Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms

In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users’ mobility patterns and the message load in the network. This new type of configuration, however, poses new challenges to security, amongst them, assessing the effect that a group of colluding malicious participants can have on the global message delivery rate in such a network is far from trivial. In this work, after modeling such a question as an optimization problem, we are able to find quite interesting results by coupling a network simulator with an evolutionary algorithm. The chosen algorithm is specifically designed to solve problems whose solutions can be decomposed into parts sharing the same structure. We demonstrate the effectiveness of the proposed approach on two medium-sized Delay-Tolerant Networks, realistically simulated in the urban contexts of two cities with very different route topology: Venice and San Francisco. In all experiments, our methodology produces attack patterns that greatly lower network performance with respect to previous studies on the subject, as the evolutionary core is able to exploit the specific weaknesses of each target configuration.<br/

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Doctor of Philosophy

dissertationThere are many bacteria that associate with insects in a mutualistic manner and offer their hosts distinct fitness advantages, and thus have likely played an important role in shaping the ecology and evolution of insects. Therefore, there is much interest in understanding how these relationships are initiated and maintained and the molecular mechanisms involved in this process, as well as interest in developing symbionts as platforms for paratransgenesis to combat disease transmission by insect hosts. However, this research has been hampered by having only a limited number of systems to work with, due to the difficulties in isolating and modifying bacterial symbionts in the lab. In this dissertation, I present my work in developing a recently described insect-bacterial symbiosis, that of the louse fly, Pseudolynchia canariensis, and its bacterial symbiont, Candidatus Arsenophonus arthropodicus, into a new model system with which to investigate the mechanisms and evolution of symbiosis. This included generating and analyzing the complete genome sequence of Ca. A. arthropodicus, which provided some evidence that Ca. A. arthropodicus has become recently associated with insects and may have evolved from an ancestor that was an insect pathogen. Additionally, I describe the development of methods for genetic modification of this bacterial symbiont and for introducing recombinant symbionts into louse fly hosts, as well as a new microinjection technique that enables the complete replacement of native symbionts with recombinant symbionts. With the generation of the symbiont genome sequence along with strategies for engineering recombinant symbionts and establishing them in an insect host, this work provides an interesting new system with which to investigate the function of specific genes in symbiosis as well as a promising new avenue of research involving paratransgenesis

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Supplementation Strategies for Tuning Glycosylation of Monoclonal Antibodies and Enhancing Growth in Mammalian Cell Culture by Omics Analysis

Two fundamental objectives of bioprocess engineering are to increase productivity and improve product quality. Glycosylation is a critical and variable factor in the quality of several therapeutic proteins, particularly those with immune-system interactions such as monoclonal antibodies (mAbs). In this thesis, supplementation of nutrients to growth medium of CHO1A7 mammalian cell cultures are examined towards enhancing cell growth, by increasing peak cell density, and improving product quality, by tuning glycosylation of EG2-hFc heavy-chain camelid antibodies. Targeted profiling via 1D-1H-NMR metabolomics and differential expression analysis via 2D-DIGE proteomics, elucidate factors to create a nutrient cocktail to enhance culture growth. Eight target nutrients corresponding with five identified metabolic systems for CHO cells including anaplerotic TCA-replenishment; NADH/NADPH replenishment; tetrahydrofolate cycle C1 cofactor conversions; limitations to lipid synthesis; and redox modulation; resulted in a ~75% improvement to peak cell densities. Towards improving product quality, nucleotide-sugar precursors, capable of shifting glycan distributions were supplemented to growth medium to tune glycosylation of EG2-hFc towards a single G0 glycoform. Growth inhibition from glucosamine-based precursors was mitigated given a priori knowledge from metabolomic analysis of the system – identifying cytosolic acetyl-CoA as a sensitive metabolic pool for CHO1A7 cell growth. Additional nucleotide-sugar precursor nutrients were examined to better resolve conflicting reports of effects to glycan distributions. These conflicts are subsequently attributed to five key factors: differences across cell platforms; differences between glycan sites of expressed proteins; the fermentation and sampling timeline; glutamine levels; finally, no standardized metrics for reporting shifts in glycan distributions with respect to controls

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier