#Wesleyan U

[justinspoliticalcorner]

Jason DeRose at NPR:

The United Methodist Church, one of the largest Protestant denominations in the U.S., has voted to repeal its ban on LGBTQ clergy as well as prohibitions on its' ministers from officiating at same-sex weddings. Delegates overwhelmingly approved the changes, 692 to 51, during the United Methodist Church's General Conference. The meeting is taking place this week in Charlotte, N.C. after the pandemic delayed the 2020 General Conference where these decisions has been slated to take place.

The tone of the Charlotte meeting has been decidedly upbeat, in sharp contrast with the last, highly contentious global meeting back in 2019, when heated floor debates left many feeling hurt. In fact, there was no floor debate over the clergy and marriages rules this time around. Rather, they were included on a consent agenda. However, in the years leading up to this General Conference, about one-quarter of United Methodist congregations in the U-S left the denomination. Those congregations tended to be among the most conservative in the church. Their departure made the decisions this year less fraught. Some of those departing congregations left to form the more conservative Global Methodist Church and others decided to become independent. The main reason many of those congregations left the denomination is that despite the church's official rules against LGBTQ clergy and same-sex weddings, some local geographic conferences chose to not enforce them.

At the United Methodist Church's General Conference today, the UMC voted overwhelmingly to lift its ban on LGBTQ+ clergy and denominational clergy officiating same-sex weddings.

This is made possible in party by the departure of more conservative churches to either the Global Methodist Church, independent of any denomination, or other Wesleyan denominations.

See Also:

LGBTQ Nation: United Methodist Church ends ban on LGBTQ+ clergy in historic vote

CNN: United Methodist Church lifts 40-year ban on LGBTQ clergy

[pwlanier]

Just picked up a painting I bought over the weekend. Had to rent a U-Haul.

“Tidal Pool” 1970

Oil on canvas. 59”x 84”.

Edward Luckey Wolfley

December 26, 1925 – July 28, 2023

Ed Wolfley was born in Indianapolis and attended Shortridge High School. Graduating from Central High School in Evansville, he then attended Wabash College, Duke University, Herron School of Art, and the University of Illinois. He earned his BFA and MFA followed by a 38 year career teaching art at University of Illinois, Ohio Wesleyan, and the University of Cincinnati’s College of Design, Architecture, Art and Planning. He primarily taught painting and drawing as well as design, anatomy, sculpture and art history. He retired Professor Emeritus of Fine Arts.

Edward Wolfley passed away on July 28, 2023 at 97 in Cincinnati, Ohio.

“Wolfley’s work from the late 1960’s and early 1970’s is displayed in the Lobby Gallery. Nine large oils on canvas are arranged so they resonate with each other. Hung on one side of the room are five large abstracts in beautifully muted colors: “Tidal Pool” (1970), Thunderhole” (1971), “Devil’s Slide” (1973), “Maine” (1969), and “Morning Mist” (1975). Each features large areas of pure color. The compositions suggest the natural world, landscapes with a horizon and submerged depths below. The titles imply the works were inspired by actual places important to Wolfley. These are bold, confident paintings in which Wolfley moves beyond the technical bravado of his earlier work to a more personal, mature view of art and the world. He does not hesitate to make big statements in paintings that are visually appealing but at the same time challenging to viewers”.

Courtesy Herrick Gallery

University of Cincinnati

[mtaartsdesign]

We are excited to continue collaborating with @poetrysociety to bring #PoetryinMotion to Grand Central Madison, digitally displayed on 5 large screens in the north end of the concourse. Together, the screens create a canvas that is over seven feet high and 91 feet long, enriching commuters’ journeys with a selection of renowned poems paired with artwork from the #MTAarts collection.

Photos:

"Train Rising Out of the Sea” © 1979, 1985, 2008 by John Ashbery. Used by arrangement with Georges Borchardt, Inc., on behalf of the author's estate. Duration (2017) © Monika Bravo, NYC Transit Prospect Avenue Station. Commissioned by MTA Arts & Design.

Mary Ruefle, “Voyager” from Memling’s Veil. Copyright © 1982 by Mary Ruefle. Reprinted by permission of the author. Bensonhurst Gardens (2012) © Francesco Simeti, NYC Transit 18 Avenue Station. Commissioned by MTA Arts & Design.

Heather McHugh, “A Night in a World” from Hinge & Sign: Poems, 1968-1993. Copyright © 1994 by Wesleyan University Press. Edges of a South Brooklyn Sky (2018) © Sally Gil, NYC Transit Avenue U Station. Commissioned by MTA Arts & Design.

Aracelis Girmay, “Noche de Lluvia, San Salvador” from Kingdom Animalia. Copyright © 2011 by Aracelis Girmay. Reprinted by permission of The Permissions Company, Inc., on behalf of BOA Editions, Ltd. Elizabeth Murray, Stream (2001) © the Murray-Holman Family Trust, NYC Transit Court Sq-23 St Station.Commissioned by MTA Arts & Design.

Toi Derricotte, "A nap" from “I": New & Selected Poems. Copyright © 2019. Reprinted with permission of the University of Pittsburgh Press. Forte (Quarropas) (2020) © Barbara Takenaga, MNR White Plains Station. Commissioned by MTA Arts & Design.

[radio-charlie]

I used to be this type of shrivelled crusty cunt, wesleyanism-tryhardism type of shit but some of the happytalk floating around does in fact make that transfer of power more likely than whatever Elite Book Club you hamster face textcels insist u belong to. it’s good to know shit but getting ppl in the right spirit is the priority rn and sometimes u need a bit of watered down medicine for that

[shihab1992]

Week 1 - Lab: Running Classification Tree

Week 1 python decision trees As part of peer review assigment I am working on decision trees in Python. This assigment is intended for Coursera course “Machine Learning for Data Analysis by Wesleyan University”. 

Installation in Linux Ubuntu.

sudo chmod +x Anaconda3-2022.10-Linux-x86_64.sh

./Anaconda3-2022.10-Linux-x86_64.sh

conda install scikit-learn

conda install -n my_environment scikit-learn

pip install sklearn

pip install -U scikit-learn scipy matplotlib

sudo apt-get install graphviz

sudo apt-get install pydotplus

conda create -c conda-forge -n spyder-env spyder numpy scipy pandas matplotlib sympy cython

conda create -c conda-forge -n spyder-env spyder

conda activate spyder-env

conda config --env --add channels conda-forge

conda config --env --set channel_priority strict

python -m pip install pydotplus

dot -Tpng tree.dot -o tree5.png

I have to perform a decision tree analysis to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. Data set is  provided by The National Longitudinal Study of Adolescent Health (AddHealth).

I will not complicate things here, therefore I am focusing on regular smoking (TREG1 variable).

I choosed few of vars to determine if they can predict regular smoking. predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']]

Therefore I did change predictor variables to just two: gender and age. I got this tree.

my code added below by pyhton

-- coding: utf-8 --

""" Created on Wed Dec 28 11:06:15 2022

@author: rfernandez """ import numpy as np import pandas as pd import matplotlib.pylab as plt

from sklearn.cross_validation import train_test_split

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report from sklearn import tree import pydotplus import sklearn.metrics

#Load the dataset

data = pd.read_csv("tree_addhealth.csv")

dc = data.dropna() dc.dtypes dc.describe() """ Modeling and Prediction """

#Split into training and testing sets

predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']] target = dc.TREG1 pr_train,pr_test,t_train,t_test= train_test_split(predictor,target, test_size=0.4) pr_train.shape pr_test.shape t_train.shape t_test.shape

#Build model on training data

classif = DecisionTreeClassifier() classif = classif.fit(pr_train,t_train) pred=classif.predict(pr_test) sklearn.metrics.confusion_matrix(t_test,pred) sklearn.metrics.accuracy_score(t_test,pred)

#Running Classification Tree

tree.export_graphviz(classif, out_file='tree_race.dot')

[rad2025]

1999 - 2024

Since learning about the 4+1 MBA program with my alma mater, SUNY College at Fredonia, and Clarkson University, way back in ‘99, I have wanted to earn my Master’s degree. When I graduated college in 2003, it wasnt the right time to earn my Masters degree. In 2006, I started to apply to the Simon School at U of R, but it wasnt the right time. In 2021, I was weighing the possibility of getting my Masters, or my CFP designation. My CFP won out, and Im glad for that decision. It just WAS NOT the right time!!! Finally. Finally. Finally. I am ready to work towards getting my Masters Degree. I was officially accepted into the Masters of Strategic Leadership program at Roberts Wesleyan University this week. I am truly thrilled to begin classes in January, 2025!

[xtruss]

In Photos: Auroral Substorm Sparks Stunning Northern Lights Visible At Mid-Latitudes

Northern Lights display viewed from Sutherland, Scotland, in the early hours of August 28, 2024. Credit: Monika Focht

Northern Lights appears as Vibrant Pink and Yellow Orange curtains filling the Sky.

Northern Lights viewed during the early hours on August 28, 2024.

Northern Lights appears as Vibrant Pink and Green curtains filling the Sky.

Northern lights above Abisko National Park, Sweden. Image Credit: Daisy Dobrijevic

Northern lights above STF Abisko Turiststation, January 2024. Image Credit: Daisy Dobrijevic

Alaska's arctic environment is the perfect place to see the northern lights. Image credit: Patrick J. Endres via Getty Images

Milky Way Photos:

Motions of stars in the Milky Way Galaxy in the next 400 thousands years based on data from the European Gaia Mission. Image Credit: ESA

The Milky Way's Mesmerizing Glowing band has had Humankind Star-Struck for Eons. Image Credit: Photo by Kendall Hoopes from Pexels

The Structure of the Milky Way Galaxy as seen from above the Galactic Disk. Image Credit: NASA/Adler/U. Chicago/Wesleyan/JPL-Caltech

[stubobnumbers]

CFB Promotion and Relegation - The Big Ten

B1G Tier One - Big Ten (FBS): Minnesota Wisconsin Iowa Illinois Northwestern Indiana Purdue Ohio State Michigan Michigan State

B1G Tier Two - Mid-American Conference (FBS): Northern Illinois Ball State Central Michigan Eastern Michigan Western Michigan Akron Bowling Green Kent State Miami (Oh.) Ohio Toledo

B1G Tier Three - Ohio Valley Conference (FCS): Dayton Youngstown State Ohio Dominican Lake Erie College Ashland University Walsh University U. of Indianapolis McKendree University Quincy University Roosevelt University

B1G Tier Four - Great Lakes Athletic Conference (D2): Michigan Tech Northern Michigan Davenport University Ferris State University Grand Valley State University Hillsdale College Northwood University – Michigan Saginaw Valley State University Wayne State University Finlandia University

B1G Tier Five - Great Midwest Athletic Conference (D2): Tiffin University University of Findlay Central State Adrian College Albion College Alma College Calvin Hope College Kalamazoo College Olivet College

B1G Tier Six - Northern Sun Conference (D2): St. Thomas Bemidji State University Concordia University, St. Paul – Minnesota Minnesota State University – Mankato Minnesota State University – Moorhead Southwest Minnesota State University University of Minnesota – Duluth Winona State University Iowa Wesleyan University

B1G Tier Seven - Wisconsin Football Association (D2): University of Wisconsin – Eau Claire University of Wisconsin – La Crosse University of Wisconsin – Oshkosh University of Wisconsin – Platteville University of Wisconsin – River Falls University of Wisconsin – Stevens Point University of Wisconsin – Stout University of Wisconsin – Whitewater Wisconsin Lutheran

B1G Tier Eight - Wisconsin Football Conference (D3): Beloit College Carroll University Carthage College Concordia University Wisconsin Lakeland University Lawrence University Ripon College St. Norbert College

B1G Tier Nine - Minnesota Football Conference (D3): Augsburg University Hamline University Macalester College Bethel University – Minnesota Carleton College St. Olaf College The College of St. Scholastica University of Northwestern – St. Paul Saint John's University – Minnesota Gustavus Adolphus College

B1G Tier Ten - Minnesota Football Coalition (D3): Concordia College – Minnesota University of Minnesota – Morris Crown College Martin Luther College Itasca CC Vermilion CC Mesabi Range College Minnesota State CTC Minnesota West CTC Rochester CTC

B1G Tier Eleven - Illinois Football Conference (D3): University of Chicago North Park University Saint Xavier University Augustana College – Illinois Aurora University Benedictine University Concordia University – Chicago Elmhurst University Eureka College Greenville University

B1G Tier Twelve - Upper Mississippi Conference (D3): Central Lakes Fond du Lac Illinois College Illinois Wesleyan University Knox College Lake Forest College Millikin University Monmouth (IL.) North Central College Rockford University

B1G Tier Thirteen - Heartland League (D3): Wheaton College – Illinois Judson University Olivet Nazarene University University of St. Francis – Illinois DuPage Community Christian College (MI) Concordia (MI.) Lawrence Technological University Madonna University Siena Heights University

B1G Tier Fourteen - Hoosier Conference (D3): Anderson University – Indiana DePauw University Franklin College Hanover College Manchester University Rose-Hulman Institute of Technology Trine University Wabash College Indiana Wesleyan University University of Saint Francis – Indiana

B1G Tier Fifteen - Ohio Conference (D3): Capital University Case Western Reserve University Baldwin Wallace University Bluffton University College of Wooster Heidelberg University Mount St. Joseph University Mount Union Kenyon College Oberlin College

B1G Tier Sixteen - Buckeye Football League (D3): Defiance College Denison University Hiram College John Carroll University Marietta College Muskingum University Notre Dame College Ohio Northern University Ohio Wesleyan University Otterbein University

B1G Tier Seventeen - Small Northern Colleges League (D3): Calumet College of St. Joseph Marian University – Indiana Oakland City University Taylor University Trinity International University Hocking College Wilmington College Wittenberg University

[smallcollegebasketball]

Bevo Francis Award Top 25 Watch List Announced

2024 Bevo Francis Top 25 Watch List Announced

Kansas City, MO – The National Awards Committee and Small College Basketball are happy to announce the Bevo Francis Top 25 Watch List for the 2023-24 season. The Top 25 Watch List consists of some of the top players from the NCAA Division II, NCAA Division III, NAIA, USCAA, and NCCAA. With the amount of talented players and phenomenal seasons across all of these levels, the National Award Committee continues to have the difficult task of narrowing the list down. John McCarthy discussed this years Top 25 list:

"Congratulations to the 25 players that have made this prestigious Bevo Francis Award Watch List,” stated McCarthy. “This is a list of the elite of the elite in college basketball at the small college levels, based on our criteria. I am very grateful to our committee members, as they have provided great insight and information." 2024 marks the eighth year of the Bevo Francis Award. Past winners include Dominez Burnett of Davenport University in 2016, Justin Pitts of Northwest Missouri State University in 2017, Emanuel Terry of Lincoln Memorial University in 2018, Aston Francis of Wheaton College in 2019, Kyle Mangas of Indiana Wesleyan University in 2020 and Trevor Hudgins of Northwest Missouri State University in 2022. In 2023, RJ Sunahara of Nova Southeastern was named the winner of the Bevo Francis Award. The award was not presented during the 2021 season.

The Clarence “Bevo” Francis Award is presented annually to the player who has had the finest overall season within Small College Basketball. Considerations will be season statistics and individual achievements, awards, personal character and team achievements. This is an incredibly prestigious award, as this award will only be given to one player within Small College Basketball per season.

Next up on April 5th, the finalists of this year’s award will be announced. The Bevo Francis Award winner will be announced on April 8th .

The Bevo Francis Awards Committee consists of the following coaches: Gary Stewart - Stevenson (Md.), Chris Briggs - Georgetown (Ky.), Bill Dreikosen - Rocky Mountain (Mont.), Mark Berokoff – Oklahoma City, Mike Donnelly – Florida Southern, Sam Hargraves – Olivet (Mich.), Richard Westerlund – UNOH, Chase Tiechmann – Florida College, Aaron Siebenthall – Ottawa(Kan.), Mark Vanderslice – USC – Aiken, Matt O’Brien – Southwestern (KS), Chris Wright – Langston (OK), Ben Thompson – Emory & Henry, Justin Leslie – Midwestern State, Raul Placeres – Maryville College (TN), Chris Martin – Loras (IA), John Lamanna – Whitman (WA), Brian Miller – MSOE, Justin Downer – Point Loma Nazarene.

To stay up to date on all things Small College Basketball or to learn more about the Bevo Francis Award, please head to smallcollegebasketball.com.

2023-24 Bevo Francis Award Top 25 Watch List

Trevor Baskin 6’8” Jr. CO Mesa Drew Blair 6’5” Sr. MN-Duluth Frank Champion 6’7” Jr. North Georgia Luke Chicone 5’10” Jr. John Carroll Cevin Clark 5’11” Jr. Southwestern (KS) Tyler Dearman 6’3” Sr. Guilford Wes Dreamer 6’7” Sr. NW MO State Gus Etchison 5’11” Jr. Marian Spencer Freedman 6’0” Sr. New York U Zach Goodline 6’1” Sr. Huntington Jake Hilmer 5’11” Sr. Upper Iowa Jeff Hunter 6’7” Sr. Keene State Zawdie Jackson 6’0” Jr. West Georgia KJ Jones 6’6” Sr. Emmanuel (GA) Jaden Lietzke 6’7” Jr. Oklahoma Wesleyan Elijah Malone 6’10” Sr. Grace (IN) Anthony Mazzeo 6’3” Sr. Case Western Reserve Jalen Overway 6’9” So. Calvin James Paterson 6’7” So. STAC Logan Pearson 6’4” Jr. Wisconsin-Platteville Anthony Roy 6’5” Jr. Langston Tyler Schmidt 6’4” Sr. Olivet Nazarene Alex VanKalsbeek 6’6” So. Northwestern (IA) Malik Willingham 6’3” Jr. Minnesota State Larry Wise 6’5” Sr. West Texas A&M

[bellamancuso]

References

www.dandad.org. (n.d.). D&AD Awards Pencil Winners 2021 | Global Advertising, Design & Digital Award Winners | D&AD. [online] Available at: https://www.dandad.org/en/d-ad-awards-pencil-winners/.

Wesleyan University, U. of C. and Tennessee, U. of (2022). What Is Guerrilla Gardening? Definition, History, and Examples. [online] Treehugger. Available at: https://www.treehugger.com/what-is-guerrilla-gardening-5196129.

Notpla Packaging. (n.d.). NOTPLA Pipettes - Revolutionising the way oil is packaged. [online] Available at: https://notpla.shop/collections/notpla-pipette/products/notpla-pipettes.

The Royal Countryside Fund. (n.d.). Home. [online] Available at: https://www.royalcountrysidefund.org.uk.

Osborne Pike. (n.d.). OsbornePike brand design | Tellus Chocolate | Rewilding nature. [online] Available at: https://osbornepike.co.uk/projects/tellus/ [Accessed 7 Mar. 2024].

www.recycle-more.co.uk. (n.d.). Dirty Recycling. [online] Available at: https://www.recycle-more.co.uk/blog/recycling-news/dirty-recycling# [Accessed 7 Mar. 2024].

Recyclenow.com. (2017). How is food waste recycled? | Recycle Now. [online] Available at: https://www.recyclenow.com/how-to-recycle/food-waste-recycling#Why-is-it-important-to-recycle-food-waste.

creativecloud.adobe.com. (n.d.). Adobe Creative Cloud | Sign in. [online] Available at: https://creativecloud.adobe.com/cc/discover/article/how-to-make-a-risograph-style-print-in-adobe-photoshop#?locale=en [Accessed 7 Mar. 2024].

latana.com. (n.d.). How Oatly Became an International Success. [online] Available at: https://resources.latana.com/post/oatly-deep-dive/.

Andrew (2021). Edible flowers guide. [online] Suttons Gardening Grow How. Available at: https://hub.suttons.co.uk/gardening-advice/edible-flowers-guide [Accessed 7 Mar. 2024].

FSC (2022). What is FSC? [online] Forest Stewardship Council. Available at: https://uk.fsc.org/what-is-fsc.

‌

[souravauddy25]

Coursera Week 1: Classification Tree

Week 1 assignment on decision trees “Machine Learning for Data Analysis by Wesleyan University”. 

Installation in Linux Ubuntu.

sudo chmod +x Anaconda3-2022.10-Linux-x86_64.sh

./Anaconda3-2022.10-Linux-x86_64.sh

conda install scikit-learn

conda install -n my_environment scikit-learn

pip install sklearn

pip install -U scikit-learn scipy matplotlib

sudo apt-get install graphviz

sudo apt-get install pydotplus

conda create -c conda-forge -n spyder-env spyder numpy scipy pandas matplotlib sympy cython

conda create -c conda-forge -n spyder-env spyder

conda activate spyder-env

conda config --env --add channels conda-forge

conda config --env --set channel_priority strict

python -m pip install pydotplus

dot -Tpng tree.dot -o tree5.png

I have to perform a decision tree analysis to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. Data set is  provided by The National Longitudinal Study of Adolescent Health (AddHealth).

I will not complicate things here, therefore I am focusing on regular smoking (TREG1 variable).

I choosed few of vars to determine if they can predict regular smoking. predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']]

Therefore I did change predictor variables to just two: gender and age. I got this tree.

my code added below by pyhton

-- coding: utf-8 --

""" Created on Friday December 29 2023

@author: souravauddy """ import numpy as np import pandas as pd import matplotlib.pyplot as plt

from sklearn.cross_validation import train_test_split

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report from sklearn import tree import pydotplus import sklearn.metrics

#Load the dataset

data = pd.read_csv("tree_addhealth.csv")

dc = data.dropna() dc.dtypes dc.describe() """ Modeling and Prediction """

#Split into training and testing sets

predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']] target = dc.TREG1 pr_train,pr_test,t_train,t_test= train_test_split(predictor,target, test_size=0.4) pr_train.shape pr_test.shape t_train.shape t_test.shape

#Build model on training data

classif = DecisionTreeClassifier() classif = classif.fit(pr_train,t_train) pred=classif.predict(pr_test) sklearn.metrics.confusion_matrix(t_test,pred) sklearn.metrics.accuracy_score(t_test,pred)

#Running Classification Tree

tree.export_graphviz(classif, out_file='tree_race.dot')

[group1mkgpp]

Pagbabago ng Pananaw - Ang Climate Change sa Mata ng Kabataan 🌏

Cobico, Crizel Jhanna A.

✏️ Edukasyon 

Nagtapos ng sekundaryang pag-aaral sa Holy Redeemer School - Dasmariñas, Cavite

Nagtapos ng senior high school sa Centro Escolar Integrated School - Makati

Kasalukuyang kumukuha ng kursong BS Pharmacy sa Centro Escolar University, Makati Campus.

📞 Contacts

Fb account: Crizel Cobico 

CEU email: cobico2121494@mkt.ceu.edu.ph

Cortez, Keana M. 

✏️ Edukasyon

Nagtapos ng sekundaryang pag-aaral sa University of the Assumption - Pampanga

Nagtapos ng senior high school sa University of the Assumption - Pampanga 

Kasalukuyang kumukuha ng kursong BS Pharmacy sa Centro Escolar University, Makati Campus.

📞 Contacts

Fb account: Keana Cortez 

CEU email: cortez2121163@mkt.ceu.edu.ph

Espinosa, Moira Alessandra L.

✏️ Edukasyon

Nagtapos ng sekundaryang pag-aaral sa La Immaculada Concepcion School - Pasig City

Nagtapos ng senior high school sa Centro Escolar Integrated School - Makati City

Kasalukuyang kumukuha ng kursong BS Pharmacy sa Centro Escolar University, Makati Campus.

📞 Contacts

Fb account: Moira Espinosa 

CEU email: espinosa2120519@mkt.ceu.edu.ph

Mabini, Kyla Marie C.

✏️ Edukasyon

Nagtapos ng sekundaryang pag-aaral sa Thy Covenant Montessori School - Taguig

Nagtapos ng senior high school sa Asia Pacific College - Makati

Kasalukuyang kumukuha ng kursong BS Pharmacy sa Centro Escolar University, Makati Campus.

📞 Contacts

Fb account: Kyla Marie Mabini 

CEU email: mabini2120582@mkt.ceu.edu.ph

Ortiona, Calila May A.

✏️ Edukasyon 

Nagtapos ng sekundaryang pag-aaral sa St. Paul School of San Antonio.

Nagtapos ng senior high school sa Wesleyan University Philippines, Cabanatuan City.

Kasalukuyang kumukuha ng kursong BS Pharmacy sa Centro Escolar University, Makati Campus.

📞 Contacts

Fb account: Calila May Ortiona 

CEU email: ortiona2121212@mkt.ceu.edu.ph 

Peciller, Francine Angela U.

✏️ Edukasyon 

Nagtapos ng sekundaryang pag-aaral sa Applied Academics for Excellence (APEX) ANNEX 1 Santa Rosa, Laguna

Nagtapos ng senior high school sa Mapua Malayan Colleges of Laguna

Kasalukuyang kumukuha ng kursong BS Pharmacy sa Centro Escolar University, Makati Campus.

📞 Contacts

Fb account: Francine Peciller 

CEU email: peciller2121599@mkt.ceu.edu.ph

[onenettvchannel]

#OnlyOnOneNETnews: A Bacoleño Student of NOHS have been reportedly accepted in 30 out of 40 International Universities with scholarship grants worth U$D1.9M

(Prepared by Luz Noceda / Gravesfield Middle School news intern of Disney XD News)

BACOLOD, NEGROS OCCIDENTAL -- A 12th Grader student and alumnus of the Negros Occidental High School (NOHS) received acceptance letters from 30 out of 40 universities in the United States of America (U.S.A.) and the United Kingdom, including merit scholarships worth U$D1,934,400 (roughly PHP110M). Julian G. Martir from Bacolod City, Negros Occidental have reportedly been accepted via electronic mailing (E-Mail) online to study abroad.

Per reports from the Digital News Exchange (DNX) when Martir saw a group of ambitious international students on his recommended videos on YouTube, he is planning to apply for scholarships to attend elite schools in a different countries to start it off. He is first submitting an essay online about his self personal life.

Various national media outlets including The Philippine Star and News5 (under PhilStar Daily Inc. and TV5 Network Inc.) are both first reported that 10 universities abroad were rejected, while 30 of them have been approved to apply. According to Martir, his difficulty was hard to understand the application process but he stopped 1 year of school after graduating from High School to prepare his college applications with the help of YouTube videos and admission experts with whom he chatted via WhatsApp, who was a parent company of Meta Platforms Inc.

The following universities that have Martin in a randomized alphabetical state and country order are The University of Arizona, Woodbury University, Regis University, The University of Colorado Boulder, University of Connecticut, Ball State University, DePaul University, Simmons University, University of Massachusetts Boston, University of Massachusetts Dartmouth, Michigan Technological University, Webster University, The University of New Hampshire, New Jersey Institute of Technology, Alfred University, Clarkson University, Fordham University, Hofstra University, Stony Brook University, Kent State University, Ohio Wesleyan University, Xavier University, Drexel University, Duquesne University, Johnson and Wales University, Clemson University, The University of Texas at Arlington, The George Washington University, Marquette University, Richmond University (all from the United States of America) and The American International University in London, UK.

Social Media netizens on Facebook and Twitter have allegedly sparked controversies of fake news that was supposed to be a national attention, but almost failed to defend the bashers due to severe hate speech of cyberbullying, extremism, death threats, defamation, trolls, toxicity and more, with a massive violation of Cyber Libel under the Republic Act #10175: Section 4.c4 or Cybercrime Prevention Act of 2012 and House Bill #129: Section 4 or The Online and Social Media Membership Accountability Act of 2022, as signed by a murderous mastermind and formerly suspended Negros Oriental congressman named Arnulfo "Arnie" Alipit Teves Jr.

In an exclusive radio interview of DWFM-FM 92.3mhz's Radyo5: True FM, he is currently planning to study in major of Mathematics & Computer Science in the United States of America and the United Kingdom per some universities. Not only that, he is a highest honor student of NOHS and it is really legal to study abroad.

Martir's parents are retirees and underpaid with their individual professions of tricycle driver and vendor. Because of the low source of income as underpaid due to poverty, they have decided to stop working and passing along with his student of Bacoleño.

According to the Philippine Consulate General (PCG) and the Bureau of Immigration (BI), Student Visa and International Passport must be legally required as a foreigner student and automatically becomes a permanent resident in whichever state or country from Bacolod to U.S.A. or United Kingdom. If approved from the officials, he is now cleared to leave the country and officially study abroad. But... there is a catch. Flights and land transportations, tuition fees & personal expenses are too expensive to travel alone. The Philippine government and institutions will soon cover all the travel and the aforementioned funds to be paid by taxpayers, authorities and officials.

Martir's gratitude was fulfilled because he was accepted by the international universities that he was applying online. His dream is to become a Mathematician and Quantum Scientist.

As he embarks on this exciting new chapter for Martir, his high school and college life continues to motivate others to pursue their dreams, regardless of past or future personal experiences of a foreign student of Negrosanon.

PHOTO COURTESY: The Philippine Star BACKGROUND PROVIDED BY: Tegna

SOURCE: *https://dnx.news/iwrite-by-sheer-will-and-grit-julian-martirs-journey-to-a-better-life/ [Referenced Editorial News Article via Digital News Exchange] *https://www.facebook.com/100083436542869/posts/228616669929550 [Referenced Archived Copy from The Philippine Star via DYQU-FM 103.9mhz's RBSFM: Bacolod] *https://philstarlife.com/self/874187-son-tricycle-driver-vendor-accepted-universities-scholarships [Referenced News Article #1 from The Philippine Star] *https://www.goodnewspilipinas.com/tricycle-drivers-son-defies-odds-filipino-student-julian-martir-accepted-to-30-us-uk-universities-with-2m-dollars-in-scholarships-find-out-how-he-did-it/ [Referenced News Article from Good News Pilipinas] *https://wethepvblic.com/julian-martir-allegedly-fake-scholarship-media-literacy/ [Referenced News Article via We The Pvblic] *https://pinoyinternalauditprofessional.wordpress.com/2023/05/20/fact-checking-and-integrity-lessons-from-the-julian-martir-incident/ [Referenced Editorial News Article via PIAP] *https://www.philstar.com/headlines/2023/01/24/2239872/author-social-media-registration-bill-decries-too-much-democracy-online [Referenced News Article #2f from The Philippine Star] *https://www.philippineconsulatela.org/consular-services-2/visa/student-visa [Referenced Guideline Rules via Philippine Consulate General] *https://www.youtube.com/watch?v=Vwbme7YnVhA [Referenced YT Video #1 via OnePH] *https://www.youtube.com/watch?v=nVnVvLNGyCk [Referenced YT Video #2f via OnePH] and *https://www.youtube.com/watch?v=k1Q760iP_Jo [Referenced YT Video via News5Everywhere]

-- OneNETnews Team

[ritzm]

Coursera Data Analysis Tools W2 Assignment

For week 2 assignment from Coursera's Data Analysis Tools (Wesleyan University) we were asked to run a Chi-Square Test of Independence.

I chose again the GapMinder dataset and decided to examine the relationship between the overall political score of a country (categorical response) and the previous year female employ rate in percentage of total population (categorical explanatory).

Therefore, the null hypothesis will be that there is no differences in overall democratic scores between countries with different female employ rates.

For my analysis purposes, I transformed:

polity score numeric variable into a categorical variable with two levels: level 'Y' for a country with an overall polity score above zero (hence, democratic) and level 'N' for a country with a polity score equal or less to zero (not democratic)

female employ rate numeric variable into a categorical variable with 4 levels, accordingly to quartile division of data

______________________________________________________________

script:

import pandas as pd import numpy as np import bisect import os import scipy.stats import seaborn import matplotlib.pyplot as plt

#set working directory

wdir = os.chdir(wdir)

data = pd.read_csv('U:\01_Learning\2_COURSERA\Data_Analysis_Tools\Datasets\gapminder.csv', low_memory=False)

sub1 = data[['femaleemployrate', 'polityscore']]

sub1.replace(' ', np.nan, inplace=True) sub1 = sub1.dropna()

sub1['femaleemployrate'] = pd.to_numeric(sub1['femaleemployrate'], errors='coerce') sub1['polityscore'] = pd.to_numeric(sub1['polityscore'], errors='coerce')

#describe female employ rate column

sub1['femaleemployrate'].describe()

sub1['Employ_rate_Females'] = pd.qcut(sub1['femaleemployrate'], q = 4, labels = ['Q1', 'Q2', 'Q3', 'Q4'])

recode = [sub1['polityscore'].between(-10,0), sub1['polityscore'].between(1,10)] values = ('N', 'Y') sub1['Democratic'] = np.select(recode, values, 0)

ct1 = pd.crosstab(sub1['Employ_rate_Females'], sub1['Democratic']) print(ct1)

colsum=ct1.sum(axis=0) colpct=ct1/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs1= scipy.stats.chi2_contingency(ct1) print (cs1)

seaborn.factorplot(x="Employ_rate_Females", y="polityscore", data=sub1, kind="bar", ci=None) plt.xlabel('Female Employ Rate Quantile') plt.ylabel('Overall democracy score')

#post-hoc Bonferroni Adjustment test

#for 4 level explanatory variable, we will need 6 comparison tests

#the significant value for p will be p/6

#eg: for 5%, p-value = 0.008

#Q1 vs Q2

recode1 = {'Q1': 'Q1', 'Q2': 'Q2'} sub1['COMP_Q1_Q2'] = sub1['Employ_rate_Females'].map(recode1)

ct2=pd.crosstab(sub1['Democratic'], sub1['COMP_Q1_Q2']) print (ct2)

colsum=ct2.sum(axis=0) colpct=ct2/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs2= scipy.stats.chi2_contingency(ct2) print (cs2)

#Q1 vs Q3

recode2 = {'Q1': 'Q1', 'Q3': 'Q3'} sub1['COMP_Q1_Q3'] = sub1['Employ_rate_Females'].map(recode2)

ct3=pd.crosstab(sub1['Democratic'], sub1['COMP_Q1_Q3']) print (ct3)

colsum=ct3.sum(axis=0) colpct=ct3/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs3= scipy.stats.chi2_contingency(ct3) print (cs3)

#Q1 vs Q4

recode3 = {'Q1': 'Q1', 'Q4': 'Q4'} sub1['COMP_Q1_Q4'] = sub1['Employ_rate_Females'].map(recode3)

ct4=pd.crosstab(sub1['Democratic'], sub1['COMP_Q1_Q4']) print (ct4)

colsum=ct4.sum(axis=0) colpct=ct4/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs4= scipy.stats.chi2_contingency(ct4) print (cs4)

#Q2 vs Q3

recode4 = {'Q2': 'Q2', 'Q3': 'Q3'} sub1['COMP_Q2_Q3'] = sub1['Employ_rate_Females'].map(recode4)

ct5=pd.crosstab(sub1['Democratic'], sub1['COMP_Q2_Q3']) print (ct5)

colsum=ct5.sum(axis=0) colpct=ct5/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs5= scipy.stats.chi2_contingency(ct5) print (cs5)

#Q2 vs Q4

recode5 = {'Q2': 'Q2', 'Q4': 'Q4'} sub1['COMP_Q2_Q4'] = sub1['Employ_rate_Females'].map(recode5)

ct6=pd.crosstab(sub1['Democratic'], sub1['COMP_Q2_Q4']) print (ct6)

colsum=ct6.sum(axis=0) colpct=ct6/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs6= scipy.stats.chi2_contingency(ct6) print (cs6)

#Q3 vs Q4

recode6 = {'Q3': 'Q3', 'Q4': 'Q4'} sub1['COMP_Q3_Q4'] = sub1['Employ_rate_Females'].map(recode6)

ct7=pd.crosstab(sub1['Democratic'], sub1['COMP_Q3_Q4']) print (ct7)

colsum=ct7.sum(axis=0) colpct=ct7/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs7= scipy.stats.chi2_contingency(ct7) print (cs7)

______________________________________________________________

From the output of the script, we can see that the female employ rate's data distributes as follows:

and when plotting the overall polity score across each female employ rate group (from the quartiles division), we see that for Q2 bin, with female employ rate between 39.6% and 48.5%, the polity score is higher than in the other groups, being the lowest for a female employ rate from 12.4% to 39.6%

From the Chi Square test we have a p-value of 0.01 < 5% which indicates that there is differences in the overall polity score between countries that have different female employ rates.

After doing the post-hoc comparisons of polity scores by pairs of female employ rate quartiles, we can confirm what we already expected: polity scores between groups Q1 and Q2 of female employ rates are statistically different, with a p-value of 0.006 (< 0.008 of the Bonferroni Adjustment).

[apoorvaml-week1]

Week 1: Peer-graded Assignment: Running a Classification Tree

This assignment is intended for Coursera course "Machine Learning for Data Analysis by Wesleyan University”.

It is for "Week 1: Peer-graded Assignment: Running a Classification Tree".

I am working on decision trees in Python.

1) Syntax used to run Classification Tree

Installation in Linux Ubuntu.

sudo chmod +x Anaconda3-2022.10-Linux-x86_64.sh

./Anaconda3-2022.10-Linux-x86_64.sh

conda install scikit-learn

conda install -n my_environment scikit-learn

pip install sklearn

pip install -U scikit-learn scipy matplotlib

sudo apt-get install graphviz

sudo apt-get install pydotplus

conda create -c conda-forge -n spyder-env spyder numpy scipy pandas matplotlib

   sympy cython

conda create -c conda-forge -n spyder-env spyder

conda activate spyder-env

conda config --env --add channels conda-forge

conda config --env --set channel_priority strict

python -m pip install pydotplus

dot -Tpng tree.dot -o tree5.png

2)  Code used to run Classification Tree

       import numpy as np

       import pandas as pd

       import matplotlib.pylab as plt

from sklearn.cross_validation import train_test_split

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report from sklearn import tree import pydotplus import sklearn.metrics

#Load the dataset

data = pd.read_csv("tree_addhealth.csv")

dc = data.dropna() dc.dtypes dc.describe() """ Modeling and Prediction """

#Split into training and testing sets

predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']]

target = dc.TREG1

pr_train,pr_test,t_train,t_test= train_test_split(predictor,target, test_size=0.4) pr_train.shape pr_test.shape t_train.shape t_test.shape

#Build model on training data

classif = DecisionTreeClassifier() classif = classif.fit(pr_train,t_train) pred=classif.predict(pr_test) sklearn.metrics.confusion_matrix(t_test,pred) sklearn.metrics.accuracy_score(t_test,pred)

#Running Classification Tree

tree.export_graphviz(classif, out_file='tree_race.dot')

3) Corresponding Output

I chose few variables to determine if they can predict regular smoking.

predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']]

Therefore I did change predictor variables to just two: gender and age. I got this tree.

4) Interpretation

I have to perform a decision tree analysis to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. Data set is provided by The National Longitudinal Study of Adolescent Health (AddHealth).

I will not complicate things here, therefore I am focusing on regular smoking (TREG1 variable).

I translated .dot file to .png using “dot -Tpng name.dot -o name.png” command.

I chose few variables to determine if they can predict regular smoking.

predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']]

Therefore I did change predictor variables to just two: gender and age.

[coding02]

Machine Learning for Data Analysis

Week 1: Running Classification Tree (ubuntu linux)

Week 1 python decision trees As part of peer review assigment I am working on decision trees in Python. This assigment is intended for Coursera course “Machine Learning for Data Analysis by Wesleyan University”. 

Installation in Linux Ubuntu.

sudo chmod +x Anaconda3-2022.10-Linux-x86_64.sh

./Anaconda3-2022.10-Linux-x86_64.sh

conda install scikit-learn

conda install -n my_environment scikit-learn

pip install sklearn

pip install -U scikit-learn scipy matplotlib

sudo apt-get install graphviz

sudo apt-get install pydotplus

conda create -c conda-forge -n spyder-env spyder numpy scipy pandas matplotlib sympy cython

conda create -c conda-forge -n spyder-env spyder

conda activate spyder-env

conda config --env --add channels conda-forge

conda config --env --set channel_priority strict

python -m pip install pydotplus

dot -Tpng tree.dot -o tree5.png

I have to perform a decision tree analysis to test nonlinear relationships among a series of explanatory variables and a binary, categorical response variable. Data set is  provided by The National Longitudinal Study of Adolescent Health (AddHealth).

I will not complicate things here, therefore I am focusing on regular smoking (TREG1 variable).

I choosed few of vars to determine if they can predict regular smoking. predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']]

Therefore I did change predictor variables to just two: gender and age. I got this tree.

And my code?

!/usr/bin/env python3

-- coding: utf-8 --

""" Created on Wed Dec 28 11:06:15 2022

@author: rfernandez """ import numpy as np import pandas as pd import matplotlib.pylab as plt

from sklearn.cross_validation import train_test_split

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report from sklearn import tree import pydotplus import sklearn.metrics

#Load the dataset

data = pd.read_csv("tree_addhealth.csv")

dc = data.dropna() dc.dtypes dc.describe() """ Modeling and Prediction """

#Split into training and testing sets

predictor = dc[['HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN']] target = dc.TREG1 pr_train,pr_test,t_train,t_test= train_test_split(predictor,target, test_size=0.4) pr_train.shape pr_test.shape t_train.shape t_test.shape

#Build model on training data

classif = DecisionTreeClassifier() classif = classif.fit(pr_train,t_train) pred=classif.predict(pr_test) sklearn.metrics.confusion_matrix(t_test,pred) sklearn.metrics.accuracy_score(t_test,pred)

#Displaying the decision tree

tree.export_graphviz(classif, out_file='tree_race.dot')

I translated .dot file to .png using “dot -Tpng name.dot -o name.png” command.

#machine#learning#python#coursera