scholarly journals SIMULATING THE FORMATION OF TIDAL CHANNELS ALONG AN OPEN-COAST TIDAL FLAT: THE EFFECTS OF INITIAL PERTURBATION

2018 ◽  
pp. 84
Author(s):  
Ying Zhang ◽  
Zeng Zhou ◽  
Liang Geng ◽  
Giovanni Coco ◽  
Jianfeng Tao ◽  
...  
Keyword(s):  
Tidal Flat ◽  
State Of The Art ◽  
Initial Perturbation ◽  
Tidal Channel ◽  
Tidal Channels ◽  
Medium Term ◽  
Short Term ◽  
Critical Range ◽  
Morphodynamic Modeling ◽  
Perturbation Magnitude

A state-of-the-art morphodynamic model (Delft3D) was used to explore the effects of bathymetric perturbation on the morphodynamic modeling of tidal channels and flats. Short-term and medium-term modeling results indicate that the two-way interaction of the hydrodynamic forcing and initial perturbation has influence on the evolution of tidal channel ontogeny. There is a critical range of the magnitude of initial perturbation, within which the morphodynamic development tends to be similar. By comparing with the case without initial perturbation, the case with a slight increase in perturbation magnitude can considerably enhance the rate of the morphodynamic development.

scholarly journals MORPHODYNAMIC MODELING OF THE MEDIUM-TERM MIGRATION OF A TIDAL CHANNEL USING PROCESS-BASED MODEL

2011 ◽  
Vol 1 (32) ◽  
pp. 70
Author(s):  
Diem Nguyen ◽  
Talal Etri ◽  
Karl-Heinz Runte ◽  
Roberto Mayerle
Keyword(s):  
Field Measurements ◽  
Tidal Channel ◽  
Medium Term ◽  
Calibration And Validation ◽  
Sediment Fraction ◽  
Morphodynamic Modeling ◽  
Set Up ◽  
Temporal Coverage ◽  
Selection Of ◽  
Process Based Models

This paper investigates the predictive capability of a morphodynamic model in capturing the development of a tidal channel on the German North Sea coast which experienced migration in the last few years. A depth-averaged version of a process-based model, Delft3D, is used. A description of the set-up, calibration and validation of the process-based models is presented. Field measurements with a dense spatial and temporal coverage were used for the development of the models. Results from the hydrodynamic and sediment transport simulations were in agreement with observations. The morphodynamic model simulations were speeded up with a morphological acceleration factor in conjunction with a representative period. Results of model calibration and validation covering periods of several years proved the capability of the model to reproduce the migration of the tidal channel. According to the standards usually adopted for checking the accuracy of morphodynamic models, the performance of the model presented here was quite good. The model ability in predicting the migration in the medium-term was found to be dependent primarily on the accuracy of the starting bathymetry, characteristics of the substrata and of the mud sediment fraction as well as on the selection of the representative period. A reduction in the rate of migration of the tidal channel is predicted from 2008 till 2010.

scholarly journals Interconnected Accommodation Space Controls Between Sand-Charged Shallow Tidal Channels and Wind-Wave Truncated Tidal Flats During Latest-Holocene Sea Level Rise (~3.0 m) in a Large Mesotidal Wave-Dominated Estuary, Grays Harbor, Washington, USA

2018 ◽  
Vol 10 (3) ◽  
pp. 26 ◽  
Author(s):  
Curt D Peterson ◽  
Sandy Vanderburgh
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Tidal Flat ◽  
Wind Wave ◽  
Tidal Flats ◽  
Tidal Channel ◽  
Tidal Channels ◽  
Accommodation Space ◽  
Global Sea Level

The late-Holocene (5–0 ka) record of accommodation space controls of tidal channel and tidal flat deposition in the shallow mesotidal wave-dominated Grays Harbor estuary (236 km2 surface area) was investigated in previously reported drill cores (n=15) and new vibracores (n=20), reaching 3–10 m depth subsurface. Continuous vibracore facies sequences (3–4 m depth subsurface) discriminate between tidal channel and tidal flat deposition and demonstrate responses of both depositional settings to interseismic uplift and coseismic subsidence (1±0.5 m vertical) from cyclic neotectonic forcing (200–800 yr recurrence intervals) in the Cascadia subduction zone. Vibracore channel samples, at 0.5 m or 1.0 m depth intervals, were analyzed for sediment grain size (sample n=124) and sand source mineralogy (sample n=67). The mean and standard deviation of sand size in the sand fraction is 175±x34 1σ µm. Sediment 14C dates (n=29) range from 376 to 6,579 median calyrBP and establish long-term sedimentation rates in subtidal channel accretionary banks (average 4.2 m ka-1), intertidal channel accretionary banks (average 3.7 m ka-1), and tidal flats (average 1.1 m ka-1). Tidal channel accretionary bank deposition largely reflects reworking of pre-existing estuary deposits. Long-term total basin sediment accumulation rates (232x106 m3 ka-1) are tied to rates of net sea level rise (1.0 m ka-1) or increasing basin accommodation space. In latest Holocene time (3–0 ka) littoral sand import (117x106 m3 ka-1) was about twice as large as the retention of river sand and mud in the estuary. The selective export of winnowed mud from the estuary provided the necessary accommodation space for the import of littoral sand in latest-Holocene time. Shallow intertidal settings in Grays Harbor (60% by surface area) are maintained by self-regulating conditions of channelized sediment import, wind-wave erosion of tidal flats, and tidal prism forcing of tidal channel discharge. Hind-casted wind-wave bottom orbital velocities (>20 cm sec-1) are sufficient to truncate tidal flat elevations to lower-intertidal levels, which maintain substantial tidal prism volumes (modern MLLW-MHHW ~6.1 x 108 m3) and associated tidal channel discharge in the shallow estuary. Net sediment deposition in the estuary is controlled by the interaction of limiting accommodation space controls in the tidal flats and tidal channels. The balance between sediment supply, energy of sediment transport/resuspension, and sediment export has survived small changes in relative sea level (1±0.5 m) from cyclic neotectonic forcing. However, the prehistoric (natural) balance could be altered by future anthropogenic impacts from sustained global sea level rise (> 1.5 m during the next century) or diminished wind-wave fetch distances, which could result from tidal flat diking/filling or uncontrolled spread of non-native invasive stabilizing sea grass (Spartina). In this regard, the susceptibilities of prehistorically-balanced sediment dynamics in Grays Harbor serve as warning for other similar mesotidal wave-dominated estuaries that could be impacted by future global sea level rise, changing sediment inputs, and/or tidal flat diking/filling, which could reduce intertidal habitat and associated ecosystem functions. 

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.

SHORT-TERM MORPHOLOGICAL CHANGES AND SEDIMENT DYNAMICS OF TIDAL FLAT DUE TO TYPHOON PASSAGE

2019 ◽  
Vol 75 (2) ◽  
pp. I_499-I_504
Author(s):  
Itsuki KURITANI ◽  
Shigeru KATO ◽  
Takahiro TABATA ◽  
Ryota NAKAMURA ◽  
Takumi OKABE
Keyword(s):  
Tidal Flat ◽  
Morphological Changes ◽  
Sediment Dynamics ◽  
Short 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

A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

2018 ◽  
Author(s):  
Xingjian Lai ◽  
Huanyi Shui ◽  
Jun Ni
Keyword(s):  
Manufacturing Systems ◽  
Short Term Memory ◽  
Complex Dynamics ◽  
State Of The Art ◽  
Short Term ◽  
Term Memory ◽  
Future Production ◽  
Effective Manner ◽  
Long Short Term Memory ◽  
Factory Floor

Throughput bottlenecks define and constrain the productivity of a production line. Prediction of future bottlenecks provides a great support for decision-making on the factory floor, which can help to foresee and formulate appropriate actions before production to improve the system throughput in a cost-effective manner. Bottleneck prediction remains a challenging task in literature. The difficulty lies in the complex dynamics of manufacturing systems. There are multiple factors collaboratively affecting bottleneck conditions, such as machine performance, machine degradation, line structure, operator skill level, and product release schedules. These factors impact on one another in a nonlinear manner and exhibit long-term temporal dependencies. State-of-the-art research utilizes various assumptions to simplify the modeling by reducing the input dimensionality. As a result, those models cannot accurately reflect complex dynamics of the bottleneck in a manufacturing system. To tackle this problem, this paper will propose a systematic framework to design a two-layer Long Short-Term Memory (LSTM) network tailored to the dynamic bottleneck prediction problem in multi-job manufacturing systems. This neural network based approach takes advantage of historical high dimensional factory floor data to predict system bottlenecks dynamically considering the future production planning inputs. The model is demonstrated with data from an automotive underbody assembly line. The result shows that the proposed method can achieve higher prediction accuracy compared with current state-of-the-art approaches.

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.

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