Historic Cities Project Task 4 – The Business Surveys: Questionnaire Design, Implementation and Initial Analysis.

The Historic Cities project examined the potential impacts of transport demand management strategies on three case study ‘historic’ cities in England. These cities are York, Cambridge and Norwich, all of which have the following characteristics: - they are cities which pre-date motorised transport, and thus tend to have city centres dominated by narrow streets; - they are all members of the Historic Towns Forum; - they have a high architectural and historic heritage, and attract many tourists each year; - they have severe congestion, and congestion related problems; - the city authorities are faced with the problems of maintaining the environmental quality of the city, while allowing the most efficient use of the transport infrastructure. The focus of the project was how transport demand management policies, particularly parking, pricing and road-space re-allocation, can contribute to the last bullet above. Task 4 in the Historic Cities project examined the predicted effects on the urban economy from a work place parking levy and road user charging. It is thought that a major barrier to the implementation of these instruments is the perception that they will have detrimental impacts on the local economy. This task examines whether this hypothesis is correct by examining the impacts on, and attitudes of, businesses in the case study cities. This working paper describes the survey work that was undertaken and presents the initial analysis of the results. It has the following sections: Section 1: introduces the research; Section 2: describes the policies to be studied; Section 3: describes the development and rationale for the questionnaire; Section 4: describes the sampling process; Section 5: presents the initial analysis of the results; Section 6: gives a summary and conclusions. This is the second Working paper that summaries the Task 4 study. The first working paper (537) outlined the business sector profile for each city. A third working paper (552) will present multi-variate analysis of the dataset

Arkansas Corn and Grain Sorghum Peformance Tests 2017

Corn and grain sorghum performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture. The tests provide information to companies marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating recommendations for producers

Arkansas Soybean Performance Tests 2018

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers

Arkansas Corn and Grain Sorghum Performance Tests 2014

Corn and grain sorghum performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture. The tests provide information to companies marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating recommendations for producers

Arkansas Soybean Performance Tests 2015

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers

Information theoretic approach to interactive learning

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.Comment: 6 page

Easter Greetings 92nd Division Italia

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Still, Lawrence

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