scholarly journals Belonging and Mattering in the First-Year Welcome Experience: A Comparison Study Before and During COVID-19.

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Ling Ning ◽  
Kimberly Kruchen ◽  
Crystal Cyr
Keyword(s):  
Machine Learning ◽  
Sense Of Belonging ◽  
First Year ◽  
Visualization Tool ◽  
Comparison Study ◽  
Sharp Decline ◽  
Machine Learning Methods ◽  
Collegiate Experience ◽  
High Level ◽  
The Impact

Institutions value knowledge about programs and services that are most effective at enhancing the collegiate experience, particularly sense of belonging and mattering for their students. The knowledge has become more pivotal due to the COVID-19 pandemic impact and as a result, the transitioning of most programs and services to a virtual environment. This study employs machine learning methods to analyze Fall Welcome survey data from Fall 2019 and Fall 2020. The purposes are threefold: 1) identify, rank, and contrast the top contributors to sense of belonging and mattering; 2) to understand quantitatively the impact of the pandemic on students’ welcoming experience; 3) to introduce and review an AI leveraged analytical visualization tool for key influencer analysis. Results indicated there has been a sharp decline in students’ connection, belonging, and mattering from fall 2019 to fall 2020 due to the pandemic. The opportunities to build connections, have an overall positive move-in experience and welcome experience are the three most common and significant contributors to students’ high level of belonging and mattering.

scholarly journals Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7232
Author(s):  
Costel Anton ◽  
Silvia Curteanu ◽  
Cătălin Lisa ◽  
Florin Leon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Raw Materials ◽  
Optimization Procedure ◽  
Exhaust Emission ◽  
Energy Potential ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Emission Changes ◽  
The Impact

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

scholarly journals Quantified uncertainties in fission yields from machine learning

2020 ◽  
Vol 242 ◽  
pp. 05003
Author(s):  
A.E. Lovell ◽  
A.T. Mohan ◽  
P. Talou ◽  
M. Chertkov
Keyword(s):  
Machine Learning ◽  
Nuclear Physics ◽  
Experimental Error ◽  
Training Data ◽  
Learning Methods ◽  
Mixture Density ◽  
Machine Learning Methods ◽  
Predicted Values ◽  
The Impact ◽  
Fission Yields

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides.

scholarly journals Section focused on machine learning methods for high-level cognitive capabilities in robotics

2019 ◽  
Vol 33 (11) ◽  
pp. 537-538
Author(s):  
Tetsunari Inamura ◽  
Hiroki Yokoyama ◽  
Emre Ugur ◽  
Xavier Hinaut ◽  
Michael Beetze ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Level

scholarly journals Review Paper on Prediction of Heart Disease using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2937-2940
Author(s):  
Aadar Pandita
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Medical Researchers ◽  
High Level ◽  
The Impact

: Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

scholarly journals Modelling the water budget and the riverflows of the Maritsa basin in Bulgaria

2007 ◽  
Vol 4 (2) ◽  
pp. 475-521 ◽  
Author(s):  
E. Artinyan ◽  
F. Habets ◽  
J. Noilhan ◽  
E. Ledoux ◽  
D. Dimitrov ◽  
...  
Keyword(s):  
Water Budget ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Slow Component ◽  
First Year ◽  
Transfer Model ◽  
Distributed Hydrological Model ◽  
Snow Water ◽  
High Level ◽  
The Impact

Abstract. A soil-vegetation-atmosphere transfer model coupled with a macroscale distributed hydrological model was used in order to simulate the water cycle for a large region in Bulgaria. To do so, an atmospheric forcing was built for two hydrological years (1 October 1995 to 30 September 1997), at an eight km resolution. It was based on the data available at the National Institute of Meteorology and Hydrology (NIMH) of Bulgaria. Atmospheric parameters were carefully checked and interpolated with a high level of detail in space and time (3-h step). Comparing computed Penman evapotranspiration versus observed pan evaporation validated the quality of the implemented forcing. The impact of the human activities on the rivers (especially hydropower or irrigation) was taken into account. Some improvements of the hydrometeorological model were made: for better simulation of summer riverflow, two additional reservoirs were added to simulate the slow component of the runoff. Those reservoirs were calibrated using the observed data of the 1st year, while the 2nd year was used for validation. 56 hydrologic stations and 12 dams were used for the model calibration while 41 rivergages were used for the validation of the model. The results compare well with the daily-observed discharges, with good results obtained over more than 25% of the rivergages. The simulated snow depth was compared to daily measurements at 174 stations and the evolution of the snow water equivalent was validated at 5 sites. The process of melting and refreezing of snow was found to be important on this region. The comparison of the normalized values of simulated versus measured soil moisture showed good correlation. The surface water budget shows large spatial variations due to the elevation influence on the precipitations, soil properties and vegetation variability. An inter annual difference was observed in the water cycle as the first year was more influenced by Mediterranean climate, while the second year was characterised by continental influence. Energy budget shows a dominating sensible heat component in summer, due to the fact that the water stress limits the evaporation. This study is a first step for the implementation of an operational hydrometeorological model that could be used for real time monitoring and forecast the water budget and the riverflow of Bulgaria.

scholarly journals One Year of Laboratory Based COVID-19 Surveillance System in Belgium: Main Indicators and Performance of the Laboratories

2021 ◽  
Author(s):  
Marjan Meurisse ◽  
Adrien Lajot ◽  
Yves Dupont ◽  
Marie Lesenfants ◽  
Sofieke Klamer ◽  
...  
Keyword(s):  
Surveillance System ◽  
National Laboratory ◽  
First Year ◽  
Positivity Ratio ◽  
And Performance ◽  
Set Up ◽  
One Year ◽  
High Level ◽  
The Impact ◽  
Epidemiological Situation

Abstract Background: With the spread of coronavirus disease 2019 (COVID-19), an existing national laboratory based surveillance system was adapted to daily monitor the epidemiological situation of SARS-CoV-2 in the Belgium by following the number of confirmed COVID-19 infections, the number of performed tests and the positivity ratio. We present these main indicators of the surveillance over a one-year period as well as the impact of the performance of the laboratories, regarding speed of processing the samples and reporting results, for surveillance.Methods: We describe the evolution of test capacity, testing strategy and the data collection methods during the first year of the epidemic in Belgium.Results: Between the 1th of March 2020 and the 28th of February 2021, 9,487,470 tests and 773,078 COVID-19 laboratory confirmed cases were reported. Two epidemic waves occurred, with a peak in April and October 2020. The capacity and performance of the laboratories improved continuously during 2020 resulting in a high level performance. Since the end of November 2020 90 to 95% of test results are reported at the latest the day after sampling was performed.Conclusions: Thanks to the effort of all laboratories a performant exhaustive national laboratory based surveillance system to monitor the epidemiological situation of SARS-CoV-2 was set up in Belgium in 2020. On top of expanding the number of laboratories performing diagnostics and significantly increasing the test capacity in Belgium, turnaround times between sampling and testing as well as reporting were optimized over the first year of this pandemic.

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