Готові списки джерел за темами / Cloud feedback

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Добірка наукової літератури з теми "Cloud feedback"

Автор: Grafiati
Опубліковано: 22 лютого 2025

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Статті в журналах з теми "Cloud feedback"

1

Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler, and Ping Yang. "An Analysis of the Short-Term Cloud Feedback Using MODIS Data." Journal of Climate 26, no. 13 (July 1, 2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.

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Анотація:
Abstract The cloud feedback in response to short-term climate variations is estimated from cloud measurements combined with offline radiative transfer calculations. The cloud measurements are made by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite and cover the period 2000–10. Low clouds provide a strong negative cloud feedback, mainly because of their impact in the shortwave (SW) portion of the spectrum. Midlevel clouds provide a positive net cloud feedback that is a combination of a positive SW feedback partially canceled by a negative feedback in the longwave (LW). High clouds have only a small impact on the net cloud feedback because of a close cancellation between large LW and SW cloud feedbacks. Segregating the clouds by optical depth, it is found that the net cloud feedback is set by a positive cloud feedback due to reductions in the thickest clouds (mainly in the SW) and a cancelling negative feedback from increases in clouds with moderate optical depths (also mainly in the SW). The global average SW, LW, and net cloud feedbacks are +0.30 ±1.10, −0.46 ±0.74, and −0.16 ±0.83 W m−2 K−1, respectively. The SW feedback is consistent with previous work; the MODIS LW feedback is lower than previous calculations and there are reasons to suspect it may be biased low. Finally, it is shown that the apparently small control that global mean surface temperature exerts on clouds, which leads to the large uncertainty in the short-term cloud feedback, arises from statistically significant but offsetting relationships between individual cloud types and global mean surface temperature.
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2

Zelinka, Mark D., Stephen A. Klein, Karl E. Taylor, Timothy Andrews, Mark J. Webb, Jonathan M. Gregory, and Piers M. Forster. "Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*." Journal of Climate 26, no. 14 (July 12, 2013): 5007–27. http://dx.doi.org/10.1175/jcli-d-12-00555.1.

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Abstract Using five climate model simulations of the response to an abrupt quadrupling of CO2, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2 cloud masking of CO2 forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2 K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2 K−1 the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.
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3

Zelinka, Mark D., Stephen A. Klein, and Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels." Journal of Climate 25, no. 11 (June 2012): 3715–35. http://dx.doi.org/10.1175/jcli-d-11-00248.1.

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Анотація:
This study proposes a novel technique for computing cloud feedbacks using histograms of cloud fraction as a joint function of cloud-top pressure (CTP) and optical depth (τ). These histograms were generated by the International Satellite Cloud Climatology Project (ISCCP) simulator that was incorporated into doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. The authors use a radiative transfer model to compute top of atmosphere flux sensitivities to cloud fraction perturbations in each bin of the histogram for each month and latitude. Multiplying these cloud radiative kernels with histograms of modeled cloud fraction changes at each grid point per unit of global warming produces an estimate of cloud feedback. Spatial structures and globally integrated cloud feedbacks computed in this manner agree remarkably well with the adjusted change in cloud radiative forcing. The global and annual mean model-simulated cloud feedback is dominated by contributions from medium thickness (3.6 < τ ≤ 23) cloud changes, but thick (τ > 23) cloud changes cause the rapid transition of cloud feedback values from positive in midlatitudes to negative poleward of 50°S and 70°N. High (CTP ≤ 440 hPa) cloud changes are the dominant contributor to longwave (LW) cloud feedback, but because their LW and shortwave (SW) impacts are in opposition, they contribute less to the net cloud feedback than do the positive contributions from low (CTP > 680 hPa) cloud changes. Midlevel (440 < CTP ≤ 680 hPa) cloud changes cause positive SW cloud feedbacks that are 80% as large as those due to low clouds. Finally, high cloud changes induce wider ranges of LW and SW cloud feedbacks across models than do low clouds.
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4

Dawson, Emma, and Kathleen A. Schiro. "Tropical High Cloud Feedback Relationships to Climate Sensitivity." Journal of Climate 38, no. 2 (January 15, 2025): 583–96. https://doi.org/10.1175/jcli-d-24-0218.1.

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Анотація:
Abstract Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback components are significantly positively correlated with ECS in the tropical mean. On the other hand, the tropical mean high cloud amount feedback is not correlated with ECS. These relationships are most pronounced outside of areas of strong climatological ascent, suggesting the importance of thin cirrus feedbacks. Finally, we explore connections between high cloud feedbacks, climate sensitivity, and mean state high cloud properties. In general, high ECS models are cloudier in the upper troposphere but have a thinner high cloud population. Moreover, we find that having more thin cirrus in the mean state relates to more positive high cloud altitude and optical depth feedbacks, and it either amplifies or dampens the high cloud amount feedback depending on the large-scale dynamical regime (amplifying in descent and dampening in ascent). In summary, our analysis highlights the importance of tropical high cloud feedbacks for driving intermodel spread in ECS and suggests that mean state high cloud characteristics might provide a unique opportunity for observationally constraining high cloud feedbacks. Significance Statement Clouds play an important role in modulating the effects of climate change through feedback processes involving changes to their amount, altitude, and opacity. In this study, we seek to understand how changes to tropical high clouds under warming are related to the magnitude of warming that global climate models simulate. We find that tropical high cloud feedbacks robustly relate to the amount of warming a model predicts and that warmer models tend to have a thinner tropical high cloud climatology. Our results highlight a potential opportunity to form a new constraint using these relationships in order to narrow the spread of warming estimates among global climate models.
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5

Yoshimori, Masakazu, F. Hugo Lambert, Mark J. Webb, and Timothy Andrews. "Fixed Anvil Temperature Feedback: Positive, Zero, or Negative?" Journal of Climate 33, no. 7 (April 1, 2020): 2719–39. http://dx.doi.org/10.1175/jcli-d-19-0108.1.

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Анотація:
AbstractThe fixed anvil temperature (FAT) theory describes a mechanism for how tropical anvil clouds respond to global warming and has been used to argue for a robust positive longwave cloud feedback. A constant cloud anvil temperature, due to increased anvil altitude, has been argued to lead to a “zero cloud emission change” feedback, which can be considered positive relative to the negative feedback associated with cloud anvil warming when cloud altitude is unchanged. Here, partial radiative perturbation (PRP) analysis is used to quantify the radiative feedback caused by clouds that follow the FAT theory (FAT–cloud feedback) and to set this in the context of other feedback components in two atmospheric general circulation models. The FAT–cloud feedback is positive in the PRP framework due to increasing anvil altitude, but because the cloud emission does not change, this positive feedback is cancelled by an equal and opposite component of the temperature feedback due to increasing emission from the cloud. To incorporate this cancellation, the thermal radiative damping with fixed relative humidity and anvil temperature (T-FRAT) decomposition framework is proposed for longwave feedbacks, in which temperature, fixed relative humidity, and FAT–cloud feedbacks are combined. In T-FRAT, the cloud feedback under the FAT constraint is zero, while that under the proportionately higher anvil temperature (PHAT) constraint is negative. The change in the observable cloud radiative effect with FAT–cloud response is also evaluated and shown to be negative due to so-called cloud masking effects. It is shown that “cloud masking” is a misleading term in this context, and these effects are interpreted more generally as “cloud climatology effects.”
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6

Zhu, Ping, James J. Hack, and Jeffrey T. Kiehl. "Diagnosing Cloud Feedbacks in General Circulation Models." Journal of Climate 20, no. 11 (June 1, 2007): 2602–22. http://dx.doi.org/10.1175/jcli4140.1.

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Abstract In this study, it is shown that the NCAR and GFDL GCMs exhibit a marked difference in climate sensitivity of clouds and radiative fluxes in response to doubled CO2 and ±2-K SST perturbations. The GFDL model predicted a substantial decrease in cloud amount and an increase in cloud condensate in the warmer climate, but produced a much weaker change in net cloud radiative forcing (CRF) than the NCAR model. Using a multiple linear regression (MLR) method, the full-sky radiative flux change at the top of the atmosphere was successfully decomposed into individual components associated with the clear sky and different types of clouds. The authors specifically examined the cloud feedbacks due to the cloud amount and cloud condensate changes involving low, mid-, and high clouds between 60°S and 60°N. It was found that the NCAR and GFDL models predicted the same sign of individual longwave and shortwave feedbacks resulting from the change in cloud amount and cloud condensate for all three types of clouds (low, mid, and high) despite the different cloud and radiation schemes used in the models. However, since the individual longwave and shortwave feedbacks resulting from the change in cloud amount and cloud condensate generally have the opposite signs, the net cloud feedback is a subtle residual of all. Strong cancellations between individual cloud feedbacks may result in a weak net cloud feedback. This result is consistent with the findings of the previous studies, which used different approaches to diagnose cloud feedbacks. This study indicates that the proposed MLR approach provides an easy way to efficiently expose the similarity and discrepancy of individual cloud feedback processes between GCMs, which are hidden in the total cloud feedback measured by CRF. Most importantly, this method has the potential to be applied to satellite measurements. Thus, it may serve as a reliable and efficient method to investigate cloud feedback mechanisms on short-term scales by comparing simulations with available observations, which may provide a useful way to identify the cause for the wide spread of cloud feedbacks in GCMs.
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7

Zelinka, Mark D., Stephen A. Klein, and Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth." Journal of Climate 25, no. 11 (June 2012): 3736–54. http://dx.doi.org/10.1175/jcli-d-11-00249.1.

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Анотація:
Cloud radiative kernels and histograms of cloud fraction, both as functions of cloud-top pressure and optical depth, are used to quantify cloud amount, altitude, and optical depth feedbacks. The analysis is applied to doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. Global, annual, and ensemble mean longwave (LW) and shortwave (SW) cloud feedbacks are positive, with the latter nearly twice as large as the former. The robust increase in cloud-top altitude in both the tropics and extratropics is the dominant contributor to the positive LW cloud feedback. The negative impact of reductions in cloud amount offsets more than half of the positive impact of rising clouds on LW cloud feedback, but the magnitude of compensation varies considerably across the models. In contrast, robust reductions in cloud amount make a large and virtually unopposed positive contribution to SW cloud feedback, though the intermodel spread is greater than for any other individual feedback component. Overall reductions in cloud amount have twice as large an impact on SW fluxes as on LW fluxes, such that the net cloud amount feedback is moderately positive, with no models exhibiting a negative value. As a consequence of large but partially offsetting effects of cloud amount reductions on LW and SW feedbacks, both the mean and intermodel spread in net cloud amount feedback are smaller than those of the net cloud altitude feedback. Finally, the study finds that the large negative cloud feedback at high latitudes results from robust increases in cloud optical depth, not from increases in total cloud amount as is commonly assumed.
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8

Sun, De-Zheng, Yongqiang Yu, and Tao Zhang. "Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations." Journal of Climate 22, no. 5 (March 1, 2009): 1287–304. http://dx.doi.org/10.1175/2008jcli2267.1.

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Анотація:
Abstract By comparing the response of clouds and water vapor to ENSO forcing in nature with that in Atmospheric Model Intercomparison Project (AMIP) simulations by some leading climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed the following two common biases in the models: 1) an underestimate of the strength of the negative cloud albedo feedback and 2) an overestimate of the positive feedback from the greenhouse effect of water vapor. Extending the same analysis to the fully coupled simulations of these models as well as other Intergovernmental Panel on Climate Change (IPCC) coupled models, it is found that these two biases persist. Relative to the earlier estimates from AMIP simulations, the overestimate of the positive feedback from water vapor is alleviated somewhat for most of the coupled simulations. Improvements in the simulation of the cloud albedo feedback are only found in the models whose AMIP runs suggest either a positive or nearly positive cloud albedo feedback. The strength of the negative cloud albedo feedback in all other models is found to be substantially weaker than that estimated from the corresponding AMIP simulations. Consequently, although additional models are found to have a cloud albedo feedback in their AMIP simulations that is as strong as in the observations, all coupled simulations analyzed in this study have a weaker negative feedback from the cloud albedo and therefore a weaker negative feedback from the net surface heating than that indicated in observations. The weakening in the cloud albedo feedback is apparently linked to a reduced response of deep convection over the equatorial Pacific, which is in turn linked to the excessive cold tongue in the mean climate of these models. The results highlight that the feedbacks of water vapor and clouds—the cloud albedo feedback in particular—may depend on the mean intensity of the hydrological cycle. Whether the intermodel variations in the feedback from cloud albedo (water vapor) in the ENSO variability are correlated with the intermodel variations of the feedback from cloud albedo (water vapor) in global warming has also been examined. While a weak positive correlation between the intermodel variations in the feedback of water vapor during ENSO and the intermodel variations in the water vapor feedback during global warming was found, there is no significant correlation found between the intermodel variations in the cloud albedo feedback during ENSO and the intermodel variations in the cloud albedo feedback during global warming. The results suggest that the two common biases revealed in the simulated ENSO variability may not necessarily be carried over to the simulated global warming. These biases, however, highlight the continuing difficulty that models have in simulating accurately the feedbacks of water vapor and clouds on a time scale of the observations available.
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9

Zhang, Minghua, and Christopher Bretherton. "Mechanisms of Low Cloud–Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3)." Journal of Climate 21, no. 18 (September 15, 2008): 4859–78. http://dx.doi.org/10.1175/2008jcli2237.1.

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Анотація:
Abstract This study investigates the physical mechanism of low cloud feedback in the Community Atmospheric Model, version 3 (CAM3) through idealized single-column model (SCM) experiments over the subtropical eastern oceans. Negative cloud feedback is simulated from stratus and stratocumulus that is consistent with previous diagnostics of cloud feedbacks in CAM3 and its predecessor versions. The feedback occurs through the interaction of a suite of parameterized processes rather than from any single process. It is caused by the larger amount of in-cloud liquid water in stratus clouds from convective sources, and longer lifetimes of these clouds in a warmer climate through their interaction with boundary layer turbulence. Thermodynamic effects are found to dominate the negative cloud feedback in the model. The dynamic effect of weaker subsidence in a warmer climate also contributes to the negative cloud feedback, but with about one-quarter of the magnitude of the thermodynamic effect, owing to increased low-level convection in a warmer climate.
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10

Lohmann, Ulrike, and David Neubauer. "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2." Atmospheric Chemistry and Physics 18, no. 12 (June 22, 2018): 8807–28. http://dx.doi.org/10.5194/acp-18-8807-2018.

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Анотація:
Abstract. How clouds change in a warmer climate remains one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that mixed-phase clouds are also important for ECS. If mixed-phase clouds in the current climate contain too few supercooled cloud droplets, too much ice will change to liquid water in a warmer climate. As shown by Tan et al. (2016), this overestimates the negative cloud-phase feedback and underestimates ECS in the CAM global climate model (GCM). Here we use the newest version of the ECHAM6-HAM2 GCM to investigate the importance of mixed-phase and ice clouds for ECS. Although we also considerably underestimate the fraction of supercooled liquid water globally in the reference version of the ECHAM6-HAM2 GCM, we do not obtain increases in ECS in simulations with more supercooled liquid water in the present-day climate, different from the findings by Tan et al. (2016). We hypothesize that it is not the global supercooled liquid water fraction that matters, but only how well low- and mid-level mixed-phase clouds with cloud-top temperatures in the mixed-phase temperature range between 0 and −35 ∘C that are not shielded by higher-lying ice clouds are simulated. These occur most frequently in midlatitudes, in particular over the Southern Ocean where they determine the amount of absorbed shortwave radiation. In ECHAM6-HAM2 the amount of absorbed shortwave radiation over the Southern Ocean is only significantly overestimated if all clouds below 0 ∘C consist exclusively of ice. Only in this simulation is ECS significantly smaller than in all other simulations and the cloud optical depth feedback is the dominant cloud feedback. In all other simulations, the cloud optical depth feedback is weak and changes in cloud feedbacks associated with cloud amount and cloud-top pressure dominate the overall cloud feedback. However, apart from the simulation with only ice below 0 ∘C, differences in the overall cloud feedback are not translated into differences in ECS in our model. This insensitivity to the cloud feedback in our model is explained with compensating effects in the clear sky.
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Більше джерел

Дисертації з теми "Cloud feedback"

1

Davis, Michael A. "Cloud-Radiative Feedback and Ocean-Atmosphere Feedback In the Southeast Pacific Ocean Simulated by IPCC AR4 GCMs." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1313350254.

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2

Ullah, Amjad. "Towards a novel biologically-inspired cloud elasticity framework." Thesis, University of Stirling, 2017. http://hdl.handle.net/1893/26064.

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Анотація:
With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches.
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3

Kropf, Dorothy Cortez. "Applying UTAUT to Determine Intent to Use Cloud Computing in K-12 Classrooms." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5212.

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Анотація:
Although school districts provide collaborative cloud computing tools such as OneDrive and Google Drive for students and teachers, the use of these tools for grading and feedback purposes remains largely unexplored. Therefore, it is difficult for school districts to make informed decisions on the use of cloud applications for collaboration. This quantitative, nonexperimental study utilized Venkatesh et al.'s unified theory of acceptance and use of technology (UTAUT) to determine teachers' intent to use collaborative cloud computing tools. Online surveys with questions pertaining to UTAUT's predictor variables of performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and UTAUT's criterion variable of behavioral intent (BI) were administered to a convenience sample of 129 teachers who responded to an email solicitation. Pearson correlation results of r = 0.781, r = 0.646, r = 0.569, and r = 0.570 indicated strong, positive correlations between BI and PE, EE, SI, and FC respectively. Spearman rho correlations results of rs = 0.746, rs = 0.587, rs = 0.569, and rs = 0.613 indicated strong, positive correlations between BI and PE, EE, SI, and FC respectively. Simple linear regression results indicated that PE and EE are strong predictors of BI when moderated by age, gender, experience, and voluntariness of use (VU). SI is a strong predictor of BI when moderated by gender, but not by age, experience, and VU. This study's application of the UTAUT model to determine teachers' BI to use collaborative cloud computing tools could transform how administrators and educational technologists introduce these tools for grading and feedback purposes. This study contributes to the growing body of literature on technology integration among K-12 teachers.
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4

Hygate, Alexander P. S. [Verfasser], and Diederik [Akademischer Betreuer] Kruijssen. "The Physics of Cloud-scale Star Formation and Feedback Across Cosmic Time / Alexander Philip Stuart Hygate ; Betreuer: J. M. Diederik Kruijssen." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-279482.

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Hygate, Alexander P. S. [Verfasser], and J. M. Diederik [Akademischer Betreuer] Kruijssen. "The Physics of Cloud-scale Star Formation and Feedback Across Cosmic Time / Alexander Philip Stuart Hygate ; Betreuer: J. M. Diederik Kruijssen." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/1205883878/34.

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6

Hygate, Alexander Philip Stuart [Verfasser], and J. M. Diederik [Akademischer Betreuer] Kruijssen. "The Physics of Cloud-scale Star Formation and Feedback Across Cosmic Time / Alexander Philip Stuart Hygate ; Betreuer: J. M. Diederik Kruijssen." Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/1205883878/34.

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7

Nobile, Pedro Northon. "Projeto de um broker de gerenciamento adaptativo de recursos em computação em nuvem baseado em técnicas de controle realimentado." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062013-110725/.

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Анотація:
Computação em nuvem refere-se a um modelo de disponibilização de recursos computacionais no qual a infraestrutura de software e hardware é ofertada como um serviço, e vem se estabelecendo como um paradigma de sucesso graças a versatilidade e ao custo-efetividade envolvidos nesse modelo de negócio, possibilitando o compartilhamento de um conjunto de recursos físicos entre diferentes usuários e aplicações. Com o advento da computação em nuvem e a possibilidade de elasticidade dos recursos computacionais virtualizados, a alocação dinâmica de recursos vem ganhando destaque, e com ela as questões referentes ao estabelecimento de contratos e de de qualidade de serviço. Historicamente, as pesquisas em QoS concentram-se na solução de problemas que envolvem duas entidades: usuários e servidores. Entretanto, em ambientes de nuvem, uma terceira entidade passa a fazer parte dessa interação, o consumidor de serviços em nuvem, que usa a infraestrutura para disponibilizar algum tipo de serviço aos usuários finais e que tem recebido pouca atenção das pesquisa até o momento, principalmente no que tange ao desenvolvimento de mecanismos automáticos para a alocação dinâmica de recursos sob variação de demanda. Este trabalho consiste na proposta de uma arquitetura de gerenciamento adaptativo de recursos sob a perspectiva do modelo de negócio envolvendo três entidades, focada na eficiência do consumidor. O trabalho inspira-se em técnicas de controle realimentado para encontrar soluções adaptativas aos problemas de alocação dinâmica de recursos, resultando em uma arquitetura de broker de consumidor, um respectivo protótipo e um método de projeto de controle para sistemas computacionais dessa natureza
CLoud computing refers to a computer resource deployment model in which software and hardware infrastructure are offered as a service. Cloud computing has become a successful paradigm due to the versatility and cost-effectiveness involved in that business model, making it possible to share a cluster of physical resources between several users and applications. With the advent of cloud computing and the computer elastic resource, dynamic allocation of virtualized resources is becoming more prominent, and along with it, the issues concerning the establishment of quality of service parameters. Historically, research on QoS has focused on solutions for problems involving two entities: users and servers. However, in cloud environments, a third party becomes part of this interaction, the cloud consumer, that uses the infrastructure to provide some kind of service to endusers, and which has received fewer attention, especially regarding the development of autonomic mechanisms for dynamic resource allocation under time-varying demand. This work aims at the development of an architecture for dynamic adaptive resource allocation involving three entities, focused on consumer revenue. The research outcome is a consumer broker architecture based on feedback control, a respective architecture prototype and a computer system feedback control methodology which may be applied in this class of problems
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DECATALDO, DAVIDE. "The Effect of Stellar and Quasar Feedback on the Interstellar Medium: Structure and Lifetime of Molecular Clouds." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/90712.

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Berekmeri, Mihaly. "La modélisation et le contrôle des services BigData : application à la performance et la fiabilité de MapReduce." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT126/document.

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Le grand volume de données généré par nos téléphones mobiles, tablettes, ordinateurs, ainsi que nos montres connectées présente un défi pour le stockage et l'analyse. De nombreuses solutions ont émergées dans l'industrie pour traiter cette grande quantité de données, la plus populaire d'entre elles est MapReduce. Bien que la complexité de déploiement des systèmes informatiques soit en constante augmentation, la disponibilité permanente et la rapidité du temps de réponse sont toujours une priorité. En outre, avec l'émergence des solutions de virtualisation et du cloud, les environnements de fonctionnement sont devenus de plus en plus dynamiques. Par conséquent, assurer les contraintes de performance et de fiabilité d'un service MapReduce pose un véritable challenge. Dans cette thèse, les problématiques de garantie de la performance et de la disponibilité de services de cloud MapReduce sont abordées en utilisant une approche basée sur la théorie du contrôle. Pour commencer, plusieurs modèles dynamiques d'un service MapReduce exécutant simultanément de multiples tâches sont introduits. Par la suite, plusieurs lois de contrôle assurant les différents objectifs de qualités de service sont synthétisées. Des contrôleurs classiques par retour de sortie avec feedforward garantissant les performances de service ont d'abord été développés. Afin d'adapter nos contrôleurs au cloud, tout en minimisant le nombre de reconfigurations et les coûts, une nouvelle architecture de contrôle événementiel a été mise en œuvre. Finalement, l'architecture de contrôle optimal MR-Ctrl a été développée. C'est la première solution à fournir aux systèmes MapReduce des garanties en termes de performances et de disponibilité, tout en minimisant le coût. Les approches de modélisation et de contrôle ont été évaluées à la fois en simulation, et en expérimentation sous MRBS, qui est une suite de tests complète pour évaluer la performance et la fiabilité des systèmes MapReduce. Les tests ont été effectuées en ligne sur un cluster MapReduce de 60 nœuds exécutant une tâche de calcul intensive de type Business Intelligence. Nos expériences montrent que le contrôle ainsi conçu, peut garantir les contraintes de performance et de disponibilité
The amount of raw data produced by everything from our mobile phones, tablets, computers to our smart watches brings novel challenges in data storage and analysis. Many solutions have arisen in the industry to treat these large quantities of raw data, the most popular being the MapReduce framework. However, while the deployment complexity of such computing systems is steadily increasing, continuous availability and fast response times are still the expected norm. Furthermore, with the advent of virtualization and cloud solutions, the environments where these systems need to run is becoming more and more dynamic. Therefore ensuring performance and dependability constraints of a MapReduce service still poses significant challenges. In this thesis we address this problematic of guaranteeing the performance and availability of MapReduce based cloud services, taking an approach based on control theory. We develop the first dynamic models of a MapReduce service running a concurrent workload. Furthermore, we develop several control laws to ensure different quality of service objectives. First, classical feedback and feedforward controllers are developed to guarantee service performance. To further adapt our controllers to the cloud, such as minimizing the number of reconfigurations and costs, a novel event-based control architecture is introduced for performance management. Finally we develop the optimal control architecture MR-Ctrl, which is the first solution to provide guarantees in terms of both performance and dependability for MapReduce systems, meanwhile keeping cost at a minimum. All the modeling and control approaches are evaluated both in simulation and experimentally using MRBS, a comprehensive benchmark suite for evaluating the performance and dependability of MapReduce systems. Validation experiments were run in a real 60 node Hadoop MapReduce cluster, running a data intensive Business Intelligence workload. Our experiments show that the proposed techniques can successfully guarantee performance and dependability constraints
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Wu, Fei. "Ultra-Low Delay in Complex Computing and Networked Systems: Fundamental Limits and Efficient Algorithms." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu155559337777619.

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Книги з теми "Cloud feedback"

1

Atlidakis, Evangelos. Structure and Feedback in Cloud Service API Fuzzing. [New York, N.Y.?]: [publisher not identified], 2021.

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Workshop on Cloud Processes and Cloud Feedbacks in Large-scale Models (1999 Reading, Berkshire, United Kingdom). Workshop on Cloud Processes and Cloud Feedbacks in Large-scale Models, European Centre for Medium-range Weather Forecasts, Reading, Berkshire, United Kingdom, 9-13 November 1999. Geneva, Switzerland: Joint Planning Staff for WCRP, World Meteorological Organization, 2000.

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Abbot, Dorian Schuyler. A high-latitude convective cloud feedback. 2008.

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Ravetto-Biagioli, Kriss. Digital Uncanny. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190853990.001.0001.

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We are confronted with a new type of uncanny experience, an uncanny evoked by parallel processing, aggregate data, and cloud-computing. The digital uncanny does not erase the uncanny feeling we experience as déjà vu or when confronted with robots that are too lifelike. Today’s uncanny refers to how nonhuman devices (surveillance technologies, algorithms, feedback, and data flows) anticipate human gestures, emotions, actions, and interactions, intimating we are machines and our behavior is predicable because we are machinic. It adds another dimension to those feelings we get when we question whether our responses are subjective or automated—automated as in reducing one’s subjectivity to patterns of data and using those patterns to present objects or ideas that would then elicit one’s genuinely subjective—yet effectively preset—response. This anticipation of our responses is a feedback loop we have produced by designing software that studies our traces, inputs, and moves. Digital Uncanny explores how digital technologies, particularly software systems working through massive amounts of data, are transforming the meaning of the uncanny that Freud tied to a return of repressed memories, desires, and experiences to their anticipation. Through a close reading of interactive and experimental art works of Rafael Lozano-Hemmer, Bill Viola, Simon Biggs, Sue Hawksley, and Garth Paine, this book is designed to explore how the digital uncanny unsettles and estranges concepts of “self,” “affect,” “feedback,” and “aesthetic experience,” forcing us to reflect on our relationship with computational media and our relationship to others and our experience of the world.
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Cloud radiation forcings and feedbacks: General circulation model tests and observational validation. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Observation of local cloud and moisture feedbacks over high ocean and desert surface temperatures. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Wang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.

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The strongest Indian summer monsoon (ISM) on the planet features prolonged clustered spells of wet and dry conditions often lasting for two to three weeks, known as active and break monsoons. The active and break monsoons are attributed to a quasi-periodic intraseasonal oscillation (ISO), which is an extremely important form of the ISM variability bridging weather and climate variation. The ISO over India is part of the ISO in global tropics. The latter is one of the most important meteorological phenomena discovered during the 20th century (Madden & Julian, 1971, 1972). The extreme dry and wet events are regulated by the boreal summer ISO (BSISO). The BSISO over Indian monsoon region consists of northward propagating 30–60 day and westward propagating 10–20 day modes. The “clustering” of synoptic activity was separately modulated by both the 30–60 day and 10–20 day BSISO modes in approximately equal amounts. The clustering is particularly strong when the enhancement effect from both modes acts in concert. The northward propagation of BSISO is primarily originated from the easterly vertical shear (increasing easterly winds with height) of the monsoon flows, which by interacting with the BSISO convective system can generate boundary layer convergence to the north of the convective system that promotes its northward movement. The BSISO-ocean interaction through wind-evaporation feedback and cloud-radiation feedback can also contribute to the northward propagation of BSISO from the equator. The 10–20 day oscillation is primarily produced by convectively coupled Rossby waves modified by the monsoon mean flows. Using coupled general circulation models (GCMs) for ISO prediction is an important advance in subseasonal forecasts. The major modes of ISO over Indian monsoon region are potentially predictable up to 40–45 days as estimated by multiple GCM ensemble hindcast experiments. The current dynamical models’ prediction skills for the large initial amplitude cases are approximately 20–25 days, but the prediction of developing BSISO disturbance is much more difficult than the prediction of the mature BSISO disturbances. This article provides a synthesis of our current knowledge on the observed spatial and temporal structure of the ISO over India and the important physical processes through which the BSISO regulates the ISM active-break cycles and severe weather events. Our present capability and shortcomings in simulating and predicting the monsoon ISO and outstanding issues are also discussed.
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Liu, Xiaodong, and Libin Yan. Elevation-Dependent Climate Change in the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.593.

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As a unique and high gigantic plateau, the Tibetan Plateau (TP) is sensitive and vulnerable to global climate change, and its climate change tendencies and the corresponding impact on regional ecosystems and water resources can provide an early alarm for global and mid-latitude climate changes. Growing evidence suggests that the TP has experienced more significant warming than its surrounding areas during past decades, especially at elevations higher than 4 km. Greater warming at higher elevations than at lower elevations has been reported in several major mountainous regions on earth, and this interesting phenomenon is known as elevation-dependent climate change, or elevation-dependent warming (EDW).At the beginning of the 21st century, Chinese scholars first noticed that the TP had experienced significant warming since the mid-1950s, especially in winter, and that the latest warming period in the TP occurred earlier than enhanced global warming since the 1970s. The Chinese also first reported that the warming rates increased with the elevation in the TP and its neighborhood, and the TP was one of the most sensitive areas to global climate change. Later, additional studies, using more and longer observations from meteorological stations and satellites, shed light on the detailed characteristics of EDW in terms of mean, minimum, and maximum temperatures and in different seasons. For example, it was found that the daily minimum temperature showed the most evident EDW in comparison to the mean and daily maximum temperatures, and EDW is more significant in winter than in other seasons. The mean daily minimum and maximum temperatures also maintained increasing trends in the context of EDW. Despite a global warming hiatus since the turn of the 21st century, the TP exhibited persistent warming from 2001 to 2012.Although EDW has been demonstrated by more and more observations and modeling studies, the underlying mechanisms for EDW are not entirely clear owing to sparse, discontinuous, and insufficient observations of climate change processes. Based on limited observations and model simulations, several factors and their combinations have been proposed to be responsible for EDW, including the snow-albedo feedback, cloud-radiation effects, water vapor and radiative fluxes, and aerosols forcing. At present, however, various explanations of the mechanisms for EDW are mainly derived from model-based research, lacking more solid observational evidence. Therefore, to comprehensively understand the mechanisms of EDW, a more extensive and multiple-perspective climate monitoring system is urgently needed in the areas of the TP with high elevations and complex terrains.High-elevation climate change may have resulted in a series of environmental consequences, such as vegetation changes, permafrost melting, and glacier shrinkage, in mountainous areas. In particular, the glacial retreat could alter the headwater environments on the TP and the hydrometeorological characteristics of several major rivers in Asia, threatening the water supply for the people living in the adjacent countries. Taking into account the climate-model projections that the warming trend will continue over the TP in the coming decades, this region’s climate change and the relevant environmental consequences should be of great concern to both scientists and the general public.
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Частини книг з теми "Cloud feedback"

1

Ahmed, Riaz. "Application Feedback." In Cloud Computing Using Oracle Application Express, 277–80. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2502-8_30.

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Ahmed, Riaz. "Application Feedback." In Cloud Computing Using Oracle Application Express, 321–24. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4243-8_30.

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Li, Jia, Lei Yao, and James Z. Wang. "Photo Composition Feedback and Enhancement." In Mobile Cloud Visual Media Computing, 113–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24702-1_5.

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Yan, Xuejun, Hongyu Yan, Jingjing Wang, Hang Du, Zhihong Wu, Di Xie, Shiliang Pu, and Li Lu. "FBNet: Feedback Network for Point Cloud Completion." In Lecture Notes in Computer Science, 676–93. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20086-1_39.

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Indrusiak, Leandro Soares, Piotr Dziurzanski, and Amit Kumar Singh. "Feedback-Based Admission Control Heuristics." In Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing, 25–49. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337997-3.

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Anson, Chris M. "Teacher Feedback Tools." In Digital Writing Technologies in Higher Education, 183–202. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36033-6_12.

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AbstractBefore digital technology, students submitted handwritten or typed papers to their instructor, who responded with handwritten marginal and end comments, often with the infamous “red pen” (Dukes & Albenisi, 2013). After the introduction of word processing, students typically printed out and submitted hard copies of their final drafts, on which the instructor would handwrite comments. Today, most instructors (and all who teach online) ask students to send their (digitally produced) writing as email attachments or upload them to a learning management site or other cloud-based repository, allowing them, in turn, to provide digital feedback. Tools for such feedback have enabled instructors to comment with greater efficiency, clarity (avoiding the longstanding problem of students having to decipher scribbled remarks), and support. After a brief historical introduction, this chapter will describe four types of digital tools for teacher feedback: digital annotation tools, text expansion tools, voice-to-text tools, and tools for audio and audio-visual feedback.
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Indrusiak, Leandro Soares, Piotr Dziurzanski, and Amit Kumar Singh. "Feedback-Based Allocation and Optimisation Heuristics." In Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing, 51–72. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337997-4.

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Wang, Xuan, Yi Li, Linna Wang, and Li Lu. "CFNet: Point Cloud Upsampling via Cascaded Feedback Network." In Artificial Neural Networks and Machine Learning – ICANN 2023, 317–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44207-0_27.

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Zhang, M. H., J. T. Kiehl, and J. J. Hack. "Cloud-Radiative Feedback as Produced by Different Parameterizations of Cloud Emissivity in CCM2." In Climate Sensitivity to Radiative Perturbations, 213–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61053-0_16.

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Li, Jinhai, Yunlei Ma, Huisheng Zhu, and Youshi He. "Research on Feedback Effects Between Perception of Internet Word of Mouth and Online Reviews Based on Dynamic Endogeneity." In Cloud Computing and Security, 658–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_58.

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Тези доповідей конференцій з теми "Cloud feedback"

1

Cappelletti, Andrea, and Mark Grechanik. "Feedback-Directed Cross-Layer Optimization of Cloud-Based Functional Actor Applications." In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), 605–16. IEEE, 2024. https://doi.org/10.1109/issre62328.2024.00063.

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Li, Xinyi, and Ganesh Gopalakrishnan. "FBTuner: A Feedback-Directed Approach for Safe Mixed-Precision Tuning." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00077.

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Jayanthi, L. N., Kannan Shanmugam, Challapalli Suma, Sagar B S, S. P. Maniraj, and T. R. GaneshBabu. "Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 1038–44. IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714624.

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Karpagam, G. R., Harsha Vardhan V M, Kabilan K K, Pranav P, Prednya Ramesh, and Suvan Sathyendira B. "Physiological Data-Based Stress Detection: From Wrist Sensors to Cloud Computing and User Feedback Integration." In 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), 386–91. IEEE, 2024. http://dx.doi.org/10.1109/icsseecc61126.2024.10649521.

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Kaneko, Yu, and Toshio Ito. "A Reliable Cloud-Based Feedback Control System." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0128.

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Kuchi, Kiran, Maaz M. Mohiuddin, T. V. Sreejith, Rijul Bansal, G. V. V. Sharma, and Shahriar Emami. "Cloud radios with limited feedback." In 2014 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2014. http://dx.doi.org/10.1109/spcom.2014.6983978.

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Ou, S. C., K. N. Liou, W. Gooch, N. Rao та Y. Takano. "Remote Sensing of Cirrus Cloud Parameters Using AVHRR 3.7 and 10.9 μm Channel Data". У Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/orsa.1993.mc.4.

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Cirrus clouds are global in nature and occupy primarily the upper troposphere and lower stratosphere. Information on cirrus cloud parameters is critically important to the development of cirrus cloud forecast models, the upgrading of real-time global cloud analysis, and the investigation of cloud-radiation feedback to the temperature perturbations in global climate change.
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Hamadache, Kahina, and Paraskevi Zerva. "Provenance of Feedback in Cloud Services." In 2014 IEEE 8th International Symposium on Service Oriented System Engineering (SOSE). IEEE, 2014. http://dx.doi.org/10.1109/sose.2014.10.

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Eberhard, W. L., R. E. Cupp, R. M. Hardesty, J. M. Intrieri, and R. J. Willis. "Design and Preliminary Results from the Cloud Lidar And Radar Exploratory Test (CLARET)." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/orsa.1990.tua5.

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Clouds and their radiative effects strongly influence climate (Ramanathan et al., 1989). High priority has been assigned (CES, 1989) to research the role of clouds’ radiative properties in climate change, especially as feedback mechanisms, and on how to incorporate these effects properly in climate models. Measurements of climate-significant properties of clouds, such as height, optical density, and particle size distribution, are needed in intensive process studies and also in extended studies to observe the large variety of cloud conditions that occur.
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Machhi, Sandip, and G. B. Jethava. "Feedback based Trust Management for Cloud Environment." In the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905330.

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Звіти організацій з теми "Cloud feedback"

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Dipankar, Anurag, and Bjorn Stevens. Cloud feedback studies with a physics grid. Office of Scientific and Technical Information (OSTI), February 2013. http://dx.doi.org/10.2172/1076961.

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Benedict, James, Amy Clement, B. Medeiros, and S. Klein. Cloud-Feedback Model Intercomparison Project: Tier 2 Simulations (Final Report). Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1769119.

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George Tselioudis. A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data. Office of Scientific and Technical Information (OSTI), August 2009. http://dx.doi.org/10.2172/962208.

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Del Genio, Anthony D. Constraints on cloud feedback from analysis of ARM observations and models. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1178045.

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Lacis, A. Analysis of cloud radiative forcing and feedback in a climate GCM. Final report. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/465803.

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Hartmann, Dennis. Understanding and Constraining the Midlatitude Cloud Optical Depth Feedback in Climate Models (Final Report). Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1558113.

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Kogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada532932.

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Kogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada541931.

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Kogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557145.

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Mitra, Sayan. Continuous Integration and Deployment Infrastructure for Rapid Testing of Autonomous Transportation Systems. Illinois Center for Transportation, June 2024. http://dx.doi.org/10.36501/0197-9191/24-017.

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This project has led to the creation of an automated-testing infrastructure for autonomy code. The framework uses Jenkins, AWS Lambda, Docker, Kubernetes, and other open-source technologies. It was utilized and evaluated both for the Generalized Racing Intelligence Competition (GRAIC) and for evaluating student programming assignments for the principles of safe autonomy course (ECE484). This infrastructure has improved our capability to evaluate (autograde) student design assignments, and students can also receive precise feedback on their work as they progress through various design challenges. This infrastructure has significantly improved our capability in both automatic, cloud-based testing of autonomy code and making this service available to a large population of students.
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