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Device identification method for internet of things based on spatial-temporal feature residuals
No full textIn recent years, the Internet of Things (IoT) has penetrated all aspects of our lives through smart cities, health, industries and others that are related to people's livelihood. With the increasing number of IoT devices, more and more personal information is exposed in the network space, which inevitably brings some network security problems. Due to the diversity and heterogeneity of IoT devices, identification of such devices in the complex IoT environments remains a major challenge. Existing deep learning-based device identification methods achieve identification of IoT devices by automatically extracting device traffic features, but usually only single modal features of device traffic are considered, which cannot achieve all-around characterization features of communication traffic and affect the identification results. Therefore, we propose an identification method, termed DMRMTT, that employs a Deep convolutional maxout network and MTT model (Multiple Time-series Transformers) to automatically extract the spatial and temporal features of IoT communication session fingerprints and perform further fusion using the structure of the residual, which makes up for the limitations of the existing methods for studying device traffic. This method can improve the characterization of device traffic behaviour and achieve a more accurate identification of IoT devices. Its efficacy is experimentally validated by using two publicly availbale datasets and compared with existing methods. Results show that our method outperforms other methods in widely used performance metrics and achieves 99.82% identification accuracy, demonstrating its superiority and usefulness in IoT device identification. © 2008-2012 IEEE
Caring for country : social workers standing in solidarity with indigenous disaster practices
No full textIndigenous knowledges are one of the keys to unlocking a better future. Indigenous communities hold time-tested, cultural knowledge and coping practices that foster their intimate connection with their natural surroundings, making them resilient to climate-related natural hazards and disasters. This knowledge has been neglected in formal disaster management policies due to colonisation and Western notions of knowledge superiority. This chapter calls for a greater recognition of Indigenous knowledges in disaster risk reduction, thus creating the opportunity for more resilient communities. Ecosocial work acknowledges the relationship within and between all living things, thus offering a potential framework for disaster practice. For social workers, this calls for partnership with Indigenous Peoples to learn about Indigenous knowledges and practices, stand in solidarity, and support the governance and care of the land. In solidarity, social workers are well placed to collaborate with Aboriginal Peoples to weave culture into the skills, capabilities, and funding of practice; and advocate for Indigenous Peoples to have stewardship for the land and governance of environmental management. © 2025 selection and editorial matter, Carole Adamson, Margaret Alston, Bindi Bennett, Jennifer Boddy, Heather Boetto, Louise Harms, and Raewyn Tudor
Unique relationship between optimum compaction properties of fine-grained soils across rational compactive efforts : a validation study
Get PDFAmong the many proposals for estimating the compaction characteristics of fine-grained soils for different compaction energy levels (CELs), energy conversion (EC) models are gaining increased attention. These models work on the premise of employing measured optimum moisture content (OMC) and maximum dry unit weight (MDUW) values obtained for a rational CEL (e.g., standard or reduced-standard Proctor (SP or RSP)) to predict the same for other CELs. This study revisits the most recently proposed EC-based compaction modeling framework, critically examining its asserted accuracy and hence identifying its true potentials. This was achieved by performing comprehensive statistical analyses on a newly compiled database of 206 compaction test results, entailing 70 different fine-grained soils (with liquid limits ranging 19–365%) and accounting for CELs of 202.0–2723.5 kJ/m3. It was demonstrated that 99% and 96% of the differences between the SP-converted and measured values for OMC and MDUW, respectively, fall within the allowable margins of OMC and MDUW measurement errors permitted by the Australian AS 1289.5.1.1/AS 1289.5.2.1 standards (satisfying their recommende
Accelerating visual anticipation in sport through temporal occlusion training : a meta-analysis
Get PDFBackground: The video-based temporal occlusion paradigm has been consistently used in visual anticipation sport research. Objective: This meta-analysis investigated the magnitude to which video-based temporal occlusion training could improve anticipation skill with transfer to representative laboratory and field tasks. Methods: As there are considerably fewer anticipation training than performance studies, the meta-analysis included 12 intervention studies with 25 effect sizes where video simulation and/or field-based tests were used. The Downs and Black checklist adapted for sports science research was used to assess methodological quality of the included studies. Decision time and accuracy of anticipation were the outcome measures because both are relevant to sports skills. The changes in these measures between experimental and control groups from baseline to the transfer test context were used to calculate the magnitude of the training effect. Results: Findings revealed a significant training effect, including a large meta-analytic effect size, and no difference in training benefit across video and field-based transfer tests. Publication bias analyses were inconclusive, likely due to the small number of available studies. Conclusions: These findings are evidence that the temporal occlusion paradigm is an effective method to improve visual anticipation skill across representative perceptual and perceptual-motor transfer tests. The theoretical implication based upon the two-stage model of visual anticipation is that temporal occlusion training can improve use of early information for body positioning by the performer, which could in turn lead to improved execution of the skill goal. © The Author(s) 2024
Modeling ammonia concentration in swine building using biophysical data and machine learning algorithms
No full textAmmonia (NH3) concentration in livestock barns is a crucial environmental parameter affecting the well-being of animals and workers’ health. Effective management and prediction of NH3 levels are essential for maintaining an efficient and enduring swine production system. This study investigated the application of machine learning algorithms, namely support vector regression (SVR), random forest regression (RFR), and multiple linear regression (MLR), to predict NH3 concentrations in pig barns and examined the impact of individual input variables on prediction accuracy. In this study, three datasets, each comprising five key biophysical variables, i.e., feed intake (FI), mass of pig (MP), carbon dioxide (CO2) levels, temperature (T), and relative humidity (RH), were utilized for training and testing these algorithms. The data were collected from three barns during the growing-finishing stage of pigs in 2022 and 2023. The study results revealed a strong positive relationship between FI and MP with NH3 concentrations. Among the three machine learning models, the SVR outperformed the MLR and RFR in predicting NH3 concentration. The result exhibited that the SVR obtained the maximum performance in both training (R2 >0.95) and testing (R2 >0.85), with R2 improvements of up to 5.43 % and 14.02 % and RMSE decreases of up to 15.97 % and 28.98 %, in comparison to the RFR and MLR across the three input datasets in NH3 prediction. The study also emphasized the importance of dataset size, with the large dataset containing all five input indicators achieving the highest accuracy compared to smaller datasets. In addition, the MLR demonstrated maximum stability, followed by the SVR, whereas the RFR exhibited minimum stability. Sensitivity analysis revealed that FI was the most influential input variable for NH3 concentration prediction. The study ranked the impact of individual input variables as FI > MP > CO2 > T > RH. The combination of FI, MP, CO2, and T as input indicators achieved the highest model performance, accounting for a substantial portion of the variance between observed and predicted data. This study demonstrated the potential of machine learning models, particularly SVR, for predicting NH3 concentrations using relevant input variables in pig barns. These findings significantly enhance understanding of NH3 concentration dynamics in pig barns, providing crucial insights into swine production and environmental management with data-driven modeling. © 2024 Elsevier B.V
Mental health nurses' empathy experiences towards consumers with dual diagnosis : a thematic analysis
Get PDFWhat Is Known on the Subject?: Dual diagnosis is one of the leading causes of disability globally. Consumers with dual diagnosis have complex needs and are at risk of relapse of their psychiatric symptoms. Mental health nurses require essential skills, including empathy, to manage consumers with dual diagnosis. No studies have explored mental health nurses' empathy towards consumers with dual diagnosis. What Does the Paper Add to Existing Knowledge?: Developing empathy towards consumers with dual diagnosis is complex. Mental health nurses' unemotional empathy experiences with consumers with dual diagnosis are related to their lack of ability to connect to their consumers' choices and feelings. Negative attitudes towards consumers with dual diagnosis contributed to nurses' poor empathy experiences. The unemotional responses of mental health nurses can be caused by factors such as novelty, insufficient information, and neutral evaluation of a consumer's situation. What Are the Implications for Practice?: The study results benefit researchers, teachers, clinicians, and administrators when designing, developing, and delivering empathy training packages for mental health nurses. Improving the empathy of mental health nurses towards consumers with dual diagnosis should be a top priority for healthcare leaders and educators. A core curriculum containing holistic awareness of the biopsychosocial components of dual diagnosis makes it easier for mental health nurses to understand and develop empathy towards consumers with dual diagnosis. Future studies must address the relationship between attitude, stress, burnout, compassion fatigue and empathy among mental health nurses in relation to consumers with dual diagnosis. Abstract: Introduction: There is a lack of evidence regarding mental health nurses' empathy towards consumers with dual diagnosis. Aims: This qualitative study aimed to describe mental health nurses' empathy towards consumers with dual diagnosis in Australian mental health settings. Method: Through purposeful sampling, interviews were conducted with 17 mental health nurses who have experience in caring for consumers with dual diagnosis. Thematic analysis, as an inductive approach was used, to generate codes and themes from participant data. To report on this qualitative study, we adhered to the ENTREQ guidelines. Results: Four themes emerged: challenges to develop empathy with consumers, lack of conducive attitude of nurses towards consumers, appraising consumers' emotions accurately and holistically responding to the appraised emotions. The findings indicated that developing empathy towards consumers with dual diagnosis is a complex task. Discussion: Mental health nurses may struggle to empathize with consumers when encountering confrontational situations. Interventional studies are required to address the relationship between mental health nurses' attitudes, stress, burnout, compassion fatigue and empathy in relation to consumers with dual diagnosis. Implications for Practice: Understanding why mental health nurses emotional experiences differ about a similar challenging situation experienced by their consumers is vital. Further research on strategies to address empathy issues among mental health nurses could enhance nursing practice and consumer care. © 2024 The Authors. Journal of Psychiatric and Mental Health Nursing published by John Wiley & Sons Ltd
Providing a localised cervical cancer screening course for general practice nurses
Get PDFCervical cancer screening programs in Australia have been developed to detect early precancerous changes in women with a cervix aged between 25 and 74. Yet, many barriers remain to the uptake of cervical screening. Barriers include a lack of culturally appropriate service provision, physical access, poor health literacy, emotional difficulties, socio-economic disadvantage and not having access to a female service provider. In remote and very remote areas of Australia, additional barriers experienced by Aboriginal or Torres Strait Islander peoples include a distrust of healthcare providers and a lack of services, resulting in a much higher rate of diagnosis and death from cervical cancer. General practice nurses (GPNs) are well placed to conduct cervical screening tests (CSTs) after they have undertaken additional education and practical training. GPNs' increase in scope of practice is beneficial to general practice as it helps to remove some barriers to cervical screening. In addition, GPNs conducting CSTs reduce GP workload and burnout and increase teamwork. GPNs working in metropolitan clinics have greater access to training facilities, whereas those working in rural and remote clinics are required to travel potentially long distances to complete practical assessments. This highlights the need for training to be made available in rural and remote areas. The aim of this forum paper is therefore to generate further discussion on the need for training programs to be made available in rural and remote areas to aid the upskilling of GPNs. © 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of La Trobe University
Core competencies of emergency nurses for the armed conflict context : experiences from the field
Get PDFBackground: Armed conflicts are usually associated with high mortality and morbidity rates, with unpredictable workload, injuries and illnesses. Identifying emergency nurses’ views of the core competencies required to enable them to work effectively in hospitals in areas of armed conflict is critical. It is important to inform the requisite standards of care and facilitate the translation of knowledge into safe, quality care. Aim: The aim of this study was to identify emergency nurses’ perceptions of core competencies necessary to work in hospitals in the context of armed conflict. Method: A descriptive qualitative phase of a mixed-method study using semi-structured interviews with participants was conducted from June to July 2019. The COREQ guideline for reporting qualitative research was followed. Findings: A sample of 15 participants was interviewed. The participant perceptions provided a different perspective of core competencies required for emergency nurses in the context of armed conflict, culminating in four main areas: (i) personal preparedness, (ii) leadership, (iii) communication and (iv) assessment and intervention. Conclusion: This study identified emergency nurses’ perceptions of their core competencies. Personal preparedness, leadership, communication, assessment and intervention were identified as contributing to calmness of character, confidence in care and cultural awareness for care in this setting and were essential for them to work effectively when managing victims of armed conflict in emergency departments. Implications for nursing practice and health policy: The findings of this study are important and novel because the researchers sought the perspectives of emergency nurses who have experience in receiving patients from armed conflict firsthand. The findings will inform policymakers in those settings regarding standard of care, education and drills for hospital nurses in optimizing armed conflict care response outcomes. © 2023 The Authors. International Nursing Review published by John Wiley & Sons Ltd on behalf of International Council of Nurses
Does radiofrequency radiation impact sleep? A double-blind, randomised, placebo-controlled, crossover pilot study
Get PDFThe most common source of Radiofrequency Electromagnetic Field (RF-EMF) exposures during sleep includes digital devices, yet there are no studies investigating the impact of multi-night exposure to electromagnetic fields emitted from a baby monitor on sleep under real-world conditions in healthy adults. Given the rise in the number of people reporting to be sensitive to manmade electromagnetic fields, the ubiquitous use of Wi-Fi enabled digital devices and the lack of real-world data, we investigated the effect of 2.45 GHz radiofrequency exposure during sleep on subjective sleep quality, and objective sleep measures, heart rate variability and actigraphy in healthy adults. This pilot study was a 4-week randomised, double-blind, crossover trial of 12 healthy adults. After a one-week run-in period, participants were randomised to exposure from either an active or inactive (sham) baby monitor for 7 nights and then crossed over to the alternate intervention after a one-week washout period. Subjective and objective assessments of sleep included the Pittsburgh Insomnia Rating Scale (PIRS-20), electroencephalography (EEG), actigraphy and heart rate variability (HRV) derived from electrocardiogram. Sleep quality was reduced significantly ( < 0.05) and clinically meaningful during RF-EMF exposure compared to sham-exposure as indicated by the PIRS-20 scores. Furthermore, at higher frequencies (gamma, beta and theta bands), EEG power density significantly increased during the Non-Rapid Eye Movement sleep ( < 0.05). No statistically significant differences in HRV or actigraphy were detected. Our findings suggest that exposure to a 2.45 GHz radiofrequency device (baby monitor) may impact sleep in some people under real-world conditions however further large-scale real-world investigations with specified dosimetry are required to confirm these findings
Dynamic fracture modeling of concrete composites based on nonlocal multiscale damage model and scaled boundary finite element methods
No full textDynamic fracture is a critical concern in the design and reliability assessment of concrete structures. This study presents a numerical prediction of dynamic fractures in concrete composites using a nonlocal multiscale damage model and the scaled boundary finite element method (SBFEM). The nonlocal multiscale damage model accurately captures the damage behavior of concrete materials by considering the nonlocal effects and predicting fractures under dynamic loading conditions. The SBFEM combined with quadtree meshes, efficiently models and discretizes concrete composites, enhancing computational efficiency, and capturing local details. The concrete mesostructure consists of aggregates, mortar matrix, and interface transition zone. The random aggregates are generated using the popular Monte Carlo simulation and take-and-place methods. By slightly offsetting the boundaries of the generated aggregates, a virtual thickness interface is obtained to approximately characterize the weakest regions. This study extensively investigates the effects of loading rate, aggregate content and shape, and interface thickness on fracture properties. The loading rate significantly influences crack morphology, with low rates suppressing crack branching, and higher rates resulting in crack branching. Moreover, an increased aggregate content in the concrete results in greater maximum reaction force. Additionally, the range of the maximum reaction force is higher when polygonal aggregates are used as compared to circular aggregates. This study examines the impact of the interface thickness on the fracture characteristics. Increasing the interface thickness makes the interface region more fragile, resulting in additional minimally damaged areas alongside the completely damaged cracked sections. This behavior can be attributed to the energy degradation functions employed in the model, thereby decreasing the load-bearing capacity of these regions. These findings contribute to a better understanding of the dynamic fracture phenomena and aid in optimizing the design and improving the reliability of concrete structures. © 2024 Elsevier Lt