Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Development of a New Process for Determining Design Year Traffic Demands, TR-528, 2007

Researchers should continuously ask how to improve the models we rely on to make financial decisions in terms of the planning, design, construction, and maintenance of roadways. This project presents an alternative tool that will supplement local decision making but maintain a full appreciation of the complexity and sophistication of today’s regional model and local traffic impact study methodologies. This alternative method is tailored to the desires of local agencies, which requested a better, faster, and easier way to evaluate land uses and their impact on future traffic demands at the sub-area or project corridor levels. A particular emphasis was placed on scenario planning for currently undeveloped areas. The scenario planning tool was developed using actual land use and roadway information for the communities of Johnston and West Des Moines, Iowa. Both communities used the output from this process to make regular decisions regarding infrastructure investment, design, and land use planning. The City of Johnston case study included forecasting future traffic for the western portion of the city within a 2,600-acre area, which included 42 intersections. The City of West Des Moines case study included forecasting future traffic for the city’s western growth area covering over 30,000 acres and 331 intersections. Both studies included forecasting a.m. and p.m. peak-hour traffic volumes based upon a variety of different land use scenarios. The tool developed took goegraphic information system (GIS)-based parcel and roadway information, converted the data into a graphical spreadsheet tool, allowed the user to conduct trip generation, distribution, and assignment, and then to automatically convert the data into a Synchro roadway network which allows for capacity analysis and visualization. The operational delay outputs were converted back into a GIS thematic format for contrast and further scenario planning. This project has laid the groundwork for improving both planning and civil transportation decision making at the sub-regional, super-project level.

Decision criteria for optimizing postemergence johnsongrass control in soybean crops in Argentina

Field studies were established in Zavalla and Oliveros, Argentina, during four years in order to optimize Johnsongrass (Sorghum halepense (L.) Pers.) chemical control by means of the thermal calendar model in comparison with other criteria (weed height or days after sowing). The effect of three application dates of postemergence herbicides was determined by visual control, density of tillers originated from rhizome bud regrowth, and from crown and shoot bud regrowth, and soybean yield. Following the thermal calendar model criterion, applications during the second date afforded the best control. Weed height for the first date showed little variability between experiments but was highly variable in the second and third application dates, achieving in some cases values greater than 120 cm. For all years, no significant differences were detected for crop yield between the first and second application dates, and yields were always lower for the third date. The greatest rhizome bud regrowth was observed for the earliest application date and the highest crown and shoot bud regrowth was determined for the last application date. Parameters associated with control efficiency showed the best behaviour for the second date. However, plant height at this moment may interfere with herbicide application and the variability exhibited by this parameter highlights the risk of determining control timing using only one decision criterion.

Ag Decision Maker, January 2014, Vol. 18, no. 3

A Business Newsletter for Agriculture

Überinfusion von Verbrennungsopfern: häufig und schädlich [Over infusion in burn victims: frequent and injurious]

Major burns are characterized by an initial capillary leak, which requires fluid resuscitation for hemodynamic stabilization. While under resuscitation was the major cause of death until the 1980s, over resuscitation has become an important source of complications, including abdominal compartment syndrome, escharosis, impaired gas exchange with prolonged mechanical ventilation and hospital stay. Fluid over infusion started in the 1990s with an increasing proportion of the fluid delivered within the first 24 h being well above the 4 ml/kg/% burn surface area (BSA) according to the Parkland formula. The first alerts were published in the form of case reports of increased mortality due to abdominal compartment syndrome and respiratory failure. This paper analyses the causes of this fluid over infusion and the ways to prevent it, which include rationing prehospital fluid delivery, avoiding early administration of colloids and prevention by permissive hypovolemia.

Ag Decision Maker, April 2002, Vol. 6, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2003, Vol. 7, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2004, Vol. 8, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2005, Vol. 9, no. 5

A Business Newsletter for Agriculture

Ag Decision Maker, April 2006, Vol. 10, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2007, Vol. 11, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2008, Vol. 12, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2009, Vol. 13, no. 6

A Business Newsletter for Agriculture