Production and characterization of electrospun composite fibers: confinement of thermosensitive microgels

Materials engineering focuses on the assembly of materials´ properties to design new products with the best performance. By using sub-micrometer size materials in the production of composites, it is possible to obtain objects with properties that none of their compounds show individually. Once three-dimensional materials can be easily customized to obtain desired properties, much interest has been paid to nanostructured poly-mers in order to build biocompatible devices. Over the past years, the thermosensitive microgels have become more common in the framework of bio-materials with potential applicability in therapy and/or diagnostics. In addition, high aspect ratio biopolymers fibers have been produced using the cost-effective method called electrospinning. Taking advantage of both microgels and electrospun fibers, surfaces with enhanced functionalities can be obtained and, therefore employed in a wide range of applications. This dissertation reports on the confinement of stimuli-responsive microgels through the colloidal electro-spinning process. The process mainly depends on the composition, properties and patterning of the precur-sor materials within the polymer jet. Microgels as well as the electrospun non-woven mats were investigated to correlate the starting materials with the final morphology of the composite fibers. PNIPAAm and PNIPAAm/Chitosan thermosensitive microgels with different compositions were obtained via surfactant free emulsion polymerization (SFEP) and characterized in terms of chemical structure, morphology, thermal sta-bility, swelling properties and thermosensitivity. Finally, the colloidal electrospinning method was carried out from spinning solutions composed of the stable microgel dispersions (up to a concentration of about 35 wt. % microgels) and a polymer solution of PEO/water/ethanol mixture acting as fiber template solution. The confinement of microgels was confirmed by Scanning Electron Microscopy (SEM). The electrospinning process was statistically analysed providing the optimum set of parameters aimed to minimize the fiber diameter, which give rise to electrospun nanofibers of PNIPAAm microgels/PEO with a mean fiber diameter of 63 ± 25 nm.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches.

An agent based architecture to support monitoring in plug and produce manufacturing systems using knowledge extraction

In recent years a set of production paradigms were proposed in order to capacitate manufacturers to meet the new market requirements, such as the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production standardized consumables. These new paradigms advocate solutions capable of facing these requirements, empowering manufacturing systems with a high capacity to adapt along with elevated flexibility and robustness in order to deal with disturbances, like unexpected orders or malfunctions. Evolvable Production Systems propose a solution based on the usage of modularity and self-organization with a fine granularity level, supporting pluggability and in this way allowing companies to add and/or remove components during execution without any extra re-programming effort. However, current monitoring software was not designed to fully support these characteristics, being commonly based on centralized SCADA systems, incapable of re-adapting during execution to the unexpected plugging/unplugging of devices nor changes in the entire system’s topology. Considering these aspects, the work developed for this thesis encompasses a fully distributed agent-based architecture, capable of performing knowledge extraction at different levels of abstraction without sacrificing the capacity to add and/or remove monitoring entities, responsible for data extraction and analysis, during runtime.

Hypertonic/hyperoncotic solution in hypovolemic patients: experience in the emergency room

Hypertonic solutions have been studied extensively in the treatment of hypovolemic shock, both in experimental and clinical models. Safety, efficacy, and long-term effects on animals and patients have been evaluated. The present article reviews indications, safety, mortality rates, and outcome in patients with hemorrhagic hypovolemic shock who were treated after admission with a hypertonic/hyperoncotic solution under strict observation in the emergency room.

Contribution to drug discovery and development for tauopathies using yeast as a model

This work aimed to contribute to drug discovery and development (DDD) for tauopathies, while expanding our knowledge on this group of neurodegenerative disorders, including Alzheimer’s disease (AD). Using yeast, a recognized model for neurodegeneration studies, useful models were produced for the study of tau interaction with beta-amyloid (Aβ), both AD hallmark proteins. The characterization of these models suggests that these proteins co-localize and that Aβ1-42, which is toxic to yeast, is involved in tau40 phosphorylation (Ser396/404) via the GSK-3β yeast orthologue, whereas tau seems to facilitate Aβ1-42 oligomerization. The mapping of tau’s interactome in yeast, achieved with a tau toxicity enhancer screen using the yeast deletion collection, provided a novel framework, composed of 31 genes, to identify new mechanisms associated with tau pathology, as well as to identify new drug targets or biomarkers. This genomic screen also allowed to select the yeast strain mir1Δ-tau40 for development of a new GPSD2TM drug discovery screening system. A library of unique 138 marine bacteria extracts, obtained from the Mid-Atlantic Ridge hydrothermal vents, was screened with mir1Δ-tau40. Three extracts were identified as suppressors of tau toxicity and constitute good starting points for DDD programs. mir1Δ strain was sensitive to tau toxicity, relating tau pathology with mitochondrial function. SLC25A3, the human homologue of MIR1, codes for the mitochondrial phosphate carrier protein (PiC). Resorting to iRNA, SLC25A3 expression was silenced in human neuroglioma cells, as a first step towards the engineering of a neural model for replicating the results obtained in yeast. This model is essential to understand the mechanisms of tau toxicity at the mitochondrial level and to validate PiC as a relevant drug target. The set of DDD tools here presented will foster the development of innovative and efficacious therapies, urgently needed to cope with tau-related disorders of high human and social-economic impact.

Bias in returns to tenure when firm wages and employment comove: A quantitative assessment and solution

It is well known that, unless worker-firm match quality is controlled for, returns to firm tenure (RTT) estimated directly via reduced form wage (Mincer) equations will be biased. In this paper we show that even if match quality is properly controlled for there is a further pervasive source of bias, namely the co-movement of firm employment and firm wages. In a simple mechanical model where human capital is absent and separation is exogenous we show that positively covarying shocks (either aggregate or firm level) to firms employment and wages cause downward bias in OLS regression estimates of RTT. We show that the long established procedures for dealing with "traditional" RTT bias do not circumvent the additional problem we have identified. We argue that if a reduced form estimation of RTT is undertaken, firm-year fixed effects must be added in order to eliminate this bias. Estimates from two large panel datasets from Portugal and Germany show that the bias is empirically important. Adding firm-year fixed effects to the regression increases estimates of RTT in the two respective countries by between 3.5% and 4.5% of wages at 20 years of tenure over 80% (50%) of the estimated RTT level itself. The results extend to tenure correlates used in macroeconomics such as the minimum unemployment rate since joining the firm. Adding firm-year fixed effects changes estimates of these effects also.

How aspiring filmmakers cope with unexpected events on set

Making the transition between plans and unexpected occurrences is something organizations are used to doing every day. However, not much is known about how actors cope with unanticipated events and how they accommodate them within predefined schedules. In this study, we draw on an inductive analysis of aspiring filmmakers’ film sets to elaborate on how they plan their shooting activities every day, only to adjust them when unforeseen complications arise. We discover that film crews anchor their expectations for the day based on a planned shooting schedule, yet they incorporate a built-in assumption that it will inevitably be disrupted. We argue that they resort to triage processes and “troubleshooting protocols” that help decipher incoming problems. Familiar problems are solved by making use of experience obtained from past situations, whereas unprecedented problems are solved through a tacit protocol used as a tool to quickly devise an appropriate game plan. This study contributes to the literature on sense-making and provides valuable information about the unexplored world of filmmaking.

Regret associated with past and future messes under a maximizing mind-set

Promotions can make you happy if you get the “best” deal or miserable if you miss it. Previous research on this topic has shown that people favor products associated with a past miss to products associated with a future miss, and people in a maximizing mind-set, i.e. people who search for the best in different domains, feel more regret in a consumption domain. This research confirms that consumers prefer purchasing a product associated with a past miss (Experiments 1 and 2) and that regret levels are higher when participants come across the future miss, under the maximizing mind-set (Experiment 2). These studies add to the notion that information on regret might prompt people to make decisions towards a more optimistic outcome.

Structured business solution for infrastructure projects

Companies seeking to continue growing and developing need to consider the possibility of international expansion and what that represents to their future. To tackle this challenge it becomes necessary to establish the company’s interests and priorities as well as defining and assessing the foreign market opportunities of a specific industry. This directed research internship proposes and conducts a simulation of the preliminary foreign market assessment and selection within the Juncker Plan: country filtering and ranking having as a frame of reference the second largest construction company in Brazil, Andrade Gutierrez.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Phenanthroimidazoles as fluorimetric and colourimetric chemosensors in aqueous solution

2,4,5-Triaryl-imidazoles are versatile compounds with application in medicine, due to their biological activity, and materials sciences, for their interesting optical properties. These properties can be tuned by careful selection of substituents at positions 2, 4 and 5: replacement of the aryl group by an heterocyclic group results in larger π-conjugated systems with improved optical properties for application in nonlinear optics, OLEDs, DNA intercalators, and chemosensors. Moreover, it is expected that introducing more conjugation and rigidity into the resulting system will further improve its properties. The development of chromo/fluorescent probes that are capable of detecting ions with high sensitivity and selectivity in aqueous media is currently a topic of strong interest and the design of heteroditopic receptors that contain two or more different binding sites for the simultaneous complexation of cationic and anionic guests is a emerging field of supramolecular chemistry. In this communication, we report the synthesis of new phenanthroimidazoles substituted at position 2 with arylthienyl or arylfuryl moieties possessing substituents of different electronic character, in order to tune the chromo/fluoro response in the presence of relevant anions and metal cations. Their photophysical properties and chemosensory ability were studied in acetonitrile and mixtures of acetonitrile and water, and selective detection of cyanide was achieved in aqueous mixtures for some of the derivatives.

Predictive control of a current-source inverter for solar photovoltaic grid interface

Solar photovoltaic systems are an increasing option for electricity production, since they produce electrical energy from a clean renewable energy resource, and over the years, as a result of the research, their efficiency has been increasing. For the interface between the dc photovoltaic solar array and the ac electrical grid is necessary the use of an inverter (dc-ac converter), which should be optimized to extract the maximum power from the photovoltaic solar array. In this paper is presented a solution based on a current-source inverter (CSI) using continuous control set model predictive control (CCS-MPC). All the power circuits and respective control systems are described in detail along the paper and were tested and validated performing computer simulations. The paper shows the simulation results and are drawn several conclusions.

A spatial multicriteria model for urban accessibility mapping

Accessibility is nowadays an important issue for the development of cities. It is seen as a priority in order toguarantee equal access to fundamental rights, to improve the quality of life of citizens and to ensure that everyone, regardless of age, mobility or ability, have equal access to all the resources and benefits cities have to offer. Consequently, factors closely related to the accessibility have gained a higher relevance for identifying and assessing the location of urban facilities. The main goal of the paper is to present an accessibility evaluation model applied in Santarém, in Brazil, a city located midway between the larger cities of Belem and Manaus. The research instruments, sampling method and data analysis proposed for mapping urban accessibility are described. Daily activities were used to identify and group key destinations. The model was implemented within a geographic information system and integrates the individualâ s perspective through the definition of each key destination weight, reflecting their significance for daily activities in the urban area. Accessibility to key destinations was mapped over 24 districts of the city of Santarém. The results of this model application can support city administration decision-making for new investments in order to improve urban quality of life.

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.