Structural behavior of uranium dioxide under pressure by LSDA+U calculations

The structural behavior of UO2 under high pressure up to 300GPa has been studied by first-principles calculations with LSDA+U approximation. The results show that a pressure-induced structural transition to the cotunnite-type (orthorhombic Pnma) phase occurs at 38GPa. It agrees well with the experimentally observed ~42 GPa. An isostructural transition following that is also predicted to take place from 80 to 130GPa, which has not yet been observed in experiments. Further high compression beyond 226GPa will result in a metallic and paramagnetic transition. It corresponds to a volume of 90A^3 per cell, in good agreement with a previous theoretical analysis in the reduction of volume required to delocalize 5f states.Comment: 10 pages, 8 figure

Pathologie discale et maladie professionnelle

Non-specific low back pain and sciatica are prevalent diseases among working adults and can sometimes affect the continuation or resumption of employment. Epidemiological studies have shown a higher prevalence of these disorders among workers exposed to cumulative lumbar load (manualhandling, awkward postures of the trunk, whole-body vibrations). In addition, morphological and biomechanical studies have compare disc narrowing and the intensity of lumbar workload.In France, the recognition of spinal diseases as such indemnifiable occupational disease has been possible since 1999. This recognition, although subject to conditions, responds to a strong social demand of employees.

Low back pain, intervertebral disc and occupational diseases

Nonspecific low back pain and sciatica are prevalent diseases among working adults and have become a worrying occupational health issue because they sometimes affect continuation or resumption of employment. Epidemiological studies that based questionnaires on workers\u27 healthcare consumption have shown a higher prevalence of these disorders in certain industrial sectors. Thus, low back disorders are usually more prevalent among workers exposed to cumulative lumbar load such as manual handling, awkward postures of the trunk and whole-body vibrations. In addition, morphological and biomechanical studies have compared disc space narrowing and the intensity of lumbar workload. Although debated, the relationship between disc degeneration and biomechanical work exposures seems to be usually accepted by most authors. In response to a considerable need of prevention and compensation for workers, low back pain and/or disc disease can be recognized as an occupational diseases in several countries but the criteria of recognition remains heterogeneous from one country to another

Chronic low back pain, chronic disability at work, chronic management issues.

Low-back  pain  (LBP)  stands  out  as  the  leading  musculoskeletal  disorder  because  it  is  both  highly prevalent and the disability with which people live for the greatest number of years (1, 2). Reaching a peak between the ages of 30–50 years, LBP affects a population at a time of career advancement (3, 4). Back pain is the most expensive disease in terms of indirect costs due to sickness absence and work disability. Indirect (or productivity) costs contribute 93% to total costs, illustrating the importance of the consequences of the disease for work performance (5, 6). On a personal level, low self-motivation and self-confidence make it harder to initiate the return-to-work (RTW) process, especially when problems at work are related to the reason for sick leave (7, 8). At the workplace level, colleagues take over the tasks of the worker on sick leave, work piles up, or another worker is hired to take over the tasks. Timely RTW is thus of great benefit for both injured workers and their employers. [...

Lifelong Augmentation of Multi-Modal Streaming Autobiographical Memories

Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing and reasoning abilities of the agent develop and further interactions with humans take place. As an example, we show how a kinematic structure learning algorithm reasons a-posteriori about the skeleton of a human hand. A partner can be asked to provide feedback about the augmented memories, which can in turn be supplied to the reasoning processes in order to adapt their parameters. We employ active, multi-modal remembering, so the robot as well as humans can gain insights of both the original and augmented memories. Our framework is capable of storing discrete and continuous data in real-time. The data can cover multiple modalities and several layers of abstraction (e.g. from raw sound signals over sentences to extracted meanings). We show a typical interaction with a human partner using an iCub humanoid robot. The framework is implemented in a platform-independent manner. In particular, we validate its multi platform capabilities using the iCub, Baxter and NAO robots. We also provide an interface to cloud based services, which allow automatic annotation of episodes. Our framework is geared towards the developmental robotics community, as it 1) provides a variety of interfaces for other modules, 2) unifies previous works on autobiographical memory, and 3) is licensed as open source software

Towards the Emergence of Procedural Memories from Lifelong Multi-Modal Streaming Memories for Cognitive Robots

Various research topics are emerging as the demand for intelligent lifelong interactions between robot and humans increases. Among them, we can find the examination of persistent storage, the continuous unsupervised annotation of memories and the usage of data at high-frequency over long periods of time. We recently proposed a lifelong autobiographical memory architecture tackling some of these challenges, allowing the iCub humanoid robot to 1) create new memories for both actions that are self-executed and observed from humans, 2) continuously annotate these actions in an unsupervised manner, and 3) use reasoning modules to augment these memories a-posteriori. In this paper, we present a reasoning algorithm which generalises the robots’ understanding of actions by finding the point of commonalities with the former ones. In particular, we generated and labelled templates of pointing actions in different directions. This represents a first step towards the emergence of a procedural memory within a long-term autobiographical memory framework for robots