This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine ...learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model's accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model's predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors.
This work aimed to test the hypothesis that the combination of arbuscular mycorrhizal fungi (AMF) and accumulation of silicon (Si) in banana plants via its uptake and transport by the fungus reduces ...the incidence of Black Leaf Steak Disease (BLSD) caused by
.
A pot experiment was conducted to compare BLSD symptoms on leaves of banana plants colonized or not by the AMF
MUCL 41833 and exposed or not to Si added to the growth substrate.
A marked increase in plant growth parameters (i.e., pseudostem diameter and height, leaf surface area, shoot, root and total dry weight) as well as accumulation of Si, P, and Ca were noticed in the AMF-colonized banana plants in presence as well as in absence of Si added to the growth substrate. Similarly Si addition to the substrate increased plant growth parameters. Leave symptoms caused by the pathogen were observed in all the treatments but were reduced in presence of AMF as well as in presence of Si added to the growth substrate. The more drastic reduction was noticed in the AMF-colonized plants with Si added to the growth substrate. The Severity Index as well as Area Under Disease Progress Curve were considerably decreased both at 21 (∼48% and 48%, respectively) and 35 days (∼21% and ∼32%, respectively) after inoculation of the pathogen as compared with non-AMF-colonized plants in absence of Si added to the substrate.
Our findings revealed that AMF-colonized banana plants grown in a subs-trate supplemented with Si were less impacted by
than non-colonized plants grown without Si added to the growth substrate. The combination of AMF-colonized banana plants (during the weaning phase or
) with the application of Si to soil seems thus a thoughtful option to mitigate the impact of BLSD in bananas, although such strategy needs first to be evaluated under field conditions to appraise its real potential.
This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in ...Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover.
Strategic alignment or “fit” is a notion that is deemed crucial in understanding how organizations can translate their deployment of information technology (IT) into actual increases in performance. ...While previous theoretical and methodological works have provided foundations for identifying the dimensions and performance impacts of the strategic alignment between IT, strategy, and structure, few attempts have been made to test the proposed theory empirically and operationalize fit systemically. Based on a gestalt perspective of fit and theory-based ideal coalignment patterns, an operational model of strategic alignment is proposed and empirically validated through a mail survey of 110 small firms. Using cluster analysis, it was found that low-performance firms exhibited a conflictual coalignment pattern of business strategy, business structure, IT strategy, and IT structure that distinguished them from other firms.
In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, ...enabling the discovery of new insights and analyses. However, the presence of noise and artefacts, particularly unwanted natural features, poses significant challenges, and their removal requires the application of filtering techniques prior to conducting analysis. Unmanned aerial vehicle-based photogrammetry is used at Melbourne Water’s Western Treatment Plant as a cost-effective and efficient method of inspecting the floating covers on the anaerobic lagoons. The focus of interest is the elevation profile of the floating covers for these sewage-processing lagoons and its implications for sub-surface scum accumulation, which can compromise the structural integrity of the engineered assets. However, unwanted artefacts due to trapped rainwater, debris, dirt, and other irrelevant structures can significantly distort the elevation profile. In this study, a machine learning algorithm is utilised to group distinct features on the floating cover based on an image segmentation process. An unsupervised k-means clustering algorithm is employed, which operates on a stacked 4D array composed of the elevation of the DEM and the RGB channels of the associated orthomosaic. In the cluster validation process, seven cluster groups were considered optimal based on the Calinski–Harabasz criterion. Furthermore, by utilising the k-means method as a filtering technique, three clusters contain features related to the elevations associated with the floating cover membrane, collectively representing 84% of the asset, with each cluster contributing at least 19% of the asset. The artefact groups constitute less than 6% of the asset and exhibit significantly different features, colour characteristics, and statistical measurements from those of the membrane groups. The study found notable improvements using the k-means filtering method, including a 59.4% average reduction in outliers and a 36.3% decrease in standard deviation compared to raw data. Additionally, employing the proposed method in the scum hardness analysis improved correlation strength by 13.1%, removing approximately 16% of the artefacts in total assets, in contrast to a 3.6% improvement with the median filtering method. This improved imaging will lead to significant benefits when integrating imagery into deep learning models for structural health monitoring and asset performance.
Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, ...and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place.
Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice.
We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t
(February 2020, before the COVID-19 pandemic) and 138 responses at t
(January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data.
Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry).
Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.
Aims
To identify and characterize the thematic foci, structure and evolution of nursing research on surveillance and patient safety.
Design
Bibliometric analysis.
Methods
Bibliometric methods were ...employed to analyse 1145 articles, using Bibliometrix and VOSviewer software.
Data Source
The Scopus bibliographic database was searched on April 7, 2023.
Results
A keyword co‐occurrence analysis found the most frequently occurring keywords to be: patient safety, nursing, nurses, adverse events, monitoring, critical care, quality improvement, vital signs, safety, alarm fatigue, education, nursing care, surveillance, clinical alarms, failure to rescue, evidence‐based practice, acute care, clinical deterioration, communication, intensive care. Network mapping, clustering and time‐tracking of the keywords revealed the focal themes, structure and evolution of the research field.
Conclusion
By assessing critical areas of the nursing research field, this study extends and enriches the current discourse on surveillance and patient safety for nursing researchers and practitioners. Critical challenges still have to be met by nurses, however, including the failure to rescue deteriorating patients. Further knowledge and understanding of surveillance and patient safety must be successfully translated from research to practice.
Implications for the Profession
This study highlights the gaps in nursing knowledge with regard to surveillance and patient safety and encourages nursing professionals to turn to evidence‐based surveillance practices.
Impact
In addressing the problem of surveillance and its effect on patient safety, this study found that, in most clinical care settings, preventing failures to rescue and adverse patient outcomes still remains a challenge for the nursing profession. This study should have an impact on nursing academics' future research themes and on nursing professionals' future clinical practices.
Reporting Method
Relevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.
Reliable and quantitative non-destructive evaluation for small fatigue cracks, in particular those in hard-to-inspect locations, is a challenging problem. Guided waves are advantageous for structural ...health monitoring due to their slow geometrical decay of amplitude with propagating distance, which is ideal for rapid wide-area inspection. This paper presents a 3D laser vibrometry experimental and finite element analysis of the interaction between an edge-guided wave and a small through-thickness hidden edge crack on a racecourse shaped hole that occurs, in practice, as a fuel vent hole. A piezoelectric transducer is bonded on the straight edge of the hole to generate the incident wave. The excitation signal consists of a 5.5 cycle Hann-windowed tone burst of centre frequency 220 kHz, which is below the cut-off frequency for the first order Lamb wave modes (SH1). Two-dimensional fast Fourier transformation (2D FFT) is applied to the incident and scattered wave field along radial lines emanating from the crack mouth, so as to identify the wave modes and determine their angular variation and amplitude. It is shown experimentally and computationally that mid-plane symmetric edge waves can travel around the hole's edge to detect a hidden crack. Furthermore, the scattered wave field due to a small crack length,
, (compared to the wavelength
of the incident wave) is shown to be equivalent to a point source consisting of a particular combination of body-force doublets. It is found that the amplitude of the scattered field increases quadratically as a function of
, whereas the scattered wave pattern is independent of crack length for small cracks
. This study of the forward scattering problem from a known crack size provides a useful guide for the inverse problem of hidden crack detection and sizing.
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