bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring
Authors: Zheng Yue , Jing Xiaoming , Lin Yonggang , Shen Dali , Zhang Yiping , Yuan Dongdong , Yu Mingquan , Zhou Yongchao 
Published: September 2023

Abstract

With the impact of global climate change and urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction on flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapidly nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with the real-time monitoring data and high accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in deduction method for water level are 0.101 and 0.144, 0.124 and 0.162 respectively, while the flooding depth deduction is more stable compared to conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.
Heterogeneous deformation in fold and thrust belts induced by anisotropies in the ocean topography; the South Portuguese variscan zone example (SW Portugal)
Authors: Bolacha Edite , Dias Rui , Fonseca Paulo 
Published: September 2023

Abstract

The Variscan fold and thrust belt related with the South Portuguese Zone presents a SW forward propagation and the coeval development of synthetic folds and thrusts pervasive at all scales. Although several models have been proposed for their formation, the estimated strain 2 pattern is more compatible with a push from the rear induced by the north-eastern subduction zone.
Although there is a strong consistence concerning the kinematics of these Variscan structures, there is an important contrast of their geometries between the inner and the external sectors, where the very low dips prevails. Although, there are some previous models suggesting that the behaviour of the more external domains could be related with the interference between the undeformed sedimentary sequence of the upper plate with the active margin of the fold and thrust belt, independent evidence of this process is lacking. The realization of scaled analogue experiments concerning the Carboniferous evolution of SW Iberia Variscides, not only show that they could induced the flat lying major thrust of the external domains, but also emphasize their important role in the structural evolution of much of the fold and thrust belt.
Unveiling Optimal Models for Phenotype Prediction in Soybean Branching: An In-depth Examination of 11 Non-linear Regression Models, Highlighting SVR and SHAP Importance
Authors: Zhou Wei , Yan Zhengxiao , Zhang Liting 
Published: September 2023

Abstract
Plant breeding is gaining importance as a sustainable tool to address the challenges posed by a growing global population and enhance food security. Advanced high-throughput omics technologies are utilized to accelerate crop improvement and develop resilient varieties with higher yield performance. These technologies generate vast genetic data, which can be exploited to manipulate key plant characteristics for crop improvement. The integration of big data and AI in plant breeding has the potential to revolutionize the field and increase food security. By using branching data (phenotype) of 1918 soybean accessions and 42k SNP polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN regression, ANN regression, Autoencoders regression, and MLP regression) and seven machine learning models (e.g., SVR, XGBoost regression, Random Forest regression, LightGBM regression, GPS regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, ANN, and Autoencoder outperformed other models and could obtain a better prediction accuracy if they were used for phenotype prediction. To support the evaluation of deep learning methods, feature importance and GO enrichment analyses were conducted. After comprehensively comparing four feature importance algorithms, there was no significant difference among the feature importance ranking score among these four algorithms, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The genes identified by the SVR model plus SHAP importance combination clearly grouped into three clusters on the soybean whole genome. Our GO enrichment results also confirmed the prediction accuracy of this methods combination. The results of this study offer valuable insights for AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.
Metabolic adaptation of a photosynthetic angiosperm to extreme CO2 levels in Yellowstone: living on the edge
Authors: Veldhuis Marie-Claire ten , Ananyev Gennady , Dismukes G. Charles 
Published: September 2023

Abstract
How do plants adapt to elevated CO2 levels? This much-studied question has returned ambiguous answers. CO2 is the carbon nutrient for all photosynthesis, underlying global biomass productivity. Small increases in CO2 levels may boost photosynthetic efficiency in the short term, but at higher levels it inhibits biomass production and increases susceptibility to stress. Whether photosynthesis can favorably adapt to extreme CO2 over prolonged periods of time remains yet unknown. Here, we investigate changes in photosynthetic metabolism in a C3 plant growing along a natural CO2 gradient in Yellowstone NP over many decades. We find that plants adapted to grow near high CO2 sources (~ 6000 ppm) require similarly high CO2 to open Linear Electron Flow (LEF) for NADPH reoxidation. Cyclic Electron Flow (CEF) for ATP generation is upregulated and photogenerated ATP is shorter-lived in high CO2-Adapted plants where it is more rapidly lost in the dark. We attribute the shift in regulation away from LEF to higher CEF to the low chloroplast ATP levels caused by the stressful growth conditions, as revealed by stunted biomass and leaf chlorosis.
Investigating the Association Between Resilience and the Impostor Syndrome in Undergraduate Nursing and Medical Students: A Cross-Sectional Study
Authors: Khalil Amal , Alharbi Ruba , Qtame Hadab Al , Bena Raneem Al , Khan Muhammad Anwar 
Published: September 2023

Abstract
Background: The prevalence of impostor syndrome has been recognized among undergraduate nursing and medical students. Resilience, on the other hand, refers to an individual's ability to adapt and bounce back from adversity. Understanding the relationship between these two constructs is important in promoting student well-being and academic success.
Methods: Thiscross-sectional study aimed to investigate the relationship between resilience and impostor syndrome among undergraduate nursing and medical students. A convenience sample of 300 students from King Saud bin Abdulaziz University for Health Sciences participated in the study. Participants completed self-report questionnaires to assess their levels of resilience and impostor syndrome.
The results revealed that less than half of the participants experienced impostor syndrome, with 41.7% classified as severe, 37.7% as moderate, and 13.0% as intense among nursing students. For medical students, 4.6% were classified as mild, 4.6% as moderate, 4.5% as severe, and 4.5% as intense. The mean resilience score was 24.3±7.15 among nursing students and 25.6±7.22 among medical students. A significant negative correlation was found between resilience and impostor syndrome scores (r = -0.220, p < .001). Regression analysis indicated that resilience was a significant predictor of impostor syndrome, with higher resilience associated with lower levels of impostor syndrome. Specifically, the odds of experiencing severe impostor phenomenon were significantly higher in the 1st quartile (odds ratio = 13.16, p = 0.023) and the 2nd quartile (odds ratio = 56.00, p = 0.008) compared to the reference category.
Conclusion and Recommendations: These findings highlight the importance of resilience in mitigating impostor syndrome among nursing and medical students. Building resilience through interventions may be beneficial in promoting student well-being and academic success. Future research should explore the effectiveness of such interventions and identify other factors contributing to impostor syndrome among healthcare students.
Human hearts have an intrinsic regenerative potential following myocardial infarction
Authors: Hume Robert , Warwick Jessica , Shim Woo Jun , Malecki Cassandra , Li Mengbo , Harney Dylan , Slaughter Eugene , Wang Xiaosuo , Nguyen Lisa , Cole Louise , Edelman James , Rashid Fairooj , Houlahan Callum , Gao Antony , Ferguson Angela , Chong James , Larance Mark , O'Sullivan John , Palpant Nathan , Bannon Paul , Lal Sean 
Published: September 2023

Abstract

Myocardial infarction (MI) is a leading cause of death worldwide and can eliminate up to a third of the cardiomyocytes (CMs) within the heart. Several non-human mammalian models and a limited number of human heart studies suggest that CMs can proliferate during early development, but most CMs stop regenerating soon after birth. In contrast, rodent MI models show that CMs increase their regenerative capacity in response to ischemia, however this has not been shown in humans. Using immunostaining, multi-omics and a unique pre-mortem human heart collected 5 days post-MI, we show that adult human CMs exhibit an increased intrinsic regenerative potential in response to ischemia. Furthermore, single nucleus ribonucleic acid sequencing (snRNAseq) reveals the unique gene expression of post-MI proliferative human CMs. Finally, we demonstrate a novel clinical method to safely biopsy peri-ischemic post-MI human heart tissue and provide further evidence of ischemic CM regeneration. Developing a means to enhance this intrinsic CM regenerative potential could lead to novel therapeutics for cardiac injury and heart failure.
Uncovering the hidden structure of small-world networks
Authors: Achahbar Abdelfattah , Lachgar Ahmed 
Published: September 2023

Abstract
The small-world (SW) network model introduced by Watts and Strogatz has had a tremendous impact on the study of complex systems, leading to the emergence of network science as an interdisciplinary field. While the Newman-Watts model is commonly used to analyze SW networks by adding a few randomly placed shortcuts to a regular lattice. We carefully study related previous works and conclude that scaling of different relevant quantities is not convincing. Here, we demonstrate that the small-world property arises mainly from the existence of clusters of nodes linked by shortcuts rather than just the mean number of shortcuts. Introducing the mean degree of clusters linked by shortcuts as a new parameter resolves the scaling ambiguity, providing a more accurate description of the network. Our findings provide a new framework for analyzing SW networks, emphasizing the importance of considering emergent structures in complex systems. We also construct a phase diagram of the crossover transition from the small to the large world, yielding valuable insights into the nature of complex networks and highlighting the power of emergence in shaping their behavior.
Measurement and Removal of Asbestos in Residential Dwellings to be Demolished - Urban Transformation Experience in Izmir-Turkey
Authors: Tetik Yilmaz Ogunc , Bayram-Zumrut Irem , Camurcu Ayse Gizem , Kale Ozge Akboga , Baradan Selim 
Published: September 2023

Abstract

Asbestos has been used extensively in the construction industry for its superior insulation properties before its health hazards were discovered and eventually getting banned. It would not be wrong to assume all residential buildings built before the 2000s in Turkey contain asbestos. Therefore, it is important to raise awareness of the potential danger of asbestos exposure during demolition work and to identify asbestos-containing materials and ensure their safe removal and disposal. This study summarizes the efforts being made in the “Asbestos Free Demolition” EU project, which is executed to determine the residential dwellings containing asbestos in Izmir. The project included field study to determine asbestos presence in the buildings that were damaged during the 2020 earthquake. 505 bulk samples were taken from 50 buildings that would go through the demolition process. 11 buildings were found to contain asbestos. This rate corresponds to 22%. In addition to asbestos removal processes in these buildings containing asbestos, air measurements were taken from each building. The detected asbestos type was 60% chrysotile (white asbestos). The concentration of asbestos in the air is ignorable also personal exposure did not exceed the permissible exposure limit (PEL) for 0.1 fiber/cm3. Results demonstrate the findings of asbestos-containing materials and suggestions before demolition work. Also, it is expected that the database created for this study could be useful in other studies involving Turkey, where accurate statistical data related with asbestos measurements is still difficult to obtain.
Experiences of rural clinicians accessing specialist support via telehealth for trauma and emergency care in Queensland, Australia
Authors: Santomauro Chiara , McLanders Mia , Rae Andrew 
Published: September 2023

Abstract

Background
Trauma and emergency patients presenting to rural facilities require time-critical treatment and management that is sometimes beyond the scope and experience of clinicians in the facility. In Queensland, Australia’s second largest state, telehealth infrastructure facilitates 24/7 communication between rural clinicians and tertiary-based critical care specialists. In this study we sought to understand the current state of Queensland’s emergency telehealth system from the perspective of direct end-users to inform future improvement efforts and resource allocation.
Methods
Semi-structured interviews were conducted with 11 rural Queensland clinicians who use telehealth to access specialist support during critical presentations. Qualitative data were analysed in three inductive phases: immersion; a combination of process coding and in vivo coding; and focused coding.
Results
Overall, the findings highlight that emergency telehealth support provides benefits beyond better patient care, as it fosters a sense of collegiality and alleviates professional isolation often experienced by rural clinicians. Four key themes were identified: (1) Challenges of trauma and emergency care in rural Queensland; (2) Strategies for trauma and emergency care in rural Queensland; (3) Factors that affect perceptions of telehealth effectiveness; and (4) How support for rural trauma and emergency care can be improved.
Conclusions
This study revealed that there are both benefits and frustrations for rural clinicians accessing telehealth specialist support for rural trauma and emergency care. Although telehealth specialist support is seen as a critical service that supports rural clinicians and benefits patient care, the findings suggest that tools, systems, and processes surrounding rural trauma and emergency care could benefit from streamlining, integration, and the introduction of new fit-for-purpose technologies. Addressing limitations of efficiencies would lead to better support for rural clinicians and would likely improve patient outcomes for rural communities.
Lived Experience, Social Support, and Challenges to Health Service Use during the COVID-19 Pandemic among HIV Key Populations in Indonesia
Authors: Sukmaningrum Evi , Levy Judith , Negara Made Diah , N/A Devika , Wardhani Brigitta Dhyah K , Wulandari Luh Putu Lila , Januraga Pande Putu 
Published: September 2023

Abstract
The COVID-19 has greatly affected the lives, health and social well-being of people globally including presenting special challenges in low to middle income countries for people living with HIV. This study investigates the pandemic experiences of the four key HIV-positive populations in Indonesia: men who have sex with men, transgender women, female sex workers, and people who use drugs. In-depth interviews were conducted with a convenience sample of 22 key population members recruited through 9 nongovernment HIV agencies in Jakarta and Bali, Indonesia. Indonesia’s Large-scale Social Restrictions Policy mandating physical distancing and stay-at-home orders had been in effect for 7–10 months. The interviews were audio-recorded, transcribed, and coded using NVivo™ (R1.7) software A grounded theory approach identified key concepts along with similarities, differences, and reoccurring patterns of COVID-related lived experience among participants. Participants recounted the impact of both the pandemic and the Restriction Policy on their interpersonal, financial, medical, and psychosocial well-being. When in need, they turned to formal and informal sources of financial and social support plus their own resourcefulness. Along with other factors, HIV medication shortages, HIV and COVID-related stigma, and fear of acquiring COVID-19 negatively impacted antiretroviral adherence and the use of health services. The results point to the latent consequences of government attempts to curb a pandemic through public health lockdowns and enforced policies of physical separation. Its findings reveal the importance of ensuring that public safety nets for HIV key populations are available to supplement more informal personal sources of needed support.
Using Hyperspectral Signatures for Predicting Foliar Nitrogen and Calcium Content of Tissue Cultured Little-leaf Mockorange (Philadelphus microphyllus A. Gray) Shoots
Authors: Khajehyar Razieh , Vahidi Milad , Tripepi Robert 
Published: September 2023

Abstract

Determining foliar mineral status of tissue cultured shoots can be costly and time consuming, yet hyperspectral signatures might be useful for determining mineral contents of these shoots. In this study, hyperspectral signatures were acquired from tissue cultured little-leaf mockorange (Philadelphus microphillus) shoots to determine the feasibility of using this technology to predict foliar nitrogen and calcium contents. After using a spectroradiometer to take hyperspectral images for determining foliar N and Ca contents, the correlation between the hyperspectral bands, vegetation indices, and hyperspectral features were calculated from the spectra. Features with high correlations were selected to develop the models via different regression methods including linear, random forest (RF), and support vector machines. The results showed that non-linear regression models developed through machine learning techniques, including RF methods and support vector machines provided satisfactory prediction models with high R2 values (%N by RF with R2 = 0.72, and %Ca by RF with R2 = 0.99), that can estimate nitrogen and calcium content of little-leaf mockorange shoots grown in vitro. Overall, the RF regression method provided the most accurate and satisfactory models for both foliar N and Ca estimation of little-leaf mockorange shoots grown in tissue culture.
An Assessment of Existing Wildfire Danger Indices in Comparison to One-class Machine Learning Models
Authors: Ismail Fathima Nuzla , Woodford Brendon J , Licorish Sherlock A , Miller Aubrey D 
Published: September 2023

Abstract

Predicting wildfires using Machine Learning (ML) models is relevant and essential to minimize wildfire threats to protect human lives and reduce significant property damage. Reliance on traditional wildfire indices for forecasting wildfires has failed to provide the expected prediction outcomes, resulting in limited application of these models. Thus, this research compares the outcome of wildfire forecasting using fire danger rating indices against ML model outcomes. Furthermore, the performance effectiveness of the fire danger rating indices and ML model outcomes are assessed using the same wildfire incidents. The Oneclass ML algorithms used are Support Vector Machine, Isolation Forest, Neural network-based Autoencoder and the Variational Autoencoder models. The two global wildfire indices investigated were US National Fire Danger Rating System (NFDRS) for California and the McArthur Forest Fire Danger Index (FFDI) for Western Australia, using similar features. For the same data sets, NFDRS and FFDI prediction outcomes were compared with ML model outcomes. Higher wildfire prediction accuracy was achieved by the One-class models, exceeding the performance of the two wildfire danger indices by at least 20%. The implications of our research findings have the potential to influence both these wildfire indices and state-of-the-art methods in wildfire prediction by proposing alternative ML methods to model the onset of wildfires.