Molecular Features for Probing Small Amphiphilic Molecules with Self-Assembled Monolayer Protected Nanoparticles

The sensing of small molecules poses the challenge to develop devices able to discriminate between compounds that may be structurally very similar. Here, attention has been paid to the use of self-assembled monolayer (SAM)-protected gold nanoparticles since they enable a modular approach to tune single-molecule affinity and selectivity simply by changing functional moieties (i.e. covering ligands), alongside with multivalent molecular recognition. To date, the discovery of monolayers suitable for a specific molecular target relies on trial-and-error approaches, with ligand chemistry being the main criteria used to modulate selectivity and sensitivity. By using molecular dynamics, we showcase that either individual molecular characteristics and/or collective features such as ligand flexibility, monolayer organization, ligand local ordering, and interfacial solvent properties can also be exploited conveniently. The knowledge of the molecular mechanisms that drive the recognition of small molecules on SAM covered nanoparticles will critically expand our ability to manipulate and control such supramolecular systems

A hydrodynamical perspective on the turbulent transport of bacteria in rivers

The transport of bacteria in turbulent river-like environments is addressed, where bacterial populations are frequently encountered attached to solids. This transport mode is investigated by studying the transient settling of heavy particles in turbulent channel flows featuring sediment beds. A numerical method is used to fully resolve turbulence and finite-size particles, which enables the assessment of the complex interplay between flow structures, suspended solids and river sediment

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Optimised meta-clustering approach for clustering Time Series Matrices

The prognostics (health state) of multiple components represented as time series data stored in vectors and matrices were processed and clustered more effectively and efficiently using the newly devised ‘Meta-Clustering’ approach. These time series data gathered from large applications and systems in diverse fields such as communication, medicine, data mining, audio, visual applications, and sensors. The reason time series data was used as the domain of this research is that meaningful information could be extracted regarding the characteristics of systems and components found in large applications. Also when it came to clustering, only time series data would allow us to group these data according to their life cycle, i.e. from the time which they were healthy until the time which they start to develop faults and ultimately fail. Therefore by proposing a technique that can better process extracted time series data would significantly cut down on space and time consumption which are both crucial factors in data mining. This approach will, as a result, improve the current state of the art pattern recognition algorithms such as K-NM as the clusters will be identified faster while consuming less space. The project also has application implications in the sense that by calculating the distance between the similar components faster while also consuming less space means that the prognostics of multiple components clustered can be realised and understood more efficiently. This was achieved by using the Meta-Clustering approach to process and cluster the time series data by first extracting and storing the time series data as a two-dimensional matrix. Then implementing an enhance K-NM clustering algorithm based on the notion of Meta-Clustering and using the Euclidean distance tool to measure the similarity between the different set of failure patterns in space. This approach would initially classify and organise each component within its own refined individual cluster. This would provide the most relevant set of failure patterns that show the highest level of similarity and would also get rid of any unnecessary data that adds no value towards better understating the failure/health state of the component. Then during the second stage, once these clusters were effectively obtained, the following inner clusters initially formed are thereby grouped into one general cluster that now represents the prognostics of all the processed components. The approach was tested on multivariate time series data extracted from IGBT components within Matlab and the results achieved from this experiment showed that the optimised Meta-Clustering approach proposed does indeed consume less time and space to cluster the prognostics of IGBT components as compared to existing data mining techniques

Network Analysis with Stochastic Grammars

Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from unstructured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst can begin the association or individualization process to answer criminal investigative questions. Three contributions resulted from this research. First , domain characteristics suitable for SCFG representation were identified and a step -by- step approach to adapt SCFG to novel domains was developed. Second, a novel iterative graph-based method of identifying similarities in context-free grammars was developed to compare behavior patterns represented as grammars. Finally, the SCFG capabilities were demonstrated in performing association and individualization in reducing the suspect pool and reducing the volume of evidence to examine in a computer network traffic analysis use case

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Breadth analysis of Online Social Networks

This thesis is mainly motivated by the analysis, understanding, and prediction of human behaviour by means of the study of their digital fingeprints. Unlike a classical PhD thesis, where you choose a topic and go further on a deep analysis on a research topic, we carried out a breadth analysis on the research topic of complex networks, such as those that humans create themselves with their relationships and interactions. These kinds of digital communities where humans interact and create relationships are commonly called Online Social Networks. Then, (i) we have collected their interactions, as text messages they share among each other, in order to analyze the sentiment and topic of such messages. We have basically applied the state-of-the-art techniques for Natural Language Processing, widely developed and tested on English texts, in a collection of Spanish Tweets and we compare the results. Next, (ii) we focused on Topic Detection, creating our own classifier and applying it to the former Tweets dataset. The breakthroughs are two: our classifier relies on text-graphs from the input text and we achieved a figure of 70% accuracy, outperforming previous results. After that, (iii) we moved to analyze the network structure (or topology) and their data values to detect outliers. We hypothesize that in social networks there is a large mass of users that behaves similarly, while a reduced set of them behave in a different way. However, specially among this last group, we try to separate those with high activity, or low activity, or any other paramater/feature that make them belong to different kind of outliers. We aim to detect influential users in one of these outliers set. We propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), labeling the outliers detected os of shape, magnitude, amplitude or combination of those. We applied this method to a subset of roughly 400 million Google+ users, identifying and discriminating automatically sets of outlier users. Finally, (iv) we find interesting to address the monitorization of real complex networks. We created a framework to dynamically adapt the temporality of large-scale dynamic networks, reducing compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.PublicadoPrograma de Doctorado en Ingeniería Matemática por la Universidad Carlos III de MadridPresidente: Rosa María Benito Zafrilla.- Secretario: Ángel Cuevas Rumín.- Vocal: José Ernesto Jiménez Merin