Forum Session at the First International Conference on Service Oriented Computing (ICSOC03)

The First International Conference on Service Oriented Computing (ICSOC) was held in Trento, December 15-18, 2003. The focus of the conference ---Service Oriented Computing (SOC)--- is the new emerging paradigm for distributed computing and e-business processing that has evolved from object-oriented and component computing to enable building agile networks of collaborating business applications distributed within and across organizational boundaries. Of the 181 papers submitted to the ICSOC conference, 10 were selected for the forum session which took place on December the 16th, 2003. The papers were chosen based on their technical quality, originality, relevance to SOC and for their nature of being best suited for a poster presentation or a demonstration. This technical report contains the 10 papers presented during the forum session at the ICSOC conference. In particular, the last two papers in the report ere submitted as industrial papers

The future of animal feeding: towards sustainable precision livestock farming

In the future, production will increasingly be affected by globalization of the trade in feed commodities and livestock products, competition for natural resources, particularly land and water, competition between feed, food and biofuel, and by the need to operate in a carbonconstrained economy, says Nutreco’s Dr. Leo den Hartog. Moreover, he suggests, livestock production will be increasingly affected by consumer and societal concerns and legislation. A way forward in the development of profi table modern pig production will be the concept of sustainable precision livestock farming, den Hartog believes. This aims to integrate the technological approach of precision livestock farming with the social and ecological aspects. Optimization of productivity and effi ciency will play a crucial role, as well as maximization of the profi t for all stakeholders in the pork chain, he says. He discusses the necessity for and rationale behind the concept, with a special focus on animal feedin

Towards understanding predictability in ecology: a forest gap model case study

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Linking remote sensing and various site factors for predicting the spatial distribution of eastern hemlock occurrence and relative basal area in Maine, USA

Introduced invasive pests are perhaps the most important and persistent catalyst for changes in forest composition. Infestation and outbreak of the hemlock woolly adelgid (Adelges tsugae; HWA) along the eastern coast of the USA, has led to widespread loss of hemlock (Tsuga canadensis (L.) Carr.), and a shift in tree species composition toward hardwood stands. Developing an understanding of the geographic distribution of individual species can inform conservation practices that seek to maintain functional capabilities of ecosystems. Modeling is necessary for understanding changes in forest composition, and subsequent changes in biodiversity, and one that can be implemented at the species level. By integrating the use of remote sensing, modeling, and Geographic Information Systems (GIS) coupled with expert knowledge in forest ecology and disturbance, we can advance the methodologies currently available in the literature on predictive modeling. This paper describes an approach to modeling the spatial distribution of the less common but foundational tree species eastern hemlock throughout the state of Maine (∼84,000 km2) at a high resolution. There are currently no published accuracy assessments on predictive models for high resolution continuous distribution of eastern hemlock relative basal area that span the geographic extent covered by our model, which is at the northern limit of the species’ range. A two stage mapping approach was used where presence/absence was predicted with an overall accuracy of 85% and the continuous distribution (percent basal area) was predicted with an accuracy of 84%. Overall, these findings are quite good despite high variability in the training dataset and the general minor component that eastern hemlock represents in the primary forest types in Maine. Eastern hemlock occurs along the southern half of the state stretching the east-west span with little to no occurrence in the northern regions. Several environmental and site characteristics, particularly average yearly maximum and minimum temperatures, were found to be positively correlated with hemlock occurrence. Eastern hemlock dominated stands appeared predominantly in the southwest corner of the state where HWA monitoring efforts can be focused. Given the importance of climate variables in predicting eastern hemlock, forecasts of future range shifts should be possible using data generated from climate scenarios

Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from RPAS in the coast of Peru

Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru

Climate sensitive diameter growth models for major tree species in Mississippi

Anticipated climate change and increasing wood demand require dependable diameter growth models for adaptive forest management. We used a mixed-effects modeling approach with Forest Inventory and Analysis (FIA) data to fit diameter growth models for loblolly pine, other softwood species (slash pine, shortleaf pine, and longleaf pine), sweetgum, and other hardwood (southern red oak, red maple, and water oak) species. Climatic variables coupled with individual tree attributes and competition factors improved climate insensitive models. Growth of loblolly pine and sweetgum was positively correlated with mean temperature of the coldest month. Mean temperature of the warmest month negatively influenced diameter growth of loblolly pine and other hardwood species. Growing season precipitation and summer precipitation balance had negative effects on the growth of softwood and hardwood species, respectively. Inclusion of FIA plot as random effect improved model fit statistics and residual distribution of climate sensitive models. These findings will be useful to managers for recalibrating diameter growth models resulting in improved biomass yield and volume estimates that will better inform decisions

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Monitoring Pollen Counts and Pollen Allergy Index Using Satellite Observations in East Coast of the United States

Allergic diseases have become increasingly common over the world during the last four decades, and they are affecting millions of people. Pollination is an important process in the life cycle of plants. However, pollen exposure is associated with allergic diseases such as asthma and seasonal allergic rhinitis (hay fever). As a result, the total annual expenditure for asthma-associated morbidity is about $56 billion in the United States, and the overall cost of allergic diseases is over$18 billion annually. For allergic rhinitis, the annual medical cost is approximately $3.4 billion. The intensity and frequency of the pollen exposures can be easily affected by many factors such as climate, vegetation, and topography, which are difficult to predict in large scales. Vegetation is very important as a pollen source, and the amount and time of pollinations depend on the flowering and growth of plants. With optimal water and temperature, vegetation can reach a maximum growth and flowering during a growing season, which means that maximum amount of pollen can be released from the plants. However, if the requirements of water and temperature cannot be met in the specific times within the growing season, pollen dispersal will be affected negatively. It is an urgent need to develop models or systems for predicting pollen events at large scales and providing early warning to prevent pollen effects on people. Unlike manual pollen counting at local sites, remote sensing facilitates the pollen estimates at large scales with temporally and spatially distributed observations, which significantly reduces the time and labor costs. With remotely sensed observations, Artificial Neural Network (ANN) helps us fill the gaps in understanding of the relationships between environmental variables and pollen concentration. At this point, I investigated pollen estimates from satellite observations in the states of East Coast United States with short and long-term data. This region is highly populated with a population of 104 million. In addition, this region has a great variety of temperature, precipitation, and vegetation. The final goal of this project is to investigate the relationships between satellite-derived variables (precipitation, land surface temperature (LST), and enhance vegetation index (EVI2)) and pollen count and further to generate a model for the prediction of pollen counts at high temporal and spatial resolutions. For this purpose, to predict pollen concentration using environmental variables, a Neural Network Analysis was performed. The results showed that strong correlations existed between pollen counts and environmental variables, except for precipitation in most locations. The validation analysis using regression models revealed strongly significant relationships between the observed and predicted pollen concentrations obtained for short and long-term data. The R squares (R2) for long term pollen counts were mostly higher than 0.5, ranging from 0.5542 for Olean, NY to 0.8589 for Savannah, GA. For short term predictions of pollen allergy index, R2 ranged from 0.53 to 0.966 except for a few sites, especially in southern Florida. The pollen distribution was mostly affected by precipitation in the southern part, whereas it was influenced by temperature in the northern part. Moreover, results demonstrated that ANN is a suitable tool for complicated statistical analysis and EVI2 combining with LST and precipitation is a reliable predictor of pollen variation. Overall the results provide a better understanding of pollen variation with vegetation seasonality and climate variables, which could assist an approach towards the establishment of an early warning system for allergy patients

Applicability of a flood forecasting system for Nebraska watersheds

Accurate and timely flood prediction can reduce the risk of flooding, bolster preparedness, and help build resilience. In this study, we have developed a flood forecasting system prototype and checked its potential for carrying out operational flood forecasting in the state of Nebraska. This system builds upon some of the core components of the Iowa Flood Information System (IFIS), which is a state-of-the-art platform widely recognized around the world. We implemented our platform on a pilot basin in Nebraska (Elkhorn River basin) by installing eight stream sensors and setting up the hydrologic model component of IFIS, i.e., the Hillslope Link Model (HLM). Due to their importance in the Midwest, we particularly emphasized the snow processes and developed an improved HLM model that can account for different aspects of snow (rain-snow-partitioning, snowmelt, and snow accumulation) through simple parameterizations. Results show that the more thorough treatment of snow processes in the hydrologic model, as proposed herein, leads to better flood peak simulations. In this paper, we discuss different steps involved in developing the flood forecasting system prototype, along with the associated challenges and opportunities. Supplementary materials attached below