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1

Fadavi, R., A. Keyhani, and S. S. Mohtasebi. "An analysis of energy use, input costs and relation between energy inputs and yield of apple orchard." Research in Agricultural Engineering 57, No. 3 (September 22, 2011): 88–96. http://dx.doi.org/10.17221/0/2010-rae.

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This study examines the energy balance between the input and the output per hectare for an apple orchard in the West Azarbaijan province in Iran (2008–2009). Data were collected by using random sampling method for 80 “face to face” questioners. Results showed that the highest share of energy consumption belongs to packaging (57%) and irrigation (16%). The highest share of expenses was found to be 34% and 30% for labor and packaging, respectively. The total energy input for apple production, energy productivity, net energy and output-input energy value were estimated as 101,505 MJ/ha, 0.23 kg/MJ, –56,320 MJ/ha and 0.44, respectively. Results indicated that 71% and 96.7% of total energy input were in indirect and non-renewable form, respectively. The benefit-cost ratio was estimated as 1.77. The regression results revealed that all exogenous variables (for machinery, fertilizers, farmyard manure and packaging energies) were found statistically significant. The packaging had the highest impact (3.23). According to the benefit-cost ratio, large farms were more successful in economic performance.
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2

Boettiger, Charlotte A., and Allison J. Doupe. "Intrinsic and Thalamic Excitatory Inputs Onto Songbird LMAN Neurons Differ in Their Pharmacological and Temporal Properties." Journal of Neurophysiology 79, no. 5 (May 1, 1998): 2615–28. http://dx.doi.org/10.1152/jn.1998.79.5.2615.

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Boettiger, Charlotte A. and Allison J. Doupe. Intrinsic and thalamic excitatory inputs onto songbird LMAN neurons differ in their pharmacological and temporal properties. J. Neurophysiol. 79: 2615–2628, 1998. In passerine songbirds, the lateral portion of the magnocellular nucleus of the anterior neostriatum (LMAN) plays a vital role in song learning, possibly by encoding sensory information and providing sensory feedback to the vocal motor system. Consistent with this, LMAN neurons are auditory, and, as learning progresses, they evolve from a broadly tuned initial state to a state of strong preference for the bird's own song and acute sensitivity to the temporal order of this song. Moreover, normal synaptic activity in LMAN is required during sensory learning for accurate tutor song copying to occur ( Basham et al. 1996 ). To explore cellular and synaptic properties of LMAN that may contribute to this crucial stage of song acquisition, we developed an acute slice preparation of LMAN from zebra finches in the early stages of sensory learning (18–25 days posthatch). We used this preparation to examine intrinsic neuronal properties of LMAN neurons at this stage and to identify two independent excitatory inputs to these neurons and compare each input's pharmacology and short-term synaptic plasticity. LMAN neurons had immature passive membrane properties, well-developed spiking behavior, and received excitatory input from two sources: afferents from the medial portion of the dorsolateral thalamus (DLM), and recurrent axon collaterals from LMAN itself (“intrinsic” input). These two inputs differed in both their pharmacology and temporal properties. Both inputs were glutamatergic, but LMAN responses to intrinsic inputs exhibited a larger N-methyl-d-aspartate component than responses to DLM inputs. Both inputs elicited temporal summation in response to pairs of stimuli delivered at short intervals, but dl-2-amino-5-phosphonovalerate (APV) significantly reduced the temporal summation only of the responses to intrinsic inputs. Moreover, responses to DLM inputs showed consistent paired-pulse depression, whereas the responses to intrinsic inputs did not. The differences between these two inputs suggest that intrinsic circuitry plays an important role in transforming DLM input patterns into the appropriate LMAN output patterns, as has been suggested for mammalian thalamocortical networks. Moreover, in LMAN, such interactions may contribute to the profound temporal and spectral selectivity that these neurons will acquire during learning.
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3

Smith, Elwin G., Ross H. McKenzie, and Cynthia A. Grant. "Optimal Input Use When Inputs Affect Price and Yield." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 51, no. 1 (March 2003): 1–13. http://dx.doi.org/10.1111/j.1744-7976.2003.tb00161.x.

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4

Laurenzano, Michael A., Parker Hill, Mehrzad Samadi, Scott Mahlke, Jason Mars, and Lingjia Tang. "Input responsiveness: using canary inputs to dynamically steer approximation." ACM SIGPLAN Notices 51, no. 6 (August 2016): 161–76. http://dx.doi.org/10.1145/2980983.2908087.

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5

Driessen, Brian J., and Nader Sadegh. "Multi-Input Square Iterative Learning Control With Bounded Inputs." Journal of Dynamic Systems, Measurement, and Control 124, no. 4 (December 1, 2002): 582–84. http://dx.doi.org/10.1115/1.1513794.

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In this paper, we present a very simple modification of the iterative learning control algorithm of S. Arimoto et al. (1984, “Bettering Operation of Robots by Learning,” J. Robot Syst., 1(2), pp. 123–140) to the case where the inputs are bounded. The Jacobian condition presented in K. Avrachenkov (1998, “Iterative Learning Control Based on Quasi-Newton Methods,” Conference on Decision Control, pp. 170–174) is specified instead of the usual condition specified by Arimoto et al. (1984). (See also K. L. Moore, 1993, Iterative Learning Control for Deterministic Systems, Advances in Industrial Control Series, Springer-Verlag, London, UK.) In particular, the former is a condition for monotonicity in the distance to the solution instead of monotonicity in the output error. This observation allows for a simple extension of the methods of Arimoto et al. (1984) to the case of bounded inputs since the process of moving an input back to a bound if it exceeds it does not affect the contraction mapping property; in fact, the distance to the solution, if anything, can only decrease even further. The usual Jacobian error condition, on the other hand, is not sufficient to guarantee the chopping rule will converge to the solution, as proved herein. To the best of our knowledge, these facts have not been previously pointed out in the iterative learning control literature.
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6

Keramati, Hossein, and Seyed-Hassan Mirian-Hosseinabadi. "Generating semantically valid test inputs using constrained input grammars." Information and Software Technology 57 (January 2015): 204–16. http://dx.doi.org/10.1016/j.infsof.2014.09.007.

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7

Onofri, Laura, and Mario Volpe. "Pricing agricultural inputs from biodiversity-rich ecosystems and habitats without input markets." African Journal of Economic and Management Studies 11, no. 1 (December 25, 2019): 122–33. http://dx.doi.org/10.1108/ajems-10-2018-0287.

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Purpose The purpose of this paper is to focus on the analysis of an understudied problem in the economic literature. It proposes a valuation methodology for inputs that come from biodiversity-rich ecosystems/habitats and are used in agro-food production at zero input cost because there is not a market for such inputs. Design/methodology/approach Following Onofri et al. (2017), the authors computed the value of the marginal productivity of different inputs in three selected case studies (Angola, Mozambique and Brazil). Results are theory based and rigorous but show a strong contingency, case based, relative dimension that is captured, in the framework, by the “relativity ratio.” The ratio expresses the relative weight of the value generated by the input that comes from biodiversity-rich ecosystems/habitats in the per capita monthly available income of the farmer and aims at conveying additional insights to the economic valuation. Findings In this paper, the assessment of agricultural inputs value (price) in the absence of inputs markets is done, with an application to three different case studies. The inputs are peculiar since they come from habitats and ecosystems that are very biodiversity-rich. Originality/value The paper proposes a practical, though rigorous, methodology for the assessment of the value (price) of agricultural inputs in absence of inputs markets. Markets do not exist since the inputs come from biodiversity-rich habitats and ecosystems.
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8

FORSYTHE, RICHARD H. "Academic Inputs." Poultry Science 69, no. 12 (December 1990): 2107–9. http://dx.doi.org/10.3382/ps.0692107.

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9

Beeber, Linda S., Regina Canuso, and Sara Emory. "Instrumental Inputs." Advances in Nursing Science 27, no. 4 (October 2004): 275–86. http://dx.doi.org/10.1097/00012272-200410000-00004.

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10

Gross, Michael. "New inputs." Current Biology 18, no. 3 (February 2008): R100—R101. http://dx.doi.org/10.1016/j.cub.2008.01.036.

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11

Yates, Darran. "Converging inputs." Nature Reviews Neuroscience 19, no. 8 (July 10, 2018): 441. http://dx.doi.org/10.1038/s41583-018-0040-1.

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12

Palm, Rainer, and Dimiter Driankov. "Fuzzy inputs." Fuzzy Sets and Systems 70, no. 2-3 (March 1995): 315–35. http://dx.doi.org/10.1016/0165-0114(94)00225-v.

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13

Ureeb, S., and M. S. H. Khiyal. "Migrating Inputs: A Comparator Based Sorting." International Journal of Machine Learning and Computing 5, no. 1 (February 2015): 31–35. http://dx.doi.org/10.7763/ijmlc.2015.v5.478.

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14

Burkitt, A. N., and G. M. Clark. "Synchronization of the Neural Response to Noisy Periodic Synaptic Input." Neural Computation 13, no. 12 (December 1, 2001): 2639–72. http://dx.doi.org/10.1162/089976601317098475.

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The timing information contained in the response of a neuron to noisy periodic synaptic input is analyzed for the leaky integrate-and-fire neural model. We address the question of the relationship between the timing of the synaptic inputs and the output spikes. This requires an analysis of the interspike interval distribution of the output spikes, which is obtained in the gaussian approximation. The conditional output spike density in response to noisy periodic input is evaluated as a function of the initial phase of the inputs. This enables the phase transition matrix to be calculated, which relates the phase at which the output spike is generated to the initial phase of the inputs. The interspike interval histogram and the period histogram for the neural response to ongoing periodic input are then evaluated by using the leading eigenvector of this phase transition matrix. The synchronization index of the output spikes is found to increase sharply as the inputs become synchronized. This enhancement of synchronization is most pronounced for large numbers of inputs and lower frequencies of modulation and also for rates of input near the critical input rate. However, the mutual information between the input phase of the stimulus and the timing of output spikes is found to decrease at low input rates as the number of inputs increases. The results show close agreement with those obtained from numerical simulations for large numbers of inputs.
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15

Dunn, Abe, Joshua D. Gottlieb, and Adam Hale Shapiro. "Administration above Administrators: The Changing Technology of Health Care Management." AEA Papers and Proceedings 110 (May 1, 2020): 274–78. http://dx.doi.org/10.1257/pandp.20201031.

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This paper measures the costs and types of administrative inputs in health care. We use data on labor and nonlabor inputs by industry and categorize them as administrative or not. We find that nonlabor inputs are a critical part of administrative spending, over and above labor inputs. Trends in nonlabor administrative input spending have differed dramatically from that of labor input spending for hospitals over the last 20 years. Hospitals have substituted away from office workers and toward externally purchased inputs. The share of managers and technical workers in administration has grown. The technology of health care administration is changing.
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16

Lee, Keum W., and Sahjendra N. Singh. "Adaptive Control of Multi-Input Aeroelastic System with Constrained Inputs." Journal of Guidance, Control, and Dynamics 38, no. 12 (December 2015): 2337–50. http://dx.doi.org/10.2514/1.g001022.

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17

Morris, Max D. "Input screening: Finding the important model inputs on a budget." Reliability Engineering & System Safety 91, no. 10-11 (October 2006): 1252–56. http://dx.doi.org/10.1016/j.ress.2005.11.022.

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18

Smallwood, David O. "Multiple-Input Multiple-Output (MIMO) Linear Systems Extreme Inputs/Outputs." Shock and Vibration 14, no. 2 (2007): 107–31. http://dx.doi.org/10.1155/2007/701837.

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A linear structure is excited at multiple points with a stationary normal random process. The response of the structure is measured at multiple outputs. If the autospectral densities of the inputs are specified, the phase relationships between the inputs are derived that will minimize or maximize the trace of the autospectral density matrix of the outputs. If the autospectral densities of the outputs are specified, the phase relationships between the outputs that will minimize or maximize the trace of the input autospectral density matrix are derived. It is shown that other phase relationships and ordinary coherence less than one will result in a trace intermediate between these extremes. Least favorable response and some classes of critical response are special cases of the development. It is shown that the derivation for stationary random waveforms can also be applied to nonstationary random, transients, and deterministic waveforms.
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19

Linder, Jonas, and Martin Enqvist. "Identification of systems with unknown inputs using indirect input measurements." International Journal of Control 90, no. 4 (November 3, 2016): 729–45. http://dx.doi.org/10.1080/00207179.2016.1222557.

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20

Morita, Kenji, Kunichika Tsumoto, and Kazuyuki Aihara. "Possible Effects of Depolarizing GABAA Conductance on the Neuronal Input–Output Relationship: A Modeling Study." Journal of Neurophysiology 93, no. 6 (June 2005): 3504–23. http://dx.doi.org/10.1152/jn.00988.2004.

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Recent in vitro experiments revealed that the GABAA reversal potential is about 10 mV higher than the resting potential in mature mammalian neocortical pyramidal cells; thus GABAergic inputs could have facilitatory, rather than inhibitory, effects on action potential generation under certain conditions. However, how the relationship between excitatory input conductances and the output firing rate is modulated by such depolarizing GABAergic inputs under in vivo circumstances has not yet been understood. We examine herewith the input–output relationship in a simple conductance-based model of cortical neurons with the depolarized GABAA reversal potential, and show that a tonic depolarizing GABAergic conductance up to a certain amount does not change the relationship between a tonic glutamatergic driving conductance and the output firing rate, whereas a higher GABAergic conductance prevents spike generation. When the tonic glutamatergic and GABAergic conductances are replaced by in vivo–like highly fluctuating inputs, on the other hand, the effect of depolarizing GABAergic inputs on the input–output relationship critically depends on the degree of coincidence between glutamatergic input events and GABAergic ones. Although a wide range of depolarizing GABAergic inputs hardly changes the firing rate of a neuron driven by noncoincident glutamatergic inputs, a certain range of these inputs considerably decreases the firing rate if a large number of driving glutamatergic inputs are coincident with them. These results raise the possibility that the depolarized GABAA reversal potential is not a paradoxical mystery, but is instead a sophisticated device for discriminative firing rate modulation.
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21

Christoffel, Daniel J., Jessica J. Walsh, Paul Hoerbelt, Boris D. Heifets, Pierre Llorach, Ricardo C. Lopez, Charu Ramakrishnan, Karl Deisseroth, and Robert C. Malenka. "Selective filtering of excitatory inputs to nucleus accumbens by dopamine and serotonin." Proceedings of the National Academy of Sciences 118, no. 24 (June 8, 2021): e2106648118. http://dx.doi.org/10.1073/pnas.2106648118.

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The detailed mechanisms by which dopamine (DA) and serotonin (5-HT) act in the nucleus accumbens (NAc) to influence motivated behaviors in distinct ways remain largely unknown. Here, we examined whether DA and 5-HT selectively modulate excitatory synaptic transmission in NAc medium spiny neurons in an input-specific manner. DA reduced excitatory postsynaptic currents (EPSCs) generated by paraventricular thalamus (PVT) inputs but not by ventral hippocampus (vHip), basolateral amygdala (BLA), or medial prefrontal cortex (mPFC) inputs. In contrast, 5-HT reduced EPSCs generated by inputs from all areas except the mPFC. Release of endogenous DA and 5-HT by methamphetamine (METH) and (±)3,4-methylenedioxymethamphetamine (MDMA), respectively, recapitulated these input-specific synaptic effects. Optogenetic inhibition of PVT inputs enhanced cocaine-conditioned place preference, whereas mPFC input inhibition reduced the enhancement of sociability elicited by MDMA. These findings suggest that the distinct, input-specific filtering of excitatory inputs in the NAc by DA and 5-HT contribute to their discrete behavioral effects.
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22

Baptist, Simon, and Cameron Hepburn. "Intermediate inputs and economic productivity." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1986 (March 13, 2013): 20110565. http://dx.doi.org/10.1098/rsta.2011.0565.

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Many models of economic growth exclude materials, energy and other intermediate inputs from the production function. Growing environmental pressures and resource prices suggest that this may be increasingly inappropriate. This paper explores the relationship between intermediate input intensity, productivity and national accounts using a panel dataset of manufacturing subsectors in the USA over 47 years. The first contribution is to identify sectoral production functions that incorporate intermediate inputs, while allowing for heterogeneity in both technology and productivity. The second contribution is that the paper finds a negative correlation between intermediate input intensity and total factor productivity (TFP)—sectors that are less intensive in their use of intermediate inputs have higher productivity. This finding is replicated at the firm level. We propose tentative hypotheses to explain this association, but testing and further disaggregation of intermediate inputs is left for further work. Further work could also explore more directly the relationship between material inputs and economic growth—given the high proportion of materials in intermediate inputs, the results in this paper are suggestive of further work on material efficiency. Depending upon the nature of the mechanism linking a reduction in intermediate input intensity to an increase in TFP, the implications could be significant. A third contribution is to suggest that an empirical bias in productivity, as measured in national accounts, may arise due to the exclusion of intermediate inputs. Current conventions of measuring productivity in national accounts may overstate the productivity of resource-intensive sectors relative to other sectors.
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23

Halliday, David M. "Weak, Stochastic Temporal Correlation of Large-Scale Synaptic Input Is a Major Determinant of Neuronal Bandwidth." Neural Computation 12, no. 3 (March 1, 2000): 693–707. http://dx.doi.org/10.1162/089976600300015754.

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We determine the bandwidth of a model neurone to large-scale synaptic input by assessing the frequency response between the outputs of a two-cell simulation that share a percentage of the total synaptic input. For temporally uncorrelated inputs, a large percentage of common inputs are required before the output discharges of the two cells exhibit significant correlation. In contrast, a small percentage (5%) of the total synaptic input that involves stochastic spike trains that are weakly correlated over a broad range of frequencies exert a clear influence on the output discharge of both cells over this range of frequencies. Inputs that are weakly correlated at a single frequency induce correlation between the output discharges only at the frequency of correlation. The strength of temporal correlation required is sufficiently weak that analysis of a sample pair of input spike trains could fail to reveal the presence of correlated input. Weak temporal correlation between inputs is therefore a major determinant of the transmission to the output discharge of frequencies present in the spike discharges of presynaptic inputs, and therefore of neural bandwidth.
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24

Tahmasian, Sevak, David W. Allen, and Craig A. Woolsey. "On averaging and input optimization of high-frequency mechanical control systems." Journal of Vibration and Control 24, no. 5 (July 6, 2016): 937–55. http://dx.doi.org/10.1177/1077546316655706.

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This paper presents the optimization of input amplitudes for mechanical control-affine systems with high-frequency, high-amplitude inputs. The problem consists of determining the input waveform shapes and the relative phases between inputs to minimize the input amplitudes while accomplishing some control objective. The effects of the input waveforms and relative phases on the dynamics are investigated using averaging. It is shown that of all zero-mean, periodic functions, square waves require the smallest amplitudes to accomplish a control objective. Using the averaging theorem the problem of input optimization is transformed into a constrained optimization problem. The constraints are algebraic nonlinear equalities in terms of the amplitudes of the inputs and their relative phases. The constrained optimization problem may be solved using analytical or numerical methods. A second approach uses finite Fourier series to solve the input optimization problem. This second approach confirms the earlier results concerning minimum amplitude inputs and is then applied to the problem of minimizing control energy.
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25

Suh, Dong Hee, and Charles B. Moss. "Examining the Input and Output Linkages in Agricultural Production Systems." Agriculture 11, no. 1 (January 11, 2021): 54. http://dx.doi.org/10.3390/agriculture11010054.

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This paper conducts a comprehensive analysis of the agricultural sector’s resource allocation and production decisions. This paper uses the differential systems with quasi-fixity to evaluate the complete agricultural production system, which examines the input and output linkages in terms of elasticities. The differential systems are estimated using the maximum likelihood estimation technique based on the two-step profit-maximizing procedure in theory. The results reveal that livestock production requires more intermediate inputs, but crop production depends on all the inputs, such as labor, capital, and intermediate inputs. In addition, the results show that input demand is inelastic, indicating that the agricultural sector has little flexibility in adjusting the demand for inputs in response to changes in input prices. Substitutable relationships among labor, capital, and intermediate inputs exist, which may reduce the pressures on production costs when input prices rise. Regarding the quasi-fixed input, land expansion changes the composition of labor and intermediate inputs, showing that the agricultural sector reduces the intensive margin when it pursues the extensive margin. Furthermore, the results show that agricultural supply is not very responsive to the respective price changes. Along with the inelastic output supply, there exist substitutable relationships between livestock and crop supply, showing that relative price changes can alter output composition in supply. The agricultural sector also reallocates more land areas into crop production rather than livestock production.
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26

Suh, Dong Hee, and Charles B. Moss. "Examining the Input and Output Linkages in Agricultural Production Systems." Agriculture 11, no. 1 (January 11, 2021): 54. http://dx.doi.org/10.3390/agriculture11010054.

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This paper conducts a comprehensive analysis of the agricultural sector’s resource allocation and production decisions. This paper uses the differential systems with quasi-fixity to evaluate the complete agricultural production system, which examines the input and output linkages in terms of elasticities. The differential systems are estimated using the maximum likelihood estimation technique based on the two-step profit-maximizing procedure in theory. The results reveal that livestock production requires more intermediate inputs, but crop production depends on all the inputs, such as labor, capital, and intermediate inputs. In addition, the results show that input demand is inelastic, indicating that the agricultural sector has little flexibility in adjusting the demand for inputs in response to changes in input prices. Substitutable relationships among labor, capital, and intermediate inputs exist, which may reduce the pressures on production costs when input prices rise. Regarding the quasi-fixed input, land expansion changes the composition of labor and intermediate inputs, showing that the agricultural sector reduces the intensive margin when it pursues the extensive margin. Furthermore, the results show that agricultural supply is not very responsive to the respective price changes. Along with the inelastic output supply, there exist substitutable relationships between livestock and crop supply, showing that relative price changes can alter output composition in supply. The agricultural sector also reallocates more land areas into crop production rather than livestock production.
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27

Xu-Friedman, Matthew A., and Wade G. Regehr. "Dynamic-Clamp Analysis of the Effects of Convergence on Spike Timing. II. Few Synaptic Inputs." Journal of Neurophysiology 94, no. 4 (October 2005): 2526–34. http://dx.doi.org/10.1152/jn.01308.2004.

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Sensory pathways in the nervous system possess mechanisms for decreasing spike-timing variability (“jitter”), probably to increase acuity. Most studies of jitter reduction have focused on convergence of many subthreshold inputs. However, many neurons receive only a few active inputs at any given time, and jitter reduction under these conditions is not well understood. We examined this issue using dynamic-clamp recordings in slices from mouse auditory brain stem. Significant jitter reduction was possible with as few as two inputs, provided the inputs had several features. First, jitter reduction was greatest and most reliable for supra-threshold inputs. Second, significant jitter reduction occurred when the distribution of input times had a rapid onset, i.e., for alpha- but not for Gaussian-distributed inputs. Third, jitter reduction was compromised unless late inputs were suppressed by the refractory period of the cell. These results contrast with the finding in the previous paper in which many subthreshold inputs contribute to jitter reduction, whether alpha- or Gaussian-distributed. In addition, convergence of many subthreshold inputs could fail to elicit any postsynaptic response when the input distribution outlasted the refractory period of the cell. These significant differences indicate that each means of reducing jitter has advantages and disadvantages and may be more effective for different neurons depending on the properties of their inputs.
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Wang, Jiaoyan, Xiaoshan Zhao, and Chao Lei. "Pulse Inputs Affect Timings of Spikes in Neurons with or Without Time Delays." International Journal of Nonlinear Sciences and Numerical Simulation 20, no. 3-4 (May 26, 2019): 257–67. http://dx.doi.org/10.1515/ijnsns-2017-0070.

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AbstractInputs can change timings of spikes in neurons. But it is still not clear how input’s parameters for example injecting time of inputs affect timings of neurons. HR neurons receiving both weak and strong inputs are considered. How pulse inputs affecting neurons is studied by using the phase-resetting curve technique. For a single neuron, weak pulse inputs may advance or delay the next spike, while strong pulse inputs may induce subthreshold oscillations depending on parameters such as injecting timings of inputs. The behavior of synchronization in a network with or without coupling delays can be predicted by analysis in a single neuron. Our results can be used to predict the effects of inputs on other spiking neurons.
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Zhao, Xiu Ying, Hong Yu Wang, Shou Yu Tong, De You Fu, and Hai Shen Zhou. "Nonlinear Spectral Subtraction Method for Elimination of Aircraft Engine’s Noise from Degraded Speech Signals." Applied Mechanics and Materials 130-134 (October 2011): 1327–30. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1327.

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The spectral subtraction is one of the best methods for elimination of approximate cyclical engine’s noise from degraded speech signal. Here we turn to research about the nonlinear spectral subtraction method and its improved model. After studying the nonlinear method we turn to this method that whether it can improve the quality of enhanced speech signal, propose the short-time spectral subtraction, which needs two inputs. The main input is containing the voice that is corrupted by noise. The other input (noise reference input) contains noise related in some way to that of the main input (background noise). Then use the main input’s frequency spectrum subtract the other input’s frequency spectrum. The results of experiment have proved it’s effective.
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30

Koert, Elisabeth, and Thomas Kuenzel. "Small dendritic synapses enhance temporal coding in a model of cochlear nucleus bushy cells." Journal of Neurophysiology 125, no. 3 (March 1, 2021): 915–37. http://dx.doi.org/10.1152/jn.00331.2020.

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We modeled dendritic inputs from the auditory nerve that spherical bushy cells of the cochlear nucleus receive. Dendritic inputs caused both tonic depolarization and modulation of the membrane potential at the input frequency. This improved the rate, entrainment, and temporal precision of output action potentials. Our simulations suggest a role for small dendritic inputs in auditory processing: they modulate the efficacy of the main input supporting temporal precision and fidelity in these central auditory neurons.
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31

Pellokila, Marthen Robinson. "PENERAPAN PENDUGAAN EFISIENSI ALOKATIF PADA USAHATANI BUNCIS (Phaseolus vulgaris, L)." Buletin Ilmiah IMPAS 21, no. 1 (September 19, 2020): 81–90. http://dx.doi.org/10.35508/impas.v21i01.2614.

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ABTRACT Efficiency is one of the important indicators to assess the performance of a company or farm. Efficiency guarantees the use of certain inputs to achieve maximum output levels (technical efficiency) and also efficiency ensures the use of certain inputs that maximize profits (price efficiency or allocative efficiency). This article discusses the application of the estimation of price efficiency / allocative efficiency of the use of production inputs in bean farming using the linearized Cobb-Douglas Production function. The results of the analysis shows that the application of price efficiency estimation for production inputs using the Cobb-Douglas production function is satisfactory as long as the classical assumptions required by the multiple regression are fulfilled. Of the five production inputs included in the model, only one production input provides a significant value to production, namely the production input for the land area use. Thus, only the production input for land area use is estimated at the value of its price efficiency. Based on the results of the analysis, it is found that the use of production inputs for land area use has not yet reached its price efficiency.
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32

Mao, Yu-Ting, Tian-Miao Hua, and Sarah L. Pallas. "Competition and convergence between auditory and cross-modal visual inputs to primary auditory cortical areas." Journal of Neurophysiology 105, no. 4 (April 2011): 1558–73. http://dx.doi.org/10.1152/jn.00407.2010.

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Sensory neocortex is capable of considerable plasticity after sensory deprivation or damage to input pathways, especially early in development. Although plasticity can often be restorative, sometimes novel, ectopic inputs invade the affected cortical area. Invading inputs from other sensory modalities may compromise the original function or even take over, imposing a new function and preventing recovery. Using ferrets whose retinal axons were rerouted into auditory thalamus at birth, we were able to examine the effect of varying the degree of ectopic, cross-modal input on reorganization of developing auditory cortex. In particular, we assayed whether the invading visual inputs and the existing auditory inputs competed for or shared postsynaptic targets and whether the convergence of input modalities would induce multisensory processing. We demonstrate that although the cross-modal inputs create new visual neurons in auditory cortex, some auditory processing remains. The degree of damage to auditory input to the medial geniculate nucleus was directly related to the proportion of visual neurons in auditory cortex, suggesting that the visual and residual auditory inputs compete for cortical territory. Visual neurons were not segregated from auditory neurons but shared target space even on individual target cells, substantially increasing the proportion of multisensory neurons. Thus spatial convergence of visual and auditory input modalities may be sufficient to expand multisensory representations. Together these findings argue that early, patterned visual activity does not drive segregation of visual and auditory afferents and suggest that auditory function might be compromised by converging visual inputs. These results indicate possible ways in which multisensory cortical areas may form during development and evolution. They also suggest that rehabilitative strategies designed to promote recovery of function after sensory deprivation or damage need to take into account that sensory cortex may become substantially more multisensory after alteration of its input during development.
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Jasso, Guillermina. "Linking Input Inequality and Outcome Inequality." Sociological Methods & Research 50, no. 3 (July 27, 2021): 944–1005. http://dx.doi.org/10.1177/00491241211014245.

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Inequality often appears in linked pairs of variables. Examples include schooling and income, income and consumption, and wealth and happiness. Consider the famous words of Veblen: “wealth confers honor.” Understanding inequality requires understanding input inequality, outcome inequality, and the relation between the two—in both inequality between persons and inequality between subgroups. This article contributes to the methodological toolkit for studying inequality by developing a framework that makes explicit both input inequality and outcome inequality and by addressing three main associated questions: (1) How do the mechanisms for generating and altering inequality differ across inputs and outcomes? (2) Which have more inequality—inputs or outcomes? (3) Under what conditions, and by what mechanisms, does input inequality affect outcome inequality? Results include the following: First, under specified conditions, distinctive mechanisms govern inequality in inputs and inequality in outcomes. Second, input inequality and outcome inequality can be the same or different; if different, whether inequality is greater among inputs or outcomes depends on the configuration of outcome function, types of inputs, distributional form of and inequality in cardinal inputs, and number of and associations among inputs. Third, the link between input inequality and outcome inequality is multiform; it can be nonexistent, linear, or nonlinear, and if nonlinear, it can be concave or convex. More deeply, this work signals the formidable empirical challenges in studying inequality, but also the fast growing toolbox. For example, even if the outcome distribution is difficult to derive, fundamental theorems on the variance make it possible to analyze the input–outcome inequality connection. Similarly, within specified distributions, the general inequality parameter makes it possible to express results in terms of both measures of overall inequality and measures of subgroup inequality.
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Fukuyama, Hirofumi, and William L. Weber. "Network Performance of Japanese Credit Cooperatives, 2004–2007." International Journal of Information Technology & Decision Making 14, no. 04 (July 2015): 825–46. http://dx.doi.org/10.1142/s0219622014500904.

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We construct a network performance measure for producers that use inputs to produce an intermediate output in a first stage of production and then use the intermediate output to produce final outputs in a second stage of production. Producers face a budget constraint and choose inputs, given input prices. Although various input combinations will be affordable, a smaller set of input combinations will be optimal in maximizing final outputs. We compare final outputs for firms that take inputs as given with final outputs when firms can optimally reallocate inputs given the fixed budget. This comparison allows measurement of the lost final output caused by a misallocation of resources in the first stage of production. We apply our method to study the performance of Japanese credit cooperatives during 2004 to 2007.
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Jamil, Nida, and Rabia Arif. "Increasing Exports through Tariff Reductions on Intermediate Goods." LAHORE JOURNAL OF ECONOMICS 24, no. 1 (January 1, 2019): 29–53. http://dx.doi.org/10.35536/lje.2019.v24.i1.a2.

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To counter the severe trade deficit problem that Pakistan faces, we explain how to move up the value chain of exports by reducing tariff rates on the intermediate inputs used by local manufacturers. The availability of cheaper intermediate inputs through tariff reductions can substantially reduce input constraints. We begin by identifying trends in the tariff rates imposed on intermediate inputs, and their imports over time by Pakistan and its counterparts. Using an instrumental variable approach, we measure the gains that can be achieved by importing more of these intermediate inputs in terms of export performance indicators. We emphasize that input tariff reductions could help Pakistan expand exports. We also identify specific sectors in which intermediate input tariff reductions could have significant gains for Pakistan in terms of export growth. We recommend the need to reduce intermediate input tariffs in these sectors only, rather than general tariff reductions across all sectors.
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36

Buckley, Neil, Stuart Mestelman, and Mohamed Shehata. "Subsidizing public inputs." Journal of Public Economics 87, no. 3-4 (March 2003): 819–46. http://dx.doi.org/10.1016/s0047-2727(01)00109-8.

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37

Vogt, Nina. "Finding exciting inputs." Nature Methods 17, no. 1 (January 2020): 30. http://dx.doi.org/10.1038/s41592-019-0719-x.

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38

Disley, Philip, Chris Hatton, and Dave Dagnan. "Inputs and outcomes." Journal of Intellectual Disabilities 16, no. 4 (November 14, 2012): 297–306. http://dx.doi.org/10.1177/1744629512466793.

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39

SZÉKELY, MIKLÓS, MÁRTA BALASKÓ, and ANDREJ A. ROMANOVSKY. "Peripheral Neural Inputs." Annals of the New York Academy of Sciences 813, no. 1 Thermoregulat (March 1997): 427–34. http://dx.doi.org/10.1111/j.1749-6632.1997.tb51728.x.

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40

Marks, Robert E. "Modelling heterogeneous inputs." Economics Letters 42, no. 2-3 (January 1993): 167–71. http://dx.doi.org/10.1016/0165-1765(93)90056-i.

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41

Yang, Ling, V. Sarath Babu, Juan Zou, Xu Can Cai, Ting Wu, and Li Lin. "The Development of an Intelligent Monitoring System for Agricultural Inputs Basing on DBN-SOFTMAX." Journal of Sensors 2018 (October 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/6025381.

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To solve the problem of unreliability of traceability information in the traceability system, we developed an intelligent monitoring system to realize the real-time online acquisition of physicochemical parameters of the agricultural inputs and to predict the varieties of input products accurately. Firstly, self-developed monitoring equipment was used to realize real-time acquisition, format conversion and pretreatment of the physicochemical parameters of inputs, and real-time communication with the cloud platform server. In this process, LoRa technology was adopted to solve the wireless communication problems between long-distance, low-power, and multinode environments. Secondly, a deep belief network (DBN) model was used to learn unsupervised physicochemical parameters of input products and extract the input features. Finally, these input features were utilized on the softmax classifier to establish the classification model, which could accurately predict the varieties of agricultural inputs. The results showed that when six kinds of pesticides, chemical fertilizers, and other agricultural inputs were predicted through the system, the prediction accuracy could reach 98.5%. Therefore, the system can be used to monitor the varieties of agrarian inputs effectively and use in real-time to ensure the authenticity and accuracy of the traceability information.
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Xu, Yang, Jun Zhao, Xiao Yan Xu, and Dan Zhu. "Response Spectrum Analysis of a Large-Span Hangar Subjected to Multi-Dimensional Seismic Inputs." Advanced Materials Research 639-640 (January 2013): 906–10. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.906.

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The multi-dimensional seismic response of a single-span hangar was studied by response spectrum analysis method. The lateral displacements of the structure, forces of its supporting columns and its roof structure were calculated and compared with each other for cases of one-, two- and three-dimensional (1D, 2D and 3D) seismic inputs. The results show that, compared with the case of 1D earthquake input, the effects of horizontally 2D earthquake inputs on the internal forces and displacements of its supporting columns in the primary direction of input are obvious when it is along the symmetrical axis of the hangar and their effects in the secondary direction of input are even more important which results in great increases of the internal forces and displacements in that direction. The vertical seismic input has almost no effect on the internal forces and displacements of columns. The internal forces in different parts of the roof structure are controlled by horizontal or vertical inputs, respectively, and, compared with those from horizontally or vertically 1D inputs, the responses from 3D inputs are increased and the effects should be considered in seismic design.
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43

Sri Ayu Kurniati and Darus. "OPTIMALISASI PENGGUNAAN INPUT USAHATANI BAWANG MERAH DI DESA SUNGAI GERINGGING KECAMATAN KAMPAR KIRI KABUPATEN KAMPAR PROPINSI RIAU." DINAMIKA PERTANIAN 34, no. 3 (August 5, 2020): 211–18. http://dx.doi.org/10.25299/dp.2018.vol34(3).5415.

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ABSTRACT Shallots are a strategic commodity but still require attention in the use of optimal inputs to achieve maximum results. All farm inputs are still limited in number while high production is highly expected by farmers. The purpose of this study was to determine the use of inputs, analyze the use of inputs in order to achieve optimal conditions and analyze the effect of input price changes on the optimal solution on red onion farming in Sungai Geringging Village, Kampar Kiri District, Kampar Regency. Descriptive qualitative and quantitative analysis methods using Linear Programming. The results of input use research state that the area of the land is narrow that is an average of 0.25 hectares, the seeds are superior but the number of uses is still below standard, more use of labor outside the family, dominant farmers use inorganic fertilizers, use pesticides to repel pests and work equipment simple one. The use of farming inputs is not optimal so that the reduction or addition of input availability will not affect the total profit in optimal conditions. The effect of changes when an input price increases and decreases by 3.25 percent does not show a difference when compared to the initial optimal conditions. Keywords: Optimization, Input, Shallot, Linear Programming, LINDO
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44

Morelli, Eugene A. "Flight Test of Optimal Inputs and Comparison with Conventional Inputs." Journal of Aircraft 36, no. 2 (March 1999): 389–97. http://dx.doi.org/10.2514/2.2469.

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45

Huang, C. S., H. Hiraba, and B. J. Sessle. "Input-output relationships of the primary face motor cortex in the monkey (Macaca fascicularis)." Journal of Neurophysiology 61, no. 2 (February 1, 1989): 350–62. http://dx.doi.org/10.1152/jn.1989.61.2.350.

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1. Somatosensory afferent input and its relationship with efferent output were examined in the primary face motor cortex (MI) and adjacent cerebral cortical areas. Excitatory afferent inputs were tested in a total of 1,654 single neurons recorded in awake or anesthetized monkeys (Macaca fascicularis), and output was characterized in these same monkeys by the movement and EMG responses evoked by intracortical microstimulation (ICMS) at the neuronal recording sites. 2. Most neurons in the MI area responded to light tactile stimulation of the orofacial region, especially the upper lip, lower lip, and tongue. Although contralateral afferent inputs predominated, 21% of the neurons received ipsilateral or bilateral orofacial inputs. The afferent input evoked by tactile stimulation of the upper and lower lips was represented especially at the medial border and the input from the tongue at the lateral border of MI. However, in most regions of MI between the medial and lateral borders, an intermingling of tactile inputs from different orofacial areas occurred. Multiple representation of tactile input from the same orofacial area was found in several, often quite separate, intracortical sites in MI. 3. Only a small proportion of the MI neurons could be activated by the deep stimuli used (e.g., stretch and pressure applied to muscle, passive jaw movement, low-intensity stimulation of hypoglossal nerve) from the orofacial region. Those neurons which did respond to these low-threshold deep inputs were not clearly segregated from those which responded to tactile input, although most of the neurons receiving deep input were located in the rostral part of MI. 4. A somatotopic pattern of representation of orofacial tactile input was more obvious in the primary face somatosensory cortex (SI). At the medial border of SI, the periorbital area was represented, then followed laterally in sequence the tactile representation of the upper lip, lower lip, and intraoral area. Contralateral afferent inputs predominated, but as in MI, a considerable proportion of SI neurons received ipsilateral or bilateral orofacial inputs. Few neurons in the region explored (areas 3b, 1, and 2) responded to deep orofacial stimuli. 5. Tactile input also dominated the input patterns of neurons in the premotor cortex (PM). Most neurons received ipsilateral or bilateral orofacial afferent inputs and no clear somatotopic pattern was noted. Several PM neurons were also activated by visual stimuli. 6. Muscle twitches evoked by ICMS were limited to MI.(ABSTRACT TRUNCATED AT 400 WORDS)
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46

Jilito, Mideksa Fufa, and Desalegn Yadeta Wedajo. "Trends and Challenges in Improved Agricultural Inputs Use by Smallholder Farmers in Ethiopia: A Review." Turkish Journal of Agriculture - Food Science and Technology 8, no. 11 (November 25, 2020): 2286–92. http://dx.doi.org/10.24925/turjaf.v8i11.2286-2292.2988.

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Much of the sustained agricultural growth necessary for poverty reduction and sustainable development comes from adequate and expanded improved agricultural inputs use like hybrid seeds, pesticides, insecticides, herbicides, fungicides, and inorganic fertilizer. However, smallholder farmers faced various and empirically unidentified constraints in the sustainable use of improved agricultural inputs. This study, therefore, is to examine the trends and challenges farmers faced to use improved inputs. The study has used secondary data from various databases such as FAOSTAT, World Bank, Ethiopian Institute of Agricultural Research and agricultural statistics. Simple graphical sketch, tabular and percentage analysis was employed for interpretation of the data. The study revealed that the trends in the use of various improved inputs are not based on the regular basis. The sustainable improved input intensification by smallholder farmers was influenced by various socio-economic, physical, and institutional factors. Understanding trends and challenges in input utilization provide baseline information for input intensification policies and strategies. Therefore, this review pointed out that it is possible to increase inputs intensification by smallholder farmers.
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47

Asadi, Haniyeh, Kaka Shahedi, Ben Jarihani, and Roy Sidle. "Rainfall-Runoff Modelling Using Hydrological Connectivity Index and Artificial Neural Network Approach." Water 11, no. 2 (January 26, 2019): 212. http://dx.doi.org/10.3390/w11020212.

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The input selection process for data-driven rainfall-runoff models is critical because input vectors determine the structure of the model and, hence, can influence model results. Here, hydro-geomorphic and biophysical time series inputs, including Normalized Difference Vegetation Index (NDVI) and Index of Connectivity (IC; a type of hydrological connectivity index), in addition to climatic and hydrologic inputs were assessed. Selected inputs were used to develop Artificial Neural Networks (ANNs) in the Haughton River catchment and the Calliope River catchment, Queensland, Australia. Results show that incorporating IC as a hydro-geomorphic parameter and remote sensing NDVI as a biophysical parameter, together with rainfall and runoff as hydro-climatic parameters, can improve ANN model performance compared to ANN models using only hydro-climatic parameters. Comparisons amongst different input patterns showed that IC inputs can contribute to further improvement in model performance, than NDVI inputs. Overall, ANN model simulations showed that using IC along with hydro-climatic inputs noticeably improved model performance in both catchments, especially in the Calliope catchment. This improvement is indicated by a slight increase (9.77% and 11.25%) in the Nash–Sutcliffe efficiency and noticeable decrease (24.43% and 37.89%) in the root mean squared error of monthly runoff from Haughton River and Calliope River, respectively. Here, we demonstrate the significant effect of hydro-geomorphic and biophysical time series inputs for estimating monthly runoff using ANN data-driven models, which are valuable for water resources planning and management.
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Su, Jinya, Baibing Li, and Wen-Hua Chen. "Simultaneous state and input estimation with partial information on the inputs." Systems Science & Control Engineering 3, no. 1 (January 2015): 445–52. http://dx.doi.org/10.1080/21642583.2015.1082512.

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49

Maksimov, V., and L. Pandolfi. "Dynamical Reconstruction of Inputs for Contraction Semigroup Systems: Boundary Input Case." Journal of Optimization Theory and Applications 103, no. 2 (November 1999): 401–20. http://dx.doi.org/10.1023/a:1021709004193.

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50

Angeli, David, Eduardo D. Sontag, and Yuan Wang. "Input-to-state stability with respect to inputs and their derivatives." International Journal of Robust and Nonlinear Control 13, no. 11 (2003): 1035–56. http://dx.doi.org/10.1002/rnc.753.

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