Showing items 13061 - 13080 of 17083

A framework for cardiac arrhythmia detection from IoT-based ECGs

- He, Jinyuan, Rong, Jia, Sun, Le, Wang, Hua, Zhang, Yanchun, Ma, Jiangang

  • Authors: He, Jinyuan , Rong, Jia , Sun, Le , Wang, Hua , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Journal article
  • Relation: World Wide Web Vol. 23, no. 5 (2020), p. 2835-2850
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  • Description: Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12% of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated method is highly demanded to provide support for physicians. Current attempts for automatic arrhythmia detection can roughly be divided as feature-engineering based and deep-learning based methods. Most of the feature-engineering based methods are suffering from adopting single classifier and use fixed features for classifying all five types of heartbeats. This introduces difficulties in identification of the problematic heartbeats and limits the overall classification performance. The deep-learning based methods are usually not evaluated in a realistic manner and report overoptimistic results which may hide potential limitations of the models. Moreover, the lack of consideration of frequency patterns and the heart rhythms can also limit the model performance. To fill in the gaps, we propose a framework for arrhythmia detection from IoT-based ECGs. The framework consists of two modules: a data cleaning module and a heartbeat classification module. Specifically, we propose two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN). DHCAF is a feature-engineering based approach, in which we introduce dynamic ensemble selection (DES) technique and develop a result regulator to improve classification performance. MCHCNN is deep-learning based solution that performs multi-channel convolutions to capture both temporal and frequency patterns from heartbeat to assist the classification. We evaluate the proposed framework with DHCAF and with MCHCNN on the well-known MIT-BIH-AR database, respectively. The results reported in this paper have proven the effectiveness of our framework. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

A good sheep run. Letters from New South Wales in Scottish newspapers between 1820 and 1850 with potential to influence decisions on emigration

- Hannaford, Graham

  • Authors: Hannaford, Graham
  • Date: 2020
  • Type: Text , Thesis , PhD
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  • Description: The primary aim of this thesis is to contribute to ongoing historical research into migration to and settlement in Australia by Scots. It achieves this by identifying and examining letters sent from the colonies in New South Wales which were printed in historic Scottish newspapers between 1820 and 1850. In examining the material, this thesis argues that the letters had potential to influence emigration decisions by Scots. The study shows some of the ways in which New South Wales was reported in the Scottish press and compares those reports with conditions in Scotland at the time. The comparisons and analyses of the letters, with consideration of their authors and likely readers as well as the newspapers in which they were printed demonstrate that the letters did have potential to influence emigration decisions. Its particular contribution to knowledge arises from demonstrating how mostly private letters which became publicly available through publication in newspapers had potential to influence emigrants’ decisions about moving to Australia. Rather than claiming direct evidence of the publication of particular letters as having influenced emigration, it shows how reporting of conditions in Australia when set into a context of contemporary events and conditions in Scotland had potential to influence decisions. It is grounded in the body of historical research about colonial Australia and sits within this Australian historiographical context. Given the motivations and attractions of Scots to colonial Australia this thesis also engages with techniques and theoretical approaches associated with Scottish diaspora studies, an area of research that often emphasises other Scottish migration patterns to Canada, New Zealand and the USA. When considered together both of these historiographical approaches lend themselves to primary source material analysis and a methodological approach that this doctoral study uses to examine the motivations of Scots who migrated to colonial Australia.
  • Description: Doctor of Philosophy

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 joint scheduling and power control scheme for hybrid I2V/V2V networks

- Nguyen, Bach, Ngo, Duy, Dao, Minh, Duong, Quang-Thang, Okada, Minoru

  • Authors: Nguyen, Bach , Ngo, Duy , Dao, Minh , Duong, Quang-Thang , Okada, Minoru
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Vehicular Technology Vol. 69, no. 12 (2020), p. 15668-15681
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  • Description: In automotive infotainment systems, vehicles using the applications are serviced via continuous infrastructure-to-vehicle (I2V) communications. Additionally, the I2V communications can be combined with vehicle-to-vehicle (V2V) connectivity owing to the small area covered by road side units (RSUs). However, dozens of vehicles have to compete for limited bandwidth when they request service simultaneously in the covered area. In this paper, we propose a joint scheduling and power control scheme for I2V and V2V links in the RSUs' coverage range. Mapping the I2V and V2V links to tuple-links, we formulate a mixed-integer nonlinear programming (MINLP) problem where frequency scheduler and power controller for those tuple-links are jointly designed. Then, we employ the delayed column generation technique and the transmission pattern definition to decompose the MINLP problem into a transmission pattern scheduling problem, as well as a power control problem. Therein, the transmission pattern scheduling problem is solved by linear programming while a greedy power control algorithm is developed. Simulation results with practical parameter settings show that our proposed scheme outperforms several conventional schemes in terms of service disruption and achieved throughput while maintaining throughput fairness among the requesting vehicles. In particular, a high channel number, a small power level number, and a large buffer size at the requesting vehicles are shown to be helpful for our proposed scheme. © 1967-2012 IEEE.

A joint SLM and precoding based PAPR reduction scheme for 5G UFMC cellular networks

- Baig, Imran, Farooq, Umer, Hasan, Najam, Zghaibeh, Manaf, Arshad, Muhammad, Imran, Muhammad

A layered investigation of Chinese in the linguistic landscape : a case study of Box Hill, Melbourne

- Yao, Xiaofang, Gruba, Paul

  • Authors: Yao, Xiaofang , Gruba, Paul
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Australian Review of Applied Linguistics Vol. 43, no. 3 (2020), p. 302-336
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  • Description: Increased attention to urban diversity as a site of study has fostered the recent development of linguistic landscape studies. To date, however, much of the research in this area has concerned the use and spread of English to the exclusion of other global languages. In a case study situated in Box Hill, a large suburb of Melbourne, we adopted a layered approach to investigate the role of Chinese language in Australia. Our data set consisted of hundreds of photographs of street signage in one square block area of the shopping district. Results of our analyses show that signage portrays a variety of code preferences and semiotic choices that in turn reveal insights into the identities, ideologies, and strategies that help to structure the urban environment. As demonstrated in our study, such complexity requires a renewed and situated understanding of key principles of linguistic landscape research (Ben-Rafael & Ben-Rafael, 2015). © John Benjamins Publishing Company

A linearized energy hub operation model at the presence of uncertainties: An adaptive robust solution approach

- Aghamohamadi, Mehrdad, Amjady, Nima, Attarha, Ahmad

  • Authors: Aghamohamadi, Mehrdad , Amjady, Nima , Attarha, Ahmad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International transactions on electrical energy systems Vol. 30, no. 3 (2020), p. n/a
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  • Description: Summary This paper presents a new adaptive robust operation optimization approach for energy hub (EH) to identify the optimal decisions on purchased energy carriers, upstream network interactions, and storing/conversion of the energy resources considering uncertainties. In this regard, a linearized framework for EH operation is first introduced. The proposed model is used to develop an EH including electrical energy, natural gas, and direct heat as inputs and electricity and heat demands as outputs. The electrical input energy is provided considering both purchased energy from upstream market and a photovoltaic (PV) generation, operated by the EH operator (EHO). The proposed approach characterizes the uncertain nature of loads, energy prices, and PV generations through polyhedral uncertainty sets, while the robustness of the proposed model can be controlled using the budget of uncertainty. The proposed adaptive robust model is formulated as a min‐max‐min optimization problem, which cannot be solved directly through an off‐the‐shelf optimization package. Thus, a new method, consisting decomposition + primal cutting plane + duality theory + exact linearization + post‐optimization analysis, is introduced to determine the EH optimal solution. The performance of the proposed approach is evaluated through a comprehensive case study.

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 microservice recommendation mechanism based on mobile architecture

- Wang, Ru, Imran, Muhammad, Saleem, Kashif

  • Authors: Wang, Ru , Imran, Muhammad , Saleem, Kashif
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 152, no. (2020), p.
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  • Description: The microservice recommendation mechanism, as a key supporting technology for providing users with intelligent and proactive application services, is one of the important research contents in mobile social networks. In this paper, a recommendation mechanism of mobile social network service based on user trajectory, trust network and collaborative filtering algorithm is proposed. The user trajectory prediction model based on Generative Adversarial Network (GAN) is introduced into the process of service recommendation, and combined with trust degree to constitute the 3D service recommendation model of user-service-trajectory prediction. This method improves the accuracy and reliability of service recommendation and avoids the blindness and randomness of service recommendation. The simulation experiments show that the service recommendation algorithm has higher accuracy than the traditional collaborative filtering algorithm. © 2019 Elsevier Ltd

A Model for human activity recognition in ambient assisted living

- do Amaral, Wagner, Dantas, Mario, Campos, Fernanda

  • Authors: do Amaral, Wagner , Dantas, Mario , Campos, Fernanda
  • Date: 2020
  • Type: Text , Book chapter
  • Relation: Advances on P2P, Parallel, Grid, Cloud and Internet Computing Chapter 29 p.
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  • Description: This work presents a model for human activity recognition, through an IoT paradigm, using location and movement data, generated from an accelerometer. The activities of five individuals from different age groups were monitored, utilizing IoT devices, using the activities of four of these individuals to train the model and the activities of the remaining individual for test data. For the prediction of the activities, the Extra Trees algorithm was used, where the results of 81.16% accuracy were obtained when only movement data were used, 92.59% when using both movement and location data, and 97.56% when using movement data and synthetic location data.

A multi-objective deep reinforcement learning framework

- Nguyen, Thanh, Nguyen, Ngoc, Vamplew, Peter, Nahavandi, Saeid, Dazeley, Richard, Lim, Chee

  • Authors: Nguyen, Thanh , Nguyen, Ngoc , Vamplew, Peter , Nahavandi, Saeid , Dazeley, Richard , Lim, Chee
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Engineering Applications of Artificial Intelligence Vol. 96, no. (2020), p.
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  • Description: This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark problems (two-objective deep sea treasure environment and three-objective Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal solutions effectively. The proposed framework is generic and highly modularized, which allows the integration of different deep reinforcement learning algorithms in different complex problem domains. This therefore overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the current literature. The proposed framework acts as a testbed platform that accelerates the development of MODRL for solving increasingly complicated multi-objective problems. © 2020 Elsevier Ltd

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 narrative synthesis of childhood injury prevention programs for pre-school children

- Peck, Blake, Terry, Daniel, Ervin, Kaye

  • Authors: Peck, Blake , Terry, Daniel , Ervin, Kaye
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Universal Journal of Public Health Vol. 8, no. 6 (2020), p. 193-197
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  • Description: Childhood injury is the leading cause of death and ongoing disability worldwide. While a cornerstone of injury prevention is education, current injury surveillance data is insufficiently nuanced to inform judgement of their effectiveness. A subsequent dearth of research evidence in the domain of childhood injury means that debate continues in regard to the most appropriate age or developmental stage of the child, as well as the most effective pedagogical approach. When considered together these issues culminate in programs that risk being inadequately targeted that are unable to justify their success in reducing injury and consequently are not sustainable. This paper reports the outcomes of a narrative synthesis approach to the review of literature in the area if childhood injury prevention programs targeted at pre-school children. Three studies met the inclusion criteria. The included studies were of poor quality and lacked convincing evidence of effectiveness due to the methods of evaluation, which in turn have shown to impact on the overall sustainability of each specific program. This paper culminates in recognition that there is insufficient evidence of the effectiveness of childhood injury prevention programs for pre-school children, requiring further high-quality studies to determine their overall effectiveness and longer-term sustainability. Copyright © 2020 by authors, all rights reserved.

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 current limiting and overload protection strategy for droop-controlled voltage-source converters in islanded AC microgrids under grid faulted conditions

- Li, Zi-Lin, Hu, Jiefeng, Chan, Ka Wing

  • Authors: Li, Zi-Lin , Hu, Jiefeng , Chan, Ka Wing
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020,Detroit, MI, USA, USA, 11-15 Oct. 2020 p. 3888-3893
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  • Description: Grid-forming voltage source converter (VSC) plays a vital role in the future renewable energy-based utility grid. Limited by the thermal capability of semiconductor switches, grid-forming VSC-based distributed generation units (DGs) cannot stand excess overcurrent like synchronous generators (SG) during large transient disturbances. In order to protect the VSC from overcurrent and ride through the transient disturbances, a new current limiting and overload protection strategy is proposed in this paper. By properly selecting the maximum current thresholds in the synchronous rotating frame, overcurrent and overload protection are achieved simultaneously. The synchronization among DGs is enhanced by feeding back the output voltage in the q-axis to the active power droop control. A comparison study between the proposed strategy and two existing methods are conducted using a networked microgrid in PSCAD/EMTDC, examining the effectiveness of the proposed strategy. © 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 effective and efficient measure for outlying aspect mining

- Samariya, Durgesh, Aryal, Sunil, Ting, Kai, Ma, Jiangang

  • Authors: Samariya, Durgesh , Aryal, Sunil , Ting, Kai , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 21st International Conference on Web Information Systems Engineering, WISE 2020, Amsterdam. 20-24 October 2020, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12343 LNCS, p. 463-474
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  • Description: Outlying Aspect Mining (OAM) aims to find the subspaces (a.k.a. aspects) in which a given query is an outlier with respect to a given data set. Existing OAM algorithms use traditional distance/density-based outlier scores to rank subspaces. Because these distance/density-based scores depend on the dimensionality of subspaces, they cannot be compared directly between subspaces of different dimensionality. Z-score normalisation has been used to make them comparable. It requires to compute outlier scores of all instances in each subspace. This adds significant computational overhead on top of already expensive density estimation—making OAM algorithms infeasible to run in large and/or high-dimensional datasets. We also discover that Z-score normalisation is inappropriate for OAM in some cases. In this paper, we introduce a new score called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which is independent of the dimensionality of subspaces. This enables the scores in subspaces with different dimensionalities to be compared directly without any additional normalisation. Our experimental results revealed that SiNNE produces better or at least the same results as existing scores; and it significantly improves the runtime of an existing OAM algorithm based on beam search. © 2020, Springer Nature Switzerland AG.

A new insight into the profitability of social housing in Australia : a real options approach

- Conteh, Augustine, Earl, George, Liu, Benjamin, Roca, Eduardo

  • Authors: Conteh, Augustine , Earl, George , Liu, Benjamin , Roca, Eduardo
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Habitat International Vol. 105, no. (2020), p.
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  • Description: There is an acute shortage of social housing in Australia; hence, the supply of social housing needs to be increased. The government, however, lacks the necessary resources to adequately respond to this need. Additionally, the level of private institutional investment in social housing is currently low as social housing investments are perceived to be illiquid, high risk, and with low returns. Through a case study on a social housing renewal project in Garbutt, Townsville, Australia, this paper demonstrates that with the application of the Real Option approach, the profitability of the social housing project is actually significantly higher, compared to what they have been shown to be when traditional methods, such as the net present value, are used. This study offers new insight into the profitability of social housing, which has important implications for policymakers and private sector investors and is the first of its kind in the Australian social housing literature. © 2020 Elsevier Ltd

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