Policy Responses to Large Accidents (Proceedings of the Conference on Policy Responses to Large Accidents, IIASA, 16-17 January 1989)

During 1988, the IIASA Risk Task Force invited a group of experts to form a collaborating network for a study on national and international responses to the accident at Chernobyl. This study dealt with responses of society to traumatic events which are able to change social structures for an extended time period. Specific issues included were: HOW do authorities react, what is the role and behavior of the media system, what are the decision making structures, how do international coordination systems function. The documentation of this activity will be published by a commercial publisher. During this work a need arose to broaden IIASA's exposure to opinions and results from experts in the field of risk research. For this reason a Conference on "Policy Responses to Large Accidents" was held at IIASA from 16-17 January, 1989. The papers presented at this conference have been compiled into this volume. They are included as delivered by the authors without additional editing

Cell Death and Disease: a new journal for a central area of pathophysiology

If pathophysiology were a fan propeller, cell death would constitute the pivot. Indeed, most diseases are connected to deregulated cell death in some way. Excessive, unwarranted cell death accounts for pathological cell loss, be it slowly degenerative as in Alzheimer's disease or dramatically acute as in stroke and myocardial infarction. Infectious pathogens manipulate cell death pathways to induce or inhibit the death of host cells at will and to subvert the immune recognition of ‘dangerous’ cell death. Finally, cancer is inexorably linked to a partial suppression of cell death programs in tumor cells, although therapy aims to (re)activate such lethal programs. Cell Death and Disease, a new open-access, online journal aims to provide center stage to fundamental, disease-oriented and translational research in cell death. [Opening paragraph

InAs-AlSb quantum wells in tilted magnetic fields

InAs-AlSb quantum wells are investigated by transport experiments in magnetic fields tilted with respect to the sample normal. Using the coincidence method we find for magnetic fields up to 28 T that the spin splitting can be as large as 5 times the Landau splitting. We find a value of the g-factor of about 13. For small even-integer filling factors the corresponding minima in the Shubnikov-de Haas oscillations cannot be tuned into maxima for arbitrary tilt angles. This indicates the anti-crossing of neighboring Landau and spin levels. Furthermore we find for particular tilt angles a crossover from even-integer dominated Shubnikov-de Haas minima to odd-integer minima as a function of magnetic field

Further Studies of the Impact of Waste Heat Release on Simulated Global Climate: Part I

The general circulation model (GCM) of the United Kingdom Meteorological Office (UKMO) has been used to investigate the impact of an input of waste heat (1.5 x 10e14 watts) into the atmosphere in a small area in the mid-latitude eastern Atlantic Ocean. The results of this experiment have been compared with those of two earlier experiments in which the waste heat was input from two energy parks, one in the Atlantic and one in the Pacific Ocean. The energy park produced significant responses in the surface pressure field, the temperature in the lowest layer of the model, and in the total precipitation distribution. The changes are of the same order of magnitude as the changes found in two earlier energy park experiments, and there are some similarities between changes in this experiment and EX01, especially over the area immediately downstream of the energy park. The results of all three energy park experiments have been investigated using zonal harmonic analysis, and the influence of the energy parks on the positions and amplitudes of waves in the temperature and wind fields are discussed

Learning sequential motor tasks

Many real robot applications require the sequential use of multiple distinct motor primitives. This requirement implies the need to learn the individual primitives as well as a strategy to select the primitives sequentially. Such hierarchical learning problems are commonly either treated as one complex monolithic problem which is hard to learn, or as separate tasks learned in isolation. However, there exists a strong link between the robots strategy and its motor primitives. Consequently, a consistent framework is needed that can learn jointly on the level of the individual primitives and the robots strategy. We present a hierarchical learning method which improves individual motor primitives and, simultaneously, learns how to combine these motor primitives sequentially to solve complex motor tasks. We evaluate our method on the game of robot hockey, which is both difficult to learn in terms of the required motor primitives as well as its strategic elements

Coronavirus infections: epidemiological, clinical and immunological features and hypotheses

Coronaviruses (CoVs) are a large family of enveloped, positivestrand RNA viruses. Four human CoVs (HCoVs), the non-severe acute respiratory syndrome (SARS)-like HCoVs (namely HCoV 229E, NL63, OC43, and HKU1), are globally endemic and account for a substantial fraction of upper respiratory tract infections. Non-SARS-like CoV can occasionally produce severe diseases in frail subjects but do not cause any major (fatal) epidemics. In contrast, SARS like CoVs (namely SARS-CoV and Middle-East respiratory syndrome coronavirus, MERS-CoV) can cause intense short-lived fatal outbreaks. The current epidemic caused by the highly contagious SARS-CoV-2 and its rapid spread globally is of major concern. There is scanty knowledge on the actual pandemic potential of this new SARS-like virus. It might be speculated that SARS-CoV-2 epidemic is grossly underdiagnosed and that the infection is silently spreading across the globe with two consequences: (i) clusters of severe infections among frail subjects could haphazardly occur linked to unrecognized index cases; (ii) the current epidemic could naturally fall into a low-level endemic phase when a significant number of subjects will have developed immunity. Understanding the role of paucisymptomatic subjects and stratifying patients according to the risk of developing severe clinical presentations is pivotal for implementing reasonable measures to contain the infection and to reduce its mortality. Whilst the future evolution of this epidemic remains unpredictable, classic public health strategies must follow rational patterns. The emergence of yet another global epidemic underscores the permanent challenges that infectious diseases pose and underscores the need for global cooperation and preparedness, even during inter-epidemic periods

Learning to predict phases of manipulation tasks as hidden states

Phase transitions in manipulation tasks often occur when contacts between objects are made or broken. A switch of the phase can result in the robot’s actions suddenly influencing different aspects of its environment. Therefore, the boundaries between phases often correspond to constraints or subgoals of the manipulation task. In this paper, we investigate how the phases of manipulation tasks can be learned from data. The task is modeled as an autoregressive hidden Markov model, wherein the hidden phase transitions depend on the observed states. The model is learned from data using the expectation-maximization algorithm. We demonstrate the proposed method on both a pushing task and a pepper mill turning task. The proposed approach was compared to a standard autoregressive hidden Markov model. The experiments show that the learned models can accurately predict the transitions in phases during the manipulation tasks