Simulated microgravity increases beta-adrenergic lipolysis in human adipose tissue

The ventrolateral periaqueductal gray is implicated as a component of the neuronal network for audiogenic seizure. This implication is based on immunocytochemical labeling of the proto-oncogene, c-fos, and microinjection studies in the severe substrain of genetically epilepsy-prone rats that exhibits tonic seizures. The present study examines changes in acoustically evoked neuronal responses within the periaqueductal gray in the awake and behaving genetically epilepsy-prone rat as compared to normal Sprague Dawley rats. Two populations of neuronal response were observed in the periaqueductal gray of both genetically epilepsy-prone and normal rats. Most of the neurons exhibited long latencies (>10 ms) and lower thresholds, and were more responsive to the acoustic stimulus. The remainder of the periaqueductal gray neurons exhibited short latencies (<10 ms) and higher thresholds, and exhibited minimal responsiveness to the acoustic stimulus. The mean threshold of periaqueductal gray acoustically evoked neuronal firing of short-latency neurons was significantly higher than normal in the genetically epilepsy-prone rat. The number of acoustically evoked action potentials was significantly elevated in the genetically epilepsy-prone rat, particularly at the highest acoustic intensity and at a repetition rate of 1/2 s. In the genetically epilepsy-prone rat, the number of action potentials exhibited adaptation (habituation) at 1/s as compared to 1/2 s across stimulus intensities. Habituation in normal rats was observed primarily at high intensities (95 dB sound pressure level or above). During wild running and tonic seizures in the genetically epilepsy-prone rat, periaqueductal gray neurons. which had diminished firing rates due to habituation, exhibited a tonic firing pattern. Just (1-5 s) prior to the onset of tonic convulsive behaviors, an increase in the rate of periaqueductal gray tonic firing was observed. These patterns of abnormal neuronal firing suggest that periaqueductal gray neurons may be involved in generation of the tonic seizure behavioral component of audiogenic seizure in the genetically epilepsy-prone rat, which will need confirmation in other audiogenic seizure models.     

Fair multi-agent task allocation for large datasets analysis

MapReduce is a design pattern for processing large datasets distributed on a cluster. Its performances are linked to the data structure and the runtime environment. Indeed, data skew can yield an unfair task allocation, but even when the initial allocation produced by the partition function is well balanced, an unfair allocation can occur during the reduce phase due to the heterogeneous performance of nodes. For these reasons, we propose an adaptive multi-agent system. In our approach, the reducer agents interact during the job and the task reallocation is based on negotiation in order to decrease the workload of the most loaded reducer and so the runtime. In this paper, we propose and evaluate two negotiation strategies. Finally, we experiment our multi-agent system with real-world datasets over heterogeneous runtime environment.     

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

     

Evaluation of direct manipulation using finger tracking for complex tasks in an immersive cube

A solution for interaction using finger tracking in a cubic immersive virtual reality system (or immersive cube) is presented. Rather than using a traditional wand device, users can manipulate objects with fingers of both hands in a close-to-natural manner for moderately complex, general purpose tasks. Our solution couples finger tracking with a real-time physics engine, combined with a heuristic approach for hand manipulation, which is robust to tracker noise and simulation instabilities. A first study has been performed to evaluate our interface, with tasks involving complex manipulations, such as balancing objects while walking in the cube. The user’s finger-tracked manipulation was compared to manipulation with a 6 degree-of-freedom wand (or flystick), as well as with carrying out the same task in the real world. Users were also asked to perform a free task, allowing us to observe their perceived level of presence in the scene. Our results show that our approach provides a feasible interface for immersive cube environments and is perceived by users as being closer to the real experience compared to the wand. However, the wand outperforms direct manipulation in terms of speed and precision. We conclude with a discussion of the results and implications for further research.     

A Spatial Regularization Approach for Vector Quantization

Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.     

Multivariate analysis of human behavior data using fuzzy windowing: Example with driver–car–environment system

In most human component system studies performed in simulators, several factors (or independent variables) (at least two, i.e., individual and time) and many variables (or dependent variables) are present. Large and complex databases have to be analyzed. Instead of using rather automatic procedures, this article suggest that, for a very first analysis at least, the human being must be present and he/she must choose a method being adapted to the data, which is different to run a method supposing that the data fit such or such model. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.     

Experimental and numerical study of the load distribution in a ball-screw system

In this work, load distribution on ball-screw systems (BSS) is determined by experimental techniques. Two optical techniques are used: photoelasticity for stress-field measurement and the mark-tracking method for displacement-field determination. In parallel to the experimental study, finite element method (FEM) and analytical solutions are used to calculate the loads applied on each ball of the BSS. Experimental results are used to validate the choice of boundary conditions and contact conditions between ball-screw and ball-nut in the FEM solution. The validation criterion is the correspondence between numerical and experimental fringes representing the differences of principal stresses. In addition to the study of load distribution, this paper presents the influence of the angle of contact direction on the stress distribution in BSS.