bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between canine hookworm infection and dog owners' awareness, perception, and behaviour: A cross‐sectional study in Brisbane, Queensland, 2019–2020
Authors: Owada Kei , Abdullah Swaid , Clark Nicholas , Nguyen Tu , Soares Magalhães Ricardo J. 
Published: May 2023

AbstractCanine hookworms are found globally, and infections have been recorded in domestic dogs visiting dog parks in Queensland, Australia. Some canine hookworms also present a risk of zoonotic transmission to humans. Potential transmission of hookworms can occur in the household and at public places because of lack of owner awareness and poor coverage of canine deworming. Between April 2019 and March 2020, faecal samples from owned dogs were collected from 39 dog parks in metropolitan Brisbane, Queensland, and tested for the presence of hookworm eggs using faecal floatation technique. Dog owners who provided samples were requested to complete a survey on their awareness, perceptions and behaviour regarding the risks and consequences of canine parasitic infection. Associations between dog owner demographics and responses to the survey were measured using Goodman and Kruskal's gamma. Statistical associations between canine hookworm infection in dogs and their owner's responses were quantified using a two‐sample Wilcoxon rank‐sum test. A total of five canine faecal samples from 175 respondents were found positive for hookworm eggs, corresponding to a sample‐ and park‐level prevalence of 2.86% and 12.82%, respectively. Female dog owners were found to be more aware of the importance of regular deworming of their dogs (|γ| = 0.405). Our results indicate that hookworm infection was associated with a lower awareness of the importance of deworming (p = 0.007), less diligence in administration of deworming (p = 0.004), lower awareness of the risk of acquiring parasites from raw meat (p = 0.010), less likelihood of cooking meat before feeding it to their dogs (p = 0.028), and less likelihood to properly dispose their dog's faeces (p = 0.027). This study not only indicates a need for improving owner education towards the importance of deworming but also a need for changing owner's behaviours to reduce potential environmental contamination with infective hookworms. The latter in particular indicates a broader public health risk of disease transmission in public places due to improper disposal of dog faeces, especially in areas with higher canine hookworm infections.
Elucidating Cryptocurrency with Trading Dashboard
Authors: Ghazali Masitah , Wong Alison Kuan Rong 
Published: May 2023

With the rise of interest in cryptocurrency in the recent decade, an ocean of financial news data has surfaced in articles, tweets, and even Reddit posts. Due to the sheer volume, it is not practical for the casual trader to read through all these news sources manually. However, only going through one or two sources alone may result in receiving biased information, or no useful information at all. With the current rise in cryptocurrency, accurately predicting market trends becomes highly beneficial to the user, providing a major opportunity for lower-income households to have a higher chance of profiting and living a substantially more comfortable lifestyle. In this study, a developer's API key was obtained for three news sources to scrape financial news from. Then, the TensorFlow Keras model and Gensim model's doc2vec NLP tool were utilized to process the data scraped online. The data is then saved as a .model and .sav file, and a website was constructed using the Flask framework. The website is now deployed and is available for all users. However, because the data obtained was too small to be utilized well, only a weak linear model that could give us a correlation between price and news sentiment was able to be constructed. The dashboard passed its functional and UAT tests with 100%, and via the usability test with SUS, the dashboard is considered to be easy to use. In all, the website summarizes the main details and sentiment of the coins and will benefit users who are just being introduced to the cryptocurrency space.
Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper
Authors: Moya Christian , Lin Guang 
Published: May 2023

Abstract
Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.
A Comparative Study of Parameter Identification Fusion Methods With a High-Precision Supercapacitor Model for Electric Vehicles
Authors: Wang Chun , Shen Chao , Tang Aihua , Liu Pu 
Published: May 2023

Abstract
To ensure energy storage system operates reliably for electric vehicles, it is vital to accurately identify supercapacitor model parameters in applications. In recent years, most of the algorithms focus on lithium-ion batteries, but few are reported to be used for supercapacitors. To fill this research gap, many algorithms and corresponding fusion methods for supercapacitors are designed in this study. First, seven popular intelligent optimization algorithms are selected to identify the supercapacitor model parameters, and the identification results are discussed in detail. Then, considering a single algorithm cannot guarantee convergence to all global optimal model parameters over state-of-charge (SOC) intervals, five fusion methods for supercapacitor parameter identification have been developed by combining information fusion technology. Finally, voltage errors are statistically analyzed to validate the effectiveness of the five proposed fusion methods. The results show that the five fusion methods can further enhance the global prediction performance of the supercapacitor model, particularly the reverse search-based parameter identification fusion (PIF-RS) method, which has better accuracy and reliability with respect to the maximum (Max) error, mean error, and root mean square (RMS) error decreasing by at least 10.1191%, 17.0024%, and 17.0989%, respectively.
Evaluation of Early-Stage Fatigue Damage in Metal Plates Using Quasi-Static Components of Low-Frequency Lamb Waves
Authors: Wu Kun , Xu Caibin , Deng Mingxi 
Published: May 2023

Abstract
Nonlinear Lamb waves including second harmonic and acoustic-radiation-induced quasi-static components (QSC) have a potential for accurately evaluating early-stage fatigue damage. Most previous studies focus on second-harmonic-based techniques that require phase velocity matching and are hard to isolate interferences from ultrasonic testing systems. The aforementioned requirement and deficiency limit applications of the second-harmonic-based techniques. In this study, a QSC-based technique of low-frequency Lamb waves is proposed for early-stage fatigue damage evaluation of metal plates, which does not need to require phase velocity matching and can remove interferences from ultrasonic testing systems. Both in simulations and in experiments, the primary Lamb wave mode at a low frequency that meets approximate group velocity matching with the generated QSC is selected. In finite element simulations, different levels of material nonlinearities by changing the third-order elastic constants are used to characterize levels of fatigue damage. Numerical results show that the magnitude of the generated QSC pulse increases with the levels of fatigue damage. Early-stage fatigue damage in aluminum plates with different fatigue cycles is further experimentally evaluated. The generated QSC pulse is extracted from received time-domain signals using the phase-inversion technique and low-pass digital filtering processing. The curve of the normalized relative acoustic nonlinearity parameter versus the cyclic loading number is obtained. Numerical simulations and experimental results show that the early-stage fatigue damage in aluminum plates can effectively be evaluated using the QSC generated by low-frequency Lamb waves.
Hepatic stellate cells: From bad reputation to mediators of liver homeostasis
Authors: Schönberger Katharina , Tchorz Jan S. 
Published: May 2023

Organ size and function critically depend on the tight regulation of cellular turnover. In this issue of
Science Signaling,
Trinh
et al
. reveal that hepatic stellate cells play an important role in maintaining liver homeostasis by stimulating midzonal hepatocyte proliferation through the secretion of neurotrophin-3.
Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases
Authors: Gu Chufeng , Wang Yujie , Jiang Yan , Xu Feiping , Wang Shasha , Liu Rui , Yuan Wen , Abudureyimu Nurbiyimu , Wang Ying , Lu Yulan , Li Xiaolong , Wu Tao , Dong Li , Chen Yuzhong , Wang Bin , Zhang Yuncheng , Wei Wen Bin , Qiu Qinghua , Zheng Zhi , Liu Deng , Chen Jili 
Published: May 2023

Background/aimsThis study evaluates the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigates the fundus disease spectrum based on ARAS.MethodsThis real-world, multicentre, cross-sectional study was conducted in Shanghai and Xinjiang, China. Six primary healthcare settings were included in this study. Colour fundus photographs were taken and graded by ARAS and retinal specialists. The performance of ARAS is described by its accuracy, sensitivity, specificity and positive and negative predictive values. The spectrum of fundus diseases in primary healthcare settings has also been investigated.ResultsA total of 4795 participants were included. The median age was 57.0 (IQR 39.0–66.0) years, and 3175 (66.2%) participants were female. The accuracy, speciﬁcity and negative predictive value of ARAS for detecting normal fundus and 14 retinal abnormalities were high, whereas the sensitivity and positive predictive value varied in detecting different abnormalities. The proportion of retinal drusen, pathological myopia and glaucomatous optic neuropathy was significantly higher in Shanghai than in Xinjiang. Moreover, the percentages of referable diabetic retinopathy, retinal vein occlusion and macular oedema in middle-aged and elderly people in Xinjiang were significantly higher than in Shanghai.ConclusionThis study demonstrated the dependability of ARAS for detecting multiple retinal diseases in primary healthcare settings. Implementing the AI-assisted fundus disease screening system in primary healthcare settings might be beneficial in reducing regional disparities in medical resources. However, the ARAS algorithm must be improved to achieve better performance.Trial registration numberNCT04592068.
Improving medication safety in a paediatric hospital: a mixed-methods evaluation of a newly implemented computerised provider order entry system
Authors: Liang Man Qing , Thibault Maxime , Jouvet Philippe , Lebel Denis , Schuster Tibor , Moreault Marie-Pierre , Motulsky Aude 
Published: May 2023

ObjectivesComputerised provider order entry (CPOE) systems have been implemented around the world as a solution to reduce ordering and transcription errors. However, previous literature documented many challenges to attain this goal, especially in paediatric settings. The objectives of this study were to (1) analyse the impact of a paediatric CPOE system on medication safety and (2) suggest potential error prevention strategies.MethodsA pre-post observational study was conducted at the pilot ward (n=60 beds) of a paediatric academic health centre through mixed methods. The implementation project and medication management workflows were described through active participation to the project management team, observation, discussions and analysis of related documents. Furthermore, using incident reports, the nature of each error and error rate was compared between the preperiod and postperiod.ResultsThe global error rate was lower, but non-statistically significant, in the post implementation phase, which was mostly driven by a significant reduction in errors during order acknowledgement, transmission and transcription. Few errors occurred at the prescription step, and most errors occurred during medication administration. Furthermore, some errors could have been prevented using a CPOE in the pre-implementation period, and the CPOE led to few technology-related errors.Discussion and conclusionThis study identified both intended and unintended effects of CPOE adoption through the entire medication management workflow. This study revealed the importance of simplifying the acknowledgement, transmission and transcribing steps through the implementation of a CPOE to reduce medication errors. Improving the usability of the electronic medication administration record could help further improve medication safety.
IT risk management for medical devices in hospital IT networks: a catalogue of measures and indicators
Authors: Richter Stefan , Ammenwerth Elske 
Published: May 2023

ObjectivesConnecting medical devices to hospital IT networks can create threats that must be covered by IT risk management. In practice, implementing such risk management is not trivial because the IEC 80001-1, as the existing state-of-the-art, do not describe sufficiently concrete implementation measures or evaluation indicators. The aim of the present work was to develop and evaluate a catalogue of measures and indicators to help hospitals implement and evaluate risk management in accordance with IEC 80001-1.MethodsWe conducted a Delphi study with 22 experts. In the first round, we performed interviews to identify implementation measures and evaluation indicators using qualitative content analysis. In the second round, a quantitative experts’ survey confirmed the results of the first survey round and identified relationships between the measures and indicators. Based on these results, we then developed a catalogue containing the identified measures and indicators. Finally, we performed a case study to verify the practicability of this catalogue.ResultsWe developed and verified a catalogue of 49 measures and 18 indicators to help hospitals implement and evaluate risk management following IEC 80001-1. The case study confirmed the practicability of the catalogue.DiscussionCompared with IEC 80001-1, our catalogue goes into further detail to offer hospitals a stepwise implementation and evaluation approach. However, the catalogue must be tested in further case studies and evaluated in terms of generalisation.ConclusionsThe catalogue will enable hospitals to overcome recent difficulties in implementing and evaluating IT risk management for medical devices according to IEC 80001-1.
Keep it simple: designing a user-centred digital information system to support chronic disease management in low/middle-income countries
Authors: Burka Daniel , Gupta Reena , Moran Andrew E , Cohn Jennifer , Choudhury Sohel R , Cheadle Tim , Mullick Rahul , Frieden Thomas R 
Published: May 2023

ObjectiveImplement a user-centred digital health information system to facilitate rapidly and substantially increasing the number of patients treated for hypertension in low/middle-income countries.MethodsUser-centred design of Simple, an offline-first app for mobile devices to record patient clinical visits and a web-based dashboard to monitor programme performance.ResultsThe Simple mobile application scaled rapidly over the past 4 years to reach more than 11 400 primary care facilities in four countries with over 3 million patients enrolled. Simple achieved median duration for new patient registration of 76 s (IQR 2 s) and follow-up visit entry of 13 s (IQR 1 s).ConclusionsA fast, easy-to-use digital information system for hypertension programmes that accommodates healthcare worker time constraints by minimising data entry and focusing on key performance indicators can successfully reach scale in low-resource settings.
Effect of a hospital command centre on patient safety: an interrupted time series study
Authors: Mebrahtu Teumzghi F , McInerney Ciarán D , Benn Jonathan , McCrorie Carolyn , Granger Josh , Lawton Tom , Sheikh Naeem , Randell Rebecca , Habli Ibrahim , Johnson Owen Ashby 
Published: May 2023

BackgroundCommand centres have been piloted in some hospitals across the developed world in the last few years. Their impact on patient safety, however, has not been systematically studied. Hence, we aimed to investigate this.MethodsThis is a retrospective population-based cohort study. Participants were patients who visited Bradford Royal Infirmary Hospital and Calderdale & Huddersfield hospitals between 1 January 2018 and 31 August 2021. A five-phase, interrupted time series, linear regression analysis was used.ResultsAfter introduction of a Command Centre, while mortality and readmissions marginally improved, there was no statistically significant impact on postoperative sepsis. In the intervention hospital, when compared with the preintervention period, mortality decreased by 1.4% (95% CI 0.8% to 1.9%), 1.5% (95% CI 0.9% to 2.1%), 1.3% (95% CI 0.7% to 1.8%) and 2.5% (95% CI 1.7% to 3.4%) during successive phases of the command centre programme, including roll-in and activation of the technology and preparatory quality improvement work. However, in the control site, compared with the baseline, the weekly mortality also decreased by 2.0% (95% CI 0.9 to 3.1), 2.3% (95% CI 1.1 to 3.5), 1.3% (95% CI 0.2 to 2.4), 3.1% (95% CI 1.4 to 4.8) for the respective intervention phases. No impact on any of the indicators was observed when only the software technology part of the Command Centre was considered.ConclusionImplementation of a hospital Command Centre may have a marginal positive impact on patient safety when implemented as part of a broader hospital-wide improvement programme including colocation of operations and clinical leads in a central location. However, improvement in patient safety indicators was also observed for a comparable period in the control site. Further evaluative research into the impact of hospital command centres on a broader range of patient safety and other outcomes is warranted.
Evaluation of race/ethnicity-specific survival machine learning models for Hispanic and Black patients with breast cancer
Authors: Park Jung In , Bozkurt Selen , Park Jong Won , Lee Sunmin 
Published: May 2023

ObjectivesSurvival machine learning (ML) has been suggested as a useful approach for forecasting future events, but a growing concern exists that ML models have the potential to cause racial disparities through the data used to train them. This study aims to develop race/ethnicity-specific survival ML models for Hispanic and black women diagnosed with breast cancer to examine whether race/ethnicity-specific ML models outperform the general models trained with all races/ethnicity data.MethodsWe used the data from the US National Cancer Institute’s Surveillance, Epidemiology and End Results programme registries. We developed the Hispanic-specific and black-specific models and compared them with the general model using the Cox proportional-hazards model, Gradient Boost Tree, survival tree and survival support vector machine.ResultsA total of 322 348 female patients who had breast cancer diagnoses between 1 January 2000 and 31 December 2017 were identified. The race/ethnicity-specific models for Hispanic and black women consistently outperformed the general model when predicting the outcomes of specific race/ethnicity.DiscussionAccurately predicting the survival outcome of a patient is critical in determining treatment options and providing appropriate cancer care. The high-performing models developed in this study can contribute to providing individualised oncology care and improving the survival outcome of black and Hispanic women.ConclusionPredicting the individualised survival outcome of breast cancer can provide the evidence necessary for determining treatment options and high-quality, patient-centred cancer care delivery for under-represented populations. Also, the race/ethnicity-specific ML models can mitigate representation bias and contribute to addressing health disparities.
Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study
Authors: Hong Julian C , Patel Pranalee , Eclov Neville C W , Stephens Sarah J , Mowery Yvonne M , Tenenbaum Jessica D , Palta Manisha 
Published: May 2023

ObjectivesClinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation.MethodsAn anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience.Results59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions.ConclusionsRandomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation.