High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT)

This study uses the Genetic Weighted Pyramid Operation Tree (GWPOT) to build a model to solve the problem of predicting high-performance concrete compressive strength. GWPOT is a new improvement of the genetic operation tree that consists of the Genetic Algorithm, Weighted Operation Structure, and Pyramid Operation Tree. The developed model obtained better results in benchmark tests against several widely used artificial intelligence (AI) models, including the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Evolutionary Support Vector Machine Inference Model (ESIM). Further, unlike competitor models that use “black-box” techniques, the proposed GWPOT model generates explicit formulas, which provide important advantages in practical application

Forward and Opposed Smoldering Combustion

A computational study has been carried out to investigate smoldering ignition and propagation in polyurethane foam. The onedimensional, transient, governing equations for smoldering combustion in a porous fuel are solved accounting for improved solid-phase chemical kinetics. A systematic methodology for the determination of solid-phase kinetics suitable for numerical models has been developed and applied to the simulation of smoldering combustion. This methodology consists in the correlation of a mathematical representation of a reaction mechanism with data from previous thermogravimetric experiments. Geneticalgorithm and trail-and-error techniques are used as the optimization procedures. The corresponding kinetic parameters for two different mechanisms of polyurethane foam smoldering kinetics are quantified: a previously proposed 3-step mechanism and a new 5-step mechanism. These kinetic mechanisms are used to model one-dimensionalsmoldering combustion, numerically solving for the solid-phase and gasphase conservation equations in microgravity with a forced flow of oxidizer gas. The results from previously conducted microgravity experiments with flexible polyurethane foam are used for calibration and testing of the model predictive capabilities. Both forward and opposed smoldering configurations are examined. The model describes well both opposed and forward propagation. Specifically, the model predicts the reaction-front thermal and species structure, the onset of smoldering ignition, and the propagation rate. The model results reproduce the most important features of the smolder process and represent a significant step forward in smoldering combustion modeling

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Wind Power

This book is the result of inspirations and contributions from many researchers of different fields. A wide verity of research results are merged together to make this book useful for students and researchers who will take contribution for further development of the existing technology. I hope you will enjoy the book, so that my effort to bringing it together for you will be successful. In my capacity, as the Editor of this book, I would like to thanks and appreciate the chapter authors, who ensured the quality of the material as well as submitting their best works. Most of the results presented in to the book have already been published on international journals and appreciated in many international conferences

Microalgae-bacteria consortia for urban wastewater treatment

Los consorcios de microalgas y bacterias surgen como una opción idónea en el marco de las tecnologías sostenibles para el tratamiento de aguas residuales. Los mecanismos de eliminación de contaminantes son el resultado de interacciones ecológicas directas y/o indirectas entre las microalgas y las bacterias autóctonas de las aguas residuales. Para diseñar sistemas eficaces que incorporen consorcios de microalgas y bacterias es necesario comprender las interacciones ecológicas entre ambas comunidades dentro de los procesos de tratamiento de aguas residuales. El objetivo principal de este trabajo de investigación es, por tanto, estudiar y explorar las interacciones ecológicas para mejorar el tratamiento de aguas residuales basado en el consorcio de microalgas y bacterias. Esta tesis aborda desde el funcionamiento de un raceway acoplado a un sistema de membranas (MHRAP), hasta el desarrollo de un modelo matemático que reproduce las interacciones ecológicas observadas experimentalmente. El hilo conductor entre los diferentes capítulos de la tesis es concretamente, la interacción entre las microalgas y las bacterias nitrificantes. Se evaluó la viabilidad del tratamiento de aguas residuales mediante un consorcio de microalgas y bacterias en la planta piloto MHRAP, variando el tiempo de retención hidráulico (TRH) y la corriente de agua residual a tratar. Tanto el TRH como la corriente de agua residual influyeron en la relación entre las microalgas y las bacterias nitrificantes. Se observaron procesos competitivos entre ambas comunidades, así como interacciones negativas, como la inhibición de la fotosíntesis debida a los nitritos. A continuación, se estudió la influencia del nitrito en la fotosíntesis en condiciones de laboratorio. El nitrito tuvo efectivamente un efecto inhibidor en la fase lumínica de la fotosíntesis. Se propuso y validó una expresión cinética que reproduce la inhibición de la fotosíntesis por nitritos. Los procesos competitivos reducen el potencial de los consorcios de microalgas y bacterias para eliminar nutrientes del agua residual, por lo que se desarrolló una guía para identificar y reducir estas interacciones negativas. Por otro lado, se evaluó la ecología microbiana de cinco períodos operativos de la planta piloto MHRAP. Se ha aplicado la secuenciación masiva de los biomarcadores 16S/18S rDNA para identificar las principales comunidades de bacterias y de microalgas, además de esclarecer la influencia de los parámetros operativos y ambientales en la microbiología del biorreactor. Coelastrella y Desmodesmus fueron los géneros de microalgas dominantes, mientras que Verrucomicrobiota y Proteobacteria fueron los filos bacterianos dominantes en los cinco periodos operativos. Todos los conocimientos recopilados durante el desarrollo de la tesis se utilizaron para desarrollar un modelo matemático capaz de reproducir las principales interacciones entre las microalgas y las bacterias. Como resultado de la revisión bibliográfica, se observó que el metabolismo de las microalgas no puede considerarse un proceso bien caracterizado, ya que algunos parámetros de los modelos matemáticos, ya publicados, son inciertos y dependen del tipo de microalga. Por ello, se realizó un análisis de sensibilidad global de los factores más influyentes de la cinética de las microalgas y un análisis de incertidumbre de los resultados del modelo matemático. Los 34 parámetros de las cinéticas de las microalgas se redujeron a 11 factores influyentes, los cuales se recomienda calibrar para cada cultivo de microalgas, con el objetivo de reducir la incertidumbre del modelo. Se desarrolló un modelo integral de microalgas y bacterias, que incluye procesos físicos, químicos y biocinéticos cruciales observados durante el desarrollo de la tesis. El modelo se utilizó para reproducir las interacciones entre las microalgas y las bacterias nitrificantes que se producen en una planta piloto de fotobiorreactores de membrana (MPBR). Además, también se simularon estrategias de control de la nitrificación para mejorar tanto la actividad de las microalgas como la tasa de recuperación de nutrientes.Microalgae-bacteria consortia appears as an ideal option within the framework of sustainable technologies for wastewater treatment. Pollutants removal mechanisms result from direct and/or indirect ecological interactions between microalgae and indigenous wastewater bacteria. Effectively designed systems that incorporate microalgae-bacteria consortia require an understanding of ecological interactions between microalgae and bacteria within wastewater treatment processes. The main objective of this research work is therefore study and explore the ecological interactions to improve microalgae-bacteria based-wastewater treatment. This thesis addresses from the operation of an outdoor membrane high rate algal pond (MHRAP) pilot plant to the development of a mathematical model that reproduces the ecological interactions observed experimentally. The common thread between the different chapters of the thesis is specifically the interaction between microalgae and nitrifying bacteria. The feasibility of wastewater treatment by microalgae-bacteria consortium was assessed at the MHRAP pilot plant by varying the hydraulic retention time (HRT) and the incoming wastewater stream. Both HRT and wastewater stream influenced the relationship between microalgae and nitrifying bacteria. Negative interactions, such as nitrite inhibition of photosynthesis and competitive interactions, were observed. The influence of nitrite on photosynthesis was then studied under laboratory conditions. Nitrite effectively had an inhibitory effect on the light-dependent phase of photosynthesis. Kinetic expression which reproduces nitrite inhibition was proposed and validated. Competitive processes reduce the potential of microalgae and bacteria consortia to recover nutrients from wastewater, therefore, a guide to identify and reduce these negative interactions was developed. Additionally, microbial ecology of five operational periods of the MHRAP pilot plant was evaluated. Massive sequencing of 16S/18S rDNA biomarkers have been applied to identify the main bacteria and microalgae communities and to detect the influence on operational and environmental parameters on bioreactor microbiology. Coelastrella and Desmodesmus were the dominant genera of microalgae, while Verrucomicrobiota and Proteobacteria were the dominant bacterial phylum in the five operating periods. All the knowledge gathered during the development of this thesis was used to develop a mathematical model, which faithfully reproduces the main interactions between microalgae and bacteria. As the literature review revealed, the metabolism of microalgae cannot be considered a well-characterized process, since some parameters of the mathematical models are uncertain and speciation-dependent. Thus, the more influential factors on microalgae kinetics and the uncertainty of the model outputs were analyzed by a global sensitivity analysis and an uncertainty analysis, respectively. The 34 parameters of the microalgae kinetics model were reduced to 11 influential factors, which should be calibrated for each microalgae culture to reduce model uncertainty. An integral microalgae-bacteria model was developed, which includes crucial physical, chemical and biokinetic processes observed during the thesis development. The model was used to reproduce microalgae and nitrifying bacteria interactions that occur in an outdoor membrane photobioreactor (MPBR) pilot plant. Moreover, nitrification control strategies were also simulated to improve both microalgae activity and nutrient recovery rate