Cognition and sub-symbolic AI Paradigms: Distributed AI as the ubiquitous future blanket for collective cognitive performance

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Symbiotic Data Platform. A Receptive-Responsive Tool for Building Thermal Comfort Optimization

By the on going research project: ‘Symbiotic Data Platform’, the main objective is to combine the existing technologies (which are; Building Information Modelling and Internet of Things), and existing data to formulize an upgraded network and make use of the floating information for optimizing the building energy performance, the user satisfaction and ambiental quality, as well as enhancing productivity, energy efficiency and sustainability. While proposing the platform, the objective is to empower the user by their ‘own’ data flow. The main aim is to create ‘Real-Time Information Models’ that takes reference from collected data from the sensor and using the existing information form BIM. The real-time data and the BIM data can be monitored, or used as a control factor for decision making as well as automation for the smart environments. Even though the platform can address various fields on a conceptual framework, yet, to simplify the testing of functioning, this paper will only focus on the thermal qualities and user comfort regarding the temperature data. On this paper, the research focuses on the data collection prototype, the current under development stage of the interface, and the implication phase of the ‘Symbiotic Data Platform’ and as well as it discusses further stages of the project

Phenotype Variability Mimicking as a Process for the Test and Optimization of Dynamic Facade Systems

A genetic algorithm and an artificial neural network are deployed for the design of a dynamic multi-layered façade system that adapts in real-time to different weather and occupants’ needs scenarios. The outputs are a set of different performances of the façade insulation cushions, optimized by the previous run of the genetic algorithm. A façade system of ETFE cushions is considered for them to learn from environmental data models. Each façade cushion is set up as an artificial neuron that is linked to the behavior and temperature of the others. The proposed outputs are a set of different performances of the façade system that are optimized through running the genetic algorithm. Façade neurons are configured as genes of the system that is abstractly represented on a digital model. The computational model manages cushion patterns’ performances through several phenotypical adaptations, suggesting that the proposed facade system maximizes its thermal efficiency in different scenarios

Urban Design vs. Science of cities. From the Digital Gap to the AI Barrier

Our future cities design challenge will be to deal with unpredictability and cognitive ethics derived from A.I. implementation at a wide range of scales. When AI machines are dealing with social-cognitive dimensions previously dealt through human-decision making, transparency of the algorithm as well as it analysis easiness in situations of un-appropriate behaviour become main issues. Added to that challenge, as Architects and Urban Designers we have a very particular challenge added to the already mentioned one:dealing with the process of changing from the Digital Gap to the AI Barrier for our built environment inhabitants. Main pillars for avoiding the appearance of the A.I. barrier will be discussed through this research taking into account most recent investigations and ethical debates regarding A.I. procedures implementation on our routines as a society

Examining the immune signatures of SARS-CoV-2 infection in pregnancy and the impact on neurodevelopment: Protocol of the SIGNATURE longitudinal study.

The COVID-19 pandemic represents a valuable opportunity to carry out cohort studies that allow us to advance our knowledge on pathophysiological mechanisms of neuropsychiatric diseases. One of these opportunities is the study of the relationships between inflammation, brain development and an increased risk of suffering neuropsychiatric disorders. Based on the hypothesis that neuroinflammation during early stages of life is associated with neurodevelopmental disorders and confers a greater risk of developing neuropsychiatric disorders, we propose a cohort study of SARS-CoV-2-infected pregnant women and their newborns. The main objective of SIGNATURE project is to explore how the presence of prenatal SARS-CoV-2 infection and other non-infectious stressors generates an abnormal inflammatory activity in the newborn. The cohort of women during the COVID-19 pandemic will be psychological and biological monitored during their pregnancy, delivery, childbirth and postpartum. The biological information of the umbilical cord (foetus blood) and peripheral blood from the mother will be obtained after childbirth. These samples and the clinical characterisation of the cohort of mothers and newborns, are tremendously valuable at this time. This is a protocol report and no analyses have been conducted yet, being currently at, our study is in the recruitment process step. At the time of this publication, we have identified 1,060 SARS-CoV-2 infected mothers and all have already given birth. From the total of identified mothers, we have recruited 537 SARS-COV-2 infected women and all of them have completed the mental health assessment during pregnancy. We have collected biological samples from 119 mothers and babies. Additionally, we have recruited 390 non-infected pregnant women

Examining the immune signatures of SARS-CoV-2 infection in pregnancy and the impact on neurodevelopment: Protocol of the SIGNATURE longitudinal study

The COVID-19 pandemic represents a valuable opportunity to carry out cohort studies that allow us to advance our knowledge on pathophysiological mechanisms of neuropsychiatric diseases. One of these opportunities is the study of the relationships between inflammation, brain development and an increased risk of suffering neuropsychiatric disorders. Based on the hypothesis that neuroinflammation during early stages of life is associated with neurodevelopmental disorders and confers a greater risk of developing neuropsychiatric disorders, we propose a cohort study of SARS-CoV-2-infected pregnant women and their newborns. The main objective of SIGNATURE project is to explore how the presence of prenatal SARS-CoV-2 infection and other non-infectious stressors generates an abnormal inflammatory activity in the newborn. The cohort of women during the COVID-19 pandemic will be psychological and biological monitored during their pregnancy, delivery, childbirth and postpartum. The biological information of the umbilical cord (foetus blood) and peripheral blood from the mother will be obtained after childbirth. These samples and the clinical characterisation of the cohort of mothers and newborns, are tremendously valuable at this time. This is a protocol report and no analyses have been conducted yet, being currently at, our study is in the recruitment process step. At the time of this publication, we have identified 1,060 SARS-CoV-2 infected mothers and all have already given birth. From the total of identified mothers, we have recruited 537 SARS-COV-2 infected women and all of them have completed the mental health assessment during pregnancy. We have collected biological samples from 119 mothers and babies. Additionally, we have recruited 390 non-infected pregnant women.This work has received support from the Fundación Alicia Koplowitz to realize the epigenetic wide association study and to the clinical assessment to the children. This work has also received public support from the Consejería de Salud y Familias para la financiación de la investigación, desarrollo e innovación (i + d + i) biomédica y en ciencias de la salud en Andalucía (CSyF 2021 - FEDER). Grant Grant number PECOVID- 0195-2020. Convocatoria financiada con Fondo Europeo de Desarrollo Regional (FEDER) al 80% dentro del Programa Operativo de Andalucía FEDER 2014-2020. Andalucía se mueve con Europa. NG-T received payment under Rio Hortega contract CM20-00015 with the Carlos III Health Institute.Peer reviewe

Phenotype Variability Mimicking as a Process for the Test and Optimization of Dynamic Facade Systems

A genetic algorithm and an artificial neural network are deployed for the design of a dynamic multi-layered façade system that adapts in real-time to different weather and occupants’ needs scenarios. The outputs are a set of different performances of the façade insulation cushions, optimized by the previous run of the genetic algorithm. A façade system of ETFE cushions is considered for them to learn from environmental data models. Each façade cushion is set up as an artificial neuron that is linked to the behavior and temperature of the others. The proposed outputs are a set of different performances of the façade system that are optimized through running the genetic algorithm. Façade neurons are configured as genes of the system that is abstractly represented on a digital model. The computational model manages cushion patterns’ performances through several phenotypical adaptations, suggesting that the proposed facade system maximizes its thermal efficiency in different scenarios

EmDeplo morphogenesis

These thesis will argue about the importance of Dynamic Parametric Architecture versus Static Parametric Architecture. Describing the architectonical and the algorithmic context within which the Emergency Deployable System emerged, it will be discussed the importance of adaptability as the missing concept of parametric architecture. Developing the concept of Human Oriented Parametric architecture, it will be discussed the need of implementing time as the lost parameter in current design techniques. Using the media-tic building, of the Spanish architect Enrique Ruiz Geli, as an example of a current design that tries to implement new technologies and parametric ideas in its design process, it will be explained the idea of the need of the building of working with the environment, not defending against it as the basis for a good adaptability. Geli¿s design is currently using 104 Arduino chips for an individual control and performance of the 104 ETFE pillows of the building façade. Through the creation of a virtual stimulus-reaction model of the system façade versus a model in which machine learning have been implemented for a better environmental performance, the efficacy on insulation of both models will be compared. Morphogenetic processes idea will be discussed through also the principle of an adaptable membrane, as the thought solution for future architecture design processes improvement. A model implementing a unique Arduino on the façade, will control the performance of the façade patterns, through, an Artificial Neural Network that will decide the kind of scenario the building is in, activating a Genetic Algorithm that will optimize insulation performance of the ETFE pillows. The final virtual model will be able to obtained the goal proposed, for this thesis, a homogeneous temperature in all the spaces of the building of 22ºC. The maximum thermal optimization obtained, nevertheless, appears if the opening of the pillows is free within and interval of 0 and 1 m thickness. The constrains of the opening of the ETFE pillows to three positions, will be demonstrated more effective than a just stimulus-reaction behaviour, but also, much less effective that an unconstrained façade system. The EmDeplo System will work with a Global behaviour, pattern performance of the façade, but also with a local behaviour for each pillow, giving the option of individual sun shading control. Machine learning implementation will give the façade the possibility to learn from the efficacy of its decisions through time, eliminating the need of an on-off behaviour for defending against the environment. Instead it will work with it, adapting to it, and evolving with its variabilities.Esta tesis debatira la importancia de la Arquitectura Parametrica Dinamica versus la Arquitectura Parametrica Estatica.Describiento el actual escenario arquitectonico y algoritmico dentro del cual el "Emergency Deployable System" emergio,sera debatida la importancia de la adaptabilidad como el concepto perdido actualmente el la arquitectura parametrica. Desarrollando el concepto de "Human Oriented Parametric Architecture", sera discutida la necesidad de implementar el tiempo como el parametro perdido en los actuales procedimientos de diseño. Empleando el edificio Media-Tic, del arquitecto español Enrique Ruiz Geli, como un ejemplo de un diseño actual que intenta implementar nuevas tecnologias e ideas parametricas en su proceso de diseño,sera explicada la idea de la necesidad del edificio de trabajar con el entorno, no de defenderse contra el, como base para una buena adaptabilidad. El diseño de Geli usa 104 chipsArduino para un control y comportamiento individual de los 104 cojines de ETFe en su fachada. A traves de la creacion de un sistema virtual estimulo-reaccion de la fachada versus un modelo virtual en el cual el aprendizaje de maquinas ha sido implementado para un mejor comportamiento ambiental, la eficacia de ambos modelos sera compararda. La idea de los procesos Morfogeneticos sera discutida a traves del pricipio de una mebrana adaptable, como la solucion propuesta para la mejora futura del proceso de diseño arquitectonico. Un modelo implementando un unico Arduino en la fachada, controlara el comportamiento de los patrones de la fachada a traves de una Red Neuronal Artificial que decidira el tipo de escenario de entorno en que el edificio se encuentra, activando un Algoritmo Genetico que optimizara el aislamiento termico de os cojines ETFE. La maqueta virtual final sera capaz de llegar a la meta propuesta para esta tesis doctoral, una temperatura homogenea en todos los espacios del edificio de 22ºC. La maxima optimizacion termica obtenida, sin embargo, aparece si la posibilidad de apertura de los cojines ETFE es libre en un intervalo entre o y 1m de espesor. La reduccion de las posibilidades de apertura de los cojines a tres posiciones unicamente, sera demostrada como una posibilidad mejoor que el comportamiento estimulo-reaccion, sin embargo, mucho menos efectivo que una posibilidad de apertura de rango libre. El sistema EmDeplo funcionara con un comportamiento global, la variabilidad del patron de la fachada, pero tambien con un comportamiento local, el de cada cojin ETFE, dando la opcion de un control de sombra-sol individual. El aprendizaje de maquinas implementado dara a la fachada la posibilidad de aprender de la eficacia de sus decisiones a lo largo del tiempo, eliminando la necisidad de un comportamiento on-off para defenderse del entorno. En lugar de esta defensa, trabajara con el, adaptamdose a el, y evolucionando con su variabilidad

Evo-Devo Strategies for Generative Architecture: Colour-Based Patterns in Polygon Meshes

Parametric design in architecture is often pigeonholed by its own definition and computational complexity. This article explores the generative capacity to integrate patterns and flows analogous to evolutionary developmental biology (Evo-Devo) strategies to develop emergent proto-architecture. Through the use of coloured patterns (genotype) and the modification of polygonal meshes (phenotype), a methodological proposal is achieved that is flexible to changes and personalization, computationally efficient, and includes a wide range of typologies. Both the process and the result are oriented towards computational lightness for a future and better integration of the workflow in genetic algorithms. Flow-based programming is used to replicate genetic properties such as multifunctionality, repeatability and interchangeability. The results reinforce the biological strategies against other more computationally abstract ones and successfully execute the parallels of universal mechanisms in Evo-Devo that are present in life