Aleix Alva

Llegit. M'ha agradat molt l'article. Gràcies.

I'd replace "experimenters with "Abusers of innocent creatures".

Completed Advent of Code 2025 #AdventOfCode

adventofcode.com/2025

#Python code: codeberg.org/aleix_alva/a...

Completed "Movie Theater" - Day 9 - Advent of Code 2025 #AdventOfCode adventofcode.com/2025/day/9

Solved part 2 by hand

codeberg.org/aleix_alva/a...

Beautiful <3

Halfway through Advent of Code 2025 #AdventOfCode adventofcode.com/2025/day/6
codeberg.org/aleix_alva/a...

Completed "Gift Shop" - Day 2 - Advent of Code 2025 #AdventOfCode adventofcode.com/2025/day/2
codeberg.org/aleix_alva/a...

What about this advent?
#advent-of-force
Is it doable?

Code: codeberg.org/aleix_alva/a...

#adventofcode day 1

Is France defined as a multipolygon? In that case, could one choose the biggest polygon in it?

Never thought of oversea territories pulling centroids. I can't suggest anything beyond asking why not use the capital or most populated city coordinates.

Really liked it. Isn't France's bubble a bit off?

2D UMAP scatter plot of countries based on five statistical metrics. Each point represents a country-year. The x-axis (umap1) loosely corresponds to income level, with high-income countries on the right. The y-axis (umap2) loosely corresponds to year, with more recent years toward the bottom. Point shape represents binned overall score (0–20, 20–40, 40–60, 60–80, 80–100), point fill represents income category (Not classified, Low, Lower middle, Upper middle, High), point colour represents year using a black-to-light-green gradient, and point size encodes population. Clusters indicate countries with similar profiles. China (large cyan point) and India (large orange point) are separated, with India moving toward the high-income region and China moving upward over time. Legends for all four aesthetics are shown below the plot for clarity.

Tried to improve the plot by adding overall_score as shape.

Scatter plot of countries in a 2D UMAP embedding based on five data metrics. Each point represents a country-year combination. The x-axis (umap1) roughly aligns with income level, with high-income countries on the right. The y-axis (umap2) roughly aligns with time, with more recent years toward the bottom. Point shape and fill encode income category, point size encodes population, and point colour represents year in a yellow-to-red gradient. Clusters indicate countries with similar metric profiles. China (large cyan point) and India (large orange point) are clearly separated, with India moving toward the high-income region and China moving upward over time. Mixed-income clusters from earlier years appear in the upper-left region.

Statistical Performance Indicators
#TidyTuesday
UMAP of the five scores, by year (circumference), income (circle) and population (radius).
#Marimo notebook: fluents.xyz/c/2025-11-25/
#Pandas #Plotnine

Thanks ^^

Relative contribution of number of published papers by country (architecture and built environment research field). The relative contribution of China notably increases since 2019.

A quick country-level bibliometric survey of architecture and built environment research
fluents.xyz/b/2025-11-22/

If you think of something that could be measured from this SH dataset, I'd be happy to try.

Now, that's a good dark list. Silly word count 0, actual knowledge 1. I wonder if a word count could capture such an insight.

It was my first TidyTuesday, so not much experience here either, just learning, and wrote the first words that came to mind. I read you like Sherlock Holmes stories: which ones would you consider the darkest? I'm suppose this little programming exercise doesn't get anywhere close.

Terms shown in line 74 here:
codeberg.org/aleix_alva/t...

Y-axis counts the number of occurrences from a dark word list, per 1000 words.

Darkest Sherlock Holmes Stories
My first ever #TidyTuesday
codeberg.org/aleix_alva/t...

The course emphasises critical thinking and data-driven decision-making, promoting ecofeminist solutions to address the climate emergency and improve liveability in built environments.

Students will acquire tools to assess the multi-scale impacts of architecture on the environment and society. They will learn to manage databases, apply statistical methods, and interpret data using open science principles, with methodologies applicable within architecture and urbanism.

Preparing a new course, "Climate and Data in Architecture". Starting Feb 2025, ETSAB, UPC.
etsab1.upc.edu/web/estudios...
It will explore the causes and implications of the climate crisis and the role, both positive and negative, played by architecture.

New preprint: Thermal Performance and Microclimate Modulation: A Year-Long, Multi-Site Study of Operational Green Roofs in Barcelona under Severe Water Scarcity
papers.ssrn.com/sol3/papers....

New project: INdoor-OUTdoor climate and energy interactions: reducing heat risks in Mediterranean cities
futur.upc.edu/42822601

Hem guanyat el Premi de Ciència Oberta 2025
futur.upc.edu/42766586

A Systematic Bibliometric Review Analysis of Research on the Use of Waste Rubber Tyres in Building and Construction Materials and Their Applications
pmc.ncbi.nlm.nih.gov/articles/PMC...