Search Results (65)
Speciation of ruthenium-based candidate drugs for cancer treatment and copper as a potential biomarker for cancer diagnosis in human serum
According to the World Health Organization (WHO), cancer represents the second leading cause of mortality, with almost 10 million deaths in 2020. A broad spectra of cancer treatments are being developed, among them are metal-based drugs. Consistent with the available data, platinum (Pt)-based chemotherapeutics are still widely used to treat …
User experience evaluation of novel air quality sensing technologies for citizen engagement in environmental health studies
The use of low-cost sensing technologies increasingly used in participatory environmental health studies brings both opportunities and challenges. While previous research mostly focused on technical aspects, this thesis brings participants to the foreground and articulates their experiences. It aims to evaluate if low-cost sensing technologies are fitfor- purpose in environmental …
Development and application of a non-targeted screening workflow for chemical exposure assessment in human biomonitoring and related studies
Human exposure to environmental stressors is widespread. Highly dynamic, these chemical, physical and social factors, collectively termed as the exposome, interact with internal factors such as genetics, sex, gut microbiome and general health status, and dictate susceptibility and risk for an onset of diseases. Human exposure to the chemicals is …
Targeted analysis of organic contaminants, exposure assessment, and vulnerability of populations to hazardous compounds
From the moment of conception until death, humans are exposed to chemicals at varying intensities and frequencies. While an individual’s level of resilience is commonly high enough to tolerate these kinds of stressors, specific exposures or genetic predispositions can lower it. Therefore, the assessment of chemical exposure, the development of …
Scalable neuro-symbolic machine learning
With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …
An automated vulnerability detection for website security
Although large research efforts on web application security have been invested for more than a decade, the security of web applications is still a challenging problem. The main focus of the cybersecurity community has been to make operating systems and communication networks more secure and harder for attackers to penetrate. …
Processing of heavy-rare-earth-lean Nd-Fe-B permanent magnets for high-temptrature applications
Permanent magnets are materials that generate a magnetic field in free space without electricity or other external energy sources. They are essential components of modern technologies, used in many electrical devices. Nowadays, they are included in technology for renewable sources of energy, such as electrical vehicles, generators and wind turbines. …
In silico approaches to understand and predict the ligand-binding interactions of the human P-glycoprotein (ABCB1) membrane transporter
The P-glycoprotein (P-gp) is responsible for the elimination of a wide variety of substances from the cell, thus it is thought to play an important role in detoxification functions. The P-gp also affects the ADMET (absorption, distribution, metabolism, excretion, and toxicity in pharmacokinetics) properties of drugs. It is involved in …
Predicting the dynamics of spatio-temporal systems based on heterogeneous data sources
As urbanisation continues to be a trend, in which centralisation of the population into cities is still growing, the importance of intelligent solutions in mobility is in high demand. Likewise, with the integration of renewable energies into all levels of the electrical grid system and the increasing amount of heavy …
Complex nodes in trees for structured output prediction
In this thesis, we integrate complex nodes into predictive clustering trees (PCTs). PCTs are well-established machine learning models that are very flexible in terms of the machine learning tasks that they can address, including structured output prediction and semisupervised learning. Like standard decision trees, they are learned with a greedy …