Data driven spatio-temporal analysis of e-cargo bike network in Lisbon and its expansion: The Yoob case study

The adoption of more environmentally friendly and sustainable fleets for last-mile parcel delivery within large urban centers has been on the rise. Cargo bikes have been the most common alternative. The implementation of this type of fleet has proven to bring benefits, but has evidenced some limitations. The infrastructure network, which supports urban logistics, had to adapt to respond to the requirements of this new type of fleet. The implementation of micro-hubs and nano-hubs was the solution. Our study has two main objectives. The first objective is to perform a spatiotemporal characterization of fleet behavior, by conducting a case study where we explored the data from YOOB (a last mile delivery logistics start-up that operates in the Lisbon area and outskirts) e-cargo bike fleet. And the second is to identify potential expansion locations to the establishment of new hubs. The work procedures followed the CRIPS-DM methodology and the collected data was based on a 4-month period (January to April 2022). By adopting data science and machine learning techniques, five types of performances of YOOB fleet were identified, with variations in distances traveled, times, volumes transported and speeds. In the perspective of expanding YOOB's e-cargo bike network, three new locations in Lisbon were signaled for potential new hub installation.A adoção de frotas mais ecológicas e sustentáveis para a distribuição das encomendas na última milha dentro dos grandes centros urbanos tem vindo a crescer. As bicicletas de carga têm sido a alternativa mais comum. A implementação deste tipo de frotas, demonstrou trazer benefícios, mas evidenciou algumas limitações. A rede de infraestruturas, que serve de suporte á logística urbana, teve de se adaptar para poder responder às necessidades deste novo tipo de frotas. A implementação de microhubs e nano-hubs foram a alternativa. O nosso estudo tem dois objetivos principais. O primeiro objetivo é o de fazer uma caracterização espácio temporal dos comportamentos da frota, através de um estudo de caso onde efetuámos a exploração dos dados da frota de e-cargo bike da YOOB (start-up logística de entregas na última milha que atua na área de Lisboa e na periferia). E o segundo consiste em identificar potenciais locais de expansão para a instalação de novos hubs no mesmo estudo de caso. Nos processos de trabalho foi seguida a metodologia CRISP-DM e os dados recolhidos foram referentes a um período de 4 meses (Janeiro a Abril de 2022). Com recurso a técnicas de ciência dos dados e aprendizagem automática, foram identificados cinco tipos de desempenhos da frota da YOOB, com variações em distâncias percorridas, tempos efetuados, volumes transportados e velocidades praticadas. Numa perspetiva de expansão da rede de e-cargo bike da YOOB, forma identificados três novos locais na cidade de Lisboa para a instalação potencial de novos hubs

Sustainable Cities: A Reflection on Potentialities and Limits based on Existing Eco-Districts in Europe

The need for more sustainable cities has become a primary objective of urban strategies. The urgency for a radical transition towards sustainability in a long term-vision has brought with it several new concepts, such as smart urbanism, and models, such as smart city, eco-city, sustainable neighborhood, eco-district, etc. While these terms are fascinating and visionary, they often lack a clear definition both in terms of theoretical insight and empirical evidence. In this light, this contribution aims at defining a conceptual framework through which to further substantiate the blurred concept of eco-district and sustainable neighborhood. It does so by reviewing the concepts of smart urbanism and sustainable neighborhood/eco-districts in the literature, including also references to other well-known sustainability-oriented models of urban development. It then explores whether several indicators, emerging from the analysis of exemplary case studies of sustainable neighborhoods in Europe, can be used to clearly identify the characteristics of a sustainable approach at the district scale. The analysis, built on a review of existing literature, allows for both the clarification of several issues related to these fields of inquiry, as well as for the identification of the potential bridges to link these issues

Sensing the Cultural Significance with AI for Social Inclusion

Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes

Performance Assessment of Routing Protocols for IoT/6LoWPAN Networks

The Internet of Things (IoT) proposes a disruptive communication paradigm that allows smart objects to exchange data among themselves to reach a common goal. IoT application scenarios are multiple and can range from a simple smart home lighting system to fully controlled automated manufacturing chains. In the majority of IoT deployments, things are equipped with small devices that can suffer from severe hardware and energy restrictions that are responsible for performing data processing and wireless communication tasks. Thus, due to their features, communication networks that are used by these devices are generally categorized as Low Power and Lossy Networks (LLNs). The considerable variation in IoT applications represents a critical issue to LLN networks, which should offer support to different requirements as well as keeping reasonable quality-of-service (QoS) levels. Based on this challenge, routing protocols represent a key issue in IoT scenarios deployment. Routing protocols are responsible for creating paths among devices and their interactions. Hence, network performance and features are highly dependent on protocol behavior. Also, based on the adopted protocol, the support for some specific requirements of IoT applications may or may not be provided. Thus, a routing protocol should be projected to attend the needs of the applications considering the limitations of the device that will execute them. Looking to attend the demand of routing protocols for LLNs and, consequently, for IoT networks, the Internet Engineering Task Force (IETF) has designed and standardized the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). This protocol, although being robust and offering features to fulfill the need of several applications, still presents several faults and weaknesses (mainly related to its high complexity and memory requirement), which limits its adoption in IoT scenarios. An alternative to RPL, the Lightweight On-demand Ad Hoc Distancevector Routing Protocol – Next Generation (LOADng) has emerged as a less complicated routing solution for LLNs. However, the cost of its simplicity is paid for with the absence of adequate support for a critical set of features required for many IoT environments. Thus, based on the challenging open issues related to routing in IoT networks, this thesis aims to study and propose contributions to better attend the network requirements of IoT scenarios. A comprehensive survey, reviewing state-of-the-art routing protocols adopted for IoT, identified the strengths and weaknesses of current solutions available in the literature. Based on the identified limitations, a set of improvements is designed to overcome these issues and enhance IoT network performance. The novel solutions are proposed to include reliable and efficient support to attend the needs of IoT applications, such as mobility, heterogeneity, and different traffic patterns. Moreover, mechanisms to improve the network performance in IoT scenarios, which integrate devices with different communication technologies, are introduced. The studies conducted to assess the performance of the proposed solutions showed the high potential of the proposed solutions. When the approaches presented in this thesis were compared with others available in the literature, they presented very promising results considering the metrics related to the Quality of Service (QoS), network and energy efficiency, and memory usage as well as adding new features to the base protocols. Hence, it is believed that the proposed improvements contribute to the state-of-the-art of routing solutions for IoT networks, increasing the performance and adoption of enhanced protocols.A Internet das Coisas, do inglês Internet of Things (IoT), propõe um paradigma de comunicação disruptivo para possibilitar que dispositivos, que podem ser dotados de comportamentos autónomos ou inteligentes, troquem dados entre eles buscando alcançar um objetivo comum. Os cenários de aplicação do IoT são muito variados e podem abranger desde um simples sistema de iluminação para casa até o controle total de uma linha de produção industrial. Na maioria das instalações IoT, as “coisas” são equipadas com um pequeno dispositivo, responsável por realizar as tarefas de comunicação e processamento de dados, que pode sofrer com severas restrições de hardware e energia. Assim, devido às suas características, a rede de comunicação criada por esses dispositivos é geralmente categorizada como uma Low Power and Lossy Network (LLN). A grande variedade de cenários IoT representam uma questão crucial para as LLNs, que devem oferecer suporte aos diferentes requisitos das aplicações, além de manter níveis de qualidade de serviço, do inglês Quality of Service (QoS), adequados. Baseado neste desafio, os protocolos de encaminhamento constituem um aspecto chave na implementação de cenários IoT. Os protocolos de encaminhamento são responsáveis por criar os caminhos entre os dispositivos e permitir suas interações. Assim, o desempenho e as características da rede são altamente dependentes do comportamento destes protocolos. Adicionalmente, com base no protocolo adotado, o suporte a alguns requisitos específicos das aplicações de IoT podem ou não ser fornecidos. Portanto, estes protocolos devem ser projetados para atender as necessidades das aplicações assim como considerando as limitações do hardware no qual serão executados. Procurando atender às necessidades dos protocolos de encaminhamento em LLNs e, consequentemente, das redes IoT, a Internet Engineering Task Force (IETF) desenvolveu e padronizou o IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). O protocolo, embora seja robusto e ofereça recursos para atender às necessidades de diferentes aplicações, apresenta algumas falhas e fraquezas (principalmente relacionadas com a sua alta complexidade e necessidade de memória) que limitam sua adoção em cenários IoT. Em alternativa ao RPL, o Lightweight On-demand Ad hoc Distance-vector Routing Protocol – Next Generation (LOADng) emergiu como uma solução de encaminhamento menos complexa para as LLNs. Contudo, o preço da simplicidade é pago com a falta de suporte adequado para um conjunto de recursos essenciais necessários em muitos ambientes IoT. Assim, inspirado pelas desafiadoras questões ainda em aberto relacionadas com o encaminhamento em redes IoT, esta tese tem como objetivo estudar e propor contribuições para melhor atender os requisitos de rede em cenários IoT. Uma profunda e abrangente revisão do estado da arte sobre os protocolos de encaminhamento adotados em IoT identificou os pontos fortes e limitações das soluções atuais. Com base nas debilidades encontradas, um conjunto de soluções de melhoria é proposto para superar carências existentes e melhorar o desempenho das redes IoT. As novas soluções são propostas para incluir um suporte confiável e eficiente capaz atender às necessidades das aplicações IoT relacionadas com suporte à mobilidade, heterogeneidade dos dispositivos e diferentes padrões de tráfego. Além disso, são introduzidos mecanismos para melhorar o desempenho da rede em cenários IoT que integram dispositivos com diferentes tecnologias de comunicação. Os vários estudos realizados para mensurar o desempenho das soluções propostas mostraram o grande potencial do conjunto de melhorias introduzidas. Quando comparadas com outras abordagens existentes na literatura, as soluções propostas nesta tese demonstraram um aumento do desempenho consistente para métricas relacionadas a qualidade de serviço, uso de memória, eficiência energética e de rede, além de adicionar novas funcionalidades aos protocolos base. Portanto, acredita-se que as melhorias propostas contribuiem para o avanço do estado da arte em soluções de encaminhamento para redes IoT e aumentar a adoção e utilização dos protocolos estudados

Social Enterprise in Central and Eastern Europe

In the last two decades, the quest for a widely accepted definition of social enterprise has been a central issue in a great number of publications. The main objective of the ICSEM Project (on which this book is based) was to show that the social enterprise field would benefit much more from linking conceptualisation efforts to the huge diversity of social enterprises than from an additional and ambitious attempt at providing an encompassing definition. Starting from a hypothesis that could be termed "the impossibility of a unified definition", the ICSEM research strategy relied on bottom-up approaches to capture the social enterprise phenomenon in its local and national contexts. This strategy made it possible to take into account and give legitimacy to locally embedded approaches, while simultaneously allowing for the identification of major social enterprise models to delineate the field on common grounds at the international level. Social Enterprise in Central and Eastern Europe – the last volume in a series of four ICSEM-based books on social enterprise worldwide — will serve as a key reference and resource for teachers, researchers, students, experts, policy makers, journalists and others who want to acquire a broad understanding of the social enterprise and social entrepreneurship phenomena as they emerge and develop in this region

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903

Computer Science for Continuous Data:Survey, Vision, Theory, and Practice of a Computer Analysis System

Building on George Boole's work, Logic provides a rigorous foundation for the powerful tools in Computer Science that underlie nowadays ubiquitous processing of discrete data, such as strings or graphs. Concerning continuous data, already Alan Turing had applied "his" machines to formalize and study the processing of real numbers: an aspect of his oeuvre that we transform from theory to practice.The present essay surveys the state of the art and envisions the future of Computer Science for continuous data: natively, beyond brute-force discretization, based on and guided by and extending classical discrete Computer Science, as bridge between Pure and Applied Mathematics