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.

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.

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.

Modeling and control of flatness in cold rolling mill using fuzzy petri nets

Today, having a good flatness control in steel industry is essential to ensure an overall product quality, productivity and successful processing. Flatness error, given as difference between measured strip flatness and target curve, can be minimized by modifying roll gap with various control functions. In most practical systems, knowing the definition of the model in order to have an acceptable control is essential. In this paper, a fuzzy Petri net method for modeling and control of flatness in cold rolling mill is developed. The method combines the concepts of Petri net and fuzzy control theories. It focuses on the fuzzy decision making problems of the fuzzy rule tree structures. The method is able to detect and recover possible errors that can occur in the fuzzy rule of the knowledge-based system. The method is implemented and simulated. The results show that its error is less than that of a PI conventional controller.

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.

Development and measurement properties of a putting skill test for high-level golf

Despite a recent increase in the amount of research investigating performance in golf, a comprehensive putting skill test has not been reported in the peer-reviewed literature. In this study, the Golf Australia Putting Test (GAPT) was developed and a series of measurement properties were assessed. Elite (n = 18) and high-level amateur (HLA; n = 22) participants completed six single putts from various areas on six concentric circles (circle radii = 0.9, 1.5, 3.0, 4.6, 6.1 and 7.6 m). Using a scoring system that rewarded participants for holing putts from longer distances, the maximum score from a single round of the test (i.e. 36 putts) was 27 points. After two rounds of the test were completed by all players, a subsample of participants (elite, n = 15; HLA, n = 7) had their putting performance recorded during tournament play for a period of 90 days to assess criterion (predictive) validity of the test. The reliability, sensitivity and discriminative validity of the GAPT were also assessed. Better agreement between Rounds 1 and 2 scores was noted in the elite group, whilst reliability values were similar for both groups. Further, the GAPT scores were shown to predict players from the elite and high-ability groups with a low classification error. An equation for predicting on-course performance from GAPT scores was also developed. Findings from this study indicate that the GAPT is a valid and reliable tool for high-level players and the GAPT may be used for player evaluation in the field.