An Artificial Immune System Strategy for Robust Chemical Spectra Classification via Distributed Heterogeneous Sensors

The timely detection and classification of chemical and biological agents in a wartime environment is a critical component of force protection in hostile areas. Moreover, the possibility of toxic agent use in heavily populated civilian areas has risen dramatically in recent months. This thesis effort proposes a strategy for identifying such agents vis distributed sensors in an Artificial Immune System (AIS) network. The system may be used to complement electronic nose ( E-nose ) research being conducted in part by the Air Force Research Laboratory Sensors Directorate. In addition, the proposed strategy may facilitate fulfillment of a recent mandate by the President of the United States to the Office of Homeland Defense for the provision of a system that protects civilian populations from chemical and biological agents. The proposed system is composed of networked sensors and nodes, communicating via wireless or wired connections. Measurements are continually taken via dispersed, redundant, and heterogeneous sensors strategically placed in high threat areas. These sensors continually measure and classify air or liquid samples, alerting personnel when toxic agents are detected. Detection is based upon the Biological Immune System (BIS) model of antigens and antibodies, and alerts are generated when a measured sample is determined to be a valid toxic agent (antigen). Agent signatures (antibodies) are continually distributed throughout the system to adapt to changes in the environment or to new antigens. Antibody features are determined via data mining techniques in order to improve system performance and classification capabilities. Genetic algorithms (GAs) are critical part of the process, namely in antibody generation and feature subset selection calculations. Demonstrated results validate the utility of the proposed distributed AIS model for robust chemical spectra recognition

Label-Free Monitoring of Tumor Models by Surface-Enhanced Raman Scattering

184 p.El objetivo general de la presente tesis se ha centrado en la monitorización de modelos celulares mediante la técnica de espectroscopia de Raman aumentada en superficies (SERS). Las tecnologías desarrolladas en la tesis han perseguido, por un lado, mejorar la recreación del ambiente tumoral a escala de laboratorio, y por otra parte, su integración junto con estructuras plasmónicas para el análisis por SERS de los modelos tumorales creados artificialmente. Más en concreto, se han analizado las alteraciones en la concentración relativa de los metabolitos presentes en el medio extracelular como resultado de la reprogramación metabólica característica de los tumores, la cual permite a su vez un crecimiento descontrolado de dichas células.La disposición conjunta de ambas tecnologías (cultivos celulares en 3D y nanoplasmónica) ofrece un marco único para la identificación de aquellos procesos celulares que se encuentran alterados durante el crecimiento de tumores. Hasta la fecha, la mayoría de las técnicas de laboratorio que se habían empleado para caracterizar ambientes celulares en el laboratorio implicaban procesos invasivos, es decir, quemodifican o incluso desintegraban la muestra para poder analizarla. En contraposición, la espectroscopia Raman había permitido adquirir información sobre la composición del medio celular de una manera mínimamente invasiva. Basada en los fenómenos de dispersión inelástica, la técnica de Raman emplea luz monocromática (generalmente de un láser) para irradiar la muestra bajo análisis, de forma que la interacción entre la muestra y el láser provoca un cambio en la energía de los fotones dispersados, específico de los modos vibraciones de las moléculas irradiadas. Por lo tanto, la luz dispersada y recogida por un detector, permite caracterizar el sistema biológico que ha sido previamente iluminado, sin marcaje previo. Sin embargo, las señales detectadas por dispersión Raman son de manera general muy débiles, por lo que se requiere una intensificación de dichas señales para poder detectar la presencia de metabolitos extracelulares (a bajas concentraciones). En esta tesis se decidió implantar la modalidad conocida como SERS, que hace uso de las propiedades plasmónicas de nanopartículas metálicas (principalmente de oro), las cuales dan lugar a campos eléctricos elevados cuando se iluminan en resonancia con los plasmones superficiales. Como resultado, la señal de Raman de las moléculas adsorbidas sobre dichas superficies metálicas se ve amplificada en varios órdenes de magnitud. Sobre esta base, se han desarrollado en la tesis diferentes plataformas destinadas a combinar sustratos plasmónicos, formados por fijación de nanopartículas de oro sobre estructuras rígidas en 2D, o bien embebidas en redes poliméricas, junto con modelos de células tumorales en crecimiento. La finalidad de la tesis ha sido pues, la monitorización de diferentes procesos celulares en dichos dispositivos mediante SERS, y su posterior interpretación biológica en el ámbito del metabolismo tumoral y la mejora del tratamiento.CICbioGUNE; CICbiomaGUN

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Advanced photonic and electronic systems - WILGA 2017

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers more than 350 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET by PAN and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2017 was the XL edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2017 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445

Air Force Institute of Technology Research Report 2003

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

DNA Oligomers - From Protein Binding to Probabilistic Modelling

This dissertation focuses on rationalised DNA design as a tool for the discovery and development of new therapeutic entities, as well as understanding the biological function of DNA beyond the storage of genetic information. The study is comprised of two main areas of study: (i) the use of DNA as a coding unit to illustrate the relationship between code-diversity and dynamics of self-assembly; and (ii) the use of DNA as an active unit that interacts and regulates a target protein. In the study of DNA as a coding unit in code-diversity and dynamics of self-assembly, we developed the DNA-Based Diversity Modelling and Analysis (DDMA) method. Using Polymerase Chain Reaction (PCR) and Real Time Polymerase Chain Reaction (RT-PCR), we studied the diversity and evolution of synthetic oligonucleotide populations. The manipulation of critical conditions, with monitoring and interpretation of their effects, lead to understanding how PCR amplification unfolding could reshape a population. This new take on an old technology has great value for the study of: (a) code-diversity, convenient in a DNA-based selection method, so semi-quantitation can evaluate a selection development and the population\'s behaviour can indicate the quality; (b) self-assembly dynamics, for the simulation of a real evolution, emulating a society where selective pressures direct the population's adaptation; and (c) development of high-entropy DNA structures, in order to understand how similar unspecific DNA structures are formed in certain pathologies, such as in auto-immune diseases. To explore DNA as an active unit in Tumour Necrosis Factor α (TNF-α) interaction and activity modulation, we investigate DNA's influence on its spatial conformation by physical environment regulation. Active TNF-α is a trimer and the protein-protein interactions between its monomers are a promising target for drug development. It has been hypothesised that TNF-α forms a very intricate network after its activation between its subunits and receptors, but the mechanism is still not completely clear. During our research, we estimate the non-specific DNA binding to TNF-α in the low micro-molar range. Cell toxicity assays confirm this interaction, where DNA consistently enhances TNF-α's cytotoxic effect. Further binding and structural studies lead to the same conclusion that DNA binds and interferes with TNF-α structure. From this protein-DNA interaction study, a new set of tools to regulate TNF-α's biological activity can be developed and its own biology can be unveiled

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

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered