bluuebunny/crossref_metadata_2025_split huggingface dataset:
Building function classification in Nanjing, China, using deep learning
Authors: Xu Yongyang , He Zhanjun , Xie Xuejing , Xie Zhong , Luo Jing , Xie Hong 
Published: May 2022

AbstractThe functional classification of buildings is important for creating and managing urban zones and assisting government departments. Existing building function classification methods are mainly designed for remote sensing imagery or zones in vector maps. These methods cannot be used for the single buildings in large‐scale vector maps. In this study, a learning strategy for multiple features and context information is developed to detect a single building function in a vector map. First, multiple features are extracted for each building based on local and regional structures. Then, a graph convolutional network, GraphSAGE, is introduced to analyze the modeled graph and building footprint features through supervised learning. Experiments show that the framework can learn local and contextual building information with the ability to distinguish different building functions. When classifying the building function, the proposed method performed better than other machine learning methods, such as random forest and support vector machines.
Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring
Authors: Shirangi Mehrdad Gharib , Aragall Roger , Osgouei Reza Ettehadi , May Roland , Furlong Edward , Dahl Thomas G. , Thompson Charles A. 
Published: May 2022

AbstractIn this work, we present our advances to develop and apply digital twins for drilling fluids and associated wellbore phenomena during drilling operations. A drilling fluid digital twin is a series of interconnected models that incorporate the learning from the past historical data in a wide range of operational settings to determine the fluids properties in real-time operations. Our specific focus is on prediction of cuttings bed thickness along the wellbore in hole cleaning and prediction of high-pressure high-temperature (HPHT) rheological properties (in downhole conditions). In both applications, we present procedures to develop accurate digital twins for prediction of drilling fluid properties in real-time drilling operations. In the hole cleaning application, we develop accurate computational fluid dynamics (CFD) models to capture the effects of rotation, eccentricity, and bed height on local fluid velocities above cuttings bed. We then run 55,000 CFD simulations for a wide range of operational settings to generate training data for machine learning. For rheology monitoring, thousands of lab experiment records are collected as training data for machine learning. In this case, the HPHT rheological properties are determined based on rheological measurement in the American Petroleum Institute (API) condition (14.7 psi and 150 °F) together with the fluid type and composition data. We compare the results of the application of several machine learning algorithms to represent CFD simulations (for hole cleaning) and lab experiments (for monitoring HPHT rheological properties). Rotating cross-validation method is applied to ensure accurate and robust results. In both cases, models from the gradient boosting and the artificial neural network algorithms provided the highest accuracy (about 0.95 in terms of R2) for test datasets. With developments presented in this paper, the hole cleaning calculations can be performed in real time, and the HPHT rheological properties of drilling fluids can be estimated at the rig site avoiding the need to wait for the laboratory experimental results.
An Optimization-Based Design Strategy for Centrifugal Compressors of Automotive Turbochargers and the Comparison of Five Particular Optimization Methods
Authors: Alpaya Mert , Kavurmacioglu Levent A. , Camci Cengiz 
Published: May 2022

AbstractIt is possible to optimize a turbocharger's performance for a broad range of engine operating points. However, most of the turbochargers have distinctive performance characteristics independent of engine operation. Therefore, the correct way to obtain the maximum performance from an engine is to design its turbocharger with the specific engine performance map in mind. This paper deals with the effective use of computational methods to create optimal turbocharger compressors for realistic engine operating conditions. The present process has four steps: preliminary analysis, throughflow analysis, optimization, and computational fluid dynamics (CFD) analysis. In the preliminary analysis, gas dynamics and Euler turbomachinery equations are used to calculate the basic dimensions. In the throughflow analysis, calculated dimensions are used to create a parametric three-dimensional (3D) geometry. A throughflow analysis generates the two-dimensional flow results using the meridional geometry of this 3D geometry. The main contribution of this research is the five different optimization methods that are employed and compared. The efficiency of the rotor is defined as the objective function to be maximized. A comparison of the five optimization schemes showed that the genetic algorithm (GA) is the most suitable method for the current design optimization problem. In the final step, a CFD solver is used to assess the created design's final performance. The CFD results' validity is checked against a reference compressor's test results. This study also indicates that preliminary sizing followed by a well-built throughflow analysis eliminates the need for a fully parametric 3D CFD-based design.
“Go Where the Big Challenges Are”: Preservice Physical Educators as Change Agents
Authors: Wickens Corrine M. , Parker Jenny 
Published: May 2022

Purpose: This study explored the tensions around physical and literacy integration initiatives from the view of physical education teacher education candidates. Method: We situated our data collection in qualitative case study methodology, emphasizing data from focus group interviews conducted during the final month of physical education teacher education candidates’ programs. Results: We demonstrated candidates’ recognition of the role of literacy integration within physical education in relation to (a) candidates’ feelings of pride and defensiveness of their field, (b) the importance of teacher collaboration, (c) teachers’ responsibility to support school goals and the students themselves, and (d) the potential of candidates to become change agents and leaders in their future school environments. Discussion/Conclusion: We assert that collaboration is required, and it must transpire within and among multiple educational contexts spaces, including K–12 settings, among teacher education faculty and programs, and across K–12 and higher education divides.
Rapid priority setting exercise on faecal incontinence for Cochrane Incontinence
Authors: Thomson Katie H , O'Connor Nicole , Dangova Kim Tuyen , Gill Sean , Jackson Sara , Bliss Donna Z , Wallace Sheila A , Pearson Fiona 
Published: May 2022

ObjectiveThis rapid priority setting exercise aimed to identify, expand, prioritise and explore stakeholder (patients, carers and healthcare practitioners) topic uncertainties on faecal incontinence (FI).DesignAn evidence gap map (EGM) was produced to give a visual overview of emerging trial evidence; existing systematic review-level evidence and FI stakeholder topic uncertainties derived from a survey. This EGM was used in a knowledge exchange workshop that promoted group discussions leading to the prioritisation and exploration of FI stakeholder identified topic uncertainties.ResultsOverall, a mismatch between the existing and emerging evidence and key FI stakeholder topic uncertainties was found. The prioritised topic uncertainties identified in the workshop were as follows: psychological support; lifestyle interventions; long-term effects of living with FI; education; constipation and the cultural impact of FI. When these six prioritised topic uncertainties were explored in more depth, the following themes were identified: education; impact and burden of living with FI; psychological support; healthcare service improvements and inconsistencies; the stigma of FI; treatments and management; culturally appropriate management and technology and its accessibility.ConclusionsTopic uncertainties identified were broad and wide ranging even after prioritisation. More research is required to unpick the themes emerging from the in-depth discussion and explore these further to achieve a consensus on deliverable research questions.
Approaches to prioritising primary health research: a scoping review
Authors: Fadlallah Racha , Daher Najla , El-Harakeh Amena , Hammam Rima , Brax Hneine , Bou Karroum Lama , Lopes Luciane Cruz , Arnous Ghida , Kassamany Inas , Baltayan Stephanie , Harb Aya , Lotfi Tamara , El-Jardali Fadi , Akl Elie A 
Published: May 2022

ObjectiveTo systematically identify and describe approaches to prioritise primary research topics in any health-related area.MethodsWe searched Medline and CINAHL databases and Google Scholar. Teams of two reviewers screened studies and extracted data in duplicate and independently. We synthesised the information across the included approaches by developing common categorisation of relevant concepts.ResultsOf 44 392 citations, 30 articles reporting on 25 approaches were included, addressing the following fields: health in general (n=9), clinical (n=10), health policy and systems (n=10), public health (n=6) and health service research (n=5) (10 addressed more than 1 field). The approaches proposed the following aspects to be addressed in the prioritisation process: situation analysis/ environmental scan, methods for generation of initial list of topics, use of prioritisation criteria, stakeholder engagement, ranking process/technique, dissemination and implementation, revision and appeal mechanism, and monitoring and evaluation. Twenty-two approaches proposed involving stakeholders in the priority setting process. The most commonly proposed stakeholder category was ‘researchers/academia’ (n=17, 77%) followed by ‘healthcare providers’ (n=16, 73%). Fifteen of the approaches proposed a list of criteria for determining research priorities. We developed a common framework of 28 prioritisation criteria clustered into nine domains. The criterion most frequently mentioned by the identified approaches was ‘health burden’ (n=12, 80%), followed by ‘availability of resources’ (n=11, 73%).ConclusionWe identified and described 25 prioritisation approaches for primary research topics in any health-related area. Findings highlight the need for greater participation of potential users (eg, policy-makers and the general public) and incorporation of equity as part of the prioritisation process. Findings can guide the work of researchers, policy-makers and funders seeking to conduct or fund primary health research. More importantly, the findings should be used to enhance a more coordinated approach to prioritising health research to inform decision making at all levels.
Oncological outcome of single-port insufflation endoscopic nipple-sparing mastectomy versus open mastectomy in early breast cancer patients: a study protocol for a randomised controlled trial
Authors: Wang Zi-han , Ding Guoqian , Wu Shanshan , Song Jian-ning , Ge Zhi-cheng , Zhang Huiming , Yuan Zhu , Gao Yin-guang , Gang Tian-ran , Zhang Zhongtao , Qu Xiang 
Published: May 2022

IntroductionBreast cancer is the most prevalent cancer and the leading cause of cancer-related death in women. Conventional open mastectomy (C-OM) is one of the most common procedures for breast cancer, which involves the removal of the nipple-areola complex and a large proportion of the breast skin, leading to poor cosmetic effect and restriction of upper extremity function. Single-port insufflation endoscopic nipple-sparing mastectomy (SIE-NSM) could conceal the incision along the wrinkles in the axilla, preserve all the breast skin and nipple-areola complex and provide a better cosmetic outcome and quality of life. This trial aims to investigate the oncological safety between SIE-NSM and C-OM in early breast cancer patients.Methods and analysisThis is a single centre, non-blinded, randomised controlled trial (RCT) and will be conducted at Beijing Friendship Hospital. Patients will be enrolled in the inpatient ward. Breast surgeons will notify patients who meet the inclusion and exclusion criteria with the instruction of this RCT. Patients will be randomly assigned to C-OM or SIE-NSM with a 3:1 allocation as per a computer-generated randomisation schedule. Patients will be followed-up for 12 months for analysing surgical outcomes. The primary outcome is the local recurrence rate at a 12-month follow-up. The secondary outcome is the distant metastasis rate, cosmetic satisfaction score and psychosocial well-being score after a 12-month follow-up. To ensure the accuracy of the cosmetic satisfaction score and psychosocial well-being score, the standard scale, Breast-Q score, will be applied.Ethics and disseminationThis study will be conducted according to the medical ethics committee of the Beijing Friendship Hospital and according to the principles of the Declaration of Helsinki. All patients will receive clear instruction of their disease and treatment plan. Informed consent will be obtained from all patients when they agree to comply with our research plan. The results will be disseminated at academic presentations and publications in peer-reviewed journals. The raw data will be confidentially stored in our electronic data capture database. Data will not be shared unless an appropriate data request is submitted after the trial completion and peer-review journal publication.Trial registration numberNCT04461847.
Chinese Neonatal Network: a national protocol for collaborative research and quality improvement in neonatal care
Authors: Hei Mingyan , Li Xiaoying , Shi Yuan , Cao Yun , Sun Jianhua , Wu Hui , Jiang Siyuan , Ma Xiaolu , Wang Yanchen , Sun Huiqing , Zhang Huayan , Du Li-Zhong , Zhou Wenhao , Lee Shoo K , Chen Chao 
Published: May 2022

IntroductionThe objective of the Chinese Neonatal Network (CHNN) is to provide a platform for collaborative research, outcomes evaluation and quality improvement for preterm infants with gestational age less than 32 weeks in China. The CHNN is the first national neonatal network and has the largest geographically representative cohort from neonatal intensive care units (NICUs) in China.Methods and analysisIndividual-level data from participating NICUs will be collected using a unique database developed by the CHNN on an ongoing basis from January 2019. Data will be prospectively collected from all infants <32 weeks gestation or <1500 g birth weight at 58 participating NICUs. Infant outcomes and inter-institutional variations in outcomes will be examined and used to inform quality improvement measures aimed at improving outcomes. Information about NICU environmental and human resource factors and processes of neonatal care will also be collected and analysed for association with outcomes. Clinical studies, including randomised controlled trials will be conducted using the CHNN data platform.Ethics and disseminationThis study was approved by the ethics review board of Children’s Hospital of Fudan University, which was recognised by all participating hospitals. Waiver of consent were granted at all sites. Only non-identifiable patient level data will be transmitted and only aggregate data will be reported in CHNN reports and publications.
Predictors for the prognosis and recurrence of ischaemic stroke among young Chinese patients: a cohort study
Authors: Wang Lijuan , Ge Jiaojiao , Chen Ying , Liu Ying , Li Cong , Dong Yanan , Liu Li , Xing Yingqi 
Published: May 2022

ObjectiveTo identify predictors of young Chinese patients with ischaemic stroke outcome and recurrence of stroke.DesignThrough considered the risk factors, neuroimaging findings, distribution, vascular stenosis, and the post-stroke treatment of young Chinese patients with ischemic stroke.SettingThe First Hospital of Jilin University.ParticipantsWe enrolled 579 patients (median age 45 years, range 15–49, men 81.0%) treated for the first occurrence of ischaemic stroke between January 2014 and December 2017.Main outcome measuresWe assessed stroke outcome based on the modified Rankin Scale (mRS) scores and recurrence of cerebrovascular events at 12 months. Multivariate logistic regression was used to identify the independent predictors of unfavourable outcomes (mRS score ≥2) and recurrence.ResultsWe observed stenosis in 295 patients (50.9%)—middle cerebral artery stenosis was the most common (18.1%). Of all 579 included patients, normal or mild, moderate and severe stenoses or occlusions were observed in 51.8%, 6.0% and 42.1% of patients, respectively. Unfavourable outcomes were observed in 91 patients (15.7%), including 10 patients (1.7%) who died. The rate of stroke recurrence was 7.9%. Independent predictors of unfavourable outcomes included a high National Institutes of Health Stroke Scale score (OR 1.151, 95% CI 1.094 to 1.210; p<0.001) and severe vascular stenosis or occlusion (OR 1.867, 95% CI 1.181 to 2.952; p=0.008). Predictors of recurrence included age of ≥45 years (OR 2.072, 95% CI 1.066 to 4.025; p=0.032) and atrial fibrillation (OR 15.207, 95% CI 4.273 to 54.120; p<0.001).ConclusionsOur research shows that when developing prevention strategies for young people, measures that focus on mitigating risk factors should be considered. In addition, vascular screening of young populations is also of vital importance for stroke prevention and poor prognosis prediction.
Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy
Authors: Galli Maria Giulia , Djuric Olivera , Besutti Giulia , Ottone Marta , Amidei Lucia , Bitton Lee , Bonilauri Carlotta , Boracchia Luca , Campanale Sergio , Curcio Vittoria , Lucchesi Davide Maria Francesco , Mulas Cesare Salvatore , Santi Francesca , Ferrari Anna Maria , Giorgi Rossi Paolo , Luppi Francesco 
Published: May 2022

ObjectiveWe aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.Design and settingRetrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.Participants450 patients discharged from the ED with diagnosis of COVID-19.Main outcome measureED readmission within 14 days, followed by hospitalisation/death.ResultsOf the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.ConclusionsA velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.
Stillbirth, neonatal and maternal mortality among caesarean births in Kenya and Uganda: a register-based prospective cohort study
Authors: Ghosh Rakesh , Santos Nicole , Butrick Elizabeth , Wanyoro Anthony , Waiswa Peter , Kim Eliana , Walker Dilys 
Published: May 2022

ObjectiveTo investigate the interaction of risks for adverse maternal and perinatal outcomes (stillbirth, predischarge neonatal and maternal mortality) among caesarean section (CS) compared with vaginal deliveries (VD).DesignProspective cohort study.Setting10 CS-capable facilities in Busoga Region, East-Central Uganda and Migori County, Kenya.ParticipantsIndividual birth data were extracted from maternity registers between October 2016 and April 2019. There were a total of 77 242 livebirths and 3734 stillbirths. Overall, 24% of deliveries were by CS with a range of 9%–49% across facilities.Primary outcome measuresStillbirth, predischarge neonatal mortality and maternal mortality.ResultsThe adjusted ORs for stillbirth, predischarge neonatal mortality and maternal mortality after a CS were 1.3 (95% CI 1.1 to 1.6), 1.9 (95% CI 1.6 to 2.2) and 3.3 (95% CI 2.2 to 4.9), respectively, compared with a VD. The association between maternal mortality and CS was 3.9 (95% CI 2.8 to 5.5) when the delivery was a live birth and 1.7 (95% CI 1.0 to 3.0) when it was a stillbirth. Post hoc analyses showed that mothers who received a CS had a lower risk of stillbirth if they were documented as a referral.ConclusionIn this context, CS births were at higher risk for worse outcomes compared with VD. Better understanding of CS use and associated adverse outcomes within the mother–baby dyad is necessary to identify opportunities to improve quality of intrapartum care.Trial registration numberNCT03112018.
NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data
Authors: Patel Rashmi , Wee Soon Nan , Ramaswamy Rajagopalan , Thadani Simran , Tandi Jesisca , Garg Ruchir , Calvanese Nathan , Valko Matthew , Rush A John , Rentería Miguel E , Sarkar Joydeep , Kollins Scott H 
Published: May 2022

PurposeNeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression—Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.ParticipantsAs of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).Findings to dateThe data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.Future plansThe NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.