Improvement of heart rate recovery after exercise training in older people.

Twenty-four subjects aged 70 and older were retrospectively selected from our archives and screened for symptoms of cardiovascular disease. Baseline exercise test was negative for myocardial ischemia in all subjects. All subjects had completed an 8-week program, performed for a variety of indications and consisting of an aerobic physical training program including 30 minutes of cycling three times per week at 65% to 75% of maximum heart rate achieved at peak exercise test performed at enrollment, an educational intervention, dietary advice, and psychological support. All subjects underwent a cardiopulmonary exercise test (CPX) before and at the end of exercise training. At the end of each CPX, peak oxygen uptake (VO2peak), the rate of increase of ventilation per unit of increase of carbon dioxide production (VE/VCO2slope), and HRR were recorded. Twenty-five healthy subjects younger than 60 with no evidence of exercise-induced myocardial ischemia and not enrolled in any exercise training program were also retrospectively selected from our archives and used as a control group for analyzing HRR. These patients performed two exercise tests several weeks apart. Several studies have shown that changes in vagal tone can be used as an outcome tool that helps identify patients or subjects with or without cardiovascular disease at risk for a cardiovascular event, although the evidence of a prognostic value of HRR in older subjects without cardiovascular disease is rather poor. In this study, exercise training resulted in HRR improvement in healthy elderly subjects, suggesting that exercise training improves vagal/sympathetic balance in older subjects without cardiovascular disease as well. Whether the observed improvement in HRR may have long-term beneficial prognostic effects was not the aim of the study, although a beneficial effect might be postulated, in light of the Framingham dat

A Monte Carlo Event Generator for W Off-shell Pair Production including Higher Order Electromagnetic Radiative Corrections

We present the Monte Carlo event generator {\tt WOPPER} for pair production of $W$'s and their decays at high energy $e^+e^-$ colliders. {\tt WOPPER} includes the effects from finite $W$ width and focusses on the calculation of higher order electromagnetic corrections in the leading log approximation including soft photon exponentiation and explicit generation of exclusive hard photons.Comment: Contribution to the Second Workshop -- Munich, Annecy, Hamburg: $e^+e^-$ Collisions at 500~GeV: The Physics Potential, November 20, 1992, to April 3, 1993. LaTeX, 6 pages + 4 uuencoded EPS figures, IKDA 93/28, SI-93-

Nuclear Ground State Observables and QCD Scaling in a Refined Relativistic Point Coupling Model

We present results obtained in the calculation of nuclear ground state properties in relativistic Hartree approximation using a Lagrangian whose QCD-scaled coupling constants are all natural (dimensionless and of order 1). Our model consists of four-, six-, and eight-fermion point couplings (contact interactions) together with derivative terms representing, respectively, two-, three-, and four-body forces and the finite ranges of the corresponding mesonic interactions. The coupling constants have been determined in a self-consistent procedure that solves the model equations for representative nuclei simultaneously in a generalized nonlinear least-squares adjustment algorithm. The extracted coupling constants allow us to predict ground state properties of a much larger set of even-even nuclei to good accuracy. The fact that the extracted coupling constants are all natural leads to the conclusion that QCD scaling and chiral symmetry apply to finite nuclei.Comment: 44 pages, 13 figures, 9 tables, REVTEX, accepted for publication in Phys. Rev.

Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC)

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/semi-automatic translation framework of LC/LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India

Using CIPP model to evaluate “NTUE 2012 Nepal Snowland school” service-learning project

In 2012, the department of Education, National Taipei University of Education (NTUE), conducted an oversea service-learning project to Nepal. The project was tied with two courses, i.e., Service-Learning Theory and Practice as well as International Education research. There were 9 university students (volunteer teachers) and 4 professors in the NTUE delegation. They served the 150 students of Snowland Ranag Light of Education School at Kathmandu, Nepal. This study aimed to use the CIPP model to evaluate the context, input, process and product of the project. Classroom observations, interviews questionnaires and document analysis were used to collect data following the triangulation principle. The research findings were as follows. 1. The context of the NTUE Nepal service-learning project includes the impact of low birth rate to NTUE, the high demand of international education in Taiwan, and the enhancement of pre- and in-service teacher’s affective domain. 2. The inputs include internal and external human resources in Taiwan and Nepal, funds from government and university, donated supplies collected by students. 3. The process includes the five phases of service-learning, with preparation from October, 2011 to August, 2012; service in the first two weeks of September, 2012; series of celebration activities in mid September at Snowland and autumn at NTUE; while reflection and evaluation phases throughout the entire process. 4. The product was evaluated according to the three instructional objectives, i.e., (1) to learn about Nepali culture and education, (2) to be able to teach Nepali 4th to 9th grade students mandarin Chinese and Taiwanese culture in English, and (3) to demonstrate affection to Snowland or Nepal. Based on the findings, recommendations were made to future practice and research

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

1. Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). 2. We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. 3. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic‐related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi‐sensor/multi‐platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. 4. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate

Fusion of Sentinel-1 data with Sentinel-2 products to overcome non-favourable atmospheric conditions for the delineation of inundation maps

729Sentinel-1 data are an alternative for monitoring flooded inland surfaces during cloudy periods. Supervised classification approaches with a single-trained model for the entire image demonstrate poor accuracy due to confusing backscatter conditions of the inundated areas in relation with the prevailing land cover features. This study follows instead a pixel-centric approach, which exploits the varying backscatter values of each pixel through a time series of Sentinel-1 images to train local Random Forest classification models per 3×3 pixels, and classifies each pixel in the target Sentinel-1 image, accordingly. Reference training data is retrieved from the timely close Sentinel-2-derived inundation maps. This study aims to identify the furthest mean day difference between the target Sentinel-1 image and available Sentinel-2 high accurate inundation maps (kappa coefficient—k > 0.9) that allows for the estimation of credible inundation maps for the Sentinel-1 target date. Various combinations of Sentinel-2 and Sentinel-1 training datasets are examined. The evaluation for eight target dates confirms that a Sentinel-1 inundation map with a k of 0.75 on average can be generated, when mean day difference is less than 30 days. The increment of the considered Sentinel-2 maps allows for the estimation of Sentinel-1 inundation maps with higher accuracy