SMEs and the potential for a collaborative path to environmental responsibility

Globally, the potential for small and medium-sized enterprises (SMEs) to collectively impact negatively on the environment is great. Therefore, the adoption, and maintenance, of environmentally responsible practices by this group of firms is especially critical. Studies of environmental practices successfully implemented by small firms have revealed that relationships with other firms, or other organizations, can contribute to greater awareness of the benefits of such activities and, therefore, enhance the possibility of environmental engagement. Collaborative relationships may provide opportunities for SMEs to overcome some of the barriers to implementing environmental initiatives associated with their size, and/or associated characteristics. This paper focuses on attitudes of SME owner-managers to a variety of environmental issues (including regulation and voluntary standards), and to collaborating with other firms (in either a formal or informal sense). The data this paper draws upon are from two waves of an ongoing longitudinal survey of New Zealand SMEs.

Prediction of pedestrians routes within a built environment in normal conditions

Modelling and prediction of pedestrian routing behaviours within known built environments has recently attracted the attention of researchers across multiple disciplines, owing to the growing demand on urban resources and requirements for efficient use of public facilities. This study presents an investigation into pedestrians' routing behaviours within an indoor environment under normal, non-panic situations. A network-based method using constrained Delaunay triangulation is adopted, and a utility-based model employing dynamic programming is developed. The main contribution of this study is the formulation of an appropriate utility function that allows an effective application of dynamic programming to predict a series of consecutive waypoints within a built environment. The aim is to generate accurate sequence waypoints for the pedestrian walking path using only structural definitions of the environment as defined in a standard CAD format. The simulation results are benchmarked against those from the A* algorithm, and the outcome positively indicates the usefulness of the proposed method in predicting pedestrians' route selection activities. © 2014 Elsevier Ltd. All rights reserved.

A genetic fuzzy system to model pedestrian walking path in a built environment

A study on the pedestrian's steering behaviour through a built environment in normal circumstances is presented in this paper. The study focuses on the relationship between the environment and the pedestrian's walking trajectory. Owing to the ambiguity and vagueness of the relationship between the pedestrians and the surrounding environment, a genetic fuzzy system is proposed for modelling and simulation of the pedestrian's walking trajectory confronting the environmental stimuli. We apply the genetic algorithm to search for the optimum membership function parameters of the fuzzy model. The proposed system receives the pedestrian's perceived stimuli from the environment as the inputs, and provides the angular change of direction in each step as the output. The environmental stimuli are quantified using the Helbing social force model. Attractive and repulsive forces within the environment represent various environmental stimuli that influence the pedestrian's walking trajectory at each point of the space. To evaluate the effectiveness of the proposed model, three experiments are conducted. The first experimental results are validated against real walking trajectories of participants within a corridor. The second and third experimental results are validated against simulated walking trajectories collected from the AnyLogic® software. Analysis and statistical measurement of the results indicate that the genetic fuzzy system with optimised membership functions produces more accurate and stable prediction of heterogeneous pedestrians' walking trajectories than those from the original fuzzy model. © 2014 Elsevier B.V. All rights reserved.

On the multicast lifetime of WANETs with multibeam antennas : Formulation, algorithms, and analysis

We explore the multicast lifetime capacity of energy-limited wireless ad hoc networks using directional multibeam antennas by formulating and solving the corresponding optimization problem. In such networks, each node is equipped with a practical smart antenna array that can be configured to support multiple beams with adjustable orientation and beamwidth. The special case of this optimization problem in networks with single beams have been extensively studied and shown to be NP-hard. In this paper, we provide a globally optimal solution to this problem by developing a general MILP formulation that can apply to various configurable antenna models, many of which are not supported by the existing formulations. In order to study the multicast lifetime capacity of large-scale networks, we also propose an efficient heuristic algorithm with guaranteed theoretical performance. In particular, we provide a sufficient condition to determine if its performance reaches optimum based on the analysis of its approximation ratio. These results are validated by experiments as well. The multicast lifetime capacity is then quantitatively studied by evaluating the proposed exact and heuristic algorithms using simulations. The experimental results also show that using two-beam antennas can exploit most lifetime capacity of the networks for multicast communications. © 2013 IEEE.

Singh v The Queen: A Path Toward Confusion, Redundancy and Doctrinal Inconsistency

In Walkington v The Queen, the English Court of Criminal Appeal enunciated criteria fordetermining whether a building contains parts thereof for purposes of ss 76 and 77 of the CrimesAct 1958 (Vic): burglary and aggravated burglary respectively. In Singh v The Queen, the VictorianCourt of Appeal was confronted with a situation in which a trespassory entry had been made into abuilding that, according to the principles enunciated in Walkington, did not consist of any part orparts. Recognizing that there was scant evidence with which to prove that the accused’s entry hadbeen accompanied by an intention to commit one of the crimes specified in ss 76 and 77, the courtnonetheless affirmed the applicant’s conviction for aggravated burglary under s 77. In so doing,the court reaffirmed its earlier decision in The Queen v Chimirri which held that a trespassoryentry into a building results in continuing trespass for as long as the accused remains in thebuilding. In Chimirri, it was further held that if an accused forms an intention to commit one ofthe specified crimes subsequent to the initial trespassory entry and enters a part of the buildingwith that intention, he or she has committed burglary, aggravated burglary, or both by virtueof the continuing trespass doctrine. The discussion to follow will demonstrate that the court’sreasoning in both Chimirri and Singh is not only flawed, but flies in the face of the very passagesfrom the judgment of Lane LJ in Walkington that were quoted with apparent approval in Singh.The discussion will further demonstrate that the continuing trespass doctrine adds nothing of valueto the law of burglary as it existed prior to Chimirri and Singh; rather, its only effect is to addconfusion and uncertainty to what had been a settled area of the law.

Rapid Physarum Algorithm for shortest path problem

As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time. © 2014 Elsevier B.V.

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

A path model of psychosocial and health behaviour change predictors of excessive gestational weight gain

This study aimed to evaluate a conceptual model of psychosocial, behaviour change, and behavioural predictors of excessive gestational weight gain (GWG). Background: Excessive GWG can place women and their babies at risk of poor health outcomes, including obesity. Models of psychosocial and behaviour change predictors of excessive GWG have not been extensively explored; understanding the mechanisms leading to excess GWG will provide crucial evidence towards the development of effective interventions. Method: Two hundred and eighty-eight pregnant women (≤18 weeks gestation) were recruited to a prospective study. Demographic, psychosocial, health behaviour change, and behavioural factors were assessed at 17 (Time 1, T1) and 33 weeks (Time 2, T2) gestation. Pre-pregnancy and final pregnancy weight were obtained and women were classified with/without excessive GWG. Logistic regressions refined the list of predictors of excessive GWG; variables with p < .1 were included in a path analysis. Results: Age, family income, T2 depression, T2 pregnancy-specific coping, T1 buttocks dissatisfaction, T2 GWG-specific self-efficacy, T1 dietary readiness, T1 dietary importance, and T1 vegetable intake predicted excessive GWG in the logistic regressions and were included in the path model. The baseline path model demonstrated poor fit. Once statistically and theoretically plausible paths were added, adequate model fit was achieved (χ² = 21.61(9), p < .05; RMSEA = .07; CFI = .93); this revised model explained 19.5% of the variance in excessive GWG. Women with high T1 buttocks dissatisfaction were more likely to exhibit low levels of dietary readiness. Women with low dietary readiness were more likely to have a lower vegetable intake, which predicted excessive GWG. Women with higher T2 depressive symptoms were more likely to report lower GWG self-efficacy and gain excessively. Conclusion: Future behavioural GWG trials should consider combining psychosocial and health behaviour change factors to optimise GWG.

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?