Towards a model to explain knowledge sharing in complex organizational environments

Effective knowledge sharing underpins the day-to-day work activities in knowledge-intensive organizational environments. This paper integrates key concepts from the literature towards a model to explain effective knowledge sharing in such environments. It is proposed that the effectiveness of knowledge sharing is determined by the maturity of informal and formal social networks and a shared information and knowledge-based artefact network (AN) in a particular work context. It is further proposed that facilitating mechanisms within the social and ANs, and mechanisms that link these networks, affect the overall efficiency of knowledge sharing in complex environments. Three case studies are used to illustrate the model, highlighting typical knowledge-sharing problems that result when certain model elements are absent or insufficient in a particular environment. The model is discussed in terms of diagnosing knowledge-sharing problems, organizational knowledge strategy, and the role of information and communication technology in knowledge sharing.

A validity-driven approach to the understanding of the personal and societal burden of low back pain : development of a conceptual and measurement model

Introduction: While the importance and magnitude of the burden of low back pain upon the individual is well recognized, a systematic understanding of the impact of the condition on individuals is currently hampered by the lack of an organized understanding of what aspects of a person’s life are affected and the lack of comprehensive measures for these effects. The aim of the present study was to develop a conceptual and measurement model of the overall burden of low back pain from the individual’s perspective using a validity-driven approach.
Methods: To define the breadth of low back pain burden we conducted three concept-mapping workshops to generate an item pool. Two face-to-face workshops (Australia) were conducted with people with low back pain and clinicians and policy-makers, respectively. A third workshop (USA) was held with international multidisciplinary experts. Multidimensional scaling, cluster analysis, participant input and thematic analyses organized participants’ ideas into clusters of ideas that then informed the conceptual model.
Results: One hundred and ninety-nine statements were generated. Considerable overlap was observed between groups, and four major clusters were observed - Psychosocial, Physical, Treatment and Employment - each with between two and six subclusters. Content analysis revealed that elements of the Psychosocial cluster were sufficiently distinct to be split into Psychological and Social, and a further cluster of elements termed Positive Effects also emerged. Finally, a hypothesized structure was proposed with six domains and 16 subdomains. New domains not previously considered in the back pain field emerged for psychometric verification: loss of independence, worry about the future, and negative or discriminatory actions by others.
Conclusions: Using a grounded approach, an explicit a priori and testable model of the overall burden of low back pain has been proposed that captures the full breadth of the burden experienced by patients and observed by experts.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

Explicit state duration HMM for abnormality detection in sequences of human activity

The importance of explicit duration modelling for classification of sequences of human activity and the reliable and timely detection of duration abnormality was highlighted. The normal classes of behavior were designed to highlight the importance of modelling duration given the limitations of the tracking system. It was found that HMM was the weakest model for classification of the unseen normal sequences with 81% accuracy. Long term abnormality was investigated by artificially varying the duration of primary activity in a randomly selected test sequence. The incorporation of duration in models of human behavior is an important consideration for systems seeking to provide cognitive support and to detect deviation in the behavorial patterns.

Exploring spatiotemporal trends in commercial fishing effort of an abalone fishing zone: a GIS-based hotspot model

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100's of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

Explicit invariant solutions associated with nonlinear atmospheric flows in a thin rotating spherical shell with and without west-to-east jets perturbations

A class of non-stationary exact solutions of two-dimensional nonlinear Navier–Stokes (NS) equations within a thin rotating spherical shell were found as invariant and approximately invariant solutions. The model is used to describe a simple zonally averaged atmospheric circulation caused by the difference in temperature between the equator and the poles. Coriolis effects are generated by pseudoforces, which support the stable west-to-east flows providing the achievable meteorological flows. The model is superimposed by a stationary latitude dependent flow. Under the assumption of no friction, the perturbed model describes zonal west-to-east flows in the upper atmosphere between the Ferrel and Polar cells. In terms of nonlinear modeling for the NS equations, two small parameters are chosen for the viscosity and the rate of the earth’s rotation and exact solutions in terms of elementary functions are found using approximate symmetry analysis. It is shown that approximately invariant solutions are also valid in the absence of the flow perturbation to a zonally averaged mean flow.

Role of index bonds in an optimal dynamic asset allocation model with real subsistence consumption

We investigate the role of index bonds in a dynamic consumption and asset allocation model where the rate of real consumption at any given time cannot fall below a fixed level. An explicit form of the optimal consumption and portfolio rule for a class of Constant Relative Risk Aversion (CRRA) utility functions is derived. Consumption increases above the subsistence level only when wealth exceeds a threshold value. Risky investments in equity and nominal bonds are initially proportional to the excess of wealth over a lower bound, and then increase nonlinearly with wealth. The desirability of investing in the risky assets are related to the agent’s risk preference, the equity premium, and the inflation risk premium. The demand for index bonds is also obtained. The results should be useful for the management of defined benefit pension funds, university endowments, and other portfolios which have a withdrawal pre-commitment in real terms.