Pharmaceutical detailing is moving to online environments because they are believed to save money, time, and be more convenient. Recently there has also been an increase in e-detailing due to the circumstances around COVID-19 pandemic. In the available literature, the transition to e-detailing is mostly evaluated through efficiency markers, with less focus on broader social implications and the interactions between people involved. To fill this gap and promote development of human-centred e-detailing technologies, this paper explores detailing interactions and related concerns which can guide the development of relevant detailing technologies. The field research and participatory Magic Machine workshops helped reveal and shape concerns which have implications for the future of e-detailing. High-level recommendations were derived to inspire further research and development of lasting, user-centred solutions. Design fiction artifacts were created to convey the research findings as boundary objects between pharmaceutical industry and academia. / Läkemedelskonsulenter flyttar sin verksamhet alltmer till en digital miljö då det anses spara pengar, tid och ska vara mer bekvämt. Covid-19 pandemin har också bidragit till en ökning av användandet av digitala verktyg för läkemedelskonsulenter. Tillgängliga vetenskapliga artiklar inom ämnet som utvärderar digitaliseringen för läkemedelskonsulenter gör det framför allt genom att mäta hur effektivt det är, och inte så mycket på de sociala följderna samt hur det inverkar på interaktionen mellan de inblandade parterna. För att fylla denna kunskapslucka och för att främja en människocentrerad digitalisering för läkemedelskonsulenter utforskar denna masteruppsats interaktionerna och dess relaterade följder, vilket kan användas som vägledning i utveckling av relevanta teknologier för läkemedelskonsulenter. Fältarbete, och en workshop som använde metoden Magic Machine, hjälpte till att ta fram och klargöra de problem som kan ha en viktig inverkan på framtida digitalt läkemedelskonsulterande. Design rekommendationer togs fram för att inspirera kommande forskning och utveckling av hållbara, användarcentrerade lösningar. Den skapade spekulativa designen visar upp forskningsresultat och på ett sätt som kan vara till användning av läkemedelsindustrin och universiteten.

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This thesis discusses the aero-acoustic characterisation of a perforate sample using a three-port technique. A rectangular T-junction with a flush mounted perforated sample at the intersection form the acoustical three-port. Under acoustic excitation from three different directions a direct method of impedance determination is incorporated to experimentally determine the passive acoustic properties of the perforate. The three-port scattering matrix and the normalised transfer impedance are calculated in the presence of grazing flow and for high-level excitation and the behaviour of these characteristics is studied. Validation of the determined results in the linear range is carried out by comparing it with existing models. Moreover, based on the experimental results for low grazing flow velocities the dependence of the real part of the transfer impedance on the grazing flow parameters as well as dimensionless numbers is described, and a semi-empirical model quantifying the behaviour is proposed. Furthermore, the thesis explains some experimental errors pertaining to standing wave patterns and operating conditions, and corrections are suggested to reduce the errors. / <p>QC 221007</p>

The complexity of the software behind autonomous systems is rapidly growing, as are the applications of what they can do. It is not unusual for the lines of code to reach the millions, which adds to the verification challenge. The machine learning algorithms involved are often "black boxes" where the precise workings are not known by the developer applying them, and their behavior is undefined when encountering an untrained scenario. With so much code, the possibility of bugs or malicious code is considerable. An approach is developed to monitor and possibly override the behavior of autonomous systems independent of the software controlling them. Application-isolated safety monitors are implemented in configurable hardware to ensure that the behavior of an autonomous system is limited to what is intended. The sensor inputs may be shared with the software, but the output from the monitors is only engaged when the system violates its prescribed behavior. For each specific rule the system is expected to follow, a monitor is present processing the relevant sensor information. The behavior is defined in linear temporal logic (LTL) and the associated monitors are implemented in a field programmable gate array (FPGA). An off-the-shelf drone is used to demonstrate the effectiveness of the monitors without any physical modifications to the drone. Upon detection of a violation, appropriate corrective actions are persistently enforced on the autonomous system. / Master of Science / Autonomous systems are surprisingly vulnerable, not just from malicious hackers, but from design errors and oversights. The lines of code required can quickly climb into the millions, and the artificial decision algorithms can be inscrutable and fully dependent upon the information they are trained on. These factors cause the verification of the core software running our autonomous cars, drones, and everything else to be prohibitively difficult by traditional means. Independent safety monitors are implemented to provide internal oversight for these autonomous systems. A semi-automatic design process efficiently creates error-free monitors from safety rules drones need to follow. These monitors remain separate and isolated from the software typically controlling the system, but use the same sensor information. They are embedded in the circuitry and act as their own small, task-specific processors watching to make sure a particular rule is not violated; otherwise, they take control of the system and force corrective behavior. The monitors are added to a consumer off-the-shelf (COTS) drone to demonstrate their effectiveness. For every rule monitored, an override is triggered when they are violated. Their effectiveness depends on reliable sensor information as with any electronic component, and the completeness of the rules detailing these monitors.

Cyber-physical systems are networked through IT infrastructure and susceptible to malware. Threats targeting process control are much more safety-critical than traditional computing systems since they jeopardize the integrity of physical infrastructure. Existing defence mechanisms address security at the network nodes but do not protect the physical infrastructure if network integrity is compromised. An interface guardian architecture is implemented on cyber-physical control leaf nodes to maintain process integrity by enforcing high-level safety and stability policies. Preemptive detection schemes are implemented to monitor process behavior and anticipate malicious activity before process safety and stability are compromised. Autonomic properties are employed to automatically protect process integrity by initiating switch-over to a verified backup controller. Subsystems adhere to strict trust requirements safeguarding them from adversarial intrusion. The preemptive detection schemes, switch-over logic, backup controller, and process communication are all trusted components that are separated from the untrusted production controller. The proposed architecture is applied to a rotary inverted pendulum experiment and implemented on a Xilinx Zynq-7000 configurable SoC. The leaf node implementation is integrated into a cyber-physical control topology. Simulated attack scenarios show strengthened resilience to both network integrity and reconfiguration attacks. Threats attempting to disrupt process behavior are successfully thwarted by having a backup controller maintain process stability. The system ensures both safety and liveness properties even under adversarial conditions. / Master of Science

In der formalen Verifikation digitaler Schaltkreise hat sich die Methodik der vollständigen Verifikation anhand spezieller Operationseigenschaften bewährt. Operationseigenschaften beschreiben das Verhalten einer Schaltung in einem festen Zeitintervall und können sequentiell miteinander verknüpft werden, um so das Gesamtverhalten zu spezifizieren. Zusätzlich beweist eine formale Vollständigkeitsprüfung, dass die Menge der Eigenschaften für jede Folge von Eingangssignalwerten die Ausgänge der zu verifizierenden Schaltung eindeutig und lückenlos determiniert. In dieser Arbeit wird untersucht, wie aus Operationseigenschaften, deren Vollständigkeit erfolgreich bewiesen wurde, automatisiert eine Schaltungsbeschreibung abgeleitet werden kann. Gegenüber der traditionellen Entwurfsmethodik auf Register-Transfer-Ebene (RTL) bietet dieses Verfahren zwei Vorteile. Zum einen vermeidet der Vollständigkeitsbeweis viele Arten von Entwurfsfehlern, zum anderen ähnelt eine Beschreibung mit Hilfe von Operationseigenschaften den in Spezifikationen häufig genutzten Zeitdiagrammen, sodass die Entwurfsebene der Spezifikationsebene angenähert wird und Fehler durch manuelle Verfeinerungsschritte vermieden werden. Das Entwurfswerkzeug vhisyn führt die High-Level-Synthese (HLS) einer vollständigen Menge von Operationseigenschaften zu einer Beschreibung auf RTL durch. Die Ergebnisse zeigen, dass sowohl die verwendeten Synthesealgorithmen, als auch die erzeugten Schaltungen effizient sind und somit die Realisierung größerer Beispiele zulassen. Anhand zweier Fallstudien kann dies praktisch nachgewiesen werden. / The complete verification approach using special operation properties is an accepted methodology for the formal verification of digital circuits. Operation properties describe the behavior of a circuit during a certain time interval. They can be sequentially concatenated in order to specify the overall behavior. Additionally, a formal completeness check proves that the sequence of properties consistently determines the exact value of the output signals for every valid sequence of input signal values. This work examines how a circuit description can be automatically derived from a set of operation properties whose completeness has been proven. In contrast to the traditional design flow at register-transfer level (RTL), this method offers two advantages. First, the prove of completeness helps to avoid many design errors. Second, the design of operation properties resembles the design of timing diagrams often used in textual specifications. Therefore, the design level is closer to the specification level and errors caused by refinement steps are avoided. The design tool vhisyn performs the high-level synthesis from a complete set of operation properties to a description at RTL. The results show that both the synthesis algorithms and the generated circuit descriptions are efficient and allow the design of larger applications. This is demonstrated by means of two case studies.

With the emergence of Industry 4.0, there is a pronounced emphasis on the necessity for enhanced flexibility in assembly processes. In the domain of bolt-tightening, this transition is evident. Tools are now required to navigate a variety of bolts and unpredictable tightening methodologies. Each bolt, possessing distinct tightening parameters, necessitates a specific sequence to prevent issues like bolt cross-talk or unbalanced force. This thesis introduces an approach that integrates advanced computing techniques with machine learning to address these challenges in the tightening areas. The primary objective is to offer edge computation for bolt detection and tightening tools' precise localization. It is realized by leveraging visual-inertial data, all encapsulated within a System-on-Chip (SoC) Field Programmable Gate Array (FPGA). The chosen approach combines visual information and motion detection, enabling tools to quickly and precisely do the localization of the tool. All the computing is done inside the SoC FPGA. The key element for identifying different bolts is the YOLOv3-Tiny-3L model, run using the Deep-learning Processor Unit (DPU) that is implemented in the FPGA. In parallel, the thesis employs the Error-State Extended Kalman Filter (ESEKF) algorithm to fuse the visual and motion data effectively. The ESEKF is accelerated via a full implementation in Register Transfer Level (RTL) in the FPGA fabric. We examined the empirical outcomes and found that the visual-inertial localization exhibited a Root Mean Square Error (RMSE) position of 39.69 mm and a standard deviation of 9.9 mm. The precision in orientation determination yields a mean error of 4.8 degrees, offset by a standard deviation of 5.39 degrees. Notably, the entire computational process, from the initial bolt detection to its final localization, is executed in 113.1 milliseconds. This thesis articulates the feasibility of executing bolt detection and visual-inertial localization using edge computing within the SoC FPGA framework. The computation trajectory is significantly streamlined by harnessing the adaptability of programmable logic within the FPGA. This evolution signifies a step towards realizing a more adaptable and error-resistant bolt-tightening procedure in industrial areas. / Med framväxten av Industry 4.0, finns det en uttalad betoning på nödvändigheten av ökad flexibilitet i monteringsprocesser. Inom området bultåtdragning är denna övergång tydlig. Verktyg krävs nu för att navigera i en mängd olika bultar och oförutsägbara åtdragningsmetoder. Varje bult, som har distinkta åtdragningsparametrar, kräver en specifik sekvens för att förhindra problem som bultöverhörning eller obalanserad kraft. Detta examensarbete introducerar ett tillvägagångssätt som integrerar avancerade datortekniker med maskininlärning för att hantera dessa utmaningar i skärpningsområdena. Det primära målet är att erbjuda kantberäkning för bultdetektering och åtdragningsverktygs exakta lokalisering. Det realiseras genom att utnyttja visuella tröghetsdata, allt inkapslat i en System-on-Chip (SoC) Field Programmable Gate Array (FPGA). Det valda tillvägagångssättet kombinerar visuell information och rörelsedetektering, vilket gör det möjligt för verktyg att snabbt och exakt lokalisera verktyget. All beräkning sker inuti SoC FPGA. Nyckelelementet för att identifiera olika bultar är YOLOv3-Tiny-3L-modellen, som körs med hjälp av Deep-learning Processor Unit (DPU) som är implementerad i FPGA. Parallellt använder avhandlingen algoritmen Error-State Extended Kalman Filter (ESEKF) för att effektivt sammansmälta visuella data och rörelsedata. ESEKF accelereras via en fullständig implementering i Register Transfer Level (RTL) i FPGA-strukturen. Vi undersökte de empiriska resultaten och fann att den visuella tröghetslokaliseringen uppvisade en Root Mean Square Error (RMSE) position på 39,69 mm och en standardavvikelse på 9,9 mm. Precisionen i orienteringsbestämningen ger ett medelfel på 4,8 grader, kompenserat av en standardavvikelse på 5,39 grader. Noterbart är att hela beräkningsprocessen, från den första bultdetekteringen till dess slutliga lokalisering, exekveras på 113,1 millisekunder. Denna avhandling artikulerar möjligheten att utföra bultdetektering och visuell tröghetslokalisering med hjälp av kantberäkning inom SoC FPGA-ramverket. Beräkningsbanan är avsevärt effektiviserad genom att utnyttja anpassningsförmågan hos programmerbar logik inom FPGA. Denna utveckling innebär ett steg mot att förverkliga en mer anpassningsbar och felbeständig skruvdragningsprocedur i industriområden.

Machine learning is evidenced as a research area with the main purpose of developing computational methods that are capable of learning with their previously acquired experiences. Although a large amount of machine learning techniques has been proposed and successfully applied in real systems, there are still many challenging issues, which need be addressed. In the last years, an increasing interest in techniques based on complex networks (large-scale graphs with nontrivial connection patterns) has been verified. This emergence is explained by the inherent advantages provided by the complex network representation, which is able to capture the spatial, topological and functional relations of the data. In this work, we investigate the new features and possible advantages offered by complex networks in the machine learning domain. In fact, we do show that the network-based approach really brings interesting features for supervised, semisupervised, and unsupervised learning. Specifically, we reformulate a previously proposed particle competition technique for both unsupervised and semisupervised learning using a stochastic nonlinear dynamical system. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition to that, data reliability issues are explored in semisupervised learning. Such matter has practical importance and is found to be of little investigation in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this work, we propose a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the semantic meaning of the data, but also is able to improve the performance of traditional classification techniques. Finally, it is expected that this study will contribute, in a relevant manner, to the machine learning area / Aprendizado de máquina figura-se como uma área de pesquisa que visa a desenvolver métodos computacionais capazes de aprender com a experiência. Embora uma grande quantidade de técnicas de aprendizado de máquina foi proposta e aplicada, com sucesso, em sistemas reais, existem ainda inúmeros problemas desafiantes que necessitam ser explorados. Nos últimos anos, um crescente interesse em técnicas baseadas em redes complexas (grafos de larga escala com padrões de conexão não triviais) foi verificado. Essa emergência é explicada pelas inerentes vantagens que a representação em redes complexas traz, sendo capazes de capturar as relações espaciais, topológicas e funcionais dos dados. Nesta tese, serão investigadas as possíveis vantagens oferecidas por redes complexas quando utilizadas no domínio de aprendizado de máquina. De fato, será mostrado que a abordagem por redes realmente proporciona melhorias nos aprendizados supervisionado, semissupervisionado e não supervisionado. Especificamente, será reformulada uma técnica de competição de partículas para o aprendizado não supervisionado e semissupervisionado por meio da utilização de um sistema dinâmico estocástico não linear. Em complemento, uma análise analítica de tal modelo será desenvolvida, permitindo o entendimento evolucional do modelo no tempo. Além disso, a questão de confiabilidade de dados será investigada no aprendizado semissupervisionado. Tal tópico tem importância prática e é pouco estudado na literatura. Com o objetivo de validar essas técnicas em problemas reais, simulações computacionais em bases de dados consagradas pela literatura serão conduzidas. Ainda nesse trabalho, será proposta uma técnica híbrica de classificação supervisionada que combina tanto o aprendizado de baixo como de alto nível. O termo de baixo nível pode ser implementado por qualquer técnica de classificação tradicional, enquanto que o termo de alto nível é realizado pela extração das características de uma rede construída a partir dos dados de entrada. Nesse contexto, aquele classifica as instâncias de teste segundo qualidades físicas, enquanto que esse estima a conformidade da instância de teste com a formação de padrões dos dados. Os estudos aqui desenvolvidos mostram que o método proposto pode melhorar o desempenho de técnicas tradicionais de classificação, além de permitir uma classificação de acordo com o significado semântico dos dados. Enfim, acredita-se que este estudo possa gerar contribuições relevantes para a área de aprendizado de máquina.

The focus of this work is to investigate the dissolution of MW25, a non-radioactive simulant of UK high-level nuclear waste borosilicate glass, and to predict its performance in the near field of a geological repository. A single-pass flow-through (SPFT) experimental system was used to measure the forward dissolution rates of MW25. Experiments were conducted in two parts. Experiment Part 1 considers the dissolution of the waste glass in deionised water at 40 and 90 oC and circum-neutral pH. Experiment Part 2 considers the dissolution of the waste glass in simulant groundwaters, with similar compositions to groundwaters of Callovo-Oxfordian clay (lower-strength sedimentary rock (LSSR)) and Borrowdale Volcanic Group rocks (higher-strength rock (HSR)), at 40 oC and pH 7. The forward dissolution rate measured in deionised water was found to be approximately one order of magnitude higher at 90 oC than at 40 oC. A similar release was observed for Si, Mg and Al at 40 oC and 90 oC, whereas the B, Cs, Na, Li and Mo showed an order of magnitude increase when the temperature was increased from 40 to 90 oC for low q/S values. The activation energy (Ea) of the reactions shows that the dissolution process is a surface phenomenon. At 90 oC the net effect of the processes governing MW25 dissolution led to the preferential release of boron and alkali metals relative to the release of Si during the transient dissolution stage, accompanied by an increase in the concentration of silicic acid. This suggests that the solution activity of silicic acid at a higher temperature has a weak influence on the release of the mobile elements. The forward dissolution rate measured in LSSR simulant groundwater was found to be slightly higher than that measured in HSR simulant groundwater. The dissolution behaviour of MW25 in both groundwaters is consistent with its behaviour in deionised water at 40 oC, with the dissolution rates of elements increasing as flow rates were increased. However, forward dissolution rates measured in the simulant groundwaters were lower than the forward dissolution rates measured in deionised water under these experimental conditions. This is attributable to the interaction of the components of the simulant groundwaters with the glass, as revealed by post-reaction surface analyses, and a consequential lower alkalinity of the leachates collected in the experiments with simulant groundwater than in deionised water. Reactive chemical transport simulations of waste glass dissolution and radionuclide release in a hypothetical near field were conducted over a time span of a million years with GoldSim. The results showed that enclosing the waste glass in a steel canister covered by a copper canister and emplacing the waste package in a granite host rock is optimal for the long-term isolation of the radionuclides. The waste glass was found to play a significant role in the overall performance of the near field. This study features a new method for estimating the surface area of reacted glass powder more accurately than the geometric surface area estimate, which is the preferred standard method among researchers.

Le calcul scientifique parallèle est un domaine en plein essor qui permet à la fois d’augmenter la vitesse des longs traitements, de traiter des problèmes de taille plus importante ou encore des problèmes plus précis. Ce domaine permet donc d’aller plus loin dans les calculs scientifiques, d’obtenir des résultats plus pertinents, car plus précis, ou d’étudier des problèmes plus volumineux qu’auparavant. Dans le monde plus particulier de la simulation numérique scientifique, la résolution d’équations aux dérivées partielles (EDP) est un calcul particulièrement demandeur de ressources parallèles. Si les ressources matérielles permettant le calcul parallèle sont de plus en plus présentes et disponibles pour les scientifiques, à l’inverse leur utilisation et la programmation parallèle se démocratisent difficilement. Pour cette raison, des modèles de programmation parallèle, des outils de développement et même des langages de programmation parallèle ont vu le jour et visent à simplifier l’utilisation de ces machines. Il est toutefois difficile, dans ce domaine dit du “parallélisme implicite”, de trouver le niveau d’abstraction idéal pour les scientifiques, tout en réduisant l’effort de programmation. Ce travail de thèse propose tout d’abord un modèle permettant de mettre en oeuvre des solutions de parallélisme implicite pour les simulations numériques et la résolution d’EDP. Ce modèle est appelé “Structured Implicit Parallelism for scientific SIMulations” (SIPSim), et propose une vision au croisement de plusieurs types d’abstraction, en tentant de conserver les avantages de chaque vision. Une première implémentation de ce modèle, sous la forme d’une librairie C++ appelée SkelGIS, est proposée pour les maillages cartésiens à deux dimensions. Par la suite, SkelGIS, et donc l’implémentation du modèle, est étendue à des simulations numériques sur les réseaux (permettant l’application de simulations représentant plusieurs phénomènes physiques). Les performances de ces deux implémentations sont évaluées et analysées sur des cas d’application réels et complexes et démontrent qu’il est possible d’obtenir de bonnes performances en implémentant le modèle SIPSim. / Parallel scientific computations is an expanding domain of computer science which increases the speed of calculations and offers a way to deal with heavier or more accurate calculations. Thus, the interest of scientific computations increases, with more precised results and bigger physical domains to study. In the particular case of scientific numerical simulations, solving partial differential equations (PDEs) is an especially heavy calculation and a perfect applicant to parallel computations. On one hand, it is more and more easy to get an access to very powerfull parallel machines and clusters, but on the other hand parallel programming is hard to democratize, and most scientists are not able to use these machines. As a result, high level programming models, framework, libraries, languages etc. have been proposed to hide technical details of parallel programming. However, in this “implicit parallelism” field, it is difficult to find the good abstraction level while keeping a low programming effort. This thesis proposes a model to write implicit parallelism solutions for numerical simulations such as mesh-based PDEs computations. This model is called “Structured Implicit Parallelism for scientific SIMulations” (SIPSim), and proposes an approach at the crossroads of existing solutions, taking advantage of each one. A first implementation of this model is proposed, as a C++ library called SkelGIS, for two dimensional Cartesian meshes. A second implementation of the model, and an extension of SkelGIS, proposes an implicit parallelism solution for network-simulations (which deals with simulations with multiple physical phenomenons), and is studied in details. A performance analysis of both these implementations is given on real case simulations, and it demonstrates that the SIPSim model can be implemented efficiently.