Landscape with Cottage and Big Tree

Rembrandt van Rijn; 5 3/64 in.x 1 ft. 19/32 in.; etching and pen on paper

Reconnaissance of Red Rock District, Cove Mesa, and Kinusta (tree) Mesa, Arizona /

Red Rock district, Cove mesa, and Kinusta mesa lie in southern Carrizo Mountain region of Apache County, Arizona, 25 miles south of the Utah State line. The Red Rock district lies along the Arizona-New Mexico line, and the two mesas are approximately 15 miles to the west (Plate I). All of these areas are within the boundaries of Navajo reservations in Arizona and New Mexico.

Corn husker and fodder cutter /

"New series" vol. II, no. 10.

The management oversight and risk tree-MORT : including systems developed by the Idaho Operations Office and Aerojet Nuclear Company,

"February 12, 1973."

Mockett's Journal : a collection of interesting matters relating to remarkable personages, ancient buildings, manners and customs, &c., beginning from the 50 : also, particulars of various churches, origin of the reculvers, parochial matters to St. Peter's : with observations on agriculture ... and the price of corn, cattle, and labour ...

Typesigned on p. vii: John Mockett.

Tracts relating to the corn trade and corn laws : including the second report ordered to be printed by the two houses of Parliament /

Goldsmiths'-Kress no. 25512.

Letters on the corn laws, and on the rights of the working classes; originally inserted in the Morning chronicle ...

Mode of access: Internet.

A supplement to Lord Western's letter to Lord John Russell: upon corn laws and commercial distress : with a brief review of the reports of the Manchester Chamber of Commerce, on December 1839, and March 1841 : together with an extract from a letter of R. Cobden, Esq., M.P., of the 12th of Sept., and Lord Western's reply.

Goldsmiths'-Kress no. 32119.

Mode of manufacturing sugar from the corn stalk, and of oil and stearine from lard, &c.

Master microform held by: ResP.

Dent corn inbreds and hybrids resistant to the corn earworm in the South /

Literature cited: p. 13.

A bibliography on forest genetics and forest tree improvement, 1958-59 /

Includes index.

Project risk management:a combined analytic hierarchy process and decision tree approach

Time, cost and quality achievements on large-scale construction projects are uncertain because of technological constraints, involvement of many stakeholders, long durations, large capital requirements and improper scope definitions. Projects that are exposed to such an uncertain environment can effectively be managed with the application of risk management throughout the project life cycle. Risk is by nature subjective. However, managing risk subjectively poses the danger of non-achievement of project goals. Moreover, risk analysis of the overall project also poses the danger of developing inappropriate responses. This article demonstrates a quantitative approach to construction risk management through an analytic hierarchy process (AHP) and decision tree analysis. The entire project is classified to form a few work packages. With the involvement of project stakeholders, risky work packages are identified. As all the risk factors are identified, their effects are quantified by determining probability (using AHP) and severity (guess estimate). Various alternative responses are generated, listing the cost implications of mitigating the quantified risks. The expected monetary values are derived for each alternative in a decision tree framework and subsequent probability analysis helps to make the right decision in managing risks. In this article, the entire methodology is explained by using a case application of a cross-country petroleum pipeline project in India. The case study demonstrates the project management effectiveness of using AHP and DTA.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.