Air pollution constitutes a critical public health challenge in rapidly urbanising sub-Saharan African cities, where industrial activity, vehicular emissions, and meteorological dynamics converge to produce complex spatiotemporal pollution patterns. This study presents the first comprehensive geospatial machine learning analysis of multivariate

Deep dive into global CO2 emissions & temperature changes. Quantifies trends, correlations, and explores lagged effects & causality. Utilizes clustering to identify climate patterns and builds a predictive model for "what-if" scenarios. Delivers critical insights into climate change impact for informed decision-making.

Galaxy/AGN Emission Line Analysis TOol by Raphael Hviding

Harnessing LSTM neural networks, this project forecasts carbon emissions from historical data. Through streamlined preprocessing, dynamic modeling, and vivid visualizations, it transforms raw data into actionable, exportable insights for impactful environmental analysis and decision-making.

A Python-based C compiler for x86-64, featuring full parsing, semantic analysis, IR generation, and assembly code emission.

The CleanEnergyBot is a Telegram bot providing real-time electricity usage, CO2 forecasts, and energy-saving tips in Ireland, using data from EirGrid and GPT-3 analysis. It helps users make eco-friendly energy choices by comparing emissions data with EU standards.

Food Waste Management web application with stats of monthly Food Wastage, Costs, Quantity Purchased, CO2 Emissions and monthly Food Donations. Donations made seamless using Map API. Personal Inventory to keep track of food items. Also the platform provides options for Recipe search and Nutrition Analysis of ingredients.

Dynamic positron emission tomography (PET) analysis

Python library unifying Positron Emission Particle Tracking (PEPT) research, including tracking, simulation, data analysis and visualisation tools.

The reporitory contains scripts and data used in the paper <Advanced Far Field EM Side-Channel Attack on AES Based on Deep Learning>. This paper presents a deep learning-based side-channel attack on AES-128 using far field electromagnetic (EM) emissions as a side channel. Unlike power or near filed EM analysis, far field EM attacks do not require a close physical proximity to the device under attack. Our neural networks are trained on traces captured from five different Bluetooth devices and tested on five other Bluetooth devices. The training set is composed as a combination of ``clean'' traces, captured through a coaxial cable, and traces captured on a distance to device. Our best model can recover the key from less than 300 traces captured in an office environment at 15 m distance to device without repeating each encryption more than once.

A comprehensive software toolkit for the measurement, analysis, and visualization of energy use, power draw, hardware performance, and carbon emissions across AI workloads.

Solution developed to identify the structural quality of ceramic floors through the analysis of acoustic emission, using digital signals processing and machine learning techniques.

🏭 A project for analyzing global CO₂ emissions using R, leveraging OWID data to create 12 visualizations that explore historical trends, economic relationships, and top emitters. Includes a linear regression of per-capita emissions on GDP per capita and supports reproducible workflows via Makefile, RMarkdown, and Docker.

acoustic emission signal process and analysis tool with matlab

The Database for Air Quality and Noise Analysis (DANA) Tool provides traffic-related inputs to the Motor Vehicle Emission Simulator (MOVES) vehicle emissions model and the Traffic Noise Model (TNM).

This project showcases a comprehensive analysis of CO2 emissions in a fictitious cheese manufacturing supply chain using both graph databases and relational databases.

Prediction of travel time has major concern in the research domain of Intel- ligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub- classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. The information from these are processed and provided back to the travellers in real time. Traffic flow modelling and driving condition analysis have many applications to various areas, such as Intelligent Trans- portation Systems (ITS), adaptive cruise control, pollutant emissions dispersion and safety.

OpenAPI ↔ C. Frontend for C, concentrating on: generation from code; single-file analysis; modification; and emission (prettyprinting).

A python package for Excitation-Emission Matrix (EEM) fluorescence analysis, including data I/O, preprocessing, decomposition (PARAFAC/NMF with optional solvers/regularizations), and validation workflows.

paceENSDF: Python Archive of Coincident Emissions from ENSDF. Package enabling interaction, manipulation, analysis, and visualization of radioactive-decay data from the ENSDF archive and corresponding coincidence gamma-gamma and gamma-X-ray emissions..

Repository for the STAR-MELT emission line analysis Jupyter notebook and Python package

This project predicts carbon emissions using various machine learning models on historical data from Kaggle. Key steps include data preprocessing, exploratory analysis, outlier treatment, and model evaluation (LSTM, GRU, SimpleRNN). The study identifies the most effective model for accurate emission forecasting.

code for retrieval analysis of emission spectra from brown dwarfs and giant planets

Repository for R-code development of GHG emissions decomposition analysis using GCAM-GLIMPSE scenario outputs and Kaya Identity method

Acoustic Emission signal analysis of Carbon Fiber Reinforced Polymers under fatigue loading

Rotterdam University Grails - Tracking Carbon Emission Analysis and Predictions

Python library for the preprocessing, correction, deconvolution and analysis of Excitation Emission Matrices (EEMs).

Several attacks on AES using far field electromagnetic (EM)emission as a side channel have been recently presented.Unlike power analysis or near filed EM analysis, far fieldEM attacks do not require a close physical proximity to thedevice under attack. However, in all previous attacks tracesfor the profiling stage are also captured at a distance (fixedor variable) from the profiling devices. This degenerates thequality of profiling traces due to noise and interference. Inthis paper, we train deep learning models on "clean" traces,captured through a coaxial cable. Our experiments show thatthe resulting models can extract the AES key from less than500 traces on average captured at 15 m from the victim devicewithout repeating each encryption more than once. This is a20-fold improvement over the previous attacks which requireabout 10K traces for the same conditions.

FHWA's Energy and Emissions Reduction Policy Analysis Tool (EERPAT)

App for emission spectra analysis