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The Australian healthcare sector is a complex mix of government departments, associations, providers, professionals, and consumers. Cybersecurity attacks, which have recently increased, challenge the sector in many ways; however, the best approaches for the sector to manage the threat are unclear. This study will report on a semi-structured focus group conducted with five representatives from the Australian healthcare and computer security sectors. An analysis of this focus group transcript yielded four themes: 1) the challenge of securing the Australian healthcare landscape; 2) the financial challenges of cybersecurity in healthcare; 3) balancing privacy and transparency; 4) education and regulation. The results indicate the need for sector-specific tools to empower the healthcare sector to mitigate cybersecurity threats, most notably using a self-evaluation tool so stakeholders can proactively prepare for incidents. Despite the vast amount of research into cybersecurity, little has been conducted on proactive cybersecurity approaches where security weaknesses are identified weaknesses before they occur. [ABSTRACT FROM AUTHOR]

Hatfield, DL, Stranieri, AM, Vincent, LM, and Earp, JE. Effect of a neoprene knee sleeve on performance and muscle activity in men and women during high-intensity, high-volume resistance training. J Strength Cond Res 35(12): 3300-3307, 2021-The purpose of this study was to assess the effects of a commercially available neoprene knee sleeve (KS) on exercise performance and muscle activity during an exhaustive leg press exercise. Twenty resistance-trained individuals, 11 men {21.0 +/- 2.2 years; 77.7 +/- 8.7 kg; 1 repetition maximum (1RM/body mass [BM]): 0.30 +/- 0.04} and 9 women (22.0 +/- 3.5 years; 66.1 +/- 9.1 kg; 1RM/BM: 0.30 +/- 0.04), all subjects (21.5 +/- 2.8 years; 72.5 +/- 10.5 kg; 1RM/BM: 0.30 +/- 0.04), participated in 3 testing sessions. The second and third sessions were performed using a counterbalanced and randomized design in which subjects exercised with (WS) or without (NS) KSs and performed 6 sets of leg press exercise at 80% of 1RM until failure with a 3-minute rest between sets. Number of repetitions, blood lactate (BL), heart rate (HR), rating of perceived exertion (RPE), and peak and average power were recorded after each set. Surface electromyography (EMG) of the right and left vastus lateralis muscles was also recorded to compare muscle activity between conditions. Significance was set at p <= 0.05, and values are presented as mean +/- SD. No significant differences were observed in the total number of repetitions for all sets (p = 0.3; WS 75.3 +/- 33.7, NS 79.8 +/- 34.3) and the number of repetitions per set between conditions (p <= 0.05) or between men and women. Similarly, no significance differences (p <= 0.05) were observed for BL, HR, RPE, or EMG per set between conditions or between men and women. These results suggest that wearing compressive neoprene KSs has no effect on improving performance and associated variables during high-load, high-volume lower-body resistance training. /=>/p> [ABSTRACT FROM AUTHOR]

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. [ABSTRACT FROM AUTHOR]

Prevention through Design has been identified as an effective means to enhance construction safety. However, design professionals, clients and other stakeholders often have limited experience of construction processes. Although processes and tools have been advanced to support stakeholders, very few have been adopted in practice. The complexities inherent in designing for safe construction suggest this to be a wicked problem using criteria advanced by Rittel and Weber. Approaches advanced to solve wicked problems outside construction are not applicable for solving construction design problems, largely because they require that stakeholders expend an impractical amount of time and energy to arrive at amenable solutions. Instead, an approach that utilises information visualisation addresses criteria for wicked problem resolution. The promise of information visualisation for this wicked problem has been illustrated through a case study involving façade design. An infographic was generated following structured interviews with building designers and safety experts. A workshop involving 20 designers, engineers and safety experts demonstrated that the infographic prompted participants to identify risks not previously considered and stimulated discussion about mitigation or reduction. There was unanimous agreement that the information visualisation could feasibly be adopted within the construction industry’s tight time and resource constraints. [ABSTRACT FROM PUBLISHER]

Background: The use of homoeopathic remedies to prevent infectious diseases, homoeoprophylaxis (HP), was first described over 100 years ago. To date, no systematic studies have been performed to identify the attitudes that current practitioners hold about HP or current trends in the use of HP. Aims: This study aimed to discover attitudes to HP amongst accredited homeopathic practitioners particularly with respect to overall acceptance, the context of use, and preferred remedies. Methodology: Two international surveys were conducted in 2014. Responses were received from 1,124 homeopaths in 35 countries; 104 of whom responded to both surveys. Results: A large proportion of respondents have positive attitudes to HP and currently use HP in their practice. However, responses also indicate that knowledge about HP is chequered. Confidence in the evidence base of HP also is variable. Conclusion: Results indicate that HP is widely practiced, however, further research is needed to improve confidence in the evidence base of HP, and better programs are needed to ensure that education about HP is enhanced. [ABSTRACT FROM AUTHOR]

Abstract: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN). We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery. This is important as not all five Ewing tests can always be applied in each situation in practice. Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN. We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests. Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery. We found the best sequences of tests for cost-function equal to the number of tests. The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93. They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests. The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure. We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained. Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure. The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test. Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence. [Copyright &y& Elsevier]

In this paper we present the notion of structured reasoning through a model, called the Generic/Actual Argument Model (GAAM). The model which has been used as a computational representation for machine modelling of reasoning and for hybrid combinations of human and machine reasoning can be used as a coalescent framework for decision making. Whilst the notion of structuring reasoning is not new, structured reasoning is advanced as a technique where group consensus on reasoning structures at various levels can be used to facilitate the comprehension of complex reasoning particularly where there are multiple perspectives. For an issue, the approach provides a scaffolding structure for cognitive co-operation and a normative reasoning structure against which group participants can identify points of difference and points in common as well as the nature of the differences and similarities. Intra-group transparency characterized by the ability to recognise points in common and understand the nature of differences is important to the process of coalescing group decisions that carry maximum group support. [ABSTRACT FROM AUTHOR]

In this article a dialectical model for practical reasoning within a community, based on the Generic/Actual Argument Model (GAAM) is advanced and its application to deliberative dialogue discussed. The GAAM, offers a dynamic template for structuring knowledge within a domain of discourse that is connected to and regulated by a community. The paper demonstrates how the community accepted generic argument structure acts to normatively influence both admissible reasoning and the progression of dialectical reasoning between participants. It is further demonstrated that these types of deliberation dialogues supported by the GAAM comply with criteria for normative principles for deliberation, specifically, Alexy’s rules for discourse ethics and Hitchcock’s Principles of Rational Mutual Inquiry. The connection of reasoning to the community in a documented and transparent structure assists in providing best justified reasons, principles of deliberation and ethical discourse which are important advantages for reasoning communities. [ABSTRACT FROM AUTHOR]

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the generic/actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training. A study of the effectiveness of this approach on learning outcomes was conducted with final year nursing students and provides evidence of improved learning outcomes. [ABSTRACT FROM AUTHOR]

Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. [ABSTRACT FROM AUTHOR]

This article describes an argumentation shell to support the formulation, representation and drafting of legal arguments. The shell can be used to capture generic arguments in many legal domains as well as to assist decision-makers in constructing their own actual arguments. The shell demonstrates that knowledge represented using the generic/actual argument model (GAAM) (a variant of Toulmin's argument structure) can be used to: (a) support the development of complex arguments, (b) add context and increase specificity for the retrieval of relevant documents, (c) incorporate background knowledge, (d) assist in the drafting of documents that represent arguments made, and (e) provide a structure for complex inferences requiring a range of mechanisms. The shell can be used to support decision making in a range of legal domains, including discretionary domains. [ABSTRACT FROM AUTHOR]

Knowledge-based systems can make a contribution to the difficult problem of regulating copyright on the internet. Regulation with law alone presents significant jurisdictional and enforcement difficulties whereas regulation with the use of encryption has the effect of removing legitimate public access to works for research, private study or criticism. Knowledge-based systems proposed here operate at five junctures: creating works, uploading works to a network, transmitting the work across the internet, downloading a work and using the creation. For each system, we apply an agent-based architecture that defines the actors, the knowledge all agents share about the world and a protocol, based on Habermasian speech acts to interact with other agents. The shared knowledge, and beliefs about the world, that each agent holds is represented with the use of arguments at two levels, the generic and the actual argument level. This application of argumentation extends our previous work in family law and refugee law. [ABSTRACT FROM AUTHOR]

Argues that discretion manifests differently in different representations of knowledge. Discretion in legal theory; First-order logic formalization; Association between discretion and a Toulmin representation of reasoning; Analysis of consistency.

An Electronic Health Record (EHR) is an individual's record of all health events that enables critical information to be documented and shared electronically amongst health care providers and patients. The introduction of an EHR, particularly a patient-accessible EHR, can be expected to lead to an escalation of enquiries, complaints and ultimately, disputes. Prevailing opinion is that Online Dispute Resolution (ODR) systems can help with the mediation of certain types of disputes electronically, particularly systems which deploy Artificial Intelligence (AI) to reduce the need for a human mediator. However, disputes regarding health tend to invoke emotional responses from patients that may conceivably impact ODR efficacy. This raises an interesting question on the influence of emotional intelligence (EI) in the process of mediation. Using a phenomenological research methodology simulating doctor–patient disputes mediated with an AI Smart ODR system in place of a human mediator, we found an association between EI and the propensity for a participant to change their previously asserted claims. Our results indicate participants with lower EI tend to prolong resolution compared to those with higher EI. Future research include trialling larger scale ODR systems for specific cohorts of patients in the area of health related dispute resolution are advanced. [ABSTRACT FROM AUTHOR]

Background: Recent investigations have questioned the role of hydration and electrolytes in cramp susceptibility and thus the efficacy of consuming electrolyte-rich carbohydrate beverages (EB) to control/prevent cramping.Methods: Nine euhydrated, cramp-prone participants had their cramp susceptibility assessed by measuring the nerve stimulation threshold frequency at which cramping occurs (TF) before and after consumption of an EB (kCal: 120, Na: 840 mg, K: 320 mg, Mg: 5 mg) and placebo beverage (PB: kCal: 5, Na: 35 mg). Cramp intensity was assessed using a verbal pain scale and poststimulation electromyography (EMG).Results: TF was greater in EB (14.86 ± 7.47 Hz) than PB (14.00 ± 5.03 Hz; P = .038) and reported pain was lower in EB (2.0 ± 0.6) than PB (2.7 ± 0.8; P = .025) while EMG was similar (P = .646).Discussion: EB consumption decreased cramp susceptibility and pain but did not prevent cramping in any participants. These results suggest that electrolyte consumption independent of hydration can influence cramp susceptibility in young people. [ABSTRACT FROM AUTHOR]

Over the last few years, business simulation games (BSGs) in higher education have attracted attention. BSGs tend to actively engage students with course material, promoting higher engagement and motivation and enabling learning outcomes. Increasingly, researchers are trying to explore the full potential of these games with an upsurge of research in the BSG field in recent years. There is a need to understand the current state of research and future research opportunities; however, there is a lack of recent systematic literature reviews in BSG literature. This study addresses this gap by systematically compiling online empirical research from January 2015 to April 2022. We followed PRISMA guidelines to identify fifty-seven (57) papers reporting empirical evidence of the effectiveness of BSGs in teaching and learning. Findings showed that BSGs improve learning outcomes such as knowledge acquisition, cognitive and interactive skills, and behaviour. The review also summarises different issues concerning the integration of BSGs into the curriculum, learning theories used in the selected studies, and assessment methods used to evaluate student achievement in learning outcomes. The findings of this review summarise the current research activities and indicate existing deficiencies and potential research directions that can be used as the basis for future research into the use of BSGs in higher education. [ABSTRACT FROM AUTHOR]

The Knowledge Discovery from Databases (KDD) technique called 'association rules' is applied to a large data set representing applicants for government-funded legal aid. Results indicate that KDD can be an invaluable tool for legal analysts. Association rules discovered identify associations between variables that are present in the data set though are not necessarily causal. Interesting rules can prompt analysts to formulate hypotheses for further investigation. The identification of interesting rules is typically performed using an objective measure of 'interesting' although this measure is often not sufficiently accurate to eliminate all uninteresting rules. In this article, a subjective measure of interestingness is adopted in conjunction with the objective measures. This leads to the ability to focus more accurately on those rules that surprise the analyst and are therefore more likely to be interesting. In general, KDD techniques have not been applied to law despite possible benefits because data is often stored in narrative form rather than in structured databases. However, the impending introduction of data warehouses that collect data from a number of organizations across a legal system presents invaluable opportunities for analysts using KDD. [ABSTRACT FROM AUTHOR]

The purpose of this study was to quantify the acute cardiometabolic responses to a novel training rope (TR) protocol in children. Fifteen boys (10.6 ± 1.4 years) were tested for peak oxygen uptake (...O2) on a treadmill and subsequently (2--7 days later) performed a progressive 10-minute protocol of 5 TR exercises (EX): EX1: standing side-to-side waves, EX2: seated alternating waves, EX3: standing alternating waves, EX4: jumping jacks, and EX5: double-arm slams. Participants performed 2 sets of each TR exercise for 30 seconds per set with a 30-second rest interval between sets and exercises. A 12.8-m TR (4.1 kg) was used for all trials, and a metronome was used to standardize cadence. Peak values for ...O2 and HR during the treadmill test were 47.4 ± 8.8 ml⋅kg-1⋅min-1 and 195.1 ± 6.6 b•min-1, respectively. During the TR protocol, mean ...O2 and HR significantly increased (p ≤ 0.05) from set 1 of EX1 (10.3 ± 2.6 ml⋅kg-1⋅min-1and 103.2 ± 11.6 b⋅min-1, respectively) to set 2 of EX5 (30.0 ± 3.9 ml⋅kg-1⋅min-1 and 168.6 ± 11.8 b⋅min-1, respectively). Percentage of peak ...O2 and peak HR values attained during the TR protocol ranged from 21.5 to 64.8% and from 52.9 to 86.4%, respectively. These descriptive data indicate that TR exercise can pose a moderate to vigorous cardiometabolic stimulus in children and with proper supervision and instruction may serve as a worthwhile complement to youth fitness programs. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this research was to conduct a cost-analysis, from a public healthcare perspective, comparing the cost and benefits of face-to-face patient examination assessments conducted by a dentist at a residential aged care facility (RACF) situated in rural areas of the Australian state of Victoria, with two teledentistry approaches utilizing virtual oral examination.Methods: The costs associated with implementing and operating the teledentistry approach were identified and measured using 2014 prices in Australian dollars. Costs were measured as direct intervention costs and programme costs. A population of 100 RACF residents was used as a basis to estimate the cost of oral examination and treatment plan development for the traditional face-to-face model vs. two teledentistry models: an asynchronous review and treatment plan preparation; and real-time communication with a remotely located oral health professional.Results: It was estimated that if 100 residents received an asynchronous oral health assessment and treatment plan, the net cost from a healthcare perspective would be AU$32.35 (AU$27.19-AU$38.49) per resident. The total cost of the conventional face-to-face examinations by a dentist would be AU$36.59 ($30.67-AU$42.98) per resident using realistic assumptions. Meanwhile, the total cost of real-time remote oral examination would be AU$41.28 (AU$34.30-AU$48.87) per resident.Discussion: Teledental asynchronous patient assessments were the lowest cost service model. Access to oral health professionals is generally low in RACFs; however, the real-time consultation could potentially achieve better outcomes due to two-way communication between the nurse and a remote oral health professional via health promotion/disease prevention delivered in conjunction with the oral examination. [ABSTRACT FROM AUTHOR]

A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min-max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions. [ABSTRACT FROM AUTHOR]

Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers' our new method does not delete attributes with missing values' does not use classifiers' and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. [ABSTRACT FROM AUTHOR]

Background: Ward rounds are an important and ubiquitous element of hospital care with a history extending well over a century. Although originally intended as a means of educating medical trainees and junior doctors, over time they have become focused on supporting clinical practice. Surprisingly, given their ubiquity and importance, they are under-researched and inadequately understood. This study aims to contribute knowledge in human reasoning within medical teams, meeting a pressing need for research concerning the reasoning occurring in rounds.Methods: The research reported here aimed to improve the understanding of ward round reasoning by conducting a critical realist case study exploring the collaborative group reasoning mechanisms in the ward rounds of two hospitals in Victoria, Australia. The data collection involved observing rounds, interviewing medical practitioners and holding focus group meetings.Results: Nine group reasoning mechanisms concerning sharing, agreeing and recording information in the categories of information accumulation, sense-making and decision-making were identified, together forming a program theory of ward round reasoning. In addition, themes spanning across mechanisms were identified, further explaining ward round reasoning and suggesting avenues for future exploration. Themes included the use of various criteria, tensions involving mechanisms, time factors, medical roles and hierarchies.Conclusions: This paper contributes to the literature by representing rounds in a manner that strengthens understanding of the form of the group reasoning occurring within, thus supporting theory-based evaluation strategies, redesigned practices and training enhancements. [ABSTRACT FROM AUTHOR]