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Journal articles on the topic 'Spatio-temporal control'

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Author: Grafiati

Published: 7 December 2022

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1

KINUGAWA, Takuma, and Toshimitsu USHIO. "Finite-Horizon Optimal Spatio-Temporal Pattern Control under Spatio-Temporal Logic Specifications." IEICE Transactions on Information and Systems E105.D, no. 10 (October 1, 2022): 1658–64. http://dx.doi.org/10.1587/transinf.2021fop0003.

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2

Smolyaninov, Vladimir V. "Spatio-temporal problems of locomotion control." Physics-Uspekhi 43, no. 10 (October 31, 2000): 991–1053. http://dx.doi.org/10.1070/pu2000v043n10abeh000796.

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3

Smolyaninov, Vladimir V. "Spatio-temporal problems of locomotion control." Uspekhi Fizicheskih Nauk 170, no. 10 (2000): 1063. http://dx.doi.org/10.3367/ufnr.0170.200010b.1063.

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4

Boutselis, George I., Ethan N. Evans, Marcus A. Pereira, and Evangelos A. Theodorou. "Leveraging Stochasticity for Open Loop and Model Predictive Control of Spatio-Temporal Systems." Entropy 23, no. 8 (July 23, 2021): 941. http://dx.doi.org/10.3390/e23080941.

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Stochastic spatio-temporal processes are prevalent across domains ranging from the modeling of plasma, turbulence in fluids to the wave function of quantum systems. This letter studies a measure-theoretic description of such systems by describing them as evolutionary processes on Hilbert spaces, and in doing so, derives a framework for spatio-temporal manipulation from fundamental thermodynamic principles. This approach yields a variational optimization framework for controlling stochastic fields. The resulting scheme is applicable to a wide class of spatio-temporal processes and can be used for optimizing parameterized control policies. Our simulated experiments explore the application of two forms of this approach on four stochastic spatio-temporal processes, with results that suggest new perspectives and directions for studying stochastic control problems for spatio-temporal systems.

5

Diebner, Hans H., Axel A. Hoff, Adolf Mathias, Horst Prehn, Marco Rohrbach, and Sven Sahle. "Control and Adaptation of Spatio-temporal Patterns." Zeitschrift für Naturforschung A 56, no. 9-10 (October 1, 2001): 663–69. http://dx.doi.org/10.1515/zna-2001-0910.

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Abstract We apply a recently introduced cognitive system for dynamics recognition to a two-dimensional array of coupled oscillators. The cognitive system allows for both the control and the adaptation of spatio-temporal patterns of that array of oscillators. One array that shows Turing-pattems in a self-organizational manner is viewed as an externally presented dynamics (stimulus) which is mapped onto a mirror dynamics, whereby the latter is capable to simulate (simulus). Two of the parameters of the stimulus are thereby regarded to be unknown and have to be estimated by the cognitive system. The cognitive system itself consists of dynamical modules that are stimulated by the external dynamics in the sense of Pyragas' external force control mechanism and thereby yield measures of how good they match the stimulus. These measures are used as weights to construct the simulus. The adaptation process is performed "on the fly", i. e., without the storage of data. The proposed cognitive system, therefore, is a prominent candidate for the construction of a control device for a permanent real time observation of an external dynamical system in order to interfere instantaneously when necessary.

6

Lu, Xu, Cong Tian, Zhenhua Duan, and Hongwei Du. "Planning with Spatio-Temporal Search Control Knowledge." IEEE Transactions on Knowledge and Data Engineering 30, no. 10 (October 1, 2018): 1915–28. http://dx.doi.org/10.1109/tkde.2018.2810144.

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7

Salamon, Nestor Z., Markus Billeter, and Elmar Eisemann. "ShutterApp: Spatio‐temporal Exposure Control for Videos." Computer Graphics Forum 38, no. 7 (October 2019): 675–83. http://dx.doi.org/10.1111/cgf.13870.

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8

Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing spatio-temporal trajectories." Computational Geometry 42, no. 9 (November 2009): 825–41. http://dx.doi.org/10.1016/j.comgeo.2009.02.002.

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9

Hayashi, Yuichiro. "Spatio-temporal control of neural activity using optogenetics." Neuroscience Research 68 (January 2010): e327. http://dx.doi.org/10.1016/j.neures.2010.07.1450.

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10

Hayashi, Yuichiro. "Spatio-temporal control of neural activity using microendoscopy." Neuroscience Research 71 (September 2011): e312. http://dx.doi.org/10.1016/j.neures.2011.07.1361.

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11

Coughlan, Matthew A., Mateusz Plewicki, and Robert J. Levis. "Parametric Spatio-Temporal Control of Focusing Laser Pulses." Optics Express 17, no. 18 (August 21, 2009): 15808. http://dx.doi.org/10.1364/oe.17.015808.

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12

Abdunabi, Ramadan, Wuliang Sun, and Indrakshi Ray. "Enforcing spatio-temporal access control in mobile applications." Computing 96, no. 4 (August 21, 2013): 313–53. http://dx.doi.org/10.1007/s00607-013-0340-2.

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13

Zhang, Lanfang, Zhiyong Zhang, and Ting Zhao. "A Novel Spatio-Temporal Access Control Model for Online Social Networks and Visual Verification." International Journal of Cloud Applications and Computing 11, no. 2 (April 2021): 17–31. http://dx.doi.org/10.4018/ijcac.2021040102.

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With the rapid development of mobile internet, a large number of online social networking platforms and tools have been widely applied. As a classic method for protecting the privacy and information security of social users, access control technology is evolving with the spatio-temporal change of social application requirements and scenarios. However, nowadays there is a lack of effective theoretical model of social spatio-temporal access control as a guide. This paper proposed a novel spatio-temporal access control model for online social network (STAC) and its visual verification, combined with the advantages of discretionary access control, using formal language to describe the access control rules based on spatio-temporal, and real-life scenarios for access control policy description, realizes a more fine-grained access control mechanism for social network. By using the access control verification tool ACPT developed by NIST to visually verify the proposed model, the security and effectiveness of the STAC model are proved.

14

Dou, Chunxia, and Shuqing Zhang. "An H ∞ fuzzy tracking control scheme for affine coupled spatio-temporal chaosfuzzy tracking control scheme for affine coupled spatio-temporal chaos." Journal of Electronics (China) 22, no. 1 (January 2005): 59–65. http://dx.doi.org/10.1007/bf02687952.

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15

Li, Yong-Jiang, Miao Yu, Chun-Dong Xue, Hai-Jun Zhang, Guo-Zhen Wang, Xiao-Ming Chen, and Kai-Rong Qin. "Modeling of Endothelial Calcium Responses within a Microfluidic Generator of Spatio-Temporal ATP and Shear Stress Signals." Micromachines 12, no. 2 (February 7, 2021): 161. http://dx.doi.org/10.3390/mi12020161.

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Intracellular calcium dynamics play essential roles in the proper functioning of cellular activities. It is a well known important chemosensing and mechanosensing process regulated by the spatio-temporal microenvironment. Nevertheless, how spatio-temporal biochemical and biomechanical stimuli affect calcium dynamics is not fully understood and the underlying regulation mechanism remains missing. Herein, based on a developed microfluidic generator of biochemical and biomechanical signals, we theoretically analyzed the generation of spatio-temporal ATP and shear stress signals within the microfluidic platform and investigated the effect of spatial combination of ATP and shear stress stimuli on the intracellular calcium dynamics. The simulation results demonstrate the capacity and flexibility of the microfluidic system in generating spatio-temporal ATP and shear stress. Along the transverse direction of the microchannel, dynamic ATP signals of distinct amplitudes coupled with identical shear stress are created, which induce the spatio-temporal diversity in calcium responses. Interestingly, to the multiple combinations of stimuli, the intracellular calcium dynamics reveal two main modes: unimodal and oscillatory modes, showing significant dependence on the features of the spatio-temporal ATP and shear stress stimuli. The present study provides essential information for controlling calcium dynamics by regulating spatio-temporal biochemical and biomechanical stimuli, which shows the potential in directing cellular activities and understanding the occurrence and development of disease.

16

Consolini, Luca, and Mario Tosques. "Spatio-temporal symmetries in control systems: an application to formation control." IFAC Proceedings Volumes 46, no. 23 (2013): 779–84. http://dx.doi.org/10.3182/20130904-3-fr-2041.00111.

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17

Li, Min, Mengshan Li, Bilong Liu, Jiang Liu, Zhen Liu, and Dijia Luo. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention." Sustainability 14, no. 12 (June 16, 2022): 7394. http://dx.doi.org/10.3390/su14127394.

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Traffic flow prediction can provide effective support for traffic management and control and plays an important role in the traffic system. Traffic flow has strong spatio-temporal characteristics, and existing traffic flow prediction models tend to extract long-term dependencies of traffic flow in the temporal and spatial dimensions individually, often ignoring the potential correlations existing between spatio-temporal information of traffic flow. In order to further improve the prediction accuracy, this paper proposes a coordinated attention-based spatio-temporal graph convolutional network (CVSTGCN) model for simultaneously and dynamically capturing the long-term dependencies existing between the spatio-temporal information of traffic flows. CVSTGCN is composed of a full convolutional network structure, which combines coordinate methods to specify the influence degrees of different feature information in different spatio-temporal dimensions, and the spatio-temporal information of different spatio-temporal dimensions by the graph convolutional network. In addition, the hard-swish activation function is introduced to replace the Rectified Linear Unit (ReLU) activation function in the prediction of traffic flow. Finally, evaluation experiments are conducted on two real datasets to demonstrate that the proposed model has the best prediction performance in both short-term and long-term forecasting.

18

Cao, Xiang Ping, Zhao Yang Li, and Mei Xing Liu. "Low-Dimensional Approximation for Control of Spatio-Temporal Processes Using Principal Interaction Patterns." Advanced Materials Research 591-593 (November 2012): 1217–20. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1217.

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Although the first-principal models of the spatio-temporal processes can accurately predict nonlinear and distributed dynamical behaviors, their infinite-dimensional nature does not allow their directly use. In this note, low-dimensional approximations for control of spatio-temporal processes using principal interaction patterns are constructed. Advanced model reduction approach based on spatial basis function expansion together with Galerkin method is used to obtain the low-dimensional approximation. Spatial structure called principal interaction patterns are extracted from the system according to a variational principle and used as basis functions in a Galerkin approximation. The simulations of the burgers equations has illustrated that low-dimensional approximation based on principal interaction patterns for spatio-temporal processes has smaller errors than more conventional approaches using Fourier modes or Empirical Eigenfunctions as basis functions.

19

Fowell, Deborah J., and Minsoo Kim. "The spatio-temporal control of effector T cell migration." Nature Reviews Immunology 21, no. 9 (February 24, 2021): 582–96. http://dx.doi.org/10.1038/s41577-021-00507-0.

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20

Schöll, E. "Pattern formation in semiconductors: control of spatio‐temporal dynamics." Annalen der Physik 516, no. 7-8 (June 14, 2004): 403–13. http://dx.doi.org/10.1002/andp.200451607-804.

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21

Ling, Bowen, and Ilenia Battiato. "Module-Fluidics: Building Blocks for Spatio-Temporal Microenvironment Control." Micromachines 13, no. 5 (May 14, 2022): 774. http://dx.doi.org/10.3390/mi13050774.

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Generating the desired solute concentration signal in micro-environments is vital to many applications ranging from micromixing to analyzing cellular response to a dynamic microenvironment. We propose a new modular design to generate targeted temporally varying concentration signals in microfluidic systems while minimizing perturbations to the flow field. The modularized design, here referred to as module-fluidics, similar in principle to interlocking toy bricks, is constructed from a combination of two building blocks and allows one to achieve versatility and flexibility in dynamically controlling input concentration. The building blocks are an oscillator and an integrator, and their combination enables the creation of controlled and complex concentration signals, with different user-defined time-scales. We show two basic connection patterns, in-series and in-parallel, to test the generation, integration, sampling and superposition of temporally-varying signals. All such signals can be fully characterized by analytic functions, in analogy with electric circuits, and allow one to perform design and optimization before fabrication. Such modularization offers a versatile and promising platform that allows one to create highly customizable time-dependent concentration inputs which can be targeted to the specific application of interest.

22

Mendoza, C., P. L. Ramazza, J. Martínez-Mardones, and S. Boccaletti. "Pinning control of spatio temporal chaos in nonlinear optics." Journal of Physics: Conference Series 134 (November 1, 2008): 012051. http://dx.doi.org/10.1088/1742-6596/134/1/012051.

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23

Hayashi, Yuichiro, Yoshiaki Tagawa, Satoshi Yawata, Shigetada Nakanishi, and Kazuo Funabiki. "Spatio-temporal control of neural activityin vivousing fluorescence microendoscopy." European Journal of Neuroscience 36, no. 6 (July 11, 2012): 2722–32. http://dx.doi.org/10.1111/j.1460-9568.2012.08191.x.

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24

Goswami, Debabrata, and Arijit Kumar De. "Spatio-Temporal Control in Multiphoton Fluorescence Laser-Scanning Microscopy." Biophysical Journal 98, no. 3 (January 2010): 586a. http://dx.doi.org/10.1016/j.bpj.2009.12.3185.

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25

Lee, Eun-Ju, Eugene W. L. Chan, and Muhammad N. Yousaf. "Spatio-Temporal Control of Cell Coculture Interactions on Surfaces." ChemBioChem 10, no. 10 (July 6, 2009): 1648–53. http://dx.doi.org/10.1002/cbic.200900277.

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26

Schöll, E. "Pattern formation in semiconductors: control of spatio-temporal dynamics." Annalen der Physik 13, no. 78 (July 1, 2004): 403–13. http://dx.doi.org/10.1002/andp.200410083.

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27

Duan, Li Hua. "Crime System Simulation and Control Based on Mapping and Inversion Mechanism." Applied Mechanics and Materials 433-435 (October 2013): 1045–48. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1045.

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The problems of crime system simulation and control are studied according to the method of relationship mapping inversion, and from the spatio-temporal analysis point of view. The rules of the criminal investigation from existence to evolution are analyzed using theories. Based on this study, the concept of crime entropy is introduced, and resulted in the establishment of a fundamental frame work for the crime simulation system based on spatio-temporal information system. Moreover, a practicable methodology is presented.

28

MISRA, A. K., and ALOK GUPTA. "A REACTION–DIFFUSION MODEL FOR THE CONTROL OF CHOLERA EPIDEMIC." Journal of Biological Systems 24, no. 04 (December 2016): 431–56. http://dx.doi.org/10.1142/s0218339016500224.

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Understanding the spatio-temporal dynamics of cholera outbreaks may help in devising more effective control procedures. In this paper, we have considered a reaction–diffusion system for biological control of cholera epidemic. Firstly, we have focused on temporal evolution of cholera in a region and its control using lytic bacteriophage in the aquatic reservoirs. Then, we have explored the effect of spatial dispersion of populations on the disease dynamics. We have observed the onset of sustained oscillations via Hopf-bifurcation for the endemic state of temporal system. This onset of fluctuations in populations depends upon the phage adsorption rate. But in the spatially extended setting, all the populations stabilize i.e., the spatio-temporal distribution of all the populations becomes uniform. Some numerical computations have been done to verify analytical results.

29

Fomina, N., C. A. Johnson, A. Maruniak, S. Bahrampour, C. Lang, R. W. Davis, S. Kavusi, and H. Ahmad. "An electrochemical platform for localized pH control on demand." Lab on a Chip 16, no. 12 (2016): 2236–44. http://dx.doi.org/10.1039/c6lc00421k.

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30

Lino, Miguel M., Susana Simões, Sónia Pinho, and Lino Ferreira. "Intracellular delivery of more than one protein with spatio-temporal control." Nanoscale 9, no. 47 (2017): 18668–80. http://dx.doi.org/10.1039/c7nr02414b.

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31

Malet-Karas, Aurore, Marion Noulhiane, and Valérie Doyère. "Dynamics of Spatio-Temporal Binding in Rats." Timing & Time Perception 7, no. 1 (January 11, 2019): 27–47. http://dx.doi.org/10.1163/22134468-20181124.

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Time and space are commonly approached as two distinct dimensions, and rarely combined together in a single task, preventing a comparison of their interaction. In this project, using a version of a timing task with a spatial component, we investigate the learning of a spatio-temporal rule in animals. To do so, rats were placed in front of a five-hole nose-poke wall in a Peak Interval (PI) procedure to obtain a reward, with two spatio-temporal combination rules associated with different to-be-timed cues and lighting contexts. We report that, after successful learning of the discriminative task, a single Pavlovian session was sufficient for the animals to learn a new spatio-temporal association. This was seen as evidence for a beneficial transfer to the new spatio-temporal rule, as compared to control animals that did not experience the new spatio-temporal association during the Pavlovian session. The benefit was observed until nine days later. The results are discussed within the framework of adaptation to a change of a complex associative rule involving interval timing processes.

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HIZANIDIS, J., A. G. BALANOV, A. AMANN, and E. SCHÖLL. "NOISE-INDUCED OSCILLATIONS AND THEIR CONTROL IN SEMICONDUCTOR SUPERLATTICES." International Journal of Bifurcation and Chaos 16, no. 06 (June 2006): 1701–10. http://dx.doi.org/10.1142/s0218127406015611.

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We consider noise-induced charge density dynamics in a semiconductor superlattice. The parameters are fixed in the regime below the Hopf bifurcation that gives birth to spatio-temporal oscillations, where in the absence of noise of the system rests at a fixed point. It is shown that in this case noise can induce in the superlattice quite coherent oscillations of the current through the device. While the regularity of these oscillations depends on the noise intensity, their dominant frequency remains almost constant with variation of the noise level in the system. Further, we demonstrate that a time-delayed feedback scheme that was previously used to control purely temporal oscillations induced by noise, can not only enhance or deteriorate the regularity of stochastic spatio-temporal patterns but also allow for the manipulation of the system's time scales with varying time delay.

33

Schug, Ann-Kathrin, and Herbert Werner. "Spatio-Temporal Loop Shaping for Distributed Control of PDE Systems." IFAC-PapersOnLine 53, no. 2 (2020): 4014–19. http://dx.doi.org/10.1016/j.ifacol.2020.12.2620.

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34

Jabbari, Milad, Rami Khushaba, and Kianoush Nazarpour. "Spatio-temporal warping for myoelectric control: an offline, feasibility study." Journal of Neural Engineering 18, no. 6 (December 1, 2021): 066028. http://dx.doi.org/10.1088/1741-2552/ac387f.

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Abstract Objective. The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach. Combining methods of long and short-term memory (LSTM) and dynamic temporal warping, we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi-channels EMG signals. Main results. Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5%–17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNNs), STW outperformed by 5%–18%. Also, STW showed enhanced performance when compared to the CNN + LSTM model by 2%–14%. All differences were statistically significant with a large effect size. Significance. This feasibility study provides evidence supporting the hypothesis that the STW feature of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

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Wang, Yun‐Zhu, Zhen Wang, Kai‐Ning Wu, and Chen‐Xu Wang. "Spatio‐temporal sampled‐data control for delay reaction‐diffusion systems." International Journal of Robust and Nonlinear Control 32, no. 2 (October 17, 2021): 600–619. http://dx.doi.org/10.1002/rnc.5839.

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36

Elliott, Liam, Ian Moore, and Charlotte Kirchhelle. "Spatio-temporal control of post-Golgi exocytic trafficking in plants." Journal of Cell Science 133, no. 4 (February 15, 2020): jcs237065. http://dx.doi.org/10.1242/jcs.237065.

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37

Sun, Jie, and Michel Sadelain. "The quest for spatio-temporal control of CAR T cells." Cell Research 25, no. 12 (November 17, 2015): 1281–82. http://dx.doi.org/10.1038/cr.2015.131.

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38

Pouysségur, Jacques, Véronique Volmat, and Philippe Lenormand. "Fidelity and spatio-temporal control in MAP kinase (ERKs) signalling." Biochemical Pharmacology 64, no. 5-6 (September 2002): 755–63. http://dx.doi.org/10.1016/s0006-2952(02)01135-8.

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39

Lee, Ki Young, Aleum Kim, Ye Eun Jeon, Jeong Joon Kim, Yong Soon Im, Gyoo Seok Choi, Sang Bong Park, Yun Sik Lim, and Jeong Jin Kang. "Spatio-temporal XACML: the expansion of XACML for access control." International Journal of Security and Networks 10, no. 1 (2015): 56. http://dx.doi.org/10.1504/ijsn.2015.068412.

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Deota, Shaunak, Namrata Shukla, and Ullas Kolthur-Seetharam. "Spatio-Temporal Control of Cellular and Organismal Physiology by Sirtuins." Journal of the Indian Institute of Science 97, no. 1 (February 24, 2017): 147–59. http://dx.doi.org/10.1007/s41745-016-0018-9.

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41

Riechelmann, Eva, Aleksandra Pieczykolan, Gernot Horstmann, Arvid Herwig, and Lynn Huestegge. "Spatio-temporal dynamics of action-effect associations in oculomotor control." Acta Psychologica 180 (October 2017): 130–36. http://dx.doi.org/10.1016/j.actpsy.2017.09.003.

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Frighetto, Daiane Frighetto, Gustavo Maia Souza, and Alexandre Molter. "Spatio-temporal population control applied to management of aquatic plants." Ecological Modelling 398 (April 2019): 77–84. http://dx.doi.org/10.1016/j.ecolmodel.2018.09.027.

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43

Prestel, Andreas, and Heiko M. Möller. "Spatio-temporal control of cellular uptake achieved by photoswitchable cell-penetrating peptides." Chemical Communications 52, no. 4 (2016): 701–4. http://dx.doi.org/10.1039/c5cc06848g.

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Koprulu, Mesru, Nihan Kesim Cicekli, and Adnan Yazici. "Spatio-temporal querying in video databases." Information Sciences 160, no. 1-4 (March 2004): 131–52. http://dx.doi.org/10.1016/j.ins.2003.08.011.

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Yonghui, Wang, and Qin Xiaoyu. "A RFID Spatio-temporal Data Model Oriented Supply Chain." Open Automation and Control Systems Journal 7, no. 1 (February 11, 2015): 31–41. http://dx.doi.org/10.2174/1874444301507010031.

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46

Bakhsh, A., K. Shahzad, and T. Husnain. "Variation in the spatio-temporal expression of insecticidal genes in cotton." Czech Journal of Genetics and Plant Breeding 47, No. 1 (March 18, 2011): 1–9. http://dx.doi.org/10.17221/131/2010-cjgpb.

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The most significant breakthrough in plant biotechnology is the development of the techniques to transform genes from unrelated sources into commercially important crop plants to develop resistance against targeted insect pests. The spatio-temporal expression of insecticidal genes in transgenic cotton varies with plant age, plant parts and environmental conditions. The understanding of this temporal and spatial variation in efficacy and the resulting mechanisms is essential for cotton protection and production. This review summarizes variations in the efficacy of introduced insecticidal genes in cotton crop. The factors contributing to the variability of endotoxins have also been highlighted. The reduction in Bt protein biosynthesis in late-season cotton tissues could be attributed to the overexpression of the Bt gene at earlier stages, which leads to gene regulation at post-transcription levels and consequently results in gene silencing at a later stage. Methylation of the promoter may also play a role in the declined expression of endotoxin proteins. In genetically modified crops several environmental factors have been reported to affect the expression of transgenes. Among environmental factors nitrogen metabolism, inhibition of synthesis, degradation, remobilization and high temperature are attributable to the quantitative reduction in Bt proteins. Applying plant growth regulators or protein enhancers such as Chaperone<sup>TM</sup> may improve Bt cotton efficacy through enhancing the synthesis of proteins. Also some agronomic practices such as nitrogen fertilization and timely irrigation favour the endotoxin expression. Thus, variations in the efficacy of insecticidal genes in transgenic cotton and the involved mechanisms need to be understood fully so as to plan rational resistance management strategies to retard the rate of resistance development and to control target pests effectively by enhancing the endotoxin expression through genetic or agronomic management.

47

Han, Shi-Yuan, Qi-Wei Sun, Qiang Zhao, Rui-Zhi Han, and Yue-Hui Chen. "Traffic Forecasting Based on Integration of Adaptive Subgraph Reformulation and Spatio-Temporal Deep Learning Model." Electronics 11, no. 6 (March 9, 2022): 861. http://dx.doi.org/10.3390/electronics11060861.

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Traffic forecasting provides the foundational guidance for many typical applications in the smart city management, such as urban traffic control, congestion avoidance, and navigation guidance. Many researchers have focused on the spatio-temporal correlations under fixed topology structure in traffic network to improve the traffic forecasting accuracy. Despite their advantages, the existing approaches are not completely discussed that the association relationship among traffic network nodes are not invariable under different traffic conditions. In this paper, a novel traffic forecasting framework is proposed by integrating the dynamic association of traffic nodes with the spatio-temporal deep learning model. To be specific, an adaptive subgraph reformulation algorithm is designed first based on the specific forecasting interval to reduce the interference of irrelevant spatio-temporal information. After that, by enhancing the attention mechanism with the generative decoder, a spatio-temporal deep learning model with only one forward operation is proposed to avoid the degradation of accuracy in the long-term prediction, in which the spatio-temporal information and the external factors (such as weather and holiday) are fused together to be as an input vector. Based on the reformulated subgraph constructed of traffic nodes with closer spatio-temporal correlation, experiments show that the proposed framework consistently outperforms other GNN (Graph Neural Network)-based state-of-the-art baselines for various forecasting intervals on a real-world dataset.

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Azzabou, Noura, and Nikos Paragios. "Spatio-temporal speckle reduction in ultrasound sequences." Inverse Problems & Imaging 4, no. 2 (2010): 211–22. http://dx.doi.org/10.3934/ipi.2010.4.211.

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Zhao, Pengpeng, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5877–84. http://dx.doi.org/10.1609/aaai.v33i01.33015877.

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Abstract:

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

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Mahmoodi, M. Mohsen, Stephanie A. Fisher, Roger Y. Tam, Philip C. Goff, Reid B. Anderson, Jane E. Wissinger, David A. Blank, Molly S. Shoichet, and Mark D. Distefano. "6-Bromo-7-hydroxy-3-methylcoumarin (mBhc) is an efficient multi-photon labile protecting group for thiol caging and three-dimensional chemical patterning." Organic & Biomolecular Chemistry 14, no. 35 (2016): 8289–300. http://dx.doi.org/10.1039/c6ob01045h.

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