A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Severe Droughts Becoming Recurrent, More Persistent in Mexico

During recent years, severe and extreme droughts in Mexico and their consequent water deficits have become more recurrent and persistent, according to historic records and the experiences of those who have lived through these events. In Mexico, agriculture consumes more than 85% of the available water. When the available water is insufficient to satisfy agricultural requirements, impacts can be acute. In extreme cases, lack of water has caused severe economic, social, and environmental crises, and recovery from these crises has taken much time and money. The regions that are most affected by drought have some common characteristics: they are the most vulnerable regions, they are more productive than other regions, and they have a greater demand for water than other regions do. The north, northwest, and northeast regions, in which are located the most important irrigation zones and most of the industrial plants, constitute 70% of the country, but these regions receive less than 40% of the country’s total rainfall. The southeast region, constituting 30% of the country, receives 60% or more of the total rain; in this part of the country, the rivers are larger with regular flows, and there are wide humid zones where irrigation is unnecessary. (Figure 1 shows the main hydrogeographic regions of Mexico.) The few remaining nonirrigated areas, which benefit from summer rains, have also been drastically affected by drought, because they do not have alternate sources of viable water or fast response capabilities

Robust Exploration/Exploitation Trade-Offs in Safety-Critical Applications

With regard to future service robots, unsafe exceptional circumstances can occur in complex systems that are hardly to foresee. In this paper, the assumption of having no knowledge about the environment is investigated using reinforcement learning as an option for learning behavior by trial-and-error. In such a scenario, action-selection decisions are made based on future reward predictions for minimizing costs in reaching a goal. It is shown that the selection of safetycritical actions leading to highly negative costs from the environment is directly related to the exploration/exploitation dilemma in temporal-di erence learning. For this, several exploration policies are investigated with regard to worst- and best-case performance in a dynamic environment. Our results show that in contrast to established exploration policies like epsilon-Greedy and Softmax, the recently proposed VDBE-Softmax policy seems to be more appropriate for such applications due to its robustness of the exploration parameter for unexpected situations

Towards Learning of Safety Knowledge from Human Demonstrations

Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, a new kind of hazards being implicated by the robot’s capability to manipulate the environment occurs: hazardous environmental object interactions. One of the open questions in safety research is integrating safety knowledge into robotic systems, enabling these systems behaving safety-conscious in hazardous situations. In this paper a safety procedure is described, in which learning of safety knowledge from human demonstration is considered. Within the procedure, a task is demonstrated to the robot, which observes object-to-object relations and labels situational data as commanded by the human. Based on this data, several supervised learning techniques are evaluated used for finally extracting safety knowledge. Results indicate that Decision Trees allow interesting opportunities

Conceptual Design of a Dynamic Risk-Assessment Server for Autonomous Robots

Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, there is a new kind of hazards being implicated by the robot’s capability to manipulate the environment: Hazardous environmental object interactions. Therefore, the realization of the Dynamic Risk-Assessment approach with special scope on object-interaction risks is addressed in this paper. A server-based architecture is proposed facilitating a feasible integration into robotic systems and realization of software and hardware redundancy as well

Principles of sound ecotoxicology

We have become progressively more concerned about the quality of some published ecotoxicology research. Others have also expressed concern. It is not uncommon for basic, but extremely important, factors to apparently be ignored. For example, exposure concentrations in laboratory experiments are sometimes not measured, and hence there is no evidence that the test organisms were actually exposed to the test substance, let alone at the stated concentrations. To try to improve the quality of ecotoxicology research, we suggest twelve basic principles that should be considered, not when presenting findings to the regulators, but at the stage of experimental design. These principles range from accurately defining the exposure through to carefully considering essential aspects of experimental design as well as unbiased analysis and reporting of the results. Although not all principles will apply to all studies, we offer these principles in the hope that they will improve the quality of the science that is presented to regulators. Science is an evidence-based discipline, and it is important that we and the regulators can trust the evidence presented to us. Significant resources often have to be devoted to refuting the results of poor research when those resources could be utilised more effectively