Time-series analysis of Campylobacter incidence in Switzerland

SUMMARY Campylobacteriosis has been the most common food-associated notifiable infectious disease in Switzerland since 1995. Contact with and ingestion of raw or undercooked broilers are considered the dominant risk factors for infection. In this study, we investigated the temporal relationship between the disease incidence in humans and the prevalence of Campylobacter in broilers in Switzerland from 2008 to 2012. We use a time-series approach to describe the pattern of the disease by incorporating seasonal effects and autocorrelation. The analysis shows that prevalence of Campylobacter in broilers, with a 2-week lag, has a significant impact on disease incidence in humans. Therefore Campylobacter cases in humans can be partly explained by contagion through broiler meat. We also found a strong autoregressive effect in human illness, and a significant increase of illness during Christmas and New Year's holidays. In a final analysis, we corrected for the sampling error of prevalence in broilers and the results gave similar conclusions.

Time-domain ptychography

Through dedicated measurements in the optical regime we demonstrate that ptychography can be applied to reconstruct complex-valued object functions that vary with time from a sequence of spectral measurements. A probe pulse of approximately 1 ps duration, time delayed in increments of 0.25 ps, is shown to recover dynamics on a ten times faster time scale with an experimental limit of approximately 5 fs.

Post-remission treatment with allogeneic stem cell transplantation in patients aged 60 years and older with acute myeloid leukaemia: a time-dependent analysis

BACKGROUND Acute myeloid leukaemia mainly affects elderly people, with a median age at diagnosis of around 70 years. Although about 50-60% of patients enter first complete remission upon intensive induction chemotherapy, relapse remains high and overall outcomes are disappointing. Therefore, effective post-remission therapy is urgently needed. Although often no post-remission therapy is given to elderly patients, it might include chemotherapy or allogeneic haemopoietic stem cell transplantation (HSCT) following reduced-intensity conditioning. We aimed to assess the comparative value of allogeneic HSCT with other approaches, including no post-remission therapy, in patients with acute myeloid leukaemia aged 60 years and older. METHODS For this time-dependent analysis, we used the results from four successive prospective HOVON-SAKK acute myeloid leukaemia trials. Between May 3, 2001, and Feb 5, 2010, a total of 1155 patients aged 60 years and older were entered into these trials, of whom 640 obtained a first complete remission after induction chemotherapy and were included in the analysis. Post-remission therapy consisted of allogeneic HSCT following reduced-intensity conditioning (n=97), gemtuzumab ozogamicin (n=110), chemotherapy (n=44), autologous HSCT (n=23), or no further treatment (n=366). Reduced-intensity conditioning regimens consisted of fludarabine combined with 2 Gy of total body irradiation (n=71), fludarabine with busulfan (n=10), or other regimens (n=16). A time-dependent analysis was done, in which allogeneic HSCT was compared with other types of post-remission therapy. The primary endpoint of the study was 5-year overall survival for all treatment groups, analysed by a time-dependent analysis. FINDINGS 5-year overall survival was 35% (95% CI 25-44) for patients who received an allogeneic HSCT, 21% (17-26) for those who received no additional post-remission therapy, and 26% (19-33) for patients who received either additional chemotherapy or autologous HSCT. Overall survival at 5 years was strongly affected by the European LeukemiaNET acute myeloid leukaemia risk score, with patients in the favourable risk group (n=65) having better 5-year overall survival (56% [95% CI 43-67]) than those with intermediate-risk (n=131; 23% [19-27]) or adverse-risk (n=444; 13% [8-20]) acute myeloid leukaemia. Multivariable analysis with allogeneic HSCT as a time-dependent variable showed that allogeneic HSCT was associated with better 5-year overall survival (HR 0·71 [95% CI 0·53-0·95], p=0·017) compared with non-allogeneic HSCT post-remission therapies or no post-remission therapy, especially in patients with intermediate-risk (0·82 [0·58-1·15]) or adverse-risk (0.39 [0·21-0·73]) acute myeloid leukaemia. INTERPRETATION Collectively, the results from these four trials suggest that allogeneic HSCT might be the preferred treatment approach in patients 60 years of age and older with intermediate-risk and adverse-risk acute myeloid leukaemia in first complete remission, but the comparative value should ideally be shown in a prospective randomised study. FUNDING None.

Time-Efficiency Analysis Comparing Digital and Conventional Workflows for Implant Crowns: A Prospective Clinical Crossover Trial

PURPOSE To compare time-efficiency in the production of implant crowns using a digital workflow versus the conventional pathway. MATERIALS AND METHODS This prospective clinical study used a crossover design that included 20 study participants receiving single-tooth replacements in posterior sites. Each patient received a customized titanium abutment plus a computer-aided design/computer-assisted manufacture (CAD/CAM) zirconia suprastructure (for those in the test group, using digital workflow) and a standardized titanium abutment plus a porcelain-fused-to-metal crown (for those in the control group, using a conventional pathway). The start of the implant prosthetic treatment was established as the baseline. Time-efficiency analysis was defined as the primary outcome, and was measured for every single clinical and laboratory work step in minutes. Statistical analysis was calculated with the Wilcoxon rank sum test. RESULTS All crowns could be provided within two clinical appointments, independent of the manufacturing process. The mean total production time, as the sum of clinical plus laboratory work steps, was significantly different. The mean ± standard deviation (SD) time was 185.4 ± 17.9 minutes for the digital workflow process and 223.0 ± 26.2 minutes for the conventional pathway (P = .0001). Therefore, digital processing for overall treatment was 16% faster. Detailed analysis for the clinical treatment revealed a significantly reduced mean ± SD chair time of 27.3 ± 3.4 minutes for the test group compared with 33.2 ± 4.9 minutes for the control group (P = .0001). Similar results were found for the mean laboratory work time, with a significant decrease of 158.1 ± 17.2 minutes for the test group vs 189.8 ± 25.3 minutes for the control group (P = .0001). CONCLUSION Only a few studies have investigated efficiency parameters of digital workflows compared with conventional pathways in implant dental medicine. This investigation shows that the digital workflow seems to be more time-efficient than the established conventional production pathway for fixed implant-supported crowns. Both clinical chair time and laboratory manufacturing steps could be effectively shortened with the digital process of intraoral scanning plus CAD/CAM technology.

The Moderating Role of Electoral Pressure on Congressional Ideological Polarization: A Cross-Sectional Time-Series Analysis

Reelection and self-interest are recurring themes in the study of our congressional leaders. To date, many studies have already been done on the trends between elections, party affiliation, and voting behavior in Congress. However, because a plethora of data has been collected on both elections and congressional voting, the ability to draw a connection between the two provides a very reasonable prospect. This project analyzes whether voting shifts in congressional elections have an effect on congressional voting. Will a congressman become ideologically more polarized when his electoral margins increase? Essentially, this paper assumes that all congressmen are ideologically polarized, and it is elections which serve to reel congressmen back toward the ideological middle. The election and ideological data for this study, which spans from the 56th to the 107th Congress, finds statistically significant relationships between these two variables. In fact, congressman pay attention to election returns when voting in Congress. When broken down by party, Democrats are more exhibitive of this phenomenon, which suggest that Democrats may be more likely to intrinsically follow the popular model of representation. Meanwhile, it can be hypothesized that insignificant results for Republicans indicate that Republicans may follow a trustee model of representation.

Implementation of active surveillance cultures and associated infection control practices for the reduction of methicillin resistant Staphylococcus aureus in the healthcare setting: An interrupted time series analysis

OBJECTIVE. To determine the effectiveness of active surveillance cultures and associated infection control practices on the incidence of methicillin resistant Staphylococcus aureus (MRSA) in the acute care setting. DESIGN. A historical analysis of existing clinical data utilizing an interrupted time series design. ^ SETTING AND PARTICIPANTS. Patients admitted to a 260-bed tertiary care facility in Houston, TX between January 2005 through December 2010. ^ INTERVENTION. Infection control practices, including enhanced barrier precautions, compulsive hand hygiene, disinfection and environmental cleaning, and executive ownership and education, were simultaneously introduced during a 5-month intervention implementation period culminating with the implementation of active surveillance screening. Beginning June 2007, all high risk patients were cultured for MRSA nasal carriage within 48 hours of admission. Segmented Poisson regression was used to test the significance of the difference in incidence of healthcare-associated MRSA during the 29-month pre-intervention period compared to the 43-month post-intervention period. ^ RESULTS. A total of 9,957 of 11,095 high-risk patients (89.7%) were screened for MRSA carriage during the intervention period. Active surveillance cultures identified 1,330 MRSA-positive patients (13.4%) contributing to an admission prevalence of 17.5% in high-risk patients. The mean rate of healthcare-associated MRSA infection and colonization decreased from 1.1 per 1,000 patient-days in the pre-intervention period to 0.36 per 1,000 patient-days in the post-intervention period (P<0.001). The effect of the intervention in association with the percentage of S. aureus isolates susceptible to oxicillin were shown to be statistically significantly associated with the incidence of MRSA infection and colonization (IRR = 0.50, 95% CI = 0.31-0.80 and IRR = 0.004, 95% CI = 0.00003-0.40, respectively). ^ CONCLUSIONS. It can be concluded that aggressively targeting patients at high risk for colonization of MRSA with active surveillance cultures and associated infection control practices as part of a multifaceted, hospital-wide intervention is effective in reducing the incidence of healthcare-associated MRSA.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Seagrass time-series analysis products, in Moreton Bay, Australia, with links to GeoTIFFs

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (~200 km**2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Periodic time-domain modulation for the electrically tunable control of optical pulse train envelope and repetition rate multiplication

An electrically tunable system for the control of optical pulse sequences is proposed and demonstrated. It is based on the use of an electrooptic modulator for periodic phase modulation followed by a dispersive device to obtain the temporal Talbot effect. The proposed configuration allows for repetition rate multiplication with different multiplication factors and with the simultaneous control of the pulse train envelope by simply changing the electrical signal driving the modulator. Simulated and experimental results for an input optical pulse train of 10 GHz are shown for different multiplication factors and envelope shapes.

Modeling the evolution of item rating networks using time-domain preferential attachment

The understanding of the structure and dynamics of the intricate network of connections among people that consumes products through Internet appears as an extremely useful asset in order to study emergent properties related to social behavior. This knowledge could be useful, for example, to improve the performance of personal recommendation algorithms. In this contribution, we analyzed five-year records of movie-rating transactions provided by Netflix, a movie rental platform where users rate movies from an online catalog. This dataset can be studied as a bipartite user-item network whose structure evolves in time. Even though several topological properties from subsets of this bipartite network have been reported with a model that combines random and preferential attachment mechanisms [Beguerisse Díaz et al., 2010], there are still many aspects worth to be explored, as they are connected to relevant phenomena underlying the evolution of the network. In this work, we test the hypothesis that bursty human behavior is essential in order to describe how a bipartite user-item network evolves in time. To that end, we propose a novel model that combines, for user nodes, a network growth prescription based on a preferential attachment mechanism acting not only in the topological domain (i.e. based on node degrees) but also in time domain. In the case of items, the model mixes degree preferential attachment and random selection. With these ingredients, the model is not only able to reproduce the asymptotic degree distribution, but also shows an excellent agreement with the Netflix data in several time-dependent topological properties.

Models for internal quality sorting of fruit, based on time-domain laser reflectance spectroscopy (TDRS).

Non-destructive measurement of fruit quality has been an important objective through recent years (Abbott, 1999). Near infrared spectroscopy (NIR) is applicable to the cuantification of chemicals in foods and NIK "laser spectroscopy" can be used to estimate the firmness of fruits. However, die main limitation of current optical techniques that measure light transmission is that they do not account for the coupling between absorption and scattering inside the tissue, when quantifying the intensity o f reemitted light. The solution o f this l i m i t a t i o n was the goal o f the present work.

Time domain passivity control for delayed teleoperation

La telepesencia combina diferentes modalidades sensoriales, incluyendo, entre otras, la visual y la del tacto, para producir una sensación de presencia remota en el operador. Un elemento clave en la implementación de sistemas de telepresencia para permitir una telemanipulación del entorno remoto es el retorno de fuerza. Durante una telemanipulación, la energía mecánica es transferida entre el operador humano y el entorno remoto. En general, la energía es una propiedad de los objetos físicos, fundamental en su mutual interacción. En esta interacción, la energía se puede transmitir entre los objetos, puede cambiar de forma pero no puede crearse ni destruirse. En esta tesis, se aplica este principio fundamental para derivar un nuevo método de control bilateral que permite el diseño de sistemas de teleoperación estables para cualquier arquitectura concebible. El razonamiento parte del hecho de que la energía mecánica insertada por el operador humano en el sistema debe transferirse hacia el entorno remoto y viceversa. Tal como se verá, el uso de la energía como variable de control permite un tratamiento más general del sistema que el control convencional basado en variables específicas del sistema. Mediante el concepto de Red de Potencia de Retardo Temporal (RPRT), el problema de definir los flujos de energía en un sistema de teleoperación es solucionado con independencia de la arquitectura de comunicación. Como se verá, los retardos temporales son la principal causa de generación de energía virtual. Este hecho se observa con retardos a partir de 1 milisegundo. Esta energía virtual es añadida al sistema de forma intrínseca y representa la causa principal de inestabilidad. Se demuestra que las RPRTs son transportadoras de la energía deseada intercambiada entre maestro y esclavo pero a la vez generadoras de energía virtual debido al retardo temporal. Una vez estas redes son identificadas, el método de Control de Pasividad en el Dominio Temporal para RPRTs se propone como mecanismo de control para asegurar la pasividad del sistema, y as__ la estabilidad. El método se basa en el simple hecho de que esta energía virtual debido al retardo debe transformarse en disipación. As__ el sistema se aproxima al sistema deseado, donde solo la energía insertada desde un extremo es transferida hacia el otro. El sistema resultante presenta dos cualidades: por un lado la estabilidad del sistema queda garantizada con independencia de la arquitectura del sistema y del canal de comunicación; por el otro, el rendimiento es maximizado en términos de fidelidad de transmisión energética. Los métodos propuestos se sustentan con sistemas experimentales con diferentes arquitecturas de control y retardos entre 2 y 900 ms. La tesis concluye con un experimento que incluye una comunicación espacial basada en el satélite geoestacionario ASTRA. ABSTRACT Telepresence combines different sensorial modalities, including vision and touch, to produce a feeling of being present in a remote location. The key element to successfully implement a telepresence system and thus to allow telemanipulation of a remote environment is force feedback. In a telemanipulation, mechanical energy must convey from the human operator to the manipulated object found in the remote environment. In general, energy is a property of all physical objects, fundamental to their mutual interactions in which the energy can be transferred among the objects and can change form but cannot be created or destroyed. In this thesis, we exploit this fundamental principle to derive a novel bilateral control mechanism that allows designing stable teleoperation systems with any conceivable communication architecture. The rationale starts from the fact that the mechanical energy injected by a human operator into the system must be conveyed to the remote environment and Vice Versa. As will be seen, setting energy as the control variable allows a more general treatment of the controlled system in contrast to the more conventional control of specific systems variables. Through the Time Delay Power Network (TDPN) concept, the issue of defining the energy flows involved in a teleoperation system is solved with independence of the communication architecture. In particular, communication time delays are found to be a source of virtual energy. This fact is observed with delays starting from 1 millisecond. Since this energy is added, the resulting teleoperation system can be non-passive and thus become unstable. The Time Delay Power Networks are found to be carriers of the desired exchanged energy but also generators of virtual energy due to the time delay. Once these networks are identified, the Time Domain Passivity Control approach for TDPNs is proposed as a control mechanism to ensure system passivity and therefore, system stability. The proposed method is based on the simple fact that this intrinsically added energy due to the communication must be transformed into dissipation. Then the system becomes closer to the ambitioned one, where only the energy injected from one end of the system is conveyed to the other one. The resulting system presents two benefits: On one hand, system stability is guaranteed through passivity independently from the chosen control architecture and communication channel; on the other, performance is maximized in terms of energy transfer faithfulness. The proposed methods are sustained with a set of experimental implementations using different control architectures and communication delays ranging from 2 to 900 milliseconds. An experiment that includes a communication Space link based on the geostationary satellite ASTRA concludes this thesis.

Time-motion analysis on Chinese male field hockey players

The aim of the study was to evaluate the match work-rate of Chinese field hockey players by analyzing the distance covered at different intensities pooled by specific positions during different periods of matches. Thirty-eight players from twenty-four male field hockey matches at the 11th Chinese National Games were filmed and analyzed.