68 resultados para passive avoidance


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As there are a myriad of micro organic pollutants that can affect the well-being of human and other organisms in the environment the need for an effective monitoring tool is eminent. Passive sampling techniques, which have been developed over the last decades, could provide several advantages to the conventional sampling methods including simpler sampling devices, more cost-effective sampling campaign, providing time-integrated load as well as representative average of concentrations of pollutants in the environment. Those techniques have been applied to monitor many pollutants caused by agricultural activities, i.e. residues of pesticides, veterinary drugs and so on. Several types of passive samplers are commercially available and their uses are widely accepted. However, not many applications of those techniques have been found in Japan, especially in the field of agricultural environment. This paper aims to introduce the field of passive sampling and then to describe some applications of passive sampling techniques in environmental monitoring studies related to the agriculture industry.

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Recent research has shown that, in general, older professors are rated to have more passive-avoidant leadership styles than younger professors by their research assistants. The current study investigated professors' age-related work concerns and research assistants' favorable age stereotypes as possible explanations for this finding. Data came from 128 university professors paired to one research assistant each. Results show that professors' age-related work concerns (decreased enthusiasm for research, growing humanism, development of exiting consciousness and increased follower empowerment) did not explain the relationships between professor age and research assistant ratings of passive-avoidant and proactive leadership. However, research assistants' favorable age stereotypes influenced the relationships between professor age and research assistant ratings of leadership, such that older professors were rated as more passive-avoidant and less proactive than younger professors by research assistants with less favorable age stereotypes, but not by research assistants with more favorable age stereotypes.

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The report of the Senate Economics References Committee inquiry into corporate tax avoidance comes with the subtitle – “You cannot tax what you cannot see”, with a strong focus on increased transparency. The majority of the 17 recommendations in the interim report relate to improved transparency of the tax affairs of corporate taxpayers. This is a significant step in the right direction. Recent experiences in the war on corporate tax avoidance both in Australia and overseas confirm that “information is power”. Most notably, we have seen increased transparency changing the behaviour of multinational enterprises as well as inducing governments to act.

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The Turnbull Government announced yet another measure aimed at addressing tax base erosion and profit shifting, placing additional requirements on new foreign investment under the existing national interest test. In the last 12 months Australia has seen various reforms within the tax system. However, this latest initiative is a shift as it links Australia’s tax regime with its foreign investment regime. It sends a broader signal to the market that Australia will look beyond the collection of tax revenues to a consideration of national interest.

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Previous research has shown that action tendencies to approach alcohol may be modified using computerized ApproacheAvoidance Task (AAT), and that this impacted on subsequent consumption. A recent paper in this journal (Becker, Jostman, Wiers, & Holland, 2015) failed to show significant training effects for food in three studies: Nor did it find effects on subsequent consumption. However, avoidance training to high calorie foods was tested against a control rather than Approach training. The present study used a more comparable paradigm to the alcohol studies. It randomly assigned 90 participants to ‘approach’ or ‘avoid’ chocolate images on the AAT, and then asked them to taste and rate chocolates. A significant interaction of condition and time showed that training to avoid chocolate resulted in faster avoidance responses to chocolate images, compared with training to approach it. Consistent with Becker et al.'s Study 3, no effect was found on amounts of chocolate consumed, although a newly published study in this journal (Schumacher, Kemps, & Tiggemann, 2016) did do so. The collective evidence does not as yet provide solid basis for the application of AAT training to reduction of problematic food consumption, although clinical trials have yet to be conducted.

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We have already seen major amendments to Australia’s tax regime to tackle base erosion and profit shifting (BEPS). Several more significant measures were announced in the federal budget, most notably the diverted profits tax, aimed at multinationals which shift tax to a lower taxing jurisdiction. Yet to date, a very simple tax minimisation strategy has been largely ignored in the ongoing reforms and was ignored in the federal budget. Excessive debt loading is a problem that not been afforded the same attention as other aggressive tax planning strategies adopted by multinationals.

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The third edition of the Australian Standard AS1742 Manual of Uniform Traffic Control Devices Part 7 provides a method of calculating the sighting distance required to safely proceed at passive level crossings based on the physics of moving vehicles. This required distance becomes greater with higher line speeds and slower, heavier vehicles so that it may return quite a long sighting distance. However, at such distances, there are also concerns around whether drivers would be able to reliably identify a train in order to make an informed decision regarding whether it would be safe to proceed across the level crossing. In order to determine whether drivers are able to make reliable judgements to proceed in these circumstances, this study assessed the distance at which a train first becomes identifiable to a driver as well as their, ability to detect the movement of the train. A site was selected in Victoria, and 36 participants with good visual acuity observed 4 trains in the 100-140 km/h range. While most participants could detect the train from a very long distance (2.2 km on average), they could only detect that the train was moving at much shorter distances (1.3 km on average). Large variability was observed between participants, with 4 participants consistently detecting trains later than other participants. Participants tended to improve in their capacity to detect the presence of the train with practice, but a similar trend was not observed for detection of the movement of the train. Participants were consistently poor at accurately judging the approach speed of trains, with large underestimations at all investigated distances.

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This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.