116320 research outputs found
Sort by
Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks
No full textIn the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors τ1 and τ2 are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM's predictions and those from the numerical outcomes with MSE in the range of 10-7, underscoring the network's effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities
Field-based calibration and operation of low-cost sensors for particulate matter by linear and nonlinear methods
Get PDFThe increasing awareness of air pollution's detrimental effects has driven the demand for affordable air quality monitoring solutions, particularly low-cost fine particulate matter (PM2.5) sensors. However, these sensors often suffer from low data accuracy and require rigorous calibration, especially in real-world settings. This study evaluates the field calibration of low-cost PM2.5 sensors under low ambient concentration conditions, utilizing both linear and nonlinear regression methods. The research was conducted in Sydney, Australia, where data were collected from both low-cost Hibou sensors and a research-grade DustTrak monitor. Our analysis compares calibration performance across various time resolutions, meteorological factors, and traffic conditions. The results indicate that nonlinear models significantly outperform linear models, achieving an R2 of 0.93 at 20-min resolution, surpassing the U.S. EPA's calibration standards. Additionally, our findings suggest that temperature, wind speed, and heavy vehicle density are the most influential factors in calibration accuracy. After comparing the corrected measurement data with WHO standards, it was observed that PM2.5 concentrations at the bus stop measurement site ranged from 7 to 76 μg/m3, with 24 % of the data exceeding the WHO 24-h standard. This finding highlights that traffic-generated PM2.5 pollution remains a significant concern in Sydney. The study concludes that nonlinear calibration methods are more effective for low-cost PM2.5 sensor deployment in urban environments, though further exploration is needed to enhance the interpretability and computational efficiency of deep learning models
Impact of memory clinics on carers of people living with dementia: An integrative review.
Get PDFPeople living with dementia and their carers require ongoing support in the community. Memory clinics can provide a range of supports including education, respite and symptom management. While these clinics improve consumer outcomes, their impact on the carers of people living with dementia is unclear. This review sought to identify and critically synthesise the literature on the effectiveness of memory clinics in supporting carers. An integrative review process was used to identify papers from CINAHL and Medline databases. Of the eight included papers, two were qualitative and six were quantitative studies. Three themes were identified, namely; psychological heath, carer burden and satisfaction with memory clinics. Four studies found decreases in caregiver burden, distress, and psychological symptoms such as anxiety and worry. Satisfaction with the clinic model was discussed as a source of support by carers, highlighting the memory clinics. The variable outcomes seen in this review require further research to elucidate the impacts of memory clinics on carers along the dementia trajectory
Overlooked tripartite microbial interactions influencing chemical cycling in the ocean.
No full textInter-microbial interactions fundamentally govern ocean ecology and biogeochemistry. Recently, Henshaw and colleagues revealed that important inter-bacterial associations in the ocean can be shaped by viral infections, whereby infected cyanobacteria release specific chemicals that attract heterotrophic bacteria, uncovering a new tripartite microbial interaction that influences carbon transfer in the surface ocean
A subtractive modelling approach for predicting the radiation of a cylindrical shell in a waveguide
Get PDFModeling the sound radiated from underwater structures immersed in various environments is necessary in ocean acoustics and naval engineering. Typically, an underwater vibroacoustic system is composed of an elastic cylindrical shell that is radiated into an unbounded fluid domain. However, in contrast to deep oceans, for a shallow water environment, the influence of the sea surface and seabed can no longer by ignored. The significant fluid-structure interaction arising from the coupling at the boundary of the structure and surrounding fluid complicates the prediction of vibroacoustic behaviour. A sub-structuring technique based on the condensed transfer function (CTF) approach and reverse condensed transfer function (rCTF) approach has been proposed recently to tackle complex vibroacoustic problems by coupling/decoupling the necessary subsystems. Its potential is demonstrated in the present study through a two-dimensional case study to predict the sound radiation from an elastic structure of a cylindrical shell excited by a harmonic line force and immersed in a fluid domain of a perfect underwater acoustic waveguide, that is composed of an upper free surface and a lower rigid floor. The targeted model is obtained from a perfect underwater waveguide in which a water disk is subtracted from, and an excited shell is added in place of the water disk. The predictions of the proposed CTF-rCTF process are verified against analytical solutions for two different partitions of the global system and two types of condensation functions
A Better Future Beyond the Walls: Narrative Review of Best Practice Components of Services and Programs for People Exiting Custody
Get PDFIncreasing imprisonment rates globally and in Australia have led to more individuals trapped in the cycles of incarceration, reinforcing disadvantage and criminal behavior. Although programs exist to support individuals during their transition post prison, there is limited knowledge about these programs, warranting further research. We conducted a narrative analysis of the program components via a systematic literature search, identifying similarities, differences, and limitations of such programs internationally, with focus on Australia. Findings suggest three main components of these programs: “what is offered,” “how they are delivered” and “when they are provided.” This highlights the need for a continuum of support offering appropriate programs from initial contact with the custodial system (“Pre-release”), continuing through “transition” and “post-release,” with tailored interventions based on the individual risk, needs, and responsivity. Results underscore the need for co-designed and evidence-based policies emphasizing the significance of early interventions and personalized approaches based on individual risk assessments
Modelling soil temperature at multiple depths in Saurashtra region (Junagadh) of Gujarat using machine learning and shapely approach
Get PDFForecasting soil temperature (ST) at multiple depths is crucial for understanding meteorological processes, enhancing agricultural resilience, and assessing ecological and environmental risks. Data driven model represents an alternative tool to the conventional measurement of ST e.g. soil thermometer. To develop the ML model, weekly ST and relevant meteorological variables for the city of Junagadh (Saurashtra region) are collected for the period of 2010–2023. A thorough feature analysis was performed to select the most promising feature using Pearson correlation coefficient and shapely approach. The model was developed using different combinations of input parameters (M1–M7) and trained using different machine learning algorithms. This research aims to evaluate four different machine learning approaches namely, Random Forest (RF), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), to predict the soil temperature at 5 cm, 10 cm and 20 cm depth. The result of this study showed that by choosing the optimum input parameter, there is no significant impact on accuracy of model. The best performance was obtained for Model 7 f(TDB, TMax, TMin, Evapo) model at the 10-cm soil depth, as it provided the greatest correlation coefficient (r = 0.9967) and the lowest value for root mean square error (RMSE = 0.3410 °C) and percent bias (PBIAS = − 0.0115). The result showed that model performance differences are often statistically significant, especially at shallower depths (ST5, ST10), but less so at ST20. In the current study, besides evaluating the potential of four machine learning models, the interpretation of the machine learning algorithm for soil temperature prediction was explored using SHapley Additive exPlanations (SHAP). The study used an explainable artificial intelligence (XAI) approach to provide novel interpretation and insights to elucidate model formulation and relative predictor importance
Day ahead scheduling of battery energy storage system operation using growth optimizer within cyber–physical–social systems
Get PDFIntegrating Battery Energy Storage Systems (BESS) into Cyber–Physical–Social Systems (CPSS) is pivotal for reducing energy costs, enhancing grid stability, and extending battery lifespan. However, existing optimization methods often struggle to balance operational cost, battery degradation, and grid reliability, particularly under uncertain demand and supply conditions. This paper introduces the Growth Optimizer (GO), a novel meta-heuristic algorithm specifically designed for day-ahead BESS scheduling in CPSS environments. Unlike traditional methods, GO explicitly incorporates cyber, physical, and social dimensions, capturing the interdependent dynamics among energy consumption behavior, grid operations, and economic incentives. By leveraging adaptive scheduling under varying battery capacities, GO effectively mitigates uncertainties such as demand fluctuations and renewable intermittency. When applied to five Australian states, GO achieves up to a 15% improvement in multi-objective performance metrics, resulting in measurable financial savings, extended battery life, and reduced infrastructure costs. This approach empowers end users to optimize energy use proactively, enhancing both economic efficiency and energy autonomy
Smart Water Conservation: A Behaviourally-Grounded Recommender System for Demand Management Programs
Get PDFWater utilities are increasingly turning to digital solutions to promote conservation behaviours among households; however, traditional campaigns often suffer from limited personalisation, low interactivity, and modest long-term impact. Though computer-tailored and recommender systems (RSs) may offer personalisation, these systems lack a generalised framework that integrates behavioural theory with system design. This study addresses this research gap by introducing a novel framework that unites behavioural science, user experience (UX) design, and adaptive digital feedback to foster water-conscious practices at the residential level. The model draws on established behavioural theories, including the Theory of Planned Behaviour, the Transtheoretical Model, and Intervention Mapping, to ensure that tailored recommendations align with users’ psychological drivers, behavioural readiness, and daily routines. An industry-first prototype RS was developed and evaluated through an online survey (N = 300), assessing user perceptions of relevance, motivation, ease of use, and likelihood of action. The results reveal strong support for personalised suggestions, with 82% of respondents agreeing that personalised recommendations would help conserve water, and 76% indicating incentives would motivate adoption. This evidence indicates early acceptance and high potential impact. This study also addresses a critical research gap: no generic model previously existed to guide the integration of RSs with behaviour change interventions in water demand management. Broader implications are also discussed for applying the model to other sustainability domains such as energy use, waste reduction, and climate adaptation
Hybrid wave–wind energy site power output augmentation using effective ensemble covariance matrix adaptation evolutionary algorithm
Get PDFFloating hybrid wind–wave systems combine offshore wind platforms and WECs to create cost-effective, reliable energy solutions. WECs that are properly designed and tuned are required to avoid unwanted loads that can interfere with turbine motion while efficiently extracting energy from waves. The systems diversify energy sources, enhance energy security, and reduce supply risks while delivering a smoother power output through the minimisation of energy production variability. However, optimisation of these systems is hindered by physical and hydrodynamic component–component interactions, which cause a challenging optimisation space. A 5-MW OC4-DeepCwind semi-submersible platform and three spherical WECs are taken into consideration in this paper in order to explore such synergies. To address these challenges, we propose an effective ensemble optimisation (EEA) technique that combines covariance matrix adaptation, novelty search, and discretisation techniques. To evaluate the EEA performance, we used four sea sites located along Australia's southern coast. In this framework, geometry and power take-off (PTO) parameters are simultaneously optimised to maximise the average power output of the hybrid wind–wave system. Ensemble optimisation methods enhance performance, flexibility, and robustness by identifying the best algorithm or combination of algorithms for a given problem, addressing issues like premature convergence, stagnation, and poor search space exploration. The EEA was benchmarked against 14 advanced optimisation methods, demonstrating superior solution quality and convergence rates. EEA improved total power output by 111%, 95%, and 52% compared to Whale Optimisation Algorithm (WOA), Equilibrium Optimiser (EO), and Artificial Hummingbird Algorithm (AHA), respectively. Additionally, in comparisons with advanced methods, Ensemble Sinusoidal Differential Covariance Matrix Adaptation (LSHADE), Self-adaptive Differential Evolution (SaNSDE), and Social Learning Particle Swarm Optimisation (SLPSO), EEA achieved absorbed power enhancements of 498%, 638%, and 349% at the Sydney sea site, showcasing its effectiveness in optimising hybrid energy systems
Data sources:
Access Repository Dashboard
Do you manage OPUS? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!