bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tomographic Reconstruction of the Solar K-Corona using Neural Fields
Authors: Ramos Andrés Asensio 
Published: June 2023

Abstract
We explore the application of neural fields for tomographic reconstructions of the solar corona using data from the LASCO-C2 instrument. We first demonstrate their ability to recover the electron density volume in a synthetic static case, utilizing a simulated 3D model of the corona. Our results show that neural fields provide an efficient and accurate representation of the electron density data. By comparing the synthesized polarized brightness from the modeled electron densities and the observations, we validate the performance of the method in recovering the electron density structure accurately. Furthermore, we extend our analysis to the dynamic case, considering time-dependent reconstructions. To this end, we incorporate the temporal dimension into the neuralfield. The results demonstrate that neural fields can effectively capture the temporal variability of the coronal electron density.We apply the developed tomographic strategy to real observations from the LASCO-C2 instrument. Using a sequence of LASCO-C2 images, we reconstruct the electron density distribution of the solar corona for a specific period. The reconstructed electron densities are able to reproduce the observed features of the polarized brighness with high fidelity. The results indicate that neural fields provide a powerful tool for tomographic reconstructions, yielding electron density maps with minimal artifacts and improved agreement with observations.Neural fields offer several advantages, including efficient interpolation, easy to implement implicit and explicit regularization, and the ability to capture temporal variability. The proposed approach has the potential to enhance our understanding of the complex dynamics and structures of the solar corona, enabling more accurate and detailed analyses of coronal features.
Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans
Authors: CHALLAGUNDLA YAGNESH , Chintalapati Lohitha Rani , Tunuguntla Trilok Sai Charan , Devarakonda Nagaraju 
Published: June 2023

Abstract
Automatic detection and segmentation of liver tumors from CT scans is a challenging task due to their complex morphology in CT images, but advances in deep learning have made it possible to achieve high accuracy in the segmentation and detection of liver tumors, eliminating the need for manual intervention and allowing radiologists to focus more on critical diseases by giving awareness about the liver tumor at an early stage, which may save a person's life by allowing them to receive treatment sooner, possibly preventing further severity or death. In our study, we worked on different approaches to detect liver tumors from CT scans and analyzed their performance in terms of accuracy. A few of the approaches employed are using Convolutional neural networks including existing ones like U-Net, ResNet50, while we proposed a combination of multiple CNN models to obtain better results. It was observed that the combination of CNN models produced higher accuracies rather than the existing models.
Green synthesis of silver nanoparticles by S.nervosum leaf extract exhibits high antibacterial activity
Authors: Cong Hong Hanh , Pham Sy Hieu , Nguyen Hong Nhung , Tran Que Chi , Tran Thi Huong , Pham Duy Khanh , Pham Thi Gam , Nguyen Van Phuong , Thi Thanh Nga Phan , Hoang Hien Y , Dao My Uyen , Hoang Anh Son 
Published: June 2023

Abstract
In this study, green synthesis and physicochemical characterization of silver nanoparticles (AgNPs) by using S.nervosum leaves extract. The characteristic surface plasmon resonance peak observed in the visible region at 435 nm is consistent with previous reports of silver nanoparticle formation. The synthesized AgNPs were also characterized by SEM, TEM, XRD, and FTIR, which reveal their nearly spherical, mainly in the 10-30 nm diameter. The antimicrobial activity of AgNPs was studied using the agar well diffusion method on four clinically significant pathogenic microorganisms (P.aeruginosa ATCC 27853; E.coli ATCC 35218, S.aureus ATCC 12493; S.aureus Mastitis isolate). The results showed that the green synthesized AgNPs exhibited antimicrobial activity at most studied concentrations, including the antibacterial effect against S.aureus (Mastitis isolate) strain was found to be resistant to Tetracycline 100 µg/L.
Well-to-Wheel Analysis of Energy Efficiency &amp; CO2 emissions for Hybrids &amp; EVs in India: Current Trends &amp; Forecasting for 2030
Authors: Nandola Yash , Krishna Uttam , Pramanik Santanu , M Himabindu , V Ravikrishna R 
Published: June 2023

Abstract
A comprehensive Well-to-Wheel (WTW) analysis was performed to evaluate WTW energy use, efficiency & CO2 emissions for 12 vehicle/fuel configurations for a passenger sedan in the Indian context. The WTW analysis covered gasoline, diesel, and compressed natural gas (CNG) powered conventional vehicles, series hybrids, and plug-in series hybrids. In addition, hydrogen fuel cell-powered series hybrid and its plug-in version, along with a battery-electric vehicle, were also studied. The WTW analysis was repeated for a couple of electricity generation scenarios for the year 2030 to forecast future trends and finally for a couple of hydrogen production scenarios, where hydrogen was produced via electrolysis of water in addition to being produced from natural gas. The electricity pathway showed minimum Well-to-Tank (WTT) efficiency and maximum WTT CO2 emissions among the five fuels being considered for the study for all three electricity generation scenarios. The hybridization of vehicles showed improvement in the Tank-to-Wheel (TTW) efficiency and reduction in TTW CO2 emissions. Plug-in hybrid configuration of gasoline, diesel, CNG, and hydrogen showed higher TTW efficiency and lower TTW CO2 emissions than the conventional and series hybrid configurations. Battery electric configuration showed the maximum TTW efficiency of 68.2% and was associated with zero TTW CO2 emissions. For the current electricity generation scenario, diesel hybrid showed maximum WTW efficiency, and CNG series hybrid showed lowest WTW CO2 emissions. With the decrease in % share of coal, increased % share of renewables and reduction in transmission and distribution losses, it is expected that there would be an increase in the WTW efficiency and decrease in WTW CO2 emissions for plug-in hybrids and battery-electric vehicles. For the year 2030, assuming that 44% of electricity is generated from renewable sources, plug-in hybrids and battery electric vehicles showed substantial improvement in WTW efficiency, with diesel plug-in hybrid still showing maximum WTW efficiency, and CO2 emissions being the lowest for the CNG plug-in hybrid. For the hydrogen generation scenarios, WTW efficiency was the lowest, and WTW CO2 emissions were the highest for the fuel cell series hybrid electric vehicle when hydrogen was produced via electrolysis using electricity from the current grid mix. Hydrogen plug-in hybrid showed the highest WTW efficiency and zero WTW CO2 emissions when hydrogen was produced via electrolysis of water using electricity generated from 100% renewable sources.
Ontology-Driven Machine Learning: A Review of applications in healthcare, finance, Natural Language Processing, and Image Analysis
Authors: ABTEW ADMAS , Demissie Dait , Kekeba Kula 
Published: June 2023

Abstract
Ontology-driven machine learning (ODML) is an emerging approach that integrates domain-specific ontologies with machine learning methods to improve the accuracy, interpretability, and explainability of predictive models. In this review, we synthesized the findings from studies that applied ODML in different domains, including healthcare, finance, natural language processing, and image analysis. The results demonstrated the potential of ODML in improving the performance of machine learning models for predicting falls, hospital readmissions, credit risk, stock prices, sentiment analysis, text classification, breast cancer histology, and liver image classification. The study highlighted the importance of domain-specific ontologies in capturing the domain knowledge and improving the performance of machine learning models. However, the quality of evidence varied across the studies, and publication bias may be present. Future studies should aim to address these limitations and develop standardized approaches for ontology development and integration with machine learning methods. Overall, ODML is a promising approach for improving the accuracy, interpretability, and explainability of machine learning models in different domains.
Quality and Safety of Wine during Storage: Packaging and Stopper
Authors: Chemisova Larisa , Ageyeva Natalia , Yakimenko Elena 
Published: June 2023

Poor packaging often affects the quality and safety indicators of wine during storage. The need to preserve and prolong wine stability during long-term storage requires a comprehensive study of processes that occur in wines packaged and capped in various ways.
This research featured 56 samples of white and red dry wines, which were bottled in four types of packages and stopper for 18 months. The effect of synthetic and natural stopper methods involved a model system that simulated the main composition of wine in line with the EAEU standards. The contents of free SO2 and dissolved oxygen, as well as the microbiology of natural corks, were studied using standard methods. The analysis of volatile substances relied on gas chromatography, mass spectrometry, and solid phase micro-extraction.
The study recorded critical changes in the quality of wine products, characterized by increased oxidation markers and low free sulfur dioxide, which fell below 10 mg/dm3. In polyethylene terephthalate bottles, these changes occurred after 12 months of storage, while it took them 18 months to develop in multilayer bags and combined packaging materials. The components of natural and artificial plugs of microbial and chemical origin entered the model systems after 10 days of contact. The microbiological contamination was twice as high as the permissible level. The migration of microbial and chemical substances into the wine samples spoiled their quality and safety.
The effect of packaging on the oxidative processes in white and red dry wines during storage resulted in different concentrations of dissolved oxygen and sulfur dioxide. The results can help wine producers to choose the most appropriate packaging and stopper means depending on the wine sort and its subsequent storage conditions. Alternative types of packaging proved to have a limited shelf life.
Effect of Moisture Content on the Quality and Quantity of Screw-Pressed Flax Seed Oil
Authors: Hasanov Jahongir , Mirzaxmedov Sherzod , Sultonova Elvira , Salikhov Shavkat 
Published: June 2023

Healthy foods and green processing are currently in the focus of scientific attention. Flax is considered a superfood that includes numerous beneficial ingredients. Its oil is a great source of polyunsaturated fatty acids. However, the heat-sensitive futures of the polyunsaturated fatty acids require low-temperature methods. Screw pressing can produce oils at lower temperatures, but a lower oil capacity makes it less advantageous. Pre-treatment of seeds as a crucial pre-processing stage affects the quality and quantity of oil. This research featured the effect of moisture content in flax seeds on the quantity and quality flax seed oil.
The study involved local flax (Linum usitatissimum L.) from the Qashqadaryo Region, Uzbekistan. Standard methods were used to define the quality parameters of oil, i.e., acid value, free fatty acids content, peroxide value, oil recovery, and sediment content. Regression equations were obtained using the method of regression analysis.
The highest oil recovery of 40.99% was observed at the moisture content of 9.56%. At 7.55%, the oil recovery dropped by 3.17%. The sediment content in the oil increased at lower moisture contents in the flax seeds (14.62–5.55%). The acid value, peroxide value, and free fatty acid content demonstrated both downward and upward trends when the moisture content increased.
The moisture content in flax seeds affected the quality and quantity profile of screw-pressed oil. The optimal parameters of oil processing can yield health-beneficial and highly nutritional oil.
The Potential Strategies for Overcoming Multidrug Resistance and
Reducing Side Effects of Monomer Tubulin Inhibitors for Cancer
Therapy
Authors: Cui Yingjie , Zhang Jing , Zhang Guifang 
Published: June 2023

Background:
Tubulin is an essential target in tumor therapy, and this is attributed
to its ability to target MT dynamics and interfere with critical cellular functions,
including mitosis, cell signaling, and intracellular trafficking. Several tubulin inhibitors
have been approved for clinical application. However, the shortcomings, such as drug resistance
and toxic side effects, limit its clinical application. Compared with single-target
drugs, multi-target drugs can effectively improve efficacy to reduce side effects and overcome
the development of drug resistance. Tubulin protein degraders do not require high
concentrations and can be recycled. After degradation, the protein needs to be resynthesized
to regain function, which significantly delays the development of drug resistance.

Methods:
Using SciFinder® as a tool, the publications about tubulin-based dual-target inhibitors
and tubulin degraders were surveyed with an exclusion of those published as patents.

Results:
This study presents the research progress of tubulin-based dual-target inhibitors
and tubulin degraders as antitumor agents to provide a reference for developing and applying
more efficient drugs for cancer therapy.

Conclusion:
The multi-target inhibitors and protein degraders have shown a development
prospect to overcome multidrug resistance and reduce side effects in the treatment of tumors.
Currently, the design of dual-target inhibitors for tubulin needs to be further optimized,
and it is worth further clarifying the detailed mechanism of protein degradation.
Optimization of Double-Gate Carbon Nanotube FET Characteristics for Short Channel Devices
Authors: Moorthy Vijai M. , Venkatesan Rethinasamy , Srivastava Viranjay M. 
Published: June 2023

Introduction:
Transistors are the fundamental electronic component integrated into electronic devices' chips Carbon Nano Tube (CNT) based field.

Methods:
Effect Transistor (FET) is a promising component for next-generation transistor technologies; as it has high carrier mobility, device stability, and mechanical flexibility. Nevertheless, some shortcomings in the CNT FET's design prevent it from providing the best performance while preserving thermal stability.

Results:
The structure and functionality of transistors with Double-Gate (DG) devices, which use carbon nanotubes as active channel regions, have been examined by the authors of this study. The DG CNT FET has been extensively simulated using an electronic device simulator with various device geometrics, including channel length, oxide thickness for its output, and transfer characteristics.

Conclusion:
A new perspective on the DG CNT FET's device performance characteristics is provided by this research work, which can be scaled down to minimum channel length without Short Channel Effects (SCEs).
Identification of Work-related Diseases in Small-scale Fishermen in Batam Island, Indonesia
Authors: Dewi Fitri Sari , Kusnoputranto Haryoto , Purwana Rachmadhi , Soesilo Tri Edhi Budhi 
Published: June 2023

Background:
Small-scale fishermen's work activities are carried out in a marine environment which has the potential to cause certain work-related diseases. AIMS: This study aims to identify the incidence of work-related diseases among small-scale fishermen in Batam Island, Indonesia.

Methods:
This study used both quantitative and qualitative approaches involving 119 small-scale fishermen. Data was collected through questionnaires, observations, documentation, interviews, focus group discussions, and secondary data collection.

Results:
The results showed that all small-scale fishermen complained of diseases caused by threats in the chemical, physical, biological, psychological, and ergonomic factors group.

Conclusion:
Most fishermen complained about colds, fever, stings, blisters, skin allergies, and eye disorders/myopia. Most complaints were obtained from small-scale fishermen over 40 years old, especially the ones with smoking habits. Identification of work-related diseases was required to prevent and control the health problems that could endanger small-scale fishermen’s health and productivity.
A Recent Update on Ayurvedic Anti-asthmatic Formulations: Highlighting the Role of Major Anti-asthmatic Plants
Authors: Bhatia Manisha , Chaudhary Jasmine , Jain Akash , Sonam  , Dhingra Ashwani , Chopra Bhawna 
Published: June 2023

Abstract:
Asthma is a chronic heterogeneous airway obstruction disorder defined by a complex
cascade of pathophysiological conditions that lead to recurring bronchial hyper-responsiveness and
underlying inflammation, lowering the patient’s quality of life. Despite the availability of various
synthetic medications for the management of asthma, it has become one of the leading causes of
death worldwide. Furthermore, the present allopathic treatment possesses severe long-term negative
effects, which make people more attracted to the safer and easily available indigenous traditional
and Ayurvedic system of medicine. Ayurveda is regarded as the “Mother of all healing” and “Science
of Life,” which treats all the ailments, including asthma, by restoring a balance between three
main components of the body- Vata, Pitta, and Dosha. Various clinically effective and less toxic anti-
asthmatic Ayurvedic herbal preparations are available for the management of asthma and related
complications which act by causing mast cell deactivation, inhibition of Th1, Th2, Th7 response, Tcell
suppression, etc., therefore, efforts have been made to compile all the relevant literature related
to the ayurvedic anti-asthmatic formulations and scrutinize the efficacy of selected plants present in
the majority in those formulations in asthma along with the structure and mode of action of phytoconstituent
responsible for designing novel molecules with better efficacy. Research on these selected
plants can also have economic significance as various constituents isolated from these plants for
their anti-asthmatic effect can further be explored to discover their potential in medicinal, diagnostic
and cosmetic field.
An Exploration of Deep Learning Techniques for the Detection of Grape
Diseases
Authors: Pandey Kavita , Chandak Abhimanyu 
Published: June 2023

Abstract:
Plant diseases are one of the major contributors to economic loss in the agriculture industry
worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent
times, a lot of emphasis has been done on disease detection due to the overall increase in production
as well as the loss of grape number. With deep learning, having a promising future and having the
advantages of automatic learning and feature extraction, the use of these techniques has now been
widely spread. This paper reviewed the existing deep-learning techniques available for grape disease
detection. Firstly, covering the various steps in a grape disease detection model ranging from
the various sources of image acquisition, the different image augmentation techniques and the various
models used, and the parameters required to evaluate. Secondly, the study summarizes the important
findings of all literature available on the theme. The paper also tries to highlight the various
challenges faced by the researchers and the common trend among them, so that future research on
the topic can achieve higher performance.
Impact of Beliefs About Local Physician Supply and Self-Rated Health on Willingness to See a Nurse Practitioner During the COVID-19 Pandemic: Web-Based Survey and Experiment
Authors: Campos-Castillo Celeste 
Published: June 2023

Background
The COVID-19 pandemic overburdened primary care clinicians. For nurse practitioners (NPs) to alleviate the burden, the public must be willing to see an NP over a physician. Those with poor health tended to continue seeking care during the pandemic, suggesting that they may be willing to see an NP.

Objective
The aim of this study is to evaluate the public’s willingness to see an NP for primary care and how this may be associated with their beliefs about the local supply of physicians and self-rated health. Two studies were conducted: (1) a survey to identify correlations and (2) an experiment to assess how willingness is dependent on information about the local supply of physicians.

Methods
The survey and experiment were conducted digitally in April and December 2020, respectively. Participants were US adults recruited from Amazon’s Mechanical Turk platform. The key independent variables were self-rated health, which was a dichotomized 5-point scale (excellent, very good, good vs fair, and poor), and beliefs about local physician supply. The survey measured beliefs about local physician supply, while the experiment manipulated beliefs by altering information the participants read about the local supply of physicians. Willingness to see an NP was assessed in 2 ways. First as an overall preference over a physician and the second as a preference given 2 clinically significant scenarios in which participants imagined they were experiencing either coughing or a headache (presentation order randomized). Multiple regressions and ANOVAs were used to assess how beliefs about the local physician supply and self-rated health were associated with overall willingness to see an NP. Bivariate probits simultaneously estimated willingness to see an NP in the 2 clinically significant scenarios.

Results
The survey showed that concerns about physician supply were associated with lower willingness to see an NP among respondents with comparatively better health but a greater willingness among respondents with comparatively worse health. The experiment suggests that only the latter is causal. For the 2 clinically significant scenarios, these patterns appeared for the coughing scenario in the survey and the headache scenario in the experiment.

Conclusions
US adults with comparatively worse self-rated health become more willing to see an NP for primary care when they hear information that raises their concerns about the local physician supply. The differences between the survey and experiment results may be useful for interpreting findings from future studies. Findings may aid in managing finite health care resources during public health crises and crafting successful messaging by NP advocacy groups. Efforts to address nursing shortages will also be needed.