Developing smart services by Internet of Things in manufacturing business

Manufacturing companies have passed from selling uniquely tangible products to adopting a service-oriented approach to generate steady and continuous revenue streams. Nowadays, equipment and machine manufacturers possess technologies to track and analyze product-related data for obtaining relevant information from customers’ use towards the product after it is sold. The Internet of Things on Industrial environments will allow manufacturers to leverage lifecycle product traceability for innovating towards an information-driven services approach, commonly referred as “Smart Services”, for achieving improvements in support, maintenance and usage processes. The aim of this study is to conduct a literature review and empirical analysis to present a framework that describes a customer-oriented approach for developing information-driven services leveraged by the Internet of Things in manufacturing companies. The empirical study employed tools for the assessment of customer needs for analyzing the case company in terms of information requirements and digital needs. The literature review supported the empirical analysis with a deep research on product lifecycle traceability and digitalization of product-related services within manufacturing value chains. As well as the role of simulation-based technologies on supporting the “Smart Service” development process. The results obtained from the case company analysis show that the customers mainly demand information that allow them to monitor machine conditions, machine behavior on different geographical conditions, machine-implement interactions, and resource and energy consumption. Put simply, information outputs that allow them to increase machine productivity for maximizing yields, save time and optimize resources in the most sustainable way. Based on customer needs assessment, this study presents a framework to describe the initial phases of a “Smart Service” development process, considering the requirements of Smart Engineering methodologies.

Motion Planning and Controls with Safety and Temporal Constraints

Motion planning, or trajectory planning, commonly refers to a process of converting high-level task specifications into low-level control commands that can be executed on the system of interest. For different applications, the system will be different. It can be an autonomous vehicle, an Unmanned Aerial Vehicle(UAV), a humanoid robot, or an industrial robotic arm. As human machine interaction is essential in many of these systems, safety is fundamental and crucial. Many of the applications also involve performing a task in an optimal manner within a given time constraint. Therefore, in this thesis, we focus on two aspects of the motion planning problem. One is the verification and synthesis of the safe controls for autonomous ground and air vehicles in collision avoidance scenarios. The other part focuses on the high-level planning for the autonomous vehicles with the timed temporal constraints. In the first aspect of our work, we first propose a verification method to prove the safety and robustness of a path planner and the path following controls based on reachable sets. We demonstrate the method on quadrotor and automobile applications. Secondly, we propose a reachable set based collision avoidance algorithm for UAVs. Instead of the traditional approaches of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking control set design for the aircraft to avoid collision. We apply our approach to collision avoidance scenarios of quadrotors and fixed-wing aircraft. In the second aspect of our work, we address the high level planning problems with timed temporal logic constraints. Firstly, we present an optimization based method for path planning of a mobile robot subject to timed temporal constraints, in a dynamic environment. Temporal logic (TL) can address very complex task specifications such as safety, coverage, motion sequencing etc. We use metric temporal logic (MTL) to encode the task specifications with timing constraints. We then translate the MTL formulae into mixed integer linear constraints and solve the associated optimization problem using a mixed integer linear program solver. We have applied our approach on several case studies in complex dynamical environments subjected to timed temporal specifications. Secondly, we also present a timed automaton based method for planning under the given timed temporal logic specifications. We use metric interval temporal logic (MITL), a member of the MTL family, to represent the task specification, and provide a constructive way to generate a timed automaton and methods to look for accepting runs on the automaton to find an optimal motion (or path) sequence for the robot to complete the task.

Exploration of the dendritic cell algorithm with the duration calculus

As one of the newest members in Articial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the eld of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-time capability of the DCA. To assess such capability, formal methods in the research of real-time systems can be employed. The ndings of the assessment can provide guideline for the future development of the algorithm. Therefore, in this paper we use an interval logic based method, named the Duration Calcu- lus (DC), to specify a simplied single-cell model of the DCA. Based on the DC specications with further induction, we nd that each individual cell in the DCA can perform its function as a detector in real-time. Since the DCA can be seen as many such cells operating in parallel, it is potentially capable of performing real-time detection. However, the analysis process of the standard DCA constricts its real-time capability. As a result, we conclude that the analysis process of the standard DCA should be replaced by a real-time analysis component, which can perform periodic analysis for the purpose of real-time detection.

EXISTENCE OF UNUSED MANAGED CAPACITY ON DEDICATED LANES AND AN ALTERNATIVE ON HOW TO SELL IT VIA AUCTIONS

This dissertation verifies whether the following two hypotheses are true: (1) High-occupancy/toll lanes (and therefore other dedicated lanes) have capacity that could still be used; (2) such unused capacity (or more precisely, “unused managed capacity”) can be sold successfully through a real-time auction. To show that the second statement is true, this dissertation proposes an auction-based metering (ABM) system, that is, a mechanism that regulates traffic that enters the dedicated lanes. Participation in the auction is voluntary and can be skipped by paying the toll or by not registering to the new system. This dissertation comprises the following four components: a measurement of unused managed capacity on an existing HOT facility, a game-theoretic model of an ABM system, an operational description of the ABM system, and a simulation-based evaluation of the system. Some other and more specific contributions of this dissertation include the following: (1) It provides a definition and a methodology for measuring unused managed capacity and another important variable referred as “potential volume increase”. (2) It proves that the game-theoretic model has a unique Bayesian Nash equilibrium. (3) And it provides a specific road design that can be applied or extended to other facilities. The results provide evidence that the hypotheses are true and suggest that the ABM system would benefit a public operator interested in reducing traffic congestion significantly, would benefit drivers when making low-reliability trips (such as work-to-home trips), and would potentially benefit a private operator interested in raising revenue.

Proposta de método para avaliação do desempenho térmico de residências unifamiliares em clima quente e úmido

Nowadays, evaluation methods to measure thermal performance of buildings have been developed in order to improve thermal comfort in buildings and reduce the use of energy with active cooling and heating systems. However, in developed countries, the criteria used in rating systems to asses the thermal and energy performance of buildings have demonstrated some limitations when applied to naturally ventilated building in tropical climates. The present research has as its main objective to propose a method to evaluate the thermal performance of low-rise residential buildings in warm humid climates, through computational simulation. The method was developed in order to conceive a suitable rating system for the athermal performance assessment of such buildings using as criteria the indoor air temperature and a thermal comfort adaptive model. The research made use of the software VisualDOE 4.1 in two simulations runs of a base case modeled for two basic types of occupancies: living room and bedroom. In the first simulation run, sensitive analyses were made to identify the variables with the higher impact over the cases´ thermal performance. Besides that, the results also allowed the formulation of design recommendations to warm humid climates toward an improvement on the thermal performance of residential building in similar situations. The results of the second simulation run was used to identify the named Thermal Performance Spectrum (TPS) of both occupancies types, which reflect the variations on the thermal performance considering the local climate, building typology, chosen construction material and studied occupancies. This analysis generates an index named IDTR Thermal Performance Resultant Index, which was configured as a thermal performance rating system. It correlates the thermal performance with the number of hours that the indoor air temperature was on each of the six thermal comfort bands pre-defined that received weights to measure the discomfort intensity. The use of this rating system showed to be appropriated when used in one of the simulated cases, presenting advantages in relation to other evaluation methods and becoming a tool for the understanding of building thermal behavior

A GEOBIA methodology for fragmented agricultural landscapes

Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

Time-of-use electricity tariffs with smart meters

This paper proposes a method for scheduling tariff time periods for electricity consumers. Europe will see a broader use of modern smart meters for electricity at residential consumers which must be used for enabling demand response. A heuristic-based method for tariff time period scheduling and pricing is proposed which considers different consumer groups with parameters studied a priori, taking advantage of demand response potential for each group and the fairness of electricity pricing for all consumers. This tool was applied to the case of Portugal, considering the actual network and generation costs, specific consumption profiles and overall electricity low voltage demand diagram. The proposed method achieves valid results. Its use will provide justification for the setting of tariff time periods by energy regulators, network operators and suppliers. It is also useful to estimate the consumer and electric sector benefits from changes in tariff time periods.

Simulação computacional como ferramenta de auxílio ao projeto: aplicação em edifícios naturalmente ventilados no clima de Natal/RN

Natural air ventilation is the most import passive strategy to provide thermal comfort in hot and humid climates and a significant low energy strategy. However, the natural ventilated building requires more attention with the architectural design than a conventional building with air conditioning systems, and the results are less reliable. Therefore, this thesis focuses on softwares and methods to predict the natural ventilation performance from the point of view of the architect, with limited resource and knowledge of fluid mechanics. A typical prefabricated building was modelled due to its simplified geometry, low cost and occurrence at the local campus. Firstly, the study emphasized the use of computational fluid dynamics (CFD) software, to simulate the air flow outside and inside the building. A series of approaches were developed to make the simulations possible, compromising the results fidelity. Secondly, the results of CFD simulations were used as the input of an energy tool, to simulate the thermal performance under different rates of air renew. Thirdly, the results of temperature were assessed in terms of thermal comfort. Complementary simulations were carried out to detail the analyses. The results show the potentialities of these tools. However the discussions concerning the simplifications of the approaches, the limitations of the tools and the level of knowledge of the average architect are the major contribution of this study

Estudio molecular en dos hermanas afectadas por ictiosis congénita autosómica recesiva : descripción de una nueva mutación causal en TGM1

Se describe la variante homocigota c.320-2A>G de TGM1 en dos hermanas con ictiosis congénita autosómica recesiva. El clonaje de los transcritos generados por esta variante permitió identificar tres mecanismos moleculares de splicing alternativos.

Leaf water potential and sap flow as indicators of water stress in Crimson ‘seedless’ grapevines under different irrigation strategies

Vitis vinifera L. cv. Crimson Seedless is a late season red table grape developed in 1989, with a high market value and increasingly cultivated under protected environments to extend the availability of seedless table grapes into the late fall. The purpose of this work was to evaluate leaf water potential and sap flow as indicators of water stress in Crimson Seedless vines under standard and reduced irrigation strategy, consisting of 70 % of the standard irrigation depth. Additionally, two sub-treatments were applied, consisting of normal irrigation throughout the growing season and a short irrigation induced stress period between veraison and harvest. Leaf water potential measurements coherently signaled crop-available water variations caused by different irrigation treatments, suggesting that this plant-based method can be reliably used to identify water-stress conditions. The use of sap flow density data to establish a ratio based on a reference ‘well irrigated vine’ and less irrigated vines can potentially be used to signal differences in the transpiration rates, which may be suitable for improving irrigation management strategies while preventing undesirable levels of water stress. Although all four irrigation strategies resulted in the production of quality table grapes, significant differences (p ≤ 0.05) were found in both berry weight and sugar content between the standard irrigation and reduced irrigation treatments. Reduced irrigation increased slightly the average berry size as well as sugar content and technical maturity index. The 2-week irrigation stress period had a negative effect on these parameters.

Leaf water potential and sap flow as indicators of water stress in Crimson 'seedless' grapevines under different irrigation strategies

Abstract Vitis vinifera L. cv. Crimson Seedless is a late season red table grape developed in 1989, with a high market value and increasingly cultivated under protected environments to extend the availability of seedless table grapes into the late fall. The purpose of this work was to evaluate leaf water potential and sap flow as indicators of water stress in Crimson Seedless vines under standard and reduced irrigation strategy, consisting of 70 % of the standard irrigation depth. Additionally, two sub-treatments were applied, consisting of normal irrigation throughout the growing season and a short irrigation induced stress period between veraison and harvest. Leaf water potential measurements coherently signaled crop-available water variations caused by different irrigation treatments, suggesting that this plant-based method can be reliably used to identify water-stress conditions. The use of sap flow density data to establish a ratio based on a reference ‘well irrigated vine’ and less irrigated vines can potentially be used to signal differences in the transpiration rates, which may be suitable for improving irrigation management strategies while preventing undesirable levels of water stress. Although all four irrigation strategies resulted in the production of quality table grapes, significant differences (p ≤ 0.05) were found in both berry weight and sugar content between the standard irrigation and reduced irrigation treatments. Reduced irrigation increased slightly the average berry size as well as sugar content and technical maturity index. The 2-week irrigation stress period had a negative effect on these parameters.

The dark and bright side of remote work: social isolation, concentration, and performance of remote workers

This dissertation, comprised of three separate studies, focuses on the relationship between remote work adoption and employee job performance, analyzing employee social isolation and job concentration as the main mediators of this relationship. It also examines the impact of concern about COVID-19 and emotional stability as moderators of these relationships. Using a survey-based method in an emergency homeworking context, the first study found that social isolation had a negative effect on remote work productivity and satisfaction, and that COVID-19 concerns affected this relationship differently for individuals with high and low levels of concern. The second study, a diary study analyzing hybrid workers, found a positive correlation between work from home (WFH) adoption and job performance through social isolation and job concentration, with emotional stability serving respectively as a buffer and booster in the relationships between WFH and the mediators. The third study, even in this case a diary study of hybrid workers, confirmed the benefits of work from home on job performance and the importance of job concentration as a mediator, while suggesting that social isolation may not be significant when studying employee job performance, but it is relevant for employee well-being. Although each study provides autonomously a discussion and research and practical implications, this dissertation also presents a general discussion on remote work and its psychological implications, highlighting areas for future research

Left Ventricle Modification After Endovascular Aortic Repair

INTRODUCTION Endograft deployment is a well-known cause of arterial stiffness increase as well as arterial stiffness increase represent a recognized cardiovascular risk factor. A harmful effect on cardiac function induced by the endograft deployment should be investigated. Aim of this study was to evaluate the impact of endograft deployment on the arterial stiffness and cardiac geometry of patients treated for aortic aneurysm in order to detect modifications that could justify an increased cardiac mortality at follow-up. MATHERIALS AND METHODS Over a period of 3 years, patients undergoing elective EVAR for infrarenal aortic pathologies in two university centers in Emilia Romagna were examined. All patients underwent pre-operative and six-months post-operative Pulse Wave Velocity (PWV) examination using an ultrasound-based method performed by vascular surgeons together with trans-thoracic echocardiography examination in order to evaluate cardiac chambers geometry before and after the treatment. RESULTS 69 patients were enrolled. After 36 months, 36 patients (52%) completed the 6 months follow-up examination.The ultrasound-based carotid-femoral PWV measurements performed preoperatively and 6 months after the procedure revealed a significant postoperative increase of cf-PWV (11,6±3,6 m/sec vs 12,3±8 m/sec; p.value:0,037).Postoperative LVtdV (90±28,3 ml/m2 vs 99,1±29,7 ml/m2; p.value:0.031) LVtdVi (47,4±15,9 ml/m2 vs 51,9±14,9 ml/m2; p.value:0.050), IVStd (12±1,5 mm vs 12,1±1,3 mm; p.value:0,027) were significantly increased if compared with preoperative measures.Postoperative E/A (0,76±0,26 vs 0,6±0,67; p.value:0,011), E’ lateral (9,5±2,6 vs 7,9±2,6; p.value:0,024) and A’ septal (10,8±1,5 vs 8,9±2; p.value0,005) were significantly reduced if compared with preoperative measurements CONCLUSION The endovascular treatment of the abdominal aorta causes an immediate and significant increase of the aortic stiffness.This increase reflects negatively on patients’ cardiac geometry inducing left ventricle hypertrophy and mild diastolic disfunction after just 6 months from endograft’s implantation.Further investigations and long-term results are necessary to access if this negative remodeling could affect the cardiac outcome of patient treated using the endovascular approach.

Analysis of earthquakes predictability

Earthquake prediction is a complex task for scientists due to the rare occurrence of high-intensity earthquakes and their inaccessible depths. Despite this challenge, it is a priority to protect infrastructure, and populations living in areas of high seismic risk. Reliable forecasting requires comprehensive knowledge of seismic phenomena. In this thesis, the development, application, and comparison of both deterministic and probabilistic forecasting methods is shown. Regarding the deterministic approach, the implementation of an alarm-based method using the occurrence of strong (fore)shocks, widely felt by the population, as a precursor signal is described. This model is then applied for retrospective prediction of Italian earthquakes of magnitude M≥5.0,5.5,6.0, occurred in Italy from 1960 to 2020. Retrospective performance testing is carried out using tests and statistics specific to deterministic alarm-based models. Regarding probabilistic models, this thesis focuses mainly on the EEPAS and ETAS models. Although the EEPAS model has been previously applied and tested in some regions of the world, it has never been used for forecasting Italian earthquakes. In the thesis, the EEPAS model is used to retrospectively forecast Italian shallow earthquakes with a magnitude of M≥5.0 using new MATLAB software. The forecasting performance of the probabilistic models was compared to other models using CSEP binary tests. The EEPAS and ETAS models showed different characteristics for forecasting Italian earthquakes, with EEPAS performing better in the long-term and ETAS performing better in the short-term. The FORE model based on strong precursor quakes is compared to EEPAS and ETAS using an alarm-based deterministic approach. All models perform better than a random forecasting model, with ETAS and FORE models showing better performance. However, to fully evaluate forecasting performance, prospective tests should be conducted. The lack of objective tests for evaluating deterministic models and comparing them with probabilistic ones was a challenge faced during the study.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.