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Purpose: Foresight J's journey started in 1999, and in 2022, it marked the conclusion of its 24 years of publication. This paper aims to provide an overall overview of important research trends published in Foresight J between 1999 and 2022 by conducting a quantitative analysis of the journal's literature. The overarching goal is to provide valuable insights into the dynamics of scholarly communication, aiding researchers, institutions and policymakers in assessing the significance and influence of academic work, guiding future research directions and academic evaluation. Design/methodology/approach: The two bibliometrics methodologies that make up the methodology of this article are scientific mapping and performance analysis. Authors have explained the development and composition of the Foresight J using these methods. The SCOPUS database is being used in current research to analyse several dimensions, such as the evolution of publications by year, the most cited papers, core authors and researchers, leading countries and prolific institutions. Moreover, the conceptual structure, scope, burst detection and co-occurrence analysis of the journal are mapped using network visualization software such as VOSviewer, CiteSpace and RStudio. Findings: With a strong track record of output over the years, Foresight J has continued to develop in terms of publications. It is determined that "Saritas" is the author with the greatest overall impact. However, according to SCOPUS bibliometric data, "Blackman" and "Richardson" are the authors with the greatest relevance in terms of the quantity of articles. In addition, it becomes apparent that the USA, Australia and the UK are very productive nations in terms of publications. The most popular fields of the journal have always been forecasting, foresight, scenario planning, strategic planning, decision-making, technology and sustainable development. These are also the author keywords that appear the most frequently. In contrast, new study themes in the Foresight J include digital technologies, innovation, sustainability, blockchain, artificial intelligence and sustainability. Research limitations/implications: Several noteworthy research implications are provided by the bibliometric study of Foresight J. "Saritas" is the author with the most overall impact, indicating that the precise contributions and influence of this researcher in the fields of forecasting, foresight and related fields. Given that "Blackman" and "Richardson" are well-known writers, it is also critical to examine the scope and complexity of their contributions to potentially identify recurring themes or patterns in their writing. The geographic productivity results, which show that the USA, Australia and the UK are the top three countries for Foresight J publications, may encourage more research into regional differences, patterns of collaboration and the worldwide distribution of research endeavours in the context of forecasting and foresight. Popular fields including scenario planning, forecasting, foresight and sustainable development are consistent, indicating persistent research interests. Examining the causes of these subjects' ongoing relevance can reveal information about the consistency and development of scholarly interests over time. Practical implications: Foresight J's bibliometric analysis has real-world applications for many stakeholders. It helps editors and publishers make strategic decisions about outreach and content by providing insights regarding the journal's influence. Assessing organizational and author productivity helps institutions allocate resources more effectively. Policymakers acquire an instrument to evaluate research patterns and distribute funds efficiently. In general, bibliometric study of a journal helps decisionmakers in academic publishing make well-informed choices that maximize the potential of options for authors, editors, institutions and policymakers. Social implications: The societal ramifications of bibliometrically analysing Foresight J from 1999 and 2022 are substantial. This analysis highlights, over the past 24 years, research trends, technological developments and societal priorities have changed by methodically looking through the journal's articles. Gaining knowledge about the academic environment covered by the journal can help raise public awareness of important topics and promote critical thinking. In addition, the analysis can support evidence-based decision-making by alerting decision makers to the influential research that was published in Foresight J. This could have an impact on the course of policies pertaining to innovation, technology and societal development. Originality/value: This study presents a first comprehensive article that provides a general overview of the main trends and patterns of the research over the Foresight J's history since its inception. Also, the paper will help the scientific community to know the value and impact of Foresight J. [ABSTRACT FROM AUTHOR]

Objectives: This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods: Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results: Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion: The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

In recent years, mine site closure and rehabilitation have emerged as significant global challenges. The escalating number of abandoned mines, exemplified by over 60,000 in Australia in 2017, underscores the urgency. Growing public concerns and governmental focus on environmental issues are now jeopardising sustainable mining practices. This paper assesses the role of unmanned aerial vehicles (UAVs) in mine closure, exploring sensor technology, artificial intelligence (AI), and mixed reality (MR) applications. Prior research validates UAV efficacy in mining, introducing various deployable sensors. Some studies delve into AI's use for UAV data analysis, but a comprehensive review integrating AI algorithms with MR methods for mine rehabilitation is lacking. The paper discusses data acquisition methods, repeatability, and barriers toward fully autonomous monitoring systems for mine closure projects. While UAVs prove adaptable with various sensors, constraints such as battery life and payload capacity impact effectiveness. Although UAVs hold potential for AI testing in mine closure studies, these applications have been overlooked. AI algorithms are pivotal for creating autonomous systems, reducing operator intervention. Moreover, MR's significance in mine closure is evident, emphasising its application in the mining industry. Ultimately, a hybrid UAV–AI–MR technology is not only viable but essential for achieving successful mine closure and sustainable mining practices in the future. [ABSTRACT FROM AUTHOR]

Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, while also detailing challenges such as data privacy, ethical considerations, and integration within existing health infrastructures. Overall, this paper aims to provide insights into the potential benefits of artificial intelligence in cardiology, while also acknowledging the barriers that need to be addressed to ensure safe and effective implementation into an Australian health system. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is one of the leading causes of cancer-related death in women. The early detection of breast cancer with screening mammograms plays a pivotal role in reducing mortality rates. Although population-based double-reading screening mammograms have reduced mortality by over 31% in women with breast cancer in Europe, continuing this program is difficult due to the shortage of radiologists. Artificial intelligence (AI) is an emerging technology that has provided promising results in medical imaging for disease detection. This study investigates the performance of AI models on an Australian mammographic database, demonstrating how transfer learning from a USA mammographic database to an Australian one, contrast enhancement on mammographic images and the quality of training data according to radiologists' concordance can improve breast cancer diagnosis. Our proposed methodology offers a more efficacious approach for AI to contribute to radiologists' decision making when interpreting mammography images. This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models. [ABSTRACT FROM AUTHOR]

Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

In light of the growing need to pay attention to general public opinions and sentiments toward AI, this paper examines the levels of understandings amongst the Australian public toward the increased societal use of AI technologies. Drawing on a nationally representative survey of 2019 adults across Australia, the paper examines how aware people consider themselves to be of recent developments in AI; variations in popular conceptions of what AI is; and the extent to which levels of support for AI are liable to alter with additional exposure to information about AI. While a majority of respondents consider themselves to have little knowledge and familiarity with the topic of AI, the survey nevertheless finds considerable range of relatively 'plausible' basic understandings of what AI is. Significantly, repeated questioning highlights a willingness among many people to reassess their opinions once having received further information about AI, and being asked to think through issues relating to AI and society. These patterns remain relatively consistent, regardless of respondents' political orientation, income, social class and other demographic characteristics. As such, the paper concludes by considering how these findings provide support for the development of public education efforts to further enhance what might be termed 'public understanding of AI'. [ABSTRACT FROM AUTHOR]

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a 'soft governance' ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. [ABSTRACT FROM AUTHOR]

Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images. [ABSTRACT FROM AUTHOR]

This paper summarises an evaluation of the application of artificial intelligence to hyperspectral drill-core scans for more effective mineral exploration. The dataset used was based on publicly available core scans and related geochemical analysis from Australia. Prior to unification, a detailed quality assessment of the geochemical data was undertaken. Special focus was paid to gold, silver, copper, iron, uranium, nickel, lead, tin, antimony, arsenic and bismuth contents. The dataset was labelled with defined ore grades related to economic cutoff values. The impact on predictions of different setups is related to the amounts of data used for learning, data design and implementation of the geological domains. Based on 1-metre bins, the results from more than 700 km of drill cores were used and analysed with the potential for geological exploration in different scenarios discussed. The results indicate the enormous potential of the use of hyperspectral scans in combination with artificial intelligence for the development of exploration scenarios and to provide support for exploration geologists and target detection. The application of predictors on scanned drill cores from Australia also indicates mineralised zones that have not been analysed chemically for all metals above economic cutoffs. This result shows the enormous potential of the approach for strategic exploration but also mining operations. Prediction of geochemical concentrations for gold, copper and iron based on a neural network in drill cores is possible. Using mineral abundances from hyperspectral core scans as learning records, and existing elemental geochemical analyses as labels, the predictions are given with an accuracy of better than 80–90%. The trained artificial intelligence system has for the first time enabled direct estimation of metal grades from hyperspectral scans. It also shows potential for applications to analyse airborne hyperspectral data for direct mapping of metal grades. Finally, it may pave the way for better plant management by the usage of hyperspectral data for direct grade estimations in operational mining and ore sorting. [ABSTRACT FROM AUTHOR]

Purpose: Artificial intelligence (AI)‐based systems have demonstrated great potential in improving the diagnostic accuracy of retinal disease but are yet to achieve widespread acceptance in routine clinical practice. Clinician attitudes are known to influence implementation. Therefore, this study aimed to identify optometrists' attitudes towards the use of AI to assist in diagnosing retinal disease. Methods: A paper‐based survey was designed to assess general attitudes towards AI in diagnosing retinal disease and motivators/barriers for future use. Two clinical scenarios for using AI were evaluated: (1) at the point of care to obtain a diagnostic recommendation, versus (2) after the consultation to provide a second opinion. Relationships between participant characteristics and attitudes towards AI were explored. The survey was mailed to 252 randomly selected practising optometrists across Australia, with repeat mail‐outs to non‐respondents. Results: The response rate was 53% (133/252). Respondents' mean (SD) age was 42.7 (13.3) years, and 44.4% (59/133) identified as female, whilst 1.5% (2/133) identified as gender diverse. The mean number of years practising in primary eye care was 18.8 (13.2) years with 64.7% (86/133) working in an independently owned practice. On average, responding optometrists reported positive attitudes (mean score 4.0 out of 5, SD 0.8) towards using AI as a tool to aid the diagnosis of retinal disease, and would be more likely to use AI if it is proven to increase patient access to healthcare (mean score 4.4 out of 5, SD 0.6). Furthermore, optometrists expressed a statistically significant preference for using AI after the consultation to provide a second opinion rather than during the consultation, at the point‐of‐care (+0.12, p = 0.01). Conclusions: Optometrists have positive attitudes towards the future use of AI as an aid to diagnose retinal disease. Understanding clinician attitudes and preferences for using AI may help maximise its clinical potential and ensure its successful translation into practice. [ABSTRACT FROM AUTHOR]

In recent years, a discourse of 'ethical artificial intelligence' has emerged and gained international traction in response to widely publicised AI failures. In Australia, the discourse around ethical AI does not accord with the reality of AI deployment in the public sector. Drawing on institutional ethnographic approaches, this paper describes the misalignments between how technology is described in government documentation, and how it is deployed in social service delivery. We argue that the propagation of ethical principles legitimates established new public management strategies, and pre-empts questions regarding the efficacy of AI development; instead positioning implementation as inevitable and, provided an ethical framework is adopted, laudable. The ethical AI discourse acknowledges, and ostensibly seeks to move past, widely reported administrative failures involving new technologies. In actuality, this discourse works to make AI implementation a reality, ethical or not. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has the potential to be an important part of education governance. It is already being built into everything from business intelligence platforms to real-time online testing. In this paper, we aim to understand how AI becomes, and forms, a legitimate part of authority in contemporary education governance in what we call the automated education governance assemblage, that incorporates technology companies and AI-supported products used in education. We focus on EduTech Australia – an education technology trade show in Sydney – as a way to look at: (i) how the different aspects of automated governance are connected at EduTech, including the relations between different participants, companies and products; and (ii) how the automated governance assemblage works to legitimise and constitute EduTech as a policy space and site of new authorities in education governance. [ABSTRACT FROM AUTHOR]

Although still in its early stages, the Fourth Industrial Revolution (FIR), which involves a broad range of artificial intelligence, robotics and machine learning technologies, will fundamentally change the way many of us work and relate to one another. Driven by technology, this transformation presents a range of challenges, as well as opportunities, where we might expect Human Resource (HR) professionals would lead the way. However, little is currently known in relation to how prepared HR professionals in Australia are to ready their organisations for this new era of work and associated resourcing. Consequently, this paper goes some way towards closing that gap. Specifically, the study reported here sets out to explore the levels of preparedness amongst Australian HRM professionals for the impact of the FIR on organisations, workplaces, jobs and skills, as well as on their own professional roles and competencies. The study utilised a sequential mixed methods research design with two phases, the first was qualitative focus groups (n = 5) followed by a quantitative online survey of selected senior HR practitioners (n = 150). The findings indicate that, while most believe that FIR technologies might be useful for their organisations and assist with improving job performance, increasing productivity and making jobs easier for employees, contrarily many did not intend to use them in the foreseeable future. Marginal support was also evident in relation to the potential contributions of FIR technologies to HR process enhancement and overall HR effectiveness. Most respondents were also not impressed with the lack of current Australian government FIR strategies and policies. Key points: Organisations differ in their development stages.Similarly, there is divergence amongst Australian HR professionals with respect to their understanding of the likely impacts of the Fourth Industrial Revolution, and their preparedness for them.HR professionals' skills, capabilities and competencies need to be upgraded.Governments and professional associations also need to become better prepared to address the challenges. [ABSTRACT FROM AUTHOR]

Objective: Continuing education (CE) is essential for a healthcare workforce, but in regional areas of Australia there are challenges to providing and accessing relevant, reliable and low-cost opportunities. The aim of the present study was to collaborate with the local regional healthcare workforce to design, deliver and evaluate an interdisciplinary CE (ICE) program. Methods: A participatory action research (PAR) model combined with an appreciative inquiry (AI) framework was used to design, deliver and evaluate an ICE program. A focus group of 11 health professionals developed an initial program. Evaluation data from 410 program participants were analysed using AI. Results: The ICE program addressed the CE barriers for the regional healthcare workforce because the locally derived content was delivered at a reasonable cost and in a convenient location. Program participants identified that they most valued shared experiences and opportunities enabling them to acquire and confirm relevant knowledge. Conclusion: ICE programs enhance interdisciplinary collaboration. However, attendance constraints for regional healthcare workforce include location, cost, workplace and personal factors. Through community engagement, resource sharing and cooperation, a local university and the interdisciplinary focus group members successfully designed and delivered the local education and research nexus program to address a CE problem for a regional healthcare workforce. What is known about the topic?: Participation in CE is mandatory for most health professionals. However, various barriers exist for regional health workers to attending CE. Innovative programs, such as webinars and travelling workshops, address some of the issues but create others. Bringing various health workers together for the simultaneous education of multiple disciplines is beneficial. Collectively, this is called ICE. What does this paper add?: Using PAR combined with AI to design an ICE program will focus attention on the enablers of the program and meet the diverse educational needs of the healthcare workforce in regional areas. Engaging regional health professionals with a local university to design and deliver CE is one way to increase access to quality, cost-effective education. What are the implications for practitioners?: Regional healthcare workers' CE needs are more likely to be met when education programs are designed by them and developed for them. ICE raises awareness of the roles of multiple healthcare disciplines. Learning together strengthens healthcare networks by bolstering relationships through a greater understanding of each other's roles. Enriching communication between local health workers has the potential to enhance patient care. [ABSTRACT FROM AUTHOR]

Due to the variability and partial unpredictability of renewable energy sources (RES), generation reserve sizing is key for managing operational risks and enhancing grid reliability. Most existing approaches for reserve sizing estimate the forecasting error for RES using either simple point forecasts, or probabilistic forecasts that assume a predefined parametric model of the underlying distribution, such as a Gaussian distribution. However, these approaches are unable to accurately model the probability of critical events that occur in the tails of the distributions, which is vital for operational risk management. Further, they do not account for multiple dynamic factors that may impact operational risk. In this paper, we introduce a data-driven dynamic probabilistic reserve sizing method based on the artificial intelligence technique of dynamic Bayesian belief networks. The method considers the actual underlying distribution of forecasting errors from the availability of conventional generators (forecasting errors of their available generation capacity), demand, RES, and prevailing conditions such as weather and market prices. In addition, we introduce a new dynamic metric for calculating the reliability level of the power grid in order to provide a real-time stochastic decision support tool for power system operators. The proposed method is demonstrated using seven years of real historical data with a granularity of 30 min from the power system in Australia, and has been implemented and is currently used by the Australian system operator. [ABSTRACT FROM AUTHOR]

• GIS analysis is conducted to quantify the hydrological information for mud pumping prediction. • In-service train data and hydrological variables are combined to develop predictive model. • Incorporating hydrological variables can improve prediction performance. • Explainable deep learning can be used to reveal the effects of a single variable on the probability of mud pumping. • The most vulnerable sections for mud pumping in railway tracks can be identified. Automatic and timely identification of mud pumping is important for the reliability and safety of railroads. The current mud pumping prediction model is based on monitoring the dynamic response of railway tracks. The essential geotechnical trigger factors such as the hydrological conditions are not well-considered in these prediction models, as that information is hard to be quantified, which unavoidably reduces the accuracy of the prediction. This paper proposes to utilize the Geographic Information System (GIS) to quantify the hydrological information along railway tracks. Through GIS analysis, the hydrological variables including elevation, near-river distance, rainfall, sink depth, and soil types are estimated and combined with in-service train monitoring data for model development. To deal with multi-attribute data, a dual-channel neural networks model is proposed to separately mine the characteristics in different attributes data for prediction. To further understand the prediction model, Shapely addictive explanations (SHAP) method is applied to estimate the importance of hydrological variables and reveal the possible relationships between the variables and the probability of mud pumping. The proposed approach is applied to a real-life case from the railway tracks in Australia to validate its effectiveness. The prediction results show that the proposed approach can predict mud pumping with balanced accuracy of 90.84%. The results confirm that integrating of GIS information and monitoring data can generate more accurate prediction and reduce the false prediction rate. Based on the explainable results, it is observed that rainfall is the most important hydrological variable that influences mud pumping occurrence. Apart from rainfall, groundwater-related variables show a greater impact on mud pumping occurrence than surface water-related variables. The explainable results also can help infrastructure managers to identify the most vulnerable sections in railway tracks, which facilitates targeted maintenance planning and track substructure design. [ABSTRACT FROM AUTHOR]

Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem. [Display omitted] • A Hybrid deep learning model is proposed for daily electricity demand prediction. • Convolutional Neural Networks and Echo State Networks are integrated into a single forecasting algorithm. • The proposed deep learning-based approach is excellent in electricity demand prediction. • The proposed algorithm has practical use in inter-daily electricity demand prediction. [ABSTRACT FROM AUTHOR]

Mean sea level rise is a significant emerging risk from climate change. This research paper is based on the use of artificial intelligence models to assess and predict the trend on mean sea level around northern Australian coastlines. The study uses sea-level times series from four sites (Broom, Darwin, Cape Ferguson, Rosslyn Bay) to make the prediction. Multivariate adaptive regression splines (MARS) and artificial neural network (ANN) algorithms have been implemented to build the prediction model. Both models show high accuracy (R2 > 0.98) and low error values (RMSE < 27%) overall. The ANN model showed slightly better performance compared to MARS over the selected sites. The ANN performance was further assessed for modelling storm surges associated with cyclones. The model reproduced the surge profile with the maximum correlation coefficients ~0.99 and minimum RMS errors ~4 cm at selected validating sites. In addition, the ANN model predicted the maximum surge at Rosslyn Bay for cyclone Marcia to within 2 cm of the measured peak and the maximum surge at Broome for cyclone Narelle to within 7 cm of the measured peak. The results are comparable with a MARS model previously used in this region; however, the ANN shows better agreement with the measured peak and arrival time, although it suffers from slightly higher predictions than the observed sea level by tide gauge station. [ABSTRACT FROM AUTHOR]

Abstract: This paper outlines the factors that affected the development of a knowledge based decision support system (KBDSS) during a period of rapid industry change. The development process was led by a steering committee consisting of farmers, extension officers and milk processor representatives. The committee requested that changes be made to the KBDSS as a response to different circumstances in the Australian dairy industry due to the deregulation of the milk market. These changes were implemented using the services of a software development, marketing and product placement company. The research indicates that the use of the recommended approaches to the development of a KBDSS did not work well in the rapidly changing environment and that the interpretation that is necessary to determine the effect of deregulation on farmers added conflict and complexity to the development process. Moreover, it was found that approaches such as prototyping and stakeholder involvement that are advocated in situations involving requirements uncertainty do not necessarily provide an adequate solution. [Copyright &y& Elsevier]

This paper identifies countries at the forefront of Artificial Intelligence (AI) development and proposes two novel patent-based indicators to differentiate structural differences in the patterns of intellectual property (IP) protection observed for AI across countries. In particular, we consider (i) the extent to which countries specialise in AI and are relevant markets for corresponding IP protection ('National Breeding Ground'); and (ii) the extent to which countries attract AI from abroad for IP protection and extend the protection of their AI-related IP to foreign markets ('International Breeding Ground'). Our investigation confirms prior findings regarding substantial changes in the technological leadership in AI, besides drastic changes in the relevance of AI techniques over time. Particularly, we find that National and International Breeding Grounds overlap only partially. China and the US can be characterised as dominant National Breeding Grounds. Australia and selected European countries, but primarily the US, are major International Breeding Grounds. We conclude that China promotes AI development with a major focus on IP protection in its domestic market, whereas the US sustains its AI progress in the international context as well. This might indicate a considerable bifurcation in the structural patterns of IP protection in global AI development. [ABSTRACT FROM AUTHOR]