Journal articles: 'Fluctuations in output'

1

Campbell, John Y., and N. Gregory Mankiw. "Are Output Fluctuations Transitory?" Quarterly Journal of Economics 102, no. 4 (November 1987): 857. http://dx.doi.org/10.2307/1884285.

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Li, Chol-Won. "Growth and Output Fluctuations." Scottish Journal of Political Economy 47, no. 2 (May 2000): 95–113. http://dx.doi.org/10.1111/1467-9485.00155.

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Yahia, Abdusalam F. "Sectoral Output Response to Fluctuations of Oil Exports in Algeria." International Journal of Trade, Economics and Finance 5, no. 6 (December 2014): 536–40. http://dx.doi.org/10.7763/ijtef.2014.v5.429.

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4

Liu, Xuan, Yan Jia, and Fei Zhao. "The Method of Suppress the Output Power Fluctuations of Off-Grid Wind Power Systems." Advanced Materials Research 608-609 (December 2012): 479–82. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.479.

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As the wind is random and intermittent, the output power of the wind turbine will also be changing, causing the generator output power fluctuation and voltage fluctuation, flicker, and early deterioration of battery. In order to improve the stability of the off-grid power systems, power quality, and better to protect the energy storage devices, the paper analyzes the main factors of the impact of fluctuations in output power from the off-grid wind power systems and energy storage technology to mitigate the off-grid wind turbine power fluctuations.

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Bental, Benjamin, and Benjamin Eden. "Reserve requirements and output fluctuations." Journal of Monetary Economics 49, no. 8 (November 2002): 1597–620. http://dx.doi.org/10.1016/s0304-3932(02)00177-0.

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6

Ghiglino, Christian, and Alain Venditti. "Wealth distribution and output fluctuations." Journal of Economic Theory 146, no. 6 (November 2011): 2478–509. http://dx.doi.org/10.1016/j.jet.2011.06.004.

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7

Moser, Debra K., Susan K. Frazier, Mary A. Woo, and Linda K. Daley. "Normal Fluctuations in Pulmonary Artery Pressures and Cardiac Output in Patients with Severe Left Ventricular Dysfunction." European Journal of Cardiovascular Nursing 1, no. 2 (June 2002): 131–37. http://dx.doi.org/10.1016/s1474-51510200013-0.

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Background: One barrier to accurate interpretation of changes in hemodynamic pressures and cardiac output is lack of data about what constitutes a normal fluctuation. Few investigators have examined normal fluctuations in these parameters and none have done so in patients with left ventricular dysfunction. Aims: To describe normal fluctuations in pulmonary artery pressures and cardiac output in patients with left ventricular dysfunction. Methods: Hemodynamically stable advanced heart failure patients ( N=39; 55±6 years old; 62% male) with left ventricular dysfunction (mean ejection fraction 22±5%) were studied. Cardiac output and pulmonary artery pressures were measured every 15 min for 2 h. Results: Mean±standard deviation fluctuations were as follows: pulmonary artery systolic pressure=7±4 mmHg; pulmonary artery diastolic pressure=6±3 mmHg; pulmonary capillary wedge pressure=5±3 mmHg; cardiac output=0.7±0.3 l/min. The coefficient of variation for fluctuations in pulmonary artery systolic pressure was 6.7%, in pulmonary artery diastolic pressure was 9.3%, in pulmonary capillary wedge pressure was 9.2%, and in cardiac output was 7.2%. Conclusions: Values that vary <8% for pulmonary artery systolic pressure, <11% for pulmonary artery diastolic pressure, <12% for pulmonary capillary wedge pressure, and <9% for cardiac output from baseline represent normal fluctuations in these parameters in patients with left ventricular dysfunction.

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Qiu, Jian Ming, Yi Xin Xu, Jin Jin Lu, Ji Zhen Liu, and Yan Bai. "Wind Farm Active Load Allocation Scheme Considering Wind Speed Fluctuations." Applied Mechanics and Materials 303-306 (February 2013): 1323–26. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1323.

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According to the structure and information flow of the active control system of large-scale wind farm, a load allocation scheme is proposed considering the wind speed fluctuations within the cluster, i.e. deciding the de-load depth of each turbine according to wind speed fluctuation when the farm is running under AGC mode, using wind turbine generator (WTG) de-load ability to reduce the impacts of wind speed fluctuations on the entire wind farm output. Use particle swarm optimization, prediction of WTG generation, wind speed fluctuation condition, wind scheduling instruction information to calculate the WTG de-load depth risk factors, and get the de-load power order distribution scheme. A simulation of 10 2MW WTGs shows that, load allocation scheme considering the wind speed fluctuations can reflect the relationship effectively between wind speed condition and output reliability, and promote the stability of wind farm output.

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9

Juan, ZHAO. "Sectoral Fluctuations and Macro-economic Fluctuation: Based on Input-Output Matrix." Energy Procedia 5 (2011): 1898–903. http://dx.doi.org/10.1016/j.egypro.2011.03.326.

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Bresnahan, T. F., and V. A. Ramey. "Output Fluctuations at the Plant Level." Quarterly Journal of Economics 109, no. 3 (August 1, 1994): 593–624. http://dx.doi.org/10.2307/2118415.

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11

Cooper, Suzanne J. "Multiple Regimes in U.S. Output Fluctuations." Journal of Business & Economic Statistics 16, no. 1 (January 1998): 92. http://dx.doi.org/10.2307/1392019.

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Cooper, Suzanne J. "Multiple Regimes in U.S. Output Fluctuations." Journal of Business & Economic Statistics 16, no. 1 (January 1998): 92–100. http://dx.doi.org/10.1080/07350015.1998.10524738.

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13

Bivin, David G., and Brad R. Humphreys. "Accounting for output fluctuations in manufacturing." Applied Economics 41, no. 18 (August 2009): 2335–52. http://dx.doi.org/10.1080/00036840701222538.

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14

Lovell, Michael C. "Input-Output simulation of inventory fluctuations." Engineering Costs and Production Economics 19, no. 1-3 (May 1990): 57–63. http://dx.doi.org/10.1016/0167-188x(90)90025-d.

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15

Seifert, Janna Kristina, Martin Kraft, Martin Kühn, and Laura J. Lukassen. "Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data." Wind Energy Science 6, no. 4 (July 23, 2021): 997–1014. http://dx.doi.org/10.5194/wes-6-997-2021.

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Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

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Demb, Jonathan B., Peter Sterling, and Michael A. Freed. "How Retinal Ganglion Cells Prevent Synaptic Noise From Reaching the Spike Output." Journal of Neurophysiology 92, no. 4 (October 2004): 2510–19. http://dx.doi.org/10.1152/jn.00108.2004.

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Synaptic vesicles are released stochastically, and therefore stimuli that increase a neuron's synaptic input might increase noise at its spike output. Indeed this appears true for neurons in primary visual cortex, where spike output variability increases with stimulus contrast. But in retinal ganglion cells, although intracellular recordings (with spikes blocked) showed that stronger stimuli increase membrane fluctuations, extracellular recordings showed that noise at the spike output is constant. Here we show that these seemingly paradoxical findings occur in the same cell and explain why. We made intracellular recordings from ganglion cells, in vitro, and presented periodic stimuli of various contrasts. For each stimulus cycle, we measured the response at the stimulus frequency (F1) for both membrane potential and spikes as well as the spike rate. The membrane and spike F1 response increased with contrast, but noise (SD) in the F1 responses and the spike rate was constant. We also measured membrane fluctuations (with spikes blocked) during the response depolarization and found that they did increase with contrast. However, increases in fluctuation amplitude were small relative to the depolarization (<10% at high contrast). A model based on estimated synaptic convergence, release rates, and membrane properties accounted for the relative magnitudes of fluctuations and depolarization. Furthermore, a cell's peak spike response preceded the peak depolarization, and therefore fluctuation amplitude peaked as the spike response declined. We conclude that two extremely general properties of a neuron, synaptic convergence and spike generation, combine to minimize the effects of membrane fluctuations on spiking.

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17

Xu, Jia Yu. "Characteristics of Wind Power Fluctuations." Advanced Materials Research 926-930 (May 2014): 919–22. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.919.

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Wind power is also known as junk. This is because wind power fluctuations affect the security and stability operation. Wind power wind turbines created is mainly concerned with the speed of wind. Because of the wind direction uncertain, intermittent, and wake effects between each unit wind farm, wind turbines cannot make that kind of power according to the demand for energy as conventional generators. Due to the lack of experimental data, assess the volatility of wind power is still a lack of effective methods. This article studies the sample in a northeast wind farm power, and based on a sliding differential algorithm, distribution fitting and quantitative calculations describe the characteristics of wind power fluctuations. This article studies the sample in a northeast wind farm power, and based on a sliding difference algorithm, through the analysis showed that wind power fluctuations obey t location scale distribution. And it is affected by factors such as spatial and temporal distribution, there is a big difference between the output power fluctuation characteristics of wind farm output power and single wind turbine. This is due to the wind turbine suffered varying differences, and wake effects between field units, making the distribution of frequent power fluctuations; relative to a single unit, the fluctuation of the whole wind farm is more gentle, that is to say with the spatial distribution increased scale, wind power fluctuations presents certain "gentle effect."

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18

Boyd, Roy, and Tony Caporale. "Resource prices, supply shocks and output fluctuations." Applied Economics Letters 2, no. 6 (June 1995): 180–83. http://dx.doi.org/10.1080/135048595357393.

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19

Bacchetta, Philippe, and Ramon Caminal. "Do capital market imperfections exacerbate output fluctuations?" European Economic Review 44, no. 3 (March 2000): 449–68. http://dx.doi.org/10.1016/s0014-2921(98)00083-x.

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20

Di Sanzo, Silvestro. "OUTPUT FLUCTUATIONS PERSISTENCE: DO CYCLICAL SHOCKS MATTER?" Bulletin of Economic Research 63, no. 1 (October 5, 2010): 28–52. http://dx.doi.org/10.1111/j.1467-8586.2010.00350.x.

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21

Gebhardt, Georg. "Inequity Aversion, Financial Markets, and Output Fluctuations." Journal of the European Economic Association 2, no. 2-3 (May 1, 2004): 229–39. http://dx.doi.org/10.1162/154247604323067943.

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22

Laurenceson, James. "Interpreting fluctuations in output growth in China." China Economic Journal 6, no. 1 (February 2013): 12–20. http://dx.doi.org/10.1080/17538963.2013.831236.

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23

Durlauf, Steven N. "Time series properties of aggregate output fluctuations." Journal of Econometrics 56, no. 1-2 (March 1993): 39–56. http://dx.doi.org/10.1016/0304-4076(93)90100-j.

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24

Hong, Haisheng, and Quanyuan Jiang. "Model Predictive Control-Based Coordinated Control Algorithm with a Hybrid Energy Storage System to Smooth Wind Power Fluctuations." Energies 12, no. 23 (December 3, 2019): 4591. http://dx.doi.org/10.3390/en12234591.

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Stochastically fluctuating wind power has an escalating impact on the stability of power grid operations. To smooth out short- and long-term fluctuations, this paper presents a coordinated control algorithm using model predictive control (MPC) to manage a hybrid energy storage system (HESS) consisting of ultra-capacitor (UC) and lithium-ion battery (LB) banks. In the HESS-computing period, the algorithm minimizes HESS operating costs in the subsequent prediction horizon by optimizing the time constant of a flexible first-delay filter (FDF) to obtain the UC power output. In the LB-computing period, the algorithm keeps the optimal time constant of the FDF from the previous period to directly obtain the power output of the UC bank to minimize the power output of the LB bank in the next prediction horizon. A relaxation technique is deployed when the problem is unsolvable. Thus, the fluctuation mitigation requirements are fulfilled with a large probability even in extreme conditions. A state-of-charge (SOC) feedback control strategy is proposed to regulate the SOC of the HESS within its proper range. Case studies and quantitative comparisons demonstrate that the proposed MPC-based algorithm uses a lower power rating and storage capacity than other conventional algorithms to satisfy one-minute and 30-min fluctuation mitigation requirements (FMR).

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25

Wang, Rui Hao. "Analysis on the Fluctuation of Wind Power." Applied Mechanics and Materials 733 (February 2015): 199–202. http://dx.doi.org/10.4028/www.scientific.net/amm.733.199.

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This paper is aimed at exploring the characteristic fluctuation of wind power based on samples from a certain wind farm. First, the paper is to analyze fluctuations of wind power at different time scales. According to a sliding difference algorithm to build wind power fluctuations evaluation. Wind power fluctuation index for different time scales are used to fit probability distributions, indicating that the best form of distribution of wind power fluctuations is t location scale distribution. Secondly, considering the wind power has the characteristics of non-linear, non-stationary signal of the data, it fully meets the wavelet neural network analysis of the characteristics of the data. Therefore, select wavelet neural network training and testing so as to make predictions about the future of the total power of wind farm. It points out the differences between different regions covered by the index from the fluctuation characteristics of wind power, thus further understanding the fluctuation characteristics of wind power: Influenced by the time and space distribution and other factors, there is a big difference between the output power fluctuation characteristics of single wind generator and wind farm, which is because of the different wind machine in the field by the wind energy differences, and the wake effect of organic groups, making frequent fluctuations in power distribution; the fluctuation of wind is gentle, i.e. with increasing spatial distribution scale, so gentle effect occurs to wind power fluctuations. Finally, through the analysis of the fluctuation characteristics of power, power factor and analyses the influence of the characteristics of fluctuation, the paper draws a conclusion of the following improvement programs to overcome the adverse effects of wind power fluctuation of power grid operation: the rational allocation of energy storage devices, expanding the coverage area of a wind farm, or improving the design of the windmill, which will make wind farms adapt to different wind directions, thus eliminating the impact of fluctuations on the power grid from the wind farm power output by the energy storage device, and covering the area of large wind farms can adapt to different wind directions, and with power complementary, it has achieved the amount of stable power transmission into the grid.

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Kim, Soyoung, and Jaewoo Lee. "INTERNATIONAL MACROECONOMIC FLUCTUATIONS." Macroeconomic Dynamics 19, no. 7 (May 16, 2014): 1509–39. http://dx.doi.org/10.1017/s1365100513000916.

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This paper investigates the international dimension of economic fluctuations and transmission of structural shocks by estimating a structural VAR model for the United States, the euro area, and Japan—the three largest economies—over the post-Bretton Woods period. The main findings are as follows: (1) Supply-side shocks (technology and supply-level shocks) explain most of the fluctuations in cross-country output deviations. (2) Real-demand shocks are the most important source of real-exchange-rate fluctuations. (3) Current account is usually influenced by all types of shocks, with technology shocks playing a stronger role. In particular, technology shocks play a prominent role in the existing global imbalance (the large external deficit of the United States). (4) Technology and supply-level shocks generate opposite-signed correlations between output differential and current account, whereas real and nominal-demand shocks generate opposite-signed correlations between real exchange rate and current account.

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27

NEMATI, MAJID. "THE DISORDERING EFFECT ON THE ONE-DIMENSIONAL APPERIODIC OPTICAL SUPER-LATTICE IN MULTI-FREQUENCY SHG." International Journal of Modern Physics: Conference Series 15 (January 2012): 191–96. http://dx.doi.org/10.1142/s2010194512007143.

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A multi-frequency second harmonic generation (SHG) in one dimensional nonlinear aperiodic optical superlattice in the presence of linear spacers is considered. The effect of random fluctuations in the length of each layer on the output efficiency of SHG process is investigated, and the favorable percentage of fluctuation in the fabrication of aperiodic optical superlattice is obtained. Comparing perfect and fluctuated aperiodic structures, the researcher realized the decrease in the amount of output efficiency with the increase of fluctuation in the linear-nonlinear aperiodic crystals.

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Schwalbe, Karsten, and Karl Heinz Hoffmann. "Optimal Control of an Endoreversible Solar Power Plant." Journal of Non-Equilibrium Thermodynamics 43, no. 3 (July 26, 2018): 255–71. http://dx.doi.org/10.1515/jnet-2018-0021.

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AbstractWhile in the classic Curzon–Ahlborn and Novikov engines the temperatures of the heat baths are kept fixed or follow a deterministic time function, it is the aim of this work to study the impact of fluctuating heat bath temperatures. As an example serves a solar power plant, where the stochastically varying cloud cover leads to fluctuations in the temperature of the hot heat bath. This solar thermal power plant is modeled as a stochastic endoreversible system. On the basis of this model the maximum expected work output of the power plant and the corresponding optimal control policy is derived. For the considered system it is found that the maximum expected work output changes with the reversion speed of the hot temperature depending on the relation of the starting hot temperature and the temperature of the power plant’s receiver. Additionally, it is found that the maximum expected work output increases with the hot temperature’s fluctuation strength.

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29

Chen, Qiting, and Meng Wang. "The Analysis of China’s Grain Output Fluctuation Based on EMD." International Journal of Economics and Finance 9, no. 11 (October 7, 2017): 64. http://dx.doi.org/10.5539/ijef.v9n11p64.

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Food is one of the most important resources for staying alive. This paper analyzes grain output fluctuations and their driving forces in China from 1978 to 2014, based on Empirical Mode Decomposition (EMD) method. These results show that there are two type cycles of cyclical fluctuation, one is 3-yearterm, and another is 8-year term. These results show that the 8-year cyclical fluctuation is the major term. Grain production’s cyclical fluctuation in 3 years was mainly influenced by yield of grain per unit area from 1978-2004 and 2007-2014, and by the area sown from 2004 to 2007. On the other hand, the longer cyclical fluctuation of 8 years is mainly affected by the yield of grain per unit area. The grain output is predicted for the next three years through the RBF neural network optimized by PSO. These results show that China’s annul grain output in the next three years will be stabilized at about 600 million tons, which may grow slowly though.

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30

Schnabel, Julius, and Seppo Valkealahti. "Energy Storage Requirements for PV Power Ramp Rate Control in Northern Europe." International Journal of Photoenergy 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2863479.

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Photovoltaic (PV) generators suffer from fluctuating output power due to the highly fluctuating primary energy source. With significant PV penetration, these fluctuations can lead to power system instability and power quality problems. The use of energy storage systems as fluctuation compensators has been proposed as means to mitigate these problems. In this paper, the behavior of PV power fluctuations in Northern European climatic conditions and requirements for sizing the energy storage systems to compensate them have been investigated and compared to similar studies done in Southern European climate. These investigations have been performed through simulations that utilize measurements from the Tampere University of Technology solar PV power station research plant in Finland. An enhanced energy storage charging control strategy has been developed and tested. Energy storage capacity, power, and cycling requirements have been derived for different PV generator sizes and power ramp rate requirements. The developed control strategy leads to lesser performance requirements for the energy storage systems compared to the methods presented earlier. Further, some differences on the operation of PV generators in Northern and Southern European climates have been detected.

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31

Anvari, M., B. Werther, G. Lohmann, M. Wächter, J. Peinke, and H. P. Beck. "Suppressing power output fluctuations of photovoltaic power plants." Solar Energy 157 (November 2017): 735–43. http://dx.doi.org/10.1016/j.solener.2017.08.038.

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32

Reinhart, Carmen M., and Vincent R. Reinhart. "Output Fluctuations and Monetary Shocks: Evidence from Colombia." Staff Papers - International Monetary Fund 38, no. 4 (December 1991): 705. http://dx.doi.org/10.2307/3867122.

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33

Šustek, Roman. "Plant-level nonconvex output adjustment and aggregate fluctuations." Journal of Monetary Economics 58, no. 4 (May 2011): 400–414. http://dx.doi.org/10.1016/j.jmoneco.2011.08.001.

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MILLS, TERENCE C. "ARE FLUCTUATIONS IN U.K. OUTPUT TRANSITORY OR PERMANENT?" Manchester School 59, no. 1 (March 1991): 1–11. http://dx.doi.org/10.1111/j.1467-9957.1991.tb00434.x.

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35

Chanut, Françoise. "Chance Fluctuations in mRNA Output in Mammalian Cells." PLoS Biology 4, no. 10 (September 12, 2006): e319. http://dx.doi.org/10.1371/journal.pbio.0040319.

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Itagaki, Hidenobu, Tadatoshi Yamada, Kiyoshi Yoda, and Satoshi Fujimura. "An Evaluation of Output Fluctuations in MRI Equipment." IEEJ Transactions on Electronics, Information and Systems 109, no. 6 (1989): 446–50. http://dx.doi.org/10.1541/ieejeiss1987.109.6_446.

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Reinhart, Vincent, and Carmen Reinhart. "Output Fluctuations and Monetary Shocks: Evidence From Colombia." IMF Working Papers 91, no. 35 (1991): 1. http://dx.doi.org/10.5089/9781451978353.001.

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38

Biolsi, Christopher, and Bocong Du. "Do shocks to animal spirits cause output fluctuations?" Southern Economic Journal 87, no. 1 (July 2020): 331–68. http://dx.doi.org/10.1002/soej.12452.

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Keating, John W., and Isaac K. Kanyama. "Is sticky price adjustment important for output fluctuations?" Review of Keynesian Economics 3, no. 3 (July 2015): 392–418. http://dx.doi.org/10.4337/roke.2015.03.08.

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Courty, Jean-Michel, and Serge Reynaud. "Generalized linear input-output theory for quantum fluctuations." Physical Review A 46, no. 5 (September 1, 1992): 2766–77. http://dx.doi.org/10.1103/physreva.46.2766.

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Sosnoff, Jacob J., Sae Young Jae, Kevin Heffernan, and Bo Fernhall. "Cardioballistic Impulse and Fluctuations in Isometric Force Output." Motor Control 15, no. 2 (April 2011): 221–31. http://dx.doi.org/10.1123/mcj.15.2.221.

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Cadoret, Isabelle, and Christophe Tavera. "The sources of decentralised output fluctuations in France." Papers in Regional Science 86, no. 2 (June 2007): 309–20. http://dx.doi.org/10.1111/j.1435-5957.2007.00124.x.

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Chakrabarti, Anindya S., and Ratul Lahkar. "Productivity dispersion and output fluctuations: An evolutionary model." Journal of Economic Behavior & Organization 137 (May 2017): 339–60. http://dx.doi.org/10.1016/j.jebo.2017.03.025.

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Adedoyin, Ramat. "Exchange rate and Industrial output within an SVAR framework: Evidence from Nigeria." International Journal of Social Sciences and Humanities Invention 8, no. 02 (March 7, 2021): 6382–95. http://dx.doi.org/10.18535/ijsshi/v8i02.04.

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The Nigerian exchange rate has gone through several reforms. Thus, this study seeks to also establish, the manner with which variations in the exchange rates influence industrial production.It focus essentially on the impact of a shock to the exchange rate on industrial production in Nigeria. The study employs the use of the SVAR model with the assumption of Cholesky decomposition as an identification scheme for four variables in the following order: exchange rate, industrial output, broad money supply, and price level.It is found that for the period under study, industrial output plays no role in explaining the fluctuations in the real exchange rate in the short run. Similarly, results show that shock to real exchange rate plays no role in explaining the fluctuations in industrial output in the short run. However by the end of a second-year period, industrial output takes 23% of the fluctuations in the real exchange rate and real exchange rate explains about 17% of the fluctuation in industrial output. As an extension, analyses show that shock to inflation and money supply have minimal influence on industrial output.It is recommended that a concentration on real factors such as savings rate, infrastructural facilities, political stability, and security can provide relatively more influence on industrial production in Nigeria.This study has contributed to knowledge through the analysis of data to identify the impact of a shock to the exchange rate on industrial production in Nigeria.

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Liu, Z., and C. W. Higgins. "Does temperature affect the accuracy of vented pressure transducer in fine-scale water level measurement?" Geoscientific Instrumentation, Methods and Data Systems 4, no. 1 (March 3, 2015): 65–73. http://dx.doi.org/10.5194/gi-4-65-2015.

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Abstract. Submersible pressure transducers have been utilized for collecting water level data since the early 1960s. Together with a digital data logger, it is a convenient way to record water level fluctuations for long-term monitoring. Despite the wide use of pressure transducers for water level monitoring, little has been reported regarding their accuracy and performance under field conditions. The effects of temperature fluctuations on the output of vented pressure transducers were considered in this study. The pressure transducers were tested under both laboratory and field conditions. The results of this study indicate that temperature fluctuation has a strong effect on the transducer output. Rapid changes in temperature introduce noise and fluctuations in the water level readings under a constant hydraulic head while the absolute temperature is also related to sensor errors. The former is attributed to venting and the latter is attributed to temperature compensation effects in the strain gauges. Individual pressure transducers responded differently to the thermal fluctuations in the same testing environment. In the field of surface hydrology, especially when monitoring fine-scale water level fluctuations, ignoring or failing to compensate for the temperature effect can introduce considerable error into pressure transducer readings. It is recommended that a performance test for the pressure transducer is conducted before field deployment.

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Liu, Z., and C. W. Higgins. "Does temperature affect the accuracy of vented pressure transducer in fine-scale water level measurement?" Geoscientific Instrumentation, Methods and Data Systems Discussions 4, no. 2 (September 29, 2014): 533–61. http://dx.doi.org/10.5194/gid-4-533-2014.

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Abstract. Submersible pressure transducers have been utilized for collecting water level data since early 1960s. Together with a digital datalogger, it is a convenient way to record water level fluctuations for long-term monitoring. Despite the widely use of pressure transducers for water level monitoring, little has been reported for their accuracy and performance under field conditions. The effect of temperature fluctuations on the output of vented pressure transducers were discussed in this study. The pressure transducer was tested under both laboratory and field conditions. The results of this study indicate that temperature fluctuation has a strong effect on the transducer output. Rapid changes in temperature introduce noise and fluctuations in the water level readings under a constant hydraulic head while the absolute temperature is also related to sensor errors. The former is attributed to venting and the latter is attributed to temperature compensation effect in the strain gauges. Individual pressure transducers responded differently to the thermal fluctuations in the same testing environment. In the field of surface hydrology, especially when monitoring fine-scale water level fluctuations, ignoring or failing to compensate for the temperature effect can introduce considerable error into pressure transducer readings. It is recommended that a performance test for the pressure transducer is conducted before field deployment.

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Li, Yansong, Weiwei Zhang, Xinying Liu, and Jun Liu. "Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology." Applied Sciences 9, no. 2 (January 18, 2019): 333. http://dx.doi.org/10.3390/app9020333.

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The straight-through magneto-optical glass current sensor has desirable temperature properties, but it is vulnerable to magnetic interference. In contrast, a polarization-type fiber optic current sensor has poor temperature performance, but the magnetic anti-interference characteristic is very good. Aiming at the problem that the accuracy of a fiber optic current sensor is susceptible to external disturbances and temperature fluctuations, we present an adaptive technology of a fiber optic current sensor that uses the magneto-optical output signal to correct the fiber output signal. The working principle of the improved method is introduced in this paper. The structure of the specific optical system and the signal processing system are presented. Temperature fluctuation and magnetic change detection units are included in the design in order to provide signal selection under different environmental fluctuations, thus stabilizing the output current data. The signal processing system was proved to be effective by building an experimental platform.

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Li, Hongmei, and Jin-Song von Storch. "On the Fluctuating Buoyancy Fluxes Simulated in a OGCM." Journal of Physical Oceanography 43, no. 7 (July 1, 2013): 1270–87. http://dx.doi.org/10.1175/jpo-d-12-080.1.

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Abstract Subgrid-scale fluctuations with zero means have generally been neglected in ocean modeling, despite their potential role in affecting the oceanic state following Hasselmann's seminal paper on stochastic climate models and series of studies conducted thereafter. When representing effects of these fluctuations in a stochastic parameterization, knowledge of basic properties of these fluctuations is essential. Here, the authors quantify these properties using hourly output of a simulation performed with a global OGCM. This study found that fluctuating buoyancy fluxes are strong in the sense that their strengths are up to one order of magnitude larger than the magnitudes of the respective mean eddy fluxes and that the fluctuations originate not only from mesoscale eddies and tropical instability waves but also from near-inertial waves, especially in the low- and midlatitude oceans. It is this wave contribution that makes the basic properties of fluctuations distinctly different from those expected from mesoscale eddies. The geographical distribution of fluctuation intensity differs from that of mesoscale eddy activity and is strongest in the low- and midlatitude oceans complemented by additional and secondary maxima in the Gulf Stream, the Kuroshio, and the Southern Ocean. The seasonality in most of the low- and midlatitude oceans, characterized by stronger fluctuations in winter than in summer, is just the opposite of that of mesoscale eddies. In the tropical oceans, the correlation length scales reach 500 km in the zonal direction but only about 30–40 km in the meridional direction, reflecting near-inertial waves with nearly zonally oriented wavecrests. Overall, these results provide an important basis for stochastically describing the effects of subgrid-scale fluctuations.

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Crosby, Mark, and Glenn Otto. "Persistence of Output Fluctuations Under Different Exchange Rate Regimes." Asian Economic Journal 17, no. 3 (September 2003): 281–96. http://dx.doi.org/10.1111/j.1467-8381.2003.00187.x.

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Caporale, Guglielmo Maria. "Common features and output fluctuations in the United Kingdom." Economic Modelling 14, no. 1 (January 1997): 1–9. http://dx.doi.org/10.1016/s0264-9993(96)01030-9.

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Journal articles on the topic 'Fluctuations in output'

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