scholarly journals WIND-GENERATED LONGSHORE CURRENTS

1978 ◽  
Vol 1 (16) ◽  
pp. 84
Author(s):  
Dag Nummedal ◽  
Robert J. Finley
Keyword(s):  
South Carolina ◽  
Surf Zone ◽  
Environmental Parameters ◽  
Stepwise Multiple Regression ◽  
Longshore Current ◽  
Statistical Data Analysis ◽  
Data Set ◽  
Seasonal Basis ◽  
Independent Variable ◽  
One Year

This paper evaluates, through the use of a stepwise multiple regression procedure, whether parameters descriptive of the surf-zone wave field adequately explain the variability in longshore current velocities, or if the inclusion of additional physical environmental parameters could significantly improve the ability to predict such currents. The data set consists of 250 LEO - observations, collected on a seasonal basis over one year, at Debidue Island beach, South Carolina. A regression analysis was performed both on linear combinations of all measured wave parameters, and on nonlinear parameter combinations proposed in various semiempirical predictive equations. Invariably, in all the regression analyses, the longshore component of the wind velocity proved to be the independent variable explaining most of the observed variance in the current velocity. Therefore, the statistical data analysis presented in this paper strongly suggests that wind stress can be a most significant factor in surf-zone current generation.

Benefits of Compiling and Analyzing Hydraulic-Design Data for Bridges

2021 ◽  
pp. 036119812110237
Author(s):  
Stephen T. Benedict ◽  
Thomas P. Knight
Keyword(s):  
South Carolina ◽  
Bridge Scour ◽  
Hydraulic Characteristics ◽  
Bridge Design ◽  
Design Data ◽  
Data Set ◽  
Engineering Knowledge ◽  
Hydraulic Design ◽  
Strong Measure ◽  
Design Characteristics

The hydraulic design of bridges is a discipline that requires a strong measure of engineering judgment. Developing good engineering judgment can take years of experience, and generally increases one project at a time. A supplemental tool that can promote the development of engineering knowledge and judgment is to compile, analyze, and graphically present hydraulic data associated with stream and bridge-design characteristics from previously analyzed bridges. If the data set is sufficiently large, graphs developed from such an effort can provide the engineer with an enhanced picture of stream and bridge-design characteristics, helping them further develop their engineering knowledge and judgment. Furthermore, such graphs can function as project scoping tools and hydraulic-design review tools. Using selected data from approximately 300 bridge-scour studies in South Carolina, previously conducted by the U.S. Geological Survey, and limited hydraulic bridge-design data for approximately 200 bridges in South Carolina, trends in stream and bridge-hydraulic characteristics were evaluated including channel width, floodplain width, flood flow depths, stream slopes, bridge backwater, bridge flow velocity, and bridge lengths. Selected relationships are presented in this paper and should serve as a valuable tool for better understanding stream and bridge-hydraulic characteristics in South Carolina.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

scholarly journals TOWARD AN IMPROVED EMPIRICAL FORMULA FOR LONGSHORE SAND TRANSPORT

1988 ◽  
Vol 1 (21) ◽  
pp. 88 ◽  
Author(s):  
Nicholas C. Kraus ◽  
Kathryn J. Gingerich ◽  
Julie Dean Rosati
Keyword(s):  
Oscillatory Flow ◽  
Field Experiments ◽  
Surf Zone ◽  
Transport Rate ◽  
Sand Transport ◽  
Longshore Current ◽  
Wave Energy Dissipation ◽  
Total Transport ◽  
Local Wave ◽  
Correction Terms

This paper presents results of two field experiments performed using portable traps to obtain point measurements of the longshore sand transport rate in the surf zone. The magnitude of the transport rate per unit width of surf zone is found to depend on the product of the local wave height and mean longshore current speed, but correlation is much improved by including two correction terms, one accounting for local wave energy dissipation and the other for the fluctuation in the longshore current. The field transport rates are also found to be compatible with laboratory rates obtained under combined unidirectional and oscillatory flow. Total transport rates previously reported for this experiment program are revised with recently determined sand trapping efficiencies.

scholarly journals Origin and transport of Mediterranean moisture and air

2010 ◽  
Vol 10 (11) ◽  
pp. 5089-5105 ◽  
Author(s):  
I. Schicker ◽  
S. Radanovics ◽  
P. Seibert
Keyword(s):  
Residence Time ◽  
Mediterranean Basin ◽  
Trade Wind ◽  
Trajectory Data ◽  
Data Set ◽  
Mediterranean Regions ◽  
Diagnosis Method ◽  
Seasonal Basis ◽  
The Mediterranean ◽  
The Mediterranean Basin

Abstract. Considering the Mediterranean as a region of high evaporation and low precipitation, evaluations of sinks and sources of moisture and precipitation in the Mediterranean basin have been carried out within the frame of the CIRCE (Climate Change and Impact Research: the Mediterranean Environment) project. Besides these evaluations, residence time and stagnation/ventilation analyses have been carried out to investigate transport to and from the Mediterranean basin and in the basin itself. A Lagrangian moisture diagnosis method calculating budgets of evaporation minus precipitation was applied to a 5.5 year (October 1999–April 2005) trajectory data set and evaluated for eight representative Mediterranean Regions Of Interest (ROI). The Mediterranean basin has been identified as a major source of moisture and precipitation to the surrounding land area and to the basin itself. Main regions of stagnation, i.e. the Po basin, have been defined on a seasonal basis through residence time analysis. Evaluation of the transport to and from the basin shows that the Mediterranean is a crossroad of airstreams where air enters mainly from the northwest and continues in two separate streams, one going southwest over North Africa into the trade wind zone and the other one to the northeast through Central Asia.

Significance of Extreme Temperament in Infancy for Clinical Status in Preschool Years

1989 ◽  
Vol 154 (4) ◽  
pp. 544-551 ◽  
Author(s):  
Michel Maziade ◽  
Jacques Thivierge ◽  
Robert Côté ◽  
Pierrette Boutin ◽  
Hugues Bernier
Keyword(s):  
Longitudinal Study ◽  
Birth Cohort ◽  
Clinical Status ◽  
Environmental Parameters ◽  
Positive Change ◽  
Stability And Change ◽  
Cognitive Characteristics ◽  
One Year ◽  
Long Term Prediction

Few, if any, of children's behavioural or cognitive characteristics assessed in the first years of life demonstrate stability until later childhood; early characteristics have so far failed to show an association with future psychopathology. This longitudinal study, from 4–8 months to 4.7 years old, focused on stability and change of extreme temperamental traits in groups of infants subselected from a large birth cohort. Persistent extreme temperament at four and eight months old did not increase stability of temperament to four years of age, relative to other children in the whole population. Sizeable change occurred, and the environmental parameters associated with negative temperamental change did not seem to be the same as those related to positive change. Boys with extreme scores were more stable, while girls appeared more prone to positive change. It is hypothesised that the direction of temperamental change in the first years could be more meaningful for long-term prediction of disorders than any one assessment of temperament taken at any one year.

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

scholarly journals FAKTOR –FAKTOR YANG MEMPENGARUHI KEPATUHAN PAJAK WAJIB PAJAK ORANG PRIBADI (Studi Pada Wajib Pajak Orang Pribadi yang terdaftar di KPP Kebayoran Baru Tiga)

2014 ◽  
Vol 1 (1) ◽  
pp. 51
Author(s):  
Fibria Anggraini P.L ◽  
Waluyo Waluyo
Keyword(s):  
Regression Analysis ◽  
Organizational Climate ◽  
Survey Design ◽  
Linear Regression Analysis ◽  
Tax Compliance ◽  
Multiple Linear Regression Analysis ◽  
Individual Behavior ◽  
Behavior Motivation ◽  
Independent Variable ◽  
One Year

The purpose of this study was to analyze the factors that affect tax compliance (tax compliance) individual taxpayers in the KPP Kebayoran Baru Tiga, South Jakarta. This study used a questionnaire survey design as an instrument. Respondents in this study were 85 individual taxpayers entrepreneur tax register at least one year and to submit the SPT.This study uses the variable individual behavior, motivation and organizational climate as the independent variable and tax compliance individual taxpayers as the dependent variable. Data analysis was performed using multiple linear regression analysis with SPSS for Windows Version 19.00. The results of this study indicate that individual behavior, motivation and organizational climate have significant effect on tax compliance individual taxpayers .

The Mid-Atlantic Twin Registry, Revisited

2012 ◽  
Vol 16 (1) ◽  
pp. 424-428 ◽  
Author(s):  
Emily C. H. Lilley ◽  
Judy L. Silberg
Keyword(s):  
South Carolina ◽  
Developmental Psychopathology ◽  
Human Microbiome ◽  
Population Based ◽  
Data Set ◽  
Twin Registry ◽  
Children Of Twins ◽  
Scientific Disciplines ◽  
Sleep Homeostasis ◽  
Population Based Registry

The Mid-Atlantic Twin Registry (MATR) is a population-based registry of more than 56,000 twins primarily born or living in Virginia, North Carolina, and South Carolina. The MATR employs several methods of ascertaining twins, and devotes considerable resources to tracking and maintaining communication with MATR participants. Researchers may utilize the MATR for administration of research services including study recruitment, collection of DNA, archival data set creation, as well as data collection through mailed, phone, or online surveys. In addition, the MATR houses the MATR Repository, with over 1,200 blood samples available for researchers interested in DNA genotyping. For over 35 years MATR twins have participated in research studies with investigators from diverse scientific disciplines and various institutions. These studies, which have resulted in numerous publications, have covered a range of topics, including the human microbiome, developmental psychopathology, depression, anxiety, substance use, epigenetics of aging, children of twins, pre-term birth, social attitudes, seizures, eating disorders, as well as sleep homeostasis. Researchers interested in utilizing twins are encouraged to contact the MATR to discuss potential research opportunities.

One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint)

2021 ◽  
Author(s):  
Thomas Ka-Luen Lui ◽  
Ka Shing, Michael Cheung ◽  
Wai Keung Leung
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negative Rate ◽  
Absolute Error ◽  
Advanced Hepatocellular Carcinoma ◽  
Data Set ◽  
One Year ◽  
Related Mortality

BACKGROUND Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. OBJECTIVE This study aimed to evaluate the role of machine learning (ML) models in predicting the one-year cancer-related mortality in advanced HCC patients treated with immunotherapy METHODS 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) in 2014 - 2019 in Hong Kong were included. The whole data set were randomly divided into training (n=316) and validation (n=79) set. The data set, including 45 clinical variables, was used to construct six different ML models in predicting the risk of one-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and the mean absolute error (MAE) using calibration analysis. RESULTS The overall one-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.93 (95%CI: 0.86-0.98), which was better than logistic regression (0.82, p=0.01) and XGBoost (0.86, p=0.04). RF also had the lowest false positive (6.7%) and false negative rate (2.8%). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSIONS ML models could predict one-year cancer-related mortality of HCC patients treated with immunotherapy, which may help to select patients who would most benefit from this new treatment option.

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