Influence of Size and Weight Variables on the Stability and Control Properties of Heavy Trucks

FH-11-9577This study has determined the influence of variations in truck size and weight constraints on the stability and control properties of heavy vehicles. The size and weight constraints of interest include axle load, gross vehicle weight, length, width, type of multiple-trailer combinations, and bridge formula allowances. Variations in location of the center of gravity of the payload were also considered as a separate subject. The influence of these parametric variations on stability and control behavior was explored by means of both full-scale vehicle tests and computer simulations. In Volume I, the findings of the study are presented in a manner which is intended to inform the non-technical reader and, specifically, the persons concerned with formulating policies and laws regarding truck size and weight. For each size and weight "issue" the stability and control problem areas are addressed and the influence of size and weight variations is quantified. The results are then reviewed in the light of their potential implications for traffic safety. Volumes II and III provide (a) background information concerning test procedures and analytical methods and (b) detailed data

Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance

Aim: To develop a causal understanding of the drivers of Species distribution model (SDM) performance. Location: United Kingdom (UK). Methods: We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model. Results: According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size. Main conclusions: Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile

Towards a university for people-centred development : a case history of reform

Hawkesbury Agricultural College (Australia) reformed their program with a student-centered curriculum and client-centered research and extension. The bases of the Systems Agriculture program are experiential learning and a systems approach. Staff and student learning projects meet the criteria of action research

A research paradigm for systems agriculture.

Commencing in the late 19708 a multidisciplinary group of staff at Hawkesbury Agricultural College embarked on what we now understand as an Action Research Project. Our experiences of agriculture in Australia, UK, Asia, Africa and South America convinced us that agriculture is a complicated human activity involving uncertainty and change. From our interactions with farmers and employers across the agricultural sector we increasingly believed that our graduates were not being sufficiently equipped to cope with this complexity and change - to be professional agriculturalists for the twenty-first century (Macadam and Bawden 1985). We were also conscious ofDahlberg's (1979) assertion that the 'conceptual maps that most people have of agriculture fail to recognise it as the basic interface between people and their environments.' We decided to investigate ways of learning about how to improve problem situations in agriculture. This required the development of insights into the learning-problem-solving- research process, which we elucidate subsequently. Through this process we have come to view problems as 'things that never disappear utterly and that cannot be solved once and for all' (Lakoff and Johnson 1980) in contrast to the present widely held view of problems as puzzles for which, typically, there is a correct solution. To convey this meaning we use here the phrase 'improve problem situations' rather than 'solve problems.' In this paper we will first outline the conditions in Australian agriculture that led us to decide to adopt a systems approach at Hawkesbury, which we are calling systems agriculture. We will follow this with an outline of the methodologies of the approach and relate these to a psychology of learning. For debate during the workshop, we will present our perception of the relative position of systems agriculture in the spectrum of systems approaches to research in agriculture and postulate a model of influences on their evolution. Finally, we will outline our views on the application of systems agriculture in researching complex problem situations in agriculture

Workshop

The challenge in using seasonal forecasting in agriculture is to assess and capture the potential benefits so that the well-being of people is improved in terms of increased food security, protection of the resource base, lower costs or better economic outcomes within the community. This report arises from an ACIAR project involving researchers in Indonesia, Zimbabwe, India and Australia

Back to the future : reflections from Hawkesbury

Systemic development is based on the logic that people can learn to be systemic, and that our efforts to effect changes in our external environment both reflect and influence changes in our 'minds and hearts'. The authors' experience of learning to be systemic is traced through an account of developments at the University of Western Sydney Hawkesbury. Their focus during the 1970s and 1980s was on reform of the agriculture curricula to produce graduates able to play a leadership role in addressing the declining situation in agricultural industries and rural communities. Central to this was the interplay between systems thinking and experiential teaming, learning concepts, educational practice, and institutional expectations and inertia. Arenas encountered included competency-based education, cognitive development, the nature of systemicity, and research and development methodologies. Emerging from this was the notion of systemic praxis and the links of insights and cognition through the Critical Learning System

A lane-departure warning and control system

Intelligent Vehicle-Highway Systems, U-M College of Engineeringhttp://deepblue.lib.umich.edu/bitstream/2027.42/108523/1/84822.pdfDescription of 84822.pdf : Final repor