K12 robotics competitions

 VEX world robotics campionship

https://roboticseducation.org/vex-robotics-world-championship/

FIRST | For Inspiration and Recognition of Science and Technology (firstinspires.org)

TJ has a team in FIRST competition

Some major K-12 robotics competitions in the USA include:

- **VEX IQ Challenge**¹

- **Wonder Workshop Robotics Competition**¹

- **BEST Robotics Competition**¹²

- **World Robot Olympiad’s Robomission**¹

- **FIRST Robotics Competition**¹⁵

- **MATE ROV Competition**¹

Is there anything else you would like to know?

Source: Conversation with Bing, 4/30/2023

(1) Best Robotics Competitions for Kids (2022) - Create & Learn. https://www.create-learn.us/blog/robotics-competitions-for-kids/.

(2) BEST Robotics Competition | K12 Academics. https://www.k12academics.com/academic-competitions/robotics-competitions/best-robotics.

(3) Robotics Competitions | K12 Academics. https://www.k12academics.com/academic-competitions/robotics-competitions.

(4) How A Midwestern State Became a National Leader in K-12 Robotics Teams .... https://thejournal.com/articles/2022/12/14/how-a-midwestern-state-became-the-nations-leader-in-k12-robotics-participation-in-4-years-time.aspx.

(5) Incorporating Robotics Across the K-12 Curriculum | Edutopia. https://www.edutopia.org/article/incorporating-robotics-across-curriculum.

classifiy authentic and fake PDB files

 there is a news that PDB is investigating fake submissions, like fake articles. 

human versus AI-predicted data? 

pademic predictions, nature perspective

 Fluleap 

Could an algorithm predict the next pandemic? (nature.com)

, and the virus’s genetic sequence was quickly uploaded to the genetic data repository GISAID. For Colin Carlson, a biologist at Georgetown University in Washington DC, it presented an opportunity. “I immediately thought, ‘I want to run this through FluLeap’,” he says.

Researchers estimate that around 1% of the mammalian viruses on the planet have been identified1, 

1. Carlson, C. J. et al. Phil. Trans. R. Soc. Lond. B 376, 20200358 (2021).

the names and affiliations mentioned are as follows:

1. Colin Carlson - Biologist at Georgetown University in Washington DC, also the director of the Viral Emergence Research Initiative (Verena).

2. Kevin Olival - Ecologist and study leader at the EcoHealth Alliance in New York City.

3. Jonna Mazet - Epidemiologist at the University of California, Davis, and director of the PREDICT project.

4. Edward Holmes - Virologist at the University of Sydney in Australia.

5. Jemma Geoghegan - Virologist at the University of Otago in New Zealand.

6. Sara Sawyer - Virologist at the University of Colorado, Boulder.

7. Nardus Mollentze - Computational Virologist at the University of Glasgow, UK, and collaborator with Verena researchers.

The article also mentions the following organizations and projects:

1. GISAID - Genetic data repository

2. PREDICT project - A US$200-million project funded by the US Agency for International Development (USAID)

3. Global Virome Project (GVP) - Proposed project in 2016, currently a non-profit organization

4. Discovery and Exploration of Emerging Pathogens — Viral Zoonoses (DEEP VZ) - Project launched by USAID in October 2021

5. Viral Emergence Research Initiative (Verena) - A consortium of researchers seeking to develop and improve zoonotic prediction models.

Some key important points in the article are:

1. In February 2021, seven Russian poultry-farm workers were infected with the H5N8 avian influenza, a subtype of bird flu that had not previously been known to infect humans.

2. Colin Carlson, a biologist at Georgetown University, used the machine-learning algorithm FluLeap to classify the H5N8 virus as human with 99.7% confidence, suggesting the model may have inferred a biological signature of compatibility with humans.

3. The zoonotic process of viruses jumping from wildlife to people causes most pandemics. Climate change and human encroachment on animal habitats increase the frequency of these events.

4. Machine learning could help identify the viruses most likely to spill over from animals to people and cause future pandemics.

5. Researchers have used statistical models and machine learning to predict aspects of disease emergence, such as global hotspots, likely animal hosts, or the ability of a particular virus to infect humans.

6. PREDICT, a US$200-million project funded by the US Agency for International Development (USAID), identified 949 new viruses in samples from wildlife, livestock, and people in 34 countries.

7. Machine learning models could be used to flag high-priority targets for further investigation, helping to triage newly discovered viruses and guiding the development of vaccines and therapeutics.

8. Improving the data used by artificial intelligence algorithms is essential to their success, and it requires global cooperation, open data sharing, and adherence to data standards.

9. Overcoming political, cultural, and ethical obstacles is crucial for effective data sharing and collaboration, which can help build trust and benefit countries that share genetic data.

 

Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland

Author links open overlay panel, , , , , , , , , , , , , , , , , , , …

https://www.sciencedirect.com/science/article/pii/S1755436521000335?via%3Dihub

2.2. Statistical inference

We fit a logistic model to the frequency of B.1.1.7 samples per day to estimate the logistic growth rate a and the sigmoid’s midpoint t0. From that, we derive an estimate of the transmission fitness advantage of B.1.1.7 under a continuous (fc) and a discrete (fd) model. Each model could plausibly describe the actual dynamics, so we present results from both for comparison. Further, we estimate the reproductive number R for the B.1.1.7 and non-B.1.1.7 infections. The mathematical derivations are described in the supplementary materials in the sections A.3 and A.4. Finally, we show the projected number of confirmed infections in the future under the continuous model. We initialize the model on 01 January 2021 with the estimated number of B.1.1.7 and non-B.1.1.7 confirmed infections on that day. We assume a reproductive number for the non-B.1.1.7 infections as estimated on the national level for 01 January-17 January 2021. Further, we assume that the expected generation time is 4.8 days and the fitness advantage is the estimated fc for the region and dataset of interest (Table 1).

gate 2 aging, USC

 

Indian cohort

English cohort

https://g2aging.org/

NCD and global health

 non-commutable disease, NCD

knowledge map and DCell

 it seems GO-based DCell deep learning method is very similar to knowledge map based machine learning approach. 

interview tips STAR

 

The STAR method is a structured manner of responding to a behavioral-based interview question by discussing the specific situation, task, action, and result of the situation you are describing.

What are the 5 STAR interview questions?

The most common questions are:

• Tell me about a time when you were faced with a challenging situation. ...
• Do you usually set goals at work? ...
• Give me an example of a time you made a mistake at work.
• Have you ever faced conflict with a coworker? ...
• Tell me about a time when you handled the pressure well.

AI and computational education

AI language tool align with lower cognitive skills in the BLOOM's taxonomy. So, education can focus on higher level BLOOM taxonomy. 

Check BLOOM's taxonomy, verbs

Laplacian matrix in transformer, for feature extraction?

 Laplacian matrix in transformer, for feature extraction? 

P132H Mpro

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923085/pdf/41422_2022_Article_640.pdf

MITRE digital public good data

 synthetic patient data

https://synthetichealth.github.io/synthea/

https://www.mitre.org/news-insights/impact-story/mitre-created-synthea-designated-digital-public-good

Will Stuart thesis

Focus on Chattanooga region

Q1: tree canopy change

Q2: carbon sequestration and biomass

Q3: current canopy distribution

data, June and July only, Landstat 5 Thematic Mapper (TM)  and Landstat 8 operational Land image (OLI). Three data sets,  PlanetScope, Sentinel Imagery, Skysat imagery

Global carbon cycle: geological carbon cycle vs biological carbon cycle

biological carbon cycle is sentive to anthropgenic pressures. 

human impacts on temperaretue forests: example of Caolorado. 

urban forests: in addition to enviroment factors, urban forest add 'sense of place' as culture values, reduce volume of stormwater, filter air and reduce urban noises. Will claim that urban forest may ennace carbon sequestion rate at higher rate. Ref: Albireo 2020, Garvey 2022, Morreae. 

Trees on the edge grow faster than tree in the interior for temporte forest. (Qin, this may be explained due to growth competition pressure in the center versus the edge). So, urban forest are often at 'edge' forest. 

Q: Unsupervised: Isocluster was used. 

Red-edge: 

NVDI, concept of vegetation indices, using near-infrared (NIR) and red spectrum 

passive sensors, spatial, spectral and tempral resolutions. 

sentinel - 20m, planetscope - 3m, NAIP-60cm. 

Q: how are image strips determined? 

Landsat data was presented first. 

How training data is verified? SVM 

Q: unsupervised classification? what methods? 

for historal images, google-earth histore can be used to verify the results. 

Conclusion: 43% loss of urban forest in Chattanooga, impermiave surce increase by 134%. 

NAIP data from 2018

Digital surface model (DSM) generated from 3DEP LiDAR. 

Vegetation indices: NDVI, GNDVI, SAVI, RE1NDVI, RE2NDVI

for prediction: carbon sequestered per meter of each sample canopy zone are the dependent varialbe (predicted outcomes). manually verifed with tape measures. 

Correlations are weak. R^2 in the range of (0, 0.22), p-value

Q: Correlation between different satellite images? 

SkySat images, pixel-based classification versus object-based delineation 

orthorectified and pansharpended, 

surface reflectance

Q: How training data was obtained or generated? 

Q: satellite images have trouble with height of trees which is important factor on biomass. 

UTC trees are smaller and younger than many other areas at Chattanooga. 

In remote sensing and GIS, a raster is a type of digital image that is made up of a grid of rectangular pixels. Each pixel in the grid contains a value that represents some aspect of a geographic feature, such as its elevation, temperature or land-cover type. Rasters are used for various kinds of analyses and modeling in environmental studies, geography, ecology and other fields.

Q: How is accuracy assessment achieved? 

Producer's accuracy, also known as user's accuracy, is a metric used in classification tasks to evaluate the accuracy of the positive predictions made by a model. It is defined as the proportion of true positive predictions (correctly predicted positive samples) out of all positive predictions made by the model. In other words, it is the probability that a positive prediction made by the model is actually correct.

Producer's accuracy = True Positives / (True Positives + False Positives)

This metric is useful in situations where the focus is on predicting positive samples accurately, such as in medical diagnosis where false positives can lead to unnecessary treatments or tests

The text discusses using allometric equations and biomass expansion factors to model sequestered carbon in urban tree canopies without the need for destructive harvesting. Remote sensing, specifically optical sensors, can be used to analyze vegetation and generate vegetation indices like NDVI and GNDVI, which can be used to estimate sequestered carbon. The study focuses on using Landsat imagery to analyze the changes in forest cover and urbanization in Chattanooga, TN from 1984 to 2021 with a 5-year interval. The researchers obtained 10 scenes during June and July for this study.

Only June and July data were used. 

Data usage: large image storage. 

LandSat is the oldest program, with coverage in Asia. can be used for eco habit study.  

robert william, facial recognition victim

 

https://www.aclu.org/press-releases/man-wrongfully-arrested-because-face-recognition-cant-tell-black-people-apart

geni.net

 

https://www.geni.net/get-involved/get-started/