Did you see that? Dissociating advanced visual information and ball flight constrains perception and action processes during one-handed catching

The integration of separate, yet complimentary, cortical pathways appears to play a role in visual perception and action when intercepting objects. The ventral system is responsible for object recognition and identification, while the dorsal system facilitates continuous regulation of action. This dual-system model implies that empirically manipulating different visual information sources during performance of an interceptive action might lead to the emergence of distinct gaze and movement pattern profiles. To test this idea, we recorded hand kinematics and eye movements of participants as they attempted to catch balls projected from a novel apparatus that synchronised or de-synchronised accompanying video images of a throwing action and ball trajectory. Results revealed that ball catching performance was less successful when patterns of hand movements and gaze behaviours were constrained by the absence of advanced perceptual information from the thrower's actions. Under these task constraints, participants began tracking the ball later, followed less of its trajectory, and adapted their actions by initiating movements later and moving the hand faster. There were no performance differences when the throwing action image and ball speed were synchronised or de-synchronised since hand movements were closely linked to information from ball trajectory. Results are interpreted relative to the two-visual system hypothesis, demonstrating that accurate interception requires integration of advanced visual information from kinematics of the throwing action and from ball flight trajectory.

Mechanism based emulation of dynamic simulation models : concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.

Advancing nursing practice : redefining the theoretical and practical integration of knowledge

Aim The aim of this paper is to offer an alternative knowing-how knowing-that framework of nursing knowledge, which in the past has been accepted as the provenance of advanced practice. Background The concept of advancing practice is central to the development of nursing practice and has been seen to take on many different forms depending on its use in context. To many it has become synonymous with the work of the advanced or expert practitioner; others have viewed it as a process of continuing professional development and skills acquisition. Moreover, it is becoming closely linked with practice development. However, there is much discussion as to what constitutes the knowledge necessary for advancing and advanced practice, and it has been suggested that theoretical and practical knowledge form the cornerstone of advanced knowledge. Design The design of this article takes a discursive approach as to the meaning and integration of knowledge within the context of advancing nursing practice. Method A thematic analysis of the current discourse relating to knowledge integration models in an advancing and advanced practice arena was used to identify concurrent themes relating to the knowing-how knowing-that framework which commonly used to classify the knowledge necessary for advanced nursing practice. Conclusion There is a dichotomy as to what constitutes knowledge for advanced and advancing practice. Several authors have offered a variety of differing models, yet it is the application and integration of theoretical and practical knowledge that defines and develops the advancement of nursing practice. An alternative framework offered here may allow differences in the way that nursing knowledge important for advancing practice is perceived, developed and coordinated. Relevance to clinical practice What has inevitably been neglected is that there are various other variables which when transposed into the existing knowing-how knowing-that framework allows for advanced knowledge to be better defined. One of the more notable variables is pattern recognition, which became the focus of Benner’s work on expert practice. Therefore, if this is included into the knowing-how knowing-that framework, the knowing-how becomes the knowledge that contributes to advancing and advanced practice and the knowing-that becomes the governing action based on a deeper understanding of the problem or issue.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Common secure index for conjunctive keyword-based retrieval over encrypted data

We consider the following problem: users in a dynamic group store their encrypted documents on an untrusted server, and wish to retrieve documents containing some keywords without any loss of data confidentiality. In this paper, we investigate common secure indices which can make multi-users in a dynamic group to obtain securely the encrypted documents shared among the group members without re-encrypting them. We give a formal definition of common secure index for conjunctive keyword-based retrieval over encrypted data (CSI-CKR), define the security requirement for CSI-CKR, and construct a CSI-CKR based on dynamic accumulators, Paillier’s cryptosystem and blind signatures. The security of proposed scheme is proved under strong RSA and co-DDH assumptions.

Enhancing interdisciplinary learning with a learning management system

Interdisciplinary learning is a form of knowledge production that is increasingly being embraced as an educational approach in higher education. A role of information and communication technologies (ICT) is to enhance interdisciplinary learning. Issues surrounding the mix of interdisciplinary pedagogic methodologies and emerging digital technologies are worthy of investigation. In this paper, the authors report the findings of a study that examined student perceptions of an interdisciplinary course on information technology (IT) and visual design that utilized a learning management system. Using questionnaire instrumentation, the authors sought the perceptions of first-year university students enrolled in a newly formed interdisciplinary IT course. Results indicate that ICT-based interdisciplinary learners prefer a self-directed and collaborative instructional modality, as well as teacher presence and interventions in the online environment. The types of student participation can significantly influence how students perceive ICT-based interdisciplinary learning design.

Business model adaptation in new ventures: are technology-based firms different?

A firm’s business model (BM) is an important driver of its relative performance. Constructive adaptation to elements of the BM can therefore sustain the position in light of changing conditions. This study takes a configurational approach to understanding drivers of business model adaptation (BMA) in new ventures. We investigate the effect of human capital, social capital, and technological environment on BMA. We find that a universal, direct effects, analysis can provide useful information, but also risks painting a distorted picture. Contingent, two-way interactions add further explanatory power, but configurational models combining elements of all three (internal resource, external activities, environment) are superior.

A framework for model integration and holistic modelling of socio-technical systems

This paper presents a layered framework for the purposes of integrating different Socio-Technical Systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another is obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.

Real-time and interactive attacks on DNP3 critical infrastructure using Scapy

The Distributed Network Protocol v3.0 (DNP3) is one of the most widely used protocols, to control national infrastructure. Widely used interactive packet manipulation tools, such as Scapy, have not yet been augmented to parse and create DNP3 frames (Biondi 2014). In this paper we extend Scapy to include DNP3, thus allowing us to perform attacks on DNP3 in real-time. Our contribution builds on East et al. (2009), who proposed a range of possible attacks on DNP3. We implement several of these attacks to validate our DNP3 extension to Scapy, then executed the attacks on real world equipment. We present our results, showing that many of these theoretical attacks would be unsuccessful in an Ethernet-based network.

Posttraumatic growth in post-surgical coronary artery bypass graft patients

Recent research in posttraumatic growth has been applied to people with life-threatening illnesses to optimise recovery. There is a lack of research exploring posttraumatic growth in coronary artery bypass graft patients. This article describes the recovery experience of 14 coronary artery bypass graft patients (13 males and 1 female) at their first outpatient review post-surgery. Grounded theory analysis was used to develop a model of distinct and shared pathways to growth depending on whether patients were symptomatic or asymptomatic pre-coronary artery bypass graft. Outcomes of posttraumatic growth in this sample included action-based healthy lifestyle growth and two forms of cognitive growth: appreciation of life and new possibilities. The model of posttraumatic growth developed in this study may be helpful in guiding future research into promoting posttraumatic growth and behaviour change in coronary artery bypass graft patients.

Considerations for a social and geographical framework for agent-based epidemics

Understanding the dynamics of disease spread is of crucial importance, in contexts such as estimating load on medical services to risk assessment and intervention policies against large-scale epidemic outbreaks. However, most of the information is available after the spread itself, and preemptive assessment is far from trivial. Here, we investigate the use of agent-based simulations to model such outbreaks in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena.Presented in this paper is the initial framework for such a model, detailing implementation of geographical features and generation of social structures. Preliminary results are a promising step towards large-scale simulations and evaluation of potential intervention policies.

Validity of the geriatric depression scale and the collateral source version of the geriatric depression scale in nursing homes

Background Depression is a common psychiatric disorder in older people. The study aimed to examine the screening accuracy of the Geriatric Depression Scale (GDS) and the Collateral Source version of the Geriatric Depression Scale (CS-GDS) in the nursing home setting. Methods Eighty-eight residents from 14 nursing homes were assessed for depression using the GDS and the CS-GDS, and validated against clinician diagnosed depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders (SCID) for residents without dementia and the Provisional Diagnostic Criteria for Depression in Alzheimer Disease (PDCdAD) for those with dementia. The screening performances of five versions of the GDS (30-, 15-, 10-, 8-, and 4-item) and two versions of the CS-GDS (30- and 15-item) were analyzed using receiver operating characteristic (ROC) curves. Results Among residents without dementia, both the self-rated (AUC = 0.75–0.79) and proxy-rated (AUC = 0.67) GDS variations performed significantly better than chance in screening for depression. However, neither instrument adequately identified depression among residents with dementia (AUC between 0.57 and 0.70). Among the GDS variations, the 4- and 8-item scales had the highest AUC and the optimal cut-offs were >0 and >3, respectively. Conclusions The validity of the GDS in detecting depression requires a certain level of cognitive functioning. While the CS-GDS is designed to remedy this issue by using an informant, it did not have adequate validity in detecting depression among residents with dementia. Further research is needed on informant selection and other factors that can potentially influence the validity of proxy-based measures in the nursing home setting.

A network DEA quantity and quality-orientated production model: An application to Australian university research services

The motivation for this analysis is the recently developed Excellence in Research for Australia (ERA) program developed to assess the quality of research in Australia. The objective is to develop an appropriate empirical model that better represents the underlying production of higher education research. In general, past studies on university research performance have used standard DEA models with some quantifiable research outputs. However, these suffer from the twin maladies of an inappropriate production specification and a lack of consideration of the quality of output. By including the qualitative attributes of peer-reviewed journals, we develop a procedure that captures both quality and quantity, and apply it using a network DEA model. Our main finding is that standard DEA models tend to overstate the research efficiency of most Australian universities.

Process querying in Apromore

This paper demonstrates the integration and usage of Process Query Language (PQL), a special-purpose programming language for querying large collections of process models based on process model behavior, in the Apromore open-source process model repository. The resulting environment provides a unique user experience when carrying out process model querying tasks. The tool is useful for researchers and practitioners working with large process model collections, and specifically for those with an interest in model retrieval tasks as part of process compliance, process redesign and process standardization initiatives.

Client learning and the performance of collaborative infrastructure projects

Australia is a leading user of collaborative procurement methods, which are used to deliver large and complex infrastructure projects. Project alliances, Early Contractor Involvement (ECI), and partnering are typical examples of collaborative procurement models. In order to increase procurement effectiveness and value for money (VfM), clients have adopted various learning strategies for new contract development. However client learning strategies and behaviours have not been systematically analysed before. Therefore, the current paper undertakes a literature review addressing the research question “How can client learning capabilities be effectively understood?”. From the resource-based and dynamic capability perspectives, this paper proposes that the collaborative learning capability (CLC) of clients drives procurement model evolution. Learning routines underpinning CLC carry out exploratory, transformative and exploitative learning phases associated with collaborative project delivery. This learning improves operating routines, and ultimately performance. The conceptualization of CLC and the three sequential learning phases is used to analyse the evidence in the construction management literature. The main contribution of this study is the presentation of a theoretical foundation for future empirical studies to unveil effective learning strategies, which help clients to improve the performance of collaborative projects in the dynamic infrastructure market.