Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds

In the last thirty years, the emergence and progression of biologging technology has led to great advances in marine predator ecology. Large databases of location and dive observations from biologging devices have been compiled for an increasing number of diving predator species (such as pinnipeds, sea turtles, seabirds and cetaceans), enabling complex questions about animal activity budgets and habitat use to be addressed. Central to answering these questions is our ability to correctly identify and quantify the frequency of essential behaviours, such as foraging. Despite technological advances that have increased the quality and resolution of location and dive data, accurately interpreting behaviour from such data remains a challenge, and analytical methods are only beginning to unlock the full potential of existing datasets. This review evaluates both traditional and emerging methods and presents a starting platform of options for future studies of marine predator foraging ecology, particularly from location and two-dimensional (time-depth) dive data. We outline the different devices and data types available, discuss the limitations and advantages of commonly-used analytical techniques, and highlight key areas for future research. We focus our review on pinnipeds - one of the most studied taxa of marine predators - but offer insights that will be applicable to other air-breathing marine predator tracking studies. We highlight that traditionally-used methods for inferring foraging from location and dive data, such as first-passage time and dive shape analysis, have important caveats and limitations depending on the nature of the data and the research question. We suggest that more holistic statistical techniques, such as state-space models, which can synthesise multiple track, dive and environmental metrics whilst simultaneously accounting for measurement error, offer more robust alternatives. Finally, we identify a need for more research to elucidate the role of physical oceanography, device effects, study animal selection, and developmental stages in predator behaviour and data interpretation.

Essays on Information Frictions, Prices and Unemployment

I investigate the effects of information frictions in price setting decisions. I show that firms' output prices and wages are less sensitive to aggregate economic conditions when firms and workers cannot perfectly understand (or know) the aggregate state of the economy. Prices and wages respond with a lag to aggregate innovations because agents learn slowly about those changes, and this delayed adjustment in prices makes output and unemployment more sensitive to aggregate shocks. In the first chapter of this dissertation, I show that workers' noisy information about the state of the economy help us to explain why real wages are sluggish. In the context of a search and matching model, wages do not immediately respond to a positive aggregate shock because workers do not (yet) have enough information to demand higher wages. This increases firms' incentives to post more vacancies, and it makes unemployment volatile and sensitive to aggregate shocks. This mechanism is robust to two major criticisms of existing theories of sluggish wages and volatile unemployment: the flexibility of wages for new hires and the cyclicality of the opportunity cost of employment. Calibrated to U.S. data, the model explains 60% of the overall unemployment volatility. Consistent with empirical evidence, the response of unemployment to TFP shocks predicted by my model is large, hump-shaped, and peaks one year after the TFP shock, while the response of the aggregate wage is weak and delayed, peaking after two years. In the second chapter of this dissertation, I study the role of information frictions and inventories in firms' price setting decisions in the context of a monetary model. In this model, intermediate goods firms accumulate output inventories, observe aggregate variables with one period lag, and observe their nominal input prices and demand at all times. Firms face idiosyncratic shocks and cannot perfectly infer the state of nature. After a contractionary nominal shock, nominal input prices go down, and firms accumulate inventories because they perceive some positive probability that the nominal price decline is due to a good productivity shock. This prevents firms' prices from decreasing and makes current profits, households' income, and aggregate demand go down. According to my model simulations, a 1% decrease in the money growth rate causes output to decline 0.17% in the first quarter and 0.38% in the second followed by a slow recovery to the steady state. Contractionary nominal shocks also have significant effects on total investment, which remains 1% below the steady state for the first 6 quarters.

Inter-industry analysis and monetary policy evaluations in the Korean flow of funds accounts

This study mainly aims to provide an inter-industry analysis through the subdivision of various industries in flow of funds (FOF) accounts. Combined with the Financial Statement Analysis data from 2004 and 2005, the Korean FOF accounts are reconstructed to form "from-whom-to-whom" basis FOF tables, which are composed of 115 institutional sectors and correspond to tables and techniques of input–output (I–O) analysis. First, power of dispersion indices are obtained by applying the I–O analysis method. Most service and IT industries, construction, and light industries in manufacturing are included in the first quadrant group, whereas heavy and chemical industries are placed in the fourth quadrant since their power indices in the asset-oriented system are comparatively smaller than those of other institutional sectors. Second, investments and savings, which are induced by the central bank, are calculated for monetary policy evaluations. Industries are bifurcated into two groups to compare their features. The first group refers to industries whose power of dispersion in the asset-oriented system is greater than 1, whereas the second group indicates that their index is less than 1. We found that the net induced investments (NII)–total liabilities ratios of the first group show levels half those of the second group since the former's induced savings are obviously greater than the latter.

Secure and Privacy-aware Data Collection and Processing in Mobile Health Systems

Healthcare systems have assimilated information and communication technologies in order to improve the quality of healthcare and patient's experience at reduced costs. The increasing digitalization of people's health information raises however new threats regarding information security and privacy. Accidental or deliberate data breaches of health data may lead to societal pressures, embarrassment and discrimination. Information security and privacy are paramount to achieve high quality healthcare services, and further, to not harm individuals when providing care. With that in mind, we give special attention to the category of Mobile Health (mHealth) systems. That is, the use of mobile devices (e.g., mobile phones, sensors, PDAs) to support medical and public health. Such systems, have been particularly successful in developing countries, taking advantage of the flourishing mobile market and the need to expand the coverage of primary healthcare programs. Many mHealth initiatives, however, fail to address security and privacy issues. This, coupled with the lack of specific legislation for privacy and data protection in these countries, increases the risk of harm to individuals. The overall objective of this thesis is to enhance knowledge regarding the design of security and privacy technologies for mHealth systems. In particular, we deal with mHealth Data Collection Systems (MDCSs), which consists of mobile devices for collecting and reporting health-related data, replacing paper-based approaches for health surveys and surveillance. This thesis consists of publications contributing to mHealth security and privacy in various ways: with a comprehensive literature review about mHealth in Brazil; with the design of a security framework for MDCSs (SecourHealth); with the design of a MDCS (GeoHealth); with the design of Privacy Impact Assessment template for MDCSs; and with the study of ontology-based obfuscation and anonymisation functions for health data.

The Essays in International Trade and Finance

The first chapter provides the first evidence on the gross capital flows reactions to the financial sector reform. I establish four new stylized facts. First, the reform is associated with an average increase of 0.03pp in both gross capital flows. Second, immediately after the reform both flows decrease, in the long term they stabilize at a higher than the pre-liberalization levels. Third, the short term dynamics is governed by debt flows, while the long term dynamics are driven by all of the components. Finally, only a complex reform leads to a positive effect. The results are robust to a wide range of robustness checks. In the second chapter we develop a novel theory to explain the recent phenomenon of reshoring, i.e. firms moving back their previously offshored business activities. We firstly provide the evidence for the importance of the quality behind the reshoring decision and then, building on Antoniades (2015) we develop a dynamic heterogeneous firms model with quality choice and offshoring. In the dynamic setting the location decision entails a tradeoff between payroll and quality-related costs. In equilibrium reshoring arises as some firms initially offshore, exploit the increase in profits due to lower wages and finally return to the domestic country in order to further increase the quality. The third chapter provides the new evidence suggesting that selling through global production networks might lead to export upgrade. I relate the sector-level GVCs participation indicators derived from the international Input-Output Tables to the data on the unit values of exports at the product-exporter level. We find a strong association between the export prices and forward participation, in particular for the developing countries. We document also a less robust negative relationship between the GVCs backward participation and unit values of exports.

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Security and privacy of resource constrained devices

The thesis aims to present a comprehensive and holistic overview on cybersecurity and privacy & data protection aspects related to IoT resource-constrained devices. Chapter 1 introduces the current technical landscape by providing a working definition and architecture taxonomy of ‘Internet of Things’ and ‘resource-constrained devices’, coupled with a threat landscape where each specific attack is linked to a layer of the taxonomy. Chapter 2 lays down the theoretical foundations for an interdisciplinary approach and a unified, holistic vision of cybersecurity, safety and privacy justified by the ‘IoT revolution’ through the so-called infraethical perspective. Chapter 3 investigates whether and to what extent the fast-evolving European cybersecurity regulatory framework addresses the security challenges brought about by the IoT by allocating legal responsibilities to the right parties. Chapters 4 and 5 focus, on the other hand, on ‘privacy’ understood by proxy as to include EU data protection. In particular, Chapter 4 addresses three legal challenges brought about by the ubiquitous IoT data and metadata processing to EU privacy and data protection legal frameworks i.e., the ePrivacy Directive and the GDPR. Chapter 5 casts light on the risk management tool enshrined in EU data protection law, that is, Data Protection Impact Assessment (DPIA) and proposes an original DPIA methodology for connected devices, building on the CNIL (French data protection authority) model.

Development and Automatization of a Planar Jet Wind Tunnel for the X-wire Calibration

The scope of this study is to design an automatic control system and create an automatic x-wire calibrator for a facility named Plane Air Tunnel; whose exit creates planar jet flow. The controlling power state as well as automatic speed adjustment of the inverter has been achieved. Thus, the wind tunnel can be run with respect to any desired speed and the x-wire can automatically be calibrated at that speed. To achieve that, VI programming using the LabView environment was learned, to acquire the pressure and temperature, and to calculate the velocity based on the acquisition data thanks to a pitot-static tube. Furthermore, communication with the inverter to give the commands for power on/off and speed control was also done using the LabView VI coding environment. The connection of the computer to the inverter was achieved by the proper cabling using DAQmx Analog/Digital (A/D) input/output (I/O). Moreover, the pressure profile along the streamwise direction of the plane air tunnel was studied. Pressure tappings and a multichannel pressure scanner were used to acquire the pressure values at different locations. Thanks to that, the aerodynamic efficiency of the contraction ratio was observed, and the pressure behavior was related to the velocity at the exit section. Furthermore, the control of the speed was accomplished by implementing a closed-loop PI controller on the LabView environment with and without using a pitot-static tube thanks to the pressure behavior information. The responses of the two controllers were analyzed and commented on by giving suggestions. In addition, hot wire experiments were performed to calibrate automatically and investigate the velocity profile of a turbulent planar jet. To be able to analyze the results, the physics of turbulent planar jet flow was studied. The fundamental terms, the methods used in the derivation of the equations, velocity profile, shear stress behavior, and the effect of vorticity were reviewed.

Qualitative Analysis of the Interdisciplinary Interaction between Data Analysis Specialists and Novice Clinical Researchers

Background: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time. Methods/Principal Findings: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of ""what if'' situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal. Conclusion/Significance: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

Shoulder Muscular Activity During Isometric Three-Point Kneeling Exercise on Stable and Unstable Surfaces

The purpose of this study was to determine if performing isometric 3-point kneeling exercises on a Swiss ball influenced the isometric force output and EMG activities of the shoulder muscles when compared with performing the same exercises on a stable base of support. Twenty healthy adults performed the isometric 3-point kneeling exercises with the hand placed either on a stable surface or on a Swiss ball. Surface EMG was recorded from the posterior deltoid, pectoralis major, biceps brachii, triceps brachii, upper trapezius, and serratus anterior muscles using surface differential electrodes. All EMG data were reported as percentages of the average root mean square (RMS) values obtained in maximum voluntary contractions for each muscle studied. The highest load value was obtained during exercise on a stable surface. A significant increase was observed in the activation of glenohumeral muscles during exercises on a Swiss ball. However, there were no differences in EMG activities of the scapulothoracic muscles. These results suggest that exercises performed on unstable surfaces may provide muscular activity levels similar to those performed on stable surfaces, without the need to apply greater external loads to the musculoskeletal system. Therefore, exercises on unstable surfaces may be useful during the process of tissue regeneration.

Adhesion of Endodontic Sealers to Human Root Dentine Submitted to Different Surface Treatments

Objective: To evaluate the adhesion of the endodontic sealers Epiphany, Apexit Plus, and AH Plus to root canal dentin submitted to different surface treatments, by using the push-out test. Methods: One hundred twenty-eight root cylinders obtained from maxillary canines were embedded in acrylic resin, had the canals prepared, and were randomly assigned to four groups (n = 32), according to root dentin treatment: (I) distilled water (control), (II) 17% EDTAC, (III) 1% NaOCl and (IV) Er:YAG laser with 16-Hz, 400-mJ input (240-mJ output) and 0.32-J/cm(2) energy density. Each group was divided into four subgroups (n = 8) filled with Epiphany (either dispensed from the automix syringe supplied by the manufacturer or prepared by hand mixing), Apexit Plus, or AH Plus. Data (MPa) were analyzed by ANOVA and Tukey's test. Results: A statistically significant difference (p < 0.01) was found among the root-canal sealers, except for the Epiphany subgroups, which had statistically similar results to each other (p > 0.01): AH Plus (4.77 +/- 0.85), Epiphany/hand mixed (3.06 +/- 1.34), Epiphany/automix syringe (2.68 +/- 1.35), and Apexit Plus (1.22 +/- 0.33). A significant difference (p < 0.01) was found among the dentin surface treatments. The highest adhesion values were obtained with AH Plus when root dentin was treated with Er: YAG laser and 17% EDTAC. Epiphany sealer presented the lowest adhesion values to root dentin treated with 17% EDTAC. Conclusions: The resin-based sealers had different adhesive behaviors, depending on the treatment of root canal walls. The mode of preparation of Epiphany (automix syringe or hand mixing) did not influence sealer adhesion to root dentin.

Comparison Between Single-Diode Low-Level Laser Therapy (LLLT) and LED Multi-Diode (Cluster) Therapy (LEDT) Applications Before High-Intensity Exercise

Background Data and Objective: There is anecdotal evidence that low-level laser therapy (LLLT) may affect the development of muscular fatigue, minor muscle damage, and recovery after heavy exercises. Although manufacturers claim that cluster probes (LEDT) maybe more effective than single-diode lasers in clinical settings, there is a lack of head-to-head comparisons in controlled trials. This study was designed to compare the effect of single-diode LLLT and cluster LEDT before heavy exercise. Materials and Methods: This was a randomized, placebo-controlled, double-blind cross-over study. Young male volleyball players (n = 8) were enrolled and asked to perform three Wingate cycle tests after 4 x 30 sec LLLT or LEDT pretreatment of the rectus femoris muscle with either (1) an active LEDT cluster-probe (660/850 nm, 10/30mW), (2) a placebo cluster-probe with no output, and (3) a single-diode 810-nm 200-mW laser. Results: The active LEDT group had significantly decreased post-exercise creatine kinase (CK) levels (-18.88 +/- 41.48U/L), compared to the placebo cluster group (26.88 +/- 15.18U/L) (p < 0.05) and the active single-diode laser group (43.38 +/- 32.90U/L) (p<0.01). None of the pre-exercise LLLT or LEDT protocols enhanced performance on the Wingate tests or reduced post-exercise blood lactate levels. However, a non-significant tendency toward lower post-exercise blood lactate levels in the treated groups should be explored further. Conclusion: In this experimental set-up, only the active LEDT probe decreased post-exercise CK levels after the Wingate cycle test. Neither performance nor blood lactate levels were significantly affected by this protocol of pre-exercise LEDT or LLLT.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

Experimental results on the nonlinear H(infinity) control via quasi-LPV representation and game theory for wheeled mobile robots

In this paper, nonlinear dynamic equations of a wheeled mobile robot are described in the state-space form where the parameters are part of the state (angular velocities of the wheels). This representation, known as quasi-linear parameter varying, is useful for control designs based on nonlinear H(infinity) approaches. Two nonlinear H(infinity) controllers that guarantee induced L(2)-norm, between input (disturbances) and output signals, bounded by an attenuation level gamma, are used to control a wheeled mobile robot. These controllers are solved via linear matrix inequalities and algebraic Riccati equation. Experimental results are presented, with a comparative study among these robust control strategies and the standard computed torque, plus proportional-derivative, controller.