Showing items 41 - 60 of 5380

Your selections:

Aerosol exposure of live bird market workers to viable influenza A/H5N1 and A/H9N2 viruses, Cambodia

- Horwood, Paul, Horm, Srey, Yann, Sokhoun, Tok, Songha, Chan, Malen, Suttie, Annika, Phalla, Y, Rith, Sareth, Siegers, Jurre, San, Sorn, Davun, Holl, Tum, Sothyra, Ly, Sowath, Tarantola, Arnaud, Dussart, Philippe, Karlsson, Erik

  • Authors: Horwood, Paul , Horm, Srey , Yann, Sokhoun , Tok, Songha , Chan, Malen , Suttie, Annika , Phalla, Y , Rith, Sareth , Siegers, Jurre , San, Sorn , Davun, Holl , Tum, Sothyra , Ly, Sowath , Tarantola, Arnaud , Dussart, Philippe , Karlsson, Erik
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Zoonoses and Public Health Vol. 70, no. 2 (2023), p. 171-175
  • Full Text:
  • Reviewed:
  • Description: Live bird markets (LBMs) have been identified as key factors in the spread, persistence and evolution of avian influenza viruses (AIVs). In addition, these settings have been associated with human infections with AIVs of pandemic concern. Exposure to aerosolised AIVs by workers in a Cambodian LBM was assessed using aerosol impact samplers. LBM vendors were asked to wear an air sampler for 30 min per day for 1 week while continuing their usual activities in the LBM during a period of high AIV circulation (February) and a period of low circulation (May). During the period of high circulation, AIV RNA was detected from 100% of the air samplers using molecular methods and viable AIV (A/H5N1 and/or A/H9N2) was isolated from 50% of air samplers following inoculation into embryonated chicken eggs. In contrast, AIV was not detected by molecular methods or successfully isolated during the period of low circulation. This study demonstrates the increased risk of aerosol exposure of LBM workers to AIVs during periods of high circulation and highlights the need for interventions during these high-risk periods. Novel approaches, such as environmental sampling, should be further explored at key high-risk interfaces as a potentially cost-effective alternative for monitoring pandemic threats. © 2022 The Authors. Zoonoses and Public Health published by Wiley-VCH GmbH.

Agile ageing : implementation considerations for a walking basketball program

- Talpey, Scott, Pascoe, Deborah, Wallen, Mathew

  • Authors: Talpey, Scott , Pascoe, Deborah , Wallen, Mathew
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Activities, Adaptation and Aging Vol. 47, no. 3 (2023), p. 301-314
  • Full Text:
  • Reviewed:
  • Description: Physical activity generally declines with increasing age and lack of enjoyment is a noted barrier to older adults participating in traditional exercise programs. Walking basketball is a modified version of basketball designed to align with the physical capabilities of older adults, where participants are required to walk rather than run and body contact is not allowed. A walking basketball program provides participants with an opportunity to obtain the physical, mental and social health benefits of exercise in a competitive and social context. Due to the dynamic environment of a walking basketball program, participants are exposed to a unique stimulus combining both physical and cognitive demands, that is unmatched by traditional exercise programs. However, an increased risk of injury coincides with the unique demands of the activity. Therefore, the purpose of this manuscript is to provide practical applications for sporting organization that wish to implement a walking basketball program. © 2022 Taylor & Francis Group, LLC.

AI apology : interactive multi-objective reinforcement learning for human-aligned AI

- Harland, Hadassah, Dazeley, Richard, Nakisa, Bahareh, Cruz, Francisco, Vamplew, Peter

  • Authors: Harland, Hadassah , Dazeley, Richard , Nakisa, Bahareh , Cruz, Francisco , Vamplew, Peter
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 23 (2023), p. 16917-16930
  • Full Text:
  • Reviewed:
  • Description: For an Artificially Intelligent (AI) system to maintain alignment between human desires and its behaviour, it is important that the AI account for human preferences. This paper proposes and empirically evaluates the first approach to aligning agent behaviour to human preference via an apologetic framework. In practice, an apology may consist of an acknowledgement, an explanation and an intention for the improvement of future behaviour. We propose that such an apology, provided in response to recognition of undesirable behaviour, is one way in which an AI agent may both be transparent and trustworthy to a human user. Furthermore, that behavioural adaptation as part of apology is a viable approach to correct against undesirable behaviours. The Act-Assess-Apologise framework potentially could address both the practical and social needs of a human user, to recognise and make reparations against prior undesirable behaviour and adjust for the future. Applied to a dual-auxiliary impact minimisation problem, the apologetic agent had a near perfect determination and apology provision accuracy in several non-trivial configurations. The agent subsequently demonstrated behaviour alignment with success that included up to complete avoidance of the impacts described by these objectives in some scenarios. © 2023, The Author(s).

An agriprecision decision support system for weed management in pastures

- Chegini, Hossein, Naha, Ranesh, Mahanti, Aniket, Gong, Mingwei, Passi, Kalpdrum

  • Authors: Chegini, Hossein , Naha, Ranesh , Mahanti, Aniket , Gong, Mingwei , Passi, Kalpdrum
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 11, no. (2023), p. 92660-92675
  • Full Text:
  • Reviewed:
  • Description: Pastures are a vital source of dairy products and cattle nutrition, and as such, play a significant role in New Zealand's agricultural economy. However, weeds can be a major problem for pastures, making it a challenge for dairy farmers to monitor and control them. Currently, most of the tasks for weed management are done manually, and farmers lack persistent technology for weed control. This motivated us to design, implement, and evaluate a Decision Support System (DSS) to detect weeds in pastures and provide decisions for the cleanup of weeds. Our proposed system uses two primary inputs: weeds and bare patches. We created a synthetic dataset to train a weed detection model and designed a fuzzy inference system to assess a pasture. We also used a neuro-fuzzy system in our DSS to evaluate our fuzzy model and tune its parameters for better functioning and accuracy. Our work aims to assist dairy farmers in better weed monitoring, as well as to provide 2D maps of weed density and yield score, which can be of significant value when no digital and meaningful images of pastures exist. The system can also support farmers in scheduling, recommending prohibitive tasks, and storing historical data for pasture analysis, collaborated by stakeholders. © 2013 IEEE.

An effective solution to the optimal power flow problem using meta-heuristic algorithms

- Aurangzeb, Khursheed, Shafiq, Sundas, Alhussein, Musaed, Pamir, Javaid, Nadeem, Imran, Muhammad

  • Authors: Aurangzeb, Khursheed , Shafiq, Sundas , Alhussein, Musaed , Pamir , Javaid, Nadeem , Imran, Muhammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Frontiers in Energy Research Vol. 11, no. (2023), p.
  • Full Text:
  • Reviewed:
  • Description: Financial loss in power systems is an emerging problem that needs to be resolved. To tackle the mentioned problem, energy generated from various generation sources in the power network needs proper scheduling. In order to determine the best settings for the control variables, this study formulates and solves an optimal power flow (OPF) problem. In the proposed work, the bird swarm algorithm (BSA), JAYA, and a hybrid of both algorithms, termed as HJBSA, are used for obtaining the settings of optimum variables. We perform simulations by considering the constraints of voltage stability and line capacity, and generated reactive and active power. In addition, the used algorithms solve the problem of OPF and minimize carbon emission generated from thermal systems, fuel cost, voltage deviations, and losses in generation of active power. The suggested approach is evaluated by putting it into use on two separate IEEE testing systems, one with 30 buses and the other with 57 buses. The simulation results show that for the 30-bus system, the minimization in cost by HJBSA, JAYA, and BSA is 860.54 $/h, 862.31, $/h and 900.01 $/h, respectively, while for the 57-bus system, it is 5506.9 $/h, 6237.4, $/h and 7245.6 $/h for HJBSA, JAYA, and BSA, respectively. Similarly, for the 30-bus system, the power loss by HJBSA, JAYA, and BSA is 9.542 MW, 10.102 MW, and 11.427 MW, respectively, while for the 57-bus system, the value of power loss is 13.473 MW, 20.552, MW and 18.638 MW for HJBSA, JAYA, and BSA, respectively. Moreover, HJBSA, JAYA, and BSA cause reduction in carbon emissions by 4.394 ton/h, 4.524, ton/h and 4.401 ton/h, respectively, with the 30-bus system. With the 57-bus system, HJBSA, JAYA, and BSA cause reduction in carbon emissions by 26.429 ton/h, 27.014, ton/h and 28.568 ton/h, respectively. The results show the outperformance of HJBSA. Copyright © 2023 Aurangzeb, Shafiq, Alhussein, Pamir, Javaid and Imran.

An efficient framework for mining outlying aspects

- Samariya, Durgesh

  • Authors: Samariya, Durgesh
  • Date: 2023
  • Type: Text , Thesis , PhD
  • Full Text:
  • Description: In the era of big data, an immense volume of information is being continuously generated. It is common to encounter errors or anomalies within datasets. These anomalies can arise due to system malfunctions or human errors, resulting in data points that deviate from expected patterns or values. Anomaly detection algorithms have been developed to identify such anomalies effectively from the data. However, these algorithms often fall short in providing insights into why a particular data point is considered as an anomaly. They cannot explain the specific feature subset(s) in which a,data point significantly differs from the majority of the data. To address this limitation, researchers have recently turned their attention,to a new research area called outlying aspect mining. This area focuses on discovering feature subset(s), known as aspects or subspaces, in which anomalous data points exhibit significant deviations from the remaining set of data. Outlying aspect mining algorithms aim to provide a more detailed,understanding of the characteristics that make a data point anomalous. Although outlying aspect mining is an emerging area of research only a few studies have been published so far. One of the key challenges in this field is scaling up these algorithms to handle large datasets, characterised by,either a large data size or high dimensionality. Many existing outlying aspect mining algorithms are not well-suited for such datasets, as they exhaustively enumerate all possible subspaces and utilise density or distance-based anomaly scores to rank subspaces. As a result, most of these algorithms struggle to handle datasets with dimensions exceeding 20. Addressing the scalability issue and developing efficient algorithms for outlying aspect mining in large datasets remain active area of research. The ability to identify and understand the specific feature subsets contributing to anomalies in big data holds great potential for various applications, including fraud detection, network intrusion detection, and anomaly-based decision support systems. Existing outlying aspect mining methods are suffering from three main problems. Firstly, these measures often rely on distance or density-based calculations, which can be biased toward high-dimensional spaces. As the dimensionality of the subspace increases, the density tends to decrease, making it difficult to assess the outlyingness of data points within specific subspaces accurately. Secondly, distances or density-based measures are computationally expensive, especially when dealing with large-scale datasets that contain millions of data points. As distance and density-based measures require computing pairwise distance, it makes them computationally expensive. In addition to that, existing work uses Z-Score normalisation to make density-based scoring measure dimensionally unbias. However, it adds additional computational overload on already computationally expensive measures. Lastly, existing outlying aspect mining uses brute-force methods to search subspaces. Thus, it is essential to tackle this efficiency issue because when the dimensionality of the data is high – candidate subspace grows exponentially, which is beyond computational resources. This research project aims to solve this challenge by developing efficient and effective methods for mining outlying aspects in high-dimensional and large datasets. I have explored and designed different scoring measures to find the outlyingness of a given data point in each subspace. The effectiveness and efficiency of these proposed measures have been verified with extensive experiments on synthetic and real-world datasets. To overcome the first problem, this thesis first identifies and analyses the condition under which Z-Score based normalisation scoring measure fails to find the most outlying aspects and proposes two different approaches called HMass and sGrid++, both measures are dimensionally unbiased in their raw form, which means they do not require any additional normalisation. sGrid++ is a simpler version of sGrid that is not only efficient and effective but also dimensionality unbiased. It does not require Z-score normalisation. HMass is a simple but effective and efficient histogram-based solution to rank outlying aspects of a given query in each subspace. In addition to detecting anomalies, HMass provides explanations on why the points are anomalous. Both sGrid++ and HMass do not require pair-wise calculation like distance or density-based measures; therefore, sGrid++ and HMass are computationally faster than distance and density-based measures, which solves the second issue of existing work. The effectiveness and efficiency of both sGrid++ and HMass are evaluated using synthetic and real-world datasets. In addition to that, I presented an exciting application of outlying aspect mining in the cybersecurity domain. To tackle the third problem, this thesis proposes an efficient and effective outlying aspect mining framework named OIMiner (for Outlying - Inlying Aspect Miner). It introduces a new scoring measure to compute outlying degree, called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which not only detects the outliers but also provides an explanation on why the selected point is an outlier. SiNNE is a dimensionally unbias measure in its raw form, which means the scores produced by SiNNE are compared directly with subspaces having different dimensions. Thus, it does not require any normalisation to make the score unbiased. Our experimental results on synthetic and publicly available real-world datasets revealed that (i) SiNNE produces better or at least the same results as existing scores. (ii) It improves the run time of the existing outlying aspect mining algorithm based on beam search by at least two orders of magnitude. SiNNE allows the existing outlying aspect mining algorithm to run in datasets with hundreds of thousands of instances and thousands of dimensions, which was not possible before.
  • Description: Doctor of Philosophy

An empirical study into the use of 7 quality control tools in Higher Education Institutions (HEIs)

- Mathur, Swati, Antony, Jiju, Olivia, McDermott, Fabiane Letícia, Lizarelli, Shreeranga, Bhat, Raja, Jayaraman, Ayon, Chakraborty

  • Authors: Mathur, Swati , Antony, Jiju , Olivia, McDermott , Fabiane Letícia, Lizarelli , Shreeranga, Bhat , Raja, Jayaraman , Ayon, Chakraborty
  • Date: 2023
  • Type: Text , Journal article
  • Relation: TQM Journal Vol. 35, no. 7 (2023), p. 1777-1798
  • Full Text:
  • Reviewed:
  • Description: Purpose: The main purpose of this study is to revisit Ishikawa's statement: “95% of problems in processes can be accomplished using the original 7 Quality Control (QC) tools”. The paper critically investigates the validity of this statement in higher education institutions (HEIs). It involves analysis of the usage of the 7 QC tools and identifying the barriers, benefits, challenges and critical success factors (CSFs) for the application of the 7 QC tools in a HEI setting. Design/methodology/approach: An online survey instrument was developed, and as this is a global study, survey participants were contacted via social networks such as LinkedIn. Target respondents were HEIs educators or professionals who are knowledgeable about the 7 QC tools promulgated by Dr Ishikawa. Professionals who work in administrative sectors, such as libraries, information technology and human resources were included in the study. A number of academics who teach the 7 basic tools of QC were also included in the study. The survey link was sent to over 200 educators and professionals and 76 complete responses were obtained. Findings: The primary finding of this study shows that the diffusion of seven QC tools is not widespread in the context of HEIs. Less than 8% of the respondents believe that more than 90% of process problems can be solved by applying the 7 QC tools. These numbers show that modern-quality problems may need more than the 7 basic QC basic tools and there may be a need to revisit the role and contribution of these tools to solve problems in the higher education sector. Tools such as Pareto chart and cause and effect diagram have been widely used in the context of HEIs. The most important barriers highlighted are related to the lack of knowledge about the benefits and about how and when to apply these tools. Among the challenges are the “lack of knowledge of the tools and their applications” and “lack of training in the use of the tools”. The main benefits mentioned by the respondents were “the identification of areas for improvement, problem definition, measurement, and analysis”. According to this study, the most important factors critical for the success of the initiative were “management support”, “widespread training” and “having a continuous improvement program in place”. Research limitations/implications: The exploratory study provides an initial understanding about the 7 QC tools application in HEIs, and their benefits, challenges and critical success factors, which can act as guidelines for implementation in HEIs. Surveys alone cannot provide deeper insights into the status of the application of 7 QC tools in HEIs, and therefore qualitative studies in the form of semi-structured interviews should be carried out in the future. Originality/value: This article contributes with an exploratory empirical study on the extent of the use of 7 QC tools in the university processes. The authors claim that this is the first empirical study looking into the use of the 7 QC tools in the university sector. © 2022, Emerald Publishing Limited.

An evaluation of low and high intensity digital mental health treatment models for anxiety and depression : an adaptive treatment randomized clinical trial

- Andrews, Brooke

  • Authors: Andrews, Brooke
  • Date: 2023
  • Type: Text , Thesis , PhD
  • Full Text:
  • Description: Doctor of Philsophy

An evidence theoretic approach for traffic signal intrusion detection

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Das, Rajkumar, Newaz, Shah

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
  • Full Text:
  • Reviewed:
  • Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.

An examination of physical exercise therapy on quality of life (QoL) and mortality in men diagnosed with Prostate Cancer (PCa)

- Khan, Nazib

  • Authors: Khan, Nazib
  • Date: 2023
  • Type: Text , Thesis , PhD
  • Full Text:
  • Description: Prostate cancer (PCa) is the second most frequent malignancy in men worldwide, accounting for 27% of new cancer cases in men and responsible for 3.8% of worldwide male cancer deaths (2018). This risk is projected to double before 204, so it is surprising that there are no well- established modifiable risk factors for PCa diagnosis orr progression. Considering this, the following thesis consists of three original research studies that examined; (1) the moderating effect of physical activity and muscular strength on prostate cancer mortality; (2) the magnitude of effect from pooled therapeutic exercise studies to impact quality of life and adverse events in PCa patients following surgery; (3) the head-to-head comparative efficacy of individual exercise therapies to impact quality of life and adverse events following surgery. The sum of works presented in this thesis lead to the conclusion that 1) when PCa is the only lifetime cancer diagnosis, life expectancy is similar to lifetime cancer free counterparts whereas diagnosis of PCa with any other lifetime cancer is associated with a 30.2% lower life expectancy during 10-year follow-up. Physical activity has negligible effect on life-expectancy whereas handgrip strength accounts for ~4% of the difference in life-expectancy between PCa with any other lifetime cancer compared with controls; 2) Pooled evidence from randomised controlled trials (n=999 participants) identifies that perioperative exercise therapy (aerobic, resistance, pelvic floor, mind-body exercise, or mixed exercise (combined aerobic and resistance) has a positive impact on patient quality of life and adverse events in PCa patients undergoing surgery which is predominant in perceptions of symptom related QoL (SMD: 0.56, 111 95% CI’s [0.22-0.89]) more so than health related quality of life (HRQoL) (SMD: 0.02, 95% 112 CI’s [-0.16-0.20]; 3) Comparison of head-to-head efficacy of different therapeutic exercise interventions [Aerobic, resistance, pelvic floor, mind-body exercise or mixed exercise (combined aerobic and resistance)] identified pelvic floor muscle exercise to be the most favorable exercise therapy to maintain QoL in PCa patients undergoing surgery, partly due to the current lack of evidence to support other exercise therapies. These findings provide new knowledge to support the effective treatment of PCa patients.
  • Description: Doctor of Philosophy

An exploration of trolling behaviours in Australian adolescents : an online survey

- Marrington, Jessica, March, Evita, Murray, Sarah, Jeffries, Carla, Machin, Tanya, March, Sonja

  • Authors: Marrington, Jessica , March, Evita , Murray, Sarah , Jeffries, Carla , Machin, Tanya , March, Sonja
  • Date: 2023
  • Type: Text , Journal article
  • Relation: PLoS ONE Vol. 18, no. 4 April (2023), p.
  • Full Text:
  • Reviewed:
  • Description: To understand why people “troll” (i.e., engage in disruptive online behaviour intended to provoke and distress for one’s own amusement), researchers have explored a range of individual differences. These studies have primarily been conducted in adult samples, despite adolescents being a particularly vulnerable group with regards to both being trolled and trolling others. In this study we aimed to (1) explore Australian adolescents’ experiences of trolling, and (2) replicate adult research that has constructed a psychological profile of the Internet troll by examining the utility of personality traits (psychopathy and sadism), self-esteem, empathy (cognitive and affective), and social rewards (negative social potency) to predict adolescents’ trolling behaviours. A sample of 157 Australian adolescents (40.8% male, 58% female, 0.6% non-binary) aged 13–18 years (M = 15.58, SD = 1.71) completed the Global Assessment of Internet Trolling-Revised, Adolescent Measure of Empathy and Sympathy, Rosenberg Self-Esteem Scale, Youth Psychopathy Traits Inventory-Short Version, Social Rewards Questionnaire, Short Sadistic Impulse Scale, and a series of questions related to the experience of trolling. Results showed in the past year, 24.2% of Australian adolescents reported being trolled and 13.4% reported having trolled others. Gender, psychopathy, sadism, self-esteem, cognitive empathy, affective empathy, and “negative social potency” (i.e., enjoyment of antisocial rewards) combined, explained 30.7% of variance in adolescents’ trolling behaviours (p < .001). When accounting for shared variance, gender (male), high psychopathy, and high negative social potency were significant predictors of trolling, aligning with findings of adult samples. Contrary to adult samples, sadism was not a unique predictor of adolescents’ trolling. For adolescents, the variance in trolling explained by sadism was nonsignificant when controlling for negative social potency. These similarities, and differences, in predictors of trolling across adult and adolescent samples may play a critical role in the development of targeted interventions to prevent or manage trolling. © 2023 Marrington et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

An optimal scheduling method in iot-fog-cloud network using combination of aquila optimizer and african vultures optimization

- Liu, Qing, Kosarirad, Houman, Meisami, Sajad, Alnowibet, Khalid, Hoshyar, Azadeh

  • Authors: Liu, Qing , Kosarirad, Houman , Meisami, Sajad , Alnowibet, Khalid , Hoshyar, Azadeh
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Processes Vol. 11, no. 4 (2023), p.
  • Full Text:
  • Reviewed:
  • Description: Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks. © 2023 by the authors.

An optimized hybrid deep intrusion detection model (HD-IDM) for enhancing network security

- Ahmad, Iftikhar, Imran, Muhammad, Qayyum, Abdul, Ramzan, Muhammad, Alassafi, Madini

  • Authors: Ahmad, Iftikhar , Imran, Muhammad , Qayyum, Abdul , Ramzan, Muhammad , Alassafi, Madini
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 11, no. 21 (2023), p.
  • Full Text:
  • Reviewed:
  • Description: Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. © 2023 by the authors.

An overview of Australian exercise and sport science degrees

- Kittel, Aden, Stevens, Christopher, Lindsay, Riki, Spittle, Sharna, Spittle, Michael

  • Authors: Kittel, Aden , Stevens, Christopher , Lindsay, Riki , Spittle, Sharna , Spittle, Michael
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Frontiers in Education Vol. 8, no. (2023), p.
  • Full Text:
  • Reviewed:
  • Description: Exercise and Sport Science (EXSS) is a common degree offered at Australian universities, yet there is no systematic overview of this multidisciplinary field of study. This study aimed to determine the broad curriculum content of Australian EXSS degrees by summarizing the units offered, identify most commonly delivered content areas, and capture course information such as work-integrated learning (WIL) requirements and majors offered. Data were gathered through publicly available university course pages, with 30 EXSS courses included and only core units identified. The most common Australian EXSS units were “Exercise Physiology,” “Biomechanics,” “Research Methods and Data Analysis,” “Exercise Prescription and Delivery,” and “Exercise and Sport Psychology.” WIL requirements ranged from 140 to 300 h per course, and five courses offered majors. This study provides an overview of Australian EXSS courses, with the focus on exercise-related components reflecting accreditation requirements. Future research should examine how these courses equip students for the multidisciplinary EXSS industry. Copyright © 2023 Kittel, Stevens, Lindsay, Spittle and Spittle.

An overview of long covid support services in australia and international clinical guidelines, with a proposed care model in a global context

- Luo, Shiqi, Zheng, Zhen, Bird, Stephen, Plebanski, Magdalena, Figueiredo, Bernardo, Jessup, Rebecca, Stelmach, Wanda, Robinson, Jennifer, Xenos, Sophia, Olasoji, Micheal, Wan, Dawn, Sheahan, Jacob, Itsiopoulos, Catherine

  • Authors: Luo, Shiqi , Zheng, Zhen , Bird, Stephen , Plebanski, Magdalena , Figueiredo, Bernardo , Jessup, Rebecca , Stelmach, Wanda , Robinson, Jennifer , Xenos, Sophia , Olasoji, Micheal , Wan, Dawn , Sheahan, Jacob , Itsiopoulos, Catherine
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Public Health Reviews Vol. 44, no. (2023), p.
  • Full Text:
  • Reviewed:
  • Description: Objective: To identify gaps among Australian Long COVID support services and guidelines alongside recommendations for future health programs. Methods: Electronic databases and seven government health websites were searched for Long COVID-specific programs or clinics available in Australia as well as international and Australian management guidelines. Results: Five Long COVID specific guidelines and sixteen Australian services were reviewed. The majority of Australian services provided multidisciplinary rehabilitation programs with service models generally consistent with international and national guidelines. Most services included physiotherapists and psychologists. While early investigation at week 4 after contraction of COVID-19 is recommended by the Australian, UK and US guidelines, this was not consistently implemented. Conclusion: Besides Long COVID clinics, future solutions should focus on early identification that can be delivered by General Practitioners and all credentialed allied health professions. Study findings highlight an urgent need for innovative care models that address individual patient needs at an affordable cost. We propose a model that focuses on patient-led self-care with further enhancement via multi-disciplinary care tools. Copyright © 2023 Luo, Zheng, Bird, Plebanski, Figueiredo, Jessup, Stelmach, Robinson, Xenos, Olasoji, Wan, Sheahan and Itsiopoulos.

An update on the influence of natural climate variability and anthropogenic climate change on tropical cyclones

- Camargo, Suzana, Murakami, Hiroyuki, Bloemendaal, Nadia, Chand, Savin, Deshpande, Medha, Dominguez-Sarmiento, Christian, González-Alemán, Juan, Knutson, Thomas, Lin, I., Moon, Il-Ju, Patricola, Christian, Reed, Kevin, Roberts, Malcolm, Scoccimarro, Enrico, Tam, Chi, Wallace, Elizabeth, Wu, Liguang, Yamada, Yohei, Zhang, Wei, Zhao, Haikun

  • Authors: Camargo, Suzana , Murakami, Hiroyuki , Bloemendaal, Nadia , Chand, Savin , Deshpande, Medha , Dominguez-Sarmiento, Christian , González-Alemán, Juan , Knutson, Thomas , Lin, I. , Moon, Il-Ju , Patricola, Christian , Reed, Kevin , Roberts, Malcolm , Scoccimarro, Enrico , Tam, Chi , Wallace, Elizabeth , Wu, Liguang , Yamada, Yohei , Zhang, Wei , Zhao, Haikun
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Tropical Cyclone Research and Review Vol. 12, no. 3 (2023), p. 216-239
  • Full Text:
  • Reviewed:
  • Description: A substantial number of studies have been published since the Ninth International Workshop on Tropical Cyclones (IWTC-9) in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. These studies have reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scales. However, there are still substantial uncertainties owing to model uncertainty in simulating historical TC decadal variability in the Atlantic, and the limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers. © 2023 The Shanghai Typhoon Institute of China Meteorological Administration

Analysis of microalgal density estimation by using lasso and image texture features

- Nguyen, Linh, Nguyen, Dung, Nguyen, Thang, Nguyen, Binh, Nghiem, Truong

  • Authors: Nguyen, Linh , Nguyen, Dung , Nguyen, Thang , Nguyen, Binh , Nghiem, Truong
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sensors Vol. 23, no. 5 (2023), p.
  • Full Text:
  • Reviewed:
  • Description: Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively © 2023 by the authors.

Animal population decline and recovery after severe fire: Relating ecological and life history traits with expert estimates of population impacts from the Australian 2019-20 megafires

- Ensbey, Michelle, Legge, Sarah, Jolly, Chris, Garnett, Stephen, Gallagher, Rachael, Lintermans, Mark, Nimmo, Dale, Rumpff, Libby, Scheele, Ben, Whiterod, Nick, Woinarski, John, Ahyong, Shane, Blackmore, Caroline, Bower, Deborah, Burbidge, Allan, Burns, Phoebe, Butler, Gavin, Catullo, Renee, Chapple, David, Dickman, Christopher, Doyle, Katie, Ferris, Jason, Fisher, Diana, Geyle, Hayley, Gillespie, Graeme, Greenlees, Matt, Hohnen, Rosemary, Hoskin, Conrad, Kennard, Mark, King, Alison, Kuchinke, Diana, Law, Brad, Lawler, Ivan, Lawler, Susan, Loyn, Richard, Lunney, Daniel, Lyon, Jarod, MacHunter, Josephine, Mahony, Michael, Mahony, Stephen, McCormack, Rob, Melville, Jane, Menkhorst, Peter, Michael, Damian, Mitchell, Nicola, Mulder, Eridani, Newell, David, Pearce, Luke, Raadik, Tarmo, Rowley, Jodi, Sitters, Holly, Southwell, Darren, Spencer, Ricky, West, Matt, Zukowski, Sylvia

Application of a universal parasite diagnostic test to biological specimens collected from animals

- Lane, Meredith, Kashani, Mitra, Barratt, Joel, Qvarnstrom, Yvonne, Yabsley, Michael, Garrett, Kayla, Bradbury, Richard

  • Authors: Lane, Meredith , Kashani, Mitra , Barratt, Joel , Qvarnstrom, Yvonne , Yabsley, Michael , Garrett, Kayla , Bradbury, Richard
  • Date: 2023
  • Type: Text , Journal article
  • Relation: International Journal for Parasitology: Parasites and Wildlife Vol. 20, no. (2023), p. 20-30
  • Full Text:
  • Reviewed:
  • Description: A previously described universal parasite diagnostic (nUPDx) based on PCR amplification of the 18S rDNA and deep-amplicon sequencing, can detect human blood parasites with a sensitivity comparable to real-time PCR. To date, the efficacy of this assay has only been assessed on human blood. This study assessed the utility of nUPDx for the detection of parasitic infections in animals using blood, tissues, and other biological sample types from mammals, birds, and reptiles, known to be infected with helminth, apicomplexan, or pentastomid parasites (confirmed by microscopy or PCR), as well as negative samples. nUPDx confirmed apicomplexan and/or nematode infections in 24 of 32 parasite-positive mammals, while also identifying several undetected coinfections. nUPDx detected infections in 6 of 13 positive bird and 1 of 2 positive reptile samples. When applied to 10 whole parasite specimens (worms and arthropods), nUPDx identified all to the genus or family level, and detected one incorrect identification made by morphology. Babesia sp. infections were detected in 5 of the 13 samples that were negative by other diagnostic approaches. While nUPDx did not detect PCR/microscopy-confirmed trichomonads or amoebae in cloacal swabs/tissue from 8 birds and 2 reptiles due to primer template mismatches, 4 previously undetected apicomplexans were detected in these samples. Future efforts to improve the utility of the assay should focus on validation against a larger panel of tissue types and animal species. Overall, nUPDx shows promise for use in both veterinary diagnostics and wildlife surveillance, especially because species-specific PCRs can miss unknown or unexpected pathogens. © 2022

Application of KRR, K-NN and GPR algorithms for predicting the soaked CBR of fine-grained plastic soils

- Verma, Gaurav, Kumar, Brind, Kumar, Chintoo, Ray, Arunava, Khandelwal, Manoj

  • Authors: Verma, Gaurav , Kumar, Brind , Kumar, Chintoo , Ray, Arunava , Khandelwal, Manoj
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Arabian Journal for Science and Engineering Vol. 48, no. 10 (2023), p. 13901-13927
  • Full Text:
  • Reviewed:
  • Description: California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. © 2023, The Author(s).