Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support

As climate change continues to impact socio-ecological systems, tools that assist conservation managers to understand vulnerability and target adaptations are essential. Quantitative assessments of vulnerability are rare because available frameworks are complex and lack guidance for dealing with data limitations and integrating across scales and disciplines. This paper describes a semi-quantitative method for assessing vulnerability to climate change that integrates socio-ecological factors to address management objectives and support decision-making. The method applies a framework first adopted by the Intergovernmental Panel on Climate Change and uses a structured 10-step process. The scores for each framework element are normalized and multiplied to produce a vulnerability score and then the assessed components are ranked from high to low vulnerability. Sensitivity analyses determine which indicators most influence the analysis and the resultant decision-making process so data quality for these indicators can be reviewed to increase robustness. Prioritisation of components for conservation considers other economic, social and cultural values with vulnerability rankings to target actions that reduce vulnerability to climate change by decreasing exposure or sensitivity and/or increasing adaptive capacity. This framework provides practical decision-support and has been applied to marine ecosystems and fisheries, with two case applications provided as examples: (1) food security in Pacific Island nations under climate-driven fish declines, and (2) fisheries in the Gulf of Carpentaria, northern Australia. The step-wise process outlined here is broadly applicable and can be undertaken with minimal resources using existing data, thereby having great potential to inform adaptive natural resource management in diverse locations.

Evolution of factors affecting mechanical olive harvesting

Harvest efficiency is defined as the percentage of fruits harvested by total production. The percentage of fruits harvested is less than 100% when working with trunk shakers to detach olives. It is important to increase the percentage of fruits harvested in order to increase farmer’s income. This objective can be achieved knowing the evolution of the main factors affecting fruit detachment. Fruit removal force (FRF), fruit weight (P) and the ratio between them are important for harvest efficiency. Field trials took place for two years (2013-2014) in Vilariça Valley, northeast Portugal in an olive orchard with ‘Cobrançosa Transmontana’ cultivar. It was adopted a mechanical harvesting system based on a trunk shaker to detach fruits, and an inverted umbrella to collect fruits. Elementary operation times were measured in seconds to evaluate work rates. FRF and P were measured in the ripening period, to evaluate their evolution. In this paper are presented the preliminary results of the ratio FRF (fruit removal force)/fruit weight evolution during the ripening period (P) and the results of the equipment work rate (trees h-1). The ratio FRF/P has predominantly descendant values in the weeks before harvest, from 140 to 80 as a result of a FRF downward variation from 4.9 to 2.94 N and an upward variation of P from 0.0294 to 0.0637 N. The FRF/P ratio stabilizes the decline in the last week of November just before harvesting, registering in some cases a slight increase in consequence of FRF increase higher than P increase (contrary to the tendency of previous weeks). Equipment work rate showed values between 40 and 57 trees h-1, confirming previous results.

Characterization of patients undergoing noninvasive ventilation admitted in unit intermediate care

Non-invasive ventilation (NIV) is the application of a ventilatory support without resorting to invasive methods. Today it’s considered a credible therapeutic option, with enough scientiic evidence to support its application in various situations and clinical settings related to the treatment of acute respiratory disease, as well as chronic respiratory disease. Objectives: Characterize patients undergoing NIV admitted in Unit Intermediate Care (ICU) in the period from October 1st 2015 to June 30th 2016. Methods: Prospective study conducted in ICU between October 2015 and June 2016. In this study were included all patients hospitalized in this unit (ICU) and in that time period a sample of 57 participants was obtained. As data collection instruments we used a questionnaire for sociodemographic and clinical data and the Braden scale. Results: Participants were mostly male 38 (66.7%), the average age 69.5 ± 11.3 years, ranging between 43 and 92 years. They weighed on average 76.6 kg (52 and 150), with an average body mass index of 28.5 kg/m2 (20 to 58.5). With skin intact 28 (49.1%) with abnormal perfusion 12 (21.1%), with altered sensitivity 11 (19.3%) and a high risk of ulcer on the scale of Braden 37 (65%). The admission diagnosis was respiratory failure 33 (57.3%) and had different backgrounds. We used reused mask 53 (93.0%), the average time of NIV was 7.1 days (1-28), 4.8 days of hospitalization (1-18) and an average of 7.8 IPAP pressure. 11 (19.3%) of the participants developed face ulcer pressure.Conclusions: The NIV is used in patients with advanced age, obesity, respiratory failure and high risk of face ulcer development.

Adaptive lighting design as a holistic approach to public lighting

Dissertação para obtenção do grau de Doutor em Design, apresentada na Universidade de Lisboa - Faculdade de Arquitetura.

Improved mechanical stability of HKUST-1 in confined nanospace

One of the main concerns in the technological application of several metal–organic frameworks (MOFs) relates to their structural instability under pressure (after a conforming step). Here we report for the first time that mechanical instability can be highly improved via nucleation and growth of MOF nanocrystals in the confined nanospace of activated carbons.

Effects of infiltration pressure on mechanical properties of Al–12Si/graphite composites for piston engines

In this work results for the flexural strength and the thermal properties of interpenetrated graphite preforms infiltrated with Al-12wt%Si are discussed and compared to those for packed graphite particles. To make this comparison relevant, graphite particles of four sizes in the range 15–124 μm, were obtained by grinding the graphite preform. Effects of the pressure applied to infiltrate the liquid alloy on composite properties were investigated. In spite of the largely different reinforcement volume fractions (90% in volume in the preform and around 50% in particle compacts) most properties are similar. Only the Coefficient of Thermal Expansion is 50% smaller in the preform composites. Thermal conductivity of the preform composites (slightly below 100 W/m K), may be increased by reducing the graphite content, alloying, or increasing the infiltration pressure. The strength of particle composites follows Griffith criterion if the defect size is identified with the particle diameter. On the other hand, the composites strength remains increasing up to unusually high values of the infiltration pressure. This is consistent with the drainage curves measured in this work. Mg and Ti additions are those that produce the most significant improvements in performance. Although extensive development work remains to be done, it may be concluded that both mechanical and thermal properties make these materials suitable for the fabrication of piston engines.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

CHARACTERIZATION OF THERMAL AND MECHANICAL PROPERTIES OF POLYPROPYLENE–BASED COMPOSITES FOR FUEL CELL BIPOLAR PLATES AND DEVELOPMENT OF EDUCATIONAL TOOLS IN HYDROGEN AND FUEL CELL TECHNOLOGIES

In this project we developed conductive thermoplastic resins by adding varying amounts of three different carbon fillers: carbon black (CB), synthetic graphite (SG) and multi–walled carbon nanotubes (CNT) to a polypropylene matrix for application as fuel cell bipolar plates. This component of fuel cells provides mechanical support to the stack, circulates the gases that participate in the electrochemical reaction within the fuel cell and allows for removal of the excess heat from the system. The materials fabricated in this work were tested to determine their mechanical and thermal properties. These materials were produced by adding varying amounts of single carbon fillers to a polypropylene matrix (2.5 to 15 wt.% Ketjenblack EC-600 JD carbon black, 10 to 80 wt.% Asbury Carbons’ Thermocarb TC-300 synthetic graphite, and 2.5 to 15 wt.% of Hyperion Catalysis International’s FIBRILTM multi-walled carbon nanotubes) In addition, composite materials containing combinations of these three fillers were produced. The thermal conductivity results showed an increase in both through–plane and in–plane thermal conductivities, with the largest increase observed for synthetic graphite. The Department of Energy (DOE) had previously set a thermal conductivity goal of 20 W/m·K, which was surpassed by formulations containing 75 wt.% and 80 wt.% SG, yielding in–plane thermal conductivity values of 24.4 W/m·K and 33.6 W/m·K, respectively. In addition, composites containing 2.5 wt.% CB, 65 wt.% SG, and 6 wt.% CNT in PP had an in–plane thermal conductivity of 37 W/m·K. Flexural and tensile tests were conducted. All composite formulations exceeded the flexural strength target of 25 MPa set by DOE. The tensile and flexural modulus of the composites increased with higher concentration of carbon fillers. Carbon black and synthetic graphite caused a decrease in the tensile and flexural strengths of the composites. However, carbon nanotubes increased the composite tensile and flexural strengths. Mathematical models were applied to estimate through–plane and in–plane thermal conductivities of single and multiple filler formulations, and tensile modulus of single–filler formulations. For thermal conductivity, Nielsen’s model yielded accurate thermal conductivity values when compared to experimental results obtained through the Flash method. For prediction of tensile modulus Nielsen’s model yielded the smallest error between the predicted and experimental values. The second part of this project consisted of the development of a curriculum in Fuel Cell and Hydrogen Technologies to address different educational barriers identified by the Department of Energy. By the creation of new courses and enterprise programs in the areas of fuel cells and the use of hydrogen as an energy carrier, we introduced engineering students to the new technologies, policies and challenges present with this alternative energy. Feedback provided by students participating in these courses and enterprise programs indicate positive acceptance of the different educational tools. Results obtained from a survey applied to students after participating in these courses showed an increase in the knowledge and awareness of energy fundamentals, which indicates the modules developed in this project are effective in introducing students to alternative energy sources.

Adaptive Software Fault Prediction Approach Using Object-Oriented Metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates

That humans and animals learn from interaction with the environment is a foundational idea underlying nearly all theories of learning and intelligence. Learning that certain outcomes are associated with specific actions or stimuli (both internal and external), is at the very core of the capacity to adapt behaviour to environmental changes. In the present work, appetitive and aversive reinforcement learning paradigms have been used to investigate the fronto-striatal loops and behavioural correlates of adaptive and maladaptive reinforcement learning processes, aiming to a deeper understanding of how cortical and subcortical substrates interacts between them and with other brain systems to support learning. By combining a large variety of neuroscientific approaches, including behavioral and psychophysiological methods, EEG and neuroimaging techniques, these studies aim at clarifying and advancing the knowledge of the neural bases and computational mechanisms of reinforcement learning, both in normal and neurologically impaired population.

Mars delivery service - development of the electro-mechanical systems of the Sample Fetch Rover for the Mars Sample Return Campaign

This thesis describes the development of the Sample Fetch Rover (SFR), studied for Mars Sample Return (MSR), an international campaign carried out in cooperation between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The focus of this document is the design of the electro-mechanical systems of the rover. After placing this work into the general context of robotic planetary exploration and summarising the state of the art for what concerns Mars rovers, the architecture of the Mars Sample Return Campaign is presented. A complete overview of the current SFR architecture is provided, touching upon all the main subsystems of the spacecraft. For each area, it is discussed what are the design drivers, the chosen solutions and whether they use heritage technology (in particular from the ExoMars Rover) or new developments. This research focuses on two topics of particular interest, due to their relevance for the mission and the novelty of their design: locomotion and sample acquisition, which are discussed in depth. The early SFR locomotion concepts are summarised, covering the initial trade-offs and discarded designs for higher traverse performance. Once a consolidated architecture was reached, the locomotion subsystem was developed further, defining the details of the suspension, actuators, deployment mechanisms and wheels. This technology is presented here in detail, including some key analysis and test results that support the design and demonstrate how it responds to the mission requirements. Another major electro-mechanical system developed as part of this work is the one dedicated to sample tube acquisition. The concept of operations of this machinery was defined to be robust against the unknown conditions that characterise the mission. The design process led to a highly automated robotic system which is described here in its main components: vision system, robotic arm and tube storage.

Experimental analysis of the aerodynamic and mixing performance of a ventilation device

Numerous types of acute respiratory failure are routinely treated using non-invasive ventilatory support (NIV). Its efficacy is well documented: NIV lowers intubation and death rates in various respiratory disorders. It can be delivered by means of face masks or head helmets. Currently the scientific community’s interest about NIV helmets is mostly focused on optimising the mixing between CO2 and clean air and on improving patient comfort. To this end, fluid dynamic analysis plays a particularly important role and a two- pronged approach is frequently employed. While on one hand numerical simulations provide information about the entire flow field and different geometries, they exhibit require huge temporal and computational resources. Experiments on the other hand help to validate simulations and provide results with a much smaller time investment and thus remain at the core of research in fluid dynamics. The aim of this thesis work was to develop a flow bench and to utilise it for the analysis of NIV helmets. A flow test bench and an instrumented mannequin were successfully designed, produced and put into use. Experiments were performed to characterise the helmet interface in terms of pressure drop and flow rate drop over different inlet flow rates and outlet pressure set points. Velocity measurements by means of Particle Image Velocimetry were performed. Pressure drop and flow rate characteristics from experiments were contrasted with CFD data and sufficient agreement was observed between both numerical and experimental results. PIV studies permitted qualitative and quantitative comparisons with numerical simulation data and offered a clear picture of the internal flow behaviour, aiding the identification of coherent flow features.

The Crosslinking Degree Controls The Mechanical, Rheological, And Swelling Properties Of Hyaluronic Acid Microparticles.

Viscosupplements, used for treating joint and cartilage diseases, restore the rheological properties of synovial fluid, regulate joint homeostasis and act as scaffolds for cell growth and tissue regeneration. Most viscosupplements are hydrogels composed of hyaluronic acid (HA) microparticles suspended in fluid HA. These microparticles are crosslinked with chemicals to assure their stability against enzyme degradation and to prolong the action of the viscosupplement. However, the crosslinking also modifies the mechanical, swelling and rheological properties of the HA microparticle hydrogels, with consequences on the effectiveness of the application. The aim of this study is to correlate the crosslinking degree (CD) with these properties to achieve modulation of HA/DVS microparticles through CD control. Because divinyl sulfone (DVS) is the usual crosslinker of HA in viscosupplements, we examined the effects of CD by preparing HA microparticles at 1:1, 2:1, 3:1, and 5:1 HA/DVS mass ratios. The CD was calculated from inductively coupled plasma spectrometry data. HA microparticles were previously sized to a mean diameter of 87.5 µm. Higher CD increased the viscoelasticity and the extrusion force and reduced the swelling of the HA microparticle hydrogels, which also showed Newtonian pseudoplastic behavior and were classified as covalent weak. The hydrogels were not cytotoxic to fibroblasts according to an MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) assay. © 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part A, 2014.

Assessment Of The Fixation Of Mandibular Symphysis Fractures Using Conical Cannulated Screws: Mechanical And Photoelastic Tests.

The aim of this study was to use mechanical and photoelastic tests to compare the performance of cannulated screws with other fixation methods in mandibular symphysis fractures. Ten polyurethane mandibles were allocated to each group and fixed as follows: group PRP, 2 perpendicular miniplates; group PLL, 1 miniplate and 1 plate, parallel; and group CS, 2 cannulated screws. Vertical linear loading tests were performed. The differences between mean values were analyzed with the Tukey test. The photoelastic test was carried out using a polariscope. The results revealed differences between the CS and PRP groups at 1, 3, 5, and 10 millimeters of displacement. The photoelastic test confirmed higher stress concentration in all groups close to the mandibular base, whereas the CS group showed it throughout the region assessed. Conical cannulated screws performed well in mechanical and photoelastic tests.