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Die Potentiale der gefrorenen Sandschalung für den Entwurf und die Herstellung funktional gradierter Betonbauteile (siehe Projekte Sobek et al. und Garrecht et al. des SPP 1542, S. 642 ff . und S. 250 ff .) wurden im Rahmen des Teilprojekts B04: Herstellung biomimetischer und biologisch inspirierter (modularer) Strukturen für die Bauindustrie des Sonderforschungsbereichs/Transregio 141 Biologischer Entwurf und integrative Strukturen – Analyse, Simulation und Umsetzung in der Architektur weiter untersucht. / Potentials of frozen sand formwork technology were further investigated in design and production of functionally graded concrete components (see also the projects Sobek et al. and Garrecht et al. of SPP 1542, pages 642 resp. 250 et seq.) in the subproject B04: Fabrication of biomimetic and biologically inspired (modular) structures for use in the construction industry in the framework of the Collaborative Research Centre/Transregio 141 Biological Design and Integrative Structures – Analysis, Simulation and Implementation in Architecture.

[pt] Atualmente as Unidades de Saúde, em um grande número de países do mundo, apresentam demandas de serviços que superam suas capacidades reais. Por esta razão, o surgimento das listas de espera é inevitável. Preparar o planejamento das mesmas, de modo otimizado resulta, portanto, em um grande desafio, devido à quantidade de recursos que devem ser considerados. O caso particular dos procedimentos cirúrgicos é particularmente crítico pela quantidade de recursos que se precisam para a realização do mesmo. Poucos projetos têm sido desenvolvidos para a gestão completa dessas listas. O trabalho desenvolvido nesta Dissertação propõe o uso de um modelo, baseado em algoritmos genéticos com inspiração quântica, para a automatização e otimização do planejamento de procedimentos cirúrgicos eletivos. Este modelo, denominado Algoritmo Evolucionário com Inspiração Quântica para a Área de Saúde (AEIQ-AS), além de alocar os pacientes e os recursos necessários para que o processo cirúrgico seja exitoso, procura reduzir o tempo total para que todas as cirurgias sejam realizadas. Este trabalho apresenta também uma ferramenta que permite a modelagem, de modo simplificado, de uma Unidade Cirúrgica de Saúde. Esta ferramenta possibilita a realização de simulações com o objetivo de ver o efeito de diferentes configurações dos recursos nas Unidades de Saúde. Para a validação do modelo proposto foi criada, de modo artificial e fazendo uso da ferramenta de simulação, uma lista de espera de 2000 cirurgias. Caso as cirurgias fossem realizadas seguindo a ordem de chegada, seriam necessárias pouco mais de 37 semanas e teria 1066 operações fora do prazo. Foram feitos vários experimentos onde se buscava a otimização destes valores. Esta busca foi feita, primeiramente, tomando em consideração só um dos parâmetros e a continuação eles em conjunto. Na primeira abordagem o AEIQ-AS consegue a realização das mesmas cirurgias em aproximadamente 31 semanas. Assim, observa se que há uma redução de aproximadamente 16,25 porcento do tempo. O número de operações fora do prazo, por sua vez, foi reduzido pelo modelo para 927 (13,04 porcento). Na abordagem simultânea, o AEIQ-AS, consegue uma diminuição do tempo total de alocação em 16,22 porcento e o número de operações fora do prazo em 9,76 porcento. Foram feitas, também, várias simulações da Unidade de Saúde mantendo as caraterísticas da lista de cirurgias para ver seu efeito no tempo total de alocação de todos os processos cirúrgicos. / [en] Currently, Health Units in a large number of countries in the world present service demand that exceed their real capacities. For this reason, is inevitable the emergence of the waiting lists. To prepare the planning of this in an optimized manner results in a substantial challenge due to the number of resources that should be considered. The case of chirurgical procedures is particularly critical by the number of resources needed for their realization. A small quantity of projects has been developed to fully manage these lists. The work developed in this Dissertation proposes the use of a model based on evolutionary algorithms with quantum inspiration for the automation and optimization of the planning of elective chirurgical procedures. This model, denominated Evolutionary Algorithm with Quantum Inspiration for the Health Field (AEIQ-AS), beyond patients and necessary resources for the successful completion of the chirurgical procedure allocation, pursue the reduction of the total time of realization of all the surgeries. The work presents also a tool that allows the modeling, in a simplified manner, of a Chirurgical Health Unit. This tool enables the realization of simulations with the objective of seeing the effect of different configurations of the resources in the Health Units. To validate the proposed model was created, in artificial mode and employing the simulation tool, a waiting list of 2000 surgeries. In case that the surgeries were realized following the arrival order, will be needed a little more than 37 weeks and will have 1066 surgeries out of time. Several experiments were conducted in order to optimize these values. This search was executed, firstly, considering only one of the parameters and, in continuation, all together. In the first approach, the AEIQ-AS obtains the realization of the same surgeries in approximately 16,25 percent of the time. The number of operations out of time was reduced by the model to 927 (13,04 percent). In the simultaneous approach, the AEIQAS achieves a decrease of the allocation total time in 16,22 percent and the number of operations out of time in 9,76 percent. It were done, also, several simulations of the Health Unit maintaining the characteristics of the surgeries list in order to look the effect in the allocation total time of all the chirurgical procedures.

[pt] Problemas reais de engenharia mecânica podem compreender tarefas de i) otimização multi-objetivo (MO) ou ii) regressão, classificação e predição. Os métodos baseados em inteligência artificial (AI) são bastante difundidos na resolução desses problemas por i) demandarem menor custo computacional e informações do domínio do problema para a resolução de uma MO, quando comparados com métodos de programação matemática, por exemplo; e ii) apresentarem melhores resultados com estrutura mais simples, adaptabilidade e interpretabilidade, em contraste com outros métodos. Sendo assim, o presente trabalho busca i) otimizar um controle proporcional-integral-derivativo (PID) aplicado a um sistema de frenagem anti-travamento de rodas (ABS) e o projeto de trocadores de calor de placas aletadas (PFHE) e casco-tubo (STHE) através de métodos de otimização baseados AI, buscando o desenvolvimento de novas versões dos métodos aplicados, e.g. multi-objective salp swarm algorithm (MSSA) e multi-objective heuristic Kalman algorithm (MOHKA), que melhorem a performance da otimização; ii) desenvolver um sistema de detecção de vazamento em dutos (LDS) sensível ao roubo de combustível a partir do treinamento de árvores de decisão (DTs) com features baseadas no tempo e na análise de componentes principais (PCA), ambas exraídas de dados de transiente de pressão de operação normal do duto e de roubo de combustível; iii) constituir um guia de aplicação para problemas de MO de controle e projeto, processo de extração de features e treinamento de classificadores baseados em aprendizado de máquina (MLCs), através de aprendizado supervisionado; e, por fim iv) demonstrar o potencial das técnicas baseadas em AI. / [en] Real-world mechanical engineering problems may comprise tasks of i) multi-objective optimization (MO) or ii) regression, classification and prediction. The use of artificial intelligence (AI) based methods for solving these problems are widespread for i) demanding less computational cost and problem domain information to solve the MO, when compared with mathematical programming for an example; and ii) presenting better results with simpler structure, adaptability and interpretability, in contrast to other methods. Therefore, the present work seeks to i) optimize a proportional-integral-derivative control (PID) applied to an anti-lock braking system (ABS) and the heat exchanger design of plate-fin (PFHE) and shell-tube (STHE) types through AI based optimization methods, seeking to develop new versions of the applied methods, e.g. multi-objective salp swarm algorithm (MSSA) and multi-objective heuristic Kalman algorithm (MOHKA), which enhance the optimization performance; ii) develop a pipeline leak detection system (LDS) sensitive to fuel theft by training decision trees (DTs) with features based on time and principal component analysis (PCA), both extracted from pressure transient data of regular pipeline operation and fuel theft; iii) constitute an application guide for control and design MO problems, feature extraction process and machine learning classifiers (MLCs) training through supervised learning; and, finally, iv) demonstrate the potential of AI-based techniques.

<p dir="ltr">The advancement of sustainable, high-performance materials is essential for the future of innovative civil infrastructure. 3D printed (3DP) concrete has emerged as an innovative technique with the potential to revolutionize construction practices by enabling advanced and novel material performance characteristics. This dissertation focuses on addressing the key challenges associated with 3DP concrete while exploring opportunities to develop innovative, high-performance materials by leveraging the unique capabilities of additive manufacturing technology, such as design flexibility and controlled internal architecture. The research presented in this work focuses on three aspects of 3DP concrete: the development of sustainable materials for 3DP concrete with enhanced rheological properties, the evaluation of mechanical performance and anisotropic behavior in 3DP fiber-reinforced mortars, and the investigation of mechanical responses of 3DP elements featuring bio-inspired designs. The first part of the research tackles the challenge of mixture formulation of 3DP concrete, focusing on improving rheological properties and sustainability by incorporating cellulose nanomaterials and supplementary cementitious materials. This study aims to enhance the performance of the material while concurrently reducing the environmental impact of 3DP concrete, making it more viable for practical applications. Furthermore, this dissertation contributes to understanding the role of fiber reinforcement in 3DP concrete, particularly its influence on the anisotropic behavior of the material. Lastly, the research introduces a novel, nature-inspired approach by taking advantage of this intrinsic anisotropy, coupled with flexibility in filament architecture design, to develop 3DP concrete with exceptional mechanical properties that are challenging to achieve through traditional casting methods. <a href="" target="_blank">We have demonstrated the ability to make concrete both stronger and more energy-absorbing than its cast counterparts by combining clever architectures inspired by extreme animals in nature with 3D concrete printing technology.</a></p>

In der heutigen Gesellschaft verzeichnet die Nutzung persönlicher Schutzausrüstung (PSA) als Vorbeugung gegen Übergriffe einen stetig wachsenden Trend. Dieser beschränkt sich nicht mehr nur auf Polizeikräfte, sondern erstreckt sich zunehmend auf weitere Berufsgruppen, einschließlich Feuerwehr- und Rettungsdienstpersonal, Mitarbeiter im öffentlichen Dienst sowie im privaten Sektor. Der Schwerpunkt dieser Forschungsarbeit liegt auf der innovativen Entwicklung additiv gefertigter Stichschutzwesten, basierend auf bioinspirierten und skalierbaren Schutzelementen. Das Ziel ist, einen hohen Schutz gegen Messerstiche gemäß der deutschen VPAM-KDIW 2004 K1 Prüfrichtlinie mit sehr gutem Tragekomfort zu kombinieren. Im Vergleich zu herkömmlichen Westen, die oft große, starre Platten verwenden, kann diese Neuentwicklung die Akzeptanz von Schutzwesten bei Sicherheitskräften und anderen Zielgruppen erhöhen, da sie neben Schutz auch Komfort in verschiedenen Anwendungsszenarien bietet. 3-D-Körperscans dienten als Grundlage für die Erstellung der Schnittmuster der Schutzwesten und die individuelle Anpassung der Schutzstrukturen an die Körperkonturen und -krümmungen. Für die Bestimmung der Körperkrümmung wurde eine neuartige, automatisierte Methode entwickelt, die auch die Analyse und Berechnung von Körperkrümmungen in dynamischen Bewegungsszenarien ermöglicht. Verschiedene bioinspirierte Schutzstrukturen wurden mittels Fused Layer Modeling (FLM) und dem Continuous Filament Fabrication (CFF)-Verfahren gefertigt und in Anlehnung gemäß der deutschen VPAM-KDIW-Richtlinie getestet. Die Forschung konzentriert sich auf die Optimierung der Additive-Manufacturing-Prozessparameter, einschließlich der Materialauswahl, der Schichtanzahl, des Faservolumengehalts und der Faserorientierung. Die finale Materialkombination besteht aus einer Hochleistungspolymermatrix und Endlosfaserverstärkungen. Das Ergebnis ist eine stichhemmende Weste mit einem individuell gefertigten Schutzpaneel für einen Anwender. Die Ergebnisse zeigen, dass individueller Schutz durch den Einsatz des CFF-Verfahrens realisierbar ist. / In today's society, the use of Personal Protective Equipment (PPE) as a prevention against assaults is experiencing a steadily growing trend. This is no longer limited to police forces, but is increasingly extending to other occupational groups, including fire and rescue service personnel, public service employees and the private sector. The focus of this research is on the innovative development of additively manufactured stab-proof vests, based on bio-inspired and scalable protective structures. The aim is to combine a high level of protection according to the German VPAM-KDIW 2004 K1 guideline with very good wearing comfort. Compared to traditional vests, which often use large, rigid panels, this new development can increase the acceptance of protective waistcoats among security forces and other target groups, as it offers not only protection but also comfort in various application scenarios. 3-D body scans served as the basis for creating the pattern for the protective vests and individual adaptation of the protective structures to the body contours and curvatures. A new, automated method was developed for determining body curvature, which also allows for the analysis and calculation of body curvatures in dynamic movement scenarios. Various bio-inspired protective structures have been manufactured using Fused Layer Modeling (FLM) and Continuous Filament Fabrication (CFF) processes and tested according to the German VPAM-KDIW guidelines. The research focuses on optimizing the Additive Manufacturing process parameters, including material selection, number of layers, fiber volume content, and fiber orientation. The final material combination consisted of a high-performance polymer matrix and continuous fibre reinforcements. The result is a stab protection vest with a customised protective panel. The results show that customised protection can be realised using the CFF process.

Tesis por compendio / [ES] La energía, la comunicación y la informática son componentes fundamentales de la sociedad moderna, ya que sientan las bases para el desarrollo tecnológico y el crecimiento económico. La estrecha interrelación entre estos pilares se ha hecho cada vez más evidente en los últimos años, a medida que los avances en computación y análisis de datos han permitido nuevos enfoques de gestión y sostenibilidad de la energía. En este contexto, el uso eficiente de la energía se ha convertido en un objetivo clave para los investigadores, los responsables políticos y las empresas por igual. Al aprovechar el poder de las técnicas informáticas y de aprendizaje automático (ML), es posible destacar los desafíos de asegurar los sistemas de energía y optimizar el uso de la energía, lo que lleva a la necesidad de técnicas avanzadas como algoritmos bio-inspirados y redes neuronales. Esta tesis doctoral tiene como objetivo analizar los programas y estrategias de gestión de la carga, el consumo y la demanda en el panorama energético actual. El núcleo central presenta un estudio exhaustivo sobre la integración de algoritmos bio-inspirados, como la optimización de enjambres de partículas (PSO) y los modelos de redes neuronales artificiales (ANN) en los sistemas de gestión de la carga para hacer frente a los retos de la gestión de la carga y utilizar la energía de forma eficiente y segura. El cuerpo principal de esta tesis comprende tres publicaciones científicas, cada una de las cuales corresponde a una etapa distinta dentro del marco general de investigación de este estudio: la primera etapa propone un sistema de monitorización de bajo coste para aplicaciones energéticas que introduce un sistema SCADA basado en web rentable que era un del 80% más barato que una solución similar. La arquitectura de bajo coste propuesta, diseñada para bancos de pruebas de microrredes, ofrece monitorización en tiempo real, accesibilidad remota y control fácil de usar para aplicaciones académicas y de investigación. La segunda etapa combina la optimización híbrida de enjambre de partículas (PSO) en cascada con redes neuronales feed-forward para pronosticar y optimizar con precisión la demanda de energía en una microrred en AC, mejorando la integración de fuentes de energía renovables como gasificación de biomasa. Los resultados muestran que el modelo PSO-ANN propuesto tiene un rendimiento un 23,2% mejor en términos de MSE que los modelos de RNA de retropropagación feed-forward (FF-BP) y propagación directa en cascada (CF-P). La tercera y última etapa se centró en un sistema inteligente de gestión de la carga reforzado con criptografía híbrida para garantizar la comunicación protegida y la privacidad de los datos, abordando así de manera efectiva los desafíos de seguridad energética en entornos residenciales. Los resultados mostraron que el modelo propuesto de Gestión de Carga aplicado a Sistemas Residenciales de Seguridad (SRS-LM) fue un 37% mejor en rendimiento (costo de energía, utilización de energía, tiempo computacional) y con una reducción de carga máxima del 60% en comparación con un modelo de Medidor de Energía Inteligente Universal (USEM). / [CA] L'energia, la comunicació i la informàtica són components fonamentals de la societat moderna, ja que establixen les bases per al desenvolupament tecnològic i el creixement econòmic. L'estreta interrelació entre estos pilars s'ha fet cada vegada més evident en els últims anys, a mesura que els avanços en computació i anàlisi de dades han permés nous enfocaments de gestió i sostenibilitat de l'energia. En este context, l'ús eficient de l'energia s'ha convertit en un objectiu clau per als investigadors, els responsables polítics i les empreses per igual. En aprofitar el poder de les tècniques informàtiques i d'aprenentatge automàtic (ML), és possible destacar els desafiaments d'assegurar els sistemes d'energia i optimitzar l'ús de l'energia, la qual cosa porta a la necessitat de tècniques avançades com a algorismes bio-inspirats i xarxes neuronals. Esta tesi doctoral té com a objectiu analitzar els programes i estratègies de gestió de la càrrega, el consum i la demanda en el panorama energètic actual. El nucli central presenta un estudi exhaustiu sobre la integració d'algorismes bio-inspirats, com l'optimització d'eixams de partícules (PSO) i els models de xarxes neuronals artificials (ANN) en els sistemes de gestió de la càrrega per a fer front als reptes de la gestió de la càrrega i utilitzar l'energia de manera eficient i segura. El cos principal d'esta tesi comprén tres publicacions científiques, cadascuna de les quals correspon a una etapa diferent dins del marc general d'investigació d'este estudi: la primera etapa proposa un sistema de monitoratge de baix cost per a aplicacions energètiques que introduïx un sistema SCADA basat en web rendible que era un del 80% més barat que una solució similar. L'arquitectura de baix cost proposada, dissenyada per a bancs de proves de microxarxes, oferix monitoratge en temps real, accessibilitat remota i control fàcil d'usar per a aplicacions acadèmiques i d'investigació. La segona etapa combina l'optimització híbrida d'eixam de partícules (PSO) en cascada amb xarxes neuronals feed-forward per a pronosticar i optimitzar amb precisió la demanda d'energia en una microxarxa en AC, millorant la integració de fonts d'energia renovables com a gasificació de biomassa. Els resultats mostren que el model PSO-ANN proposat té un rendiment un 23,2% millor en termes de MSE que els models d'RNA de retropropagació feed-forward (FF-BP) i propagació directa en cascada (CF-P). La tercera i última etapa es va centrar en un sistema intel·ligent de gestió de la càrrega reforçat amb criptografia híbrida per a garantir la comunicació protegida i la privacitat de les dades, abordant així de manera efectiva els desafiaments de seguretat energètica en entorns residencials. Els resultats van mostrar que el model proposat de Gestió de Càrrega aplicat a Sistemes Residencials de Seguretat (SRS-LM) va ser un 37% millor en rendiment (cost d'energia, utilització d'energia, temps computacional) i amb una reducció de càrrega màxima del 60% en comparació amb un model de Mesurador d'Energia Intel·ligent Universal (USEM). / [EN] Energy, communication, and computing are critical components of modern society, providing the foundation for technological development and economic growth. The close interrelation between these pillars has become increasingly apparent in recent years, as computing and data analysis advances have enabled new energy management and sustainability approaches. In this context, efficient energy usage has become a key focus for researchers, policymakers, and businesses alike. By harnessing the power of computing and machine learning (ML) techniques, it is possible to highlight the challenges of securing energy systems and optimizing energy usage, leading to the need for advanced techniques such as bio-inspired algorithms and neural networks. This doctoral thesis aims to analyse load consumption and demand management programs and strategies in the current energy landscape. The central core presents an study on integrating bio-inspired algorithms, such as particle swarm optimization (PSO) and artificial neural networks (ANN) models in load management systems to meet load management challenges and use energy efficiently and securely. The main body of this thesis comprises three scientific publications, each corresponding to a distinct stage within the overarching research framework of this study: the first stage covers the proposal of a low-cost architecture in energy systems introducing a cost-effective web-based SCADA system that was over 80% cheaper than a similar solution. The proposed low-cost architecture, tailored for microgrid testbeds, offers real-time monitoring, remote accessibility, and user-friendly control for academic and research applications. The second stage combined a cascade hybrid Particle Swarm Optimization (PSO) with feed-forward neural networks to accurately forecast and optimize energy demand in an AC microgrid, notably enhancing the integration of renewable energy sources like biomass gasification. The results showed that the proposed PSO-ANN model performs 23.2% better in terms of MSE than Feedforward Backpropagation (FF-BP) and Cascade forward propagation (CF-P) ANN models. The third and final stage focused on a smart load management system fortified with hybrid cryptography to ensure protected communication and data privacy, thereby effectively addressing energy security challenges in residential settings. Results showed that the proposed Security Residential System Load Management (SRS-LM) model was 37% better in performance (power cost, power utilization, computational time) and with a 60% peak load reduction compared to a Universal Smart Energy Meter (USEM) model. / Chiñas Palacios, CD. (2024). Bio-Inspired Algorithms and Artificial Neural Networks Applied to Smart Load Management Systems to Optimize Energy Usage [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203128 / Compendio

This thesis addresses orchestration of servers amongst multiple co-hosted internet services such as e-Banking, e-Auction and e-Retail in hosting centres. The hosting paradigm entails levying fees for hosting third party internet services on servers at guaranteed levels of service performance. The orchestration of server ensemble in hosting centres is considered in the context of maximising the hosting centre's revenue over a lengthy time horizon. The inspiration for the server orchestration approach proposed in this thesis is drawn from nature and generally classed as swarm intelligence, specifically, sophisticated collective behaviour of social insects borne out of primitive interactions amongst members of the group to solve problems beyond the capability of individual members. Consequently, the approach is self-organising, adaptive and robust. A new scheme for server ensemble orchestration is introduced in this thesis. This scheme exploits the many similarities between server orchestration in an internet hosting centre and forager allocation in a honeybee (Apis mellifera) colony. The scheme mimics the way a honeybee colony distributes foragers amongst flower patches to maximise nectar influx, to orchestrate servers amongst hosted internet services to maximise revenue. The scheme is extended by further exploiting inherent feedback loops within the colony to introduce self-tuning and energy-aware server ensemble orchestration. In order to evaluate the new server ensemble orchestration scheme, a collection of server ensemble orchestration methods is developed, including a classical technique that relies on past history to make time varying orchestration decisions and two theoretical techniques that omnisciently make optimal time varying orchestration decisions or an optimal static orchestration decision based on complete knowledge of the future. The efficacy of the new biomimetic scheme is assessed in terms of adaptiveness and versatility. The performance study uses representative classes of internet traffic stream behaviour, service user's behaviour, demand intensity, multiple services co-hosting as well as differentiated hosting fee schedule. The biomimetic orchestration scheme is compared with the classical and the theoretical optimal orchestration techniques in terms of revenue stream. This study reveals that the new server ensemble orchestration approach is adaptive in a widely varying external internet environments. The study also highlights the versatility of the biomimetic approach over the classical technique. The self-tuning scheme improves on the original performance. The energy-aware scheme is able to conserve significant energy with minimal revenue performance degradation. The simulation results also indicate that the new scheme is competitive or better than classical and static methods.

This thesis deals with the phenomenon of Carpathian Ruthenia, or Zakarpattia, which is one of the most discussed issues in Czech literature. It expands the traditional view of the issue with a chronological survey of works by Czech authors who were inspired by Carpathian Ruthenia, and maps their writings created from the end of the 19th century to the beginning of the 21st century. It focuses on their interpretation, with particular reference to the topology. The first chapter presents the terminological problems associated with the territory of the Transcarpathian region and briefly summarizes its history. The next chapter submits an account of the exceptional nature of this area (its contrasts, periphery, regionalism, myths, exoticism, idylls and adventures), and attempts to characterize its uniqueness (backwardness, belief in superstitions, Jews, alcoholism, poachers, enchanting countryside and outlaws). Carpathian Ruthenia appears to be a place of secrecy, where hypothetical characters grow. The topology of the mountain is also an important element. The following chapter, the longest, presents most of the works by the Czech authors who were inspired by this region. The first of these authors came to Carpathian Ruthenia during the 1920s as government workers or tourists. Their works are arranged...

Terra Mirabilis is a three-movement musical composition for symphony orchestra with piano solo inspired by natural landscapes photographed by the composer. The three movement composition and its corresponding landscapes portray three times of a day: early morning (I. The Mists), evening (II. Oceanus), and late night (III. Nocturne). Each chapter is devoted to the discussion of one movement, wherein overall concept and form are addressed, followed by detailed analyses of harmonic structure, motivic and thematic development, orchestration, and representational elements. As a complement to the score and the text, a CD-R audio recording of orchestral mock-ups accompanies this dissertation.