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Generalised rational approximation and its application to improve deep learning classifiers

- Peiris, V, Sharon, Nir, Sukhorukova, Nadezda, Ugon, Julien

  • Authors: Peiris, V , Sharon, Nir , Sukhorukova, Nadezda , Ugon, Julien
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Applied Mathematics and Computation Vol. 389, no. (2021), p.
  • Relation: http://purl.org/au-research/grants/arc/DP180100602
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  • Description: A rational approximation (that is, approximation by a ratio of two polynomials) is a flexible alternative to polynomial approximation. In particular, rational functions exhibit accurate estimations to nonsmooth and non-Lipschitz functions, where polynomial approximations are not efficient. We prove that the optimisation problems appearing in the best uniform rational approximation and its generalisation to a ratio of linear combinations of basis functions are quasiconvex even when the basis functions are not restricted to monomials. Then we show how this fact can be used in the development of computational methods. This paper presents a theoretical study of the arising optimisation problems and provides results of several numerical experiments. We apply our approximation as a preprocessing step to deep learning classifiers and demonstrate that the classification accuracy is significantly improved compared to the classification of the raw signals. © 2020
  • Description: This research was supported by the Australian Research Council (ARC), Solving hard Chebyshev approximation problems through nonsmooth analysis (Discovery Project DP180100602 ).  This research was partially sponsored by Tel Aviv-Swinburne Research Collaboration Grant (2019).

2020 International Society of Hypertension global hypertension practice guidelines

- Unger, Thomas, Borghi, Claudio, Charchar, Fadi, Khan, Nadia, Poulter, Neil, Prabhakaran, Dorairaj, Ramirez, Agustin, Schlaich, Markus, Stergiou, George, Tomaszewski, Maciej, Wainford, Richard, Williams, Bryan, Schutte, Aletta

A combined virtual element method and the scaled boundary finite element method for linear elastic fracture mechanics

- Adak, Dibyendu, Pramod, ALN, Ooi, Ean Tat, Natarajan, Sundararajan

  • Authors: Adak, Dibyendu , Pramod, ALN , Ooi, Ean Tat , Natarajan, Sundararajan
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Engineering Analysis with Boundary Elements Vol. 113, no. (2020), p. 9-16
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  • Description: In this paper, we propose a framework that combines the recently introduced virtual element method (VEM) and the scaled boundary finite element method (SBFEM) to evaluate the fracture parameters. The domain is discretized with arbitrary polygons and the element that contains the crack tip is treated within the framework of the SBFEM. This facilitates a semi-analytical treatment of the crack tip singularity allowing the fracture parameters are estimated directly from the definition. The VEM is employed for the rest of the domain. The salient feature of the VEM is that the terms in the stiffness matrix are computed without requiring higher order quadrature schemes. As both the methods satisfy partition of unity and the compatibility condition, the matrices are assembled as in the conventional FEM. The accuracy of the proposed formulation is demonstrated with two standard benchmark examples. The proposed VEM-SBFEM framework yields accurate results. © 2019 Elsevier Ltd

A cross-sectional survey of nursing students' patient safety knowledge

- Levett-Jones, Tracy, Andersen, Patrea, Bogossian, Fiona, Cooper, Simon, Guinea, Stephen, Hopmans, Ruben, McKenna, Lisa, Pich, Jacqui, Reid-Searl, Kerry, Seaton, Philippa

  • Authors: Levett-Jones, Tracy , Andersen, Patrea , Bogossian, Fiona , Cooper, Simon , Guinea, Stephen , Hopmans, Ruben , McKenna, Lisa , Pich, Jacqui , Reid-Searl, Kerry , Seaton, Philippa
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Nurse Education Today Vol. 88, no. (2020), p.
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  • Description: Background: Knowledge provides a foundation for safe and effective nursing practice. However, most previous studies have focused on exploring nursing students' self-reported perceptions of, or confidence in, their level of patient safety knowledge, rather than examining their actual levels of knowledge. Objective: The overarching objective of this study was to examine final year nursing students' levels of knowledge about key patient safety concepts. Design: A cross-sectional design was used for this study. Data collection was undertaken during 2018 using a web-based patient safety quiz with 45 multiple choice questions informed by the Patient Safety Competency Framework for Nursing Students. A Modified Angoff approach was used to establish a pass mark or ‘cut score’ for the quiz. Setting and participants: Nursing students enrolled in the final year of a pre-registration nursing program in Australia or New Zealand were invited to participate in the study. Results: In total, 2011 final year nursing students from 23 educational institutions completed the quiz. Mean quiz scores were 29.35/45 or 65.23% (SD 5.63). Participants achieved highest scores in the domains of person-centred care and therapeutic communication, and lowest scores for infection prevention and control and medication safety. Based on the pass mark of 67.3% determined by the Modified Angoff procedure, 44.7% of students (n = 899) demonstrated passing performance on the quiz. For eight of the institutions, less than half of their students achieved a passing mark. Conclusions: Given the pivotal role that nurses play in maintaining patient safety, the results from this quiz raise important questions about the preparation of nursing students for safe and effective clinical practice. The institutional results also suggest the need for increased curricula attention to patient safety. © 2020 Elsevier Ltd

A distributed and anonymous data collection framework based on multilevel edge computing architecture

- Usman, Muhammad, Jan, Mian, Jolfaei, Alireza, Xu, Min, He, Xiangjian, Chen, Jinjun

  • Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza , Xu, Min , He, Xiangjian , Chen, Jinjun
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
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  • Description: Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.

A DNA toolbox for non-invasive genetic studies of sambar deer (Rusa unicolor)

- Davies, Chris, Wright, Wendy, Wedrowicz, Faye, Hogan, Fiona

  • Authors: Davies, Chris , Wright, Wendy , Wedrowicz, Faye , Hogan, Fiona
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Australian Mammalogy Vol. 42, no. 1 (2020), p. 58-66
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  • Description: Invasive sambar deer (Rusa unicolor) are having significant detrimental impacts on natural environments in south-eastern Australia. Little, however, is known about their ecology, limiting evidence-based management strategies directed at reducing deer impacts. Genetic data, generated from DNA isolated from deer scats, can be used to fill ecological knowledge gaps. This study outlines a non-invasive genetic sampling strategy by which good-quality DNA from a single deer scat can be used to determine (1) species of origin, (2) sex and (3) a unique DNA profile. DNA from deer tissue and sambar deer scat samples were used to develop and optimise molecular methods to collect reliable genetic information. A DNA toolbox is presented that describes how to find, collect and store scat samples, isolate DNA and use molecular markers to generate informative genetic data. Generating genetic data using this approach will support studies aimed at acquiring ecological knowledge about sambar deer. Such knowledge will be critical for developing evidence-based recommendations to improve on-ground management decisions for sambar deer.

A dual scaled boundary finite element formulation over arbitrary faceted star convex polyhedra

- Ooi, Ean Tat, Saputra, Albert, Natarajan, Sundararajan, Ooi, Ean Hin, Song, Chongmin

  • Authors: Ooi, Ean Tat , Saputra, Albert , Natarajan, Sundararajan , Ooi, Ean Hin , Song, Chongmin
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Computational Mechanics Vol. 66, no. 1 (2020), p. 27-47
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  • Description: A novel technique to formulate arbritrary faceted polyhedral elements in three-dimensions is presented. The formulation is applicable for arbitrary faceted polyhedra, provided that a scaling requirement is satisfied and the polyhedron facets are planar. A triangulation process can be applied to non-planar facets to generate an admissible geometry. The formulation adopts two separate scaled boundary coordinate systems with respect to: (i) a scaling centre located within a polyhedron and; (ii) a scaling centre on a polyhedron’s facets. The polyhedron geometry is scaled with respect to both the scaling centres. Polygonal shape functions are derived using the scaled boundary finite element method on the polyhedron facets. The stiffness matrix of a polyhedron is obtained semi-analytically. Numerical integration is required only for the line elements that discretise the polyhedron boundaries. The new formulation passes the patch test. Application of the new formulation in computational solid mechanics is demonstrated using a few numerical benchmarks. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

A guide to the short, long and circular RNAs in hypertension and cardiovascular disease

- Prestes, Priscilla, Maier, Michelle, Woods, Bradley, Charchar, Fadi

  • Authors: Prestes, Priscilla , Maier, Michelle , Woods, Bradley , Charchar, Fadi
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: International Journal of Molecular Sciences Vol. 21, no. 10 (2020)
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  • Description: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in adults in developed countries. CVD encompasses many diseased states, including hypertension, coronary artery disease and atherosclerosis. Studies in animal models and human studies have elucidated the contribution of many genetic factors, including non-coding RNAs. Non-coding RNAs are RNAs not translated into protein, involved in gene expression regulation post-transcriptionally and implicated in CVD. Of these, circular RNAs (circRNAs) and microRNAs are relevant. CircRNAs are created by the back-splicing of pre-messenger RNA and have been underexplored as contributors to CVD. These circRNAs may also act as biomarkers of human disease, as they can be extracted from whole blood, plasma, saliva and seminal fluid. CircRNAs have recently been implicated in various disease processes, including hypertension and other cardiovascular disease. This review article will explore the promising and emerging roles of circRNAs as potential biomarkers and therapeutic targets in CVD, in particular hypertension. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices

- Solyman, Ahmad, Attar, Hani, Khosravi, Mohammad, Menon, Varun, Jolfaei, Alireza, Balasubramanian, Venki, Selvaraj, Buvana, Tavallali, Pooya

  • Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
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  • Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.

A machine learning approach for prediction of pregnancy outcome following IVF treatment

- Hassan, Md Rafiul, Al-Insaif, Sadiq, Hossain, Muhammad, Kamruzzaman, Joarder

  • Authors: Hassan, Md Rafiul , Al-Insaif, Sadiq , Hossain, Muhammad , Kamruzzaman, Joarder
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 32, no. 7 (2020), p. 2283-2297
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  • Description: Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature. © 2018, The Natural Computing Applications Forum.

A narrative review of pharmacotherapy treatment for opioid addiction and application in a community-based model in Victoria, Australia

- Vishwanath, Tejaswini, Cash, Penelope, Penney, Wendy, Cant, Robyn

  • Authors: Vishwanath, Tejaswini , Cash, Penelope , Penney, Wendy , Cant, Robyn
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Mental Health and Addiction Vol. 18, no. 1 (2020), p. 66-76
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  • Description: To review the literature regarding the value and consumer focus of research on opioid replacement therapy (ORT) programs, relevant to the Australian rural context and community pharmacy. We conducted a narrative review of literature published between 1995 and 2015, searching EBSCOhost, PubMed and Google Scholar. Three authors collaborated to screen and synthesise studies. Effectiveness, cost affordability, diversion and retention benefits were reported from pharmacological or prescribers’ or dispensers’ standpoint. Research was fragmented and evaluated contributions of these individual types of stakeholders with less discussion of consumers, or consumers’ everyday experiences of ORT. No studies took into account consumers’ psychosocial aspects or power discourses. We found ORT as a beneficial treatment for opioid dependence is well researched, although overall Australian program evaluation was lacking. A gap within ORT literature should be addressed by making consumer experiences central in designs for future research and for program evaluation. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

A new coupling structure and position detection method for segmented control dynamic wireless power transfer systems

- Li, Xiaofei, Hu, Jiefeng, Wang, Heshou, Dai, Xin, Sun, Yue

  • Authors: Li, Xiaofei , Hu, Jiefeng , Wang, Heshou , Dai, Xin , Sun, Yue
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Power Electronics Vol. 35, no. 7 (2020), p. 6741-6745
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  • Description: In this letter, a new coupling structure for dynamic wireless power transfer (DWPT) systems is proposed. Bipolar coils are symmetrically placed on the transmitter unipolar coils, resulting in natural decoupling between the bipolar coils and the unipolar coils. This special structure can mitigate the self-couplings between the adjacent unipolar transmitter coils and hence facilitate the design of the compensation circuit. Another remarkable advantage of this design is that it can lead to a stable mutual coupling between the transmitter array and the receiver when the receiver moves along the transmitter, making it a natural fit for DWPT applications. Furthermore, to reduce the electromagnetic interference and power loss, an automatic segmented control scheme is implemented, and a position detection method by monitoring the primary current is developed. The feasibility of the proposed coupling structure and the position detection method are verified on a laboratory prototype with 72-V output voltage. The experimental results show that the power fluctuation is within ±2.5%, and system efficiency is around 90%. (This letter is accompanied by a video demonstrating the experimental test). © 2020 IEEE.

A new data driven long-term solar yield analysis model of photovoltaic power plants

- Ray, Biplob, Shah, Rakibuzzaman, Islam, Md Rabiul, Islam, Syed

  • Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
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  • Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.

A new data driven long-term solar yield analysis model of photovoltaic power plants

- Ray, Biplob, Shah, Rakibuzzaman, Islam, M.D. Rabiul, Islam, Syed

  • Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, M.D. Rabiul , Islam, Syed
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
  • Full Text: false
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  • Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.

A note on primal-dual stability in infinite linear programming

- Goberna, Miguel, López, Marco, Ridolfi, Andrea, Vera de Serio, Virginia

  • Authors: Goberna, Miguel , López, Marco , Ridolfi, Andrea , Vera de Serio, Virginia
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Optimization Letters Vol. 14, no. 8 (2020), p. 2247-2263
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  • Description: In this note we analyze the simultaneous preservation of the consistency (and of the inconsistency) of linear programming problems posed in infinite dimensional Banach spaces, and their corresponding dual problems, under sufficiently small perturbations of the data. We consider seven different scenarios associated with the different possibilities of perturbations of the data (the objective functional, the constraint functionals, and the right hand-side function), i.e., which of them are known, and remain fixed, and which ones can be perturbed because of their uncertainty. The obtained results allow us to give sufficient and necessary conditions for the coincidence of the optimal values of both problems and for the stability of the duality gap under the same type of perturbations. There appear substantial differences with the finite dimensional case due to the distinct topological properties of cones in finite and infinite dimensional Banach spaces. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
  • Description: Funding details: Australian Research Council, ARC, DP180100602: http://purl.org/au-research/grants/arc/DP180100602

A pupil-positioning method based on the starburst model

- Yu, Pingping, Duan, Wenjie, Sun, Yi, Cao, Ning, Wang, Zhenzhou, Lu, Guojun

  • Authors: Yu, Pingping , Duan, Wenjie , Sun, Yi , Cao, Ning , Wang, Zhenzhou , Lu, Guojun
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Cmc-Computers Materials & Continua Vol. 64, no. 2 (2020), p. 1199-1217
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  • Description: Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction, disease diagnosis, and psychological and physiological studies. Gaze-tracking systems are an important research topic in the human-computer interaction field. As one of the core modules of the head-mounted gaze-tracking system, pupil positioning affects the accuracy and stability of the system. By tracking eye movements to better locate the center of the pupil, this paper proposes a method for pupil positioning based on the starburst model. The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye. In this paper, we propose a method for detecting the feature points of the pupil edge based on the starburst model, which clusters feature points and uses the RANdom SAmple Consensus (RANSAC) algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center. Our experimental results show that the algorithm has higher precision, higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.
  • Description: Science and Technology Support Plan Project of Hebei Province (grant numbers 17210803D and 19273703D Science and Technology Spark Project of the Hebei Seismological Bureau (grant number DZ20180402056) Education Department of Hebei Province (grant number QN2018095) Polytechnic College of Hebei University of Science and Technology

A review of code blue activations in a single regional Australian healthcare service : a retrospective descriptive study of RISKMAN data

- Porter, Joanne, Peck, Blake, McNabb, Tiffinee, Missen, Karen

  • Authors: Porter, Joanne , Peck, Blake , McNabb, Tiffinee , Missen, Karen
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Clinical Nursing Vol. 29, no. 1-2 (2020), p. 221-227
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  • Description: Background: In the case of life-threatening conditions such as respiratory or cardiac arrest, or the clinical deterioration of the patient, a Code Blue activation may be instigated. A Code Blue activation involves a team of advanced trained clinicians attending the emergency needs of the patient. Aims and objectives: The aim of the study was to explore the number of cases of Code Blue activations, looking at the timing, clinical ward, diagnosis and activation criteria while noting cases where escalation from a Medical Emergency Team (MET) call occurs in one Regional Healthcare Service in Victoria, Australia, over a six-year period. Methods: A quantitative retrospective descriptive study of Code Blue emergencies over a six-year period from June 2010 to June 2016 was conducted. Data collected from the RISKMAN program operating at a single site was imported into SPSS (V 22) for descriptive statistical analysis. A STROBE EQUATOR checklist was used for this study (see File S1). Findings: The majority of Code Blue activations were male (59%, n = 127) and aged between 70 and 89 years of age (43%, n = 93). A Code Blue activation was more likely to occur at 08:00 hr, 14:00 hr or 22:00 hr, corresponding to the nurses’ change in shift, with the majority of Code Blues (27.8%, n = 60) occurring in the emergency department. Cardiac arrest was the main activation criterion with 54.6% (n = 118) cases followed by respiratory arrest (14%, n = 32). Interestingly, 20% (n = 45) of the Code Blue activations were upgraded from a Medical Emergency Team (MET) call. Conclusion: This project has produced several interesting findings surrounding Code Blue activations at one regional healthcare service which are not present in existing literature and is worthwhile for further investigation. Relevance to clinical practice: Understanding Code Blue activation criteria, common timings (month, time of day) and patient demographics ensures clinicians can remain vigilant in watching for the signs of patient deterioration and improve staff preparedness Code Blue events. © 2019 John Wiley & Sons Ltd

A role for MAIT cells in colorectal cancer

- Berzins, Stuart, Wallace, Morgan, Kannourakis, George, Kelly, Jason

  • Authors: Berzins, Stuart , Wallace, Morgan , Kannourakis, George , Kelly, Jason
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: Frontiers in Immunology Vol. 11, no. (2020), p.
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  • Description: MAIT cells are MR1-restricted T cells that are well-known for their anti-microbial properties, but they have recently been associated with different forms of cancer. Several studies have reported activated MAIT cells within the microenvironment of colorectal tumors, but there is conjecture about the nature of their response and whether they are contributing to anti-tumor immunity, or to the progression of the disease. We have reviewed the current state of knowledge about the role of MAIT cells in colorectal cancer, including their likely influence when activated and potential sources of stimulation in the tumor microenvironment. The prospects for MAIT cells being used in clinical settings as biomarkers or as targets of new immunotherapies designed to harness their function are discussed. © Copyright © 2020 Berzins, Wallace, Kannourakis and Kelly.

A shared bus profiling scheme for smart cities based on heterogeneous mobile crowdsourced data

- Kong, Xiangjie, Xia, Feng, Li, Jianxin, Hou, Mingliang, Li, Menglin, Xiang, Yong

  • Authors: Kong, Xiangjie , Xia, Feng , Li, Jianxin , Hou, Mingliang , Li, Menglin , Xiang, Yong
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 2 (2020), p. 1436-1444
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  • Description: Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers' travel requirements. © 2005-2012 IEEE.

A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Shahriar Shafin, Sakib, Bhuiyan, Md Zakirul

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
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  • Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.