scholarly journals An equivariant index for proper actions I

2017 ◽  
Vol 272 (2) ◽  
pp. 661-704 ◽  
Author(s):  
Peter Hochs ◽  
Yanli Song
Keyword(s):  
Proper Actions ◽  
Equivariant Index

scholarly journals Achieving a Sustainable Development Process by Deployment of Solar PV Power in ASEAN: A SWOT Analysis

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 630
Author(s):  
Hengtian Wang ◽  
Xiaolong Yang ◽  
Qihe Lou ◽  
Xinxin Xu
Keyword(s):  
Sustainable Development ◽  
Development Process ◽  
Swot Analysis ◽  
Primary Energy ◽  
Solar Pv ◽  
Proper Actions ◽  
External Threats ◽  
Process Deployment ◽  
Asean Countries ◽  
Primary Energy Supply

The Association of Southeast Asian Nations (ASEAN) has experienced rapid social and economic development in the past decades, while energy shortage, environmental pollution, and climate change are the factors that prevent a sustainable development process. Deployment of solar photovoltaic (PV) power is one of the effective alternatives to overcome the above barriers and assist ASEAN to achieve the aspirational target of 23% renewable energy (RE) in the total primary energy supply (TPES). In this study, SWOT analysis is adopted to analyze the internal strengths and weaknesses and the external threats and opportunities tightly related to the development of solar PV power in ASEAN countries. Through the SWOT analysis, great potential for the development of solar PV power in ASEAN is found. As long as appropriate policies are implemented and proper actions are taken, huge space for deployment of solar PV power can be expected. Based on the SWOT analysis, countermeasures that emphasize further deployment of solar PV power in ASEAN countries are put forward. The tactics include arousing people’s awareness of a sustainable development process, government issue coherence and stable incentive policies, fostering a solar PV industry chain and master key technology, and seek opportunities via an international cooperation.

PROPER ACTIONS ON SOME EXPONENTIAL SOLVABLE HOMOGENEOUS SPACES

2005 ◽  
Vol 16 (09) ◽  
pp. 941-955 ◽  
Author(s):  
ALI BAKLOUTI ◽  
FATMA KHLIF
Keyword(s):  
Maximal Subgroup ◽  
Homogeneous Space ◽  
Lie Group ◽  
Homogeneous Spaces ◽  
Intersection Property ◽  
Nilpotent Lie Group ◽  
Proper Actions ◽  
Simply Connected

Let G be a connected, simply connected nilpotent Lie group, H and K be connected subgroups of G. We show in this paper that the action of K on X = G/H is proper if and only if the triple (G,H,K) has the compact intersection property in both cases where G is at most three-step and where G is special, extending then earlier cases. The result is also proved for exponential homogeneous space on which acts a maximal subgroup.

Integrated Production Data Analysis and Water Injection Optimization in A Giant Carbonate Field

2021 ◽  
Author(s):  
Adel Mehrabadi ◽  
Gabriele Urbani ◽  
Simona Renna ◽  
Lucia Rossi ◽  
Italo Luciani ◽  
...  
Keyword(s):  
Data Analysis ◽  
Water Injection ◽  
Water Flooding ◽  
Integrated Analysis ◽  
Production Data ◽  
Data Set ◽  
Proper Actions ◽  
Injection Scheme ◽  
Vertical Heterogeneity ◽  
Current Water

Abstract In case of giant brown fields, a proper water injection management can result in a very complex process, due to the quality and quantity of data to be analysed. Main issue is the understanding of the injected water preferential paths, especially in carbonate environment characterized by strong vertical and areal heterogeneities (karst). A structured workflow is presented to analyze and integrate a massive data set, in order to understand and optimize the water injection scheme. An extensive Production Data Analysis (PDA) has been performed, based on the integration of available geological data (including NMR and Cased Hole Logs), production (allocated rates, Well Tests, PLT), pressure (SBHP, RFT, MDT, ESP) and salinity data. The applied workflow led to build a Fluid Path Conceptual Model (FPCM), an easy but powerful tool to visualize the complex dynamic connections between injectors-producers and aquifer influence areas. Several diagnostic plots were performed to support and validate the main outcomes. On this basis, proper actions were implemented to optimize the current water injection scheme. The workflow was applied on a carbonate giant brown field characterized by three different reservoir members, hydraulically communicating at original conditions, characterized by high vertical heterogeneity and permeability contrast. Moreover, dissolution phenomena, localized in the uppermost reservoir section, led to important permeability enhancement through a wide network of connected vugs, acting as water preferential communication pathways. The geological analysis played a key role to investigate the reservoir water flooding mechanism in dynamic conditions. The water rising mechanism was identified to be driven by the high permeability contrast, hence characterized by lateral independent movements in the different reservoir members. The integrated analysis identified room for optimization of the current water injection strategy. In particular, key factor was the analysis and optimization at block scale, intended as areal and vertical sub-units, as identified by the PDA and visualized through the FPCM. Actions were suggested, including injection rates optimization and the definition of new injections points. A detailed surveillance plan was finally implemented to monitor the effects of the proposed actions on the field performances, proving the robustness of the methodology. Eni workflow for water injection analysis and optimization was previously successfully tested only in sandstone reservoirs. This paper shows the robustness of the methodology also in carbonate environment, where water encroachment is strongly driven by karst network. The result is a clear understanding of the main dynamics in the reservoir, which allows to better tune any action aimed to optimize water injection and increase the value of mature assets.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Heterogeneous Inertia Estimation for Power Systems with High Penetration of Converter-Interfaced Generation

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5047
Author(s):  
Diala Nouti ◽  
Ferdinanda Ponci ◽  
Antonello Monti
Keyword(s):  
Power Systems ◽  
Estimation Algorithm ◽  
System Stability ◽  
Rotational Inertia ◽  
Heterogeneous Distribution ◽  
Proper Actions ◽  
High Penetration ◽  
System Operator ◽  
And Control ◽  
System Inertia

The increasing and fast deployment of distributed generation is posing challenges to the operation and control of power systems due to the resulting reduction in the overall system rotational inertia and damping. Therefore, it becomes quite crucial for the transmission system operator to monitor the varying system inertia and damping in order to take proper actions to maintain the system stability. This paper presents an inertia estimation algorithm for low-inertia systems to estimate the inertia (both mechanical and virtual) and damping of systems with mixed generation resources and/or the resource itself. Moreover, the effect of high penetration of distributed energy resources and the resulting heterogeneous distribution of inertia on the overall system inertia estimation is investigated. A comprehensive set of case studies and scenarios of the IEEE 39-bus system provides results to demonstrate the performance of the proposed estimator.

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