Review and future outlook for the removal of microplastics by physical, biological and chemical methods in water bodies and wastewaters

German Miranda Flores

Environmental Monitoring and Assessment

agosto 2025

Microplastics are emerging pollutants threatening ecosystems. This work compares physical, biological, and chemical methods for their removal from water bodies and wastewater.

Crecimiento de biogranulos en agua sintetica de origen industrial en un reactor tipo batch para la remocion de fenol como contaminante prioritario

German Miranda Flores

Repositorio UDLAP

agosto 2025

Se evaluó el uso de lodos granulares en un reactor batch para remover fenol en aguas residuales industriales. Los resultados muestran una metodología eficiente de biogranulación capaz de eliminar hasta 16 mgL de fenol.

Pathways to hydrogen production through variable renewable energy capacity: A study of Mexico and Jamaica

German Miranda Flores

Energy Conversion and Management

agosto 2025

This review assesses green hydrogen potential in Mexico and Jamaica, highlighting renewable energy capacities, key regions, production technologies, and barriers such as costs, infrastructure, and legislation.

El marketing social desde un enfoque interdisciplinario

Rodrigo Barcenas Lopez

Contexto UDLAP

agosto 2025

El marketing social usa técnicas del marketing tradicional para visibilizar problemas y promover cambios. Su éxito exige compromiso real, participación activa y estrategias ligadas a políticas públicas que aborden causas estructurales.

Analysis of correlations in the growth of biogranules from synthetic wastewater of industrial origin

German Miranda Flores

ECORFAN

agosto 2025

Pearson correlation was applied to analyze variables in a biogranulation process with synthetic textile wastewater, identifying key relations in aerobic and anaerobic cycles for system monitoring

La generación que ya no necesita a Google para buscar

Hugo Lopez Alvarez

Blog personal

agosto 2025

La forma de buscar información está cambiando: mientras los mayores de 40 aún dependen de Google, las nuevas generaciones prefieren IA, redes sociales y búsquedas multimodales. El futuro será conversacional, visual y distribuido.

A Monte Carlo-based approach for the evaluation of Mexicos geothermal potential

German Miranda Flores

Renewable Energy

agosto 2025

Mexico’s geothermal reserves (~8000 MWe) can support green hydrogen generation. Using Monte Carlo simulations on 12 hot springs, the study estimates ~147.6 MWe capacity over 25 years.

El problema de las dos sigmas y los tutores de inteligencia artificial

Raul Gonzalez Lopez

https:ia.udlap.mx

agosto 2025

El fenómeno conocido como el “problema de las dos sigmas” de Bloom se refiere a la diferencia significativa en el rendimiento académico entre los estudiantes que reciben instrucción convencional en grupo y aquellos que reciben tutoría individualizada.

Importancia de las “Habilidades Blandas” en un mercado laboral dominado por la IA

Raul Gonzalez Lopez

https:ia.udlap.mx

agosto 2025

El artículo destaca cómo la rápida transformación tecnológica y económica impulsada por la Inteligencia Artificial (IA) está afectando profundamente el mercado laboral.'El artículo destaca cómo la rápida transformación tecnológica y económica impulsada por la Inteligencia Artificial (IA) está afectando profundamente el mercado laboral.

Fraudulent Event Detection via Temporal Graph Networks

Diego Saldana Ulloa

IEEE

julio 2025

The development of new objective functions in Neural Architecture Search (NAS) may include temporal and dynamic aspects of the problem to be modeled. In this line, we show the application of a temporal graph neural algorithm for detecting fraud on real data of an online payment platform, which is made up of different events that a user can perform, such as card registration, device registration, bank account registration, and IP registration. A combination of previous information generates different Event-Based Temporal Graphs (ETG) used in constructing the objective function for our neural network. The results show that combining different events effectively impacts AUC and recall, which is directly related to the proportion of fraudulent observations captured, and that there is a direct correlation between better metrics values and the graph density (measured as the proportion between vertices and edges). Few works focus on applying ETGs for fraud detection using real data and its analysis through the chara

A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms

Diego Saldana Ulloa

MDPI

diciembre 2024

A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph (ETG) were sourced from an online payment platform and include details such as users, cards, devices, bank accounts, and features related to all these entities. Based on these data, seven distinct graphs were created; the first three represent individual interaction events (card registration, device registration, and bank account registration), while the remaining four are combinations of these graphs (card–device, card–bank account, device–bank account, and card–device–bank account registration). This approach was adopted to determine if the graph’s structure influenced the detection of fraudulent transactions. The results demonstrate that integrating more interaction events into the graph enhances the metrics, meaning graphs c

A Process for Topic Modelling Via Word Embeddings

Diego Saldana Ulloa

Research in Computing Science

septiembre 2023

This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a dimensionality reduction technique called UMAP is used. This method manages to reduce the dimensions while preserving part of the local and global information of the original data. K-Means is used as the clustering algorithm to obtain the topics. Then, the topics are evaluated using the TF-IDF statistics, Topic Diversity, and Topic Coherence to get the meaning of the words on the clusters. The results of the process show good values, so the topic modeling of this process is a viable option for classifying or clustering texts without labels.