Optimization Operation Planning Method for Microgrid System that Combines Short-Term and Medium-Term Calculation using PSO

2021 ◽  
Vol 141 (12) ◽  
pp. 1397-1404
Author(s):  
Yu Tanahashi ◽  
Hiroshi Kobayashi ◽  
Yuta Nakamura ◽  
Mutsumi Aoki
Keyword(s):  
Planning Method ◽  
Operation Planning ◽  
Medium Term ◽  
Short Term

Is money the creation of the state or the market? (On the “modern monetary theory” as described in the textbook by W. Mitchell, L. R. Wray and M. Watts “Macroeconomics”)

2020 ◽  
pp. 121-134
Author(s):  
S. A. Andryushin
Keyword(s):  
Public Debt ◽  
Scientific Community ◽  
Full Employment ◽  
Term Effect ◽  
Scientific Concept ◽  
Monetary Theory ◽  
Left Wing ◽  
Medium Term ◽  
Short Term ◽  
Stimulation Of

In 2019, a textbook “Macroeconomics” was published in London, on the pages of which the authors presented a new monetary doctrine — Modern Monetary Theory, MMT, — an unorthodox concept based on the postulates of Post-Keynesianism, New Institutionalism, and the theory of Marxism. The attitude to this scientific concept in the scientific community is ambiguous. A smaller part of scientists actively support this doctrine, which is directly related to state monetary and fiscal stimulation of full employment, public debt servicing and economic growth. Others, the majority of economists, on the contrary, strongly criticize MMT, arguing that the new theory hides simple left-wing populism, designed for a temporary and short-term effect. This article considers the origins and the main provisions of MMT, its discussions with the mainstream, criticism of the basic tenets of MMT, and also assesses possible prospects for the development of MMT in the medium term.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Multidimensional assessment of women after severe maternal morbidity: the COMMAG cohort study

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e041138
Author(s):  
Elton C Ferreira ◽  
Maria Laura Costa ◽  
Rodolfo C Pacagnella ◽  
Carla Silveira ◽  
Carla B Andreucci ◽  
...  
Keyword(s):  
Cohort Study ◽  
Sexual Function ◽  
Screening Test ◽  
Maternal Morbidity ◽  
Severe Maternal Morbidity ◽  
Medium Term ◽  
Short Term ◽  
Multidimensional Assessment ◽  
Index Pregnancy ◽  
Sf 36

ObjectivesTo perform a multidimensional assessment of women who experienced severe maternal morbidity (SMM) and its short-term and medium-term impact on the lives and health of women and their children.DesignA retrospective cohort study.SettingA tertiary maternity hospital from the southeast region of Brazil.ParticipantsThe exposed population was selected from intensive care unit admissions if presenting any diagnostic criteria for SMM. Controls were randomly selected among women without SMM admitted to the same maternity and same time of childbirth.Primary and secondary outcome variablesValidated tools were applied, addressing post-traumatic stress disorder (PTSD) and quality of life (SF-36) by phone, and then general and reproductive health, functioning (WHO Disability Assessment Schedule), sexual function (Female Sexual Function Index (FSFI)), substance abuse (Alcohol, Smoking and Substance Involvement Screening Test 2.0) and growth/development (Denver Developmental Screening Test) of children born in the index pregnancy in a face-to-face interview.ResultsAll instruments were applied to 638 women (315 had SMM; 323 were controls, with the assessment of 264 and 307 children, respectively). SF-36 score was significantly lower in the SMM group, while PTSD score was similar between groups. Women who had SMM became more frequently sterile, had more abnormal clinical conditions after the index pregnancy and a higher score for altered functioning, while proportions of FSFI score or any drug use were similar between groups. Furthermore, children from the SMM group were more likely to have weight (threefold) and height (1.5 fold) for age deficits and also impaired development (1.5-fold).ConclusionSMM impairs some aspects of the lives of women and their children. The focus should be directed towards monitoring these women and their children after birth, ensuring accessibility to health services and reducing short-term and medium-term repercussions on physical, reproductive and psychosocial health.

scholarly journals Short-term and medium-term health effects of 9/11

The Lancet ◽  
2011 ◽  
Vol 378 (9794) ◽  
pp. 925-934 ◽  
Author(s):  
Sharon E Perlman ◽  
Stephen Friedman ◽  
Sandro Galea ◽  
Hemanth P Nair ◽  
Monika Erős-Sarnyai ◽  
...  
Keyword(s):  
Health Effects ◽  
Medium Term ◽  
Short Term

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

Short-term operation planning on cogeneration systems: A survey

2008 ◽  
Vol 78 (5) ◽  
pp. 835-848 ◽  
Author(s):  
Fabricio Salgado ◽  
Pedro Pedrero
Keyword(s):  
Operation Planning ◽  
Short Term ◽  
Term Operation

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

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