The first real-time worldwide ionospheric predictions network: an advance in support of spaceborne experimentation, on-line model validation, and space weather

We report on the first realtime ionospheric predictions network and its capabilities to ingest a global database and forecast F-layer characteristics and "in situ" electron densities along the track of an orbiting spacecraft. A global network of ionosonde stations reported around-the-clock observations of F-region heights and densities, and an on-line library of models provided forecasting capabilities. Each model was tested against the incoming data; relative accuracies were intercompared to determine the best overall fit to the prevailing conditions; and the best-fit model was used to predict ionospheric conditions on an orbit-to-orbit basis for the 12-hour period following a twice-daily model test and validation procedure. It was found that the best-fit model often provided averaged (i.e., climatologically-based) accuracies better than 5% in predicting the heights and critical frequencies of the F-region peaks in the latitudinal domain of the TSS-1R flight path. There was a sharp contrast however, in model-measurement comparisons involving predictions of actual, unaveraged, along-track densities at the 295 km orbital altitude of TSS-1R In this case, extrema in the first-principle models varied by as much as an order of magnitude in density predictions, and the best-fit models were found to disagree with the "in situ" observations of Ne by as much as 140%. The discrepancies are interpreted as a manifestation of difficulties in accurately and self-consistently modeling the external controls of solar and magnetospheric inputs and the spatial and temporal variabilities in electric fields, thermospheric winds, plasmaspheric fluxes, and chemistry.

Chemical characterisation of wild populations of Thymus from different climatic regions in southeast Spain

Thymus is taxonomically a very complex genus with a high frequency of hybridisation and introgression among sympatric species. The variation in accumulation of leaf-surface flavonoids was investigated in 71 wild populations of Thymus front different putative hybrid swarm areas in Andalucia, Spain. Twenty-two flavones, five flavanones, two dihydroflavonols, a flavonol and two unknowns were detected by HPLC-DAD combined with LC-APCI-MS analysis. The majority of compounds were flavones with a lutelin-type substitution of the B-ring, in contrast to previous reports on Macedonian taxa, which predominantly accumulate flavones with apigenin-type substitution of the B-ring. Anatomical and morphometric studies, supported by cluster analysis, identified pure Thymus hyemalis and Thymus baeticus populations, and a large number of putative hybrids. Flavonoid variation was closely related to morphological variation in all populations and is suspected to be a result of genetic polymorphism. Principal component analysis identified the presence of species-specific and geographically linked chemotypes and putative hybrids with mixed morphological and chemical characteristics. Qualitative and quantitative flavonoid accumulation appears to be genetically regulated, while external factors play a secondary role. Flavonoid profiles can thus provide diagnostic markers for the taxonomy of Thymus and are also useful in detecting hybridising taxa. (C) 2007 Elsevier Ltd. All rights reserved.

Beyond words: Aesthetic knowledge and knowing in design

Aesthetic knowledge comes from practitioners understanding the look, feel, smell, taste and sound of things. It is vital to work in many organizational contexts. In this paper, we explore aesthetic knowledge and knowing in organizations through detailed observation of design work in the architectural practice Edward Cullinan Architects. Through our research, we explore aesthetic knowledge in the context of architectural work, we unpack what it is, how it is generated, and how it is applied in design projects, shared between practitioners and developed at the level of the organization. Our analysis suggests that aesthetic knowledge plays an important part in organizational practice, not only as the symbolic context for work, but as an integral part of the work that people do. It suggests that aesthetic reflexivity, which involves an opening up and questioning of what is known, is experienced as part of practice as well as a `time out' from practice.

Multi-agent building control in shared environment

Multi-agent systems have been adopted to build intelligent environment in recent years. It was claimed that energy efficiency and occupants' comfort were the most important factors for evaluating the performance of modem work environment, and multi-agent systems presented a viable solution to handling the complexity of dynamic building environment. While previous research has made significant advance in some aspects, the proposed systems or models were often not applicable in a "shared environment". This paper introduces an ongoing project on multi-agent for building control, which aims to achieve both energy efficiency and occupants' comfort in a shared environment.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Peripersonal space: a multisensory interface for body-object interactions

Research in the last four decades has brought a considerable advance in our understanding of how the brain synthesizes information arising from different sensory modalities. Indeed, many cortical and subcortical areas, beyond those traditionally considered to be ‘associative,’ have been shown to be involved in multisensory interaction and integration (Ghazanfar and Schroeder 2006). Visuo-tactile interaction is of particular interest, because of the prominent role played by vision in guiding our actions and anticipating their tactile consequences in everyday life. In this chapter, we focus on the functional role that visuo-tactile processing may play in driving two types of body-object interactions: avoidance and approach. We will first review some basic features of visuo-tactile interactions, as revealed by electrophysiological studies in monkeys. These will prove to be relevant for interpreting the subsequent evidence arising from human studies. A crucial point that will be stressed is that these visuo-tactile mechanisms have not only sensory, but also motor-related activity that qualifies them as multisensory-motor interfaces. Evidence will then be presented for the existence of functionally homologous processing in the human brain, both from neuropsychological research in brain-damaged patients and in healthy participants. The final part of the chapter will focus on some recent studies in humans showing that the human motor system is provided with a multisensory interface that allows for continuous monitoring of the space near the body (i.e., peripersonal space). We further demonstrate that multisensory processing can be modulated on-line as a consequence of interacting with objects. This indicates that, far from being passive, the monitoring of peripersonal space is an active process subserving actions between our body and objects located in the space around us.

The treatment of mental contamination: a case series

The recommended treatment for obsessive-compulsive disorder (OCD) is cognitive behavior therapy (CBT) incorporating exposure and response prevention (ERP), which is effective for approximately 50% of patients. However, there has been little advance in treatment outcomes since the introduction of ERP in 1979. It has been suggested that some progress can be made in treating contamination obsessions and washing compulsions by addressing feelings of dirtiness and contamination that arise without physical contact with a tangible contaminant. To date, the treatment of these “mental contamination” fears in OCD has not been systematically explored. This paper reports on a case series of 12 participants with OCD who received 10 to 20 sessions of a CBT-based treatment for mental contamination. At the end of treatment, 7 participants no longer met the diagnostic criteria for OCD and mental contamination and these gains were maintained at 6-month follow-up. The clinical implications of these findings are discussed.

Pollen-based biomes for Beringia 18,000, 6000 and 0 14C yr B.P.

The objective biomization method developed by Prentice et al. (1996) for Europe was extended using modern pollen samples from Beringia and then applied to fossil pollen data to reconstruct palaeovegetation patterns at 6000 and 18,000 14C yr bp. The predicted modern distribution of tundra, taiga and cool conifer forests in Alaska and north-western Canada generally corresponds well to actual vegetation patterns, although sites in regions characterized today by a mosaic of forest and tundra vegetation tend to be preferentially assigned to tundra. Siberian larch forests are delimited less well, probably due to the extreme under-representation of Larix in pollen spectra. The biome distribution across Beringia at 6000 14C yr bp was broadly similar to today, with little change in the northern forest limit, except for a possible northward advance in the Mackenzie delta region. The western forest limit in Alaska was probably east of its modern position. At 18,000 14C yr bp the whole of Beringia was covered by tundra. However, the importance of the various plant functional types varied from site to site, supporting the idea that the vegetation cover was a mosaic of different tundra types.

Decomposition of surface electromyographic signal using Hidden Markov Model

The detection of physiological signals from the motor system (electromyographic signals) is being utilized in the practice clinic to guide the therapist in a more precise and accurate diagnosis of motor disorders. In this context, the process of decomposition of EMG (electromyographic) signals that includes the identification and classification of MUAP (Motor Unit Action Potential) of a EMG signal, is very important to help the therapist in the evaluation of motor disorders. The EMG decomposition is a complex task due to EMG features depend on the electrode type (needle or surface), its placement related to the muscle, the contraction level and the health of the Neuromuscular System. To date, the majority of researches on EMG decomposition utilize EMG signals acquired by needle electrodes, due to their advantages in processing this type of signal. However, relatively few researches have been conducted using surface EMG signals. Thus, this article aims to contribute to the clinical practice by presenting a technique that permit the decomposition of surface EMG signal via the use of Hidden Markov Models. This process is supported by the use of differential evolution and spectral clustering techniques. The developed system presented coherent results in: (1) identification of the number of Motor Units actives in the EMG signal; (2) presentation of the morphological patterns of MUAPs in the EMG signal; (3) identification of the firing sequence of the Motor Units. The model proposed in this work is an advance in the research area of decomposition of surface EMG signals.