Structural and spectroscopic properties of the diazocarbene radical (CNN) and its ions CNN(+) and CNN(-): a high-level theoretical investigation

The diazocarbene radical, CNN, and the ions CNN(+) and CNN(-) were investigated at a high level of theory. Very accurate structural parameters for the states X(3)Sigma(-) and A(3)Pi of CNN, and X(2)Pi of both CNN(+) and CNN(-) were obtained with the UCCSD(T) method using correlated-consistent basis functions with extrapolations to the complete basis set limit, with valence only and also with all electrons correlated. Harmonic and anharmonic frequencies were obtained for all species and the Renner parameter and average frequencies evaluated for the Pi states. At the UCCSD(T)/CBS(T-5) level of theory, Delta(f)H(0 K) = 138.89 kcal/mol and Delta(f)H(298 K) = 139.65 kcal/mol were obtained for diazocarbene; for the ionization potential and the electron affinity of CNN, 10.969 eV (252.95 kcal/mol), and 1.743 eV (40.19 kcal/mol), respectively, are predicted. Geometry optimization was also carried out with the CASSCF/MRCI/CBS(T-5) approach for the states X(3)Sigma(-) A(3)Pi, and a(1)Delta of CNN, and with the CASSCF/MRSDCI/aug-cc-pVTZ approach for the states b(1)Sigma(+), c(1)Pi, d(1)Sigma(-), and B(3)Sigma(-), and excitation energies (T(e)) evaluated. Vertical energies were calculated for 15 electronic states, thus improving on the accuracy of the five transitions already described, and allowing for a reliable overview of a manifold of other states, which is expected to guide future spectroscopic experiments. This study corroborates the experimental assignment for the vertical transition X (3)Sigma(-) <- E (3)Pi.

Isomers on the [H, S(2), Cl] potential energy surface: A high level investigation

This study reports a systematic state-of-the-art characterization of new sulfur-chlorine species on the [H, S(2), Cl] potential energy surface. Coupled cluster theory singles and doubles with perturbative contributions of connected triples, using the series of correlation consistent basis sets with extrapolations to the complete basis set limit (CBS), were employed to quantify the energetic quantities involved in the isomerization processes on this surface. The structures and vibrational frequencies are unique for some species and represent the most accurate investigation to date. These molecules are potentially a new route of coupling the sulfur and chlorine chemistries in the atmosphere, and conditions of high concentration of H(2)S (HS) like in volcanic eruptions might contribute to their formation. Also an assessment of the MP2/CBS approach relative to CCSD(T)/CBS provides insights on the expected performance of MP2/CBS on the characterization of polysulfides, and also of more complex systems containing disulfide bridges. (C) 2009 Elsevier B.V. All rights reserved.

Supporting adaptive learning with high level timed petri nets

Supporting adaptive learning is one of the key problems for hypertext-based learning applications. This paper proposed a timed Petri Net based approach that provides adaptation to learning activities by controlling the visualization of hypertext information nodes. Simple examples were given while explaining ways to realize adaptive operations. Future directions were also discussed at the end of this paper.

Dynamically Adapting BDI Agent Architectures based on High-level User Specifications

Users are facing an increasing challenge of managing information and being available anytime anywhere, as the web exponentially grows. As a consequence, assisting them in their routine tasks has become a relevant issue to be addressed. In this paper, we introduce a software framework that supports the development of Personal Assistance Software (PAS). It relies on the idea of exposing a high level user model in order to increase user trust in the task delegation process as well as empowering them to manage it. The framework provides a synchronization mechanism that is responsible for dynamically adapting an underlying BDI agent-based running implementation in order to keep this high-level view of user customizations consistent with it.

Supporting adaptive learning in hypertext environment : a high level timed Petri net based approach

One problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduced related concepts of hypertext learning state space and Petri net, then proposed a high level timed Petri Net based approach to provide some kinds of adaptation for learning activities. Examples were given while explaining ways to realizing adaptive instructions. Possible future directions were also discussed at the end of this paper.

Negative beliefs about low back pain are associated with high pain intensity and high level disability in community-based women

Background Although previous studies have investigated beliefs about back pain in clinical and employed populations, there is a paucity of data examining the beliefs of the broader community. We aimed to characterize the beliefs that community-dwelling women have about back pain and its consequences, and to determine whether those with varying levels of pain intensity and disability differ in their beliefs. Methods 542 community-dwelling women, aged 24 to 80 years, were recruited from a research database. Participants completed a self-administered questionnaire that included detailed demographic information, the Chronic Pain Grade Questionnaire (CPG) and the Back Beliefs Questionnaire (BBQ). The CPG examined individuals' levels of pain intensity and disability, and the BBQ investigated their beliefs about back pain and its consequences. Results 506 (93.4%) women returned the study questionnaire. The mean (SD) BBQ score for the cohort was 30.7 (6.0), indicating generally positive beliefs about back pain. However, those women with high intensity pain and high level disability had a mean (SD) score of 28.5 (5.7) and 24.8 (5.7) respectively, which reflects greater negativity about back pain and its consequences. There was an association between negative beliefs and high pain intensity (OR = 0.94 (95% CI: 0.90, 0.99), p = 0.01) and high level disability (OR = 0.93 (95% CI: 0.89, 0.97), p = 0.001), after adjusting for confounders. Conclusion This study highlights that although women living in the community were generally positive about back pain, subgroups of women with high pain intensity and high level disability were identified who had more pessimistic views. While a causal relationship cannot be inferred from these cross-sectional data, the results suggest that negative beliefs individuals have about back pain may be predictive of chronic, disabling spinal pain.

Supporting adaptive learning in hypertext environments : a high level timed Petri net-based approach

A problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduces related concepts of hypertext learning state space and high level Petri Nets (PNs), then proposes a high level timed PN based approach used to providing kinds of adaptation for learning activities by adjusting time attributes of targeted learning state space. Examples are given while explaining ways to realising adaptive instructions. Possible future directions are also discussed at the end of this paper.

Recognising and monitoring high-level behaviours in complex spatial environments

The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. This paper presents a system for recognising high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviours. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviours of people from observing their trajectories within a real, complex indoor environment.

High level segmentation of instructional videos based on content density

Automatically partitioning instructional videos into topic sections is a challenging problem in e-learning environments for efficient content management and cataloging. This paper addresses this problem by proposing a novel density function to delineate sections underscored by changes in topics in instructional and training videos. The content density function draws guidance from the observation that topic boundaries coincide with the ebb and flow of the 'density' of content shown in these videos. Based on this function, we propose two methods for high-level segmentation by determining topic boundaries. We study the performance of the two methods on eight training videos, and our experimental results demonstrate the effectiveness and robustness of the two proposed high-level segmentation algorithms for learning media.

Deduction of high level spatial relations

The problem of deriving spatial relationships between objects in general requires high level abstract representation, and it would pose difficulties even for human observer. Based on a formalism for spatial layouts proposed earlier [KiV92, VeK921, we present methods for deducing high level spatial relations between objects by an active, sighted agent in a large-scale environment. The deduction of spatial relations is based on simple visual clues, and thus this technique is more feasible than schemes that rely on complex object recognition.

Discrete event simulation enabled high level emulation of a distribution centre

Emulation facilitates the testing of control systems through the use of a simulation model. Typically emulation has focused on low level control, to ensure that resources within a system are commissioned correctly. Higher level control that deals with complex issues such as throughput, in-system time and stacking, has not received as much attention. In this paper, a higher level agent-based emulation framework was proposed. Then an emulation model for a distribution centre is described that can test distribution centre level algorithms directly. This methodology also allows playback of real world operations, making it an ideal tool to analyse problems with performance of commissioned systems.