Performance Optimization and Statistical Analysis of Basic Immune Simulator (BIS) Using the FLAME GPU Environment

Agent-based models (ABMs) are increasingly being used to study population dynamics in complex systems such as the human immune system. Previously, Folcik et al. developed a Basic Immune Simulator (BIS) and implemented it using the RePast ABM simulation framework. However, frameworks such as RePast are designed to execute serially on CPUs and therefore cannot efficiently handle large simulations. In this thesis, we developed a parallel implementation of immune simulator using FLAME GPU, a parallel ABM simulation framework designed to execute of Graphics Processing Units(GPUs). The parallel implementation was tested against the original RePast implementation for accuracy by running a simulation of immune response to a viral infection of generic tissue cells. Finally, a performance benchmark done against the original RePast implementation demonstrated a significant performance gain 13X for the parallel FLAME GPU implementation

A reputation framework for behavioural history: developing and sharing reputations from behavioural history of network clients

The open architecture of the Internet has enabled its massive growth and success by facilitating easy connectivity between hosts. At the same time, the Internet has also opened itself up to abuse, e.g. arising out of unsolicited communication, both intentional and unintentional. It remains an open question as to how best servers should protect themselves from malicious clients whilst offering good service to innocent clients. There has been research on behavioural profiling and reputation of clients, mostly at the network level and also for email as an application, to detect malicious clients. However, this area continues to pose open research challenges. This thesis is motivated by the need for a generalised framework capable of aiding efficient detection of malicious clients while being able to reward clients with behaviour profiles conforming to the acceptable use and other relevant policies. The main contribution of this thesis is a novel, generalised, context-aware, policy independent, privacy preserving framework for developing and sharing client reputation based on behavioural history. The framework, augmenting existing protocols, allows fitting in of policies at various stages, thus keeping itself open and flexible to implementation. Locally recorded behavioural history of clients with known identities are translated to client reputations, which are then shared globally. The reputations enable privacy for clients by not exposing the details of their behaviour during interactions with the servers. The local and globally shared reputations facilitate servers in selecting service levels, including restricting access to malicious clients. We present results and analyses of simulations, with synthetic data and some proposed example policies, of client-server interactions and of attacks on our model. Suggestions presented for possible future extensions are drawn from our experiences with simulation

Urubu: energy scavenging in wireless sensor networks

For the past years wireless sensor networks (WSNs) have been coined as one of the most promising technologies for supporting a wide range of applications. However, outside the research community, few are the people who know what they are and what they can offer. Even fewer are the ones that have seen these networks used in real world applications. The main obstacle for the proliferation of these networks is energy, or the lack of it. Even though renewable energy sources are always present in the networks environment, designing devices that can efficiently scavenge that energy in order to sustain the operation of these networks is still an open challenge. Energy scavenging, along with energy efficiency and energy conservation, are the current available means to sustain the operation of these networks, and can all be framed within the broader concept of “Energetic Sustainability”. A comprehensive study of the several issues related to the energetic sustainability of WSNs is presented in this thesis, with a special focus in today’s applicable energy harvesting techniques and devices, and in the energy consumption of commercially available WSN hardware platforms. This work allows the understanding of the different energy concepts involving WSNs and the evaluation of the presented energy harvesting techniques for sustaining wireless sensor nodes. This survey is supported by a novel experimental analysis of the energy consumption of the most widespread commercially available WSN hardware platforms.Há já alguns anos que as redes de sensores sem fios (do Inglês Wireless Sensor Networks - WSNs) têm sido apontadas como uma das mais promissoras tecnologias de suporte a uma vasta gama de aplicações. No entanto, fora da comunidade científica, poucas são as pessoas que sabem o que elas são e o que têm para oferecer. Ainda menos são aquelas que já viram a sua utilização em aplicações do dia-a-dia. O principal obstáculo para a proliferação destas redes é a energia, ou a falta dela. Apesar da existência de fontes de energia renováveis no local de operação destas redes, continua a ser um desafio construir dispositivos capazes de aproveitar eficientemente essa energia para suportar a operação permanente das mesmas. A colheita de energia juntamente com a eficiência energética e a conservação de energia, são os meios disponíveis actualmente que permitem a operação permanente destas redes e podem ser todos englobados no conceito mais amplo de “Sustentabilidade Energética”. Esta tese apresenta um estudo extensivo das várias questões relacionadas com a sustentabilidade energética das redes de sensores sem fios, com especial foco nas tecnologias e dispositivos explorados actualmente na colheita de energia e no consumo energético de algumas plataformas comercias de redes de sensores sem fios. Este trabalho permite compreender os diferentes conceitos energéticos relacionados com as redes de sensores sem fios e avaliar a capacidade das tecnologias apresentadas em suportar a operação permanente das redes sem fios. Este estudo é suportado por uma inovadora análise experimental do consumo energético de algumas das mais difundidas plataformas comerciais de redes de sensores sem fios

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

Digital Transformation

The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing.The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing

THIESEL 2020.Thermo-and Fluid Dynamic Processes in Direct Injection Engines.8th-11th September

'The THIESEL 2020 Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines planned in Valencia (Spain) for 8th to 11th September 2020 has been successfully held in a virtual format, due to the COVID19 pandemic. In spite of the very tough environmental demands, combustion engines will probably remain the main propulsion system in transport for the next 20 to 50 years, at least for as long as alternative solutions cannot provide the flexibility expected by customers of the 21st century. But it needs to adapt to the new times, and so research in combustion engines is nowadays mostly focused on the new challenges posed by hybridization and downsizing. The topics presented in the papers of the conference include traditional ones, such as Injection & Sprays, Combustion, but also Alternative Fuels, as well as papers dedicated specifically to CO2 Reduction and Emissions Abatement.Papers stem from the Academic Research sector as well as from the IndustryXandra Marcelle, M.; Desantes Fernández, JM. (2020). THIESEL 2020.Thermo-and Fluid Dynamic Processes in Direct Injection Engines.8th-11th September. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/150759EDITORIA