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Normal Hardbound Edition is also available on request. Being a history of his religious opinions World's classics ; no. Shows some signs of wear, and may have some markings on the inside. Better World Books Condition: Cloud 9 Books Condition: Apologia Pro Vita Sua: Lightly rubbed textured brown cloth covers, paper title plate on spine, spine and portion of back cover faded.
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Quaker Meeting library stamps and pocket inside back cover, hinges cracking, else pages very good. Vintage Quaker Books Published: This is an older printing. Russell Books Ltd Condition: Longmans, Green, Reader, and Dyer, A purple cloth hardcover book in fair condition. Spine deeply tanned and stained. Edges lightly bumped and worn. Front hinge starting to crack, but repaired. Text is unmarked and binding, though shaken, is still secure enough for reading.
A classic work by the Anglican convert to Catholicism, explaining and defending his conversion. Being a History of His Religious Opinions. Newman, John Henry Cardinal. First printing on title page, no additional printings indicated. Reddish brown cloth hardcover without dust jacket, pages. Exterior almost like slightest loss of color on edges.
Name on first page and 3rd page. Rear endpaper slightly split but firmly held by cloth mesh. Ex-Library with the usual treatments. Text in English pp. An approach for generating synthetic fine temporal resolution solar radiation time series from hourly gridded datasets. Full Text Available A tool has been developed to statistically increase the temporal resolution of solar irradiance time series. Fine temporal resolution time series are an important input into the planning process for solar power plants, and lead to increased understanding of the likely short-term variability of solar energy.
The approach makes use of the spatial variability of hourly gridded datasets around a location of interest to make inferences about the temporal variability within the hour.
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The unique characteristics of solar irradiance data are modelled by classifying each hour into a typical weather situation. Low variability situations are modelled using an autoregressive process which is applied to ramps of clear-sky index. High variability situations are modelled as a transition between states of clear sky conditions and different levels of cloud opacity. These stations, together with an independent dataset, have also been used to verify the quality of the results using a number of relevant metrics.
The results show that the method generates realistic fine resolution synthetic time series.
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The synthetic time series correlate well with observed data on monthly and annual timescales as they are constrained to the nearest grid-point value on each hour. The probability distributions of the synthetic and observed global irradiance data are similar, with Kolmogorov-Smirnov test statistic less than 0. The tool could be useful for the estimation of solar power output for integration studies. The aim of the present work is to study the dependence of temporal resolution with the activity using statistical techniques applied to the series of values time series measurements of temporal resolution during daily equipment checks.
Describing temporal variability of the mean Estonian precipitation series in climate time scale. Applicability of the random walk type models to represent the temporal variability of various atmospheric temperature series has been successfully demonstrated recently e. The break separates short-range strong non-stationarity from nearly stationary longer range variability region. This is an indication of the fact that several geophysical time series show a short-range non-stationary behaviour and a stationary behaviour in longer range Davis et al.
In order to model series like that the choice of time step appears to be crucial. To characterize the long-range variability we can neglect the short-range non-stationary fluctuations, provided that we are able to model properly the long-range tendencies. The structure function Monin and Yaglom, was used to determine an approximate segregation line between the short and the long scale in terms of modeling.
The longer scale can be called climate one, because such models are applicable in scales over some decades. In order to get rid of the short-range fluctuations in daily series the variability can be examined using sufficiently long time step. In the present paper, we show that the same philosophy is useful to find a model to represent a climate-scale temporal variability of the Estonian daily mean precipitation amount series over 45 years Temporal variability of the obtained daily time series is examined by means of an autoregressive and integrated moving average ARIMA family model of the type 0,1,1.
This model is applicable for daily precipitation simulating if to select an appropriate time step that enables us to neglet the short-range non-stationary fluctuations. A considerably longer time step than one day 30 days is used in the current paper to model the precipitation time series variability.
An advection-based model to increase the temporal resolution of PIV time series. A numerical implementation of the advection equation is proposed to increase the temporal resolution of PIV time series. The method is based on the principle that velocity fluctuations are transported passively, similar to Taylor's hypothesis of frozen turbulence. In the present work, the advection model is extended to unsteady three-dimensional flows.
The main objective of the method is that of lowering the requirement on the PIV repetition rate from the Eulerian frequency toward the Lagrangian one. The local trajectory of the fluid parcel is obtained by forward projection of the instantaneous velocity at the preceding time instant and backward projection from the subsequent time step. The trajectories are approximated by the instantaneous streamlines, which yields accurate results when the amplitude of velocity fluctuations is small with respect to the convective motion.
The verification is performed with two experiments conducted at temporal resolutions significantly higher than that dictated by Nyquist criterion. The flow past the trailing edge of a NACA airfoil closely approximates frozen turbulence , where the largest ratio between the Lagrangian and Eulerian temporal scales is expected.
An order of magnitude reduction of the needed acquisition frequency is demonstrated by the velocity spectra of super-sampled series. The application to three-dimensional data is made with time-resolved tomographic PIV measurements of a transitional jet. Here, the 3D advection equation is implemented to estimate the fluid trajectories. The reduction in the minimum sampling rate by the use of super-sampling in this case is less, due to the fact that vortices occurring in the jet shear layer are not well approximated by sole advection at large time separation.
Both cases reveal that the current requirements for time-resolved PIV experiments can be revised when information is poured from space to time. An additional favorable effect is observed by the analysis in the. Full Text Available This article aims to provide new results about the intraday degree sequence distribution considering phone call network graph evolution in time. More specifically, it tackles the following problem. Given a large amount of landline phone call data records, what is the best way to summarize the distinct number of calling partners per client per day?
In order to answer this question, a series of undirected phone call network graphs is constructed based on data from a local telecommunication source in Albania. All network graphs of the series are simplified. Further, a longitudinal temporal study is made on this network graphs series related to the degree distributions. Power law and log-normal distribution fittings on the degree sequence are compared on each of the network graphs of the series.
The maximum likelihood method is used to estimate the parameters of the distributions, and a Kolmogorov—Smirnov test associated with a p-value is used to define the plausible models. A direct distribution comparison is made through a Vuong test in the case that both distributions are plausible. Study findings suggested that log-normal distribution models better the intraday degree sequence data of the network graphs.
It is not possible to say that the distributions of log-normal parameters are normal. Extremely high fine particulate matter PM2. In order to formulate mitigation measures and policies, knowledge on PM2. While previous studies are limited either because of availability of data, or because of problematic a priori assumptions that PM2. To achieve this, we conduct two time- series cluster analyses on full-year PM2.
Insights from the analysis on temporal variation of PM2. These findings advance current understanding on temporal patterns in PM2. Multiple approaches for reverse-engineering biological networks from time- series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co- temporal modeling.
The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co- temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1 identify two types of entity relationships, parent and sibling, between biological entities, 2 deal with experimental sparse time course data, and 3 handle experimental noise seen in replicate data sets.
These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co- temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.
Increasing the temporal resolution of direct normal solar irradiance forecasted series. A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power CSP plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology.
Numerical weather prediction NWP models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance DNI exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies.
In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h or higher temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data. Las series televisivas juveniles: Plots and Conflicts Portrayed in a Teen Series. This paper presents the main findings of a research project on teen series , which are television fiction series featuring teenagers and specifically targeted at a young audience.
The analysis of the portrayal of young people in television fictional series specifically targeted at a young audience has a meaningful value both for television production and for audience reception. Spatio- temporal chaos and thermal noise in Josephson junction series arrays. We study underdamped Josephson junction series arrays that are globally coupled through a resistive shunting load and driven by an rf bias current.
We find that they can be an experimental realization of many phenomena currently studied in globally coupled logistic map. Depending on the bias current the array can show Shapiro steps but also spatio- temporal chaos or ''turbulence'' in the IV characteristics.
In the turbulent phase there is a saturation of the broad band noise for a large number of junctions. This corresponds to a break down of the law of large numbers as seen in globally coupled maps. We study this phenomenon as a function of thermal noise. We find that when increasing the temperature the broad band noise decreases. Improved efficiency of maximum likelihood analysis of time series with temporally correlated errors. Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes.
Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model for cases where there are numerous observations.
Here, I address the second problem of computational efficiency using maximum likelihood estimates MLE. With missing data, standard spectral techniques involving FFTs are not appropriate. Instead, time domain techniques involving construction and inversion of large data covariance matrices are employed. That restriction can be removed by simply forming a data filter that adds noise processes rather than combining them in quadrature.
Consequently, the inversion of the data covariance matrix is simplified yet provides robust results for a wider range of power-law indices. Analysis of series temporal vegetation obtained by tele detection as tool for a tracking processes of desertification. This risk of desertification in the Mediterranean Basin, is evident in the southeast of the Iberian Peninsula, where land degradation reaches unsustainable levels.
Various climatic variables as possible causes of land covers behaviour were considered. The existence of a large inter-annual and intra-annual variability for all land covers and precipitation was observed. It is showed an association between temporal patterns of vegetation series of different land uses and rainfall. Temporal trend of carpal tunnel release surgery: Carpal tunnel release CTR is among the most common hand surgeries, although little is known about its pattern. In this study, we aimed to investigate temporal trends, age and gender variation and current practice patterns in CTR surgeries.
We conducted a population-based time series analysis among over 13 million residents of Ontario, who underwent operative management for carpal tunnel syndrome CTS from April 1, to March 31, using administrative claims data. Secondary analyses revealed different trends in procedure rates according to age. A computational framework to generate daily temperature maps using time- series of publicly available MODIS MOD11A2 product Land Surface Temperature LST images 1 km resolution; 8-day composites is illustrated using temperature measurements from the national network of meteorological stations in Croatia.
The input data set contains 57, ground measurements of daily temperature for the year The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio- temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy.
The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results of fold cross-validation show that use of spatio- temporal regression-kriging and incorporation of time- series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. Further software advancement—interactive space-time variogram exploration and automated retrieval.
Integrating field plots, lidar, and landsat time series to provide temporally consistent annual estimates of biomass from to present. Domke; Zhiqiang Yang; Robert E. We are developing a system that provides temporally consistent biomass estimates for national greenhouse gas inventory reporting to the United Nations Framework Convention on Climate Change.
Our model-assisted estimation framework relies on remote sensing to scale from plot measurements to lidar strip samples, to Landsat time series -based maps. As a demonstration, new The invasion of exotic species compromises ecosystem functions and causes substantial economic losses at the global scale. Over the past century, non-native saltcedar has expanded into most riparian zones in southwestern United States and posed significant threats to the native biotic communities.
Repeated monitoring of saltcedar distribution is essential for conservation agencies to locate highly susceptible areas and develop corresponding control strategies. Throughout the phenological cycle, the leaf senescence stage has been found to be the most crucial in spectrally detecting saltcedar. However, due to climate variability and anthropogenic forcing, the timing of saltcedar leaf senescence may vary over space and time. This spatial and inter-annual variation need to be accommodated to pinpoint the appropriate remotely sensed imagery for saltcedar mapping.
The objective of this study was to develop a Landsat-based Multiyear Spectral Angle Clustering MSAC model to monitor the inter-annual leaf senescence of exotic saltcedar. At the Landsat scale, the time series analysis of vegetation phenology is usually limited by the temporal resolution of images. The MSAC model can overcome this limit and take advantage of the Landsat images from multiple years to compensate the lack of images in a single year.
Results indicated the MSAC model provided a Landsat-based solution to capture the inter-annual leaf senescence of saltcedar. Compared to traditional NDVI-based phenological approaches, the proposed model achieved a more accurate classification results of saltcedar across years. The MSAC model provides unique opportunities to guide the selection of appropriate remotely sensed image for repetitive saltcedar mapping.
To determine the level of work morbidity with temporary disability by occupations, we used information from work records and medical certificates of permanent workers of an oil refinery, who have been working in the same occupation and department or workplace for 8 years or more. Sex had an influence on the studied morbidity, and on most of the diagnostic groups when we applied ratio difference tests. Those occupations that contributed the most to morbidity and the most frequent causes of disability were identified. This type of study provides the family physician that takes care of a working population with useful information.
To determine the level of work morbidity with temporary disability by departments or workplaces, we use information from work records and medical certificates of permanent workers of an oil refinery, who have been working in the same occupation and department or workplace for 8 years or more. Sex had an influence on the studied morbidity, and similarly, the differences were statistically significant in the group of workers with frequent diseases when we applied ratio difference tests.
Those departments that contributed the most to morbidity and the most frequent causes of disability were identified. This type of study provides the family physician who takes care of a working population with useful information. Los rasgos analizados son: Separation of spatial- temporal patterns 'climatic modes' by combined analysis of really measured and generated numerically vector time series. The new method of decomposition of the Earth's climate system into well separated spatial- temporal patterns 'climatic modes' is discussed.
The method is based on: The application of the method allows by means of vector time series generated numerically by the INM RAS Coupled Climate Model [2] to separate from real SST anomalies data [3] two climatic modes possessing by noticeably different time scales: Possible applications of spatial- temporal climatic patterns concept to prognosis of climate system evolution is discussed. Full Text Available Antecedentes. El compromiso bilateral fue del Funcionalmente no fue posible homogeneizar un instrumento pre y posoperatorio.
Full Text Available Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio- temporal fusion problem in both research and practice.
First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment.
Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.
Full Text Available Objetivo: The used methodology follows the guidelines marked by the Health Council, as for processes and indicators that are picked up in the Annual Plan of Inspection. We highlight the lack of the sanitary professionals' responsible for the benefit prescription of the of Temporary Disability DT formation. Unanimity exists regarding the necessity of unifying approaches to administrate the DT. It would be interesting staying according to this document with other Medical Units for Disabilities Valuation that carries out its work in the Andalusia Autonomous Community, being able to extend to other Autonomies.
Full Text Available Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency.
Our approach is based on the availability of multi- temporal satellite images i. Seeds corresponding to clouds are firstly extracted through a pixel-to-pixel comparison between the images contained in time series the presence of a cloud is here assumed to be related to a high variation of reflectance between two images. Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi- temporal satellite images.
Our preliminary experiments show the good performances of our method. In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery. A computational framework to generate daily temperature maps using time- series of publicly available MODIS MOD11A2 product Land Surface Temperature LST images 1 km resolution; 8-day composites is illustrated using temperature measurements from the national network of meteorological stations.
Temporal relationships between awakening cortisol and psychosocial variables in inpatients with anorexia nervosa - A time series approach. The aim of the study was to investigate the characteristics of the awakening salivary cortisol in patients with anorexia nervosa AN using a time series design. Patients collected salivary cortisol daily upon awakening. In addition, before retiring, the patients answered questions daily on the handheld regarding disorder-related psychosocial variables. The analysis of cortisol and diary data was conducted by using a time series approach.
Time series showed that the awakening cortisol of the AN patients was elevated as compared to a control group. Cortisol measurements of patients with LSS essentially fluctuated in a stationary manner around a constant mean. The series of patients with HSS were generally less stable; four HSS patients showed a non-stationary cortisol awakening series. Antipsychotic medication did not change awakening cortisol in a specific way.
The lagged dependencies between cortisol and depressive feelings became significant for four patients. Here, higher cortisol values were temporally associated with higher values of depressive feelings. Upon awakening, the cortisol of all AN patients was in the standard range but elevated as compared to healthy controls. Evaluation of agreement between temporal series obtained from electrocardiogram and pulse wave. Leikan, GM; Rossi, E.
Heart rate variability allows to study the cardiovascular autonomic nervous system modulation. Usually, this signal is obtained from the electrocardiogram ECG. A simpler method for recording the pulse wave PW is by means of finger photoplethysmography PPG , which also provides information about the duration of the cardiac cycle. In this study, the correlation and agreement between the time series of the intervals between heartbeats obtained from the ECG with those obtained from the PPG, were studied.
Signals analyzed were obtained from young, healthy and resting subjects. For statistical analysis, the Pearson correlation coefficient and the Bland and Altman limits of agreement were used. Results show that the time series constructed from the PW would not replace the ones obtained from ECG. Analyzing temporal changes in maximum runoff volume series of the Danube River.
Several hypotheses claim that more extremes in climatic and hydrologic phenomena are anticipated. In order to verify such hypotheses it is inevitable to examine the past periods by thoroughly analyzing historical data. In the present study, the annual maximum runoff volumes with t-day durations were calculated for a year series of mean daily discharge of Danube River at Bratislava gauge Slovakia.
Statistical methods were used to clarify how the maximum runoff volumes of the Danube River changed over two historical periods and The conclusion is that the runoff volume regime during floods has not changed significantly during the last years. Automated land cover change detection: Full Text Available and methodologies on sequential time series extracted from satellite data. A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series.
Full Text Available Abstract Background There is an increasing need for processing and understanding relevant information generated by the systematic collection of public health data over time.
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However, the analysis of those time series usually requires advanced modeling techniques, which are not necessarily mastered by staff, technicians and researchers working on public health and epidemiology. Here a user-friendly tool, EPIPOI, is presented that facilitates the exploration and extraction of parameters describing trends, seasonality and anomalies that characterize epidemiological processes.
It also enables the inspection of those parameters across geographic regions. Although the visual exploration and extraction of relevant parameters from time series data is crucial in epidemiological research, until now it had been largely restricted to specialists. Its friendly interface guides users intuitively through useful comparative analyses including the comparison of spatial patterns in temporal parameters. A prototype has already been used to assist researchers in a variety of contexts from didactic use in public health workshops to the main analytical tool in published research.
Conclusions EPIPOI can assist public health officials and students to explore time series data using a broad range of sophisticated analytical and visualization tools. It also provides an analytical environment where even advanced users can benefit by enabling a higher degree of control over model assumptions, such as those associated with detecting disease outbreaks and pandemics. Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion. To better understand the progression of cotton root rot within the season, time series monitoring is required.
Then, the phenology of healthy cotton and infected cotton was modeled using a logistic model. The logistic model could describe the phenology curves with R-squared values from 0. Moreover, the phenology curve of infected cotton showed a significant difference from that of healthy cotton. A Fast Fourier Transform was used to decompose the series into dynamic parameters: A classification is made based on those parameters with larger information content inter- and intra-annual variability, achieving a map of 18 areas of phenological behaviour.
However, the successor states of the SU in Central Asia face on-going environmental damages and soil degradation that are endangering the sustainability of agricultural production. Classification trees were generated by interpreting multitemporal normalized difference vegetation index data and crop phenological knowledge. Assessments based on image-derived validation samples showed good accuracy. Official statistics were found to be of limited use for analyzing the plausibility of the results, because they hardly represent the area that is cropped in the very dry study region.
Overlaying a historical soil map illustrated that initially sierozems were preferred for irrigated agriculture, but later the less favorable solonchaks and solonetzs were also explored, illustrating the strategy of agricultural expansion in the Aral Sea Basin. Winter wheat cultivation doubled between and to approximately , ha demonstrating its growing relevance for modern Uzbekistan.
The spatial- temporal approach used enhances the understanding of natural conditions before irrigation is employed and supports decision-making for investments in irrigation infrastructure and land cultivation throughout the Landsat era. En Colombia, estas series parecen mostrar una tendencia general de crecimiento.
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms EEGs , electrooculograms EOGs , electrocardiograms, and electromyograms EMGs.
We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography PSG signals EEG, EMG, and EOG , and that can exploit the temporal context of each s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier.
Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available.
As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost. In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems e. Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions.
To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing.
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The objective will be achieved through the following aims: Taking invasive saltcedar as an example, four methods i. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of. Tierra y Casas en Chile. Tierra y Casas en Chile This note presents previously unpublished data on prices of agricultural land in Chile's central region and houses both rental and purchasing prices in Santiago, with the corresponding methodology.
As such, it is a follow-up to an Appendix in Morande Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution spatial and temporal NDVI time- series datasets, which face some limitations including high computation loads and unreasonable assumptions.
Analysis of series temporal vegetation obtained by tele detection as tool for a tracking processes of desertification; Analisis de series temporales de vegetacion obtenidas mediante teledeteccion como herramienta para el seguimiento de procesos de desertificacion. Full Text Available Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. The test over a forest site shows high accuracy average difference: Experiments over more complex landscape and long-term time- series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations.
The proposed method will benefit land surface process research, which requires a dense NDVI time- series dataset with high spatial resolution. Remote Sensing of River Delta Inundation: Full Text Available River deltas belong to the most densely settled places on earth. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes.
Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal , but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series , which indicate inundation duration in each of the deltas.
Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid. Anomalous origin of the left coronary artery from the pulmonary artery ALCAPA is a congenital cardiac anomaly with low incidence and a broad clinical spectrum. Its main form of presentation is congestive heart failure due to dilated cardiomyopathy. We reviewed clinical histories and collected 5 consecutive ALCAPA cases; its clinical symptoms, diagnosis and treatment were described.
All five patients were discharged in better clinical conditions and continue attending to periodic medical follow-up. Las complicaciones son frecuentes, principalmente, retraso del desarrollo psicomotor, del crecimiento pondoestatural, difcultades alimentarias y const The topic of this paper is temporal interpolation of precipitation observed by weather radars.
Precipitation measurements with high spatial and temporal resolution are, in general, desired for urban drainage applications. An advection-based interpolation method is developed which uses methods Full Text Available Objectives. The presented research problem concerns data regularities for an unspecified time series based on an approach to the expert formalisation of knowledge integrated into a decision-making mechanism.
A context-free grammar, consisting of a modification of universal temporal grammar, is used to describe regularities. Using the rules of the developed grammar, an expert can describe patterns in the group of time series. A multi-dimensional matrix pattern of the behaviour of a group of time series is used in a real-time decision-making regime in the expert system to implements a universal approach to the description of the dynamics of these changes in the expert system.
The multidimensional matrix pattern is specifically intended for decision-making in an expert system; the modified temporal grammar is used to identify patterns in the data. A syntactically oriented converter of descriptions is developed. A schema for the creation and application of matrix patterns in expert systems is drawn up. The advantage of the implementation of the proposed hybrid approaches consists in a reduction of the time taken for identifying temporal patterns and an automation of the matrix pattern of the decision-making system based on expert descriptions verified using live data derived from relationships in the monitoring data.
Based on the Latent Pattern and Morphology. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization.
Specifically, spatio- temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k -means and the Back-Propagation neural networks BP-Net. The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k - c DBA as the optimal time series clustering algorithm for our study.
The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST. Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data.
The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by The minimum sensitivity MS of the paddy rice classification has also been improved.
The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.
In order to determine the level of occupational morbidity with temporary disability according to organizative units, we used the information taken from the working records and from the scientific and medical records of 1 permanent workers with more than 8 years of work. The grouped sections and the organizative units were identified with the highest levels of morbidity, as well as with the most frequent causes of disability.
The longer they had worked the higher the severity index was. This type of study provides useful information to the family physician giving attention at a working center. Full Text Available Objetivo. The entertainment has taken the shape of transmedia storytelling, in which the way media is consumed is as important as the way it is produced. This article attempts to exemplify the current narrative and media situation with the case of the TV show Dexter. This corpus has, on one hand, interesting characteristics in the story it narrates.
In turn, the Dexter universe is another example of the convergence culture, since it has narrations in different media, and a large number of fans active in the consumption and production of that world. First, the paper describes briefly the plot of the show, to then approach the characteristics of its story. After that, it is analyzed its insertion in the convergence culture, investigating the influence of its transmedia narrative in.
However, other structurally-related compounds are reported to exert neuroprotective activity and are also included in food for human consumption. Sin embargo, algunos comportamientos de estas series hacen que el modelo no sea apropiado. Una de las razones para ello puede ser la no linealidad de esos comportamientos. Se propone tratar estas series con modelos TAR modelo autorregresivo por tramos; dichos modelos se definen por una variable umbral, por lo que en general resulta ser un modelo temporal no lineal.
Un modelo de este tipo se formula como una serie temporal con su rezago como variable umbral, donde d es un entero positivo denominado retardo umbral. Full Text Available Reliable multi- temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy.
For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series.
Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer MODIS land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method.
To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function.
We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter EKF. The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data. Detecting trends in forest disturbance and recovery using yearly Landsat time series: LandTrendr — Temporal segmentation algorithms.
We introduce and test LandTrendr Landsat-based detection of Trends in Disturbance and Recovery , a new approach to extract spectral trajectories of land surface change from yearly Landsat time- series stacks LTS. The method brings together two themes in time- series analysis of LTS: Temporal relationship between antibiotic use and respiratory virus activities in the Republic of Korea: Inappropriate use of antibiotics increases resistance and reduces their effectiveness. Despite evidence-based guidelines, antibiotics are still commonly used to treat infections likely caused by respiratory viruses.
In this study, we examined the temporal relationships between antibiotic usage and respiratory infections in the Republic of Korea. The number of monthly antibiotic prescriptions and the incidence of acute respiratory tract infections between and at all primary care clinics were obtained from the Korean Health Insurance Review and Assessment Service.
The monthly detection rates of respiratory viruses, including adenovirus, respiratory syncytial virus, influenza virus, human coronavirus, and human rhinovirus, were collected from Korea Centers for Disease Control and Prevention. Cross-correlation analysis was conducted to quantify the temporal relationship between antibiotic use and respiratory virus activities as well as respiratory infections in primary clinics. The monthly use of different classes of antibiotic, including penicillins, other beta-lactam antibacterials, macrolides and quinolones, was significantly correlated with influenza virus activity.
These correlations peaked at the 0-month lag with cross-correlation coefficients of 0. This relationship indicates that interventions aimed at reducing influenza cases in addition to effort to discourage the prescription of antibiotics by physicians may help to decrease unnecessary antibiotic consumption. The North Atlantic Oscillation represents a significant mode of atmospheric variability for the Arctic and sub- Artic climate system.
Developing a longer-term record of the spatial and temporal variability of the NAO could improve our understanding of natural climate variability in the region. Previous work has shown a significant relationship between Greenland ice core records and the NAO. Here, we have compared sea-salt and dust records from nine ice cores around the Arctic region to sea level pressure and NAO indices to evaluate the extent to which these ice cores can be used to reconstruct the NAO.
Wang, Zhuosen; Schaaf, Crystal B. The traditional Landsat Albedo Shuai et al. The available cloud free Landsat 5 albedos due to clouds, generated every 16 days at best were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites Harvard Forest in , Santa Rita in and Walker Branch in These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network NEON , and thus represent locations which will be served by spatially paired albedo measures in the near future.
The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error RMSE less than 0. Se buscaron los siguientes datos: Por el contrario, Temporal change in deep-sea benthic ecosystems: Societal concerns over the potential impacts of recent global change have prompted renewed interest in the long-term ecological monitoring of large ecosystems.
The deep sea is the largest ecosystem on the planet, the least accessible, and perhaps the least understood. Nevertheless, deep-sea data collected over the last few decades are now being synthesised with a view to both measuring global change and predicting the future impacts of further rises in atmospheric carbon dioxide concentrations.
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For many years, it was assumed by many that the deep sea is a stable habitat, buffered from short-term changes in the atmosphere or upper ocean. However, recent studies suggest that deep-seafloor ecosystems may respond relatively quickly to seasonal, inter-annual and decadal-scale shifts in upper-ocean variables. In this review, we assess the evidence for these long-term i. We have identified 11 deep-sea sedimented ecosystems for which published analyses of long-term biological data exist. At three of these, we have found evidence for a progressive trend that could be potentially linked to recent climate change, although the evidence is not conclusive.
At the other sites, we have concluded that the changes were either not significant, or were stochastically variable without being clearly linked to climate change or climate variability indices. For chemosynthetic ecosystems, we have identified 14 sites for which there are some published long-term data. Data for temporal changes at chemosynthetic ecosystems are scarce, with few sites being subjected to repeated visits.
However, the limited evidence from hydrothermal vents suggests that at fast-spreading centres such as the East Pacific Rise, vent communities are impacted on decadal scales. The spatiotemporal dynamic patterns of vegetation in mining area are still unclear. Combining with the shape features of the fitted trajectory, this paper extracted five vegetation dynamic patterns including pre-disturbance type, continuous disturbance type, stabilization after disturbance type, stabilization between disturbance and recovery type, and recovery after disturbance type.
The result indicated that recovery after disturbance type was the dominant vegetation change pattern among the five types of vegetation dynamic pattern, which accounted for