Petals of beauty: Random selections from Bharati's poems. Madras: Manivachagar Pathipagam, 1994.

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Pāratiyār. Petals of beauty: Random selections from Bharati's poems. Madras: Manivachagar Pathipagam, 1994.

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Book chapters on the topic "Random selection"

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Hilbert, Sven. "Random Selection." In Encyclopedia of Personality and Individual Differences, 4262–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1344.

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Hilbert, Sven. "Random Selection." In Encyclopedia of Personality and Individual Differences, 1–3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-28099-8_1344-1.

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L’Ecuyer, Pierre, and Peter Hellekalek. "Random Number Generators: Selection Criteria and Testing." In Random and Quasi-Random Point Sets, 223–65. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1702-2_5.

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Bos, Izak, and Peter Caligari. "Random variation of allele frequencies." In Selection Methods in Plant Breeding, 63–72. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8432-6_6.

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Krizanc, Danny, and Sanguthevar Rajasekaran. "Random Sampling: Sorting and Selection." In Handbook of Randomized Computing, 1–21. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0013-1_1.

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Shi, Zhan. "Branching Random Walks with Selection." In Lecture Notes in Mathematics, 99–105. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25372-5_6.

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Stepanov, Timofey. "Random Selection in Few Rounds." In Computer Science – Theory and Applications, 354–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38536-0_31.

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Pfeiffer, Paul E. "Random Selection and Counting Processes." In Springer Texts in Statistics, 491–540. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4615-7676-1_25.

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Buhrman, Harry, Matthias Christandl, Michal Koucký, Zvi Lotker, Boaz Patt-Shamir, and Nikolai Vereshchagin. "High Entropy Random Selection Protocols." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 366–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74208-1_27.

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Färber, Michael, and Cezary Kaliszyk. "Random Forests for Premise Selection." In Frontiers of Combining Systems, 325–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24246-0_20.

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Conference papers on the topic "Random selection"

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Hao, Fang-Fang, and Yan-Kui Liu. "Fuzzy Random Portfolio Selection Problem." In 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.101.

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Urruty, Thierry, Syntyche Gbèhounou, Huu Ton Le, Jean Martinet, and Christine Fernandez. "Iterative Random Visual Word Selection." In ICMR '14: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2578726.2578758.

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Wuller, Stefan, Ulrike Meyer, Fabian Forg, and Susanne Wetzel. "Privacy-preserving conditional random selection." In 2015 13th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2015. http://dx.doi.org/10.1109/pst.2015.7232953.

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Wagner, Jorg, Ying-chang Liang, and Rui Zhang. "Random Beamforming with Systematic Beam Selection." In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2006. http://dx.doi.org/10.1109/pimrc.2006.253936.

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Rajagopalan, Sundararaman, Lakshmi Chandrasekaran, Amirtharajan Rengarajan, Sivaraman Rethinam, Sridevi Arumugham, and Mohan Kandhaiya. "Image Encryption: A Random Selection Approach." In 2018 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2018. http://dx.doi.org/10.1109/iccci.2018.8441267.

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Pitts, Rowland. "Random Selection Might Just be Indomitable." In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2021. http://dx.doi.org/10.1109/icstw52544.2021.00014.

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Zhang, Lu, Shan-Shan Hou, Jun-Jue Hu, Tao Xie, and Hong Mei. "Is operator-based mutant selection superior to random mutant selection?" In the 32nd ACM/IEEE International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1806799.1806863.

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Gutierrez-Madronal, Lorena, Antonio Garcia-Dominguez, and Inmaculada Medina-Bulo. "Combining Evolutionary Mutation Testing with Random Selection." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185618.

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Ren, Ya-Zhou, Guo-Ji Zhang, and Guo-Xian Yu. "Random subspace based semi-supervised feature selection." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016706.

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Losee, R. "Random and best-first document selection models." In the 10th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/42005.42024.

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Reports on the topic "Random selection"

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Eastlake, D. Publicly Verifiable Nomcom Random Selection. RFC Editor, February 2000. http://dx.doi.org/10.17487/rfc2777.

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Eastlake, D. Publicly Verifiable Nominations Committee (NomCom) Random Selection. RFC Editor, June 2004. http://dx.doi.org/10.17487/rfc3797.

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Collins, Joseph C. Testing, Selection, and Implementation of Random Number Generators. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada486379.

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Gautier, Eric, and Stefan Hoderlein. A triangular treatment effect model with random coefficients in the selection equation. Institute for Fiscal Studies, December 2012. http://dx.doi.org/10.1920/wp.cem.2012.3912.

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DeLeire, Thomas, and Christopher Timmins. Roy Model Sorting and Non-Random Selection in the Valuation of a Statistical Life. Cambridge, MA: National Bureau of Economic Research, September 2008. http://dx.doi.org/10.3386/w14364.

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Kott, Phillip S. Calibration-Weighting a Stratified Simple Random Sample with SUDAAN. RTI Press, March 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0048.2204.

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This report shows how to apply the calibration-weighting procedures in SAS-callable SUDAAN (Version 11) to a stratified simple random sample drawn from a complete list frame for an establishment survey. The results are calibrated weights produced via raking, raking to a size variable, and pseudo-optimal calibration that potentially reduce and appropriately measure the standard errors of estimated totals. The report then shows how to use these procedures to remove selection bias caused by unit nonresponse under a plausible response model. Although unit nonresponse is usually assumed to be a function of variables with known population or full-sample estimated totals, calibration weighting can often be used when nonresponse is assumed to be a function of a variable known only for unit respondents (i.e., not missing at random). When producing calibrated weights for an establishment survey, one advantage the SUDAAN procedures have over most of their competitors is that their linearization-based variance estimators can capture the impact of finite-population correction.
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Bobashev, Georgiy, R. Joey Morris, Elizabeth Costenbader, and Kyle Vincent. Assessing network structure with practical sampling methods. RTI Press, May 2018. http://dx.doi.org/10.3768/rtipress.2018.op.0049.1805.

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Using data from an enumerated network of worldwide flight connections between airports, we examine how sampling designs and sample size influence network metrics. Specifically, we apply three types of sampling designs: simple random sampling, nonrandom strategic sampling (i.e., selection of the largest airports), and a variation of snowball sampling. For the latter sampling method, we design what we refer to as a controlled snowball sampling design, which selects nodes in a manner analogous to a respondent-driven sampling design. For each design, we evaluate five commonly used measures of network structure and examine the percentage of total air traffic accounted for by each design. The empirical application shows that (1) the random and controlled snowball sampling designs give rise to more efficient estimates of the true underlying structure, and (2) the strategic sampling method can account for a greater proportion of the total number of passenger movements occurring in the network.
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Graves, Todd L. Automatic step size selection in randon walk Metropolis algorithms. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1057119.

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Zlotkin, Eliahu, Shizuo G. Kamita, Nor Chejanovsky, and S. Maeda. Targeting of an Expressed Insect Selective Neurotoxin by its Recombinant Baculovirus: Pharmacokinetic and Pharmacodynamic Aspects. United States Department of Agriculture, July 1995. http://dx.doi.org/10.32747/1995.7571354.bard.

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Objectives: 1) Clarification of the mode of potentiation of an expressed insect selective neurotoxin (AaIT) by its recombinant baculovirus. 2) In vitro formation and/or modification of neuroactive polypeptides for the design of new improved recombinant baculoviruses. Results: 1) A combined utilization of bioassays, LM-cytochemistry, the highly resolutive EM immunogold and electrical recording from the CNS of baculovirus and AaIT - expressing – recombinant baculovirus infected larvae it has been shown that the recombinant virus potentiates the effect of the toxin. Potentiation is achieved through its continuous expression in the infected tracheal epithelia thus providing a: a) Local supply of freshly produced toxin in the vicinity of its traget sites; b) Translocation of the expressed toxin to the insect CNS. The latter exposes the recombinant toxin to new, critical, target sites which are inaccessible through the natural route of scorpion envenomation. 2) Subjecting a recombinant AaIT toxin to a newly designed system of random mutagenesis results in large numbers of new AaIT genes with amino acid substitutions. The new or modified toxin genes were inserted into a linear BmNPV expressed in silkworm cell culture and assayed on blowfly and silkworm larvae. Thus a system for mass formation and screening of neuroactive agents was developed. Contribution to agriculture: 1) Demonstration of the insecticidal mechanism, capacity and utility of the combination of neuroactive polypeptides and recombinant pathogens. 2) Development of a simple in vitro system for the formation and selection of new neuroactive polypeptides.
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10

Zhang, Yongping, Wen Cheng, and Xudong Jia. Enhancement of Multimodal Traffic Safety in High-Quality Transit Areas. Mineta Transportation Institute, February 2021. http://dx.doi.org/10.31979/mti.2021.1920.

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Numerous extant studies are dedicated to enhancing the safety of active transportation modes, but very few studies are devoted to safety analysis surrounding transit stations, which serve as an important modal interface for pedestrians and bicyclists. This study bridges the gap by developing joint models based on the multivariate conditionally autoregressive (MCAR) priors with a distance-oriented neighboring weight matrix. For this purpose, transit-station-centered data in Los Angeles County were used for model development. Feature selection relying on both random forest and correlation analyses was employed, which leads to different covariate inputs to each of the two jointed models, resulting in increased model flexibility. Utilizing an Integrated Nested Laplace Approximation (INLA) algorithm and various evaluation criteria, the results demonstrate that models with a correlation effect between pedestrians and bicyclists perform much better than the models without such an effect. The joint models also aid in identifying significant covariates contributing to the safety of each of the two active transportation modes. The research results can furnish transportation professionals with additional insights to create safer access to transit and thus promote active transportation.
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