The impact of mechanical compression on cortical microtubules in Arabidopsis: a quantitative pipeline

Exogenous mechanical perturbations on living tissues are commonly used to investigate whether cell effectors can respond to mechanical cues. However, in most of these experiments, the applied mechanical stress and/or the biological response are described only qualitatively. We developed a quantitative pipeline based on microindentation and image analysis to investigate the impact of a controlled and prolonged compression on microtubule behaviour in the Arabidopsis shoot apical meristem, using microtubule fluorescent marker lines. We found that a compressive stress, in the order of magnitude of turgor pressure, induced apparent microtubule bundling. Importantly, that response could be reversed several hours after the release of compression. Next, we tested the contribution of microtubule severing to compression-induced bundling: microtubule bundling seemed less pronounced in the katanin mutant, in which microtubule severing is dramatically reduced. Conversely, some microtubule bundles could still be observed 16 hours after the release of compression in the spiral2 mutant, in which severing rate is instead increased. To quantify the impact of mechanical stress on anisotropy and orientation of microtubule arrays, we used the nematic tensor based FibrilTool ImageJ/Fiji plugin. To assess the degree of apparent bundling of the network, we developed several methods, some of which were borrowed from geostatistics. The final microtubule bundling response could notably be related to tissue growth velocity that was recorded by the indenter during compression. Because both input and output are quantified, this pipeline is an initial step towards correlating more precisely the cytoskeleton response to mechanical stress in living tissues.

Avaliação do desempenho das entidades gestoras dos serviços de resíduos urbanos em Portugal

Dissertação de Mestrado, Gestão Empresarial, Faculdade de Economia, Universidade do Algarve, 2016

Estudo comparativo do desempenho de controladores robustos aplicados a um sistema de tanques acoplados.

Currently the uncertain system has attracted much academic community from the standpoint of scientific research and also practical applications. A series of mathematical approaches emerge in order to troubleshoot the uncertainties of real physical systems. In this context, the work presented here focuses on the application of control theory in a nonlinear dynamical system with parametric variations in order and robustness. We used as the practical application of this work, a system of tanks Quanser associates, in a configuration, whose mathematical model is represented by a second order system with input and output (SISO). The control system is performed by PID controllers, designed by various techniques, aiming to achieve robust performance and stability when subjected to parameter variations. Other controllers are designed with the intention of comparing the performance and robust stability of such systems. The results are obtained and compared from simulations in Matlab-simulink.

Gestão das universidades federais brasileiras : um estudo sobre a eficiência do ensino superior no Brasil

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Departamento de Economia, 2015.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Best-practice model for technology enhanced learning in the creative arts

This paper presents a best-practice model for the redesign of virtual learning environments (VLEs) within creative arts to augment blended learning. In considering a blended learning best-practice model, three factors should be considered: the conscious and active human intervention, good learning design and pedagogical input, and the sensitive handling of the process by trained professionals. This study is based on a comprehensive VLE content analysis conducted across two academic schools within the creative arts at one Post-92 higher education (HE) institution. It was found that four main barriers affect the use of the VLE within creative arts: lack of flexibility in relation to navigation and interface, time in developing resources, competency level of tutors (confidence in developing online resources balanced against other flexible open resources) and factors affecting the engagement of ‘digital residents’. The experimental approach adopted in this study involved a partnership between the learning technology advisor and academic staff, which resulted in a VLE best-practice model that focused directly on improving aesthetics and navigation. The approach adopted in this study allowed a purposive sample of academic staff to engage as participants, stepping back cognitively from their routine practices in relation to their use of the VLE and questioning approaches to how they embed the VLE to support teaching and learning. The model presented in this paper identified a potential solution to overcome the challenges of integrating the VLE within creative arts. The findings of this study demonstrate positive impact on staff and student experience and provide a sustainable model of good practice for the redesign of the VLE within creative disciplines.

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.

An Input-Process-Output Approach to Interorganizational Teams: The Influence of Work Group Diversity, Trust and Shared Leadership on Communication Network and Team Outputs.

The market’s challenges bring firms to collaborate with other organizations in order to create Joint Ventures, Alliances and Consortia that are defined as “Interorganizational Networks” (IONs) (Provan, Fish and Sydow; 2007). Some of these IONs are managed through a shared partecipant governance (Provan and Kenis, 2008): a team composed by entrepreneurs and/or directors of each firm of an ION. The research is focused on these kind of management teams and it is based on an input-process-output model: some input variables (work group’s diversity, intra-team's friendship network density) have a direct influence on the process (team identification, shared leadership, interorganizational trust, team trust and intra-team's communication network density), which influence some team outputs, individual innovation behaviors and team effectiveness (team performance, work group satisfaction and ION affective commitment). Data was collected on a sample of 101 entrepreneurs grouped in 28 ION’s government teams and the research hypotheses are tested trough the path analysis and the multilevel models. As expected trust in team and shared leadership are positively and directly related to team effectiveness while team identification and interorganizational trust are indirectly related to the team outputs. The friendship network density among the team’s members has got positive effects on the trust in team and on the communication network density, and also, through the communication network density it improves the level of the teammates ION affective commitment. The shared leadership and its effects on the team effectiveness are fostered from higher level of team identification and weakened from higher level of work group diversity, specifically gender diversity. Finally, the communication network density and shared leadership at the individual level are related to the frequency of individual innovative behaviors. The dissertation’s results give a wider and more precise indication about the management of interfirm network through “shared” form of governance.

Optimal input states and feedback for interferometric phase estimation

We derive optimal N-photon two-mode input states for interferometric phase measurements. Under canonical measurements the phase variance scales as N-2 for these states, as compared to N-1 or N-1/2 for states considered bq previous authors. We prove, that it is not possible to realize the canonical measurement by counting photons in the outputs of the interferometer, even if an adjustable auxiliary phase shift is allowed in the interferometer. However. we introduce a feedback algorithm based on Bayesian inference to control this auxiliary phase shift. This makes the measurement close to a canonical one, with a phase variance scaling slightly above N-2. With no feedback, the best result (given that the phase to be measured is completely unknown) is a scaling of N-1. For optimal input states having up to four photons, our feedback scheme is the best possible one, but for higher photon numbers more complicated schemes perform marginally better.

Coreleased orexin and glutamate evoke nonredundant spike outputs and computations in histamine neurons.

Stable wakefulness requires orexin/hypocretin neurons (OHNs) and OHR2 receptors. OHNs sense diverse environmental cues and control arousal accordingly. For unknown reasons, OHNs contain multiple excitatory transmitters, including OH peptides and glutamate. To analyze their cotransmission within computational frameworks for control, we optogenetically stimulated OHNs and examined resulting outputs (spike patterns) in a downstream arousal regulator, the histamine neurons (HANs). OHR2s were essential for sustained HAN outputs. OHR2-dependent HAN output increased linearly during constant OHN input, suggesting that the OHN→HAN(OHR2) module may function as an integral controller. OHN stimulation evoked OHR2-dependent slow postsynaptic currents, similar to midnanomolar OH concentrations. Conversely, glutamate-dependent output transiently communicated OHN input onset, peaking rapidly then decaying alongside OHN→HAN glutamate currents. Blocking glutamate-driven spiking did not affect OH-driven spiking and vice versa, suggesting isolation (low cross-modulation) of outputs. Therefore, in arousal regulators, cotransmitters may translate distinct features of OHN activity into parallel, nonredundant control signals for downstream effectors.

Integrating structural and input design of a 2-DOF high-speed parallel manipulator: A flexible model-based approach

This paper discusses the integrated design of parallel manipulators, which exhibit varying dynamics. This characteristic affects the machine stability and performance. The design methodology consists of four main steps: (i) the system modeling using flexible multibody technique, (ii) the synthesis of reduced-order models suitable for control design, (iii) the systematic flexible model-based input signal design, and (iv) the evaluation of some possible machine designs. The novelty in this methodology is to take structural flexibilities into consideration during the input signal design; therefore, enhancing the standard design process which mainly considers rigid bodies dynamics. The potential of the proposed strategy is exploited for the design evaluation of a two degree-of-freedom high-speed parallel manipulator. The results are experimentally validated. (C) 2010 Elsevier Ltd. All rights reserved.

Comparison between DC(+) and Square Wave AC SAW Current Outputs to Weld AISI 304 for Low-Temperature Applications

Welded equipment for cryogenic applications is utilized in chemical, petrochemical, and metallurgical industries. One material suitable for cryogenic application is austenitic stainless steel, which usually doesn`t present ductile/brittle transition temperature, except in the weld metal, where the presence of ferrite and micro inclusions can promote a brittle failure, either by ferrite cleavage or dimple nucleation and growth, respectively. A 25-mm- (1-in.-) thick AISI 304 stainless steel base metal was welded with the SAW process using a 308L solid wire and two kinds of fluxes and constant voltage power sources with two types of electrical outputs: direct current electrode positive and balanced square wave alternating current. The welded joints were analyzed by chemical composition, microstructure characterization, room temperature mechanical properties, and CVN impact test at -100 degrees C (-73 degrees F). Results showed that an increase of chromium and nickel content was observed in all weld beads compared to base metal. The chromium and nickel equivalents ratio for the weld beads were always higher for welding with square wave AC for the two types of fluxes than for direct current. The modification in the Cr(eq)/Ni(eq) ratio changes the delta ferrite morphology and, consequently, modifies the weld bead toughness at lower temperatures. The oxygen content can also affect the toughness in the weld bead. The highest absorbed energy in a CVN impact test was obtained for the welding condition with square wave AC electrical output and neutral flux, followed by DC(+) electrical output and neutral flux, and square wave AC electrical output and alloyed flux.

Robust model predictive controller with output feedback and target tracking

A model predictive controller (MPC) is proposed, which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered as the case of open-loop stable systems, where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended to the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The method is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state-space model that is built using past output measurements and past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low-order multivariable system.

A Functional Verification Methodology Based on Parameter Domains for Efficient Input Stimuli Generation and Coverage Modeling

Modern Integrated Circuit (IC) design is characterized by a strong trend of Intellectual Property (IP) core integration into complex system-on-chip (SOC) architectures. These cores require thorough verification of their functionality to avoid erroneous behavior in the final device. Formal verification methods are capable of detecting any design bug. However, due to state explosion, their use remains limited to small circuits. Alternatively, simulation-based verification can explore hardware descriptions of any size, although the corresponding stimulus generation, as well as functional coverage definition, must be carefully planned to guarantee its efficacy. In general, static input space optimization methodologies have shown better efficiency and results than, for instance, Coverage Directed Verification (CDV) techniques, although they act on different facets of the monitored system and are not exclusive. This work presents a constrained-random simulation-based functional verification methodology where, on the basis of the Parameter Domains (PD) formalism, irrelevant and invalid test case scenarios are removed from the input space. To this purpose, a tool to automatically generate PD-based stimuli sources was developed. Additionally, we have developed a second tool to generate functional coverage models that fit exactly to the PD-based input space. Both the input stimuli and coverage model enhancements, resulted in a notable testbench efficiency increase, if compared to testbenches with traditional stimulation and coverage scenarios: 22% simulation time reduction when generating stimuli with our PD-based stimuli sources (still with a conventional coverage model), and 56% simulation time reduction when combining our stimuli sources with their corresponding, automatically generated, coverage models.

Submarine groundwater discharge and associated chemical input to a coastal sea

This paper presents a theoretical model of flow and chemical transport processes in subterranean estuaries (unconfined brackish groundwater aquifers at the ocean-land interface). The model shows that groundwater circulation and oscillating flow, caused by wave setup and tide, may constitute up to 96% of submarine groundwater discharge (SGWD) compared with 4% due to the net groundwater discharge. While these local flow processes do not change the total amount of land-derived chemical input to the ocean over a long period (e.g., yearly), they induce fluctuations of the chemical transfer rate as the aquifer undergoes saltwater intrusion. This may result in a substantial increase in chemical fluxes to the ocean over a short period (e.g., monthly and by a factor of 20 above the averaged level), imposing a possible threat to the marine environment. These results are essentially consistent with the experimental findings of Moore [1996] and have important implications for coastal resources management.